milvus-io--milvus/docs/design-docs/design_docs/20260129-add-function-field-design.md

Design Document: Add Function Field Feature

Commit: 513c92d7f2 feat: support add function field (#44444) Author: MrPresent-Han Date: September 2025 Scope: 156 files, +10,129/-3,475 lines

---

1. Overview

1.1 Motivation

Function fields enable users to dynamically add computed/derived fields to existing collections without requiring data re-ingestion. The primary use case is adding BM25 (sparse vector) fields to collections that were originally created with only dense vector fields, enabling hybrid search capabilities post-creation.

1.2 Key Requirements

1. Non-disruptive schema evolution: Add function fields without collection recreation
2. Backward compatibility: Existing segments must remain queryable during and after the transition
3. Consistency guarantees: All components must have a unified view of schema changes
4. Performance: Minimize impact on ongoing read/write operations
5. Backfill support: Optionally compute function outputs for existing data

1.3 Design Principles

• Schema versioning: Every schema change increments a version number for tracking
• Lazy evaluation: Function outputs can be computed on-demand rather than requiring physical backfill
• Write-ahead semantics: Schema changes are durably logged before in-memory state updates
• Graceful degradation: Queries handle missing function field data without crashing

---

2. Architecture Overview

2.1 High-Level Data Flow

┌─────────────────────────────────────────────────────────────────────────────┐
│                           AlterCollectionSchema Request                      │
└─────────────────────────────────────────────────────────────────────────────┘
                                        │
                                        ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                                   PROXY                                      │
│  • Validate request (field types, names, schema version consistency)         │
│  • Check all segments have aligned schema versions                           │
│  • Create alterCollectionSchemaTask and enqueue to DDL queue                 │
│  • Optionally create indexes for new fields                                  │
└─────────────────────────────────────────────────────────────────────────────┘
                                        │
                                        ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                                ROOTCOORD                                     │
│  • Validate function schema (input/output fields, uniqueness)                │
│  • Assign field IDs and function IDs                                         │
│  • Increment schema version                                                  │
│  • Broadcast AlterCollectionMessage to WAL + all virtual channels            │
└─────────────────────────────────────────────────────────────────────────────┘
                                        │
                    ┌───────────────────┼───────────────────┐
                    ▼                   ▼                   ▼
┌──────────────────────────┐ ┌──────────────────────┐ ┌──────────────────────┐
│      STREAMING/WAL       │ │       DATACOORD      │ │      QUERYNODE       │
│ • Flush existing segments│ │ • Track segment      │ │ • SyncSchema to      │
│ • Log schema change      │ │   schema versions    │ │   segments           │
│ • Update in-memory schema│ │ • Trigger backfill   │ │ • Update IDF oracle  │
│ • Validate insert schema │ │   compaction if      │ │ • Rebuild function   │
│   versions               │ │   enabled            │ │   runners            │
└──────────────────────────┘ └──────────────────────┘ └──────────────────────┘
                                        │
                                        ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                             DATANODE (Backfill)                              │
│  • Execute backfill compaction for segments with outdated schema             │
│  • Compute function outputs (e.g., BM25 sparse vectors)                      │
│  • Write new binlogs with function field data                                │
│  • Update BM25 statistics                                                    │
└─────────────────────────────────────────────────────────────────────────────┘

2.2 Component Responsibilities

Component	Responsibility
Proxy	API gateway, request validation, schema consistency checks, task orchestration
RootCoord	Schema management, ID assignment, metadata persistence, broadcast coordination
Streaming/WAL	Durable schema change logging, write consistency, version mismatch detection
DataCoord	Segment metadata tracking, backfill compaction policy, schema version monitoring
DataNode	Backfill compaction execution, function output computation, binlog writing
QueryNode	Schema synchronization, function runner management, IDF oracle updates
Segcore (C++)	Low-level schema sync, field accessibility checks, data storage

---

3. API Design

3.1 New RPC Endpoints

RootCoord: AlterCollectionSchema

rpc AlterCollectionSchema(AlterCollectionSchemaRequest) returns (AlterCollectionSchemaResponse) {}

Request Structure:

• db_name: Database name
• collection_name: Collection name
• action: Schema alteration action (currently only ADD supported)
• add_request: Contains field infos and function schemas to add
• do_physical_backfill: Whether to backfill existing data

