google-datacatalog-postgresql-connector 0.7.0

Library for ingesting Postgresql metadata into Google Cloud Data Catalog

google-datacatalog-postgresql-connector

Library for ingesting PostgreSQL metadata into Google Cloud Data Catalog.

Disclaimer: This is not an officially supported Google product.

Table of Contents

• 1. Installation
  • 1.1. Mac/Linux
  • 1.2. Windows
  • 1.3. Install from source
    • 1.3.1. Get the code
    • 1.3.2. Create and activate a virtualenv
    • 1.3.3. Install the library
• 2. Environment setup
  • 2.1. Auth credentials
    • 2.1.1. Create a service account and grant it below roles
    • 2.1.2. Download a JSON key and save it as
  • 2.2. Set environment variables
• 3. Adapt user configurations
• 4. Run entry point
  • 4.1. Run Python entry point
  • 4.2. Run Docker entry point
• 5. Scripts inside tools
  • 5.1. Run clean up
  • 5.2. Extract CSV
• 6. Developer environment
  • 6.1. Install and run Yapf formatter
  • 6.2. Install and run Flake8 linter
  • 6.3. Run Tests
• 7. Metrics
• 8. Troubleshooting

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1. Installation

Install this library in a virtualenv using pip. virtualenv is a tool to create isolated Python environments. The basic problem it addresses is one of dependencies and versions, and indirectly permissions.

With virtualenv, it's possible to install this library without needing system install permissions, and without clashing with the installed system dependencies. Make sure you use Python 3.6+.

1.1. Mac/Linux

pip3 install virtualenv
virtualenv --python python3.6 <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install google-datacatalog-postgresql-connector

1.2. Windows

pip3 install virtualenv
virtualenv --python python3.6 <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install google-datacatalog-postgresql-connector

1.3. Install from source

1.3.1. Get the code

git clone https://github.com/GoogleCloudPlatform/datacatalog-connectors-rdbms/
cd datacatalog-connectors-rdbms/google-datacatalog-postgresql-connector

1.3.2. Create and activate a virtualenv

pip3 install virtualenv
virtualenv --python python3.6 <your-env>
source <your-env>/bin/activate

1.3.3. Install the library

pip install .

2. Environment setup

2.1. Auth credentials

2.1.1. Create a service account and grant it below roles

• Data Catalog Admin

2.1.2. Download a JSON key and save it as

• <YOUR-CREDENTIALS_FILES_FOLDER>/postgresql2dc-credentials.json

Please notice this folder and file will be required in next steps.

2.2. Set environment variables

Replace below values according to your environment:

export GOOGLE_APPLICATION_CREDENTIALS=data_catalog_credentials_file

export POSTGRESQL2DC_DATACATALOG_PROJECT_ID=google_cloud_project_id
export POSTGRESQL2DC_DATACATALOG_LOCATION_ID=google_cloud_location_id
export POSTGRESQL2DC_POSTGRESQL_SERVER=postgresql_server
export POSTGRESQL2DC_POSTGRESQL_USERNAME=postgresql_username
export POSTGRESQL2DC_POSTGRESQL_PASSWORD=postgresql_password
export POSTGRESQL2DC_POSTGRESQL_DATABASE=postgresql_database
export POSTGRESQL2DC_RAW_METADATA_CSV=postgresql_raw_csv (If supplied ignores the POSTGRESQL server credentials)

3. Adapt user configurations

Along with default metadata, the connector can ingest optional metadata as well, such as number of rows in each table. The table below shows what metadata is scraped by default, and what is configurable.

Metadata	Description	Scraped by default	Config option
schema_name	Name of a schema	Y	---
table_name	Name of a table	Y	---
table_type	Type of a table (BASE, VIEW, etc)	Y	---
table_size_mb	Size of a table, in MB	Y	---
column_name	Name of a column	Y	---
column_type	Type of a column (ARRAY, USER-DEFINED, etc)	Y	---
column_default_value	Default value of a column	Y	---
column_nullable	Whether a column is nullable	Y	---
column_char_length	Char length of values in a column	Y	---
column_numeric_precision	Numeric precision of values in a column	Y	---
column_enum_values	List of enum values for a column	Y	---
ANALYZE statement	Statement to refresh metadata information	N	refresh_metadata_tables
table_rows	Number of rows in a table	N	sync_row_counts

Sample configuration file ingest_cfg.yaml in the repository root shows what kind of configuration is expected. If you want to run optional queries, please add ingest_cfg.yaml to your working directory and adapt it to your needs.

