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Configure OpenSearch for advanced search Pro ​

Plane uses OpenSearch to provide advanced search capabilities across your workspace. This guide walks you through setting up OpenSearch integration on your self-hosted instance.

Before you begin ​

You'll need:

  • An OpenSearch 2.x instance (self-hosted or managed service like AWS OpenSearch).

Once configured, advanced search provides:

  • Full-text search across work items, projects, cycles, modules, pages, and more
  • Fuzzy matching that tolerates typos and variations in spelling
  • Autocomplete with instant suggestions as you type
  • Multi-entity search that searches across all content types in a single query

Users can access advanced search using the global search shortcut (Cmd/Ctrl + K) or search within specific projects and sections.

Configure OpenSearch ​

Set environment variables in your Plane configuration. See Environment variables reference for details.

For Docker deployments ​

  1. Add configuration to your environment file

    Edit /opt/plane/plane.env.

    bash
    # OpenSearch Settings
OPENSEARCH_ENABLED=1
OPENSEARCH_URL=https://your-opensearch-instance:9200/
OPENSEARCH_USERNAME=admin
OPENSEARCH_PASSWORD=your-secure-password
OPENSEARCH_INDEX_PREFIX=plane

  2. Restart Plane services

    bash
    prime-cli restart

    or if managing containers directly:

    bash
    docker compose down
docker compose up -d

  3. Create search indices

    Access the API container and create the necessary indices:

    bash
    # Access the API container
docker exec -it plane-api-1 sh

# Create all search indices (run once)
python manage.py manage_search_index index rebuild --force

  4. Index your existing data

    Index all existing content into OpenSearch:

    bash
    # For small datasets
python manage.py manage_search_index document index --force

# For large datasets (recommended)
python manage.py manage_search_index --background document index --force

    The background option processes indexing through Celery workers, which is better for instances with large amounts of data.

For Kubernetes deployments ​

The Plane Helm chart provides auto-setup for OpenSearch. If you're using your own OpenSearch instance, configure it through Helm values.

  1. Configure Helm values

    Get the current values file:

    bash
    helm show values plane/plane-enterprise > values.yaml

    Edit values.yaml to add OpenSearch configuration:

    yaml
    env:
  # OpenSearch configuration
  opensearch_remote_url: 'https://your-opensearch-instance:9200/'
  opensearch_remote_username: 'admin'
  opensearch_remote_password: 'your-secure-password'
  opensearch_index_prefix: 'plane'

    Refer to the Plane Helm chart documentation for complete values structure.

  2. Upgrade your deployment

    bash
    helm upgrade --install plane-app plane/plane-enterprise \
    --create-namespace \
    --namespace plane \
    -f values.yaml \
    --timeout 10m \
    --wait \
    --wait-for-jobs

  3. Create search indices

    Run these commands in the API pod.

    bash
    # Get the API pod name
API_POD=$(kubectl get pods -n plane --no-headers | grep api | head -1 | awk '{print $1}')

# Create all search indices (run once)
kubectl exec -n plane $API_POD -- python manage.py manage_search_index index rebuild --force

  4. Index your existing data Run these commands in the API pod.

    bash
    # For small datasets
kubectl exec -n plane $API_POD -- python manage.py manage_search_index document index --force

# For large datasets (recommended)
kubectl exec -n plane $API_POD -- python manage.py manage_search_index --background document index --force

Verify the setup ​

Check OpenSearch connection ​

Test that Plane can connect to your OpenSearch instance:

bash
# Access your API container or pod
docker exec -it plane-api-1 sh  # For Docker
# OR
kubectl exec -n plane $API_POD -- sh  # For Kubernetes

# Start Python shell
python manage.py shell

Then run:

python
from django.conf import settings
from opensearchpy import OpenSearch

client = OpenSearch(
    hosts=[settings.OPENSEARCH_DSL['default']['hosts']],
    http_auth=settings.OPENSEARCH_DSL['default']['http_auth'],
    use_ssl=True,
    verify_certs=False
)

# Check cluster health
print(client.cluster.health())

# List indices - you should see your Plane indices
print(client.cat.indices(format='json'))

Verify indices were created ​

List all created indices:

bash
python manage.py manage_search_index list

You should see indices for work items, projects, cycles, modules, pages, and other searchable entities.

