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Singer.io target for writing CSV files and upload to S3 - PipelineWise compatible

Project description

pipelinewise-target-s3-csv

PyPI version PyPI - Python Version License: Apache2

Singer target that uploads loads data to S3 in CSV format 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 S3 CSV

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

Install

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
  pip install pipelinewise-target-s3-csv

or

  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-s3-csv --config [config.json]

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

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. An example with the minimal settings:

{
  "s3_bucket": "my_bucket"
}

Profile based authentication

Profile based authentication used by default using the default profile. To use another profile set aws_profile parameter in config.json or set the AWS_PROFILE environment variable.

Non-Profile based authentication

For non-profile based authentication set aws_access_key_id , aws_secret_access_key and optionally the aws_session_token parameter in the config.json. Alternatively you can define them out of config.json by setting AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and AWS_SESSION_TOKEN environment variables.

Full list of options in config.json:

Property Type Required? Description
aws_access_key_id String No S3 Access Key Id. If not provided, AWS_ACCESS_KEY_ID environment variable will be used.
aws_secret_access_key String No S3 Secret Access Key. If not provided, AWS_SECRET_ACCESS_KEY environment variable will be used.
aws_session_token String No AWS Session token. If not provided, AWS_SESSION_TOKEN environment variable will be used.
aws_profile String No AWS profile name for profile based authentication. If not provided, AWS_PROFILE environment variable will be used.
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
delimiter String (Default: ',') A one-character string used to separate fields.
quotechar String (Default: '"') A one-character string used to quote fields containing special characters, such as the delimiter or quotechar, or which contain new-line characters.
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 snowflake etc.) Metadata columns are creating automatically by adding extra columns to the tables with a column prefix _SDC_. The column names are following the stitch naming conventions documented at https://www.stitchdata.com/docs/data-structure/integration-schemas#sdc-columns. 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 Snowflake.
encryption_type String No (Default: 'none') The type of encryption to use. Current supported options are: 'none' and 'KMS'.
encryption_key String No A reference to the encryption key to use for data encryption. For KMS encryption, this should be the name of the KMS encryption key ID (e.g. '1234abcd-1234-1234-1234-1234abcd1234'). This field is ignored if 'encryption_type' is none or blank.
compression String No The type of compression to apply before uploading. Supported options are none (default) and gzip. For gzipped files, the file extension will automatically be changed to .csv.gz for all files.
naming_convention String No (Default: None) Custom naming convention of the s3 key. Replaces tokens date, stream, and timestamp with the appropriate values.

Supports "folders" in s3 keys e.g. folder/folder2/{stream}/export_date={date}/{timestamp}.csv.

Honors the s3_key_prefix, if set, by prepending the "filename". E.g. naming_convention = folder1/my_file.csv and s3_key_prefix = prefix_ results in folder1/prefix_my_file.csv
temp_dir String (Default: platform-dependent) Directory of temporary CSV files with RECORD messages.

To run tests:

  1. Define environment variables that requires running the tests
  export TARGET_S3_CSV_ACCESS_KEY_ID=<s3-access-key-id>
  export TARGET_S3_CSV_SECRET_ACCESS_KEY=<s3-secret-access-key>
  export TARGET_S3_CSV_BUCKET=<s3-bucket>
  export TARGET_S3_CSV_KEY_PREFIX=<s3-key-prefix>
  1. Install python test dependencies in a virtual env and run nose unit and integration tests
  python3 -m venv venv
  . venv/bin/activate
  pip install --upgrade pip
  pip install .[test]
  1. To run unit tests:
  nosetests --where=tests/unit
  1. To run integration tests:
  nosetests --where=tests/integration

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_s3_csv -d C,W,unexpected-keyword-arg,duplicate-code

License

Apache License Version 2.0

See LICENSE to see the full text.

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