OpenTelemetry support

The Pinecone Java SDK provides built-in support for capturing per-operation response metadata, making it straightforward to monitor your Pinecone usage with OpenTelemetry or any other observability system. With this feature, you can track client-side latency, server processing time, network overhead, error rates, and more for every data plane operation your application makes.

How it all fits together

The SDK’s observability support is designed to be flexible. You don’t need to adopt the entire observability stack at once — start simple and add layers as your needs grow. Here are the components involved and how they relate to each other:

Pinecone Java SDK: Exposes a ResponseMetadataListener callback, a plain Java interface with no external dependencies. At its simplest, you can log the metadata to the console. No additional tools required.
OpenTelemetry (OTel): An open standard and SDK for producing structured telemetry data (metrics, traces, logs). If you want standardized metrics that follow semantic conventions, you add the OTel SDK and wire it to the listener. This is optional.
OTel Collector: A vendor-neutral service that receives telemetry from your app and forwards it to a storage backend. Optional — many setups export directly from the app to a backend.
Prometheus: A time-series database that stores metrics, making them queryable over time. One popular storage option.
Grafana: A visualization and dashboarding tool that queries Prometheus (or other backends) and displays charts and alerts. One popular visualization option.

A common setup chains these together:

Your App (OTel SDK) → OTel Collector → Prometheus (storage) → Grafana (visualization)

This is just one example pipeline. You can substitute Datadog, New Relic, or any OTel-compatible backend. You can also skip OTel entirely and use Micrometer, custom logging, or any approach that suits your stack.

Response metadata listener

The Java SDK captures response metadata through a ResponseMetadataListener — a functional interface you provide when building the Pinecone client. The listener is called after each data plane operation completes (whether it succeeds or fails), and receives a ResponseMetadata object containing timing, status, and context information. The SDK itself has no OpenTelemetry dependency. You bring your own observability library and decide what to do with the metadata.

Supported operations

The following data plane operations are instrumented, for both synchronous (Index) and asynchronous (AsyncIndex) usage:

Operation	Description
`upsert`	Insert or update vectors
`query`	Search for similar vectors
`fetch`	Retrieve vectors by ID
`update`	Update vector metadata
`delete`	Delete vectors

Available metadata

Each ResponseMetadata object provides the following fields:

Method	Description	OTel attribute
`getOperationName()`	Operation type (e.g., `upsert`, `query`)	`db.operation.name`
`getIndexName()`	Pinecone index name	`pinecone.index_name`
`getNamespace()`	Namespace (empty string if default)	`db.namespace`
`getServerAddress()`	Pinecone server host	`server.address`
`getClientDurationMs()`	Total round-trip time in ms (always available)	—
`getServerDurationMs()`	Server processing time in ms (may be `null`)	—
`getNetworkOverheadMs()`	Client minus server duration in ms (may be `null`)	—
`getStatus()`	`"success"` or `"error"`	`status`
`getGrpcStatusCode()`	Raw gRPC status code (e.g., `OK`, `UNAVAILABLE`)	`db.response.status_code`
`getErrorType()`	Error category, or `null` if successful	`error.type`

Possible errorType values: validation, connection, server, rate_limit, timeout, auth, not_found, unknown.

Recommended metrics

If you’re recording OTel metrics, the SDK example project uses these metric names, which follow OTel semantic conventions for database clients:

Metric	Type	Unit	Description
`db.client.operation.duration`	Histogram	ms	Client-measured round-trip time
`pinecone.server.processing.duration`	Histogram	ms	Server processing time
`db.client.operation.count`	Counter	—	Total number of operations

Quick start: Simple logging

The simplest way to use the listener is to log the metadata directly. This requires no additional dependencies beyond the Pinecone SDK:

import io.pinecone.clients.Pinecone;

