Search and Graph Reindexing

If your search infrastructure (Elasticsearch/OpenSearch) or graph services (Elasticsearch/OpenSearch/Neo4j) become inconsistent or out-of-sync with your primary metadata store, you can rebuild them from the source of truth: the metadata_aspect_v2 table in your relational database (MySQL/Postgres).

This process works by fetching the latest version of each aspect from the database and replaying them as Metadata Change Log (MCL) events. These events will regenerate your search and graph indexes, effectively restoring a consistent view.

⚠️ Note: By default, this process does not remove stale documents from the index that no longer exist in the database. To ensure full consistency, we recommend reindexing into a clean instance, or using the -a clean option to wipe existing index contents before replay.

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How it Works

Reindexing is powered by the datahub-upgrade utility (packaged as the datahub-upgrade container in Docker/Kubernetes). It supports a special upgrade task called RestoreIndices, which replays aspects from the database back into search and graph stores.

You can run this utility in three main environments:

• Quickstart (via CLI)
• Docker Compose (via shell script)
• Kubernetes (via Helm + CronJob)

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Reindexing Configuration Options

When running the RestoreIndices job, you can pass additional arguments to customize the behavior:

🔄 Pagination & Performance

Argument	Description
urnBasedPagination	Use URN-based pagination instead of offset. Recommended for large datasets.
startingOffset	Starting offset for offset-based pagination.
lastUrn	Resume from this URN (used with URN pagination).
lastAspect	Resume from this aspect name (used with lastUrn).
numThreads	Number of concurrent threads for reindexing.
batchSize	Number of records per batch.
batchDelayMs	Delay in milliseconds between each batch (throttling).

📅 Time Filtering

Argument	Description
gePitEpochMs	Only restore aspects created after this timestamp (in ms).
lePitEpochMs	Only restore aspects created before this timestamp (in ms).

🔍 Content Filtering

Argument	Description
aspectNames	Comma-separated list of aspects to restore (e.g., ownership,status).
urnLike	SQL LIKE pattern to filter URNs (e.g., urn:li:dataset%).

🧱 Other Options

Argument	Description
createDefaultAspects	Whether to create default aspects in SQL & index if missing. Disable this if using a read-only replica.
clean	Deletes existing index documents before restoring. Use with caution.

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Running the Restore Job

🧪 Quickstart CLI

If you're using DataHub's quickstart image, you can restore indices using a single CLI command:

datahub docker quickstart --restore-indices

info

This command automatically clears the search and graph indices before restoring them.

More details in the Quickstart Docs.

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🐳 Docker Compose

If you're using Docker Compose and have cloned the DataHub source repo, run:

./docker/datahub-upgrade/datahub-upgrade.sh -u RestoreIndices

To clear existing index contents before restore (recommended if you suspect inconsistencies), add -a clean:

./docker/datahub-upgrade/datahub-upgrade.sh -u RestoreIndices -a clean

info

Without the -a clean flag, old documents may remain in your search/graph index, even if they no longer exist in your SQL database.

Refer to the Upgrade Script Docs for more info on environment configuration.

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☸️ Kubernetes (Helm)

1. Check if the Job Template Exists

Run:

kubectl get cronjobs

You should see a result like:

datahub-datahub-restore-indices-job-template

If not, make sure you're using the latest Helm chart version that includes the restore job.

2. Trigger the Restore Job

Run:

kubectl create job --from=cronjob/datahub-datahub-restore-indices-job-template datahub-restore-indices-adhoc

This will create and run a one-off job to restore indices from your SQL database.

3. To Enable Clean Reindexing

Edit your values.yaml to include the -a clean argument:

datahubUpgrade:
  restoreIndices:
    image:
      args:
        - "-u"
        - "RestoreIndices"
        - "-a"
        - "batchSize=1000"
        - "-a"
        - "batchDelayMs=100"
        - "-a"
        - "clean"

info

The default job does not delete existing documents before restoring. Add -a clean to ensure full sync.

Through APIs

See also the Best Practices section below, however note that the APIs are able to handle a few thousand aspects. In this mode one of the GMS instances will perform the required actions, however it is subject to timeout. Use one of the approaches above for longer running restoreIndices.

OpenAPI

There are two primary APIs, one which exposes the common parameters for restoreIndices and another one designed to accept a list of URNs where all aspects are to be restored.

Full configuration:

All Aspects:

Rest.li

For Rest.li, see Restore Indices API.

Best Practices

In general, this process is not required to run unless there has been a disruption of storage services or infrastructure, such as Elasticsearch/Opensearch cluster failures, data corruption events, or significant version upgrade inconsistencies that have caused the search and graph indices to become out of sync with the local database.

