Connect a REST API to Meilisearch with Kestra

Not all of your data lives in a database you control. Plenty of it sits behind someone else’s REST API: a SaaS product catalog, a CMS, a CRM, a public data feed. You still want it searchable in Meilisearch, and you still want it to stay current. This guide wires an arbitrary REST API to Meilisearch with Kestra, covering both the first load and keeping it in sync. Because “a REST API” is not one thing, the incremental strategy depends on what the API offers. This guide covers the two cases you’ll actually meet: APIs that support “give me what changed since X”, and APIs that don’t.

Why orchestrate the sync

Pulling from an API on a schedule, handling pagination, converting the payload, indexing it, and tracking what you’ve already seen is exactly the kind of multi-step, stateful, must-not-silently-die job orchestration exists for. Kestra gives you the HTTP client, the format converters, a schedule, a KV store for watermarks, retries, and logging, all declaratively.

Prerequisites

A running Kestra with the Meilisearch and serdes plugins (the HTTP client is built into Kestra core), plus a Meilisearch Cloud project. Only Kestra runs locally, since Meilisearch is managed:

services:
  kestra:
    image: kestra/kestra:latest
    command: server local
    ports: ["8080:8080"]
    environment:
      # your Meilisearch Cloud Default Admin API key, base64-encoded
      SECRET_MEILISEARCH_API_KEY: <base64 of your admin API key>

FROM kestra/kestra:latest
RUN /app/kestra plugins install \
      io.kestra.plugin:plugin-meilisearch:LATEST \
      io.kestra.plugin:plugin-serdes:LATEST

Get your Cloud credentials. In the Meilisearch Cloud dashboard, create a project and copy its Project URL (the url in the flows below) and its Default Admin API Key (Settings, then API Keys). Keep that key and any upstream API tokens in Kestra secrets, referenced as {{ secret('NAME') }}.

Step 1: The first load (backfill)

The pipeline is three steps: download the JSON from the API, convert it to Kestra’s ION format, index it. Here’s the pattern against a public API:

id: api_to_meilisearch
namespace: company.search

variables:
  meilisearch_url: https://ms-xxxxxxxxxxxx-xxxx.meilisearch.io   # your Meilisearch Cloud Project URL
  index: pokemon

tasks:
  - id: http_download
    type: io.kestra.plugin.core.http.Download
    uri: https://pokeapi.co/api/v2/pokemon/jigglypuff

  - id: to_ion
    type: io.kestra.plugin.serdes.json.JsonToIon
    from: "{{ outputs.http_download.uri }}"

  - id: add_documents
    type: io.kestra.plugin.meilisearch.DocumentAdd
    from: "{{ outputs.to_ion.uri }}"
    index: "{{ vars.index }}"
    url: "{{ vars.meilisearch_url }}"
    key: "{{ secret('MEILISEARCH_API_KEY') }}"

http.Download fetches the response into internal storage, JsonToIon converts it, and DocumentAdd indexes it. If the API needs authentication, add headers:

  - id: http_download
    type: io.kestra.plugin.core.http.Download
    uri: https://api.example.com/v1/products
    headers:
      Authorization: "Bearer {{ secret('API_TOKEN') }}"

Shaping the response. APIs rarely hand back a flat array of clean documents. When the records are nested under a key, or need renaming/flattening for Meilisearch, drop in a JSONata transform between conversion and indexing:

  - id: reshape
    type: io.kestra.plugin.transform.jsonata.TransformItems
    from: "{{ outputs.to_ion.uri }}"
    expression: 'results.{ "id": id, "name": name, "category": type }'

(That needs the plugin-transform-json plugin.) Make sure each document has a field Meilisearch can use as a primary key. Pagination. For APIs that page, wrap the download in a loop that walks pages until the response is empty, appending each page’s documents. Kestra’s ForEach/EachSequential tasks or a paginating HTTP pattern handle this; index each page as you go so memory stays flat.

Step 2: Incremental sync

This is where REST APIs differ from databases: you can’t run a WHERE updated_at > X query unless the API gives you one. Two cases.