Constraints:

• Only one function field can be added per request (current limitation)
• All segments must have consistent schema versions before alteration

QueryNode: UpdateIndex

rpc UpdateIndex(UpdateIndexRequest) returns (common.Status) {}

message UpdateIndexRequest {
    common.MsgBase base = 1;
    int64 collectionID = 2;
    oneof Action {
        AddIndex add_index_request = 3;
        DropIndex drop_index_request = 4;
    }
}

3.2 Proto Message Changes

Schema Versioning

Multiple message types now include schema_version for tracking:

// data_coord.proto
message AllocSegmentRequest {
    int32 schema_version = 7;
}

message SegmentInfo {
    int32 schema_version = 33;
}

// messages.proto
message InsertMessageHeader {
    int32 schema_version = 3;
}

message CreateSegmentMessageHeader {
    int32 schema_version = 8;
}

Backfill Compaction

// data_coord.proto
enum CompactionType {
    BackfillCompaction = 12;
}

message CompactionPlan {
    repeated schema.FunctionSchema functions = 30;
}

message CompactionTask {
    repeated schema.FunctionSchema diff_functions = 29;
}

Index Versioning

// index_coord.proto
message FieldIndex {
    int32 min_schema_version = 4;
}

Streaming Error Handling

// streaming.proto
enum StreamingCode {
    STREAMING_CODE_SCHEMA_VERSION_MISMATCH = 14;
}

---

4. Component Design Details

4.1 Proxy Layer

File: internal/proxy/impl.go, internal/proxy/task.go

AlterCollectionSchema Flow

1. Health Check
      │
      ▼
2. DescribeCollection (get current schema)
      │
      ▼
3. Schema Version Consistency Check
   • GetCollectionStatistics
   • Verify SchemaVersionConsistencyProportion == 100%
      │
      ▼
4. Create alterCollectionSchemaTask
      │
      ▼
5. Enqueue to DDL Queue
      │
      ▼
6. PreExecute: Validate fields and function schema
   • Check max field count
   • Check no duplicate names
   • Validate data types
   • Check not system field
      │
      ▼
7. Execute: Call RootCoord.AlterCollectionSchema
      │
      ▼
8. Post-Execute: Create indexes if configured

Schema Version Consistency Check

Before allowing schema changes, the proxy validates that all segments have aligned schema versions:

stats, _ := s.GetCollectionStatistics(ctx, collectionID)
proportion := stats[common.SchemaVersionConsistencyProportionKey]
if proportion != "100" {
    return errors.New("segments have inconsistent schema versions")
}

4.2 RootCoord Layer

File: internal/rootcoord/ddl_callbacks_alter_collection_schema.go

Schema Change Processing

1. Acquire broadcast lock on collection
      │
      ▼
2. Retrieve current collection metadata
      │
      ▼
3. Validate request:
   • Exactly one function schema
   • Valid field schemas
   • No duplicate field names
   • Function name doesn't exist
      │
      ▼
4. Assign IDs:
   • Field IDs from nextFieldID(coll)
   • Function ID from nextFunctionID(coll)
   • Resolve field names → field IDs for function I/O
      │
      ▼
5. Construct new schema:
   • Copy existing fields and functions
   • Append new fields and function
   • Increment schema version
   • Set DoPhysicalBackfill flag
      │
      ▼
6. Broadcast AlterCollectionMessage:
   • Send to control channel
   • Send to all virtual channels

ID Assignment

// Field ID assignment
func nextFieldID(coll *model.Collection) int64 {
    maxFieldID := findMaxFieldID(coll.Fields, coll.StructArraySubFields)
    return maxFieldID + 1
}

// Function ID assignment
func nextFunctionID(coll *model.Collection) int64 {
    maxFunctionID := common.StartOfUserFunctionID
    for _, fn := range coll.Functions {
        if fn.ID > maxFunctionID {
            maxFunctionID = fn.ID
        }
    }
    return maxFunctionID + 1
}

