graphimplementationreadinessdependenciestracking

Status: DraftAuthor: Platform TeamCreated: Nov 16, 2025Updated: Nov 16, 2025

MEMO-081: Graph Implementation Readiness Assessment

Status: Draft Author: Platform Team Created: 2025-11-16 Updated: 2025-11-16

Executive Summary

This memo assesses the prism-data-layer's readiness to implement the massive-scale graph features defined in RFC-057 through RFC-061. After analyzing the five graph RFCs against the current prism-data-layer codebase, I've identified critical foundational gaps that must be addressed before graph implementation can proceed.

Bottom Line: The prism-data-layer currently lacks the fundamental infrastructure needed for 100B-scale graph operations. We need to implement 15 foundational components across 4 phases before we can begin graph feature development.

Timeline: 16-20 weeks of foundational work before graph implementation can begin.

Risk: Attempting to implement graph features without these foundations will result in unbuildable prototypes and architectural debt.

RFC Requirements Summary

RFC-057: Massive-Scale Graph Sharding (100B Vertices)

Core Requirements:

Three-tier hierarchical sharding (cluster → proxy → partition)
Support for 1000+ lightweight compute nodes
64 partitions per proxy (16,000 partitions cluster-wide)
Vertex ID format: {cluster_id}:{proxy_id}:{partition_id}:{local_vertex_id}
Opaque vertex ID routing tables for rebalancing flexibility
Dynamic partition rebalancing without downtime
100M vertices per proxy, 1.56M vertices per partition

Scale Targets:

100 billion vertices
10 trillion edges
30 TB total memory across cluster
Sub-second query latency for common traversals

RFC-058: Multi-Level Graph Indexing

Core Requirements:

Four-tier index hierarchy (partition → proxy → cluster → global)
Online index construction without blocking queries
Incremental index updates via distributed WAL
Bloom filter cascade for fast negative lookups
Index types: hash, range, inverted, edge (outgoing/incoming)
Index schema versioning and migration
Partition pruning using cluster-level indexes
Space efficiency: indexes <20% of graph data size

Performance Targets:

Sub-millisecond index lookups
Partition pruning: 99% reduction in queried partitions
20,000× speedup for property-filtered queries

RFC-059: Hot/Cold Storage with S3 Snapshot Loading

Core Requirements:

Three-tier storage (hot: memory, warm: local SSD, cold: S3)
Hot tier: 10-20% of data in-memory
Cold tier: 80-90% on S3 Standard
Multi-format snapshot support (Parquet, Protobuf, JSON Lines, HDFS, Prometheus)
Parallel snapshot loading: 1000 workers, 210 TB in <20 minutes
Temperature classification (hot/warm/cold/frozen)
ML-based partition temperature prediction
WAL replay during snapshot load for consistency
Multi-tier caching (Varnish → CloudFront → S3 Express → Batch S3)

Cost Optimization:

86% cost reduction vs pure in-memory ($7M/year vs $49M/year)
S3 request cost optimization (88.5% reduction via caching)

RFC-060: Distributed Gremlin Query Execution

Core Requirements:

Full Apache TinkerPop Gremlin 3.x specification support
Distributed query decomposition across 1000+ nodes
Cost-based query optimizer with selectivity estimation
Partition pruning via multi-level indexes
Adaptive parallelism based on intermediate result sizes
Result streaming without full materialization
Query resource limits (memory, timeout, fan-out, depth)
Super-node handling (100M+ degree vertices) with sampling
Batch authorization for large result sets
Distributed tracing and slow query logging

Performance Targets:

Sub-second latency for common traversals
2,500× speedup via partition pruning
10,000× speedup for batch authorization

RFC-061: Fine-Grained Graph Authorization

Core Requirements:

Vertex-level label-based access control (LBAC)
Security labels on vertices: ['org:acme', 'pii', 'confidential']
Principal clearances with hierarchical wildcard matching
Authorization policy push-down to partition level
Traversal filtering (auto-filter vertices during queries)
Batch authorization using Roaring bitmaps
Audit logging with intelligent sampling
Compliance support (SOC 2, GDPR, HIPAA)

Performance Targets:

<100 μs authorization overhead per vertex
10,000× speedup for batch authorization (1M vertices in 1.1 ms)
90% audit log cost reduction via sampling