Test search functionality ​

  1. Sign in to your Plane instance.
  2. Press Cmd/Ctrl + K to open global search.
  3. Type a search query and verify results appear.
  4. Test search within projects, work items, and pages.

Maintenance ​

Resync data ​

If search results become stale or inconsistent, resync your data:

bash
python manage.py manage_search_index document index --force

This reindexes all content without recreating the index structure.

Complete rebuild ​

For a complete reset (recreates indices and reindexes all data):

bash
# Recreate all indices
python manage.py manage_search_index index rebuild --force

# Reindex all documents
python manage.py manage_search_index document index --force

Use this if index structure needs updating or if you're experiencing persistent issues.

Monitor logs ​

Check API logs OpenSearch-related errors:

Docker:

bash
docker compose logs api | grep -i opensearch

Kubernetes:

bash
kubectl logs -n plane -l app.kubernetes.io/component=api | grep -i opensearch

Understanding how it works ​

Advanced search in Plane maintains search indices separately from your main database. This separation is why search can be fast even with thousands of work items - OpenSearch is purpose-built for search operations, while your database handles transactional operations.

Why Plane uses OpenSearch ​

Traditional database searches struggle with fuzzy matching and typos. If you search for "authentcation" (with a typo), a database won't find "authentication". OpenSearch handles this naturally because it analyzes text differently - it breaks words into tokens, normalizes variations, and understands linguistic patterns.

This is why autocomplete feels instant. OpenSearch pre-processes text to match partial words, while your database would need to scan entire tables to achieve similar results.

The synchronization challenge ​

The trade-off with separate search indices is keeping them synchronized with your database. When someone updates a work item, that change must reach OpenSearch for search results to remain accurate.

Plane solves this through an event-driven architecture. Every time data changes in your database, Django emits a signal. These signals trigger updates to OpenSearch.

Batching for efficiency ​

Direct, immediate updates would overwhelm both your database and OpenSearch. Imagine a user creating 50 work items in quick succession, that would mean 50 separate API calls to OpenSearch, each with network overhead.

Instead, Plane batches updates through Redis. When a signal fires, the update goes into a Redis queue. A Celery worker processes this queue every 5 seconds, combining multiple updates into efficient batch operations. This is why you might notice a brief delay (up to 5 seconds) before new content appears in search results.

The batching pattern also provides resilience. If OpenSearch is temporarily unavailable, updates accumulate in Redis and process once connectivity returns. This requires Redis 6.2+ which supports the LPOP count operation needed for efficient batch retrieval.

The complete flow ​

OpenSeach flow

When you search, queries bypass this synchronization process entirely. The Plane API sends your search query directly to OpenSearch, which returns results almost instantly. Your database isn't involved in search queries at all — this is the key to search performance.

Index organization ​

Plane creates nine separate indices in OpenSearch, one for each searchable entity type. This separation might seem redundant - why not put everything in one index?

The answer lies in how different entities need different search behaviors. Work items use fuzzy matching and field prioritization (title matches rank higher than description matches). Projects emphasize metadata filtering—status, member counts, and timelines. Pages analyze long-form content structure.

Each index is optimized for its content type:

IndexContentSearch Features
{prefix}_issuesWork itemsFull-text search, field weighting (title > description), state filtering
{prefix}_issue_commentsCommentsComment search within work items, parent-child relationships
{prefix}_projectsProjectsProject discovery, metadata filtering (dates, counts, status)
{prefix}_cyclesCyclesCycle search, time-based filtering and aggregations
{prefix}_modulesModulesModule/sprint search, planning aggregations
{prefix}_pagesPagesPage content search, rich text analysis for long-form content
{prefix}_workspacesWorkspacesWorkspace search and discovery
{prefix}_issue_viewsSaved viewsSaved view search and filtering
{prefix}_teamspacesTeamspacesTeamspace discovery

The {prefix} is whatever you configured in OPENSEARCH_INDEX_PREFIX, or empty if you didn't set a prefix. This prefix exists because you might run multiple Plane instances pointing to the same OpenSearch cluster. The prefix prevents different instances from accidentally sharing or conflicting with each other's indices.