> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pinecone.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Decrease latency
> Reduce query and upsert latency in Pinecone using namespaces, metadata filters, targeting indexes by host, and regional colocation strategies.
## Use namespaces
When you divide records into [namespaces](/guides/index-data/indexing-overview#namespaces) in a logical way, you speed up queries by ensuring only relevant records are scanned. The same applies to [fetching records](/guides/manage-data/fetch-data), [listing record IDs](/guides/manage-data/list-record-ids), and other data operations.
## Filter by metadata
In addition to increasing search accuracy and relevance, [searching with metadata filters](/guides/search/filter-by-metadata) can also help decrease latency by retrieving only records that match the filter.
## Target indexes by host
When you target an index by name for data operations such as `upsert` and `query`, the SDK gets the unique DNS host for the index using the `describe_index` operation. This is convenient for testing but should be avoided in production because `describe_index` uses a different API than data operations and therefore adds an additional network call and point of failure. Instead, you should get an index host once and cache it for reuse or specify the host directly.
You can get index hosts in the [Pinecone console](https://app.pinecone.io/organizations/-/projects/-/indexes) or using the [`describe_index`](/guides/manage-data/manage-indexes#describe-an-index) operation.
The following example shows how to target an index by host directly:
When using Private Endpoints for private connectivity between your application and Pinecone, you must target the index using the [Private Endpoint URL](/guides/production/configure-private-endpoints#read-and-write-data) for the host.
```Python Python {5} theme={null}
from pinecone.grpc import PineconeGRPC as Pinecone
pc = Pinecone(api_key="YOUR_API_KEY")
index = pc.Index(host="INDEX_HOST")
```
```javascript JavaScript {6} theme={null}
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' });
// For the Node.js SDK, you must specify both the index host and name.
const index = pc.index("INDEX_NAME", "INDEX_HOST");
```
```java Java {11} theme={null}
import io.pinecone.clients.Index;
import io.pinecone.configs.PineconeConfig;
import io.pinecone.configs.PineconeConnection;
public class TargetIndexByHostExample {
public static void main(String[] args) {
PineconeConfig config = new PineconeConfig("YOUR_API_KEY");
config.setHost("INDEX_HOST");
PineconeConnection connection = new PineconeConnection(config);
// For the Java SDK, you must specify both the index host and name.
Index index = new Index(connection, "INDEX_NAME");
}
}
```
```go Go {21} theme={null}
package main
import (
"context"
"fmt"
"log"
"github.com/pinecone-io/go-pinecone/v4/pinecone"
)
func main() {
ctx := context.Background()
pc, err := pinecone.NewClient(pinecone.NewClientParams{
ApiKey: "YOUR_API_KEY",
})
if err != nil {
log.Fatalf("Failed to create Client: %v", err)
}
idxConnection, err := pc.Index(pinecone.NewIndexConnParams{Host: "INDEX_HOST", Namespace: "example-namespace"})
if err != nil {
log.Fatalf("Failed to create IndexConnection for Host %v: %v", idx.Host, err)
}
}
```
## Reuse connections
When you target an index for upserting or querying, the client establishes a TCP connection, which is a three-step process. To avoid going through this process on every request, and reduce average request latency, [cache and reuse the index connection object](/reference/api/authentication#initialize-a-client) whenever possible.
## Use a cloud environment
If you experience slow uploads or high query latencies, it might be because you are accessing Pinecone from your home network. To decrease latency, access Pinecone/deploy your application from a cloud environment instead, ideally from the same [cloud and region](/guides/index-data/create-an-index#cloud-regions) as your index.
## Avoid batching queries
If you're batching queries, try reducing the number of queries per call to a single query vector. You can run these [queries in parallel](/guides/search/semantic-search#parallel-queries) and expect roughly the same performance as with batching.
## Avoid including vector values when not needed
Including vector values increases response size, especially at higher `top_k` values, and larger responses can raise round-trip latency. If you don't need vector values in your response, leave `include_values` at its default of `false` on [`query`](/reference/api/latest/data-plane/query). [`fetch`](/reference/api/latest/data-plane/fetch) always returns values, so use `query` when you're searching and only need IDs or metadata.
On-demand indexes retrieve vector values from object storage, so `fetch` operations and queries with `include_values=true` may occasionally experience higher tail latency before values are cached on disk. Dedicated read nodes indexes cache values locally and aren't affected.
## Work with database limits
Pinecone has [rate limits](/reference/api/database-limits/rate-limits) to protect your applications and maintain infrastructure health. Rate limits vary based on pricing plan and apply to serverless indexes only.
Indexes built on [Dedicated Read Nodes](/guides/index-data/dedicated-read-nodes/overview) aren't subject to read unit limits for query, fetch, list, and full-text search operations. For sizing and capacity planning guidance, see the [Dedicated Read Nodes](/guides/index-data/dedicated-read-nodes/overview) guide.
To handle rate limits effectively:
* [Implement retry logic with exponential backoff](/guides/production/error-handling#handle-rate-limits-429).
* If you need higher limits for your use case, [contact Support](https://app.pinecone.io/organizations/-/settings/support/ticket). Most limits can be adjusted to accommodate your scaling needs.