milvus/docs/design_docs/entitiy_level_ttl.md

Entity-level TTL Design

Background

Currently, Milvus supports collection-level TTL for data expiration, but does not support defining an independent expiration time for individual entities (rows). As application scenarios become more diverse, for example:

• Data from different tenants or businesses stored in the same collection but with different lifecycles;
• Hot and cold data mixed together, where short-lived data should be cleaned automatically while long-term data is retained;
• IoT / logging / MLOps data that requires record-level retention policies;

Relying solely on collection-level TTL can no longer satisfy these requirements. If users want to retain only part of the data, they must periodically perform upsert operations to refresh the timestamps of those entities. This approach is unintuitive and increases operational and maintenance costs.

Therefore, Entity-level TTL becomes a necessary feature.

Related issues:

• milvus-io/milvus#45917
• milvus-io/milvus#45923

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Design Principles

• Fully compatible with existing collection-level TTL behavior.
• Allow users to choose whether to enable entity-level TTL.
• User-controllable: support explicit declaration in schema or transparent system management.
• Minimize changes to compaction and query logic; expiration is determined only by the TTL column and write timestamp.
• Support dynamic upgrade for existing collections.

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Basic Approach

Milvus already supports the TIMESTAMPTZ data type. Entity TTL information will therefore be stored in a field of this type.

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Design Details

Entity-level TTL is implemented by allowing users to explicitly add a TIMESTAMPTZ column in the schema and mark it in collection properties:

"ttl_field": "ttl"

Here, ttl is the name of the column that stores TTL information. This mechanism is mutually exclusive with collection-level TTL.

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Terminology and Conventions

• ExpireAt : The value stored in the TTL field, representing the absolute expiration timestamp of an entity (UTC by default if no timezone is specified).
• Collection-level TTL : The existing mechanism where retention duration is defined at the collection level (e.g., retain 30 days).
• insert_ts / mvcc_ts : Existing Milvus write or MVCC timestamps, used as fallback when needed.
• expirQuantiles : A time point corresponding to a certain percentile of expired data within a segment, used to quickly determine whether compaction should be triggered.

Example:

• 20% of data expires at time t1
• 40% of data expires at time t2

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1. Collection Properties and Constraints

• Only fields with DataType == TIMESTAMPTZ can be configured as a TTL field.
• Mutually exclusive with collection-level TTL:
  • If collection-level TTL is enabled, specifying a TTL field is not allowed.
  • Collection-level TTL must be disabled first.
• One TTL field per collection:
  • A collection may contain multiple TIMESTAMPTZ fields, but only one can be designated as the TTL field.

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2. Storage Semantics

• Unified convention: the TTL field stores an absolute expiration time (ExpireAt).
• Duration-based TTL is not supported.
• NULL value semantics:
  • A NULL TTL value means the entity never expires.

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3. Compatibility Rules

Existing Collections

For an existing collection to enable entity-level TTL:

1. Disable collection-level TTL using AlterCollection.
2. Add a new TIMESTAMPTZ field using AddField.
3. Update collection properties via AlterCollection to mark the new field as the TTL field.

If historical data should also have expiration times, users must perform an upsert operation to backfill the TTL field.

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4. SegmentInfo Extension and Compaction Trigger

4.1 SegmentInfo Metadata Extension

A new field expirQuantiles is added to the segment metadata:

message SegmentInfo {
  int64 ID = 1;
  int64 collectionID = 2;
  int64 partitionID = 3;
  string insert_channel = 4;
  int64 num_of_rows = 5;
  common.SegmentState state = 6;
  int64 max_row_num = 7 [deprecated = true]; // deprecated, we use the binary size to control the segment size but not a estimate rows.
  uint64 last_expire_time = 8;
  msg.MsgPosition start_position = 9;
  msg.MsgPosition dml_position = 10;
  // binlogs consist of insert binlogs
  repeated FieldBinlog binlogs = 11;
  repeated FieldBinlog statslogs = 12;
  // deltalogs consists of delete binlogs. FieldID is not used yet since delete is always applied on primary key
  repeated FieldBinlog deltalogs = 13;
  bool createdByCompaction = 14;
  repeated int64 compactionFrom = 15;
  uint64 dropped_at = 16; // timestamp when segment marked drop
  // A flag indicating if:
  // (1) this segment is created by bulk insert, and
  // (2) the bulk insert task that creates this segment has not yet reached `ImportCompleted` state.
  bool is_importing = 17;
  bool is_fake = 18;

  // denote if this segment is compacted to other segment.
  // For compatibility reasons, this flag of an old compacted segment may still be False.
  // As for new fields added in the message, they will be populated with their respective field types' default values.
  bool compacted = 19;

  // Segment level, indicating compaction segment level
  // Available value: Legacy, L0, L1, L2
  // For legacy level, it represent old segment before segment level introduced
  // so segments with Legacy level shall be treated as L1 segment
  SegmentLevel level = 20;
  int64 storage_version = 21;

  int64 partition_stats_version = 22;
  // use in major compaction, if compaction fail, should revert segment level to last value
  SegmentLevel last_level = 23;
  // use in major compaction, if compaction fail, should revert partition stats version to last value 
  int64 last_partition_stats_version = 24;

