## IdOrUniqueName The ID or unique name input. If both ID and unique name are provided, the ID will take precedence. The unique identifier of the object. The unique name of the object. ## CreateEnvironmentInput The fields for creating an Environment. The Environment's unique name. The Environment's description. ## ModifyEnvironmentInput The fields for modifying an Environment. The Environment's unique name. The Environment's description. ## CreateApplicationInput The fields for creating an Application. The Application's unique name. If not specified, Propel will set the ID as unique name. The Application's description. The Application's Propeller. If no Propeller is provided, Propel will set the Propeller to `P1_X_SMALL`. A Propeller determines your Application's query processing power. The larger the Propeller, the faster the queries and the higher the cost. Every Propel Application (and therefore every set of API credentials) has a Propeller that determines the speed and cost of queries. * `P1_X_SMALL`: Max records per second: 5,000,000 records per second * `P1_SMALL`: Max records per second: 25,000,000 records per second * `P1_MEDIUM`: Max records per second: 100,000,000 records per second * `P1_LARGE`: Max records per second: 250,000,000 records per second * `P1_X_LARGE`: Max records per second: 500,000,000 records per second The Application's API authorization scopes. If specified, at least one scope must be provided; otherwise, all scopes will be granted to the Application by default. The API operations an Application is authorized to perform. * `ADMIN`: Grant read/write access to Data Sources, Data Pools, Metrics and Policies. * `APPLICATION_ADMIN`: Grant read/write access to Applications. * `DATA_POOL_QUERY`: Grant read access to query Data Pools. * `DATA_POOL_READ`: Grant read access to read Data Pools. * `DATA_POOL_STATS`: Grant read access to fetch column statistics from Data Pools. * `ENVIRONMENT_ADMIN`: Grant read/write access to Environments. * `METRIC_QUERY`: Grant read access to query Metrics. * `METRIC_STATS`: Grant read access to fetch Dimension statistics from Metrics. * `METRIC_READ`: Grant read access to Metrics. This does not allow querying Metrics. For that, see `METRIC_QUERY`. ## ModifyApplicationInput The fields for modifying an Application. The ID or unique name of the Application to modify. The ID or unique name input. If both ID and unique name are provided, the ID will take precedence. The unique identifier of the object. The unique name of the object. The Application's new unique name. The Application's new description. The Application's new Propeller. A Propeller determines your Application's query processing power. The larger the Propeller, the faster the queries and the higher the cost. Every Propel Application (and therefore every set of API credentials) has a Propeller that determines the speed and cost of queries. * `P1_X_SMALL`: Max records per second: 5,000,000 records per second * `P1_SMALL`: Max records per second: 25,000,000 records per second * `P1_MEDIUM`: Max records per second: 100,000,000 records per second * `P1_LARGE`: Max records per second: 250,000,000 records per second * `P1_X_LARGE`: Max records per second: 500,000,000 records per second The Application's new API authorization scopes. The API operations an Application is authorized to perform. * `ADMIN`: Grant read/write access to Data Sources, Data Pools, Metrics and Policies. * `APPLICATION_ADMIN`: Grant read/write access to Applications. * `DATA_POOL_QUERY`: Grant read access to query Data Pools. * `DATA_POOL_READ`: Grant read access to read Data Pools. * `DATA_POOL_STATS`: Grant read access to fetch column statistics from Data Pools. * `ENVIRONMENT_ADMIN`: Grant read/write access to Environments. * `METRIC_QUERY`: Grant read access to query Metrics. * `METRIC_STATS`: Grant read access to fetch Dimension statistics from Metrics. * `METRIC_READ`: Grant read access to Metrics. This does not allow querying Metrics. For that, see `METRIC_QUERY`. ## CreateSnowflakeDataSourceInput The fields for creating a Snowflake Data Source. The Data Source's unique name. If not specified, Propel will set the ID as unique name. The Data Source's description. The Data Source's connection settings. The fields for creating a Snowflake Data Source's connection settings. The Snowflake account. Only include the part before the "snowflakecomputing.com" part of your Snowflake URL (make sure you are in classic console, not Snowsight). For AWS-based accounts, this looks like "znXXXXX.us-east-2.aws". For Google Cloud-based accounts, this looks like "ffXXXXX.us-central1.gcp". The Snowflake database name. The Snowflake warehouse name. It should be "PROPELLING" if you used the default name in the setup script. The Snowflake schema. The Snowflake username. It should be "PROPEL" if you used the default name in the setup script. The Snowflake password. The Snowflake role. It should be "PROPELLER" if you used the default name in the setup script. ## ModifySnowflakeDataSourceInput The fields for modifying a Snowflake Data Source. The ID or unique name of the Data Source to modify. The ID or unique name input. If both ID and unique name are provided, the ID will take precedence. The unique identifier of the object. The unique name of the object. The Data Source's new unique name. The Data Source's new description. The Data Source's new connection settings. The fields for modifying a Snowflake Data Source's connection settings. The Snowflake account. Only include the part before the "snowflakecomputing.com" part of your Snowflake URL (make sure you are in classic console, not Snowsight). For AWS-based accounts, this looks like "znXXXXX.us-east-2.aws". For Google Cloud-based accounts, this looks like "ffXXXXX.us-central1.gcp". If not provided this property will not be modified. The Snowflake database name. If not provided this property will not be modified. The Snowflake warehouse name. It should be "PROPELLING" if you used the default name in the setup script. If not provided this property will not be modified. The Snowflake schema. If not provided this property will not be modified. The Snowflake username. It should be "PROPEL" if you used the default name in the setup script. If not provided this property will not be modified. The Snowflake password. If not provided this property will not be modified. The Snowflake role. It should be "PROPELLER" if you used the default name in the setup script. If not provided this property will not be modified. ## SnowflakeConnectionSettingsInput The fields for creating a Snowflake Data Source's connection settings. The Snowflake account. Only include the part before the "snowflakecomputing.com" part of your Snowflake URL (make sure you are in classic console, not Snowsight). For AWS-based accounts, this looks like "znXXXXX.us-east-2.aws". For Google Cloud-based accounts, this looks like "ffXXXXX.us-central1.gcp". The Snowflake database name. The Snowflake warehouse name. It should be "PROPELLING" if you used the default name in the setup script. The Snowflake schema. The Snowflake username. It should be "PROPEL" if you used the default name in the setup script. The Snowflake password. The Snowflake role. It should be "PROPELLER" if you used the default name in the setup script. ## PartialSnowflakeConnectionSettingsInput The fields for modifying a Snowflake Data Source's connection settings. The Snowflake account. Only include the part before the "snowflakecomputing.com" part of your Snowflake URL (make sure you are in classic console, not Snowsight). For AWS-based accounts, this looks like "znXXXXX.us-east-2.aws". For Google Cloud-based accounts, this looks like "ffXXXXX.us-central1.gcp". If not provided this property will not be modified. The Snowflake database name. If not provided this property will not be modified. The Snowflake warehouse name. It should be "PROPELLING" if you used the default name in the setup script. If not provided this property will not be modified. The Snowflake schema. If not provided this property will not be modified. The Snowflake username. It should be "PROPEL" if you used the default name in the setup script. If not provided this property will not be modified. The Snowflake password. If not provided this property will not be modified. The Snowflake role. It should be "PROPELLER" if you used the default name in the setup script. If not provided this property will not be modified. ## HttpBasicAuthInput The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. ## HttpDataSourceTableInput The fields for specifying an HTTP Data Source's table. The name of the table All the columns present in the table The fields for specifying a column in an HTTP Data Source's table. The column name. It has to be unique within a table. The column type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. The ClickHouse type to use when `type` is set to `CLICKHOUSE`. Whether the column's type is nullable or not. ## HttpDataSourceColumnInput The fields for specifying a column in an HTTP Data Source's table. The column name. It has to be unique within a table. The column type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. The ClickHouse type to use when `type` is set to `CLICKHOUSE`. Whether the column's type is nullable or not. ## S3DataSourceTableInput The fields for specifying an Amazon S3 Data Source's table. The name of the table The path to the table's files in Amazon S3. All the columns present in the table The fields for specifying a column in an Amazon S3 Data Source's table. The column name. It has to be unique within a table. The column type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. Whether the column's type is nullable or not. ## S3DataSourceColumnInput The fields for specifying a column in an Amazon S3 Data Source's table. The column name. It has to be unique within a table. The column type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. Whether the column's type is nullable or not. ## AmazonDataFirehoseDataSourceColumnInput The fields for specifying a column in an Amazon Data Firehose Data Source's table. The column name. It has to be unique within a table. The JSON property that the column will be derived from. For example, if you send a JSON event like this: ```json theme={"system"} { "greeting": { "message": "hello, world" } } ``` Then you can use the JSON property "greeting.message" to extract "hello, world" to a column. The column type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. Whether the column's type is nullable or not. ## AmazonDynamoDBDataSourceColumnInput The fields for specifying a column in an Amazon DynamODB Data Source's table. The column name. It has to be unique within a table. The JSON property that the column will be derived from. For example, if you send a JSON event like this: ```json theme={"system"} { "greeting": { "message": "hello, world" } } ``` Then you can use the JSON property "greeting.message" to extract "hello, world" to a column. The column type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. Whether the column's type is nullable or not. ## TwilioSegmentDataSourceColumnInput The fields for specifying a column in a Twilio Segment Data Source's table. The column name. It has to be unique within a table. The JSON property that the column will be derived from. For example, if you POST a JSON event like this: ```json theme={"system"} { "greeting": { "message": "hello, world" } } ``` Then you can use the JSON property "greeting.message" to extract "hello, world" to a column. The column type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. Whether the column's type is nullable or not. ## WebhookDataSourceColumnInput The fields for specifying a column in a Webhook Data Source's table. The column name. It has to be unique within a table. The JSON property that the column will be derived from. For example, if you POST a JSON event like this: ```json theme={"system"} { "greeting": { "message": "hello, world" } } ``` Then you can use the JSON property "greeting.message" to extract "hello, world" to a column. The column type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. Whether the column's type is nullable or not. ## TableSettingsInput A Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse. The ClickHouse table engine for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. A Data Pool's table engine. Field for specifying the MergeTree table engine. Parameters for the MergeTree table engine. The type is always `MERGE_TREE`. See TableEngineType Field for specifying the ReplacingMergeTree table engine. Parameters for the ReplacingMergeTree table engine. The type is always `REPLACING_MERGE_TREE`. See TableEngineType The `ver` parameter to the ReplacingMergeTree engine. Field for specifying the SummingMergeTree table engine. Parameters for the SummingMergeTree table engine. The type is always `SUMMING_MERGE_TREE`. See TableEngineType The columns argument for the SummingMergeTree table engine Field for specifying the AggregatingMergeTree table engine. Parameters for the AggregatingMergeTree table engine. The type is always `AGGREGATING_MERGE_TREE`. See TableEngineType Field for specifying the PostgreSQL table engine. Parameters for the PostgreSQL table engine. The type is always `POSTGRESQL`. See TableEngineType The PARTITION BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The PRIMARY KEY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The ORDER BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The TTL clause for the Data Pool's table. ## TableEngineInput A Data Pool's table engine. Field for specifying the MergeTree table engine. Parameters for the MergeTree table engine. The type is always `MERGE_TREE`. ClickHouse table engine types. * `MERGE_TREE`: The MergeTree table engine. * `REPLACING_MERGE_TREE`: The ReplacingMergeTree table engine. * `SUMMING_MERGE_TREE`: The SummingMergeTree table engine. * `AGGREGATING_MERGE_TREE`: The AggregatingMergeTree table engine. * `POSTGRESQL`: The PostgreSQL table engine. Field for specifying the ReplacingMergeTree table engine. Parameters for the ReplacingMergeTree table engine. The type is always `REPLACING_MERGE_TREE`. ClickHouse table engine types. * `MERGE_TREE`: The MergeTree table engine. * `REPLACING_MERGE_TREE`: The ReplacingMergeTree table engine. * `SUMMING_MERGE_TREE`: The SummingMergeTree table engine. * `AGGREGATING_MERGE_TREE`: The AggregatingMergeTree table engine. * `POSTGRESQL`: The PostgreSQL table engine. The `ver` parameter to the ReplacingMergeTree engine. Field for specifying the SummingMergeTree table engine. Parameters for the SummingMergeTree table engine. The type is always `SUMMING_MERGE_TREE`. ClickHouse table engine types. * `MERGE_TREE`: The MergeTree table engine. * `REPLACING_MERGE_TREE`: The ReplacingMergeTree table engine. * `SUMMING_MERGE_TREE`: The SummingMergeTree table engine. * `AGGREGATING_MERGE_TREE`: The AggregatingMergeTree table engine. * `POSTGRESQL`: The PostgreSQL table engine. The columns argument for the SummingMergeTree table engine Field for specifying the AggregatingMergeTree table engine. Parameters for the AggregatingMergeTree table engine. The type is always `AGGREGATING_MERGE_TREE`. ClickHouse table engine types. * `MERGE_TREE`: The MergeTree table engine. * `REPLACING_MERGE_TREE`: The ReplacingMergeTree table engine. * `SUMMING_MERGE_TREE`: The SummingMergeTree table engine. * `AGGREGATING_MERGE_TREE`: The AggregatingMergeTree table engine. * `POSTGRESQL`: The PostgreSQL table engine. Field for specifying the PostgreSQL table engine. Parameters for the PostgreSQL table engine. The type is always `POSTGRESQL`. ClickHouse table engine types. * `MERGE_TREE`: The MergeTree table engine. * `REPLACING_MERGE_TREE`: The ReplacingMergeTree table engine. * `SUMMING_MERGE_TREE`: The SummingMergeTree table engine. * `AGGREGATING_MERGE_TREE`: The AggregatingMergeTree table engine. * `POSTGRESQL`: The PostgreSQL table engine. ## MergeTreeTableEngineInput Parameters for the MergeTree table engine. The type is always `MERGE_TREE`. ClickHouse table engine types. * `MERGE_TREE`: The MergeTree table engine. * `REPLACING_MERGE_TREE`: The ReplacingMergeTree table engine. * `SUMMING_MERGE_TREE`: The SummingMergeTree table engine. * `AGGREGATING_MERGE_TREE`: The AggregatingMergeTree table engine. * `POSTGRESQL`: The PostgreSQL table engine. ## ReplacingMergeTreeTableEngineInput Parameters for the ReplacingMergeTree table engine. The type is always `REPLACING_MERGE_TREE`. ClickHouse table engine types. * `MERGE_TREE`: The MergeTree table engine. * `REPLACING_MERGE_TREE`: The ReplacingMergeTree table engine. * `SUMMING_MERGE_TREE`: The SummingMergeTree table engine. * `AGGREGATING_MERGE_TREE`: The AggregatingMergeTree table engine. * `POSTGRESQL`: The PostgreSQL table engine. The `ver` parameter to the ReplacingMergeTree engine. ## SummingMergeTreeTableEngineInput Parameters for the SummingMergeTree table engine. The type is always `SUMMING_MERGE_TREE`. ClickHouse table engine types. * `MERGE_TREE`: The MergeTree table engine. * `REPLACING_MERGE_TREE`: The ReplacingMergeTree table engine. * `SUMMING_MERGE_TREE`: The SummingMergeTree table engine. * `AGGREGATING_MERGE_TREE`: The AggregatingMergeTree table engine. * `POSTGRESQL`: The PostgreSQL table engine. The columns argument for the SummingMergeTree table engine ## AggregatingMergeTreeTableEngineInput Parameters for the AggregatingMergeTree table engine. The