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Keep PostgreSQL as the source of truth and use change data capture (CDC) to send committed row changes to Meilisearch. Your application writes to PostgreSQL once; a separate CDC process reads the database’s logical replication stream, turns changes into search documents, and submits index or delete operations to Meilisearch.
This removes application dual-writes, not the downstream write to the search index. The two systems update asynchronously, so a successful PostgreSQL transaction does not guarantee that its change is already searchable. A reliable setup also needs an initial load of existing rows, a stable document ID, delete handling, and monitoring for replication and indexing failures.
How does PostgreSQL-to-Meilisearch CDC work?
PostgreSQL records database changes in its write-ahead log (WAL). With logical decoding enabled, an authorized CDC reader can interpret committed changes as row-level events. It then maps those events to Meilisearch documents and submits the appropriate index or delete operations.
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The flow is:
- Your application commits a change in PostgreSQL.
- PostgreSQL’s logical replication stream makes the change available to the CDC reader.
- The reader transforms the row into a search document, or recognizes that the row was deleted.
- The reader submits an indexing or deletion request to the relevant Meilisearch index.
- Meilisearch processes the request asynchronously; the document becomes searchable after the indexing task completes.
PostgreSQL remains authoritative. Meilisearch is a derived search view that can lag behind the database, and it should not be treated as a transactional copy. CDC avoids making the application coordinate two writes, but it does not provide atomic commits across PostgreSQL and Meilisearch, guarantee zero lag, or prove exactly-once delivery end to end. Design for retries, duplicate events, and recovery.
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What should you decide before connecting the systems?
Settle the document shape and identity before you start importing data. These decisions affect backfill, updates, deletion, and later recovery.
- Choose the index and fields. Decide which PostgreSQL table or tables supply the index and which fields belong in each document. A database row does not have to become a fully searchable copy of every column.
- Choose a stable document ID. Map each source row to the same deterministic Meilisearch primary key during both backfill and live updates. If an update produces a different ID from the original document, the old document can remain in the index.
- Define delete behavior. A deleted source row must cause a Meilisearch deletion. Verify that the selected connector can route deletes and that the row identity is available when it does so.
- Check the PostgreSQL deployment. Confirm that it permits logical decoding and that the chosen CDC implementation’s prerequisites are available. PostgreSQL documents that
wal_level=logicaladds information needed for logical decoding; changingwal_levelrequires a server restart and can increase WAL volume. - Plan credentials and connectivity. Give the CDC process only the access required for its source and destination. Keep the Meilisearch API key out of application code and examples, and use the access method and permissions required by your chosen deployment.
- Consider primary keys and replica identity. Tables without a suitable primary key need special scrutiny. Under default replica identity, PostgreSQL may not provide enough old-row identity information for the connector to handle updates or deletes as you expect.
Which integration route should you choose?
For a direct documented destination, consider Sequin’s Meilisearch sink. Meilisync is another PostgreSQL-capable integration listed by Meilisearch. Choose Debezium with Kafka when Kafka is part of the architecture you want; the documented Debezium PostgreSQL connector captures changes but does not, by itself, write Meilisearch documents.
| Route | Useful when | Initial load | Main responsibility |
|---|---|---|---|
| Sequin direct sink | You want a documented PostgreSQL-to-Meilisearch destination. | The quickstart includes an initial backfill. | Configure the index, document key, field transformation, API key, and task-status monitoring. |
| Meilisync | You want a configurable sync project listed by Meilisearch. | Its configuration supports full sync. | Meet its documented PostgreSQL prerequisites, including wal2json, and account for its refresh warning. |
| Debezium → Kafka → custom consumer | You need Kafka’s event stream for this or other consumers. | Debezium takes a snapshot and then streams changes. | Build and operate the consumer that maps events to Meilisearch writes, deletes, retries, and task checks. |
These routes are alternatives, not independently benchmarked rankings. Compare the operational components you already run, whether a ready-made Meilisearch sink exists, database and plugin prerequisites, transformation work, delete and schema-change handling, replay design, and measured end-to-end lag in your own deployment. The cited documentation does not establish a generally applicable latency or throughput figure.
How do you set up the direct Sequin route?
Sequin’s documented quickstart walks through a source connection, a Meilisearch sink, field transformation, and initial backfill. Exact interface details can change, so use the current quickstart for the labels and deployment-specific setup rather than copying sample credentials or treating an example container image as a production default.
- Connect PostgreSQL as the source. Confirm logical decoding is available in the database deployment and configure the access required by Sequin. If the database needs a change to
wal_level, plan the required restart and assess the additional WAL volume before making it. - Create the Meilisearch index. Decide its name and the document primary-key field before the first import. Use an ID derived consistently from the PostgreSQL row.
