Upgrade on Kubernetes

This guide will help you upgrade your OpenMetadata Kubernetes Application with automated helm hooks.

Requirements

This guide assumes that you have an OpenMetadata deployment that you installed and configured following the Kubernetes Deployment guide.

We also assume that your helm chart release names are openmetadata and openmetadata-dependencies and namespace used is default.

Prerequisites

Everytime that you plan on upgrading OpenMetadata to a newer version, make sure to go over all these steps:

Backup your Metadata

Before upgrading your OpenMetadata version we strongly recommend backing up the metadata.

The source of truth is stored in the underlying database (MySQL and Postgres supported). During each version upgrade there is a database migration process that needs to run. It will directly attack your database and update the shape of the data to the newest OpenMetadata release.

It is important that we backup the data because if we face any unexpected issues during the upgrade process, you will be able to get back to the previous version without any loss.

Since version 1.4.0, OpenMetadata encourages using the builtin-tools for creating logical backups of the metadata:

• mysqldump for MySQL
• pg_dump for Postgres

For PROD deployment we recommend users to rely on cloud services for their databases, be it AWS RDS, Azure SQL or GCP Cloud SQL.

If you're a user of these services, you can leverage their backup capabilities directly:

• Creating a DB snapshot in AWS
• Backup and restore in Azure MySQL
• About GCP Cloud SQL backup

You can refer to the following guide to get more details about the backup and restore:

Update sort_buffer_size (MySQL) or work_mem (Postgres)

Before running the migrations, it is important to update these parameters to ensure there are no runtime errors. A safe value would be setting them to 20MB.

If using MySQL

You can update it via SQL (note that it will reset after the server restarts):

To make the configuration persistent, you'd need to navigate to your MySQL Server install directory and update the my.ini or my.cnf files with sort_buffer_size = 20971520.

If using RDS, you will need to update your instance's Parameter Group to include the above change.

If using Postgres

You can update it via SQL (not that it will reset after the server restarts):

To make the configuration persistent, you'll need to update the postgresql.conf file with work_mem = 20MB.

If using RDS, you will need to update your instance's Parameter Group to include the above change.

Note that this value would depend on the size of your query_entity table. If you still see Out of Sort Memory Errors during the migration after bumping this value, you can increase them further.

After the migration is finished, you can revert this changes.

New Versioning System for Ingestion Docker Image

We are excited to announce a recent change in our version tagging system for our Ingestion Docker images. This update aims to improve consistency and clarity in our versioning, aligning our Docker image tags with our Python PyPi package versions.

Ingestion Docker Image Tags

To maintain consistency, our Docker images will now follow the same 4-digit versioning system as of Python Package versions. For example, a Docker image version might look like 1.0.0.0.

Additionally, we will continue to provide a 3-digit version tag (e.g., 1.0.0) that will always point to the latest corresponding 4-digit image tag. This ensures ease of use for those who prefer a simpler version tag while still having access to the most recent updates.

Benefits

Consistency: Both Python applications and Docker images will have the same versioning format, making it easier to track and manage versions. Clarity: The 4-digit system provides a clear and detailed versioning structure, helping users understand the nature and scope of changes. Non-Breaking Change: This update is designed to be non-disruptive. Existing Ingestions and dependencies will remain unaffected.

Example

Here’s an example of how the new versioning works:

Python Application Version: 1.5.0.0 Docker Image Tags:

• 1.5.0.0 (specific version)
• 1.5.0 (latest version in the 1.5.0.x series)

We believe this update will bring greater consistency and clarity to our versioning system. As always, we value your feedback and welcome any questions or comments you may have.

Backward Incompatible Changes

1.5.0

Multi Owners

OpenMetadata allows a single user or a team to be tagged as owners for any data assets. In Release 1.5.0, we allow users to tag multiple individual owners or a single team. This will allow organizations to add ownership to multiple individuals without necessarily needing to create a team around them like previously.

This is a backward incompatible change, if you are using APIs, please make sure the owner field is now changed to “owners”

Import/Export Format

To support the multi-owner format, we have now changed how we export and import the CSV file in glossary, services, database, schema, table, etc. The new format will be user:userName;team:TeamName

If you are importing an older file, please make sure to make this change.

Pydantic V2

The core of OpenMetadata are the JSON Schemas that define the metadata standard. These schemas are automatically translated into Java, Typescript, and Python code with Pydantic classes.

In this release, we have migrated the codebase from Pydantic V1 to Pydantic V2.