4.3 Streaming/WAL Layer

Files: internal/streamingnode/server/wal/interceptors/shard/

Schema Version Validation

The shard interceptor validates schema versions at write time:

func handleInsertMessage(ctx context.Context, msg InsertMessage) error {
    schemaVersion := msg.Header.GetSchemaVersion()
    correctVersion, err := shardManager.CheckIfCollectionSchemaVersionMatch(
        msg.Header.GetCollectionId(),
        schemaVersion,
    )
    if err != nil {
        return status.NewSchemaVersionMismatch(
            "schema version mismatch, input: %d, collection: %d",
            schemaVersion, correctVersion,
        )
    }
    // Process insert...
}

Schema Change Ordering (Critical)

Schema changes follow strict ordering to maintain consistency:

func handleAlterCollection(ctx context.Context, msg AlterCollectionMessage) error {
    // 1. FLUSH existing segments FIRST (creates checkpoint)
    if messageutil.IsSchemaChange(header) {
        segmentIDs, _ := flushSegments(ctx, collectionID)
        header.FlushedSegmentIds = segmentIDs
    }

    // 2. APPEND to WAL (durable record)
    msgID, err := appendOp(ctx, msg)
    if err != nil {
        return err
    }

    // 3. UPDATE in-memory state LAST (after WAL success)
    alterCollectionMsg := message.AsImmutableAlterCollectionMessageV2(msg)
    if err := shardManager.AlterCollection(alterCollectionMsg); err != nil {
        panic("failed to alter collection after WAL append")
    }
}

Why panic() is Used (Critical Design Decision):

The panic at step 3 is intentional and represents an unrecoverable state where:

1. WAL-Memory Inconsistency: The schema change has been durably written to WAL but failed to apply to in-memory state. This creates a dangerous inconsistency where:

   • The WAL contains the new schema version
   • In-memory state still has the old schema version
   • Subsequent writes would be validated against the wrong schema

2. Why Alternatives Don't Work:

   • Retry: Cannot retry because WAL append succeeded—retrying would create duplicate schema change entries
   • Rollback: Cannot rollback WAL append (write-ahead log is append-only)
   • Ignore: Would allow writes with mismatched schema versions, causing data corruption
   • Flag for Manual Intervention: Would leave the node in a zombie state serving stale schema

3. Recovery Process After Panic:

   • Node crashes and restarts
   • On restart, node replays WAL from last checkpoint
   • Replayed AlterCollectionMessage updates in-memory state correctly
   • Node reaches consistent state (WAL and memory both have new schema)
   • Service resumes with correct schema version

4. Consistency Guarantees:

   • Crash-recovery ensures WAL is the source of truth
   • Other nodes will also replay WAL and converge to same schema
   • No data corruption occurs (all flushed segments have old schema)
   • New segments will be created with new schema after recovery

This ordering ensures:

• All old-schema data is flushed before schema change
• Schema change is durably recorded before being visible
• System can recover to consistent state after crash
• Panic prevents silent schema inconsistencies that would corrupt data

4.4 DataCoord Layer

Files: internal/datacoord/meta.go, internal/datacoord/compaction_policy_backfill.go

Segment Schema Version Tracking

Each segment tracks its schema version:

type SegmentInfo struct {
    // ... other fields
    SchemaVersion int32
}

The DataCoord calculates schema consistency metrics:

func GetCollectionStatistics(ctx context.Context, req Request) Response {
    collectionSchemaVersion := collection.Schema.GetVersion()
    segments := meta.SelectSegments(ctx, WithCollection(req.CollectionID))

    consistentCount := 0
    for _, segment := range segments {
        if segment.GetSchemaVersion() == collectionSchemaVersion {
            consistentCount++
        }
    }

    proportion := float64(consistentCount) / float64(len(segments)) * 100.0
    return Response{
        Stats: map[string]string{
            SchemaVersionConsistencyProportionKey: fmt.Sprintf("%.2f", proportion),
        },
    }
}

Backfill Compaction Policy

Trigger Conditions:

1. Collection has DoPhysicalBackfill = true
2. Segment's SchemaVersion < Collection.SchemaVersion
3. Segment is healthy, flushed, not compacting, not importing, visible

Policy Flow:

func (p *backfillCompactionPolicy) Trigger(ctx context.Context) ([]CompactionView, error) {
    for _, collection := range collections {
        segments := getEligibleSegments(collection.ID)

        for _, segment := range segments {
            if segment.SchemaVersion < collection.SchemaVersion {
                if collection.DoPhysicalBackfill {
                    // Get schema diff to identify new functions
                    oldSchema := getSchemaByVersion(segment.SchemaVersion)
                    funcDiff := util.SchemaDiff(oldSchema, collection.Schema)

