google-datacatalog-greenplum-connector

Library for ingesting Greenplum 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. Run entry point
  • 3.1. Run Python entry point
  • 3.2. Run the Python entry point with a user-defined entry resource URL prefix
  • 3.3. Run Docker entry point
• 4 Scripts inside tools
  • 4.1. Run clean up
  • 4.2. Extract CSV
• 5. Developer environment
  • 5.1. Install and run Yapf formatter
  • 5.2. Install and run Flake8 linter
  • 5.3. Run Tests
• 6. Metrics
• 7. 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-greenplum-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-greenplum-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-greenplum-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>/greenplum2dc-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 GREENPLUM2DC_DATACATALOG_PROJECT_ID=google_cloud_project_id
export GREENPLUM2DC_DATACATALOG_LOCATION_ID=google_cloud_location_id
export GREENPLUM2DC_GREENPLUM_SERVER=greenplum_server
export GREENPLUM2DC_GREENPLUM_USERNAME=greenplum_username
export GREENPLUM2DC_GREENPLUM_PASSWORD=greenplum_password
export GREENPLUM2DC_GREENPLUM_DATABASE=greenplum_database
export GREENPLUM2DC_RAW_METADATA_CSV=greenplum_raw_csv (If supplied ignores the GREENPLUM server credentials)

3. Run entry point

3.1. Run Python entry point

• Virtualenv

google-datacatalog-greenplum-connector \
--datacatalog-project-id=$GREENPLUM2DC_DATACATALOG_PROJECT_ID \
--datacatalog-location-id=$GREENPLUM2DC_DATACATALOG_LOCATION_ID \
--greenplum-host=$GREENPLUM2DC_GREENPLUM_SERVER \
--greenplum-user=$GREENPLUM2DC_GREENPLUM_USERNAME \
--greenplum-pass=$GREENPLUM2DC_GREENPLUM_PASSWORD \
--greenplum-database=$GREENPLUM2DC_GREENPLUM_DATABASE \
--raw-metadata-csv=$GREENPLUM2DC_RAW_METADATA_CSV      

3.2. Run the Python entry point with a user-defined entry resource URL prefix

This option is useful when the connector cannot accurately determine the database hostname. For example when running under proxies, load balancers or database read replicas, you can specify the prefix of your master instance so the resource URL will point to the exact database where the data is stored.

• Virtualenv

google-datacatalog-greenplum-connector \
--datacatalog-project-id=$GREENPLUM2DC_DATACATALOG_PROJECT_ID \
--datacatalog-location-id=$GREENPLUM2DC_DATACATALOG_LOCATION_ID \
--datacatalog-entry-resource-url-prefix project/database-instance \
--greenplum-host=$GREENPLUM2DC_GREENPLUM_SERVER \
--greenplum-user=$GREENPLUM2DC_GREENPLUM_USERNAME \
--greenplum-pass=$GREENPLUM2DC_GREENPLUM_PASSWORD \
--greenplum-database=$GREENPLUM2DC_GREENPLUM_DATABASE \
--raw-metadata-csv=$GREENPLUM2DC_RAW_METADATA_CSV

3.3. Run Docker entry point

docker build -t greenplum2datacatalog .
docker run --rm --tty -v YOUR-CREDENTIALS_FILES_FOLDER:/data greenplum2datacatalog \ --datacatalog-project-id=$GREENPLUM2DC_DATACATALOG_PROJECT_ID \
--datacatalog-location-id=$GREENPLUM2DC_DATACATALOG_LOCATION_ID \
--greenplum-host=$GREENPLUM2DC_GREENPLUM_SERVER \
--greenplum-user=$GREENPLUM2DC_GREENPLUM_USERNAME \
--greenplum-pass=$GREENPLUM2DC_GREENPLUM_PASSWORD \
--greenplum-database=$GREENPLUM2DC_GREENPLUM_DATABASE  \
--raw-metadata-csv=$GREENPLUM2DC_RAW_METADATA_CSV       

4 Scripts inside tools

4.1. Run clean up

# List of projects split by comma. Can be a single value without comma
export GREENPLUM2DC_DATACATALOG_PROJECT_IDS=my-project-1,my-project-2

# Run the clean up
python tools/cleanup_datacatalog.py --datacatalog-project-ids=$TGREENPLUM2DC_DATACATALOG_PROJECT_IDS 

4.2. Extract CSV

# Run  inside your greenplum 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/gpadmin/greenplum_full_dump.csv'  CSV HEADER;

5. Developer environment

5.1. 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

5.2. Install and run Flake8 linter

pip install --upgrade flake8
flake8 src tests

5.3. Run Tests

python setup.py test

6. Metrics

Metrics README.md

7. 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.