Snapshot Use CasesCompatible with Milvus 3.0.x

In this guide, you will find common use cases for snapshots.

Data backup and restoration

Snapshots are quick, point-in-time images of data, suitable for fast rollbacks or testing (days to weeks). At the same time, backups are independent, complete copies stored separately for long-term disaster recovery (weeks to years) and for better protection against total storage failure.

The following table compares snapshots and backups.

	Backup	Snapshot
Backup creation	Copies all data files (time-consuming)	Creates metadata only (in milliseconds)
Restoration	Imports data and rebuilds indexes	Copies existing data and index files only
Performance	Slow and resource-intensive	Fast and lightweight (in seconds to minutes)
System impact	High I/O and CPU usage	Minimal impact

Creating a snapshot usually takes milliseconds, and restoring it takes seconds to minutes, depending on the data volume.

For more details on snapshot limits, restrictions, and their system impacts, refer to Snapshots.

Data processing with external collections

Snapshots can provide stable, point-in-time sources for analytical or validation workloads. For Milvus snapshots, use the milvus-table external collection format instead of reading snapshot files directly as generic Spark input. A Milvus snapshot stores collection metadata, segment manifests, delete logs, and primary-key statistics, so Milvus needs the snapshot metadata JSON and the milvus-table reader to preserve the correct schema and delete semantics.

This workflow creates a queryable external collection over the snapshot data. The main column data remains referenced from the snapshot source, and refresh maps the source StorageV3 manifests into target external segments.

Step 1: Get the snapshot metadata path

Create or choose a snapshot from a normal Milvus collection, and then describe it to get its object-storage location.

from pymilvus import DataType, MilvusClient

client = MilvusClient(
    uri="http://localhost:19530",
    token="root:Milvus"
)

snapshot_info = client.describe_snapshot(
    snapshot_name="analytics_snapshot_20260321",
    collection_name="my_collection",
    include_collection_info=True
)

external_source = f"s3://bucket/{snapshot_info.s3_location}"

Step 2: Create and refresh a milvus-table external collection

Create an external collection whose schema matches the snapshot source collection. Set external_spec.format to "milvus-table", and set each target data field’s external_field to the corresponding source field name.

schema = client.create_schema(
    external_source=external_source,
    external_spec="""{
        "format": "milvus-table",
        "extfs": {
            "cloud_provider": "aws",
            "region": "us-west-2",
            "access_key_id": "YOUR_ACCESS_KEY",
            "access_key_value": "YOUR_SECRET_KEY"
        }
    }""",
)

schema.add_field(
    field_name="id",
    datatype=DataType.INT64,
    is_primary=True,
    external_field="id",
)
schema.add_field(
    field_name="embedding",
    datatype=DataType.FLOAT_VECTOR,
    dim=768,
    external_field="embedding",
)

client.create_collection(
    collection_name="snapshot_external_collection",
    schema=schema,
)

job_id = client.refresh_external_collection(
    collection_name="snapshot_external_collection"
)

After refresh completes, you can create indexes, load the external collection, and run search or query operations against the snapshot-backed view.