                    // Create backfill compaction view
                    views = append(views, BackfillSegmentsView{
                        segmentID: segment.ID,
                        funcDiff:  funcDiff,
                    })
                } else {
                    // Just update metadata (no physical backfill)
                    segment.SchemaVersion = collection.SchemaVersion
                }
            }
        }
    }
    return views, nil
}

4.5 DataNode Layer (Backfill Compactor)

File: internal/datanode/compactor/backfill_compactor.go

Backfill Execution Pipeline

┌─────────────────────────────────────────────────────────────────┐
│                    Backfill Compaction Pipeline                  │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  1. Pre-Validation                                               │
│     • Exactly one segment in plan                                │
│     • Field binlogs present                                      │
│     • Exactly one backfill function                              │
│     • FunctionRunner validates successfully                      │
│                                                                  │
│  2. Read Input Data                                              │
│     • Read input field binlogs (e.g., varchar for BM25)          │
│     • Decompress and parse via BinlogRecordReader                │
│     • Build input data array                                     │
│                                                                  │
│  3. Execute Function                                             │
│     • Run FunctionRunner.BatchRun() on input data                │
│     • For BM25: Compute sparse float vectors                     │
│     • Build InsertData with function outputs                     │
│                                                                  │
│  4. Write Output                                                 │
│     • Create PackedWriter for new field binlogs                  │
│     • Allocate new log IDs                                       │
│     • Write records to object storage                            │
│                                                                  │
│  5. Update Statistics (BM25)                                     │
│     • Serialize BM25 stats (term frequencies)                    │
│     • Write to dedicated BM25 stats log files                    │
│                                                                  │
│  6. Merge Logs                                                   │
│     • Combine new function field binlogs with original binlogs   │
│     • Create FieldBinlog entries with sizes                      │
│                                                                  │
│  7. Return Result                                                │
│     • CompactionPlanResult with merged logs                      │
│     • Segment ID, row count, BM25 logs                           │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Performance Tracking

type backfillMetrics struct {
    getInputDataDuration time.Duration
    executeBM25Duration  time.Duration
    writeRecordDuration  time.Duration
    updateStatsDuration  time.Duration
}

4.6 QueryNode Layer

Files: internal/querynodev2/delegator/delegator.go, internal/querynodev2/pipeline/embedding_node.go

Schema Update Flow

func (sd *shardDelegator) UpdateSchema(ctx context.Context, schema *schemapb.CollectionSchema) error {
    // Update collection manager
    sd.collection.UpdateSchema(schema)

    // Update BM25 function runners
    sd.updateBM25Functions(schema, ctx)

    // Propagate to all segments
    return sd.propagateSchemaToSegments(ctx, schema)
}

BM25 Function Detection

func (sd *shardDelegator) updateBM25Functions(schema *schemapb.CollectionSchema, ctx context.Context) {
    // Get current BM25 output field IDs
    currentOutputFields := getCurrentBM25OutputFields(sd.schema)

    // Get new BM25 output field IDs
    newOutputFields := getBM25OutputFields(schema)

    // Find only NEW functions (not in current set)
    for fieldID := range newOutputFields {
        if _, exists := currentOutputFields[fieldID]; !exists {
            // Create function runner for new BM25 function
            runner := createFunctionRunner(schema, fieldID)
            sd.functionRunners[fieldID] = runner
            sd.analyzerRunners[inputFieldID] = runner
            sd.isBM25Field[fieldID] = true
        }
    }

    // Update or create IDF Oracle
    if sd.idfOracle == nil {
        sd.idfOracle = NewIDFOracle(schema.Functions)
    } else {
        sd.idfOracle.UpdateCurrent(schema.Functions)
    }
}

Embedding Node Dynamic Schema Handling

The embedding node dynamically adapts to schema changes:

type embeddingNode struct {
    curSchema       *schemapb.CollectionSchema
    functionRunners map[int64]function.FunctionRunner  // keyed by function ID
}

func (en *embeddingNode) Operate(msgs []flowgraph.Msg) []flowgraph.Msg {
    for _, msg := range msgs {
        insertMsg := msg.(*insertNodeMsg)

