Loading Data from Jira to Timescale with dlt in Python

tip

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

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Loading data from Jira to Timescale is now easier with the open-source Python library, dlt. Jira is the leading project management tool for agile teams, helping you plan, track, and release software efficiently. On the other hand, Timescale is designed to handle demanding workloads, including time series, vector, events, and analytics data, all built on PostgreSQL. With dlt, you can seamlessly integrate and transfer data from Jira to Timescale, leveraging dlt's capabilities for efficient data extraction and loading. For more details 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.
• Scalable Data Extraction: 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. Learn more
• Pipeline Metadata for Governance: dlt pipelines leverage metadata to provide governance capabilities. This metadata includes load IDs, which consist of a timestamp and pipeline name, enabling incremental transformations and data vaulting by tracking data loads and facilitating data lineage and traceability. Learn 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. Learn more
• Implicit Extraction DAGs: dlt incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically, ensuring data consistency and integrity. Learn 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 Timescale:

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 Timescale. You can run the following commands to create a starting point for loading data from Jira to Timescale:

# 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 Timescale 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 Timescale 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: Utilize Github Actions for a free CI/CD runner to automate your pipeline deployment.
• Deploy with Airflow and Google Composer: Follow this guide to deploy your pipeline using Airflow and Google Composer.
• Deploy with Google Cloud Functions: Learn how to deploy your pipeline with Google Cloud Functions.
• Explore other deployment options: Check out other deployment options for various environments and tools.

The running in production section will teach you about:

• How to Monitor your pipeline: Learn how to effectively monitor your pipeline to ensure smooth operations and quickly identify any issues. How to Monitor your pipeline
• Set up alerts: Set up alerts to get notified of any critical issues or changes in your pipeline's performance. Set up alerts
• And set up tracing: Implement tracing to gain detailed insights into the execution of your pipeline, helping you debug and optimize performance. 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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