Increase Your Vector Database Read Throughput with In-Memory Replicas

Use in-memory replicas to enhance read throughput and the utilization of hardware resources.


Concepts related to in-memory replica

Replica concepts.

Replica group

Shard replica

Streaming replica

Historical replica

Shard leader

What is in-memory replica?

In-memory replicas.

Why are in-memory replicas important?

Enable in-memory replicas in the Milvus vector database

from pymilvus import Collection
collection = Collection("book") # Get an existing collection.
collection.load(replica_number=2) # load collection as 2 replicas
Replica groups:
- Group: <group_id:435309823872729305>, <group_nodes:(21, 20)>, <shards:[Shard: <channel_name:milvus-zong-rootcoord-dml_27_435367661874184193v0>, <shard_leader:21>, <shard_nodes:[21]>, Shard: <channel_name:milvus-zong-rootcoord-dml_28_435367661874184193v1>, <shard_leader:20>, <shard_nodes:[20, 21]>]>
- Group: <group_id:435309823872729304>, <group_nodes:(25,)>, <shards:[Shard: <channel_name:milvus-zong-rootcoord-dml_28_435367661874184193v1>, <shard_leader:25>, <shard_nodes:[25]>, Shard: <channel_name:milvus-zong-rootcoord-dml_27_435367661874184193v0>, <shard_leader:25>, <shard_nodes:[25]>]>

What’s next



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