feat(backend): Replace MLMD with KFP Server APIs

[HumairAK]

Description of your changes:

This PR removes MLMD as per the KEP here

Resolves: #11760

Overview

Core Change: Replaced MLMD (ML Metadata) service with direct database storage via KFP API server.

This is a major architectural shift that eliminates the external ML Metadata service dependency and consolidates all artifact and task metadata operations directly into the KFP API server with MySQL/database backend.

NOTE: Migration and UI changes will follow this PR. UI will be in a broken state until then. The UI change is a blocker to merge the mlmd-removal branch to master.

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Components Removed

MLMD Service Infrastructure

• metadata-writer component (backend/metadata_writer/)
  • Python-based service that wrote execution metadata to MLMD
  • Dockerfile and all source code removed
• metadata-grpc deployment
  • MLMD gRPC service and envoy proxy
  • All Kustomize manifests and configurations removed
  • DNS configuration patches removed from all deployment variants
• MLMD Client Library (backend/src/v2/metadata/)
  • ~1,800 lines of Go client code removed
  • Client, converter, and test utilities deleted

Deployment Changes

• Removed from all Kustomization variants (standalone, multiuser, kubernetes-native)
• Removed metadata-writer from CI image builds
• Removed metadata service from proxy NO_PROXY configurations
• Removed metadata-grpc port forwarding from integration test workflows

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Components Added

New API Layer

Artifact Service API (backend/api/v2beta1/artifact.proto)

• CRUD Operations:

  • CreateArtifact - Create single artifact
  • GetArtifact - Retrieve artifact by ID
  • ListArtifacts - Query artifacts with filtering
  • BatchCreateArtifacts - Bulk artifact creation

• Artifact Task Operations:

  • CreateArtifactTask - Track artifact usage in tasks
  • ListArtifactTasks - Query artifact-task relationships
  • BatchCreateArtifactTasks - Bulk task-artifact linking

• Generated Clients:

  • Go HTTP client (~4,000 lines)
  • Python HTTP client (~3,500 lines)
  • Swagger documentation

Extended Run Service API (backend/api/v2beta1/run.proto)

• New Task Endpoints:

  • CreateTask - Create pipeline task execution record
  • GetTask - Retrieve task details
  • ListTasks - Query tasks with filtering
  • UpdateTask - Update task status/metadata
  • BatchUpdateTasks - Efficient bulk task updates

• ViewMode Feature:

  • BASIC - Minimal response (IDs, status, timestamps)
  • RUNTIME_ONLY - Include runtime details without full spec
  • FULL - Complete task/run details with spec
  • Reduces payload size for list operations by 80%+

Storage Layer

Artifact Storage (backend/src/apiserver/storage/artifact_store.go)

• Direct MySQL table for artifacts
• Stores: name, URI, type, metadata, custom properties
• Supports filtering by run_id, task_name, artifact_type
• ~300 lines with comprehensive test coverage

Artifact Task Store (backend/src/apiserver/storage/artifact_task_store.go)

• Junction table linking artifacts to tasks
• Tracks: IO type (input/output), producer task, artifact metadata
• Bulk insert optimization for batch operations
• ~400 lines with test coverage

Enhanced Task Store (backend/src/apiserver/storage/task_store.go)

• Expanded from ~500 to ~1,400 lines
• Added task state tracking (PENDING, RUNNING, SUCCEEDED, FAILED, etc.)
• Input/output artifact and parameter tracking
• Pod information (name, namespace, type)
• Batch update support for efficient status synchronization

API Server Implementation

Artifact Server (backend/src/apiserver/server/artifact_server.go)

• Implements all artifact service endpoints
• Request validation and conversion
• Pagination support for list operations
• ~600 lines with 1,000+ lines of tests

Extended Run Server (backend/src/apiserver/server/run_server.go)

• Added task CRUD operation handlers
• ViewMode implementation for optimized responses
• Batch task update endpoint
• ~350 lines of new code with comprehensive tests

Client Infrastructure

KFP API Client (backend/src/v2/apiclient/)

• New client package for driver/launcher to call API server
• OAuth2/OIDC authentication support
• Retry logic and error handling
• Mock implementation for testing
• ~800 lines total

Driver/Launcher Refactoring

Parameter/Artifact Resolution (backend/src/v2/driver/resolver/)

• Extracted resolution logic from monolithic resolve.go (~1,100 lines removed)
• New focused modules:
  • parameters.go - Parameter resolution (~560 lines)
  • artifacts.go - Artifact resolution (~314 lines)
  • resolve.go - Orchestration (~90 lines)
• Improved testability and maintainability

Driver Changes (backend/src/v2/driver/)

• Removed MLMD client dependency
• Added KFP API client for task/artifact operations
• Refactored execution flow to use API server
• Container/DAG execution updated for new storage model

Launcher Changes (backend/src/v2/cmd/launcher-v2/)

• Replaced MLMD calls with API server calls
• Uses batch updater for efficient status reporting
• Artifact publishing through artifact API

Batch Updater (backend/src/v2/component/batch_updater.go)

• Efficient batching mechanism for task updates
• Reduces API calls during execution
• Configurable batch size and flush intervals
• ~250 lines with interfaces for testing

Testing Infrastructure

Test Data Pipelines (backend/src/v2/driver/test_data/)

• 15+ new compiled pipeline YAMLs for integration testing:
  • cache_test.yaml - Cache hit/miss scenarios
  • componentInput.yaml - Input parameter testing
  • k8s_parameters.yaml - Kubernetes-specific features
  • oneof_simple.yaml - Conditional execution
  • nested_naming_conflicts.yaml - Name resolution edge cases
  • Loop iteration scenarios
  • Optional input handling
  • And more...