type is always `AGGREGATING_MERGE_TREE`. ClickHouse table engine types. * `MERGE_TREE`: The MergeTree table engine. * `REPLACING_MERGE_TREE`: The ReplacingMergeTree table engine. * `SUMMING_MERGE_TREE`: The SummingMergeTree table engine. * `AGGREGATING_MERGE_TREE`: The AggregatingMergeTree table engine. * `POSTGRESQL`: The PostgreSQL table engine. ## PostgreSqlTableEngineInput Parameters for the PostgreSQL table engine. The type is always `POSTGRESQL`. ClickHouse table engine types. * `MERGE_TREE`: The MergeTree table engine. * `REPLACING_MERGE_TREE`: The ReplacingMergeTree table engine. * `SUMMING_MERGE_TREE`: The SummingMergeTree table engine. * `AGGREGATING_MERGE_TREE`: The AggregatingMergeTree table engine. * `POSTGRESQL`: The PostgreSQL table engine. ## BackfillOptionsInput Whether historical data should be backfilled or not Defines the order in which historical data is backfilled. Defaults to `OLDEST_FIRST` if not specified. The available options for partition processing order. * `OLDEST_FIRST`: Partitions are processed in ascending order, starting with the oldest. * `NEWEST_FIRST`: Partitions are processed in descending order, starting with the newest. ## CreateMaterializedViewInput The fields for creating a Materialized View. The Materialized View's unique name. If not specified, Propel will set the ID as the unique name. The Materialized View's description. The SQL that the Materialized View will execute. By default, a destination Data Pool with default settings will be created for the Materialized View; however, you can customize the destination Data Pool (or point to an existing Data Pool), by setting this field. Use this to target an existing Data Pool or the engine settings of a new Data Pool. The fields for targeting an existing Data Pool or a new Data Pool. If specified, the Materialized View will target an existing Data Pool. Ensure the Data Pool's schema is compatible with your Materialized View's SQL statement. The ID of the Data Pool. The name of the Data Pool. If specified, the Materialized View will create and target a new Data Pool. You can further customize the new Data Pool's engine settings. The fields for customizing a new Data Pool that a Materialized View will target. The Data Pool's unique name. The Data Pool's description. Optionally specify the Data Pool's primary timestamp. This will influence the Data Pool's engine settings. See TimestampInput Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics. Override the Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse, and a default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if any. You can override these defaults in order to specify a custom table engine, custom ORDER BY, etc. See TableSettingsInput Optionally specify the Data Pool's unique ID. This will influence the Data Pool's engine settings. deprecated: Will be removed; use table settings to define the primary key. See UniqueIdInput By default, a Materialized View only applies to records added after its creation. This option allows to backfill all the data that was present before the Materialized View creation. Whether historical data should be backfilled or not Defines the order in which historical data is backfilled. Defaults to `OLDEST_FIRST` if not specified. The available options for partition processing order. * `OLDEST_FIRST`: Partitions are processed in ascending order, starting with the oldest. * `NEWEST_FIRST`: Partitions are processed in descending order, starting with the newest. ## ModifyMaterializedViewInput The fields for modifying a Materialized View. The ID of the Materialized View to modify. The Materialized View's new unique name. The Materialized View's new description. ## CreateMaterializedViewDestinationInput The fields for targeting an existing Data Pool or a new Data Pool. If specified, the Materialized View will target an existing Data Pool. Ensure the Data Pool's schema is compatible with your Materialized View's SQL statement. The ID of the Data Pool. The name of the Data Pool. If specified, the Materialized View will create and target a new Data Pool. You can further customize the new Data Pool's engine settings. The fields for customizing a new Data Pool that a Materialized View will target. The Data Pool's unique name. The Data Pool's description. Optionally specify the Data Pool's primary timestamp. This will influence the Data Pool's engine settings. The fields to specify the Data Pool's primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It will serve as the time dimension for your Metrics. The name of the column that represents the primary timestamp. Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics. Override the Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse, and a default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if any. You can override these defaults in order to specify a custom table engine, custom ORDER BY, etc. A Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse. The ClickHouse table engine for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. See TableEngineInput The PARTITION BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The PRIMARY KEY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The ORDER BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The TTL clause for the Data Pool's table. Optionally specify the Data Pool's unique ID. This will influence the Data Pool's engine settings. deprecated: Will be removed; use table settings to define the primary key. The fields to specify the Data Pool's unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool. The name of the column that represents the unique ID. ## CreateMaterializedViewDestinationNewDataPoolInput The fields for customizing a new Data Pool that a Materialized View will target. The Data Pool's unique name. The Data Pool's description. Optionally specify the Data Pool's primary timestamp. This will influence the Data Pool's engine settings. The fields to specify the Data Pool's primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It will serve as the time dimension for your Metrics. The name of the column that represents the primary timestamp. Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics. Override the Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse, and a default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if any. You can override these defaults in order to specify a custom table engine, custom ORDER BY, etc. A Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse. The ClickHouse table engine for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. A Data Pool's table engine. Field for specifying the MergeTree table engine. See MergeTreeTableEngineInput Field for specifying the ReplacingMergeTree table engine. See ReplacingMergeTreeTableEngineInput Field for specifying the SummingMergeTree table engine. See SummingMergeTreeTableEngineInput Field for specifying the AggregatingMergeTree table engine. See AggregatingMergeTreeTableEngineInput Field for specifying the PostgreSQL table engine. See PostgreSqlTableEngineInput The PARTITION BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The PRIMARY KEY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The ORDER BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The TTL clause for the Data Pool's table. Optionally specify the Data Pool's unique ID. This will influence the Data Pool's engine settings. deprecated: Will be removed; use table settings to define the primary key. The fields to specify the Data Pool's unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool. The name of the column that represents the unique ID. ## DimensionInput The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. ## TimestampInput The fields to specify the Data Pool's primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It will serve as the time dimension for your Metrics. The name of the column that represents the primary timestamp. ## TenantInput The fields to specify the Data Pool's tenant ID column. The tenant ID column is used to control access to your data with access policies. The name of the column that represents the tenant ID. ## UniqueIdInput The fields to specify the Data Pool's unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool. The name of the column that represents the unique ID. ## DataPoolColumnInput The name of the Data Source column that this Data Pool column derives from. The Data Pool column's type. This may differ from the corresponding Data Source column's type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. The ClickHouse type to use when `type` is set to `CLICKHOUSE`. Whether the column is nullable, meaning whether it accepts a null value. ## CreateDataPoolInputV2 The fields for creating a Data Pool. The Data Source that will be used to create the Data Pool. The table that the Data Pool will sync from. The table's primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It's part of what makes Propel fast for larger data sets. It will also serve as the time dimension for your Metrics. If you do not provide a primary timestamp column, you will need to supply an alternate timestamp when querying your Data Pool or its Metrics using the TimeRangeInput. The fields to specify the Data Pool's primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It will serve as the time dimension for your Metrics. The name of the column that represents the primary timestamp. The Data Pool's unique name. If not specified, Propel will set the ID as the unique name. The Data Pool's description. The list of columns. The name of the Data Source column that this Data Pool column derives from. The Data Pool column's type. This may differ from the corresponding Data Source column's type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. The ClickHouse type to use when `type` is set to `CLICKHOUSE`. Whether the column is nullable, meaning whether it accepts a null value. The Data Pool's syncing settings. The fields for modifying the Data Pool syncing. The available Data Pool sync intervals. Specify unit of time between attempts to sync data from your data warehouse. Note that the syncing interval is approximate. For example, setting the syncing interval to `EVERY_1_HOUR` does not mean that syncing will occur exactly on the hour. Instead, the syncing interval starts relative to when the Data Pool goes `LIVE`, and Propel will attempt to sync approximately every hour. Additionally, if you pause or resume syncing, this too can shift the syncing interval around. * `EVERY_1_MINUTE` * `EVERY_5_MINUTES` * `EVERY_15_MINUTES` * `EVERY_30_MINUTES` * `EVERY_1_HOUR` * `EVERY_4_HOURS` * `EVERY_12_HOURS` * `EVERY_24_HOURS` Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics. Override the Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse, and a default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if any. You can override these defaults in order to specify a custom table engine, custom ORDER BY, etc. A Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse. The ClickHouse table engine for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. A Data Pool's table engine. Field for specifying the MergeTree table engine. See MergeTreeTableEngineInput Field for specifying the ReplacingMergeTree table engine. See ReplacingMergeTreeTableEngineInput Field for specifying the SummingMergeTree table engine. See SummingMergeTreeTableEngineInput Field for specifying the AggregatingMergeTree table engine. See AggregatingMergeTreeTableEngineInput Field for specifying the PostgreSQL table engine. See PostgreSqlTableEngineInput The PARTITION BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The PRIMARY KEY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The ORDER BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The TTL clause for the Data Pool's table. The Data Pool's optional tenant ID column. The tenant ID column is used to control access to your data with access policies. deprecated: Will be removed; use Data Pool Access Policies instead The fields to specify the Data Pool's tenant ID column. The tenant ID column is used to control access to your data with access policies. The name of the column that represents the tenant ID. The Data Pool's unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool. deprecated: Will be removed; use table settings to define the primary key. The fields to specify the Data Pool's unique ID column. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated within the Data Pool. The name of the column that represents the unique ID. ## ModifyDataPoolInput The fields for modifying a Data Pool. The ID or unique name of the Data Pool to modify. The ID or unique name input. If both ID and unique name are provided, the ID will take precedence. The unique identifier of the object. The unique name of the object. The Data Pool's new unique name. The Data Pool's new description. The Data Pool's new data retention in days. The Data Pool's new syncing settings. The fields for modifying the Data Pool syncing. The available Data Pool sync intervals. Specify unit of time between attempts to sync data from your data warehouse. Note that the syncing interval is approximate. For example, setting the syncing interval to `EVERY_1_HOUR` does not mean that syncing will occur exactly on the hour. Instead, the syncing interval starts relative to when the Data Pool goes `LIVE`, and Propel will attempt to sync approximately every hour. Additionally, if you pause or resume syncing, this too can shift the syncing interval around. * `EVERY_1_MINUTE` * `EVERY_5_MINUTES` * `EVERY_15_MINUTES` * `EVERY_30_MINUTES` * `EVERY_1_HOUR` * `EVERY_4_HOURS` * `EVERY_12_HOURS` * `EVERY_24_HOURS` The table's primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It's part of what makes Propel fast for larger data sets. It will also serve as the time dimension for your Metrics. If you do not provide a primary timestamp column, you will need to supply an alternate timestamp when querying your Data Pool or its Metrics using the TimeRangeInput. The fields to specify the Data Pool's primary timestamp column. Propel uses the primary timestamp to order and partition your data in Data Pools. It will serve as the time dimension for your Metrics. The name of the column that represents the primary timestamp. Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics. ## DataPoolSyncingInput The fields for modifying the Data Pool syncing. The available Data Pool sync intervals. Specify unit of time between attempts to sync data from your data warehouse. Note that the syncing interval is approximate. For example, setting the syncing interval to `EVERY_1_HOUR` does not mean that syncing will occur exactly on the hour. Instead, the syncing interval starts relative to when the Data Pool goes `LIVE`, and Propel will attempt to sync approximately every hour. Additionally, if you pause or resume syncing, this too can shift the syncing interval around. * `EVERY_1_MINUTE` * `EVERY_5_MINUTES` * `EVERY_15_MINUTES` * `EVERY_30_MINUTES` * `EVERY_1_HOUR` * `EVERY_4_HOURS` * `EVERY_12_HOURS` * `EVERY_24_HOURS` ## DataGridInput The fields for querying Data Grid records. The Data Pool to be queried. The ID of the Data Pool. The name of the Data Pool. The time range for retrieving the records. The fields required to specify the time range for a time series, counter, or leaderboard Metric query. If no relative or absolute time ranges are provided, Propel defaults to an absolute time range beginning with the earliest record in the Metric's Data Pool and ending with the latest record. If both relative and absolute time ranges are provided, the relative time range will take precedence. If a `LAST_N` relative time period is selected, an `n` ≥ 1 must be provided. If no `n` is provided or `n` \< 1, a `BAD_REQUEST` error will be returned. The timestamp field to use when querying. Defaults to the timestamp configured on the Data Pool or Metric, if any. Set this to filter on an alternative timestamp field. The relative time period. The Relative time ranges are based on the current date and time. `THIS` - The current unit of time. For example, if today is June 8, 2022, and `THIS_MONTH` is selected, then data for June 2022 would be returned. `PREVIOUS` - The previous unit of time. For example, if today is June 8, 2022, and `PREVIOUS_MONTH` is selected, then data for May 2022 would be returned. It excludes the current unit of time. `NEXT` - The next unit of time. For example, if today is June 8, 2022, and `NEXT_MONTH` is selected, then data for July 2022 would be returned. It excludes the current unit of time. `LAST_N` - The last `n` units of time, including the current one. For example, if today is June 8, 2022 and `LAST_N_YEARS` with `n` = 3 is selected, then data for 2020, 2021, and 2022 will be returned. It will include the current time period. * `THIS_HOUR`: Starts at the zeroth minute of the current hour and continues for 60 minutes. * `TODAY`: Starts at 12:00:00 AM of the current day and continues for 24 hours. * `THIS_WEEK`: Starts on Monday, 12:00:00 AM of the current week and continues for seven days. * `THIS_MONTH`: Starts at 12:00:00 AM on the first day of the current month and continues for the duration of the month. * `THIS_QUARTER`: Starts at 12:00:00 AM on the first day of the current calendar quarter and continues for the duration of the quarter. * `THIS_YEAR`: Starts on January 1st, 12:00:00 AM of the current year and continues for the duration