Pinecone client = new Pinecone.Builder("PINECONE_API_KEY")
    .withResponseMetadataListener(metadata -> {
        System.out.printf("Operation: %s | Client: %dms | Server: %sms | Network: %sms | Status: %s%n",
            metadata.getOperationName(),
            metadata.getClientDurationMs(),
            metadata.getServerDurationMs(),
            metadata.getNetworkOverheadMs(),
            metadata.getStatus());
    })
    .build();

Once configured, every data plane operation automatically triggers the listener:

Index index = client.getIndexConnection("my-index");
index.upsert("id-1", Arrays.asList(0.1f, 0.2f, 0.3f));
// Output: Operation: upsert | Client: 47ms | Server: 40ms | Network: 7ms | Status: success

Quick start: OpenTelemetry integration

To record structured metrics with OpenTelemetry, add the OTel SDK dependencies and wire a metrics recorder to the listener.

1. Add dependencies

Add the following to your pom.xml:

<dependencies>
    <!-- Pinecone SDK -->
    <dependency>
        <groupId>io.pinecone</groupId>
        <artifactId>pinecone-client</artifactId>
        <version>LATEST</version>
    </dependency>

    <!-- OpenTelemetry SDK -->
    <dependency>
        <groupId>io.opentelemetry</groupId>
        <artifactId>opentelemetry-sdk</artifactId>
    </dependency>
    <dependency>
        <groupId>io.opentelemetry</groupId>
        <artifactId>opentelemetry-sdk-metrics</artifactId>
    </dependency>

    <!-- OTLP exporter (sends metrics to an OTel Collector or compatible backend) -->
    <dependency>
        <groupId>io.opentelemetry</groupId>
        <artifactId>opentelemetry-exporter-otlp</artifactId>
    </dependency>
</dependencies>

<!-- Use the OTel BOM to manage versions -->
<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>io.opentelemetry</groupId>
            <artifactId>opentelemetry-bom</artifactId>
            <version>1.35.0</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

2. Create a metrics recorder

The SDK’s example project includes a reusable PineconeMetricsRecorder class you can copy into your project. It implements ResponseMetadataListener and records all three recommended metrics with proper OTel attributes:

import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.common.Attributes;
import io.opentelemetry.api.common.AttributesBuilder;
import io.opentelemetry.api.metrics.LongCounter;
import io.opentelemetry.api.metrics.LongHistogram;
import io.opentelemetry.api.metrics.Meter;
import io.pinecone.configs.ResponseMetadata;
import io.pinecone.configs.ResponseMetadataListener;

public class PineconeMetricsRecorder implements ResponseMetadataListener {

    private static final AttributeKey<String> DB_SYSTEM = AttributeKey.stringKey("db.system");
    private static final AttributeKey<String> DB_OPERATION_NAME = AttributeKey.stringKey("db.operation.name");
    private static final AttributeKey<String> DB_NAMESPACE = AttributeKey.stringKey("db.namespace");
    private static final AttributeKey<String> PINECONE_INDEX_NAME = AttributeKey.stringKey("pinecone.index_name");
    private static final AttributeKey<String> SERVER_ADDRESS = AttributeKey.stringKey("server.address");
    private static final AttributeKey<String> STATUS = AttributeKey.stringKey("status");
    private static final AttributeKey<String> ERROR_TYPE = AttributeKey.stringKey("error.type");

    private final LongHistogram clientDurationHistogram;
    private final LongHistogram serverDurationHistogram;
    private final LongCounter operationCounter;

    public PineconeMetricsRecorder(Meter meter) {
        this.clientDurationHistogram = meter.histogramBuilder("db.client.operation.duration")
                .setDescription("Duration of Pinecone operations from client perspective")
                .setUnit("ms")
                .ofLongs()
                .build();

        this.serverDurationHistogram = meter.histogramBuilder("pinecone.server.processing.duration")
                .setDescription("Server processing time from x-pinecone-response-duration-ms header")
                .setUnit("ms")
                .ofLongs()
                .build();

        this.operationCounter = meter.counterBuilder("db.client.operation.count")
                .setDescription("Total number of Pinecone operations")
                .setUnit("{operation}")
                .build();
    }