Some pointers to keep in mind when running this process:

• Always test reindexing in a staging environment first.
• Consider taking a backup of your Elasticsearch/OpenSearch index before a clean restore.
• For very large deployments, use urnBasedPagination and limit batchSize to avoid overloading your backend.
• Monitor Elasticsearch/OpenSearch logs during the restore for throttling or memory issues.

K8 Job vs. API

When to Use Kubernetes Jobs

For operations affecting 2,000 or more aspects, it's strongly recommended to use the Kubernetes job approach. This job is designed for long-running processes and provide several advantages:

• Won't time out like API calls
• Can be monitored through Kubernetes logging
• Won't consume resources from your primary GMS instances
• Can be scheduled during off-peak hours to minimize system impact

When to Use APIs

The RestoreIndices APIs (available through both Rest.li and OpenAPI) is best suited for:

• Targeted restores affecting fewer than 2,000 aspects
• Emergencies where you need to quickly restore critical metadata
• Testing or validating the restore process before running a full-scale job
• Scenarios where you don't have direct access to the Kubernetes cluster

Remember that API-based restoration runs within one of your GMS instances and is subject to timeouts, which could lead to incomplete restorations for larger installations.

Targeted Restoration Strategies

Being selective about what you restore is crucial for efficiency. Combining these filtering strategies can dramatically reduce the restoration scope, saving resources and time.

Entity Type Filtering

Entity Type Filtering: Use the urnLike parameter to target specific entity types:

• For datasets: urnLike=urn:li:dataset:%
• For users: urnLike=urn:li:corpuser:%
• For dashboards: urnLike=urn:li:dashboard:%

Single Entity

Single Entity Restoration: When only one entity is affected, provide the specific URN to minimize processing overhead. Aspect-Based Filtering: Use aspectNames to target only the specific aspects that need restoration:

• For ownership inconsistencies: aspectNames=ownership
• For tag issues: aspectNames=globalTags

Time-Based

Time-Based Recovery: If you know when the inconsistency began, use time-based filtering:

• gePitEpochMs={timestamp} to process only records created after the incident
• lePitEpochMs={timestamp} to limit processing to records before a certain time

Parallel Processing Strategies

To optimize restoration speed while managing system load:

Multiple Parallel Jobs

Run several restoreIndices processes simultaneously by:

• Work on non-overlapping sets of aspects or entities
  • Dividing work by entity type (one job for datasets, another for users, etc.)
  • Splitting aspects among different jobs (one for ownership aspects, another for lineage, etc.)
  • Partitioning large entity types by prefix or time range
• Have staggered start times to prevent initial resource contention
• Monitor system metrics closely during concurrent restoration to ensure you're not overloading your infrastructure.

caution

Avoid Conflicts: Ensure that concurrent jobs:

Never specify the --clean argument in concurrent jobs

Temporary Workload Reduction

• Pause scheduled ingestion jobs during restoration
• Temporarily disable or reduce frequency of the datahub-gc job to prevent conflicting deletes
• Consider pausing automated workflows or integrations that generate metadata events

Infrastructure Tuning

Implementing these expanded best practices should help ensure a smoother, more efficient restoration process while minimizing impact on your DataHub environment.

This operation can be I/O intensive from the read-side from SQL and on the Elasticsearch write side. If you're able to leverage provisioned I/O. or throughput, you might want to monitor your infrastructure for a possible.

Elasticsearch/Opensearch Optimization

To improve write performance during restoration:

Refresh Interval Adjustment:

Temporarily increase the refresh_interval setting from the default (typically 1s) to something like 30s or 60s. Run the system update job with the following environment variable ELASTICSEARCH_INDEX_BUILDER_REFRESH_INTERVAL_SECONDS=60

caution

Remember to reset this after restoration completes!

Bulk Processing Improvements:

• Adjust the Elasticsearch batching parameters to optimize bulk request size (try values between 2000-5000)
  • Run your GMS or mae-consumer with environment variables
    • ES_BULK_REQUESTS_LIMIT=3000
    • ES_BULK_FLUSH_PERIOD=60
• Configure batchDelayMs on restoreIndices to add breathing room between batches if your cluster is struggling

Shard Management:

• Ensure your indices have an appropriate number of shards for your cluster size.
• Consider temporarily adding nodes to your search cluster during massive restorations.

SQL/Primary Storage

Consider using a read replica as the source of the job's data. If you configure a read-only replica you must also provide the parameter createDefaultAspects=false.

Kafka & Consumers

Partition Strategy:

• Verify that the Kafka Metadata Change Log (MCL) topic have enough partitions to allow for parallel processing.
• Recommended: At least 10-20 partitions for the MCL topic in production environments.

Consumer Scaling:

• Temporarily increase the number of mae-consumer pods to process the higher event volume.
• Scale GMS instances if they're handling consumer duties.

Monitoring:

• Watch consumer lag metrics closely during restoration.
• Be prepared to adjust scaling or batch parameters if consumers fall behind.