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Copyright 2012 The Obvious Corporation and contributors.
<http://obvious.com/>

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

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123 changes: 123 additions & 0 deletions README.md
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# pipelinewise-target-bigquery

[![PyPI version](https://badge.fury.io/py/pipelinewise-target-bigquery.svg)](https://badge.fury.io/py/pipelinewise-target-bigquery)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pipelinewise-target-bigquery.svg)](https://pypi.org/project/pipelinewise-target-bigquery/)
[![License: Apache2](https://img.shields.io/badge/License-Apache2-yellow.svg)](https://opensource.org/licenses/Apache-2.0)

[Singer](https://www.singer.io/) target that loads data into BigQuery following the [Singer spec](https://github.com/singer-io/getting-started/blob/master/docs/SPEC.md).

This is a [PipelineWise](https://transferwise.github.io/pipelinewise) compatible target connector.

## How to use it

The recommended method of running this target is to use it from [PipelineWise](https://transferwise.github.io/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 BigQuery](https://transferwise.github.io/pipelinewise/connectors/targets/bigquery.html)

If you want to run this [Singer Target](https://singer.io) independently please read further.

### Install

First, make sure Python 3 is installed on your system or follow these
installation instructions for [Mac](http://docs.python-guide.org/en/latest/starting/install3/osx/) or
[Ubuntu](https://www.digitalocean.com/community/tutorials/how-to-install-python-3-and-set-up-a-local-programming-environment-on-ubuntu-16-04).

It's recommended to use a virtualenv:

```bash
python3 -m venv venv
pip install pipelinewise-target-bigquery
```

or

```bash
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-bigquery --config [config.json]`

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

**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:

```json
{
"host": "localhost",
"port": 5432,
"user": "my_user",
"password": "secret",
"dbname": "my_db_name",
"default_target_schema": "my_target_schema"
}
```

Full list of options in `config.json`:

| Property | Type | Required? | Description |
|-------------------------------------|---------|------------|---------------------------------------------------------------|
| host | String | Yes | BigQuery host |
| port | Integer | Yes | BigQuery port |
| user | String | Yes | BigQuery user |
| password | String | Yes | BigQuery password |
| dbname | String | Yes | BigQuery database name |
| 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 BigQuery. |
| 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
| schema_mapping | Object | | Useful if you want to load multiple streams from one tap to multiple BigQuery schemas.<br><br>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.<br><br> **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](https://transferwise.github.io/pipelinewise/connectors/taps/mysql.html#configuring-what-to-replicate). |
| 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 bigquery 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 BigQuery. |
| hard_delete | Boolean | | (Default: False) When `hard_delete` option is true then DELETE SQL commands will be performed in BigQuery 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. |


### To run tests:

1. Define environment variables that requires running the tests
```
export GOOGLE_APPLICATION_CREDENTIALS=<credentials-json-file>
```

2. Install python 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 .
pip install nose
```

3. To run unit tests:
```
nosetests --where=tests/unit
```

4. 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_bigquery -d C,W,unexpected-keyword-arg,duplicate-code
```

## License

Apache License Version 2.0

See [LICENSE](LICENSE) to see the full text.
3 changes: 3 additions & 0 deletions requirements.txt
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singer-python==5.1.1
psycopg2==2.7.5
inflection==0.3.1
33 changes: 33 additions & 0 deletions setup.py
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#!/usr/bin/env python

from setuptools import setup

with open('README.md') as f:
long_description = f.read()

setup(name="pipelinewise-target-bigquery",
version="1.0.4",
description="Singer.io target for loading data to BigQuery - PipelineWise compatible",
long_description=long_description,
long_description_content_type='text/markdown',
author="TransferWise",
url='https://github.com/transferwise/pipelinewise-target-bigquery',
classifiers=[
'License :: OSI Approved :: Apache Software License',
'Programming Language :: Python :: 3 :: Only'
],
py_modules=["target_bigquery"],
install_requires=[
'singer-python==5.1.1',
'psycopg2==2.8.2',
'inflection==0.3.1',
'joblib==0.13.2'
],
entry_points="""
[console_scripts]
target-bigquery=target_bigquery:main
""",
packages=["target_bigquery"],
package_data = {},
include_package_data=True,
)
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