Current prism-data-layer State Analysis

Existing Capabilities

What We Have:

Basic Graph Pattern (RFC-055):
- 1B vertex scale (not 100B)
- 10 proxy architecture (not 1000)
- In-memory MemStore backend
- Simple consistent hashing
- Basic vertex/edge CRUD operations
Foundational Infrastructure:
- gRPC-based proxy architecture
- Protobuf message definitions
- Basic admin protocol
- Pattern registry system
- Plugin architecture (RFC-008)
Development Tooling:
- Local testing infrastructure (RFC-016)
- Documentation framework
- CI/CD pipelines

Critical Gaps

I've identified 15 critical gaps across 4 phases that must be addressed before graph implementation:

Phase 1: Distributed Systems Foundations (4-6 weeks)

Gap 1.1: Hierarchical Partition Registry

What's Missing:

No three-tier partition model (cluster → proxy → partition)
Current consistent hashing is flat (no hierarchy)
No partition ID format: {cluster}:{proxy}:{partition}:{local_id}
No cluster/proxy/partition metadata storage

Required Implementation:

message ClusterTopology {
  string cluster_id = 1;
  repeated ProxyNode proxies = 2;
  PartitionStrategy strategy = 3;
}

message ProxyNode {
  string proxy_id = 1;
  string cluster_id = 2;
  repeated Partition partitions = 3;
  int64 vertex_count = 4;
  int64 memory_bytes = 5;
}

message Partition {
  string partition_id = 1;  // "07:0042:05"
  PartitionTemperature temperature = 2;
  int64 vertex_count = 3;
  string storage_location = 4;  // S3 path or "memory"
}

Acceptance Criteria:

Protobuf definitions for cluster topology
In-memory partition registry with hierarchical lookup
Partition ID parsing: {cluster}:{proxy}:{partition}:{local_id}
Unit tests: 1000 proxies × 64 partitions = 16,000 partitions
Benchmark: <100ns partition ID parse time

Effort: 1-2 weeks

Gap 1.2: Distributed Routing Table for Opaque Vertex IDs

What's Missing:

No opaque vertex ID support (currently hierarchical only)
No distributed routing table (vertex ID → partition location)
No routing cache with 99% hit rate
No routing table sharding across cluster

Required Implementation:

type OpaqueVertexRouter struct {
    routingTable *DistributedHashTable  // 256 shards
    bloomFilter  *bloom.BloomFilter     // Fast negative lookup
    cache        *RoutingCache          // 1M entries, 100 MB
}

func (ovr *OpaqueVertexRouter) RouteVertex(vertexID string) (*PartitionLocation, error)
func (ovr *OpaqueVertexRouter) UpdateLocation(vertexID string, location *PartitionLocation) error
func (ovr *OpaqueVertexRouter) InvalidateCache(vertexID string) error

Acceptance Criteria:

Distributed routing table with consistent hashing
LRU cache with configurable size (default: 1M entries)
Bloom filter for negative lookups (0.1% false positive rate)
Routing table replication (3× for fault tolerance)
Unit tests: 100M vertex ID lookups
Benchmark: <1 μs cold lookup, <10 ns hot lookup (cached)

Effort: 2 weeks

Gap 1.3: Distributed Coordination Service

What's Missing:

No distributed coordination (no Raft/etcd integration)
No cluster membership management
No partition assignment consensus
No leader election for coordinators

Required Implementation:

Integrate etcd or implement Raft consensus
Cluster membership registry (proxies join/leave)
Partition assignment coordination
Leader election for query coordinators

Acceptance Criteria:

etcd client integration for cluster metadata
Cluster membership with health checks
Partition assignment API
Leader election for coordinators
Failure detection and failover (<5 second recovery)

Effort: 2-3 weeks

Gap 1.4: Distributed Write-Ahead Log (WAL)

What's Missing:

No distributed WAL (RFC-051 not implemented)
No Kafka integration for durable writes
No WAL consumer for multi-tier updates
No WAL replay for consistency during snapshot load

Required Implementation:

type WALProducer struct {
    kafkaProducer *kafka.Producer
    topic         string  // "graph-wal-cluster-{cluster_id}"
    partitions    int     // 1000 (one per proxy)
}

type WALConsumer struct {
    kafkaConsumer *kafka.Consumer
    groupID       string
}

type WALOperation struct {
    Type         OperationType  // CREATE_VERTEX, UPDATE_VERTEX, DELETE_VERTEX, etc.
    PartitionID  string
    Vertex       *Vertex
    Edge         *Edge
    Timestamp    int64
}

