Loading Jira Data into ClickHouse with Python's dlt Library

Need help deploying these pipelines, or figuring out how to run them in your data stack?

Join our Slack community or book a call with our support engineer Violetta.

This page provides technical documentation about using the open-source Python library, dlt, to load data from Jira into ClickHouse. Jira is the top project management tool for agile teams, enabling them to plan, track, and release high-quality software. On the other hand, ClickHouse is a swift, open-source, column-oriented database management system that facilitates the generation of real-time analytical data reports using SQL queries. By using dlt, you can bridge the gap between these two powerful tools, making the impossible, possible. For additional information about Jira, please visit Atlassian's official website.

dlt Key Features

• Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about pipeline metadata, schema enforcement and curation, and schema evolution.
• Scalability and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines. It supports running extraction, normalization and load in parallel, writing sources and resources that are run in parallel via thread pools and async execution, and finetuning the memory buffers, intermediary file sizes and compression options. Read more about performance.
• 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. It also incorporates the concept of implicit extraction Directed Acyclic Graphs (DAGs) to handle the dependencies between data sources and their transformations automatically. Learn more here.
• Data Types: dlt supports various data types including text, double, bool, timestamp, date, time, bigint, binary, complex, decimal, and wei. Each data type has its own precision and scale that are interpreted by the particular destination and are validated when a column is created. Learn more about the data types.
• Resource Grouping and Secrets: dlt provides tutorials to help users understand resource grouping and secrets. It also provides a list of building blocks, including destinations, verified sources, SQL or Pandas for data access, and guides for appending, replacing and merging tables, setting up "last value" incremental loading, defining the columns nullability and data types, and using the built-in requests client. Continue your journey with the Resource Grouping and Secrets tutorial.

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 ClickHouse:

pip install "dlt[clickhouse]"

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

# create a new directory
mkdir jira_pipeline
cd jira_pipeline
# initialize a new pipeline with your source and destination
dlt init jira clickhouse
# 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[clickhouse]>=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.clickhouse]
dataset_name = "dataset_name" # please set me up!

[destination.clickhouse.credentials]
database = "default"
password = "password" # please set me up!
username = "default"
host = "host" # please set me up!
port = 9440
http_port = 8443

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 ClickHouse 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='clickhouse', 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='clickhouse',
        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 ClickHouse 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: dlt supports deployment with Github Actions. This feature allows you to schedule your pipelines to run at specified intervals. Learn more about this deployment method here.
• Deploy with Airflow: With dlt, you can deploy your pipelines with Airflow, a platform used to programmatically author, schedule and monitor workflows. Find out more about this method here.
• Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions, a serverless execution environment for building and connecting cloud services. Learn more about this deployment method here.
• Other Deployment Methods: dlt supports a variety of deployment methods to suit your specific needs. Discover other deployment methods here.

The running in production section will teach you about:

• Monitoring your pipeline: dlt provides a comprehensive monitoring system for your data pipelines. It helps you keep track of the performance and health of your pipelines, ensuring that they are running efficiently and effectively. Learn more about it here.
• Setting up alerts: With dlt, you can set up alerts that notify you about any issues or errors that occur during the running of your pipelines. This allows you to quickly address and resolve these issues, minimizing any potential impact on your data processing. Check out the guide here.
• Tracing your operations: dlt allows you to trace your operations, providing you with detailed insights into each step of your data processing. This can be invaluable for debugging and optimizing your pipelines. Read more about tracing here.

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.

Additional pipeline guides

• Load data from HubSpot to Google Cloud Storage in python with dlt
• Load data from Rest API to Databricks in python with dlt
• Load data from Spotify to EDB BigAnimal in python with dlt
• Load data from Shopify to AWS Athena in python with dlt
• Load data from Oracle Database to PostgreSQL in python with dlt
• Load data from Salesforce to Azure Cloud Storage in python with dlt
• Load data from Google Analytics to DuckDB in python with dlt
• Load data from Apple App-Store Connect to Azure Cloud Storage in python with dlt
• Load data from Microsoft SQL Server to Microsoft SQL Server in python with dlt
• Load data from Pipedrive to AWS Athena in python with dlt