Monitor LanceDB with OpenTelemetry

LanceDB emits internal metrics (currently object store request counts, bytes transferred, request latency, retryable errors, and throttles) and can bridge them into any OpenTelemetry backend. Use this to watch how your application interacts with S3, GCS, Azure Blob, or the local filesystem in production: spot latency regressions, catch retry storms, and size your storage tier from real workload data. The bridge is available in the Python and TypeScript SDKs. It is a thin wrapper over LanceDB’s metrics recorder; your application supplies and configures the OpenTelemetry SDK.

This page covers LanceDB Embedded instrumentation. LanceDB Enterprise clusters emit their own metrics from the server side. See Enterprise monitoring below.

Available metrics

Once instrumented, LanceDB registers one observable instrument per metric on your MeterProvider. The current catalog covers the object store layer:

Metric	Kind	Description
`lance_object_store_requests_total`	Counter	Total object store requests, labelled by `operation` and `base` (store scheme).
`lance_object_store_request_duration_seconds`	Histogram	Request latency in seconds.
`lance_object_store_request_bytes_total`	Counter	Bytes read from or written to the store.
`lance_object_store_errors_total`	Counter	Object store requests that failed.
`lance_object_store_retryable_responses_total`	Counter	Retryable HTTP responses (5xx, 429 and 408) from a store reached over HTTP.
`lance_object_store_throttle_total`	Counter	Throttle responses (429 and 503) from a store reached over HTTP.
`lance_object_store_in_flight_requests`	Gauge	Currently outstanding object store requests.

The recorder is process-global and pull-based: your configured MetricReader collects on its own schedule, so there is no hot-path overhead beyond the atomic aggregation that LanceDB does anyway.

Histograms are exported Prometheus-style. OpenTelemetry has no asynchronous histogram instrument, so each histogram surfaces as three observable counters: <name>_bucket (with an le attribute per bucket boundary, including +Inf), <name>_count, and <name>_sum. Only _sum carries the histogram’s unit; _bucket and _count are cumulative sample counts.

Python

Install LanceDB with the otel extra to pull in the OpenTelemetry API, plus an OpenTelemetry SDK of your choice. The SDK is intentionally not bundled, so you configure it and its readers and exporters however your platform expects.

pip install "lancedb[otel]" opentelemetry-sdk opentelemetry-exporter-otlp-proto-grpc

Call instrument_lancedb_metrics() once at startup, before opening any tables. It returns True when the recorder is installed and instruments are registered.

from lancedb.otel import instrument_lancedb_metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import (
    PeriodicExportingMetricReader,
)
from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import (
    OTLPMetricExporter,
)

reader = PeriodicExportingMetricReader(OTLPMetricExporter())
provider = MeterProvider(metric_readers=[reader])

instrument_lancedb_metrics(provider)

# Any object store activity from this point on is now recorded.
db = lancedb.connect("s3://my-bucket/lancedb")

If you omit meter_provider, LanceDB uses the global provider returned by opentelemetry.metrics.get_meter_provider().

instrument_lancedb_metrics() returns False and emits a warning if another metrics-crate recorder is already installed in the process. Only one global recorder is permitted, so instrument LanceDB before any other library that installs its own recorder.

TypeScript

Install the OpenTelemetry SDK and exporter in your application:

npm install @opentelemetry/sdk-metrics @opentelemetry/exporter-metrics-otlp-grpc

import { connect, instrumentLanceDbMetrics } from "@lancedb/lancedb";
import { MeterProvider, PeriodicExportingMetricReader } from "@opentelemetry/sdk-metrics";
import { OTLPMetricExporter } from "@opentelemetry/exporter-metrics-otlp-grpc";

const reader = new PeriodicExportingMetricReader({
  exporter: new OTLPMetricExporter(),
});
const provider = new MeterProvider({ readers: [reader] });

instrumentLanceDbMetrics(provider);

const db = await connect("s3://my-bucket/lancedb");

instrumentLanceDbMetrics() also accepts no arguments, in which case it uses the global provider from @opentelemetry/api. Calling it more than once is safe: instruments are created only on the first successful call.

Build dashboards and alerts

A few starting points for dashboards and alerts:

Request rate by operation: rate(lance_object_store_requests_total[1m]) broken down by operation shows read vs. write pressure and helps size ingestion and serving traffic separately.
Tail latency: histogram quantiles over lance_object_store_request_duration_seconds_bucket catch object store slowdowns before they surface as query timeouts.
Retryable responses: a rising lance_object_store_retryable_responses_total typically means you are being throttled and should back off or shard writes.
In-flight requests: a growing lance_object_store_in_flight_requests gauge without a matching rise in throughput indicates queueing.

Enterprise monitoring

Enterprise A deployment emits its own metrics from the server side, using Prometheus for collection and OpenTelemetry (OTel) to export them with data enrichment. No client-side instrumentation is needed.

Component	Metrics
Query node	RPS, query latency, error codes, slow-query count, CPU/memory utilization
Executor	SSD cache hit/miss rate, CPU/memory utilization

Contact support for the comprehensive metric list and help integrating them with your monitoring dashboard.

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Monitor LanceDB with OpenTelemetry

Available metrics

Python

TypeScript

Build dashboards and alerts

Enterprise monitoring

Related guides

Performance tips

Object storage

​Available metrics

​Python

​TypeScript

​Build dashboards and alerts

​Enterprise monitoring

​Related guides

Performance tips

Object storage

Available metrics

Python

TypeScript

Build dashboards and alerts

Enterprise monitoring

Related guides