  // used to indicate whether the segment is sorted by primary key.
  bool is_sorted = 25;

  // textStatsLogs is used to record tokenization index for fields.
  map<int64, TextIndexStats> textStatsLogs = 26;
  repeated FieldBinlog bm25statslogs = 27;

  // This field is used to indicate that some intermediate state segments should not be loaded.
  // For example, segments that have been clustered but haven't undergone stats yet.
  bool is_invisible = 28;


  // jsonKeyStats is used to record json key index for fields.
  map<int64, JsonKeyStats> jsonKeyStats = 29;
    // This field is used to indicate that the segment is created by streaming service.
  // This field is meaningful only when the segment state is growing.
  // If the segment is created by streaming service, it will be a true.
  // A segment generated by datacoord of old arch, will be false.
  // After the growing segment is full managed by streamingnode, the true value can never be seen at coordinator.
  bool is_created_by_streaming = 30;
  bool is_partition_key_sorted = 31;

  // manifest_path stores the fullpath of LOON manifest file of segemnt data files.
  // we could keep the fullpath since one segment shall only have one active manifest
  // and we could keep the possiblity that manifest stores out side of collection/partition/segment path
  string manifest_path = 32;

  // expirQuantiles records the expiration timestamps of the segment
  // at the 20%, 40%, 60%, 80%, and 100% data distribution levels
  repeated int64 expirQuantiles = 33;
}

Meaning:

• expirQuantiles: The expiration timestamps corresponding to the 20%, 40%, 60%, 80%, and 100% percentiles of data within the segment.

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4.2 Metadata Writing

• Statistics are collected only during compaction .
• expirQuantiles is computed during sort or mix compaction tasks.
• For streaming segments, sort compaction is required as the first step, making this approach sufficient.

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4.3 Compaction Trigger Strategy

• Based on a configured expired-data ratio, select the corresponding percentile from expirQuantiles (rounded down).
• Compare the selected expiration time with the current time.
• If the expiration condition is met, trigger a compaction task.

Special cases:

• If expirQuantiles is NULL, the segment is treated as non-expiring.
• For old segments without a TTL field, expiration logic is skipped.
• Subsequent upsert operations will trigger the corresponding L0 compaction.

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5. Query / Search Logic

• Each query is executed with an MVCC timestamp assigned by Milvus.
• When loading a collection, the system records which field is configured as the TTL field.

During query execution, expired data is filtered by comparing the TTL field value with the MVCC timestamp inside mask_with_timestamps.

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PyMilvus Example

1. Create Collection

Specify the TTL field in the schema:

schema = client.create_schema(auto_id=False, description="test entity ttl")
schema.add_field("id", DataType.INT64, is_primary=True)
schema.add_field("ttl", DataType.TIMESTAMPTZ, nullable=True)
schema.add_field("vector", DataType.FLOAT_VECTOR, dim=dim)

prop = {"ttl_field": "ttl"}
client.create_collection(
    collection_name,
    schema=schema,
    enable_dynamic_field=True,
    properties=prop,
)

---

2. Insert Data

Insert data the same way as a normal TIMESTAMPTZ field:

rows = [
    {"id": 0, "vector": [random.random() for _ in range(dim)], "ttl": None},
    {"id": 1, "vector": [random.random() for _ in range(dim)], "ttl": None},
    {"id": 2, "vector": [random.random() for _ in range(dim)], "ttl": None},
    {"id": 3, "vector": [random.random() for _ in range(dim)], "ttl": "2025-12-31T00:00:00Z"},
    {"id": 4, "vector": [random.random() for _ in range(dim)], "ttl": "2025-12-31T01:00:00Z"},
    {"id": 5, "vector": [random.random() for _ in range(dim)], "ttl": "2025-12-31T02:00:00Z"},
    {"id": 6, "vector": [random.random() for _ in range(dim)], "ttl": "2025-12-31T03:00:00Z"},
    {"id": 7, "vector": [random.random() for _ in range(dim)], "ttl": "2025-12-31T04:00:00Z"},
    {"id": 8, "vector": [random.random() for _ in range(dim)], "ttl": "2025-12-31T23:59:59Z"},
]

insert_result = client.insert(collection_name, rows)
client.flush(collection_name)

---

3. Index and Load

Index creation and loading are unaffected. Indexes can still be built on the TTL field if needed.

index_params = client.prepare_index_params()
index_params.add_index(
    field_name="vector",
    index_type="IVF_FLAT",
    index_name="vector",
    metric_type="L2",
    params={"nlist": 128},
)
client.create_index(collection_name, index_params=index_params)

client.load_collection(collection_name)

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4. Search / Query

Use queries with different timestamps to validate expiration behavior:

query_expr = "id > 0"
res = client.query(
    collection_name,
    query_expr,
    output_fields=["id", "ttl"],
    limit=100,
)
print(res)