- Choose the source data. In the quickstart’s example, the selected table is
public.products. Select the actual table or data you intend to index, rather than assuming that example applies to your schema. - Transform rows into documents. Map only the fields needed for search and include a top-level ID that matches the configured primary-key field. Ensure future updates produce the same ID.
- Enable the initial backfill. A live change stream alone does not populate rows that existed before capture began. Use the sink’s initial backfill so existing records are loaded while the transition to streaming changes is coordinated.
- Configure both change paths. Route inserts and updates to indexing, and route deletes to document deletion. The sink reference describes
indexanddeleterouting actions, document writes, delete batches, and task-status checks. - Verify the destination. Check the sink’s status and Meilisearch task results, then search for a known record after a test update. Do not treat an accepted write request as proof that the document is already searchable.
Keep the initial setup distinct from production hardening. Example environment settings, credentials, and pinned images in a walkthrough illustrate a path; they are not automatically safe or suitable production defaults.
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What does Meilisync require?
The upstream Meilisync README documents a PostgreSQL route that uses logical decoding and the wal2json extension. Its documented installation command is pip install meilisync[postgres]; its configuration selects a source table, target index, fields, and optional full sync, progress tracking, and batch settings.
- Confirm that the PostgreSQL server supports the required logical decoding setup and that
wal2jsonis available. - Install the PostgreSQL extra using the project’s documented command.
- Configure the source table, destination index, fields, and a stable mapping from source rows to Meilisearch document IDs.
- Decide whether to use full sync and progress tracking, and configure batches to suit the deployment.
- Run the integration in staging and verify inserts, updates, and deletes before relying on it for a production index.
Take the project’s refresh warning seriously: its documented workflow says to stop sync before a refresh to avoid inconsistency. Plan reindexing around that behavior, and validate the current project’s refresh and restart behavior in staging. Do not assume that every connector needs wal2json: Debezium documents PostgreSQL’s built-in pgoutput option instead.
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Use Debezium with Kafka when you want PostgreSQL changes in Kafka for multiple consumers or already operate that event-streaming architecture. It adds a custom Meilisearch consumer to the work: Debezium captures database changes, but a consumer still has to produce the documents and handle the destination.
Debezium documents an initial snapshot followed by streaming committed inserts, updates, and deletes into per-table Kafka topics. Its PostgreSQL connector supports the built-in pgoutput logical decoding option. A consumer must then:
- read the relevant table events and distinguish inserts, updates, and deletes;
- transform each event into the chosen Meilisearch document shape;
- map the source row key to the stable Meilisearch document ID;
- submit index or delete operations and handle errors, retries, and duplicate delivery safely;
- check Meilisearch task outcomes rather than stopping at HTTP acceptance; and
- provide a replay or reconciliation plan for outages and failed indexing.
This route offers Kafka as an event transport; it is not a turnkey Debezium-to-Meilisearch sink in the cited connector documentation. It also creates more components to operate. Debezium’s logical decoding stream does not deliver DDL events, so handle schema changes separately: coordinate database migrations with the consumer’s transformations and any required index changes.
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How should you handle the initial load without missing changes?
Coordinate the existing-row backfill with the live change stream. Starting a CDC connection without a snapshot or backfill leaves older rows out; loading a snapshot without a consistent handoff risks missing changes that occur during the load.
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Use the same deterministic document ID for a row during backfill and live processing. Then test the handoff with a controlled set of records: insert a row, update it, and delete it while the integration is running. Confirm that the final Meilisearch state matches PostgreSQL. If your source data spans several tables or requires joins to form a document, determine how the chosen connector represents those dependencies and how changes to related rows trigger a document refresh; do not assume a single-table row stream will maintain a denormalized search document automatically.
How can you tell whether a change is actually searchable?
Check both sides of the pipeline: PostgreSQL’s replication progress and Meilisearch’s indexing task result. An accepted Meilisearch document request can mean the operation is queued, not that indexing has finished or that a query can already return the updated document.
Meilisearch task status can be enqueued, processing, succeeded, or failed. Alert on failures, inspect the task result, and issue an actual search query for a known changed record. A successful task is useful evidence that Meilisearch processed the operation; a search query verifies the outcome from a reader’s perspective.
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For a controlled acceptance check, record a source row’s expected ID and searchable fields, make one update in PostgreSQL, and follow it through the CDC worker to Meilisearch. Confirm the expected document appears with the updated values. Repeat for a delete and confirm the document no longer appears. This is a deployment check, not a promise of a particular delay: measure the commit-to-searchable interval in your own environment and choose operational alert thresholds based on that behavior.