of the year. * `PREVIOUS_HOUR`: Starts at the zeroth minute of the previous hour and continues for 60 minutes. * `YESTERDAY`: Starts at 12:00:00 AM on the day before the today and continues for 24 hours. * `PREVIOUS_WEEK`: Starts on Monday, 12:00:00 AM, a week before the current week, and continues for seven days. * `PREVIOUS_MONTH`: Starts at 12:00:00 AM on the first day of the month before the current month and continues for the duration of the month. * `PREVIOUS_QUARTER`: Starts at 12:00:00 AM on the first day of the calendar quarter before the current quarter and continues for the duration of the quarter. * `PREVIOUS_YEAR`: Starts on January 1st, 12:00:00 AM, the year before the current year, and continues for the duration of the year. * `NEXT_HOUR`: Starts at the zeroth minute of the next hour and continues for 60 minutes. * `TOMORROW`: " Starts at 12:00:00 AM, the day after the current day, and continues for 24 hours. * `NEXT_WEEK`: Starts on Monday, 12:00:00 AM, the week after the current week, and continues for the duration of the week. * `NEXT_MONTH`: Starts at 12:00:00 AM on the first day of the next month and continues for the duration of the month. * `NEXT_QUARTER`: Starts at 12:00:00 AM on the first day of the next calendar quarter and continues for the duration of the quarter. * `NEXT_YEAR`: Starts on January 1st, 12:00:00 AM of the next year and continues for the duration of the year. * `LAST_N_MINUTES`: Starts at the zeroth second `n` - 1 minute(s) before the current minute and continues through the current minute. It includes this minute. * `LAST_N_HOURS`: Starts at the zeroth minute of the `n` - 1 hour(s) before the current hour, and continues through the current hour. It includes this hour. * `LAST_N_DAYS`: Starts at 12:00:00 AM, `n` - 1 day(s) before the current day, and continues through the current day. It includes today. * `LAST_N_WEEKS`: Starts on Monday, 12:00:00 AM, `n` - 1 week(s) before the current week, and continues through the current week. It includes this week. * `LAST_N_MONTHS`: Starts at 12:00:00 AM on the first day of the month, `n` - 1 month(s) before the current month, and continues through the current month. It includes this month. * `LAST_N_QUARTERS`: Starts at 12:00:00 AM on the first day of the calendar quarter `n` - 1 quarter(s) before the current quarter and continues through the current quarter. It includes this quarter. * `LAST_N_YEARS`: Starts on January 1st, 12:00:00 AM of the year `n` - 1 year(s) before the current year and continues through the current year. It includes this year. * `LAST_15_MINUTES` * `LAST_30_MINUTES` * `LAST_HOUR` * `LAST_4_HOURS` * `LAST_12_HOURS` * `LAST_24_HOURS` * `LAST_7_DAYS` * `LAST_30_DAYS` * `LAST_90_DAYS` * `LAST_3_MONTHS` * `LAST_6_MONTHS` * `LAST_YEAR` * `LAST_2_YEARS` * `LAST_5_YEARS` The number of time units for the `LAST_N` relative periods. The optional start timestamp (inclusive). Defaults to the timestamp of the earliest record in the Data Pool. The optional end timestamp (exclusive). Defaults to the timestamp of the latest record in the Data Pool. The time zone to use. Dates and times are always returned in UTC, but setting the time zone influences relative time ranges and granularities. You can set this to "America/Los\_Angeles", "Europe/Berlin", or any other value in the [IANA time zone database](https://en.wikipedia.org/wiki/Tz_database). Defaults to "UTC". The columns to retrieve. The index of the column to order the table by. The index is 1-based. If not provided, records will be ordered by their timestamp by default. The sort order of the rows. It can be ascending (`ASC`) or descending (`DESC`) order. Defaults to descending (`DESC`) order when not provided. The available sort orders. * `ASC`: Sort in ascending order. * `DESC`: Sort in descending order. The filters to apply to the records, in the form of SQL. You may only filter on columns included in the `columns` array input. The number of rows to be returned when paging forward. It can be a number between 1 and 1,000. The cursor to use when paging forward. The number of rows to be returned when paging forward. It can be a number between 1 and 1,000. The cursor to use when paging backward. The filters to apply to the records. You may only filter on columns included in the `columns` array input. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## MetricReportInput The fields for querying a Metric Report. A Metric Report is a table whose columns include dimensions and Metric values, calculated over a given time range. The time range for calculating the Metric Report. The fields required to specify the time range for a time series, counter, or leaderboard Metric query. If no relative or absolute time ranges are provided, Propel defaults to an absolute time range beginning with the earliest record in the Metric's Data Pool and ending with the latest record. If both relative and absolute time ranges are provided, the relative time range will take precedence. If a `LAST_N` relative time period is selected, an `n` ≥ 1 must be provided. If no `n` is provided or `n` \< 1, a `BAD_REQUEST` error will be returned. The timestamp field to use when querying. Defaults to the timestamp configured on the Data Pool or Metric, if any. Set this to filter on an alternative timestamp field. The relative time period. The Relative time ranges are based on the current date and time. `THIS` - The current unit of time. For example, if today is June 8, 2022, and `THIS_MONTH` is selected, then data for June 2022 would be returned. `PREVIOUS` - The previous unit of time. For example, if today is June 8, 2022, and `PREVIOUS_MONTH` is selected, then data for May 2022 would be returned. It excludes the current unit of time. `NEXT` - The next unit of time. For example, if today is June 8, 2022, and `NEXT_MONTH` is selected, then data for July 2022 would be returned. It excludes the current unit of time. `LAST_N` - The last `n` units of time, including the current one. For example, if today is June 8, 2022 and `LAST_N_YEARS` with `n` = 3 is selected, then data for 2020, 2021, and 2022 will be returned. It will include the current time period. * `THIS_HOUR`: Starts at the zeroth minute of the current hour and continues for 60 minutes. * `TODAY`: Starts at 12:00:00 AM of the current day and continues for 24 hours. * `THIS_WEEK`: Starts on Monday, 12:00:00 AM of the current week and continues for seven days. * `THIS_MONTH`: Starts at 12:00:00 AM on the first day of the current month and continues for the duration of the month. * `THIS_QUARTER`: Starts at 12:00:00 AM on the first day of the current calendar quarter and continues for the duration of the quarter. * `THIS_YEAR`: Starts on January 1st, 12:00:00 AM of the current year and continues for the duration of the year. * `PREVIOUS_HOUR`: Starts at the zeroth minute of the previous hour and continues for 60 minutes. * `YESTERDAY`: Starts at 12:00:00 AM on the day before the today and continues for 24 hours. * `PREVIOUS_WEEK`: Starts on Monday, 12:00:00 AM, a week before the current week, and continues for seven days. * `PREVIOUS_MONTH`: Starts at 12:00:00 AM on the first day of the month before the current month and continues for the duration of the month. * `PREVIOUS_QUARTER`: Starts at 12:00:00 AM on the first day of the calendar quarter before the current quarter and continues for the duration of the quarter. * `PREVIOUS_YEAR`: Starts on January 1st, 12:00:00 AM, the year before the current year, and continues for the duration of the year. * `NEXT_HOUR`: Starts at the zeroth minute of the next hour and continues for 60 minutes. * `TOMORROW`: " Starts at 12:00:00 AM, the day after the current day, and continues for 24 hours. * `NEXT_WEEK`: Starts on Monday, 12:00:00 AM, the week after the current week, and continues for the duration of the week. * `NEXT_MONTH`: Starts at 12:00:00 AM on the first day of the next month and continues for the duration of the month. * `NEXT_QUARTER`: Starts at 12:00:00 AM on the first day of the next calendar quarter and continues for the duration of the quarter. * `NEXT_YEAR`: Starts on January 1st, 12:00:00 AM of the next year and continues for the duration of the year. * `LAST_N_MINUTES`: Starts at the zeroth second `n` - 1 minute(s) before the current minute and continues through the current minute. It includes this minute. * `LAST_N_HOURS`: Starts at the zeroth minute of the `n` - 1 hour(s) before the current hour, and continues through the current hour. It includes this hour. * `LAST_N_DAYS`: Starts at 12:00:00 AM, `n` - 1 day(s) before the current day, and continues through the current day. It includes today. * `LAST_N_WEEKS`: Starts on Monday, 12:00:00 AM, `n` - 1 week(s) before the current week, and continues through the current week. It includes this week. * `LAST_N_MONTHS`: Starts at 12:00:00 AM on the first day of the month, `n` - 1 month(s) before the current month, and continues through the current month. It includes this month. * `LAST_N_QUARTERS`: Starts at 12:00:00 AM on the first day of the calendar quarter `n` - 1 quarter(s) before the current quarter and continues through the current quarter. It includes this quarter. * `LAST_N_YEARS`: Starts on January 1st, 12:00:00 AM of the year `n` - 1 year(s) before the current year and continues through the current year. It includes this year. * `LAST_15_MINUTES` * `LAST_30_MINUTES` * `LAST_HOUR` * `LAST_4_HOURS` * `LAST_12_HOURS` * `LAST_24_HOURS` * `LAST_7_DAYS` * `LAST_30_DAYS` * `LAST_90_DAYS` * `LAST_3_MONTHS` * `LAST_6_MONTHS` * `LAST_YEAR` * `LAST_2_YEARS` * `LAST_5_YEARS` The number of time units for the `LAST_N` relative periods. The optional start timestamp (inclusive). Defaults to the timestamp of the earliest record in the Data Pool. The optional end timestamp (exclusive). Defaults to the timestamp of the latest record in the Data Pool. The time zone to use. Dates and times are always returned in UTC, but setting the time zone influences relative time ranges and granularities. You can set this to "America/Los\_Angeles", "Europe/Berlin", or any other value in the [IANA time zone database](https://en.wikipedia.org/wiki/Tz_database). Defaults to "UTC". One or many dimensions to group the Metric values by. Typically, dimensions in a report are what you want to compare and rank. The fields for specifying a dimension to include in a Metric Report. The column name of the dimension to include in a Metric Report. This must match the name of a Data Pool column. The name to display in the `headers` array when displaying the report. This defaults to the column name if unspecified. The sort order for the dimension. It can be ascending (`ASC`) or descending (`DESC`) order. Defaults to ascending (`ASC`) order when not provided. The available sort orders. * `ASC`: Sort in ascending order. * `DESC`: Sort in descending order. One or more Metrics to include in the Metric Report. These will be broken down by `dimensions`. The fields for specifying a Metric to include in a Metric Report. The Metric to query. You can query a pre-configured Metric by ID or name, or you can query an ad hoc Metric that you define inline. The ID of a pre-configured Metric. The name of a pre-configured Metric. An ad hoc Custom Metric. See CustomMetricQueryInput An ad hoc Count Metric. See CountMetricQueryInput An ad hoc Sum Metric. See SumMetricQueryInput An ad hoc Average Metric. See AverageMetricQueryInput An ad hoc Min Metric. See MinMetricQueryInput An ad hoc Max Metric. See MaxMetricQueryInput An ad hoc Count Distinct Metric. See CountDistinctMetricQueryInput The name to display in the `headers` array when displaying the report. This defaults to the Metric's unique name if unspecified. The Query Filters to apply when calculating the Metric, in the form of SQL. The sort order for the Metric. It can be ascending (`ASC`) or descending (`DESC`) order. Defaults to descending (`DESC`) order when not provided. The available sort orders. * `ASC`: Sort in ascending order. * `DESC`: Sort in descending order. The Metric's unique name. If not specified, Propel will lookup the Metric by ID. deprecated: Use `metric` The Metric's ID. If not specified, Propel will lookup the Metric by unique name. deprecated: Use `metric` The Query Filters to apply when calculating the Metric. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. See FilterOperator The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. The Query Filters to apply when building the Metric Report, in the form of SQL. These can be used to filter out rows. The index of the column to order the Metric Report by. The index is 1-based and defaults to the first Metric column. In other words, by default, reports are ordered by the first Metric; however, you can order by the second Metric, third Metric, etc., by overriding the `orderByColumn` input. You can also order by dimensions this way. The number of rows to be returned when paging forward. It can be a number between 1 and 1,000. The cursor to use when paging forward. The number of rows to be returned when paging forward. It can be a number between 1 and 1,000. The cursor to use when paging backward. The Query Filters to apply when building the Metric Report. These can be used to filter out rows. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## MetricReportDimensionInput The fields for specifying a dimension to include in a Metric Report. The column name of the dimension to include in a Metric Report. This must match the name of a Data Pool column. The name to display in the `headers` array when displaying the report. This defaults to the column name if unspecified. The sort order for the dimension. It can be ascending (`ASC`) or descending (`DESC`) order. Defaults to ascending (`ASC`) order when not provided. The available sort orders. * `ASC`: Sort in ascending order. * `DESC`: Sort in descending order. ## MetricReportMetricInput The fields for specifying a Metric to include in a Metric Report. The Metric to query. You can query a pre-configured Metric by ID or name, or you can query an ad hoc Metric that you define inline. The ID of a pre-configured Metric. The name of a pre-configured Metric. An ad hoc Custom Metric. The Data Pool to which this Metric belongs. See DataPoolInput Custom expression for defining the Metric. An ad hoc Count Metric. The Data Pool to which this Metric belongs. See DataPoolInput An ad hoc Sum Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to be summed. See DimensionInput An ad hoc Average Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to be averaged. See DimensionInput An ad hoc Min Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to calculate the minimum from. See DimensionInput An ad hoc Max Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to calculate the maximum from. See DimensionInput An ad hoc Count Distinct Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to count distinct values from. See DimensionInput The name to display in the `headers` array when displaying the report. This defaults to the Metric's unique name if unspecified. The Query Filters to apply when calculating the Metric, in the form of SQL. The sort order for the Metric. It can be ascending (`ASC`) or descending (`DESC`) order. Defaults to descending (`DESC`) order when not provided. The available sort orders. * `ASC`: Sort in ascending order. * `DESC`: Sort in descending order. The Metric's unique name. If not specified, Propel will lookup the Metric by ID. deprecated: Use `metric` The Metric's ID. If not specified, Propel will lookup the Metric by unique name. deprecated: Use `metric` The Query Filters to apply when calculating the Metric. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## CreateCountMetricInput The fields for creating a new Count Metric. The Data Pool that powers this Metric. The Metric's unique name. If not specified, Propel will set the ID as unique name. The Metric's description. The Metric's Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. The Metric's Dimensions. Dimensions define the columns that will be available to filter the Metric at query time. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The Metric's Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## CreateSumMetricInput The fields for creating a new Sum Metric. The Data Pool that powers this Metric. The Metric's unique name. If not specified, Propel will set the ID as unique name. The Metric's description. The Metric's Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. The Metric's Dimensions. Dimensions define the columns that will be available to filter the Metric at query time. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The column to be summed. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The Metric's Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## CreateAverageMetricInput The fields for creating a new Average Metric. The Data Pool that powers this Metric. The Metric's unique name. The Metric's description. The Metric's Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. The Metric's Dimensions. Dimensions define the columns that will be available to filter the Metric at query time. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The column to be averaged. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The Metric's Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## CreateMinMetricInput The fields for creating a new Minimum (Min) Metric. The Data Pool that powers this Metric. The Metric's unique name. If not specified, Propel will set the ID as unique name. The Metric's description. The Metric's Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. The Metric's Dimensions. Dimensions define the columns that will be available to filter the Metric at query time. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The Metric's Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## CreateMaxMetricInput The fields for creating a new Maximum (Max) Metric. The Data Pool that powers this Metric. The Metric's unique name. If not specified, Propel will set the ID as unique name. The Metric's description. The Metric's Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. The Metric's Dimensions. Dimensions define the columns that will be available to filter the Metric at query time. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The column to calculate the maximum from. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The Metric's Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## CreateCountDistinctMetricInput The fields for creating a new Count Distinct Metric. The Data Pool that powers this Metric. The Metric's unique name. If not specified, Propel will set the ID as unique name. The Metric's description. The Metric's Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. The Metric's Dimensions. Dimensions define the columns that will be available to filter the Metric at query time. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The Dimension over which the count distinct operation is going to be performed. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The Metric's Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## CreateCustomMetricInput The fields for creating a new Custom Metric. The Data Pool that powers this Metric. The Metric's unique name. If not specified, Propel will set the ID as unique name. The Metric's description. The Metric's Filters, in the form of SQL. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. The Metric's Dimensions. Dimensions define the columns that will be available to filter the Metric at query time. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The expression that defines the aggregation function for this Metric. The Metric's Filters. Metric Filters allow defining a Metric with a subset of records from the given Data Pool. If no Filters are present, all records will be included. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## ModifyMetricInput The fields for modifying a Metric. The ID of the Metric to modify. The Metric's new unique name. The Metric's new description. The Metric's new Dimensions. Used to add or remove Dimensions. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The Metric's new Filters, in the form of SQL. Used to add or remove Metric Filters. Enables or disables access control for the Metric. The Metric's new Filters. Used to add or remove Metric Filters. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## MigrateMetricInput The fields for migrating a Metric's Data Pool. The Metric that is going to be migrated. The DataPool to which the Metric is going to be migrated. ## DataPoolInput The ID of the Data Pool. The name of the Data Pool. ## CustomMetricQueryInput The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. Custom expression for defining the Metric. ## CountMetricQueryInput The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. ## SumMetricQueryInput The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. The column to be summed. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. ## AverageMetricQueryInput The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. The column to be averaged. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. ## MinMetricQueryInput The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. The column to calculate the minimum from. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. ## MaxMetricQueryInput The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. The column to calculate the maximum from. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. ## CountDistinctMetricQueryInput The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. The column to count distinct values from. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. ## MetricInput The ID of a pre-configured Metric. The name of a pre-configured Metric. An ad hoc Custom Metric. The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. Custom expression for defining the Metric. An ad hoc Count Metric. The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. An ad hoc Sum Metric. The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. The column to be summed. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. An ad hoc Average Metric. The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. The column to be averaged. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. An ad hoc Min Metric. The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. The column to calculate the minimum from. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. An ad hoc Max Metric. The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. The column to calculate the maximum from. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. An ad hoc Count Distinct Metric. The Data Pool to which this Metric belongs. The ID of the Data Pool. The name of the Data Pool. The column to count distinct values from. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. ## CounterInput The fields for querying a Metric in counter format. A Metric's counter query returns a single value over a given time range. The Metric to query. You can query a pre-configured Metric by ID or name, or you can query an ad hoc Metric that you define inline. The ID of a pre-configured Metric. The name of a pre-configured Metric. An ad hoc Custom Metric. The Data Pool to which this Metric belongs. See DataPoolInput Custom expression for defining the Metric. An ad hoc Count Metric. The Data Pool to which this Metric belongs. See DataPoolInput An ad hoc Sum Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to be summed. See DimensionInput An ad hoc Average Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to be averaged. See DimensionInput An ad hoc Min Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to calculate the minimum from. See DimensionInput An ad hoc Max Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to calculate the maximum from. See DimensionInput An ad hoc Count Distinct Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to count distinct values from. See DimensionInput The time range for calculating the counter. The fields required to specify the time range for a time series, counter, or leaderboard Metric query. If no relative or absolute time ranges are provided, Propel defaults to an absolute time range beginning with the earliest record in the Metric's Data Pool and ending with the latest record. If both relative and absolute time ranges are provided, the relative time range will take precedence. If a `LAST_N` relative time period is selected, an `n` ≥ 1 must be provided. If no `n` is provided or `n` \< 1, a `BAD_REQUEST` error will be returned. The timestamp field to use when querying. Defaults to the timestamp configured on the Data Pool or Metric, if any. Set this to filter on an alternative timestamp field. The relative time period. The Relative time ranges are based on the current date and time. `THIS` - The current unit of time. For example, if today is June 8, 2022, and `THIS_MONTH` is selected, then data for June 2022 would be returned. `PREVIOUS` - The previous unit of time. For example, if today is June 8, 2022, and `PREVIOUS_MONTH` is selected, then data for May 2022 would be returned. It excludes the current unit of time. `NEXT` - The next unit of time. For example, if today is June 8, 2022, and `NEXT_MONTH` is selected, then data for July 2022 would be returned. It excludes the current unit of time. `LAST_N` - The last `n` units of time, including the current one. For example, if today is June 8, 2022 and `LAST_N_YEARS` with `n` = 3 is selected, then data for 2020, 2021, and 2022 will be returned. It will include the current time period. * `THIS_HOUR`: Starts at the zeroth minute of the current hour and continues for 60 minutes. * `TODAY`: Starts at 12:00:00 AM of the current day and continues for 24 hours. * `THIS_WEEK`: Starts on Monday, 12:00:00 AM of the current week and continues for seven days. * `THIS_MONTH`: Starts at 12:00:00 AM on the first day of the current month and continues for the duration of the month. * `THIS_QUARTER`: Starts at 12:00:00 AM on the first day of the current calendar quarter and continues for the duration of the quarter. * `THIS_YEAR`: Starts on January 1st, 12:00:00 AM of the current year and continues for the duration of the year. * `PREVIOUS_HOUR`: Starts at the zeroth minute of the previous hour and continues for 60 minutes. * `YESTERDAY`: Starts at 12:00:00 AM on the day before the today and continues for 24 hours. * `PREVIOUS_WEEK`: Starts on Monday, 12:00:00 AM, a week before the current week, and continues for seven days. * `PREVIOUS_MONTH`: Starts at 12:00:00 AM on the first day of the month before the current month and continues for the duration of the month. * `PREVIOUS_QUARTER`: Starts at 12:00:00 AM on the first day of the calendar quarter before the current quarter and continues for the duration of the quarter. * `PREVIOUS_YEAR`: Starts on January 1st, 12:00:00 AM, the year before the current year, and continues for the duration of the year. * `NEXT_HOUR`: Starts at the zeroth minute of the next hour and continues for 60 minutes. * `TOMORROW`: " Starts at 12:00:00 AM, the day after the current day, and continues for 24 hours. * `NEXT_WEEK`: Starts on Monday, 12:00:00 AM, the week after the current week, and continues for the duration of the week. * `NEXT_MONTH`: Starts at 12:00:00 AM on the first day of the next month and continues for the duration of the month. * `NEXT_QUARTER`: Starts at 12:00:00 AM on the first day of the next calendar quarter and continues for the duration of the quarter. * `NEXT_YEAR`: Starts on January 1st, 12:00:00 AM of the next year and continues for the duration of the year. * `LAST_N_MINUTES`: Starts at the zeroth second `n` - 1 minute(s) before the current minute and continues through the current minute. It includes this minute. * `LAST_N_HOURS`: Starts at the zeroth minute of the `n` - 1 hour(s) before the current hour, and continues through the current hour. It includes this hour. * `LAST_N_DAYS`: Starts at 12:00:00 AM, `n` - 1 day(s) before the current day, and continues through the current day. It includes today. * `LAST_N_WEEKS`: Starts on Monday, 12:00:00 AM, `n` - 1 week(s) before the current week, and continues through the current week. It includes this week. * `LAST_N_MONTHS`: Starts at 12:00:00 AM on the first day of the month, `n` - 1 month(s) before the current month, and continues through the current month. It includes this month. * `LAST_N_QUARTERS`: Starts at 12:00:00 AM on the first day of the calendar quarter `n` - 1 quarter(s) before the current quarter and continues through the current quarter. It includes this quarter. * `LAST_N_YEARS`: Starts on January 1st, 12:00:00 AM of the year `n` - 1 year(s) before the current year and continues through the current year. It includes this year. * `LAST_15_MINUTES` * `LAST_30_MINUTES` * `LAST_HOUR` * `LAST_4_HOURS` * `LAST_12_HOURS` * `LAST_24_HOURS` * `LAST_7_DAYS` * `LAST_30_DAYS` * `LAST_90_DAYS` * `LAST_3_MONTHS` * `LAST_6_MONTHS` * `LAST_YEAR` * `LAST_2_YEARS` * `LAST_5_YEARS` The number of time units for the `LAST_N` relative periods. The optional start timestamp (inclusive). Defaults to the timestamp of the earliest record in the Data Pool. The optional end timestamp (exclusive). Defaults to the timestamp of the latest record in the Data Pool. The time zone to use. Dates and times are always returned in UTC, but setting the time zone influences relative time ranges and granularities. You can set this to "America/Los\_Angeles", "Europe/Berlin", or any other value in the [IANA time zone database](https://en.wikipedia.org/wiki/Tz_database). Defaults to "UTC". The Query Filters to apply before retrieving the counter data, in the form of SQL. If no Query Filters are provided, all data is included. The ID of the Metric to query. Required if `metricName` is not specified. deprecated: Use `metric` The name of the Metric to query. Required if `metricId` is not specified. deprecated: Use `metric` The Query Filters to apply before retrieving the counter data. If no Query Filters are provided, all data is included. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## TimeSeriesInput The fields for querying a Metric in time series format. A Metric's time series query returns the values over a given time range aggregated by a given time granularity; day, month, or year, for example. The Metric to Query. It can be a pre-created one or it can be inlined here. The ID of a pre-configured Metric. The name of a pre-configured Metric. An ad hoc Custom Metric. The Data Pool to which this Metric belongs. See DataPoolInput Custom expression for defining the Metric. An ad hoc Count Metric. The Data Pool to which this Metric belongs. See DataPoolInput An ad hoc Sum Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to be summed. See DimensionInput An ad hoc Average Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to be averaged. See DimensionInput An ad hoc Min Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to calculate the minimum from. See DimensionInput An ad hoc Max Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to calculate the maximum from. See DimensionInput An ad hoc Count Distinct Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to count distinct values from. See DimensionInput The time range for calculating the time series. The fields required to specify the time range for a time series, counter, or leaderboard Metric query. If no relative or absolute time ranges are provided, Propel defaults to an absolute time range beginning with the earliest record in the Metric's Data Pool and ending with the latest record. If both relative and absolute time ranges are provided, the relative time range will take precedence. If a `LAST_N` relative time period is selected, an `n` ≥ 1 must be provided. If no `n` is provided or `n` \< 1, a `BAD_REQUEST` error will be returned. The timestamp field to use when querying. Defaults to the timestamp configured on the Data Pool or Metric, if any. Set this to filter on an alternative timestamp field. The relative time period. The Relative time ranges are based on the current date and time. `THIS` - The current unit of time. For example, if today is June 8, 2022, and `THIS_MONTH` is selected, then data for June 2022 would be returned. `PREVIOUS` - The previous unit of time. For example, if today is June 8, 2022, and `PREVIOUS_MONTH` is selected, then data for May 2022 would be returned. It excludes the current unit of time. `NEXT` - The next unit of time. For example, if today is June 8, 2022, and `NEXT_MONTH` is selected, then data for July 2022 would be returned. It excludes the current unit of time. `LAST_N` - The last `n` units of time, including the current one. For example, if today is June 8, 2022 and `LAST_N_YEARS` with `n` = 3 is selected, then data for 2020, 2021, and 2022 will be returned. It will include the current time period. * `THIS_HOUR`: Starts at the zeroth minute of the current hour and continues for 60 minutes. * `TODAY`: Starts at 12:00:00 AM of the current day and continues for 24 hours. * `THIS_WEEK`: Starts on Monday, 12:00:00 AM of the current week and continues for seven days. * `THIS_MONTH`: Starts at 12:00:00 AM on the first day of the current month and continues for the duration of the month. * `THIS_QUARTER`: Starts at 12:00:00 AM on the first day of the current calendar quarter and continues for the duration of the quarter. * `THIS_YEAR`: Starts on January 1st, 12:00:00 AM of the current year and continues for the duration of the year. * `PREVIOUS_HOUR`: Starts at the zeroth minute of the previous hour and continues for 60 minutes. * `YESTERDAY`: Starts at 12:00:00 AM on the day before the today and continues for 24 hours. * `PREVIOUS_WEEK`: Starts on Monday, 12:00:00 AM, a week before the current week, and continues for seven days. * `PREVIOUS_MONTH`: Starts at 12:00:00 AM on the first day of the month before the current month and continues for the duration of the month. * `PREVIOUS_QUARTER`: Starts at 12:00:00 AM on the first day of the calendar quarter before the current quarter and continues for the duration of the quarter. * `PREVIOUS_YEAR`: Starts on January 1st, 12:00:00 AM, the year before the current year, and continues for the duration of the year. * `NEXT_HOUR`: Starts at the zeroth minute of the next hour and continues for 60 minutes. * `TOMORROW`: " Starts at 12:00:00 AM, the day after the current day, and continues for 24 hours. * `NEXT_WEEK`: Starts on Monday, 12:00:00 AM, the week after the current week, and continues for the duration of the week. * `NEXT_MONTH`: Starts at 12:00:00 AM on the first day of the next month and continues for the duration of the month. * `NEXT_QUARTER`: Starts at 12:00:00 AM on the first day of the next calendar quarter and continues for the duration of the quarter. * `NEXT_YEAR`: Starts on January 1st, 12:00:00 AM of the next