Loading Jira Data to YugabyteDB with dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to YugabyteDB. You can get the connection string for your YugabyteDB database as described in the YugabyteDB Docs.

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Loading data from Jira to YugabyteDB can be a complex task, but with the open-source Python library dlt, it becomes straightforward. Jira is the leading project management tool for agile teams, enabling efficient planning, tracking, and releasing of world-class software. YugabyteDB, on the other hand, is a distributed PostgreSQL database designed for modern applications, offering built-in resilience, seamless scalability, and flexible geo-distribution. By using dlt, you can easily extract data from Jira and load it into YugabyteDB, leveraging dlt's capabilities to handle data extraction, transformation, and loading. For more information about Jira, visit here.

dlt Key Features

• Easy to get started: dlt is a Python library that is easy to use and understand. It is designed to be simple to use and easy to understand. Type pip install dlt and you are ready to go. Read more
• Scalability via iterators, chunking, and parallelization: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This approach allows for efficient processing of large datasets by breaking them down into manageable chunks. Read more
• Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Schemas define the structure of normalized data and guide the processing and loading of data. Read more
• Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities. This metadata includes load IDs, which consist of a timestamp and pipeline name. Load IDs enable incremental transformations and data vaulting by tracking data loads and facilitating data lineage and traceability. Read more
• Implicit extraction DAGs: dlt incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. This ensures data consistency and integrity. Read more

Getting started with your pipeline locally

0. Prerequisites

dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt

First you need to install the dlt library with the correct extras for YugabyteDB:

pip install "dlt[postgres]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from Jira to YugabyteDB. You can run the following commands to create a starting point for loading data from Jira to YugabyteDB:

# create a new directory
mkdir jira_pipeline
cd jira_pipeline
# initialize a new pipeline with your source and destination
dlt init jira postgres
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:

dlt[postgres]>=0.3.25

You now have the following folder structure in your project:

jira_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── jira/                   # folder with source specific files
│   └── ...
├── jira_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

2. Configuring your source and destination credentials

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml

# put your configuration values here

[runtime]
log_level="WARNING"  # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
dlthub_telemetry = true

generated secrets.toml

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.jira]
subdomain = "subdomain" # please set me up!
email = "email" # please set me up!
api_token = "api_token" # please set me up!

[destination.postgres]
dataset_name = "dataset_name" # please set me up!

[destination.postgres.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
port = 5432
connect_timeout = 15

2.1. Adjust the generated code to your usecase

Further help setting up your source and destinations

• Read more about setting up the Jira source in our docs.
• Read more about setting up the YugabyteDB destination in our docs.

3. Running your pipeline for the first time

The dlt cli has also created a main pipeline script for you at jira_pipeline.py, as well as a folder jira that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:

from typing import List, Optional

import dlt
from jira import jira, jira_search


def load(endpoints: Optional[List[str]] = None) -> None:
    """
    Load data from specified Jira endpoints into a dataset.

    Args:
        endpoints: A list of Jira endpoints. If not provided, defaults to all resources.
    """
    if not endpoints:
        endpoints = list(jira().resources.keys())

    pipeline = dlt.pipeline(
        pipeline_name="jira_pipeline", destination='postgres', dataset_name="jira"
    )

    load_info = pipeline.run(jira().with_resources(*endpoints))

    print(f"Load Information: {load_info}")


def load_query_data(queries: List[str]) -> None:
    """
    Load issues from specified Jira queries into a dataset.

    Args:
        queries: A list of JQL queries.
    """
    pipeline = dlt.pipeline(
        pipeline_name="jira_search_pipeline",
        destination='postgres',
        dataset_name="jira_search",
    )

    load_info = pipeline.run(jira_search().issues(jql_queries=queries))

    print(f"Load Information: {load_info}")


if __name__ == "__main__":
    # Add your desired endpoints to the list 'endpoints'
    load(endpoints=None)

    queries = [
        "created >= -30d order by created DESC",
        'project = KAN AND status = "In Progress" order by created DESC',
    ]

    load_query_data(queries=queries)

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python jira_pipeline.py

4. Inspecting your load result

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline jira_pipeline info

You can also use streamlit to inspect the contents of your YugabyteDB destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline jira_pipeline show

5. Next steps to get your pipeline running in production

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to

• Deploy with GitHub Actions: Learn how to set up and deploy your dlt pipeline using GitHub Actions. Check the detailed guide here.
• Deploy with Airflow and Google Composer: Follow this tutorial to deploy your dlt pipeline using Airflow managed by Google Composer. The step-by-step instructions are available here.
• Deploy with Google Cloud Functions: This guide walks you through deploying your dlt pipeline using Google Cloud Functions. Access the guide here.
• Explore other deployment options: Discover more ways to deploy your dlt pipeline by exploring other available guides here.

The running in production section will teach you about:

• How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline to ensure smooth and efficient data processing. How to Monitor your pipeline
• Set up alerts: Stay informed about your pipeline's status by setting up alerts for various events and metrics. Set up alerts
• And set up tracing: Implement tracing to track the execution flow and performance of your dlt pipeline. And set up tracing

Available Sources and Resources

For this verified source the following sources and resources are available

Source jira

The Jira source provides data on project management tasks, including details on issues, users, workflows, and projects.

Resource Name	Write Disposition	Description
issues	replace	Individual pieces of work to be completed. Contains various fields such as assignee, comments, created time, reporter, status, summary, updated time, etc.
projects	replace	A collection of tasks that need to be completed to achieve a certain outcome. Contains fields such as avatar URL, description, ID, key, lead, name, etc.
users	replace	Administrator of a given project. Contains fields such as account ID, account type, avatar URL, display name, email address, etc.
workflows	replace	The key aspect of managing and tracking the progress of issues or tasks within a project. Contains fields such as created time, description, ID, updated time, etc.

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