Supported OS Linux Windows Mac OS

Información general

  • Optimización del rendimiento y control del uso: observa y realiza un seguimiento de acciones específicas (por ejemplo, recuento de solicitudes) dentro de Pinecone para identificar solicitudes de aplicaciones con alta latencia o uso. Monitoriza tendencias y obtén información procesable para mejorar la utilización de recursos y reducir el gasto.

  • Alerta automática en métricas: recibe alertas cuando la plenitud del índice alcance un determinado umbral. También puedes crear tus propios monitores personalizados para alertar sobre métricas y umbrales específicos.

  • Localización y clasificación de los picos inesperados de uso o latencia: visualiza rápidamente anomalías en uso o latencia en un dashboard de Datadog de Pinecone. Visualiza métricas a lo largo del tiempo para comprender mejor las tendencias y determinar la gravedad de un pico.

Requisitos

La monitorización de Pinecone con Datadog requiere:

  • Un plan Enterprise o Enterprise Dedicated de Pinecone.
  • Índices basados en pods o en sin servidor: Datadog admite tanto la captura de métricas basadas en pods como sin servidor.

Configuración

Instalación

  1. Inicia sesión en tu cuenta de Pinecone.
  2. Navega hasta la pestaña API Keys (Claves de API).
  3. Crea una clave de API.
  4. Copia la clave de API creada en el portapapeles.

Configuración

  1. Navega hasta la pestana de configuración dentro del cuadro de integración de Pinecone de Datadog.
  2. Introduce el ID de tu proyecto de Pinecone, que encontrarás en la lista del proyecto en la consola de Pinecone.
  3. Sólo para entornos basados en pods: selecciona tu entorno. Los proyectos en entornos sin servidor pueden dejarlo en blanco.
  4. Pega tu clave de API copiada.

Datos recopilados

Métricas

pinecone.vector.count
(gauge)
Number of records per pod in the index.
Shown as record
pinecone.request.count.total
(count)
Number of data plane calls made by clients.
Shown as request
pinecone.request.error.count.total
(count)
Number of data plane calls made by clients that resulted in errors.
Shown as request
pinecone.request.latency.seconds.min
(gauge)
Minimum of the distribution of server-side processing latency for Pinecone data plane calls.
Shown as second
pinecone.request.latency.seconds.max
(gauge)
Maximum of the distribution of server-side processing latency for Pinecone data plane calls.
Shown as second
pinecone.request.latency.seconds.avg
(gauge)
Average of the distribution of server-side processing latency for Pinecone data plane calls.
Shown as second
pinecone.request.latency.seconds.50percentile
(gauge)
p50 of the distribution of server-side processing latency for Pinecone data plane calls.
Shown as second
pinecone.request.latency.seconds.90percentile
(gauge)
p90 of the distribution of server-side processing latency for Pinecone data plane calls.
Shown as second
pinecone.request.latency.seconds.95percentile
(gauge)
p95 of the distribution of server-side processing latency for Pinecone data plane calls.
Shown as second
pinecone.request.latency.seconds.99percentile
(gauge)
p99 of the distribution of server-side processing latency for Pinecone data plane calls.
Shown as second
pinecone.request.latency.seconds.99.9percentile
(gauge)
p99.9 of the distribution of server-side processing latency for Pinecone data plane calls.
Shown as second
pinecone.request.latency.seconds.count
(count)
Count of the distribution of server-side processing latency for Pinecone data plane calls.
Shown as request
pinecone.index.fullness
(gauge)
Fullness of the index on a scale of 0 to 1.
Shown as unit
pinecone.db.op.query.total
(count)
The number of Query Request made to an Index (Serverless)
Shown as request
pinecone.db.op.fetch.total
(count)
The number of Fetch Request made to an Index (Serverless)
Shown as request
pinecone.db.op.update.total
(count)
The number of Update Request made to an Index (Serverless)
Shown as request
pinecone.db.op.delete.total
(count)
The number of Delete Request made to an Index (Serverless)
Shown as request
pinecone.db.op.upsert.total
(count)
The number of Upsert Request made to an Index (Serverless)
Shown as request
pinecone.db.op.query.duration.total
(count)
Total time taken processing Query Request for an Index (Serverless)
Shown as millisecond
pinecone.db.op.fetch.duration.total
(count)
Total time taken processing Fetch Request for an Index (Serverless)
Shown as millisecond
pinecone.db.op.update.duration.total
(count)
Total time taken processing Update Request for an Index (Serverless)
Shown as millisecond
pinecone.db.op.delete.duration.total
(count)
Total time taken processing Delete Request for an Index (Serverless)
Shown as millisecond
pinecone.db.op.upsert.duration.total
(count)
Total time taken processing Upsert Request for an Index (Serverless)
Shown as millisecond
pinecone.db.op.write.unit.total
(count)
Total number of write units consumed (Serverless)
Shown as request
pinecone.db.op.read.unit.total
(count)
Total number of read units consumed (Serverless)
Shown as request
pinecone.db.storage.size.bytes
(gauge)
Total size of the index in bytes (Serverless)
Shown as byte
pinecone.db.record.total
(gauge)
Total number of records (Serverless)
Shown as record

Logs

Pinecone no recopila logs.

Checks de servicio

Pinecone no incluye ningún check de servicio.

Eventos

Pinecone no incluye ningún evento.

Solucionar problemas

¿Necesitas ayuda? Ponte en contacto con el soporte de Datadog.