target-db2 0.1.1

`target-db2` is a Singer target for db2, built with the Meltano Singer SDK.

target-db2

target-db2 is a Singer target for db2.

Build with the Meltano Target SDK, by the Infostrux Team

Installation

Install from PyPi:

pipx install target-db2

Install from GitHub:

pipx install git+https://github.com/Infostrux-Solutions/target-db2.git@main

Install via Meltano Configuration (meltano.yml)

  loaders:
  - name: target-db2
    namespace: target_db2
    # uncomment one of the following
    # pip_url: git+https://github.com/Infostrux-Solutions/target-db2.git@main
    # pip_url: target-db2

complete the meltano installation with appropriate configuration settings described in Settings section below.

Configuration

Capabilities

• about
• stream-maps
• schema-flattening
• validate-records

Settings

Setting	Required	Default	Description
host	True	None	IBM Db2 Database Host
port	True	None	IBM Db2 Database Port
user	True	None	IBM Db2 Database User Name
password	True	None	IBM Db2 Database User Password
database	True	None	IBM Db2 Database Name
varchar_size	False	None	Field size for Varchar type. Default 10000. Since JSON values are serialized to varchar, it may be necessary to increase this value. Max possible value 32764
add_record_metadata	False	None	Add metadata to records.
load_method	False	TargetLoadMethods.APPEND_ONLY	The method to use when loading data into the destination. append-only will always write all input records whether that records already exists or not. upsert will update existing records and insert new records. overwrite will delete all existing records and insert all input records.
batch_size_rows	False	None	Maximum number of rows in each batch.
validate_records	False	1	Whether to validate the schema of the incoming streams.
stream_maps	False	None	Config object for stream maps capability. For more information check out Stream Maps.
stream_map_config	False	None	User-defined config values to be used within map expressions.
faker_config	False	None	Config for the Faker instance variable fake used within map expressions. Only applicable if the plugin specifies faker as an addtional dependency (through the singer-sdk faker extra or directly).
faker_config.seed	False	None	Value to seed the Faker generator for deterministic output: https://faker.readthedocs.io/en/master/#seeding-the-generator
faker_config.locale	False	None	One or more LCID locale strings to produce localized output for: https://faker.readthedocs.io/en/master/#localization
flattening_enabled	False	None	'True' to enable schema flattening and automatically expand nested properties.
flattening_max_depth	False	None	The max depth to flatten schemas.

A full list of supported settings and capabilities is available by running: target-db2 --about

Supported Python Versions

• 3.8
• 3.9
• 3.10
• 3.11
• 3.12

Configure using environment variables

This Singer target will automatically import any environment variables within the working directory's .env if the --config=ENV is provided, such that config values will be considered if a matching environment variable is set either in the terminal context or in the .env file.

Db2 Authentication and Authorization

Currently, only username / password (UID / PWD) based authentication is supported. If you need support for additional authentication mechanisms, please open an issue.

The username & password can be provided through meltano.yml or the target-db2's config.json. The user must have the following permissions in order to be able to load data into Db2.

• TODO: figure out the minimal permissions requied by meltano to load data to Db2.

Usage

You can easily run target-db2 by itself or in a pipeline using Meltano.

Executing the Target Directly

note: requires installing tap-carbon-intensity

target-db2 --version
target-db2 --help
# Test using the "Carbon Intensity" sample:
tap-carbon-intensity | target-db2 --config /path/to/target-db2-config.json

Developer Resources

Follow these instructions to contribute to this project.

Initialize your Development Environment

pipx install poetry
poetry install

Create and Run Tests

Create tests within the tests subfolder and then run:

poetry run pytest

You can also test the target-db2 CLI interface directly using poetry run:

poetry run target-db2 --help

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.

This project is supplied with a custom meltano.yml project file already created, as well as a docker-compose.yml file to set up source & target systems for end-to-end testing.

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

# Install meltano
pipx install meltano
# Initialize meltano within this directory
cd target-db2
meltano install

Now you can test and orchestrate using Meltano:

# Test invocation:
meltano invoke target-db2 --version

# OR run a test `elt` pipeline with the Carbon Intensity sample tap:
docker compose up -d
meltano run tap-carbon-intensity target-db2
# after testing is complete, remember to shut down the container resources
docker compose down

# OR run a test `elt` pipeline with the supplied postgresql
# source & sample data generator
docker compose up -d
python generate_postgresql_data.py
meltano run tap-postgres target-db2
# after testing is complete, remember to shut down the container resources
docker compose down

SDK Dev Guide

See the dev guide for more instructions on how to use the Meltano Singer SDK to develop your own Singer taps and targets.

Minimal Permissions Required on DB2

This library will perform the following actions on DB2.

• CREATE TABLE
• DROP TABLE
• ALTER TABLE ADD COLUMN
• ALTER TABLE ALTER COLUMN
• INSERT INTO TABLE
• MERGE INTO TABLE USING
• [OPTIONALLY] CREATE SCHEMA

NOTE: CREATE SCHEMA is used to create a new schema where data will be loaded. If the stated target_schema, specified via default_target_schema exists, this library will not issue a CREATE SCHEMA command

Known Limitations & Issues

Complex Data Structures (arrays & maps)

Complex values such as dict or list will be json encoded and stored as VARCHAR. The VARCHAR column has a default size of 10000, and it is user configurable via the setting varchar_size.

IBM Db2 allows VARCHAR columns up to 32704 bytes.

This target currently does not write to CLOB fields, PRs welcome!