Acceptance Criteria:

Kafka producer for WAL writes
Kafka consumer for WAL replay
WAL operation protobuf definitions
WAL consumer with offset tracking
WAL replay during partition load
Unit tests: 10k ops/sec per partition
Benchmark: <5 ms end-to-end WAL write latency

Effort: 2 weeks

Phase 2: Storage Tier Infrastructure (4-5 weeks)

Gap 2.1: Hot/Cold/Warm Storage Tier Management

What's Missing:

No storage tier abstraction (only MemStore)
No S3 integration for cold storage
No local SSD management for warm storage
No temperature classification for partitions
No promotion/demotion policies

Required Implementation:

type PartitionManager struct {
    hotTier   *MemStore
    warmTier  *LocalSSD
    coldTier  *S3Backend
    classifier *TemperatureClassifier
}

func (pm *PartitionManager) GetVertex(vertexID string) (*Vertex, error) {
    // Try hot → warm → cold
}

func (pm *PartitionManager) PromotePartition(partitionID string, targetTemp PartitionTemperature) error
func (pm *PartitionManager) DemotePartition(partitionID string, targetTemp PartitionTemperature) error

Acceptance Criteria:

Storage tier interface (hot/warm/cold)
S3 backend client (read/write partitions)
Local SSD backend (NVMe-optimized)
Tier-aware vertex/edge lookup with fallback
Temperature classification algorithm (ML-based or rule-based)
Promotion/demotion policies with hysteresis
Unit tests: Tier fallback chain
Benchmark: <50 μs hot, <100 μs warm, <50 ms cold

Effort: 2-3 weeks

Gap 2.2: Snapshot Loading Infrastructure

What's Missing:

No bulk snapshot loading (only incremental inserts)
No Parquet/Protobuf/JSON Lines readers
No parallel loading across 1000 workers
No WAL replay during snapshot load
No dual-version loading (active + loading)

Required Implementation:

type SnapshotLoader struct {
    loaders map[SnapshotFormat]FormatLoader
}

type FormatLoader interface {
    LoadVertices(s3Path string, partition *Partition) error
    LoadEdges(s3Path string, partition *Partition) error
}

// Implementations:
// - ParquetLoader (Apache Arrow)
// - ProtobufLoader (zero-copy mmap)
// - JSONLinesLoader

Acceptance Criteria:

Parquet loader using Apache Arrow
Protobuf loader with memory-mapped files
JSON Lines loader
Parallel loading coordinator (1000 workers)
WAL replay integration for consistency
Dual-version partition loading (active + loading)
Atomic swap after load complete
Unit tests: Load 1M vertex snapshot
Benchmark: <60 seconds for 210 TB (1000 workers)

Effort: 3 weeks

Gap 2.3: S3 Multi-Tier Caching

What's Missing:

No S3 request cost optimization
No CloudFront CDN integration
No proxy-local Varnish cache
No S3 Express One Zone for hot cold partitions
No batch S3 reads

Required Implementation:

type CacheManager struct {
    localCache  *VarnishCache     // Tier 0: 100 GB SSD per proxy
    cdnCache    *CloudFrontCache  // Tier 1: Global CDN
    s3Express   *S3ExpressClient  // Tier 2: Hot cold partitions
    s3Standard  *S3StandardClient // Tier 3: Batch reads
}

func (cm *CacheManager) FetchPartition(partitionID string) (*Partition, error) {
    // Try local → CDN → S3 Express → S3 Standard
}

Acceptance Criteria:

Varnish HTTP cache integration (per-proxy)
CloudFront CDN configuration
S3 Express One Zone client
Batch S3 read prefetching (10 partitions per request)
Cache invalidation on partition update
Unit tests: Cache tier fallback
Benchmark: 72% cache hit rate (avoid S3)

Effort: 2 weeks

Phase 3: Graph Query Infrastructure (4-5 weeks)

Gap 3.1: Multi-Level Index Infrastructure

What's Missing:

No index abstraction layer
No partition-level indexes (hash, range, inverted, edge)
No proxy-level aggregated indexes
No cluster-level directory indexes
No Bloom filter cascade
No index schema versioning

Required Implementation:

type PartitionIndex struct {
    HashIndexes     map[string]*PropertyHashIndex
    RangeIndexes    map[string]*PropertyRangeIndex
    InvertedIndexes map[string]*InvertedIndex
    EdgeIndexes     map[string]*EdgeIndex
    BloomFilter     *bloom.BloomFilter
    SchemaVersion   int32  // Current: 5
}

type IndexBuilder struct {
    partition *Partition
}

func (ib *IndexBuilder) BuildOnline() error  // Online index construction
func (ib *IndexBuilder) UpdateIncremental(walOp *WALOperation) error

Acceptance Criteria:

Effort: 3 weeks

Gap 3.2: Gremlin Query Parser and Planner

What's Missing:

No Gremlin AST parser
No query decomposition (no stages)
No cost-based optimizer
No partition pruning strategy
No selectivity estimation

Required Implementation:

type GremlinParser struct {
    // Parse Gremlin query string to AST
}

type QueryPlanner struct {
    indexManager *IndexManager
    costModel    *CostModel
}

func (qp *QueryPlanner) Parse(gremlinQuery string) (*QueryAST, error)
func (qp *QueryPlanner) Analyze(ast *QueryAST) (*QueryPlan, error)
func (qp *QueryPlanner) Optimize(plan *QueryPlan) (*QueryPlan, error)

Acceptance Criteria:

Gremlin query string parser (Apache TinkerPop subset)
Query AST representation (V, E, hasLabel, has, out, in, limit, etc.)
Query decomposition into stages
Cost-based optimizer with selectivity estimation
Partition pruning using multi-level indexes
Filter ordering optimization (most selective first)
Unit tests: Parse 100 common Gremlin queries
Benchmark: <10 ms query planning

Effort: 3 weeks

Gap 3.3: Distributed Query Execution Engine

What's Missing:

No distributed query coordinator
No partition executor
No cross-partition traversal coordinator
No result streaming
No adaptive parallelism
No query resource limits

Required Implementation:

type QueryCoordinator struct {
    planner      *QueryPlanner
    partitionMgr *PartitionManager
}

type PartitionExecutor struct {
    partition *Partition
    indexes   *PartitionIndex
}

func (qc *QueryCoordinator) ExecuteGremlin(
    ctx context.Context,
    gremlinQuery string,
    principal *Principal,
) (*ResultStream, error)

func (pe *PartitionExecutor) ExecuteStep(
    ctx context.Context,
    step *GremlinStep,
    inputVertices []*Vertex,
) ([]*Vertex, error)

Acceptance Criteria:

Query coordinator (fan-out to partitions)
Partition executor (local query execution)
Cross-partition traversal coordinator
Result streaming with backpressure
Adaptive parallelism (1-1000 workers)
Query resource limits (memory, timeout, fan-out, depth)
Circuit breaker for runaway queries
Unit tests: Execute 2-hop traversal across 100 partitions
Benchmark: <1 second for 1M vertex query

Effort: 3 weeks

Phase 4: Authorization and Observability (3-4 weeks)

Gap 4.1: Label-Based Access Control (LBAC)

What's Missing:

No security label support on vertices/edges
No principal clearance model
No authorization policy engine
No batch authorization with bitmaps
No traversal filtering

Required Implementation:

type AuthorizationManager struct {
    policy      *AuthorizationPolicy
    bitmapCache *AuthBitmapCache
    auditLogger *AuditLogger
}

func (am *AuthorizationManager) CanAccessVertex(
    ctx *AuthorizationContext,
    vertex *Vertex,
) (bool, error)

func (am *AuthorizationManager) FilterVerticesBatch(
    principal *Principal,
    vertices []*Vertex,
) ([]*Vertex, error)  // Batch authorization with bitmaps

Acceptance Criteria:

Security label protobuf definitions
Principal clearance model
Authorization policy evaluation (<100 μs per vertex)
Hierarchical wildcard matching (org:acme:**)
Batch authorization with Roaring bitmaps
Partition-level authorization bitmap cache
Traversal filtering (auto-inject authz filters)
Unit tests: Authorize 1M vertices
Benchmark: 1.1 ms for 1M vertex batch authorization