year and continues for the duration of the year. * `LAST_N_MINUTES`: Starts at the zeroth second `n` - 1 minute(s) before the current minute and continues through the current minute. It includes this minute. * `LAST_N_HOURS`: Starts at the zeroth minute of the `n` - 1 hour(s) before the current hour, and continues through the current hour. It includes this hour. * `LAST_N_DAYS`: Starts at 12:00:00 AM, `n` - 1 day(s) before the current day, and continues through the current day. It includes today. * `LAST_N_WEEKS`: Starts on Monday, 12:00:00 AM, `n` - 1 week(s) before the current week, and continues through the current week. It includes this week. * `LAST_N_MONTHS`: Starts at 12:00:00 AM on the first day of the month, `n` - 1 month(s) before the current month, and continues through the current month. It includes this month. * `LAST_N_QUARTERS`: Starts at 12:00:00 AM on the first day of the calendar quarter `n` - 1 quarter(s) before the current quarter and continues through the current quarter. It includes this quarter. * `LAST_N_YEARS`: Starts on January 1st, 12:00:00 AM of the year `n` - 1 year(s) before the current year and continues through the current year. It includes this year. * `LAST_15_MINUTES` * `LAST_30_MINUTES` * `LAST_HOUR` * `LAST_4_HOURS` * `LAST_12_HOURS` * `LAST_24_HOURS` * `LAST_7_DAYS` * `LAST_30_DAYS` * `LAST_90_DAYS` * `LAST_3_MONTHS` * `LAST_6_MONTHS` * `LAST_YEAR` * `LAST_2_YEARS` * `LAST_5_YEARS` The number of time units for the `LAST_N` relative periods. The optional start timestamp (inclusive). Defaults to the timestamp of the earliest record in the Data Pool. The optional end timestamp (exclusive). Defaults to the timestamp of the latest record in the Data Pool. The time zone to use. Dates and times are always returned in UTC, but setting the time zone influences relative time ranges and granularities. You can set this to "America/Los\_Angeles", "Europe/Berlin", or any other value in the [IANA time zone database](https://en.wikipedia.org/wiki/Tz_database). Defaults to "UTC". The time granularity (hour, day, month, etc.) to aggregate the Metric values by. The available time series granularities. Granularities define the unit of time to aggregate the Metric data for a time series query. For example, if the granularity is set to `DAY`, then the the time series query will return a label and a value for each day. If there are no records for a given time series granularity, Propel will return the label and a value of "0" so that the time series can be properly visualized. * `MINUTE`: Aggregates values by minute intervals. * `FIVE_MINUTES`: Aggregates values by 5-minute intervals. * `TEN_MINUTES`: Aggregates values by 10-minute intervals. * `FIFTEEN_MINUTES`: Aggregates values by 15-minute intervals. * `HOUR`: Aggregates values by hourly intervals. * `DAY`: Aggregates values by daily intervals. * `WEEK`: Aggregates values by weekly intervals. * `MONTH`: Aggregates values by monthly intervals. * `YEAR`: Aggregates values by yearly intervals. The Query Filters to apply before retrieving the time series data, in the form of SQL. If no Query Filters are provided, all data is included. Columns to group by. The ID of the Metric to query. Required if `metricName` is not specified. deprecated: Use `metric` The name of the Metric to query. Required if `metricId` is not specified. deprecated: Use `metric` The Query Filters to apply before retrieving the time series data. If no Query Filters are provided, all data is included. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## LeaderboardInput The fields for querying a Metric in leaderboard format. A Metric's leaderboard query returns an ordered table of Dimension and Metric values over a given time range. The Metric to query. You can query a pre-configured Metric by ID or name, or you can query an ad hoc Metric that you define inline. The ID of a pre-configured Metric. The name of a pre-configured Metric. An ad hoc Custom Metric. The Data Pool to which this Metric belongs. See DataPoolInput Custom expression for defining the Metric. An ad hoc Count Metric. The Data Pool to which this Metric belongs. See DataPoolInput An ad hoc Sum Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to be summed. See DimensionInput An ad hoc Average Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to be averaged. See DimensionInput An ad hoc Min Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to calculate the minimum from. See DimensionInput An ad hoc Max Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to calculate the maximum from. See DimensionInput An ad hoc Count Distinct Metric. The Data Pool to which this Metric belongs. See DataPoolInput The column to count distinct values from. See DimensionInput The time range for calculating the leaderboard. The fields required to specify the time range for a time series, counter, or leaderboard Metric query. If no relative or absolute time ranges are provided, Propel defaults to an absolute time range beginning with the earliest record in the Metric's Data Pool and ending with the latest record. If both relative and absolute time ranges are provided, the relative time range will take precedence. If a `LAST_N` relative time period is selected, an `n` ≥ 1 must be provided. If no `n` is provided or `n` \< 1, a `BAD_REQUEST` error will be returned. The timestamp field to use when querying. Defaults to the timestamp configured on the Data Pool or Metric, if any. Set this to filter on an alternative timestamp field. The relative time period. The Relative time ranges are based on the current date and time. `THIS` - The current unit of time. For example, if today is June 8, 2022, and `THIS_MONTH` is selected, then data for June 2022 would be returned. `PREVIOUS` - The previous unit of time. For example, if today is June 8, 2022, and `PREVIOUS_MONTH` is selected, then data for May 2022 would be returned. It excludes the current unit of time. `NEXT` - The next unit of time. For example, if today is June 8, 2022, and `NEXT_MONTH` is selected, then data for July 2022 would be returned. It excludes the current unit of time. `LAST_N` - The last `n` units of time, including the current one. For example, if today is June 8, 2022 and `LAST_N_YEARS` with `n` = 3 is selected, then data for 2020, 2021, and 2022 will be returned. It will include the current time period. * `THIS_HOUR`: Starts at the zeroth minute of the current hour and continues for 60 minutes. * `TODAY`: Starts at 12:00:00 AM of the current day and continues for 24 hours. * `THIS_WEEK`: Starts on Monday, 12:00:00 AM of the current week and continues for seven days. * `THIS_MONTH`: Starts at 12:00:00 AM on the first day of the current month and continues for the duration of the month. * `THIS_QUARTER`: Starts at 12:00:00 AM on the first day of the current calendar quarter and continues for the duration of the quarter. * `THIS_YEAR`: Starts on January 1st, 12:00:00 AM of the current year and continues for the duration of the year. * `PREVIOUS_HOUR`: Starts at the zeroth minute of the previous hour and continues for 60 minutes. * `YESTERDAY`: Starts at 12:00:00 AM on the day before the today and continues for 24 hours. * `PREVIOUS_WEEK`: Starts on Monday, 12:00:00 AM, a week before the current week, and continues for seven days. * `PREVIOUS_MONTH`: Starts at 12:00:00 AM on the first day of the month before the current month and continues for the duration of the month. * `PREVIOUS_QUARTER`: Starts at 12:00:00 AM on the first day of the calendar quarter before the current quarter and continues for the duration of the quarter. * `PREVIOUS_YEAR`: Starts on January 1st, 12:00:00 AM, the year before the current year, and continues for the duration of the year. * `NEXT_HOUR`: Starts at the zeroth minute of the next hour and continues for 60 minutes. * `TOMORROW`: " Starts at 12:00:00 AM, the day after the current day, and continues for 24 hours. * `NEXT_WEEK`: Starts on Monday, 12:00:00 AM, the week after the current week, and continues for the duration of the week. * `NEXT_MONTH`: Starts at 12:00:00 AM on the first day of the next month and continues for the duration of the month. * `NEXT_QUARTER`: Starts at 12:00:00 AM on the first day of the next calendar quarter and continues for the duration of the quarter. * `NEXT_YEAR`: Starts on January 1st, 12:00:00 AM of the next year and continues for the duration of the year. * `LAST_N_MINUTES`: Starts at the zeroth second `n` - 1 minute(s) before the current minute and continues through the current minute. It includes this minute. * `LAST_N_HOURS`: Starts at the zeroth minute of the `n` - 1 hour(s) before the current hour, and continues through the current hour. It includes this hour. * `LAST_N_DAYS`: Starts at 12:00:00 AM, `n` - 1 day(s) before the current day, and continues through the current day. It includes today. * `LAST_N_WEEKS`: Starts on Monday, 12:00:00 AM, `n` - 1 week(s) before the current week, and continues through the current week. It includes this week. * `LAST_N_MONTHS`: Starts at 12:00:00 AM on the first day of the month, `n` - 1 month(s) before the current month, and continues through the current month. It includes this month. * `LAST_N_QUARTERS`: Starts at 12:00:00 AM on the first day of the calendar quarter `n` - 1 quarter(s) before the current quarter and continues through the current quarter. It includes this quarter. * `LAST_N_YEARS`: Starts on January 1st, 12:00:00 AM of the year `n` - 1 year(s) before the current year and continues through the current year. It includes this year. * `LAST_15_MINUTES` * `LAST_30_MINUTES` * `LAST_HOUR` * `LAST_4_HOURS` * `LAST_12_HOURS` * `LAST_24_HOURS` * `LAST_7_DAYS` * `LAST_30_DAYS` * `LAST_90_DAYS` * `LAST_3_MONTHS` * `LAST_6_MONTHS` * `LAST_YEAR` * `LAST_2_YEARS` * `LAST_5_YEARS` The number of time units for the `LAST_N` relative periods. The optional start timestamp (inclusive). Defaults to the timestamp of the earliest record in the Data Pool. The optional end timestamp (exclusive). Defaults to the timestamp of the latest record in the Data Pool. The time zone to use. Dates and times are always returned in UTC, but setting the time zone influences relative time ranges and granularities. You can set this to "America/Los\_Angeles", "Europe/Berlin", or any other value in the [IANA time zone database](https://en.wikipedia.org/wiki/Tz_database). Defaults to "UTC". One or many Dimensions to group the Metric values by. Typically, Dimensions in a leaderboard are what you want to compare and rank. The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. The sort order of the rows. It can be ascending (`ASC`) or descending (`DESC`) order. Defaults to descending (`DESC`) order when not provided. The available sort orders. * `ASC`: Sort in ascending order. * `DESC`: Sort in descending order. The number of rows to be returned. It can be a number between 1 and 1,000. The Query Filters to apply before retrieving the leaderboard data, in the form of SQL. If no Query Filters are provided, all data is included. The ID of the Metric to query. Required if `metricName` is not specified. deprecated: Use `metric` The name of the Metric to query. Required if `metricId` is not specified. deprecated: Use `metric` The Query Filters to apply before retrieving the leaderboard data. If no Query Filters are provided, all data is included. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## TimeRangeInput The fields required to specify the time range for a time series, counter, or leaderboard Metric query. If no relative or absolute time ranges are provided, Propel defaults to an absolute time range beginning with the earliest record in the Metric's Data Pool and ending with the latest record. If both relative and absolute time ranges are provided, the relative time range will take precedence. If a `LAST_N` relative time period is selected, an `n` ≥ 1 must be provided. If no `n` is provided or `n` \< 1, a `BAD_REQUEST` error will be returned. The timestamp field to use when querying. Defaults to the timestamp configured on the Data Pool or Metric, if any. Set this to filter on an alternative timestamp field. The relative time period. The Relative time ranges are based on the current date and time. `THIS` - The current unit of time. For example, if today is June 8, 2022, and `THIS_MONTH` is selected, then data for June 2022 would be returned. `PREVIOUS` - The previous unit of time. For example, if today is June 8, 2022, and `PREVIOUS_MONTH` is selected, then data for May 2022 would be returned. It excludes the current unit of time. `NEXT` - The next unit of time. For example, if today is June 8, 2022, and `NEXT_MONTH` is selected, then data for July 2022 would be returned. It excludes the current unit of time. `LAST_N` - The last `n` units of time, including the current one. For example, if today is June 8, 2022 and `LAST_N_YEARS` with `n` = 3 is selected, then data for 2020, 2021, and 2022 will be returned. It will include the current time period. * `THIS_HOUR`: Starts at the zeroth minute of the current hour and continues for 60 minutes. * `TODAY`: Starts at 12:00:00 AM of the current day and continues for 24 hours. * `THIS_WEEK`: Starts on Monday, 12:00:00 AM of the current week and continues for seven days. * `THIS_MONTH`: Starts at 12:00:00 AM on the first day of the current month and continues for the duration of the month. * `THIS_QUARTER`: Starts at 12:00:00 AM on the first day of the current calendar quarter and continues for the duration of the quarter. * `THIS_YEAR`: Starts on January 1st, 12:00:00 AM of the current year and continues for the duration of the year. * `PREVIOUS_HOUR`: Starts at the zeroth minute of the previous hour and continues for 60 minutes. * `YESTERDAY`: Starts at 12:00:00 AM on the day before the today and continues for 24 hours. * `PREVIOUS_WEEK`: Starts on Monday, 12:00:00 AM, a week before the current week, and continues for seven days. * `PREVIOUS_MONTH`: Starts at 12:00:00 AM on the first day of the month before the current month and continues for the duration of the month. * `PREVIOUS_QUARTER`: Starts at 12:00:00 AM on the first day of the calendar quarter before the current quarter and continues for the duration of the quarter. * `PREVIOUS_YEAR`: Starts on January 1st, 12:00:00 AM, the year before the current year, and continues for the duration of the year. * `NEXT_HOUR`: Starts at the zeroth minute of the next hour and continues for 60 minutes. * `TOMORROW`: " Starts at 12:00:00 AM, the day after the current day, and continues for 24 hours. * `NEXT_WEEK`: Starts on Monday, 12:00:00 AM, the week after the current week, and continues for the duration of the week. * `NEXT_MONTH`: Starts at 12:00:00 AM on the first day of the next month and continues for the duration of the month. * `NEXT_QUARTER`: Starts at 12:00:00 AM on the first day of the next calendar quarter and continues for the duration of the quarter. * `NEXT_YEAR`: Starts on January 1st, 12:00:00 AM of the next year and continues for the duration of the year. * `LAST_N_MINUTES`: Starts at the zeroth second `n` - 1 minute(s) before the current minute and continues through the current minute. It includes this minute. * `LAST_N_HOURS`: Starts at the zeroth minute of the `n` - 1 hour(s) before the current hour, and continues through the current hour. It includes this hour. * `LAST_N_DAYS`: Starts at 12:00:00 AM, `n` - 1 day(s) before the current day, and continues through the current day. It includes today. * `LAST_N_WEEKS`: Starts on Monday, 12:00:00 AM, `n` - 1 week(s) before the current week, and continues through the current week. It includes this week. * `LAST_N_MONTHS`: Starts at 12:00:00 AM on the first day of the month, `n` - 1 month(s) before the current month, and continues through the current month. It includes this month. * `LAST_N_QUARTERS`: Starts at 12:00:00 AM on the first day of the calendar quarter `n` - 1 quarter(s) before the current quarter and continues through the current quarter. It includes this quarter. * `LAST_N_YEARS`: Starts on January 1st, 12:00:00 AM of the year `n` - 1 year(s) before the current year and continues through the current year. It includes this year. * `LAST_15_MINUTES` * `LAST_30_MINUTES` * `LAST_HOUR` * `LAST_4_HOURS` * `LAST_12_HOURS` * `LAST_24_HOURS` * `LAST_7_DAYS` * `LAST_30_DAYS` * `LAST_90_DAYS` * `LAST_3_MONTHS` * `LAST_6_MONTHS` * `LAST_YEAR` * `LAST_2_YEARS` * `LAST_5_YEARS` The number of time units for the `LAST_N` relative periods. The optional start timestamp (inclusive). Defaults to the timestamp of the earliest record in the Data Pool. The optional end timestamp (exclusive). Defaults to the timestamp of the latest record in the Data Pool. ## FilterInput The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## UpdateDataPoolRecordsJobSetColumnInput The fields for creating an Update Data Pool Records Job. ``` { "column": "status", "expression": "'completed'" } { "column": "counter", "expression": "counter + 1" } { "column": "full_name", "expression": "concat(first_name, ' ', last_name)" } ``` The name of the column to update. The value to which the column will be updated. Once evaluated, it should be of the same data type as the column. ## CreateBoosterInput The fields for creating a new Booster. Boosters can be understood as an aggregating index. The index is formed from left to right as follows: 1. The Data Pool's Tenant ID column (if present) 2. Metric Filter columns (if present) 3. Query Filter Dimensions (see `dimensions`) 4. The Data Pool's timestamp column The Booster's Metric. Dimensions to include in the Booster. Follow these guidelines when specifying Dimensions: 1. Specify Dimensions in descending order of importance for filtering and in ascending order of cardinality. 