Skip to main content target for loading data to Amazon Redshift - PipelineWise compatible

Project description


PyPI version PyPI - Python Version License: Apache2

Singer target that loads data into Amazon Redshift following the Singer spec.

This is a PipelineWise compatible target connector.

How to use it

The recommended method of running this target is to use it from PipelineWise. When running it from PipelineWise you don't need to configure this tap with JSON files and most of things are automated. Please check the related documentation at Target Redshift

If you want to run this Singer Target independently please read further.


First, make sure Python 3 is installed on your system or follow these installation instructions for Mac or Ubuntu.

It's recommended to use a virtualenv:

  python3 -m venv venv
  . venv/bin/activate
  pip install --upgrade pip
  pip install pipelinewise-target-redshift


  python3 -m venv venv
  . venv/bin/activate
  pip install --upgrade pip
  pip install .

To run

Like any other target that's following the singer specificiation:

some-singer-tap | target-redshift --config [config.json]

It's reading incoming messages from STDIN and using the properites in config.json to upload data into Amazon Redshift.

Note: To avoid version conflicts run tap and targets in separate virtual environments.

Configuration settings

Running the the target connector requires a config.json file. Example with the minimal settings:


  "host": "",
  "port": 5439,
  "user": "my_user",
  "password": "password",
  "dbname": "database_name",
  "aws_access_key_id": "secret",
  "aws_secret_access_key": "secret",
  "s3_bucket": "bucket_name",
  "default_target_schema": "my_target_schema"

Full list of options in config.json:

Property Type Required? Description
host String Yes Redshift Host
port Integer Yes Redshift Port
user String Yes Redshift User
password String Yes Redshift Password
dbname String Yes Redshift Database name
aws_access_key_id String No S3 Access Key Id. Used for S3 and Redshfit copy operations. If not provided, credentials will be collected from the environment
aws_secret_access_key String No S3 Secret Access Key. Used for S3 and Redshfit copy operations. If not provided, credentials will be collected from the environment
aws_session_token String No S3 AWS STS token for temporary credentials
aws_redshift_copy_role_arn String No 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_bucket String Yes S3 Bucket name
s3_key_prefix String (Default: None) A static prefix before the generated S3 key names. Using prefixes you can upload files into specific directories in the S3 bucket.
copy_options String (Default: EMPTYASNULL BLANKSASNULL TRIMBLANKS TRUNCATECOLUMNS TIMEFORMAT 'auto' COMPUPDATE OFF STATUPDATE OFF). 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
batch_size Integer (Default: 100000) Maximum number of rows in each batch. At the end of each batch, the rows in the batch are loaded into Redshift.
parallelism Integer (Default: no. of CPUs) The maximum parallelism to load streams into Redshift. Desirable if running in a container on a host which has many more CPUs than are allocated to the container, as Python will infer the number of CPUs as the number of host CPUs.
default_target_schema String 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.
default_target_schema_select_permission String Grant USAGE privilege on newly created schemas and grant SELECT privilege on newly created tables to a specific list of users or groups. Example: {"users": ["user_1","user_2"], "groups": ["group_1", "group_2"]} If schema_mapping is not defined then every stream sent by the tap is granted accordingly.
schema_mapping Object 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_permission value to grant SELECT permissions to different groups per schemas or optionally you can create indices automatically for the replicated tables.

Note: This is an experimental feature and recommended to use via PipelineWise YAML files that will generate the object mapping in the right JSON format. For further info check a [PipelineWise YAML Example]
disable_table_cache Boolean (Default: False) 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.
add_metadata_columns Boolean (Default: False) 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 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.
hard_delete Boolean (Default: False) 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.
data_flattening_max_level Integer (Default: 0) 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.

To run tests:

  1. Install python dependencies in a virtual env:
  python3 -m venv venv
  . venv/bin/activate
  pip install --upgrade pip
  pip install .
  pip install pytest coverage
  1. To run unit tests:
  coverage run -m pytest --disable-pytest-warnings tests/unit && coverage report
  1. To run integration tests define environment variables first:
  export TARGET_REDSHIFT_HOST=<redshift-host>
  export TARGET_REDSHIFT_PORT=<redshift-port>
  export TARGET_REDSHIFT_USER=<redshift-user>
  export TARGET_REDSHIFT_PASSWORD=<redshift-password>
  export TARGET_REDSHIFT_DBNAME=<redshift-database-name>
  export TARGET_REDSHIFT_SCHEMA=<redshift-target-schema>
  export TARGET_REDSHIFT_AWS_ACCESS_KEY=<aws-access-key-id>
  export TARGET_REDSHIFT_AWS_SECRET_ACCESS_KEY=<aws-access-secret-access-key>
  export TARGET_REDSHIFT_S3_BUCKET=<s3-bucket>
  export TARGET_REDSHIFT_S3_KEY_PREFIX=<s3-bucket-directory>

  coverage run -m pytest --disable-pytest-warnings tests/integration && coverage report

To run pylint:

  1. Install python dependencies and run python linter
  python3 -m venv venv
  . venv/bin/activate
  pip install --upgrade pip
  pip install .
  pip install pylint
  pylint target_redshift -d C,W,unexpected-keyword-arg,duplicate-code


Apache License Version 2.0

See LICENSE to see the full text.

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