Deployment Related Changes (OSS only)

./bootstrap/bootstrap_storage.sh removed

OpenMetadata community has built rolling upgrades to database schema and the data to make upgrades easier. This tool is now called as ./bootstrap/openmetadata-ops.sh and has been part of our releases since 1.3. The bootstrap_storage.sh doesn’t support new native schemas in OpenMetadata. Hence, we have deleted this tool from this release.

While upgrading, please refer to our Upgrade Notes in the documentation. Always follow the best practices provided there.

Database Connection Pooling

OpenMetadata uses Jdbi to handle database-related operations such as read/write/delete. In this release, we introduced additional configs to help with connection pooling, allowing the efficient use of a database with low resources.

Please update the defaults if your cluster is running at a large scale to scale up the connections efficiently.

For the new configuration, please refer to the doc here

Data Insights

The Data Insights application is meant to give you a quick glance at your data's state and allow you to take action based on the information you receive. To continue pursuing this objective, the application was completely refactored to allow customizability.

Part of this refactor was making Data Insights an internal application, no longer relying on an external pipeline. This means triggering Data Insights from the Python SDK will no longer be possible.

With this change you will need to run a backfill on the Data Insights for the last couple of days since the Data Assets data changed.

UI Changes

New Explore Page

Explore page displays hierarchically organized data assets by grouping them into services > database > schema > tables/stored procedures. This helps users organically find the data asset they are looking for based on a known database or schema they were using. This is a new feature and changes the way the Explore page was built in previous releases.

Connector Schema Changes

In the latest release, several updates and enhancements have been made to the JSON schema across various connectors. These changes aim to improve security, configurability, and expand integration capabilities. Here's a detailed breakdown of the updates:

• KafkaConnect: Added schemaRegistryTopicSuffixName to enhance topic configuration flexibility for schema registries.
• GCS Datalake: Introduced bucketNames field, allowing users to specify targeted storage buckets within the Google Cloud Storage environment.
• OpenLineage: Added saslConfig to enhance security by enabling SASL (Simple Authentication and Security Layer) configuration.
• Salesforce: Added sslConfig to strengthen the security layer for Salesforce connections by supporting SSL.
• DeltaLake: Updated schema by moving metastoreConnection to a newly created metastoreConfig.json file. Additionally, introduced configSource to better define source configurations, with new support for metastoreConfig.json and storageConfig.json.
• Iceberg RestCatalog: Removed clientId and clientSecret as mandatory fields, making the schema more flexible for different authentication methods.
• DBT Cloud Pipelines: Added as a new connector to support cloud-native data transformation workflows using DBT.
• Looker: Expanded support to include connections using GitLab integration, offering more flexible and secure version control.
• Tableau: Enhanced support by adding capabilities for connecting with TableauPublishedDatasource and TableauEmbeddedDatasource, providing more granular control over data visualization and reporting.

Include DDL

During the Database Metadata ingestion, we can optionally pick up the DDL for both tables and views. During the metadata ingestion, we use the view DDLs to generate the View Lineage.

To reduce the processing time for out-of-the-box workflows, we are disabling the include DDL by default, whereas before, it was enabled, which potentially led to long-running workflows.

Secrets Manager

Starting with the release 1.5.0, the JWT Token for the bots will be sent to the Secrets Manager if you configured one. It won't appear anymore in your dag_generated_configs in Airflow.

Python SDK

The metadata insight command has been removed. Since Data Insights application was moved to be an internal system application instead of relying on external pipelines the SDK command to run the pipeline was removed.

Upgrade Process

Step 1: Get an overview of what has changed in Helm Values

You can get changes from artifact hub of openmetadata helm chart release. Click on Default Values >> Compare to Version.

Step 2: Upgrade Helm Repository with a new release

Update Helm Chart Locally for OpenMetadata with the below command:

It will result in the below output on screen.

Verify with the below command to see the latest release available locally.

Step 3: Upgrade OpenMetadata Dependencies

You can run the below command to upgrade the dependencies with the new chart

The above command uses configurations defined here. You can modify any configuration and deploy by passing your own values.yaml.

Step 4: Upgrade OpenMetadata

Finally, we upgrade OpenMetadata with the below command:

You might need to pass your own values.yaml with the --values flag.

Note that in every version upgrade there is a migration process that updates your database to the newest version.

For kubernetes, this process will happen automatically as an upgrade hook.

