target-singer-jsonl

This is a Singer target that receives Singer messages following the Singer spec and writes them stream-wise to JSONL formatted and g-zipped files. File writing is done via the smart_open python package, supporting local disk as well as many other destinations (e.g. S3).

Installation

pip install target-singer-jsonl

Settings

{
    "destination": "local",
    "add_record_metadata": False,
    "local": {
        "folder": ".secrets/output/"
    },
    "s3": {
        "bucket": "my-s3-bucket",
        "prefix": "put/files/in/here/"
    },
}

Setting	Required	Default	Description
destination	False	local	The destination configuration to use. Currently local and s3 are supported.
add_record_metadata	False	true	Whether to inject _sdc_* metadata columns.
local.folder	False		Output folder to write .singer.gz stream files to.
s3.bucket	False		S3 bucket.
s3.prefix	False		S3 key prefix to write .singer.gz stream files under.

Note: target-singer-jsonl will automatically create a folder/key per stream (using the stream name) as well as using the stream name and a timestamp for each file. Therefore the complete scheme is:

# local
<folder>/<stream name>/<stream name>-<timestamp>.singer.gz
# s3
s3://<bucket>/<prefix>/<stream name>/<stream name>-<timestamp>.singer.gz

Timestamps are of the form %Y%m%dT%H%M%S%z, and if folders/keys do not exist, they will be created.

Usage

# pipe stream input from a single file
cat output/records.jsonl | target-singer-jsonl --config example_config.json
# pipe target input from a Singer tap
tap-carbon-intensity | target-singer-jsonl --config example_config.json

Limitations

target-singer-jsonl is in development so may not work 100% as expected. It was also not built using the Meltano Singer SDK, due to its low-level handling of Singer messages. This may change in future, however it currently means that some advanced features are not yet available.

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.

Your project comes with a custom meltano.yml project file already created.

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

# Install meltano
pipx install meltano
# Initialize meltano within this directory
meltano install

Now you can test and orchestrate using Meltano:

# Test invocation:
meltano run job-simple-test