target-parquet

target-parquet is a Singer target for parquet.

Build with the Meltano Target SDK.

Installation

Install from GitHub:

pipx install git+https://github.com/Automattic/target-parquet.git@main

Configuration

Accepted Config Options

Capabilities

• about
• stream-maps
• schema-flattening

Settings

Setting	Required	Default	Description
destination_path	False	output	Destination Path
compression_method	False	gzip	(Default - gzip) Compression methods have to be supported by Pyarrow, and currently the compression modes available are - snappy, zstd, brotli and gzip.
max_pyarrow_table_size	False	800	Max size of pyarrow table in MB (before writing to parquet file). It can control the memory usage of the target.
max_batch_size	False	10000	Max records to write in one batch. It can control the memory usage of the target.
extra_fields	False	None	Extra fields to add to the flattened record. (e.g. extra_col1=value1,extra_col2=value2)
extra_fields_types	False	None	Extra fields types. (e.g. extra_col1=string,extra_col2=integer)
partition_cols	False	None	Extra fields to add to the flattened record. (e.g. extra_col1,extra_col2)

A full list of supported settings and capabilities for this target is available by running:

target-parquet --about

Configure using environment variables

This Singer target will automatically import any environment variables within the working directory's .env if the --config=ENV is provided, such that config values will be considered if a matching environment variable is set either in the terminal context or in the .env file.

Source Authentication and Authorization

Usage

You can easily run target-parquet by itself or in a pipeline using Meltano.

Executing the Target Directly

target-parquet --version
target-parquet --help
# Test using the "Carbon Intensity" sample:
tap-carbon-intensity | target-parquet --config /path/to/target-parquet-config.json

Developer Resources

Follow these instructions to contribute to this project.

Initialize your Development Environment

pipx install poetry
poetry install

Create and Run Tests

Create tests within the tests subfolder and then run:

poetry run pytest

You can also test the target-parquet CLI interface directly using poetry run:

poetry run target-parquet --help

Testing with Meltano

Note: This target will work in any Singer environment and does not require Meltano. Examples here are for convenience and to streamline end-to-end orchestration scenarios.

Next, install Meltano (if you haven't already) and any needed plugins:

# Install meltano
pipx install meltano
# Initialize meltano within this directory
cd target-parquet
meltano install

Now you can test and orchestrate using Meltano:

# Test invocation:
meltano invoke target-parquet --version
# OR run a test `elt` pipeline with the Carbon Intensity sample tap:
meltano run tap-carbon-intensity target-parquet

SDK Dev Guide

See the dev guide for more instructions on how to use the Meltano Singer SDK to develop your own Singer taps and targets.