Case A: the API supports “changed since”

Many well-designed APIs accept a filter like ?updated_since= or ?modified_after=. When yours does, incremental sync is clean: on each run, ask only for what changed since the last successful run. Kestra exposes the previous execution’s scheduled time, and its KV store can persist a precise watermark. Using the schedule’s own trigger date as the lower bound:

id: api_incremental_sync
namespace: company.search

variables:
  meilisearch_url: https://ms-xxxxxxxxxxxx-xxxx.meilisearch.io   # your Meilisearch Cloud Project URL
  index: products

triggers:
  - id: schedule
    type: io.kestra.plugin.core.trigger.Schedule
    cron: "*/15 * * * *"
    recoverMissedSchedules: NONE

tasks:
  - id: http_download
    type: io.kestra.plugin.core.http.Download
    # request only records changed since this run's scheduled time
    uri: "https://api.example.com/v1/products?updated_since={{ trigger.date | date(\"yyyy-MM-dd'T'HH:mm:ss'Z'\", timeZone='UTC') }}"
    headers:
      Authorization: "Bearer {{ secret('API_TOKEN') }}"

  - id: to_ion
    type: io.kestra.plugin.serdes.json.JsonToIon
    from: "{{ outputs.http_download.uri }}"

  - id: upsert_documents
    type: io.kestra.plugin.meilisearch.DocumentAdd
    from: "{{ outputs.to_ion.uri }}"
    index: "{{ vars.index }}"
    url: "{{ vars.meilisearch_url }}"
    key: "{{ secret('MEILISEARCH_API_KEY') }}"

For a precise, gap-free watermark (rather than the schedule time), read a stored timestamp at the start of the run with io.kestra.plugin.core.kv.Get, use it in the query, and write the new high-water mark with io.kestra.plugin.core.kv.Set after a successful index. Overlap is safe regardless: DocumentAdd is add-or-replace, so re-fetching a few already-seen records just overwrites them by primary key.

Case B: the API has no change filter

When the API can only return the full collection, you have two honest options:

Periodic full re-index. Just run the Step 1 backfill on a schedule. It’s the simplest thing that works, and because upserts are idempotent it’s completely safe: every run reconciles the index to the current API state. Fine for small-to-medium collections.
Diff against a fresh index plus alias swap. Index each full pull into a new index, then atomically point a Meilisearch alias at it. This also handles deletes for free (anything no longer returned by the API simply isn’t in the new index), at the cost of re-indexing everything each time.

Handling deletes

Deletes are the hard part with REST sources, because most APIs don’t tell you what was removed: a record just stops appearing.

If the API exposes deletions (a deleted flag, a /deletions endpoint, or a status field), fetch those ids and call Meilisearch’s documents/delete-batch endpoint with an http.Request task. The exact pattern is in Connect PostgreSQL to Meilisearch with Kestra.
If it doesn’t, use the fresh-index-plus-alias-swap approach from Case B. It’s the only reliable way to drop records the API no longer returns.

Going to production

Rate limits. Add a retry block with backoff so 429 responses are retried politely; space out pagination requests if the API is strict.
Watermark durability. Prefer the KV-store watermark over the schedule date when you need exactly-once semantics. It survives missed runs and backfills correctly after downtime.
Auth refresh. If the API uses short-lived tokens, add a first task that fetches a fresh token (another http.Request) and pass it downstream.
Choose the interval by freshness need. A 15-minute schedule is a sensible default; tighten or loosen it to match how fast the source changes and the API’s rate limits.

Wrap-up

Any REST API can feed Meilisearch through the same download, convert, index pipeline. For the initial load it’s three tasks; for incremental sync, lean on the API’s “changed since” filter when it has one, and fall back to a scheduled full re-index (with an alias swap for deletes) when it doesn’t. Kestra supplies the HTTP client, converters, scheduling, watermark storage, and retries, so keeping search in sync with a third-party API becomes a short, observable YAML flow.

Introduction

Back-end SDKs

Frontend SDKs

Framework integrations

Integrations

Connect a REST API to Meilisearch with Kestra

Why orchestrate the sync

Prerequisites

Step 1: The first load (backfill)

Step 2: Incremental sync

Case A: the API supports “changed since”

Case B: the API has no change filter

Handling deletes

Going to production

Wrap-up

​Why orchestrate the sync

​Prerequisites

​Step 1: The first load (backfill)

​Step 2: Incremental sync

​Case A: the API supports “changed since”

​Case B: the API has no change filter

​Handling deletes

​Going to production

​Wrap-up

Why orchestrate the sync

Prerequisites

Step 1: The first load (backfill)

Step 2: Incremental sync

Case A: the API supports “changed since”

Case B: the API has no change filter

Handling deletes

Going to production

Wrap-up