Indexing performance

The indexing performance section of the monitoring dashboard shows how quickly Meilisearch processes indexing tasks. It is labeled Beta in the Cloud UI. You can filter all charts by index using the All indexes dropdown, set a date range, or enable real-time mode. Timestamps are displayed in UTC.

Indexing latency (TTS)

The indexing latency chart tracks time-to-search (TTS): the time from when an indexing task is enqueued to when the indexed documents become searchable. Latency is shown at four percentiles, measured in milliseconds: p75, p90, p95, and p99.

Percentile	What it means
p75	75% of indexing tasks completed within this time
p90	90% of indexing tasks completed within this time
p95	95% of indexing tasks completed within this time
p99	99% of indexing tasks completed within this time — the slowest tasks

TTS is the metric that matters most for use cases where freshness is important, such as e-commerce catalog updates or live content indexing.

Batches

The Batches tab gives you a per-batch view of every indexing operation, along with a detailed trace of where time was spent. Use it when the TTS chart shows high latency and you need to identify the bottleneck.

Each row shows:

Status: succeeded, failed, or in progress
Index id: which index was written to
Batch id: unique identifier for the batch
Duration: total wall-clock time for the batch
Started date: when the batch began processing

Click a batch to open its details page.

Progress trace

The details page includes a progressTrace section with timing for every internal step of the indexing pipeline:

Key steps visible in the trace:

Trace path	What it measures
`processing tasks > retrieving config`	Loading index configuration
`processing tasks > computing document changes`	Diff between incoming and existing documents
`processing tasks > reading payload stats`	Parsing incoming document payloads
`processing tasks > indexing > extracting documents`	Extracting fields from documents
`processing tasks > indexing > extracting facets`	Building facet data
`processing tasks > indexing > merging facets`	Merging facet updates into the index
`processing tasks > indexing > extracting words`	Tokenizing document content
`processing tasks > indexing > merging words`	Merging word data into the inverted index
`processing tasks > indexing > writing embeddings to database`	Persisting vector embeddings
`processing tasks > indexing > post processing facets`	Finalizing facet search structures
`processing tasks > indexing > post processing words`	Finalizing word prefix structures
`processing tasks > indexing > building geo json`	Building geo search structures
`processing tasks > indexing > finalizing`	Committing the batch to disk
`writing tasks to disk`	Persisting the task record

Internal database sizes

The Internal DB table shows the current on-disk size of each internal data structure, along with the delta from this batch:

Field	What it stores
`wordPrefixPositionDocids`	Word prefix position data for prefix search
`fieldIdDocidFacetStrings`	Facet string data for filtering and faceting
`vectorStore`	Vector embeddings for semantic/hybrid search
`documents`	Raw document storage

The delta (shown as +N KiB or +N MiB) tells you how much space each batch adds. A vectorStore growing much faster than documents indicates a high-dimensional embedding model.

Other fields

Field	What it shows
`embedderRequests.total`	Number of embedding API calls made during this batch
`embedderRequests.failed`	Failed embedding calls (non-zero means some documents may not be indexed for vector search)
`writeChannelCongestion.attempts`	Number of write attempts
`writeChannelCongestion.blocking_attempts`	Write attempts that had to wait (high values indicate write pressure)

Expert support for Enterprise customers

In most cases, the simplest way to improve indexing performance is to upgrade to a larger resource tier. More RAM and CPU directly reduce indexing time and TTS. You can change your resource tier at any time from the project settings. If upgrading does not resolve the issue, the Meilisearch team can help. Enterprise customers have direct access to experts who can analyze your batch traces, database sizes, and index configuration to optimize for your specific workload. Contact sales@meilisearch.com to learn more.

Common issues and fixes

Symptom	Likely cause	Fix
High TTS across all percentiles	Large document batches or many indexed attributes	Reduce batch size, or reduce the number of `filterableAttributes` and `sortableAttributes`
`merging words` step slow	Large inverted index update	Reduce the number of `searchableAttributes` or batch size
`writing embeddings to database` slow	High vector dimensions or large batch	Reduce batch size; consider a lower-dimension model
`embedderRequests.failed` non-zero	Embedder API errors or rate limits	Check your embedder configuration and API key validity
High `writeChannelCongestion.blocking_attempts`	Concurrent write contention	Avoid concurrent indexing operations on the same index
TTS spikes periodically	Scheduled bulk imports competing with search	Stagger indexing operations to off-peak hours
`vectorStore` growing faster than expected	High embedding dimensions	Switch to a lower-dimension embedding model

Capabilities

Full-text search

Hybrid and semantic search

Geo search

Conversational search

Multi-search

Filtering, sorting, and faceting

Personalization

Analytics

Security and tenant tokens

Search rules

Platform

Indexing

Indexing latency (TTS)

Batches

Progress trace

Internal database sizes

Other fields

Expert support for Enterprise customers

Common issues and fixes

​Indexing latency (TTS)

​Batches

​Progress trace

​Internal database sizes

​Other fields

​Expert support for Enterprise customers

​Common issues and fixes

Indexing latency (TTS)

Batches

Progress trace

Internal database sizes

Other fields

Expert support for Enterprise customers

Common issues and fixes