2. Take into consideration hierarchical relationships as well (for example, a "country" Dimension should appear before a "state" Dimension). The fields for creating or modifying a Dimension. The name of the column to create the Dimension from. ## CreatePolicyInput The fields for creating a Policy. The Metric to which the Policy will be applied. The type of Policy to create. The types of Policies that can be applied to a Metric. * `ALL_ACCESS`: Grants access to all Metric data. * `TENANT_ACCESS`: Grants access to a specified tenant's Metric data. The Application that will be granted access to the Metric. ## ModifyPolicyInput The fields for modifying a Policy. The Policy's unique identifier. The type of Policy. The types of Policies that can be applied to a Metric. * `ALL_ACCESS`: Grants access to all Metric data. * `TENANT_ACCESS`: Grants access to a specified tenant's Metric data. ## DeletionRequestInput The fields for creating a Deletion Job. The Data Pool that is going to get the data deleted The filters that will be used for deleting data, in the form of SQL. Data matching these filters will be deleted. The list of filters that will be used for deleting data. Data matching these filters will be deleted. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## CreateDeletionJobInput The fields for creating a Deletion Job. The Data Pool that is going to get the data deleted The filters that will be used for deleting data, in the form of SQL. Data matching these filters will be deleted. The list of filters that will be used for deleting data. Data matching these filters will be deleted. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## CreateAddColumnToDataPoolJobInput The fields for creating an Add Column Job. The Data Pool to which the column will be added. Name of the new column. Type of the new column. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. The ClickHouse type of the new column when `columnType` is set to `CLICKHOUSE`. JSON property to which the new column corresponds. ## CreateUpdateDataPoolRecordsJobInput The fields for creating an Update Data Pool Records Job. The Data Pool that is going to get its records updated. The filters that will be used for updating records, in the form of SQL. Records matching these filters will be updated. Describes how the job will update the records. The fields for creating an Update Data Pool Records Job. ``` { "column": "status", "expression": "'completed'" } { "column": "counter", "expression": "counter + 1" } { "column": "full_name", "expression": "concat(first_name, ' ', last_name)" } ``` The name of the column to update. The value to which the column will be updated. Once evaluated, it should be of the same data type as the column. The list of filters that will be used for updating records. Records matching these filters will be updated. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## CreateDataPoolAccessPolicyInput The Data Pool Access Policy's unique name. If not specified, Propel will set the ID as unique name. The Data Pool Access Policy's description. The Data Pool to which the Access Policy belongs. Columns that the Access Policy makes available for querying. If set to `["*"]`, all columns will be available for querying. Row-level filters that the Access Policy applies before executing queries, in the form of SQL. Row-level filters that the Access Policy applies before executing queries. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## ModifyDataPoolAccessPolicyInput The Data Pool Access Policy's new unique name. The Data Pool Access Policy's new description. Columns that the Access Policy makes available for querying. If not provided this property will not be modified. Row-level filters that the Access Policy applies before executing queries, in the form of SQL. If not provided this property will not be modified. Row-level filters that the Access Policy applies before executing queries. If not provided this property will not be modified. deprecated: Use `filterSql` instead The fields of a filter. You can construct more complex filters using `and` and `or`. For example, to construct a filter equivalent to ``` (value > 0 AND value <= 100) OR status = "confirmed" ``` you could write ``` { "column": "value", "operator": "GREATER_THAN", "value": "0", "and": [{ "column": "value", "operator": "LESS_THAN_OR_EQUAL_TO", "value": "0" }], "or": [{ "column": "status", "operator": "EQUALS", "value": "confirmed" }] } ``` Note that `and` takes precedence over `or`. The name of the column to filter on. The operation to perform when comparing the column and filter values. The available Filter operators. * `EQUALS`: Selects values that are equal to the specified value. * `NOT_EQUALS`: Selects values that are not equal to the specified value. * `GREATER_THAN`: Selects values that are greater than the specified value. * `GREATER_THAN_OR_EQUAL_TO`: Selects values that are greater or equal to the specified value. * `LESS_THAN`: Selects values that are less than the specified value. * `LESS_THAN_OR_EQUAL_TO`: Selects values that are less or equal to the specified value. * `IS_NULL`: Selects values that are null. This operator does not accept a value. * `IS_NOT_NULL`: Selects values that are not null. This operator does not accept a value. * `LIKE`: Selects values that match the specified pattern. * `NOT_LIKE`: "Selects values that do not match the specified pattern. The value to compare the column to. Additional filters to AND with this one. AND takes precedence over OR. Additional filters to OR with this one. AND takes precedence over OR. ## SqlV1Input Input to the SqlV1 api. The SQL query. The SQL dialect to use. If not provided, the query is parsed on a best-effort basis. The SQL dialect to use when parsing queries. * `POSTGRESQL`: Parse as PostgreSQL-compatible SQL. * `CLICKHOUSE`: Parse as ClickHouse-compatible SQL. ## DescribeSqlV1Input Input for describing SqlV1 inputs. The SQL query. The SQL dialect to use. If not provided, the query is parsed on a best-effort basis. The SQL dialect to use when parsing queries. * `POSTGRESQL`: Parse as PostgreSQL-compatible SQL. * `CLICKHOUSE`: Parse as ClickHouse-compatible SQL. ## RecordsByUniqueIdInput The fields for querying records by unique ID. The Data Pool to be queried. A Data Pool ID or unique name can be provided. The ID of the Data Pool. The name of the Data Pool. The columns to retrieve. The unique IDs of the records to retrieve. ## TopValuesInput The fields for querying the top values in a given column. The Data Pool to be queried. A Data Pool ID or unique name can be provided. The ID of the Data Pool. The name of the Data Pool. The column to fetch the unique values from. The time range for calculating the top values. The fields required to specify the time range for a time series, counter, or leaderboard Metric query. If no relative or absolute time ranges are provided, Propel defaults to an absolute time range beginning with the earliest record in the Metric's Data Pool and ending with the latest record. If both relative and absolute time ranges are provided, the relative time range will take precedence. If a `LAST_N` relative time period is selected, an `n` ≥ 1 must be provided. If no `n` is provided or `n` \< 1, a `BAD_REQUEST` error will be returned. The timestamp field to use when querying. Defaults to the timestamp configured on the Data Pool or Metric, if any. Set this to filter on an alternative timestamp field. The relative time period. The Relative time ranges are based on the current date and time. `THIS` - The current unit of time. For example, if today is June 8, 2022, and `THIS_MONTH` is selected, then data for June 2022 would be returned. `PREVIOUS` - The previous unit of time. For example, if today is June 8, 2022, and `PREVIOUS_MONTH` is selected, then data for May 2022 would be returned. It excludes the current unit of time. `NEXT` - The next unit of time. For example, if today is June 8, 2022, and `NEXT_MONTH` is selected, then data for July 2022 would be returned. It excludes the current unit of time. `LAST_N` - The last `n` units of time, including the current one. For example, if today is June 8, 2022 and `LAST_N_YEARS` with `n` = 3 is selected, then data for 2020, 2021, and 2022 will be returned. It will include the current time period. * `THIS_HOUR`: Starts at the zeroth minute of the current hour and continues for 60 minutes. * `TODAY`: Starts at 12:00:00 AM of the current day and continues for 24 hours. * `THIS_WEEK`: Starts on Monday, 12:00:00 AM of the current week and continues for seven days. * `THIS_MONTH`: Starts at 12:00:00 AM on the first day of the current month and continues for the duration of the month. * `THIS_QUARTER`: Starts at 12:00:00 AM on the first day of the current calendar quarter and continues for the duration of the quarter. * `THIS_YEAR`: Starts on January 1st, 12:00:00 AM of the current year and continues for the duration of the year. * `PREVIOUS_HOUR`: Starts at the zeroth minute of the previous hour and continues for 60 minutes. * `YESTERDAY`: Starts at 12:00:00 AM on the day before the today and continues for 24 hours. * `PREVIOUS_WEEK`: Starts on Monday, 12:00:00 AM, a week before the current week, and continues for seven days. * `PREVIOUS_MONTH`: Starts at 12:00:00 AM on the first day of the month before the current month and continues for the duration of the month. * `PREVIOUS_QUARTER`: Starts at 12:00:00 AM on the first day of the calendar quarter before the current quarter and continues for the duration of the quarter. * `PREVIOUS_YEAR`: Starts on January 1st, 12:00:00 AM, the year before the current year, and continues for the duration of the year. * `NEXT_HOUR`: Starts at the zeroth minute of the next hour and continues for 60 minutes. * `TOMORROW`: " Starts at 12:00:00 AM, the day after the current day, and continues for 24 hours. * `NEXT_WEEK`: Starts on Monday, 12:00:00 AM, the week after the current week, and continues for the duration of the week. * `NEXT_MONTH`: Starts at 12:00:00 AM on the first day of the next month and continues for the duration of the month. * `NEXT_QUARTER`: Starts at 12:00:00 AM on the first day of the next calendar quarter and continues for the duration of the quarter. * `NEXT_YEAR`: Starts on January 1st, 12:00:00 AM of the next year and continues for the duration of the year. * `LAST_N_MINUTES`: Starts at the zeroth second `n` - 1 minute(s) before the current minute and continues through the current minute. It includes this minute. * `LAST_N_HOURS`: Starts at the zeroth minute of the `n` - 1 hour(s) before the current hour, and continues through the current hour. It includes this hour. * `LAST_N_DAYS`: Starts at 12:00:00 AM, `n` - 1 day(s) before the current day, and continues through the current day. It includes today. * `LAST_N_WEEKS`: Starts on Monday, 12:00:00 AM, `n` - 1 week(s) before the current week, and continues through the current week. It includes this week. * `LAST_N_MONTHS`: Starts at 12:00:00 AM on the first day of the month, `n` - 1 month(s) before the current month, and continues through the current month. It includes this month. * `LAST_N_QUARTERS`: Starts at 12:00:00 AM on the first day of the calendar quarter `n` - 1 quarter(s) before the current quarter and continues through the current quarter. It includes this quarter. * `LAST_N_YEARS`: Starts on January 1st, 12:00:00 AM of the year `n` - 1 year(s) before the current year and continues through the current year. It includes this year. * `LAST_15_MINUTES` * `LAST_30_MINUTES` * `LAST_HOUR` * `LAST_4_HOURS` * `LAST_12_HOURS` * `LAST_24_HOURS` * `LAST_7_DAYS` * `LAST_30_DAYS` * `LAST_90_DAYS` * `LAST_3_MONTHS` * `LAST_6_MONTHS` * `LAST_YEAR` * `LAST_2_YEARS` * `LAST_5_YEARS` The number of time units for the `LAST_N` relative periods. The optional start timestamp (inclusive). Defaults to the timestamp of the earliest record in the Data Pool. The optional end timestamp (exclusive). Defaults to the timestamp of the latest record in the Data Pool. The time zone to use. Dates and times are always returned in UTC, but setting the time zone influences relative time ranges and granularities. You can set this to "America/Los\_Angeles", "Europe/Berlin", or any other value in the [IANA time zone database](https://en.wikipedia.org/wiki/Tz_database). Defaults to "UTC". The maximum number of values to return. It can be a number between 1 and 1,000. If the parameter is omitted, default value 10 is used. ## AmazonDataFirehoseConnectionSettingsInput The Amazon Data Firehose Data Source's connection settings. Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics. HTTP basic access authentication credentials. You must configure these same credentials to be included in the `X-Amz-Firehose-Access-Key` header when Amazon Data Firehose issues requests to its custom HTTP endpoint. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. Additional columns for the table in Propel. The fields for specifying a column in an Amazon Data Firehose Data Source's table. The column name. It has to be unique within a table. The JSON property that the column will be derived from. For example, if you send a JSON event like this: ```json theme={"system"} { "greeting": { "message": "hello, world" } } ``` Then you can use the JSON property "greeting.message" to extract "hello, world" to a column. The column type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. Whether the column's type is nullable or not. Override the Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse, and a default will be chosen based on the Data Pool's `timestamp` value, if any. You can override these defaults in order to specify a custom table engine, custom ORDER BY, etc. A Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse. The ClickHouse table engine for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. A Data Pool's table engine. Field for specifying the MergeTree table engine. See MergeTreeTableEngineInput Field for specifying the ReplacingMergeTree table engine. See ReplacingMergeTreeTableEngineInput Field for specifying the SummingMergeTree table engine. See SummingMergeTreeTableEngineInput Field for specifying the AggregatingMergeTree table engine. See AggregatingMergeTreeTableEngineInput Field for specifying the PostgreSQL table engine. See PostgreSqlTableEngineInput The PARTITION BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The PRIMARY KEY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The ORDER BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The TTL clause for the Data Pool's table. The primary timestamp column, if any. ## CreateAmazonDataFirehoseDataSourceInput The Amazon Data Firehose Data Source's connection settings The Amazon Data Firehose Data Source's connection settings. Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics. HTTP basic access authentication credentials. You must configure these same credentials to be included in the `X-Amz-Firehose-Access-Key` header when Amazon Data Firehose issues requests to its custom HTTP endpoint. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. Additional columns for the table in Propel. The fields for specifying a column in an Amazon Data Firehose Data Source's table. The column name. It has to be unique within a table. The JSON property that the column will be derived from. For example, if you send a JSON event like this: ```json theme={"system"} { "greeting": { "message": "hello, world" } } ``` Then you can use the JSON property "greeting.message" to extract "hello, world" to a column. The column type. See ColumnType Whether the column's type is nullable or not. Override the Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse, and a default will be chosen based on the Data Pool's `timestamp` value, if any. You can override these defaults in order to specify a custom table engine, custom ORDER BY, etc. A Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse. The ClickHouse table engine for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. See TableEngineInput The PARTITION BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The PRIMARY KEY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The ORDER BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The TTL clause for the Data Pool's table. The primary timestamp column, if any. The Amazon Data Firehose Data Source's description. The Amazon Data Firehose Data Source's unique name. If not specified, Propel will set the ID as unique name. ## ModifyAmazonDataFirehoseDataSourceInput The Amazon Data Firehose Data Source's new connection settings. If not provided this property will not be modified. The Amazon Data Firehose Data Source's connection settings. HTTP basic access authentication credentials. You must configure these same credentials to be included in the `X-Amz-Firehose-Access-Key` header when Amazon Data Firehose issues requests to its custom HTTP endpoint. If not provided this property will not be modified. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The Amazon Data Firehose Data Source's new description. If not provided this property will not be modified. The ID or unique name of the Amazon Data Firehose Data Source to modify. The ID or unique name input. If both ID and unique name are provided, the ID will take precedence. The unique identifier of the object. The unique name of the object. The Amazon Data Firehose Data Source's new unique name. If not provided this property will not be modified. ## PartialAmazonDataFirehoseConnectionSettingsInput The Amazon Data Firehose Data Source's connection settings. HTTP basic access authentication credentials. You must configure these same credentials to be included in the `X-Amz-Firehose-Access-Key` header when Amazon Data Firehose issues requests to its custom HTTP endpoint. If not provided this property will not be modified. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. ## AmazonDynamoDBConnectionSettingsInput The Amazon DynamoDB Data Source's connection settings. Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics. HTTP basic access authentication credentials. You must configure these same credentials to be included in the `X-Amz-Firehose-Access-Key` header when Amazon Data Firehose transmits records from your DynamoDB table to its custom HTTP endpoint. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. Additional columns for the table in Propel. The fields for specifying a column in an Amazon DynamODB Data Source's table. The column name. It has to be unique within a table. The JSON property that the column will be derived from. For example, if you send a JSON event like this: ```json theme={"system"} { "greeting": { "message": "hello, world" } } ``` Then you can use the JSON property "greeting.message" to extract "hello, world" to a column. The column type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. Whether the column's type is nullable or not. Override the Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse, and a default will be chosen based on the Data Pool's `timestamp` value, if any. You can override these defaults in order to specify a custom table engine, custom ORDER BY, etc. A Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse. The ClickHouse table engine for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. A Data Pool's table engine. Field for specifying the MergeTree table engine. See MergeTreeTableEngineInput Field for specifying the ReplacingMergeTree table engine. See ReplacingMergeTreeTableEngineInput Field for specifying the SummingMergeTree table engine. See SummingMergeTreeTableEngineInput Field for specifying the AggregatingMergeTree table engine. See AggregatingMergeTreeTableEngineInput Field for specifying the PostgreSQL table engine. See PostgreSqlTableEngineInput The PARTITION BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The PRIMARY KEY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The ORDER BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The TTL clause for the Data Pool's table. The primary timestamp column, if any. ## CreateAmazonDynamoDBDataSourceInput The Amazon DynamoDB Data Source's connection settings The Amazon DynamoDB Data Source's connection settings. Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics. HTTP basic access authentication credentials. You must configure these same credentials to be included in the `X-Amz-Firehose-Access-Key` header when Amazon Data Firehose transmits records from your DynamoDB table to its custom HTTP endpoint. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. Additional columns for the table in Propel. The fields for specifying a column in an Amazon DynamODB Data Source's table. The column name. It has to be unique within a table. The JSON property that the column will be derived from. For example, if you send a JSON event like this: ```json theme={"system"} { "greeting": { "message": "hello, world" } } ``` Then you can use the JSON property "greeting.message" to extract "hello, world" to a column. The column type. See ColumnType Whether the column's type is nullable or not. Override the Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse, and a default will be chosen based on the Data Pool's `timestamp` value, if any. You can override these defaults in order to specify a custom table engine, custom ORDER BY, etc. A Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse. The ClickHouse table engine for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. See TableEngineInput The PARTITION BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The PRIMARY KEY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The ORDER BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The TTL clause for the Data Pool's table. The primary timestamp column, if any. The Amazon DynamoDB Data Source's description. The Amazon DynamoDB Data Source's unique name. If not specified, Propel will set the ID as unique name. ## ModifyAmazonDynamoDBDataSourceInput The Amazon DynamoDB Data Source's new connection settings. If not provided this property will not be modified. The Amazon DynamoDB Data Source's connection settings. HTTP basic access authentication credentials. You must configure these same credentials to be included in the `X-Amz-Firehose-Access-Key` header when Amazon Data Firehose transmits records from your DynamoDB table to its custom HTTP endpoint. If not provided this property will not be modified. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The Amazon DynamoDB Data Source's new description. If not provided this property will not be modified. The ID or unique name of the Amazon DynamoDB Data Source to modify. The ID or unique name input. If both ID and unique name are provided, the ID will take precedence. The unique identifier of the object. The unique name of the object. The Amazon DynamoDB Data Source's new unique name. If not provided this property will not be modified. ## PartialAmazonDynamoDBConnectionSettingsInput The Amazon DynamoDB Data Source's connection settings. HTTP basic access authentication credentials. You must configure these same credentials to be included in the `X-Amz-Firehose-Access-Key` header when Amazon Data Firehose transmits records from your DynamoDB table to its custom HTTP endpoint. If not provided this property will not be modified. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. ## ClickHouseConnectionSettingsInput The ClickHouse Data Source connection settings. Which database to connect to The password for the provided user The URL where the ClickHouse host is listening to HTTP\[S] connections The user for authenticating against the ClickHouse host ## CreateClickHouseDataSourceInput The ClickHouse Data Source's connection settings The ClickHouse Data Source connection settings. Which database to connect to The password for the provided user The URL where the ClickHouse host is listening to HTTP\[S] connections The user for authenticating against the ClickHouse host The ClickHouse Data Source's description. The ClickHouse Data Source's unique name. If not specified, Propel will set the ID as unique name. ## ModifyClickHouseDataSourceInput The ClickHouse Data Source's new connection settings. If not provided this property will not be modified. The ClickHouse Data Source connection settings. Which database to connect to If not provided this property will not be modified. The password for the provided user If not provided this property will not be modified. The URL where the ClickHouse host is listening to HTTP\[S] connections If not provided this property will not be modified. The user for authenticating against the ClickHouse host If not provided this property will not be modified. The ClickHouse Data Source's new description. If not provided this property will not be modified. The ID or unique name of the ClickHouse Data Source to modify. The ID or unique name input. If both ID and unique name are provided, the ID will take precedence. The unique identifier of the object. The unique name of the object. The ClickHouse Data Source's new unique name. If not provided this property will not be modified. ## PartialClickHouseConnectionSettingsInput The ClickHouse Data Source connection settings. Which database to connect to If not provided this property will not be modified. The password for the provided user If not provided this property will not be modified. The URL where the ClickHouse host is listening to HTTP\[S] connections If not provided this property will not be modified. The user for authenticating against the ClickHouse host If not provided this property will not be modified. ## CreateHttpDataSourceInput The HTTP Data Source's connection settings The HTTP Data Source connection settings. The HTTP Basic authentication settings for uploading new data. If this parameter is not provided, anyone with the URL to your tables will be able to upload data. While it's OK to test without HTTP Basic authentication, we recommend enabling it. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The HTTP Data Source's tables. The fields for specifying an HTTP Data Source's table. The name of the table All the columns present in the table See HttpDataSourceColumnInput The HTTP Data Source's description. The HTTP Data Source's unique name. If not specified, Propel will set the ID as unique name. ## HttpConnectionSettingsInput The HTTP Data Source connection settings. The HTTP Basic authentication settings for uploading new data. If this parameter is not provided, anyone with the URL to your tables will be able to upload data. While it's OK to test without HTTP Basic authentication, we recommend enabling it. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The HTTP Data Source's tables. The fields for specifying an HTTP Data Source's table. The name of the table All the columns present in the table The fields for specifying a column in an HTTP Data Source's table. The column name. It has to be unique within a table. The column type. See ColumnType The ClickHouse type to use when `type` is set to `CLICKHOUSE`. Whether the column's type is nullable or not. ## ModifyHttpDataSourceInput The HTTP Data Source's new connection settings. If not provided this property will not be modified. The HTTP Data Source connection settings. The HTTP Basic authentication settings for uploading new data. If this parameter is not provided, anyone with the URL to your tables will be able to upload data. While it's OK to test without HTTP Basic authentication, we recommend enabling it. If not provided this property will not be modified. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. Set this to `false` to disable HTTP Basic authentication. Any previously stored HTTP Basic authentication settings will be cleared out. If not provided this property will not be modified. The HTTP Data Source's tables. If not provided this property will not be modified. The fields for specifying an HTTP Data Source's table. The name of the table All the columns present in the table See HttpDataSourceColumnInput The HTTP Data Source's new description. If not provided this property will not be modified. The ID or unique name of the HTTP Data Source to modify. The ID or unique name input. If both ID and unique name are provided, the ID will take precedence. The unique identifier of the object. The unique name of the object. The HTTP Data Source's new unique name. If not provided this property will not be modified. ## PartialHttpConnectionSettingsInput The HTTP Data Source connection settings. The HTTP Basic authentication settings for uploading new data. If this parameter is not provided, anyone with the URL to your tables will be able to upload data. While it's OK to test without HTTP Basic authentication, we recommend enabling it. If not provided this property will not be modified. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. Set this to `false` to disable HTTP Basic authentication. Any previously stored HTTP Basic authentication settings will be cleared out. If not provided this property will not be modified. The HTTP Data Source's tables. If not provided this property will not be modified. The fields for specifying an HTTP Data Source's table. The name of the table All the columns present in the table The fields for specifying a column in an HTTP Data Source's table. The column name. It has to be unique within a table. The column type. See ColumnType The ClickHouse type to use when `type` is set to `CLICKHOUSE`. Whether the column's type is nullable or not. ## CreateKafkaDataSourceInput The Kafka Data Source's connection settings The Kafka Data Source connection settings. The type of authentication to use. Can be SCRAM-SHA-256, SCRAM-SHA-512, PLAIN or NONE The bootstrap server(s) to connect to The password for the provided user Whether the the connection to the Kafka servers is encrypted or not The user for authenticating against the Kafka servers The Kafka Data Source's description. The Kafka Data Source's unique name. If not specified, Propel will set the ID as unique name. ## KafkaConnectionSettingsInput The Kafka Data Source connection settings. The type of authentication to use. Can be SCRAM-SHA-256, SCRAM-SHA-512, PLAIN or NONE The bootstrap server(s) to connect to The password for the provided user Whether the the connection to the Kafka servers is encrypted or not The user for authenticating against the Kafka servers ## ModifyKafkaDataSourceInput The Kafka Data Source's new connection settings. If not provided this property will not be modified. The Kafka Data Source connection settings. The type of authentication to use. Can be SCRAM-SHA-256, SCRAM-SHA-512, PLAIN or NONE If not provided this property will not be modified. The bootstrap server(s) to connect to If not provided this property will not be modified. The password for the provided user If not provided this property will not be modified. Whether the the connection to the Kafka servers is encrypted or not If not provided this property will not be modified. The user for authenticating against the Kafka servers If not provided this property will not be modified. The Kafka Data Source's new description. If not provided this property will not be modified. The ID or unique name of the Kafka Data Source to modify. The ID or unique name input. If both ID and unique name are provided, the ID will take precedence. The unique identifier of the object. The unique name of the object. The Kafka Data Source's new unique name. If not provided this property will not be modified. ## PartialKafkaConnectionSettingsInput The Kafka Data Source connection settings. The type of authentication to use. Can be SCRAM-SHA-256, SCRAM-SHA-512, PLAIN or NONE If not provided this property will not be modified. The bootstrap server(s) to connect to If not provided this property will not be modified. The password for the provided user If not provided this property will not be modified. Whether the the connection to the Kafka servers is encrypted or not If not provided this property will not be modified. The user for authenticating against the Kafka servers If not provided this property will not be modified. ## CreatePostgreSqlDataSourceInput The PostgreSQL Data Source's connection settings The PostgreSQL Data Source connection settings. Which database to connect to The host where PostgreSQL is listening The password for the provided user The port where PostgreSQL is listening (usually 5432) Which schema to use The user for authenticating against PostgreSQL The PostgreSQL Data Source's description. The PostgreSQL Data Source's unique name. If not specified, Propel will set the ID as unique name. ## ModifyPostgreSqlDataSourceInput The PostgreSQL Data Source's new connection settings. If not provided this property will not be modified. The PostgreSQL Data Source connection settings. Which database to connect to If not provided this property will not be modified. The host where PostgreSQL is listening If not provided this property will not be modified. The password for the provided user If not provided this property will not be modified. The port where PostgreSQL is listening (usually 5432) If not provided this property will not be modified. Which schema to use If not provided this property will not be modified. The user for authenticating against PostgreSQL If not provided this property will not be modified. The PostgreSQL Data Source's new description. If not provided this property will not be modified. The ID or unique name of the PostgreSQL Data Source to modify. The ID or unique name input. If both ID and unique name are provided, the ID will take precedence. The unique identifier of the object. The unique name of the object. The PostgreSQL Data Source's new unique name. If not provided this property will not be modified. ## PartialPostgreSqlConnectionSettingsInput The PostgreSQL Data Source connection settings. Which database to connect to If not provided this property will not be modified. The host where PostgreSQL is listening If not provided this property will not be modified. The password for the provided user If not provided this property will not be modified. The port where PostgreSQL is listening (usually 5432) If not provided this property will not be modified. Which schema to use If not provided this property will not be modified. The user for authenticating against PostgreSQL If not provided this property will not be modified. ## PostgreSqlConnectionSettingsInput The PostgreSQL Data Source connection settings. Which database to connect to The host where PostgreSQL is listening The password for the provided user The port where PostgreSQL is listening (usually 5432) Which schema to use The user