Effort: 2 weeks

Gap 4.2: Audit Logging Infrastructure

What's Missing:

No audit event definitions
No Kafka-based audit log sink
No intelligent sampling (1% base, 100% sensitive)
No compliance query support
No audit log retention policies

Required Implementation:

type AuditLogger struct {
    kafkaProducer *kafka.Producer
    sampler       *ComplianceSampler
}

type ComplianceSampler struct {
    baseSampleRate float64  // 0.01 = 1%
    alwaysLog      []string // ['pii', 'confidential', 'financial']
}

func (al *AuditLogger) LogAccess(ctx *AuthorizationContext, vertex *Vertex)
func (al *AuditLogger) LogDenial(ctx *AuthorizationContext, vertex *Vertex, reason string)

Acceptance Criteria:

Audit event protobuf definitions
Kafka producer for audit events
Intelligent sampling (1% base, 100% sensitive)
Compliance rules (SOC 2, GDPR, HIPAA)
Audit log retention (hot: 7d, warm: 30d, cold: 90d)
Unit tests: 10M events/sec sampling
Benchmark: <10 μs audit log overhead

Effort: 1 week

Gap 4.3: Distributed Tracing and Observability

What's Missing:

No OpenTelemetry integration
No distributed tracing across 1000 nodes
No slow query logging
No query timeline visualization
No EXPLAIN plan for queries

Required Implementation:

type QueryTracer struct {
    tracer trace.Tracer  // OpenTelemetry
}

func (qt *QueryTracer) TraceQueryExecution(
    ctx context.Context,
    query string,
) (*QueryTimeline, error)

type QueryTimeline struct {
    QueryID    string
    TotalTime  time.Duration
    Stages     []*StageTimeline
    Bottlenecks []Bottleneck
}

Acceptance Criteria:

OpenTelemetry SDK integration
Distributed trace context propagation
Query timeline capture (per stage, per partition)
Slow query logging (threshold: 5 seconds)
EXPLAIN plan implementation
SignOz/Jaeger trace visualization
Prometheus metrics (query latency, partition execution time)
Unit tests: Trace 3-hop query across 100 partitions
Benchmark: <100 μs tracing overhead

Effort: 2 weeks

Phase 5: Graph-Specific Features (4-6 weeks, after foundations)

Gap 5.1: Super-Node Handling

What's Missing:

No vertex degree classification (normal/hub/super/mega)
No sampling strategies (random, top-K, HyperLogLog)
No circuit breaker for high-degree vertices
No Gremlin .sample() and .approximate() extensions

Required Implementation:

type SuperNodeCircuitBreaker struct {
    DegreeThreshold   int64  // 100k neighbors = super-node
    AutoSample        bool
    DefaultSampleSize int    // 10k samples
}

func (scb *SuperNodeCircuitBreaker) CheckDegree(vertexID string) (int64, VertexClass)
func (scb *SuperNodeCircuitBreaker) SampleNeighbors(vertexID string, size int) ([]*Vertex, error)

Acceptance Criteria:

Vertex degree classification (based on out-degree)
Random sampling strategy (reservoir sampling)
Top-K sampling (by property: engagement, recency)
HyperLogLog cardinality estimation (0.8% error)
Circuit breaker for queries to super-nodes
Gremlin .sample(N) extension
Gremlin .approximate() extension
Unit tests: Sample 100M neighbors to 10k
Benchmark: <100 ms super-node sampling

Effort: 2 weeks

Gap 5.2: Dynamic Partition Rebalancing

What's Missing:

No partition migration without downtime
No dual-read during migration
No rebalancing coordinator
No partition copy/verify/swap

Required Implementation:

type RebalancingCoordinator struct {
    sourceProxy string
    targetProxy string
}

func (rc *RebalancingCoordinator) MigratePartition(
    partitionID string,
    targetProxy string,
) error {
    // 1. Copy partition to target
    // 2. Dual-read (source + target)
    // 3. Verify consistency
    // 4. Update routing table
    // 5. Delete source
}

Acceptance Criteria:

Partition copy (source → target)
Dual-read during migration
Consistency verification (checksum)
Routing table update (atomic swap)
Source partition cleanup
Zero downtime guarantee
Unit tests: Migrate 64 partitions
Benchmark: <10 seconds per partition (opaque IDs)

