feat(ingest/fivetran): support filtering on destination ids

[matthew-coudert-cko]

Add ability for users to filter to only include specific destination IDs. This would let us exclude non-prod environments in our PROD DataHub deployment and speed up the ingestion (as its pretty slow given the 100s of connectors we have).

Checklist

• The PR conforms to DataHub's Contributing Guideline (particularly Commit Message Format)
• Links to related issues (if applicable)
• Tests for the changes have been added/updated (if applicable)
• Docs related to the changes have been added/updated (if applicable). If a new feature has been added a Usage Guide has been added for the same.
• For any breaking change/potential downtime/deprecation/big changes an entry has been made in Updating DataHub

Summary by CodeRabbit

• New Features

  • Introduced a new configuration field, destination_patterns, for enhanced control over Fivetran source ingestion.
  • Updated methods to support filtering based on destination patterns.

• Bug Fixes

  • Corrected indentation issues in logging statements.

• Tests

  • Enhanced test configurations to validate the new destination_patterns functionality.
  • Added warnings for deprecated configuration keys.

[coderabbitai]

Walkthrough

The pull request introduces modifications to the Fivetran ingestion components within the metadata ingestion framework. A new field, destination_patterns, has been added to the FivetranSourceConfig class, enhancing configuration capabilities. Correspondingly, the get_workunits_internal and get_allowed_connectors_list methods have been updated to incorporate this new parameter for improved filtering of connectors. Additionally, tests have been updated to reflect these changes, ensuring that the integration with Snowflake and BigQuery functions correctly with the new configuration.

Changes

File	Change Summary
metadata-ingestion/src/datahub/ingestion/source/fivetran/config.py	Added field destination_patterns: AllowDenyPattern to FivetranSourceConfig class.
metadata-ingestion/src/datahub/ingestion/source/fivetran/fivetran.py	Updated get_workunits_internal method to include self.config.destination_patterns in allowed connectors list.
metadata-ingestion/src/datahub/ingestion/source/fivetran/fivetran_log_api.py	Enhanced get_allowed_connectors_list method by adding destination_patterns parameter for filtering.
metadata-ingestion/tests/integration/fivetran/test_fivetran.py	Added destination_patterns to test configurations; raised warning for deprecated destination_config.

Poem

🐰 In the fields where patterns play,
A new config has come to stay.
With Fivetran's dance, we now can see,
Destination dreams, wild and free!
So hop along, let’s test and cheer,
For every connector, we hold dear! 🌼

---

Thank you for using CodeRabbit. We offer it for free to the OSS community and would appreciate your support in helping us grow. If you find it useful, would you consider giving us a shout-out on your favorite social media?

❤️ Share

• X
• Mastodon
• Reddit
• LinkedIn

🪧 Tips

Chat

There are 3 ways to chat with CodeRabbit:

• Review comments: Directly reply to a review comment made by CodeRabbit. Example:
  • I pushed a fix in commit <commit_id>, please review it.
  • Generate unit testing code for this file.
  • Open a follow-up GitHub issue for this discussion.
• Files and specific lines of code (under the "Files changed" tab): Tag @coderabbitai in a new review comment at the desired location with your query. Examples:
  • @coderabbitai generate unit testing code for this file.
  • @coderabbitai modularize this function.
• PR comments: Tag @coderabbitai in a new PR comment to ask questions about the PR branch. For the best results, please provide a very specific query, as very limited context is provided in this mode. Examples:
  • @coderabbitai gather interesting stats about this repository and render them as a table. Additionally, render a pie chart showing the language distribution in the codebase.
  • @coderabbitai read src/utils.ts and generate unit testing code.
  • @coderabbitai read the files in the src/scheduler package and generate a class diagram using mermaid and a README in the markdown format.
  • @coderabbitai help me debug CodeRabbit configuration file.

Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments.

CodeRabbit Commands (Invoked using PR comments)

• @coderabbitai pause to pause the reviews on a PR.
• @coderabbitai resume to resume the paused reviews.
• @coderabbitai review to trigger an incremental review. This is useful when automatic reviews are disabled for the repository.
• @coderabbitai full review to do a full review from scratch and review all the files again.
• @coderabbitai summary to regenerate the summary of the PR.
• @coderabbitai resolve resolve all the CodeRabbit review comments.
• @coderabbitai configuration to show the current CodeRabbit configuration for the repository.
• @coderabbitai help to get help.

Other keywords and placeholders

• Add @coderabbitai ignore anywhere in the PR description to prevent this PR from being reviewed.
• Add @coderabbitai summary to generate the high-level summary at a specific location in the PR description.
• Add @coderabbitai anywhere in the PR title to generate the title automatically.

CodeRabbit Configuration File (.coderabbit.yaml)

• You can programmatically configure CodeRabbit by adding a .coderabbit.yaml file to the root of your repository.
• Please see the configuration documentation for more information.
• If your editor has YAML language server enabled, you can add the path at the top of this file to enable auto-completion and validation: # yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json

Documentation and Community

• Visit our Documentation for detailed information on how to use CodeRabbit.
• Join our Discord Community to get help, request features, and share feedback.
• Follow us on X/Twitter for updates and announcements.

[hsheth2]

@matthew-coudert-cko how slow is the Fivetran connector for you? I strongly suspect that there's some low-hanging fruit in terms of optimizations there (e.g. we're probably doing a bunch of N+1 queries)

[matthew-coudert-cko]

@matthew-coudert-cko how slow is the Fivetran connector for you? I strongly suspect that there's some low-hanging fruit in terms of optimizations there (e.g. we're probably doing a bunch of N+1 queries)

@hsheth2

Not horribly slow, but around 40/45 minutes. My main worry is actually ingesting too many data process instances as it tends to slow down our graphQL queries when we have lots and lots of them (we've had the same issue with Airflow since we have 1000s of tasks running every hour). We solve that by just going into the DB and deleting old ones and reindexing whenever it gets slow.

[hsheth2]

@matthew-coudert-cko yup makes sense. By the way, you might be interested in this #11102

[maggiehays]

@coderabbitai summary