    @Override
    public void onResponse(ResponseMetadata metadata) {
        AttributesBuilder attributesBuilder = Attributes.builder()
                .put(DB_SYSTEM, "pinecone")
                .put(DB_OPERATION_NAME, metadata.getOperationName())
                .put(PINECONE_INDEX_NAME, metadata.getIndexName())
                .put(SERVER_ADDRESS, metadata.getServerAddress())
                .put(STATUS, metadata.getStatus());

        String namespace = metadata.getNamespace();
        if (namespace != null && !namespace.isEmpty()) {
            attributesBuilder.put(DB_NAMESPACE, namespace);
        }

        if (!metadata.isSuccess() && metadata.getErrorType() != null) {
            attributesBuilder.put(ERROR_TYPE, metadata.getErrorType());
        }

        Attributes attributes = attributesBuilder.build();

        clientDurationHistogram.record(metadata.getClientDurationMs(), attributes);

        Long serverDuration = metadata.getServerDurationMs();
        if (serverDuration != null) {
            serverDurationHistogram.record(serverDuration, attributes);
        }

        operationCounter.add(1, attributes);
    }
}

3. Wire it into the Pinecone client

Initialize the OTel SDK, create the recorder, and pass it to the Pinecone client builder:

import io.opentelemetry.api.metrics.Meter;
import io.opentelemetry.sdk.OpenTelemetrySdk;
import io.opentelemetry.sdk.metrics.SdkMeterProvider;
import io.opentelemetry.sdk.metrics.export.PeriodicMetricReader;
import io.opentelemetry.exporter.otlp.metrics.OtlpGrpcMetricExporter;
import io.pinecone.clients.Pinecone;

// Set up OTel with OTLP exporter
OtlpGrpcMetricExporter exporter = OtlpGrpcMetricExporter.builder()
    .setEndpoint("http://localhost:4317")
    .build();

SdkMeterProvider meterProvider = SdkMeterProvider.builder()
    .registerMetricReader(PeriodicMetricReader.builder(exporter).build())
    .build();

OpenTelemetrySdk openTelemetry = OpenTelemetrySdk.builder()
    .setMeterProvider(meterProvider)
    .build();

// Create the metrics recorder
Meter meter = openTelemetry.getMeter("pinecone.client");
PineconeMetricsRecorder recorder = new PineconeMetricsRecorder(meter);

// Build the Pinecone client with the recorder
Pinecone client = new Pinecone.Builder("PINECONE_API_KEY")
    .withResponseMetadataListener(recorder)
    .build();

// Use the client normally -- metrics are recorded automatically
Index index = client.getIndexConnection("my-index");
index.upsert("id-1", Arrays.asList(0.1f, 0.2f, 0.3f));
index.query(3, Arrays.asList(0.1f, 0.2f, 0.3f));

For a complete runnable example with Docker Compose, Prometheus, and Grafana, see the java-otel-metrics example project in the SDK repository.

Example: Micrometer/Prometheus

If your application uses Micrometer (common in Spring Boot), you can wire the listener to Micrometer instead of the OTel SDK:

import io.micrometer.core.instrument.MeterRegistry;
import io.micrometer.core.instrument.Timer;
import io.pinecone.clients.Pinecone;
import java.util.concurrent.TimeUnit;

Pinecone client = new Pinecone.Builder("PINECONE_API_KEY")
    .withResponseMetadataListener(metadata -> {
        Timer.builder("pinecone.client.duration")
            .tag("operation", metadata.getOperationName())
            .tag("index", metadata.getIndexName())
            .tag("status", metadata.getStatus())
            .register(meterRegistry)
            .record(metadata.getClientDurationMs(), TimeUnit.MILLISECONDS);
    })
    .build();