Test Coverage

• Storage layer: ~650 lines of tests for artifact/task stores
• API server: ~1,700 lines of tests for artifact/run servers
• Driver: ~1,400 lines of new integration tests
• Setup utilities: ~900 lines of test infrastructure

Utility Additions

Scope Path (backend/src/common/util/scope_path.go)

• Hierarchical DAG navigation for nested pipelines
• Tracks execution context through task hierarchy
• Used for parameter/artifact resolution
• ~230 lines with tests

Proto Helpers (backend/src/common/util/proto_helpers.go)

• Conversion utilities for proto messages
• Type-safe helpers for common operations
• ~44 lines

YAML Parser (backend/src/common/util/yaml_parser.go)

• Pipeline spec parsing utilities
• ~108 lines

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Key Behavioral Changes

Artifact Tracking

• Before: Driver writes to MLMD via gRPC, launcher writes execution metadata via metadata-writer
• After: Driver/launcher call artifact API endpoints directly, writes to MySQL

Task State Management

• Before: State inferred from MLMD execution contexts
• After: Explicit task records with status, pod info, I/O tracking in task_store

Performance Optimizations

• ViewMode: List operations can request minimal data, reducing response size dramatically
• Batch Updates: Task status updates batched to reduce API overhead
• Direct DB Access: Eliminates gRPC hop to separate MLMD service

API Response Size

• ListRuns with VIEW_MODE=DEFAULT: ~80% smaller payloads
• Improves UI responsiveness for pipeline listing

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Migration Considerations

Database Schema

• New tables: artifacts, artifact_tasks
• Extended tasks table with new columns
• Proto test golden files updated to reflect new response formats

Backwards Compatibility

• API endpoints maintain backward compatibility
• Existing pipeline specs continue to work
• No changes required to user-facing SDK

Deployment

• Simpler deployment (2 fewer services)
• Reduced resource requirements (no metadata-grpc, metadata-writer pods)
• Fewer network policies needed

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Testing Strategy

Unit Tests

• Comprehensive coverage for all new storage/server components
• Mock implementations for API client
• Isolated testing of resolver logic

Integration Tests

• 15+ compiled test pipelines covering edge cases
• Driver integration tests with real Kubernetes API server
• Task/artifact lifecycle validation

Golden File Updates

• Proto test golden files regenerated
• Reflects new API response structure

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Files Changed Summary

• Total files changed: ~550
• Lines added: ~50,000
• Lines removed: ~15,000
• Net addition: ~35,000 (mostly generated client code and tests)

Breakdown

• Generated API clients (Go/Python): ~15,000 lines
• Test code and test data: ~10,000 lines
• Storage layer implementation: ~2,000 lines
• API server implementation: ~1,500 lines
• Driver/launcher refactoring: ~1,000 lines
• Removed MLMD code: ~15,000 lines

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Risks & Considerations

Testing

• Extensive test coverage added
• Integration tests validate end-to-end flows
• Proto compatibility tests ensure API stability

Performance

• Direct database access should be faster than gRPC → MLMD → DB
• Batch updates reduce API call overhead
• ViewMode optimization for large lists

Operational

• Simpler deployment reduces operational complexity
• Fewer moving parts = fewer failure modes
• All metadata operations auditable through API server logs

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Recommended Follow-up

1. Monitor database performance under load with new artifact tables
2. Consider adding database indexes if artifact queries become slow
3. Document migration path for existing MLMD data (if applicable)
4. Update deployment documentation to reflect MLMD removal
5. Performance benchmarking comparing MLMD vs. direct storage

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Conclusion

This is an architectural improvement that:

• Reduces system complexity
• Improves maintainability
• Maintains API compatibility
• Includes comprehensive testing
• Simplifies deployment

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[HumairAK]

Upgrade Test failures are expected until we add migration logic (to follow this PR). Note also UI changes are not included in this, those too - will follow this pr.

[CarterFendley]

First off, this is amazing! Not sure where you find the time 😂

A couple questions because this overlaps with an area of interest. My understanding is that this PR is reporting / updating the status of tasks (components) directly from the launcher such as here. So to check my understanding, this means that we are moving completely away from the persistence agent, correct? I have been running into issues with the persistence agent at scale & with short lived workflows so I am excited about new approaches.

Secondly, I see the added RPCs to update task state. Are these the counter part to the ones used by the V1 persistence agent to populate tasks table here? If this is the case, should we remove the V2 equivalent which, unless I am mistaken, seems to be currently unused (even before this PR).