Monitor performance

Monitor Pinecone index performance metrics (query latency, throughput, and errors) in the Pinecone console or via Prometheus and Datadog.

Pinecone generates time-series performance metrics for each Pinecone index. You can monitor these metrics directly in the Pinecone console or with tools like Prometheus or Datadog.

Monitor in the Pinecone Console

To view performance metrics in the Pinecone console:

Open the Pinecone console.
Select the project containing the index you want to monitor.
Go to Database > Indexes.
Select the index.
Go to the Metrics tab.

Monitor with Datadog

To monitor Pinecone with Datadog, use Datadog's Pinecone integration.

Monitor with Prometheus

To monitor all serverless indexes in a project, insert the following snippet into the scrape_configs section of your prometheus.yml file and update it with values for your Prometheus integration:

YAML

global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'pinecone-serverless-metrics'
    http_sd_configs:
      - url: https://api.pinecone.io/prometheus/projects/PROJECT_ID/metrics/discovery
        refresh_interval: 1m
        authorization:
          type: Bearer
          credentials: API_KEY
    authorization:
      type: Bearer
      credentials: API_KEY

Replace PROJECT_ID with the unique ID of the project you want to monitor. You can find the project ID in the Pinecone console.
Replace both instances of API_KEY with an API key for the project you want to monitor. The first instance is for service discovery, and the second instance is for the discovered targets. If necessary, you can create an new API key in the Pinecone console.

For more configuration details, see the Prometheus docs.

Available metrics

The following metrics are available when you integrate Pinecone with Prometheus:

Name	Type	Description
`pinecone_db_record_total`	gauge	The total number of records in the index.
`pinecone_db_storage_size_bytes`	gauge	The total size of the index in bytes.
`pinecone_db_op_upsert_count`	counter	The number of upsert requests.
`pinecone_db_op_upsert_duration_sum`	counter	The total time taken processing upsert requests in milliseconds.
`pinecone_db_op_query_count`	counter	The number of query requests.
`pinecone_db_op_query_duration_sum`	counter	The total time taken processing query requests in milliseconds.
`pinecone_db_op_fetch_count`	counter	The number of fetch requests.
`pinecone_db_op_fetch_duration_sum`	counter	The total time taken processing fetch requests in milliseconds.
`pinecone_db_op_update_count`	counter	The number of update requests.
`pinecone_db_op_update_duration_sum`	counter	The total time taken processing update requests in milliseconds.
`pinecone_db_op_delete_count`	counter	The number of delete requests.
`pinecone_db_op_delete_duration_sum`	counter	The total time taken processing delete requests in milliseconds.
`pinecone_db_op_list_count`	counter	The number of list requests.
`pinecone_db_op_list_duration_sum`	counter	The total time taken processing list requests in milliseconds.
`pinecone_db_write_unit_count`	counter	The total number of write units consumed by an index.
`pinecone_db_read_unit_count`	counter	The total number of read units consumed by an index.
`pinecone_db_scheduled_backup_failure_total`	counter	The number of terminal failures for scheduled backup create operations.
`pinecone_db_drn_cpu_usage_percent`	gauge	The CPU usage percentage for a dedicated read node shard, averaged across replicas.
`pinecone_db_index_fullness`	gauge	The greater of storage and memory fullness for a dedicated read nodes index, on a scale of 0 to 1.
`pinecone_db_index_memory_fullness`	gauge	The memory used by a dedicated read nodes index as a ratio of its total memory capacity, on a scale of 0 to 1.
`pinecone_db_index_storage_fullness`	gauge	The storage used by a dedicated read nodes index as a ratio of its total storage capacity, on a scale of 0 to 1.

Metric labels

Each metric contains the following labels:

Label	Description
`index_name`	Name of the index to which the metric applies.
`cloud`	Cloud where the index is deployed: `aws`, `gcp`, or `azure`.
`region`	Region where the index is deployed.
`capacity_mode`	Type of index: `serverless` or `byoc`.
`instance`	Server instance (only available for counter metrics).
`shard_id`	Shard identifier (only available for per-shard dedicated read node metrics).

Example queries

Return the total number of records per index:

Shell

sum by (index_name) (pinecone_db_record_total)

Return the total number of records in Pinecone index docs-example:

Shell

pinecone_db_record_total{index_name="docs-example"}

For each index, return the total number of upsert requests per second:

Shell

sum by (index_name) (rate(pinecone_db_op_upsert_count[5m]))

Return the average processing time in milliseconds for upsert requests per index:

Shell

(sum by (index_name) (rate(pinecone_db_op_upsert_duration_sum[1m])))/(sum by (index_name) (rate(pinecone_db_op_upsert_count[1m])))

For each index, return the total number of read units consumed per second:

Shell

sum by (index_name) (rate(pinecone_db_read_unit_count[5m]))

Return the total write units consumed per second for the Pinecone index docs-example:

Shell

sum (rate(pinecone_db_write_unit_count{index_name="docs-example"}[5m]))

Return the highest CPU usage percentage across all shards of Pinecone index docs-example:

Shell

max(pinecone_db_drn_cpu_usage_percent{index_name="docs-example"})

For each dedicated read nodes index, return the highest CPU usage percentage across its shards:

Shell

max by (index_name) (pinecone_db_drn_cpu_usage_percent)

Return the fullness of Pinecone index docs-example:

Shell

pinecone_db_index_fullness{index_name="docs-example"}

Return the indexes that are at least 80% full:

Shell

pinecone_db_index_fullness >= 0.8