        // Check for schema update
        if insertMsg.schema != nil && insertMsg.schema != en.curSchema {
            en.curSchema = insertMsg.schema
            en.setupFunctionRunners()  // Rebuild runners for new schema
        }

        // Process with current function runners
        if len(en.functionRunners) > 0 {
            en.processWithFunctions(insertMsg)
        }
    }
}

4.7 Segcore (C++) Layer

Files: internal/core/src/common/Schema.h, internal/core/src/segcore/SegmentInterface.h

Schema Synchronization

New SyncSchema() operation allows runtime schema updates:

class SegmentInternalInterface {
public:
    void SyncSchema(SchemaPtr new_schema) {
        std::unique_lock<std::shared_mutex> lock(sch_mutex_);
        if (new_schema->get_schema_version() > schema_->get_schema_version()) {
            schema_ = new_schema;
        }
    }

protected:
    SchemaPtr schema_;
    mutable std::shared_mutex sch_mutex_;  // Thread-safe schema access
};

Field Accessibility Checks

New method to check if a field is accessible (either has data or index):

bool FieldAccessible(FieldId field_id) const {
    return HasFieldData(field_id) || HasIndex(field_id);
}

Safe Search Handling

Vector search operations now gracefully handle missing function fields:

std::unique_ptr<SearchResult> AsyncSearch(SearchInfo& search_info) {
    FieldId target_field = search_info.GetFieldId();

    // Check if function field is accessible
    if (!segment->FieldAccessible(target_field)) {
        // Return empty result instead of crashing
        return std::make_unique<SearchResult>(
            make_empty_search_result(search_info)
        );
    }

    // Proceed with normal search
    return DoSearch(search_info);
}

---

5. Schema Diff Utility

File: internal/util/schema_util.go

5.1 Data Structures

type FieldDiff struct {
    Added []*schemapb.FieldSchema  // Fields in new but not in old
}

type FuncDiff struct {
    Added []*schemapb.FunctionSchema  // Functions in new but not in old
}

5.2 Comparison Logic

func SchemaDiff(oldSchema, newSchema *schemapb.CollectionSchema) (*FieldDiff, *FuncDiff, error) {
    if oldSchema == nil || newSchema == nil {
        return nil, nil, errors.New("schema cannot be nil")
    }

    fieldDiff := compareFields(oldSchema.Fields, newSchema.Fields)
    funcDiff := compareFunctions(oldSchema.Functions, newSchema.Functions)

    return fieldDiff, funcDiff, nil
}

func compareFunctions(oldFuncs, newFuncs []*schemapb.FunctionSchema) *FuncDiff {
    // Build map of old function IDs for O(1) lookup
    oldMap := make(map[int64]bool)
    for _, fn := range oldFuncs {
        if fn != nil {
            oldMap[fn.Id] = true
        }
    }

    // Find functions in new but not in old
    var added []*schemapb.FunctionSchema
    for _, fn := range newFuncs {
        if fn != nil && !oldMap[fn.Id] {
            added = append(added, fn)
        }
    }

    return &FuncDiff{Added: added}
}

---

6. IDF Oracle Updates

File: internal/querynodev2/delegator/idf_oracle.go

6.1 UpdateCurrent Method

New method to handle function field additions:

func (oracle *IDFOracle) UpdateCurrent(functions []*schemapb.FunctionSchema) {
    oracle.mu.Lock()
    defer oracle.mu.Unlock()

    for _, fn := range functions {
        if fn.Type == schemapb.FunctionType_BM25 {
            outputFieldID := fn.OutputFieldIds[0]

            // Initialize stats for new BM25 fields
            if _, exists := oracle.currentStats[outputFieldID]; !exists {
                oracle.currentStats[outputFieldID] = NewBM25Stats()
            }
        }
    }
}

6.2 Stats Merging

Enhanced merging for backfilled segments:

func (seg *segmentStats) MergeStats(newStats bm25Stats) bool {
    seg.mu.Lock()
    defer seg.mu.Unlock()

    // Load from disk if needed
    if seg.stats == nil && seg.statsPath != "" {
        seg.stats = loadStatsFromLocalNoLock(seg.statsPath)
    }