Effort: 2 weeks

Implementation Roadmap

Phase 1: Foundations (4-6 weeks)

Deliverables:

Hierarchical partition registry
Distributed routing table
Distributed coordination (etcd)
Distributed WAL (Kafka)

Milestone: Can route queries to correct partition using hierarchical IDs

Phase 2: Storage (4-5 weeks)

Deliverables:

Hot/cold/warm storage tier management
Snapshot loading infrastructure
S3 multi-tier caching

Milestone: Can load 1M vertex snapshot from S3 and serve queries from hot/cold tiers

Phase 3: Query Engine (4-5 weeks)

Deliverables:

Multi-level index infrastructure
Gremlin query parser and planner
Distributed query execution engine

Milestone: Can execute Gremlin queries across 100 partitions with partition pruning

Phase 4: Security and Observability (3-4 weeks)

Deliverables:

Label-based access control
Audit logging infrastructure
Distributed tracing

Milestone: Can authorize queries with vertex-level labels and audit access

Phase 5: Graph Features (4-6 weeks)

Deliverables:

Super-node handling
Dynamic partition rebalancing

Milestone: Can handle celebrity vertices (100M degree) and rebalance partitions without downtime

Total Timeline: 19-26 weeks (4.75-6.5 months)

Risk Assessment

High-Risk Dependencies

Risk 1: Distributed Coordination Complexity

Likelihood: High
Impact: Critical
Mitigation: Use etcd (proven) instead of building Raft from scratch
Fallback: Start with single-coordinator (not HA) for prototyping

Risk 2: WAL Replay Consistency

Likelihood: Medium
Impact: Critical
Mitigation: Comprehensive WAL replay tests with simulated failures
Fallback: Disable snapshot loading until WAL replay is proven

Risk 3: S3 Request Cost Explosion

Likelihood: High (identified in RFC-059)
Impact: High ($1M/year without mitigation)
Mitigation: Implement multi-tier caching in Phase 2 (mandatory)
Fallback: N/A - caching is non-negotiable at 100B scale

Risk 4: Index Size Explosion

Likelihood: Medium
Impact: High (16 TB indexes cluster-wide)
Mitigation: Roaring bitmap compression, Bloom filter optimization
Fallback: Reduce indexed properties to top 10 most queried

Risk 5: Query Resource Exhaustion

Likelihood: High (celebrity super-nodes)
Impact: Critical (cluster-wide OOM)
Mitigation: Circuit breaker + sampling (Phase 5, but prototype in Phase 3)
Fallback: Hard-code .limit(10000) on all traversals during development

Medium-Risk Dependencies

Risk 6: Gremlin Compatibility

Likelihood: Medium
Impact: Medium
Mitigation: Implement TinkerPop test suite subset
Fallback: Document unsupported Gremlin steps

Risk 7: Authorization Performance

Likelihood: Low (bitmap approach proven in RFC-061)
Impact: Medium
Mitigation: Benchmark early in Phase 4
Fallback: Reduce clearance check frequency (sample 10% of queries)

Testing Strategy

Unit Test Coverage Targets

Phase 1: 90% coverage (foundations must be solid)

Partition registry: 95%
Routing table: 90%
WAL producer/consumer: 95%

Phase 2: 85% coverage

Storage tier management: 85%
Snapshot loaders: 80%

Phase 3: 80% coverage

Index construction: 85%
Query planner: 80%
Query executor: 75%

Phase 4: 85% coverage

Authorization: 90%
Audit logging: 80%

Phase 5: 75% coverage

Super-node handling: 75%
Rebalancing: 80%

Integration Test Requirements

Critical Integration Tests:

End-to-End Query Test (Phase 3 milestone):
- Load 1M vertex graph
- Execute Gremlin query: g.V().has('city', 'SF').out('FOLLOWS').limit(100)
- Verify: Query completes in <1 second
- Verify: Correct 100 results returned
Snapshot Load + WAL Replay Test (Phase 2 milestone):
- Load 1M vertex snapshot
- Inject 10k WAL operations during load
- Verify: All WAL operations applied to loaded snapshot
- Verify: No data loss
Authorization + Query Test (Phase 4 milestone):
- Create graph with 3 organizations (org:acme, org:widget, org:globex)
- Principal with clearance: ['org:acme']
- Execute query: g.V().hasLabel('User')
- Verify: Only org:acme users returned
Super-Node Handling Test (Phase 5 milestone):
- Create vertex with 100M neighbors
- Execute query: g.V('celebrity').out('FOLLOWS')
- Verify: Circuit breaker triggers sampling
- Verify: Query returns 10k sampled neighbors (not 100M)
- Verify: Query completes in <1 second