for authenticating against PostgreSQL ## CreateS3DataSourceInput The Amazon S3 Data Source's connection settings The connection settings for an Amazon S3 Data Source. These include the Amazon S3 bucket name, the AWS access key ID, and the tables (along with their paths). We do not allow fetching the AWS secret access key after it has been set. The AWS access key ID for an IAM user with sufficient access to the Amazon S3 bucket. The AWS secret access key for an IAM user with sufficient access to the Amazon S3 bucket. The name of the Amazon S3 bucket. The Amazon S3 Data Source's tables. The fields for specifying an Amazon S3 Data Source's table. The name of the table The path to the table's files in Amazon S3. All the columns present in the table See S3DataSourceColumnInput The Amazon S3 Data Source's description. The Amazon S3 Data Source's unique name. If not specified, Propel will set the ID as unique name. ## ModifyS3DataSourceInput The Amazon S3 Data Source's new connection settings. If not provided this property will not be modified. The connection settings for an Amazon S3 Data Source. These include the Amazon S3 bucket name, the AWS access key ID, and the tables (along with their paths). We do not allow fetching the AWS secret access key after it has been set. The AWS access key ID for an IAM user with sufficient access to the Amazon S3 bucket. If not provided this property will not be modified. The AWS secret access key for an IAM user with sufficient access to the Amazon S3 bucket. If not provided this property will not be modified. The name of the Amazon S3 bucket. If not provided this property will not be modified. The Amazon S3 Data Source's tables. If not provided this property will not be modified. The fields for specifying an Amazon S3 Data Source's table. The name of the table The path to the table's files in Amazon S3. All the columns present in the table See S3DataSourceColumnInput The Amazon S3 Data Source's new description. If not provided this property will not be modified. The ID or unique name of the Amazon S3 Data Source to modify. The ID or unique name input. If both ID and unique name are provided, the ID will take precedence. The unique identifier of the object. The unique name of the object. The Amazon S3 Data Source's new unique name. If not provided this property will not be modified. ## PartialS3ConnectionSettingsInput The connection settings for an Amazon S3 Data Source. These include the Amazon S3 bucket name, the AWS access key ID, and the tables (along with their paths). We do not allow fetching the AWS secret access key after it has been set. The AWS access key ID for an IAM user with sufficient access to the Amazon S3 bucket. If not provided this property will not be modified. The AWS secret access key for an IAM user with sufficient access to the Amazon S3 bucket. If not provided this property will not be modified. The name of the Amazon S3 bucket. If not provided this property will not be modified. The Amazon S3 Data Source's tables. If not provided this property will not be modified. The fields for specifying an Amazon S3 Data Source's table. The name of the table The path to the table's files in Amazon S3. All the columns present in the table The fields for specifying a column in an Amazon S3 Data Source's table. The column name. It has to be unique within a table. The column type. See ColumnType Whether the column's type is nullable or not. ## S3ConnectionSettingsInput The connection settings for an Amazon S3 Data Source. These include the Amazon S3 bucket name, the AWS access key ID, and the tables (along with their paths). We do not allow fetching the AWS secret access key after it has been set. The AWS access key ID for an IAM user with sufficient access to the Amazon S3 bucket. The AWS secret access key for an IAM user with sufficient access to the Amazon S3 bucket. The name of the Amazon S3 bucket. The Amazon S3 Data Source's tables. The fields for specifying an Amazon S3 Data Source's table. The name of the table The path to the table's files in Amazon S3. All the columns present in the table The fields for specifying a column in an Amazon S3 Data Source's table. The column name. It has to be unique within a table. The column type. See ColumnType Whether the column's type is nullable or not. ## CreateTwilioSegmentDataSourceInput The Twilio Segment Data Source's connection settings The Twilio Segment Data Source connection settings. Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics. The HTTP basic authentication settings for the Twilio Segment Data Source URL. If this parameter is not provided, anyone with the webhook URL will be able to send events. While it's OK to test without HTTP Basic authentication, we recommend enabling it. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The additional columns for the table in Propel. The fields for specifying a column in a Twilio Segment Data Source's table. The column name. It has to be unique within a table. The JSON property that the column will be derived from. For example, if you POST a JSON event like this: ```json theme={"system"} { "greeting": { "message": "hello, world" } } ``` Then you can use the JSON property "greeting.message" to extract "hello, world" to a column. The column type. See ColumnType Whether the column's type is nullable or not. Override the Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse, and a default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if any. You can override these defaults in order to specify a custom table engine, custom ORDER BY, etc. A Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse. The ClickHouse table engine for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. See TableEngineInput The PARTITION BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The PRIMARY KEY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The ORDER BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The TTL clause for the Data Pool's table. The primary timestamp column, if any. The Twilio Segment Data Source's description. The Twilio Segment Data Source's unique name. If not specified, Propel will set the ID as unique name. ## ModifyTwilioSegmentDataSourceInput The Twilio Segment Data Source's new connection settings. If not provided this property will not be modified. The Twilio Segment Data Source connection settings. The HTTP basic authentication settings for the Twilio Segment Data Source URL. If this parameter is not provided, anyone with the webhook URL will be able to send events. While it's OK to test without HTTP Basic authentication, we recommend enabling it. If not provided this property will not be modified. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. Set this to `false` to disable HTTP Basic authentication. Any previously stored HTTP Basic authentication settings will be cleared out. If not provided this property will not be modified. The Twilio Segment Data Source's new description. If not provided this property will not be modified. The ID or unique name of the Twilio Segment Data Source to modify. The ID or unique name input. If both ID and unique name are provided, the ID will take precedence. The unique identifier of the object. The unique name of the object. The Twilio Segment Data Source's new unique name. If not provided this property will not be modified. ## PartialTwilioSegmentConnectionSettingsInput The Twilio Segment Data Source connection settings. The HTTP basic authentication settings for the Twilio Segment Data Source URL. If this parameter is not provided, anyone with the webhook URL will be able to send events. While it's OK to test without HTTP Basic authentication, we recommend enabling it. If not provided this property will not be modified. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. Set this to `false` to disable HTTP Basic authentication. Any previously stored HTTP Basic authentication settings will be cleared out. If not provided this property will not be modified. ## TwilioSegmentConnectionSettingsInput The Twilio Segment Data Source connection settings. Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics. The HTTP basic authentication settings for the Twilio Segment Data Source URL. If this parameter is not provided, anyone with the webhook URL will be able to send events. While it's OK to test without HTTP Basic authentication, we recommend enabling it. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The additional columns for the table in Propel. The fields for specifying a column in a Twilio Segment Data Source's table. The column name. It has to be unique within a table. The JSON property that the column will be derived from. For example, if you POST a JSON event like this: ```json theme={"system"} { "greeting": { "message": "hello, world" } } ``` Then you can use the JSON property "greeting.message" to extract "hello, world" to a column. The column type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. Whether the column's type is nullable or not. Override the Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse, and a default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if any. You can override these defaults in order to specify a custom table engine, custom ORDER BY, etc. A Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse. The ClickHouse table engine for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. A Data Pool's table engine. Field for specifying the MergeTree table engine. See MergeTreeTableEngineInput Field for specifying the ReplacingMergeTree table engine. See ReplacingMergeTreeTableEngineInput Field for specifying the SummingMergeTree table engine. See SummingMergeTreeTableEngineInput Field for specifying the AggregatingMergeTree table engine. See AggregatingMergeTreeTableEngineInput Field for specifying the PostgreSQL table engine. See PostgreSqlTableEngineInput The PARTITION BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The PRIMARY KEY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The ORDER BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The TTL clause for the Data Pool's table. The primary timestamp column, if any. ## CreateWebhookDataSourceInput The Webhook Data Source's connection settings The Webhook Data Source connection settings. Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics. The HTTP basic authentication settings for the Webhook Data Source URL. If this parameter is not provided, anyone with the webhook URL will be able to send events. While it's OK to test without HTTP Basic authentication, we recommend enabling it. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The additional columns for the table in Propel. The fields for specifying a column in a Webhook Data Source's table. The column name. It has to be unique within a table. The JSON property that the column will be derived from. For example, if you POST a JSON event like this: ```json theme={"system"} { "greeting": { "message": "hello, world" } } ``` Then you can use the JSON property "greeting.message" to extract "hello, world" to a column. The column type. See ColumnType Whether the column's type is nullable or not. Override the Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse, and a default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if any. You can override these defaults in order to specify a custom table engine, custom ORDER BY, etc. A Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse. The ClickHouse table engine for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. See TableEngineInput The PARTITION BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The PRIMARY KEY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The ORDER BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The TTL clause for the Data Pool's table. The tenant ID column, if any. The primary timestamp column, if any. The unique ID column, if any. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated. The Webhook Data Source's description. The Webhook Data Source's unique name. If not specified, Propel will set the ID as unique name. ## ModifyWebhookDataSourceInput The Webhook Data Source's new connection settings. If not provided this property will not be modified. The Webhook Data Source connection settings. The HTTP basic authentication settings for the Webhook Data Source URL. If this parameter is not provided, anyone with the webhook URL will be able to send events. While it's OK to test without HTTP Basic authentication, we recommend enabling it. If not provided this property will not be modified. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. Set this to `false` to disable HTTP Basic authentication. Any previously stored HTTP Basic authentication settings will be cleared out. If not provided this property will not be modified. The Webhook Data Source's new description. If not provided this property will not be modified. The ID or unique name of the Webhook Data Source to modify. The ID or unique name input. If both ID and unique name are provided, the ID will take precedence. The unique identifier of the object. The unique name of the object. The Webhook Data Source's new unique name. If not provided this property will not be modified. ## PartialWebhookConnectionSettingsInput The Webhook Data Source connection settings. The HTTP basic authentication settings for the Webhook Data Source URL. If this parameter is not provided, anyone with the webhook URL will be able to send events. While it's OK to test without HTTP Basic authentication, we recommend enabling it. If not provided this property will not be modified. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. Set this to `false` to disable HTTP Basic authentication. Any previously stored HTTP Basic authentication settings will be cleared out. If not provided this property will not be modified. ## WebhookConnectionSettingsInput The Webhook Data Source connection settings. Enables or disables access control for the Data Pool. If the Data Pool has access control enabled, Applications must be assigned Data Pool Access Policies in order to query the Data Pool and its Metrics. The HTTP basic authentication settings for the Webhook Data Source URL. If this parameter is not provided, anyone with the webhook URL will be able to send events. While it's OK to test without HTTP Basic authentication, we recommend enabling it. The fields for specifying an HTTP Data Source's Basic authentication settings. The username for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The password for HTTP Basic authentication that must be included in the Authorization header when uploading new data. The additional columns for the table in Propel. The fields for specifying a column in a Webhook Data Source's table. The column name. It has to be unique within a table. The JSON property that the column will be derived from. For example, if you POST a JSON event like this: ```json theme={"system"} { "greeting": { "message": "hello, world" } } ``` Then you can use the JSON property "greeting.message" to extract "hello, world" to a column. The column type. The Propel data types. * `BOOLEAN`: True or false. * `STRING`: A variable-length string. * `FLOAT`: A 32-bit signed double-precision floating point number. * `DOUBLE`: A 64-bit signed double-precision floating point number. * `INT8`: An 8-bit signed integer, with a minimum value of -2⁷ and a maximum value of 2⁷-1. * `INT16`: A 16-bit signed integer, with a minimum value of -2¹⁵ and a maximum value of 2¹⁵-1. * `INT32`: A 32-bit signed integer, with a minimum value of -2³¹ and a maximum value of 2³¹-1. * `INT64`: A 64-bit signed integer, with a minimum value of -2⁶³ and a maximum value of 2⁶³-1. * `DATE`: A date without a timestamp. For example, "YYYY-MM-DD". * `TIMESTAMP`: A date with a timestamp. For example, "yyy-MM-dd HH:mm:ss". * `JSON`: A JavaScript Object Notation (JSON) document. * `CLICKHOUSE`: A ClickHouse-specific type. Whether the column's type is nullable or not. Override the Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse, and a default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if any. You can override these defaults in order to specify a custom table engine, custom ORDER BY, etc. A Data Pool's table settings. These describe how the Data Pool's table is created in ClickHouse. The ClickHouse table engine for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. A Data Pool's table engine. Field for specifying the MergeTree table engine. See MergeTreeTableEngineInput Field for specifying the ReplacingMergeTree table engine. See ReplacingMergeTreeTableEngineInput Field for specifying the SummingMergeTree table engine. See SummingMergeTreeTableEngineInput Field for specifying the AggregatingMergeTree table engine. See AggregatingMergeTreeTableEngineInput Field for specifying the PostgreSQL table engine. See PostgreSqlTableEngineInput The PARTITION BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The PRIMARY KEY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The ORDER BY clause for the Data Pool's table. This field is optional. A default will be chosen based on the Data Pool's `timestamp` and `uniqueId` values, if specified. The TTL clause for the Data Pool's table. The tenant ID column, if any. The primary timestamp column, if any. The unique ID column, if any. Propel uses the primary timestamp and a unique ID to compose a primary key for determining whether records should be inserted, deleted, or updated.