4. Run entry point

4.1. Run Python entry point

• Virtualenv

google-datacatalog-postgresql-connector \
--datacatalog-project-id=$POSTGRESQL2DC_DATACATALOG_PROJECT_ID \
--datacatalog-location-id=$POSTGRESQL2DC_DATACATALOG_LOCATION_ID \
--postgresql-host=$POSTGRESQL2DC_POSTGRESQL_SERVER \
--postgresql-user=$POSTGRESQL2DC_POSTGRESQL_USERNAME \
--postgresql-pass=$POSTGRESQL2DC_POSTGRESQL_PASSWORD \
--postgresql-database=$POSTGRESQL2DC_POSTGRESQL_DATABASE  \
--raw-metadata-csv=$POSTGRESQL2DC_RAW_METADATA_CSV

4.2. Run Docker entry point

docker build -t postgresql2datacatalog .
docker run --rm --tty -v YOUR-CREDENTIALS_FILES_FOLDER:/data postgresql2datacatalog \
--datacatalog-project-id=$POSTGRESQL2DC_DATACATALOG_PROJECT_ID \
--datacatalog-location-id=$POSTGRESQL2DC_DATACATALOG_LOCATION_ID \
--postgresql-host=$POSTGRESQL2DC_POSTGRESQL_SERVER \
--postgresql-user=$POSTGRESQL2DC_POSTGRESQL_USERNAME \
--postgresql-pass=$POSTGRESQL2DC_POSTGRESQL_PASSWORD \
--postgresql-database=$POSTGRESQL2DC_POSTGRESQL_DATABASE  \
--raw-metadata-csv=$POSTGRESQL2DC_RAW_METADATA_CSV       

5. Scripts inside tools

5.1. Run clean up

# List of projects split by comma. Can be a single value without comma
export POSTGRESQL2DC_DATACATALOG_PROJECT_IDS=my-project-1,my-project-2

# Run the clean up
python tools/cleanup_datacatalog.py --datacatalog-project-ids=$POSTGRESQL2DC_DATACATALOG_PROJECT_IDS 

5.2. Extract CSV

# Run  inside your postgresql database instance

COPY (
    select t.table_schema as schema_name, t.table_name, t.table_type, c.column_name, c.column_default as column_default_value, c.is_nullable as column_nullable, c.data_type as column_type,
            c.character_maximum_length as column_char_length, c.numeric_precision as column_numeric_precision  
        from information_schema.tables t
            join  information_schema.columns c on c.table_name = t.table_name
        where t.table_schema not in ('pg_catalog', 'information_schema', 'pg_toast', 'gp_toolkit')
            and c.table_schema not in ('pg_catalog', 'information_schema', 'pg_toast', 'gp_toolkit')
    ) 
    TO '/home/postgre/postgresql_full_dump.csv'  CSV HEADER;

6. Developer environment

6.1. Install and run Yapf formatter

pip install --upgrade yapf

# Auto update files
yapf --in-place --recursive src tests

# Show diff
yapf --diff --recursive src tests

# Set up pre-commit hook
# From the root of your git project.
curl -o pre-commit.sh https://raw.githubusercontent.com/google/yapf/master/plugins/pre-commit.sh
chmod a+x pre-commit.sh
mv pre-commit.sh .git/hooks/pre-commit

6.2. Install and run Flake8 linter

pip install --upgrade flake8
flake8 src tests

6.3. Run Tests

python setup.py test

7. Metrics

Metrics README.md

8. Troubleshooting

In the case a connector execution hits Data Catalog quota limit, an error will be raised and logged with the following detailement, depending on the performed operation READ/WRITE/SEARCH:

status = StatusCode.RESOURCE_EXHAUSTED
details = "Quota exceeded for quota metric 'Read requests' and limit 'Read requests per minute' of service 'datacatalog.googleapis.com' for consumer 'project_number:1111111111111'."
debug_error_string = 
"{"created":"@1587396969.506556000", "description":"Error received from peer ipv4:172.217.29.42:443","file":"src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Quota exceeded for quota metric 'Read requests' and limit 'Read requests per minute' of service 'datacatalog.googleapis.com' for consumer 'project_number:1111111111111'.","grpc_status":8}"

For more info about Data Catalog quota, go to: Data Catalog quota docs.