Performance Benchmarks

Critical Benchmarks:

Partition ID parse: <100 ns
Routing table lookup (cold): <1 μs
Routing table lookup (hot): <10 ns
Index lookup: <1 ms
Query planning: <10 ms
Authorization check (single vertex): <100 μs
Batch authorization (1M vertices): <1.1 ms
WAL write latency: <5 ms

Success Criteria

Phase 1 Success:

✅ Route 1M queries to correct partition using hierarchical IDs
✅ WAL write throughput: 10k ops/sec per partition
✅ Partition registry supports 16,000 partitions

Phase 2 Success:

✅ Load 1M vertex Parquet snapshot in <5 seconds (single proxy)
✅ Tier fallback: hot (50 μs) → warm (100 μs) → cold (50 ms)
✅ S3 cache hit rate: 72% (avoid S3 requests)

Phase 3 Success:

✅ Execute Gremlin 2-hop traversal across 100 partitions in <1 second
✅ Partition pruning: 99% reduction in queried partitions
✅ Index lookup: <1 ms per property filter

Phase 4 Success:

✅ Authorize 1M vertex query in <1.1 ms (batch authorization)
✅ Audit log sampling: 1% base, 100% sensitive
✅ Distributed traces for all queries (OpenTelemetry)

Phase 5 Success:

✅ Super-node query (100M neighbors) completes in <1 second (sampled)
✅ Partition migration: <10 seconds per partition (opaque IDs)
✅ Zero downtime during partition rebalancing

Recommendations

Immediate Actions (Next 2 Weeks)

Create Phase 1 Implementation Plan:
- Break down Gap 1.1-1.4 into 2-week sprints
- Assign developers to each gap
- Set up dev environments with Kafka + etcd
Prototype Critical Paths:
- Prototype hierarchical partition ID parsing (1 day)
- Prototype Kafka WAL producer (2 days)
- Prototype etcd cluster membership (3 days)
Set Up Testing Infrastructure:
- Docker compose for Kafka + etcd + SQLite
- Performance benchmark harness
- Integration test framework

Medium-Term Actions (Weeks 3-8)

Complete Phase 1:
- Deliver all 4 gaps by end of Week 6
- Milestone demo: Route 1M queries across 16,000 partitions
Begin Phase 2 in Parallel:
- Start snapshot loader prototyping (Week 5)
- S3 backend client implementation (Week 6)

Long-Term Actions (Weeks 9-26)

Incremental Delivery:
- Phase 2: Complete by Week 12
- Phase 3: Complete by Week 18
- Phase 4: Complete by Week 22
- Phase 5: Complete by Week 26
Weekly Demos:
- Every Friday: Demo incremental progress
- Monthly milestones with stakeholder reviews

Conclusion

The prism-data-layer requires 16-20 weeks of foundational work before graph implementation can begin. This is not optional—attempting to implement graph features without these foundations will result in unbuildable systems and architectural debt.

The path forward:

Accept the 16-20 week timeline for foundations
Begin Phase 1 immediately (distributed systems foundations)
Deliver incrementally with weekly demos
Prioritize critical gaps (WAL, storage tiers, indexes)

Alternative (not recommended): Attempt graph implementation without foundations, resulting in:

Unbuildable prototypes (no WAL, no distributed coordination)
Performance failures (no indexes, no caching)
Cost explosions (no S3 optimization)
Security gaps (no authorization)

Recommended approach: Invest 16-20 weeks in solid foundations, then implement graph features on proven infrastructure.