Visualizing metrics

Once your metrics are flowing to a backend, you can build dashboards to monitor your Pinecone operations. If you’re using Prometheus and Grafana, here are some useful queries: P50 and P95 client latency:

histogram_quantile(0.5, sum(rate(db_client_operation_duration_milliseconds_bucket[5m])) by (le))
histogram_quantile(0.95, sum(rate(db_client_operation_duration_milliseconds_bucket[5m])) by (le))

P95 latency by operation type:

histogram_quantile(0.95, sum(rate(db_client_operation_duration_milliseconds_bucket[5m])) by (le, db_operation_name))

Operation count by type:

sum by (db_operation_name) (db_client_operation_count_total)

Understanding the latency breakdown

The ResponseMetadata object provides three timing values that help you pinpoint the source of latency issues:

Component	Method	What it measures
Client duration	`getClientDurationMs()`	Total round-trip time from request start to response completion. Always available.
Server duration	`getServerDurationMs()`	Time the Pinecone backend spent processing the request. Extracted from the `x-pinecone-response-duration-ms` response header. May be `null`.
Network overhead	`getNetworkOverheadMs()`	The difference: client duration minus server duration. Includes network latency, serialization, and deserialization. May be `null`.

Use these values to diagnose performance issues:

High server duration: The bottleneck is on the Pinecone backend. Consider optimizing your query (e.g., reducing topK, using metadata filters), or check the Pinecone status page.
High network overhead: The bottleneck is in the network path between your application and Pinecone. Consider deploying your application closer to your index’s cloud region, or check for network issues.

Limitations

Data plane operations only. Control plane operations (e.g., creating or deleting indexes) are not currently instrumented.
Bulk import operations are not yet instrumented.
Server duration may be unavailable. The getServerDurationMs() method returns null if the x-pinecone-response-duration-ms header is not present in the response.
Synchronous callback. The listener is called synchronously after the gRPC response is received. Keep implementations lightweight and non-blocking to avoid adding latency to your operations. For heavy processing, queue the metadata for async handling.
Exceptions are swallowed. Exceptions thrown by the listener are logged but do not affect the operation result.

Best practices

Keep listeners lightweight. Record metrics or enqueue work — don’t do I/O or heavy computation in the callback.
Follow OTel semantic conventions. Use the attribute names shown in the recommended metrics table for interoperability with standard dashboards and tooling.
Monitor both client and server duration. Tracking both lets you separate Pinecone backend performance from network conditions.
Set alerts on error rates. Use the status and error.type attributes to build alerts for elevated error rates across operations.

APIs

Database

Inference

Admin

SDKs

CLI

Tools

How it all fits together

Response metadata listener

Supported operations

Available metadata

Recommended metrics

Quick start: Simple logging

Quick start: OpenTelemetry integration

1. Add dependencies

2. Create a metrics recorder

3. Wire it into the Pinecone client

Example: Micrometer/Prometheus

Visualizing metrics

Understanding the latency breakdown

Limitations

Best practices

APIs

Database

Inference

Admin

SDKs

CLI

Tools

Documentation Index

​How it all fits together

​Response metadata listener

​Supported operations

​Available metadata

​Recommended metrics

​Quick start: Simple logging

​Quick start: OpenTelemetry integration

​1. Add dependencies

​2. Create a metrics recorder

​3. Wire it into the Pinecone client

​Example: Micrometer/Prometheus

​Visualizing metrics

​Understanding the latency breakdown

​Limitations

​Best practices

How it all fits together

Response metadata listener

Supported operations

Available metadata

Recommended metrics

Quick start: Simple logging

Quick start: OpenTelemetry integration

1. Add dependencies

2. Create a metrics recorder

3. Wire it into the Pinecone client

Example: Micrometer/Prometheus

Visualizing metrics

Understanding the latency breakdown

Limitations

Best practices