What should you monitor and how do you recover?
Monitor the replication slot, retained WAL, CDC lag, worker errors, and Meilisearch task failures. A stalled consumer can leave changes unapplied and cause WAL to be retained; an indexing failure can leave the search view stale even while PostgreSQL continues accepting writes.
- Replication slot activity: Inspect PostgreSQL’s
pg_replication_slotsview and verify that the expected slot is active and progressing. An inactive or stalled slot warrants investigation before retained WAL grows further. - WAL retention: Track retained WAL and available storage. Logical replication can increase WAL volume, and a consumer that stops advancing can prevent old log data from being discarded.
- CDC lag and errors: Watch whether the reader is keeping up and whether it can connect, decode, transform, and send events. A healthy database connection alone does not prove that destination writes are succeeding.
- Meilisearch task failures: Check failed tasks as well as succeeded ones. A request accepted by the API may still fail later during task processing.
- End-to-end behavior: Periodically compare known source records with search results, including after a schema or connector change.
When a failure occurs, identify where progress stopped: PostgreSQL capture, the CDC reader or transformation, the Meilisearch request, or asynchronous task processing. Correct the underlying issue, then use the connector’s supported retry or replay mechanism. Reconcile or rebuild the index when you cannot establish that all changes were applied. Keep the source database authoritative during recovery; do not treat the search index as the repair source.
For schema changes, update the document transformation and any relevant index configuration deliberately. Debezium does not deliver DDL events through logical decoding, so a consumer cannot rely on the row-change stream alone to communicate database schema migrations.
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What are the common failure modes?
| Symptom | Likely cause | What to check |
|---|---|---|
| Older rows are missing from search. | Live capture started without an initial snapshot or backfill. | Confirm the integration’s initial-load process covered pre-existing rows and completed. |
| Updates create duplicate or stale documents. | The document ID is not stable, or an update maps to a different key. | Compare the source-row key and Meilisearch primary key used by both backfill and live changes. |
| Deleted rows still appear. | Deletes are not routed, or the connector lacks sufficient row identity. | Check delete routing and the source table’s primary key or replica identity behavior. |
| The API accepted a write, but search is unchanged. | The task is still queued, processing, or failed. | Inspect the Meilisearch task status and result, then run a query after completion. |
| WAL storage grows while indexing falls behind. | A replication slot or CDC consumer may have stopped advancing. | Check slot activity, retained WAL, consumer lag, and errors before restarting or rebuilding. |
| A database migration breaks document creation. | The transformation or index expectations no longer match the row shape. | Coordinate schema migrations with consumer code and destination configuration; do not expect logical decoding to supply DDL events. |
A connector restart is not automatically a full recovery plan. Know whether it resumes from a stored position, retries failed destination tasks, or requires replay or reconciliation. The answer depends on the connector and its configuration; verify it before a production incident.
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Does this make search fully real time?
No. It makes search updates event-driven rather than dependent on a second application write or a periodic full export, but the pipeline still has several asynchronous stages: PostgreSQL capture, CDC processing, the network request, and Meilisearch task execution.
There is no generally applicable latency or throughput number established by the cited documentation. Measure the time from a committed PostgreSQL change until the corresponding Meilisearch query returns the new state in your own deployment. Account for load, batch settings, network conditions, consumer health, and destination indexing work when setting expectations. Do not promise a fixed subsecond delay or transactional visibility without deployment-specific evidence.
Frequently Asked Questions
Can I use a PostgreSQL read replica as the CDC source?
The cited material establishes logical decoding prerequisites and connector behaviors, but it does not confirm replica support for every deployment or integration. Check the documentation for your specific PostgreSQL service and CDC tool before pointing capture at a replica.
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Should my application read search results from Meilisearch or PostgreSQL?
Use Meilisearch when you need search-oriented retrieval, and keep PostgreSQL authoritative for writes and canonical records. If a user action requires the very latest committed value, account for the possibility that the index has not caught up yet.
What happens if the CDC worker is offline for a while?
Recovery depends on whether the replication slot and connector can retain and resume from the required position, and whether the retained WAL remains available. Monitor slot progress and storage, then follow the connector’s documented replay or reconciliation procedure rather than assuming a restart will repair the index.
Can one Meilisearch document combine data from multiple PostgreSQL tables?
It can be designed that way, but the CDC process must know how changes in each contributing table affect the resulting document. Plan the joins or transformation and ensure related-row updates trigger the necessary document re-indexing; a row stream from one table does not provide that behavior automatically.