Post-Upgrade Steps

Reindex

Go to Settings -> Applications -> Search Indexing

Reindex

Click on Run Now.

In the configuration section, you can select the entities you want to reindex.

Reindex

Since this is required after the upgrade, we want to reindex All the entities.

(Optional) Update your OpenMetadata Ingestion Client

If you are running the ingestion workflows externally or using a custom Airflow installation, you need to make sure that the Python Client you use is aligned with the OpenMetadata server version.

For example, if you are upgrading the server to the version x.y.z, you will need to update your client with

The plugin parameter is a list of the sources that we want to ingest. An example would look like this openmetadata-ingestion[mysql,snowflake,s3]==1.2.0. You will find specific instructions for each connector here.

Moreover, if working with your own Airflow deployment - not the openmetadata-ingestion image - you will need to upgrade as well the openmetadata-managed-apis version:

Re Deploy Ingestion Pipelines

Go to Settings -> {service entity} -> Pipelines

Re-deploy

Select the pipelines you want to Re Deploy click Re Deploy.

If you are seeing broken dags select all the pipelines from all the services and re deploy the pipelines.

Openmetadata-ops Script

Overview

The openmetadata-ops script is designed to manage and migrate databases and search indexes, reindex existing data into Elastic Search or OpenSearch, and redeploy service pipelines.

Usage

Commands

• analyze-tables

Migrates secrets from the database to the configured Secrets Manager. Note that this command does not support migrating between external Secrets Managers.

• changelog

Prints the change log of database migration.

• check-connection

Checks if a connection can be successfully obtained for the target database.

• deploy-pipelines

Deploys all the service pipelines.

• drop-create

Deletes any tables in the configured database and creates new tables based on the current version of OpenMetadata. This command also re-creates the search indexes.

• info

Shows the list of migrations applied and the pending migrations waiting to be applied on the target database.

• migrate

Migrates the OpenMetadata database schema and search index mappings.

• migrate-secrets

Migrates secrets from the database to the configured Secrets Manager. Note that this command does not support migrating between external Secrets Managers.

• reindex

Reindexes data into the search engine from the command line.

• repair

Repairs the DATABASE_CHANGE_LOG table, which is used to track all the migrations on the target database. This involves removing entries for the failed migrations and updating the checksum of migrations already applied on the target database.

• validate

Checks if all the migrations have been applied on the target database.

Examples

Display Help To display the help message:

Migrate Database Schema

To migrate the database schema and search index mappings:

Reindex Data

To reindex data into the search engine:

Troubleshooting

Helm Upgrade fails with additional property airflow not allowed

With Release 1.0.0, if you see your helm charts failing to deploy with the below issue -

This means the values passed to the helm charts has a section global.airflow. As per the breaking changes mentioned here, Airflow configs are replaced with pipelineServiceClient for Helm Charts.

The Helm Chart Values JSON Schema helps to catch the above breaking changes and this section will help you resolve and update your configurations for the same. You can read more about JSON Schema with Helm Charts here.

You will need to update the existing section of global.airflow values to match the new configurations.

⛔ Before 1.0.0 Helm Chart Release, the global.airflow section would be like -

✅ After 1.0.0 Helm Chart Release, the global.pipelineServiceClient section will replace the above airflow section -

Run the helm lint command on your custom values after making the changes to validate with the JSON Schema.

MySQL Pod fails on Upgrade

If your helm dependencies upgrade fails with the below command result -

This issue is related to a minor change that affected the MySQL Database Engine version upgrade from 8.0.28 to 8.0.29 for the Helm Chart Release 0.0.49 and 0.0.50. Then the registry url was updated as we found a work around to fetch previous versions of bitnami/mysql Helm Releases.

As a result of the above fixes, anyone who is on OpenMetadata Dependencies Helm Chart Version 0.0.49 and 0.0.50 is affected with the above issue when upgrading for mysql. In order to fix this issue, make sure to follow the below steps -

1. Backup the Database using Metadata Backup CLI as mentioned here
2. Uninstall OpenMetadata Dependencies Helm Chart (helm uninstall openmetadata-dependencies)
3. Remove the unmanaged volume for MySQL Stateful Set Kubernetes Object (kubectl delete pvc data-mysql-0)
4. Install the latest version of OpenMetadata Dependencies Helm Chart
5. Restore the Database using Metadata Restore CLI as mentioned here
6. Next, Proceed with upgrade for OpenMetadata Helm Chart as mentioned here