    // Merge stats
    for fieldID, newFieldStats := range newStats {
        if oldStats, exists := seg.stats[fieldID]; exists {
            oldStats.Merge(newFieldStats)
        } else {
            seg.stats[fieldID] = newFieldStats.Clone()
        }
    }

    return seg.activated  // Return whether to update current stats
}

---

7. Index Service Changes

File: internal/datacoord/index_service.go

7.1 MinSchemaVersion Tracking

Indexes now track the minimum schema version required:

func (s *Server) CreateIndex(ctx context.Context, req *indexpb.CreateIndexRequest) error {
    // Get latest schema
    schema, _ := s.broker.DescribeCollectionInternal(ctx, collectionID, typeutil.MaxTimestamp)

    index := &model.Index{
        // ... other fields
        MinSchemaVersion: schema.GetVersion(),  // NEW: Track schema version
    }

    // Broadcast to all channels including control channel
    channels := append([]string{streaming.WAL().ControlChannel()}, vchannels...)

    return s.saveAndBroadcastIndex(ctx, index, channels)
}

---

8. Error Handling

8.1 Schema Version Mismatch

New streaming error code for version conflicts:

const STREAMING_CODE_SCHEMA_VERSION_MISMATCH = 14

func (e *StreamingError) IsSchemaVersionMismatch() bool {
    return e.Code == streamingpb.StreamingCode_STREAMING_CODE_SCHEMA_VERSION_MISMATCH
}

func (e *StreamingError) IsUnrecoverable() bool {
    return e.Code == STREAMING_CODE_UNRECOVERABLE ||
           e.IsReplicateViolation() ||
           e.IsTxnUnavailable() ||
           e.IsSchemaVersionMismatch()  // Schema mismatches are unrecoverable
}

8.2 Graceful Degradation

Segments without function field data return empty results rather than failing:

// In VectorSearchNode
if (!segment->FieldAccessible(target_vector_field_id)) {
    return make_empty_search_result(num_queries, topK);
}

---

9. Configuration

9.1 New Parameters

// component_param.go
type BackfillConfig struct {
    // Whether backfill compaction is enabled
    Enabled bool

    // Maximum concurrent backfill tasks
    MaxConcurrentTasks int

    // Backfill batch size
    BatchSize int
}

---

10. Sequence Diagrams

10.1 Add Function Field Flow

Client              Proxy           RootCoord        Streaming         DataCoord        QueryNode
  │                   │                 │                │                 │                │
  │ AlterCollectionSchema               │                │                 │                │
  ├──────────────────►│                 │                │                 │                │
  │                   │ DescribeCollection              │                 │                │
  │                   ├────────────────►│                │                 │                │
  │                   │◄────────────────┤                │                 │                │
  │                   │                 │                │                 │                │
  │                   │ GetCollectionStatistics         │                 │                │
  │                   ├─────────────────────────────────────────────────►│                │
  │                   │◄─────────────────────────────────────────────────┤                │
  │                   │ (check schema version consistency = 100%)         │                │
  │                   │                 │                │                 │                │
  │                   │ AlterCollectionSchema            │                 │                │
  │                   ├────────────────►│                │                 │                │
  │                   │                 │                │                 │                │
  │                   │                 │ Broadcast AlterCollectionMessage │                │
  │                   │                 ├───────────────►│                 │                │
  │                   │                 │                │                 │                │
  │                   │                 │       (handleAlterCollection)   │                │
  │                   │                 │       • Flush existing segments │                │
  │                   │                 │       • Append to WAL           │                │
  │                   │                 │       • Update in-memory state  │                │
  │                   │                 │                │                 │                │
  │                   │                 │                │ Forward to      │                │
  │                   │                 │                ├────────────────►│                │
  │                   │                 │                │  DataCoord      │                │
  │                   │                 │                │                 │                │
  │                   │                 │                │ UpdateSchema    │                │
  │                   │                 │                ├────────────────────────────────►│
  │                   │                 │                │                 │                │
  │                   │◄────────────────┤                │                 │                │
  │◄──────────────────┤                 │                │                 │                │
  │                   │                 │                │                 │                │

Key Points:

1. RootCoord broadcasts a single AlterCollectionMessage to the Streaming node
2. The Streaming node's handleAlterCollection function internally performs three steps in strict order:
   • Step 1: Flush existing segments (creates checkpoint with old schema)
   • Step 2: Append schema change to WAL (durability)
   • Step 3: Update in-memory state (visibility)
3. This ordering (described in Section 4.3) ensures crash consistency and prevents mixed-schema segments
4. The message is then forwarded to DataCoord and QueryNode for metadata updates

10.2 Backfill Compaction Flow

DataCoord                 DataNode                  ObjectStorage
    │                         │                          │
    │ (Backfill policy detects                           │
    │  segment with old schema)                          │
    │                         │                          │
    │ SubmitBackfillCompaction│                          │
    ├────────────────────────►│                          │
    │                         │                          │
    │                         │ Read input field binlogs │
    │                         ├─────────────────────────►│
    │                         │◄─────────────────────────┤
    │                         │                          │
    │                         │ Execute BM25 function    │
    │                         │ (compute sparse vectors) │
    │                         │                          │
    │                         │ Write output binlogs     │
    │                         ├─────────────────────────►│
    │                         │◄─────────────────────────┤
    │                         │                          │
    │                         │ Write BM25 stats         │
    │                         ├─────────────────────────►│
    │                         │◄─────────────────────────┤
    │                         │                          │
    │ CompactionPlanResult    │                          │
    │◄────────────────────────┤                          │
    │                         │                          │
    │ Update segment metadata │                          │
    │ (schemaVersion = new)   │                          │
    │                         │                          │

---

11. Key Design Decisions

11.1 Schema Versioning Strategy

Decision: Use monotonically increasing integer version numbers.

Rationale:

• Simple comparison (<, >, ==)
• No timestamp synchronization issues
• Easy to track in all components
• Supports partial ordering of schema changes

11.2 Physical vs Logical Backfill

Decision: Support both modes via DoPhysicalBackfill flag.

Physical Backfill (DoPhysicalBackfill = true):

• Computes and stores function outputs
• Higher storage cost
• Better query performance
• Required for complex functions

Logical Backfill (DoPhysicalBackfill = false):

• Only updates metadata
• Function outputs computed on-demand
• Lower storage cost
• Higher query latency

11.3 Single Function Per Request

Decision: Limit to one function field addition per request.

Rationale:

• Simplifies validation and rollback
• Easier to track progress
• Reduces complexity of partial failures
• Can be relaxed in future versions

11.4 Write-Ahead Schema Changes

Decision: Flush segments before schema changes, log to WAL before updating in-memory state.

Rationale:

• Ensures no mixed-schema segments
• Provides durability guarantees
• Enables crash recovery
• Maintains consistency across components

11.5 Graceful Search Degradation

Decision: Return empty results for inaccessible function fields instead of failing.

Rationale:

• Maintains availability during transitions
• Allows gradual backfill
• Better user experience
• Consistent with eventual consistency model

---

12. Testing Strategy

12.1 Unit Tests

Component	Test File	Coverage
Schema Util	internal/util/schema_util_test.go	Field diff, function diff, nil handling
Backfill Policy	internal/datacoord/compaction_policy_backfill_test.go	Trigger conditions, segment selection
Backfill Task	internal/datacoord/compaction_task_backfill_test.go	State machine, progress tracking
Backfill Compactor	internal/datanode/compactor/backfill_compactor_test.go	Execution pipeline, error handling

12.2 Integration Tests

• End-to-end function field addition
• Hybrid search with backfilled BM25 fields
• Schema version consistency during concurrent operations
• Recovery after crash during schema change

---

13. Future Enhancements

13.1 Multi-Function Addition

Support adding multiple function fields in a single request for efficiency.

13.2 Function Field Modification

Support modifying function parameters without full re-computation.

13.3 Function Field Deletion

Support removing function fields with proper cleanup of binlogs and indexes.

13.4 Incremental Backfill

Support pausing and resuming backfill operations for large collections.

13.5 Custom Function Types

Extend beyond BM25 to support user-defined function types.

---

14. References

• Milvus Architecture: docs/architecture.md
• Segcore Pipeline: docs/segcore-pipeline.md
• Reduce Mechanism: docs/reduce-mechanism.md
• BM25 Algorithm: pkg/util/bm25/bm25.go