Monitoring

Pinecone generates time-series performance metrics for each Pinecone index. You can monitor these metrics directly in the Pinecone Console, or you can pull them into Prometheus or Prometheus- and OpenMetrics-compatible tools like Datadog.

Monitor in the Pinecone Console

To view performance metrics in the Pinesone Console:

1. Log in to the Pinecone Console.
2. Select the index that you want to monitor.
3. Go to the Metrics tab.

Monitor with Prometheus

This feature is available only on Enterprise or Enterprise Dedicated plans.

Pinecone metrics are available at the following URL:

https://metrics.YOUR_ENVIRONMENT.pinecone.io/metrics.

To monitor Pinecone with your own Prometheus instance, configure the prometheus.yml file to target the Pinecone metrics URL, using your API key for authorization:

scrape_configs:
  - job_name: pinecone-job-1
    authorization:
      credentials: <api-key-here>
    scheme: https
    static_configs:
      - targets: ['metrics.YOUR_ENVIRONMENT.pinecone.io/metrics']

Your API key must be passed via the Authorization header as a bearer token like Authorization: Bearer \<api-key\>. Also, note that only the metrics for the project associated with the API key will be ingested by Prometheus.

For more configuration details, see the Prometheus docs.

Example Prometheus queries

Return the average latency in seconds for all requests against the Pinecone index example-index:

avg by (request_type) (pinecone_request_latency_seconds{index_name="example-index"})

Return the vector count for the Pinecone index example-index:

sum ((avg by (app) (pinecone_vector_count{index_name="example-index"})))

Return the total number of requests against the Pinecone index example-index over one minute:

sum by (request_type)(increase(pinecone_request_count_total{index_name="example-index"}[60s]))

Return the total number of upsert requests against the Pinecone index example-index over one minute:

sum by (request_type)(increase(pinecone_request_count_total{index_name="example-index", request_type="upsert"}[60s]))

Return the total errors returned by the Pinecone index example-index over one minute:

sum by (request_type) (increase(pinecone_request_error_count{
      index_name="example-index"}[60s]))

Return the index fullness metric for the Pinecone index example-index:

round(max (pinecone_index_fullness{index_name="example-index"} * 100))

Monitor with Datadog

To monitor Pinecone with Datadog, use Pinecone’s direct Datadog integration.

Available Metrics

The metrics available are as follows:

Name	Type	Description	Labels
pinecone_vector_count	gauge	The number of records per pod in the index.	- pid: Process identifier - index_name: Name of the index - project_name: Pinecone project name
pinecone_request_count_total	counter	The number of data plane calls made by clients.	- pid: Process identifier - index_name: Name of the index - project_name: Pinecone project name - request_type: One of upsert, delete, fetch, query, describe_index_stats
pinecone_request_error_count_total	counter	The number of data plane calls made by clients that resulted in errors.	- pid: Process identifier - index_name: Name of the index - project_name: Pinecone project name - request_type: One of upsert, delete, fetch, query, describe_index_stats
pinecone_request_latency_seconds	histogram	The distribution of server-side processing latency for pinecone data plane calls.	- pid: Process identifier - index_name: Name of the index - project_name: Pinecone project name - request_type: One of upsert, delete, fetch, query, describe_index_stats
pinecone_index_fullness	gauge	The fullness of the index on a scale of 0 to 1.	- pid: Process identifier - index_name: Name of the index - project_name: Pinecone project name