Redshift host

Redshift port

Redshift database name

Redshift user name

Redshift password

AWS S3 bucket name

Note $MELTANO_EXTRACT__LOAD_SCHEMA will expand to the value of the load_schema extra for the extractor used in the pipeline, which defaults to the extractor's namespace, e.g. tap_gitlab for tap-gitlab.

Name of the schema where the tables will be created. If schema_mapping is not defined then every stream sent by the tap is loaded into this schema.

AWS profile name for profile based authentication. If not provided, AWS_PROFILE environment variable will be used.

S3 Access Key Id. Used for S3 and Redshift copy operations. If not provided, AWS_ACCESS_KEY_ID environment variable will be used.

S3 Secret Access Key. Used for S3 and Redshift copy operations. If not provided, AWS_SECRET_ACCESS_KEY environment variable will be used.

S3 AWS STS token for temporary credentials. If not provided, AWS_SESSION_TOKEN environment variable will be used.

AWS Role ARN to be used for the Redshift COPY operation. Used instead of the given AWS keys for the COPY operation if provided - the keys are still used for other S3 operations

S3 Object ACL

A static prefix before the generated S3 key names. Using prefixes you can upload files into specific directories in the S3 bucket. Default(None)

Parameters to use in the COPY command when loading data to Redshift.

Some basic file formatting parameters are fixed values and not recommended overriding them by custom values. They are like: CSV GZIP DELIMITER ',' REMOVEQUOTES ESCAPE.

Maximum number of rows in each batch. At the end of each batch, the rows in the batch are loaded into Redshift.

Flush and load every stream into Redshift when one batch is full. Warning - This may trigger the COPY command to use files with low number of records, and may cause performance problems.

The number of threads used to flush tables. 0 will create a thread for each stream, up to parallelism_max. -1 will create a thread for each CPU core. Any other positive number will create that number of threads, up to parallelism_max.

Max number of parallel threads to use when flushing tables.

Grant USAGE privilege on newly created schemas and grant SELECT privilege on newly created tables to a specific list of users or groups. If schema_mapping is not defined then every stream sent by the tap is granted accordingly.

Useful if you want to load multiple streams from one tap to multiple Redshift schemas.

If the tap sends the stream_id in <schema_name>-<table_name> format then this option overwrites the default_target_schema value.

Note, that using schema_mapping you can overwrite the default_target_schema_select_permissions value to grant SELECT permissions to different groups per schemas or optionally you can create indices automatically for the replicated tables.

By default the connector caches the available table structures in Redshift at startup. In this way it doesn't need to run additional queries when ingesting data to check if altering the target tables is required. With disable_table_cache option you can turn off this caching. You will always see the most recent table structures but will cause an extra query runtime.

Metadata columns add extra row level information about data ingestions, (i.e. when was the row read in source, when was inserted or deleted in redshift etc.) Metadata columns are creating automatically by adding extra columns to the tables with a column prefix _SDC_.

The metadata columns are documented at https://transferwise.github.io/pipelinewise/data_structure/sdc-columns.html. Enabling metadata columns will flag the deleted rows by setting the _SDC_DELETED_AT metadata column.

Without the add_metadata_columns option the deleted rows from singer taps will not be recongisable in Redshift.

When hard_delete option is true then DELETE SQL commands will be performed in Redshift to delete rows in tables. It's achieved by continuously checking the _SDC_DELETED_AT metadata column sent by the singer tap. Due to deleting rows requires metadata columns, hard_delete option automatically enables the add_metadata_columns option as well.

Object type RECORD items from taps can be loaded into VARIANT columns as JSON (default) or we can flatten the schema by creating columns automatically. When value is 0 (default) then flattening functionality is turned off.

Log based and Incremental replications on tables with no Primary Key cause duplicates when merging UPDATE events. When set to true, stop loading data if no Primary Key is defined.

Validate every single record message to the corresponding JSON schema. This option is disabled by default and invalid RECORD messages will fail only at load time by Redshift. Enabling this option will detect invalid records earlier but could cause performance degradation.

Do not update existing records when Primary Key is defined. Useful to improve performance when records are immutable, e.g. events

The compression method to use when writing files to S3 and running Redshift COPY.

The number of slices to split files into prior to running COPY on Redshift. This should be set to the number of Redshift slices. The number of slices per node depends on the node size of the cluster - run SELECT COUNT(DISTINCT slice) slices FROM stv_slices to calculate this. Defaults to 1.

(Default: platform-dependent) Directory of temporary CSV files with RECORD messages.