Troubleshooting

This section describes common issues and how to solve them. Need help? Ask your question in our support forum. Standard, Enterprise, and Dedicated customers can also email support@pinecone.io for help.

Unable to pip install

Version 3 of Python uses pip3. Use the following commands at the command line (the terminal):

pip3 install -U pinecone-client

Index is missing after inactivity

Indexes on the Starter (free) plan are deleted after 7 days of inactivity. To prevent this, you can send any API request to Pinecone and the counter will reset.

Slow uploads or high latencies

To minimize latency when accessing Pinecone:

  • Switch to a cloud environment. For example: EC2, GCE, Google Colab, GCP AI Platform Notebook, or SageMaker Notebook. If you experience slow uploads or high query latencies, it might be because you are accessing Pinecone from your home network.
  • Consider deploying your application in the same environment as your Pinecone service. For users on the Starter (free) plan, the environment is GCP US-West (Oregon).
  • See performance tuning for more tips.

High query latencies with batching

If you're batching queries, try reducing the number of queries per call to 1 query vector. You can make these calls in parallel and expect roughly the same performance as with batching.

Upsert throttling when using the gRPC client

It's possible to get write-throttled sooner when upserting using the gRPC index. If you see this often, then we recommend using a backoff algorithm while upserting.

Pods are full

There is a limit to how much vector data a single pod can hold. Create an index with more pods to hold more data. Check the usage estimator to determine the minimum number of pods, or contact us for help estimating and configuring larger indexes.

If your metadata has high cardinality, such as having a unique value for every vector in a large index, the index will take up more memory than estimated. This could result in the pods being full sooner than you expected. Consider only indexing metadata to be used for filtering, and storing the rest in a separate key-value store.

See the Manage Indexes documentation for information on how to specify the number of pods for your index.

Security concerns

We work hard to earn and maintain trust by treating security and reliability as a cornerstone of our company and product. Pinecone is SOC 2 Type II compliant and GDPR-ready. See the Trust & Security page for more information. Contact us to report any security concerns.