target-duckdb 0.4.4

Singer.io target for loading data into DuckDB

target-duckdb

Singer target that loads data into DuckDB following the Singer spec.

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:

make venv

To run

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

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

It's reading incoming messages from STDIN and using the properties in config.json to load data into DuckDB.

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

Configuration settings

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

{
    "filepath": "/path/to/local/duckdb.file",
    "default_target_schema": "main"
}

Additional options in config.json:

Property	Type	Required?	Description
filepath	String	Yes	The path to the DuckDB file that you want to write on the local filesystem.
batch_size_rows	Integer		(Default: 100000) Maximum number of rows in each batch. At the end of each batch, the rows in the batch are loaded into DuckDB.
flush_all_streams	Boolean		(Default: False) Flush and load every stream into DuckDB when one batch is full. Warning: This may trigger the COPY command to use files with low number of records.
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.
schema_mapping	Object		Useful if you want to load multiple streams from one tap to multiple DuckDB schemas. If the tap sends the stream_id in <schema_name>-<table_name> format then this option overwrites the default_target_schema value.
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 postgres 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 recognisable in DuckDB.
hard_delete	Boolean		(Default: False) When hard_delete option is true then DELETE SQL commands will be performed in DuckDB 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 transformed to flattened columns by creating columns automatically. When value is 0 (default) then flattening functionality is turned off.
primary_key_required	Boolean		(Default: True) 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_records	Boolean		(Default: False) 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 DuckDB. Enabling this option will detect invalid records earlier but could cause performance degradation.
temp_dir	String		(Default: platform-dependent) Directory of temporary CSV files with RECORD messages.

To run tests:

1. Install python dependencies in a virtual env and run unit and integration tests

  make venv

2. To run unit tests:

  make unit_test

3. To run integration tests:

  make integration_test

To run pylint:

1. Install python dependencies and run python linter

 make venv pylint

License

Apache License Version 2.0

See LICENSE to see the full text.