Executive Summary​

RFC Requirements Summary​

RFC-057: Massive-Scale Graph Sharding (100B Vertices)​

RFC-058: Multi-Level Graph Indexing​

RFC-059: Hot/Cold Storage with S3 Snapshot Loading​

RFC-060: Distributed Gremlin Query Execution​

RFC-061: Fine-Grained Graph Authorization​

Current prism-data-layer State Analysis​

Existing Capabilities​

Critical Gaps​

Phase 1: Distributed Systems Foundations (4-6 weeks)​

Gap 1.1: Hierarchical Partition Registry​

Gap 1.2: Distributed Routing Table for Opaque Vertex IDs​

Gap 1.3: Distributed Coordination Service​

Gap 1.4: Distributed Write-Ahead Log (WAL)​

Phase 2: Storage Tier Infrastructure (4-5 weeks)​

Gap 2.1: Hot/Cold/Warm Storage Tier Management​

Gap 2.2: Snapshot Loading Infrastructure​

Gap 2.3: S3 Multi-Tier Caching​

Phase 3: Graph Query Infrastructure (4-5 weeks)​

Gap 3.1: Multi-Level Index Infrastructure​

Gap 3.2: Gremlin Query Parser and Planner​

Gap 3.3: Distributed Query Execution Engine​

Phase 4: Authorization and Observability (3-4 weeks)​

Gap 4.1: Label-Based Access Control (LBAC)​

Gap 4.2: Audit Logging Infrastructure​

Gap 4.3: Distributed Tracing and Observability​

Phase 5: Graph-Specific Features (4-6 weeks, after foundations)​

Gap 5.1: Super-Node Handling​

Gap 5.2: Dynamic Partition Rebalancing​

Implementation Roadmap​

Phase 1: Foundations (4-6 weeks)​

Phase 2: Storage (4-5 weeks)​

Phase 3: Query Engine (4-5 weeks)​

Phase 4: Security and Observability (3-4 weeks)​

Phase 5: Graph Features (4-6 weeks)​

Risk Assessment​

High-Risk Dependencies​

Medium-Risk Dependencies​

Testing Strategy​

Unit Test Coverage Targets​

Integration Test Requirements​

Performance Benchmarks​

Success Criteria​

Recommendations​

Immediate Actions (Next 2 Weeks)​

Medium-Term Actions (Weeks 3-8)​

Long-Term Actions (Weeks 9-26)​

Conclusion​

Executive Summary

RFC Requirements Summary

RFC-057: Massive-Scale Graph Sharding (100B Vertices)

RFC-058: Multi-Level Graph Indexing

RFC-059: Hot/Cold Storage with S3 Snapshot Loading

RFC-060: Distributed Gremlin Query Execution

RFC-061: Fine-Grained Graph Authorization

Current prism-data-layer State Analysis

Existing Capabilities

Critical Gaps

Phase 1: Distributed Systems Foundations (4-6 weeks)

Gap 1.1: Hierarchical Partition Registry

Gap 1.2: Distributed Routing Table for Opaque Vertex IDs

Gap 1.3: Distributed Coordination Service

Gap 1.4: Distributed Write-Ahead Log (WAL)

Phase 2: Storage Tier Infrastructure (4-5 weeks)

Gap 2.1: Hot/Cold/Warm Storage Tier Management

Gap 2.2: Snapshot Loading Infrastructure

Gap 2.3: S3 Multi-Tier Caching

Phase 3: Graph Query Infrastructure (4-5 weeks)

Gap 3.1: Multi-Level Index Infrastructure

Gap 3.2: Gremlin Query Parser and Planner

Gap 3.3: Distributed Query Execution Engine

Phase 4: Authorization and Observability (3-4 weeks)

Gap 4.1: Label-Based Access Control (LBAC)

Gap 4.2: Audit Logging Infrastructure

Gap 4.3: Distributed Tracing and Observability

Phase 5: Graph-Specific Features (4-6 weeks, after foundations)

Gap 5.1: Super-Node Handling

Gap 5.2: Dynamic Partition Rebalancing

Implementation Roadmap

Phase 1: Foundations (4-6 weeks)

Phase 2: Storage (4-5 weeks)

Phase 3: Query Engine (4-5 weeks)

Phase 4: Security and Observability (3-4 weeks)

Phase 5: Graph Features (4-6 weeks)

Risk Assessment

High-Risk Dependencies

Medium-Risk Dependencies

Testing Strategy

Unit Test Coverage Targets

Integration Test Requirements

Performance Benchmarks

Success Criteria

Recommendations

Immediate Actions (Next 2 Weeks)

Medium-Term Actions (Weeks 3-8)

Long-Term Actions (Weeks 9-26)

Conclusion