CONJUGATE

Cloud-native Observability + Natural-language Joint Understanding Granular search Analytics Tunable Engine

OpenSearch-Compatible | Diagon-Powered | Cloud-Native

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What is CONJUGATE?

CONJUGATE is a cloud-native distributed search engine that provides 100% OpenSearch API compatibility while leveraging the high-performance Diagon search engine core.

The name CONJUGATE represents our core capabilities: Cloud-native Observability + Natural-language Joint Understanding with Granular search Analytics in a Tunable Engine. Just as conjugate pairs work together in harmony, CONJUGATE nodes coordinate seamlessly across distributed Kubernetes environments to deliver high-performance search with deep observability.

Key Features

✅ 100% OpenSearch API Compatibility

• Index Management, Document APIs, Search APIs
• Full Query DSL support
• 90% PPL (Piped Processing Language) support planned (Phase 4)

✅ High Performance

• Diagon core: Lucene-style inverted index + ClickHouse columnar storage
• SIMD-accelerated BM25 scoring (4-8× faster)
• Advanced compression (40-70% storage savings)
• Skip indexes for granule pruning (90%+ data skipping)

✅ Distributed Architecture

• Specialized node types (Master, Coordination, Data)
• Horizontal scalability (10-1000+ nodes)
• Multi-tier storage (Hot/Warm/Cold/Frozen)
• Dual-mode control plane: Traditional (Raft) or K8S-native (Operator)
• Auto-detection of deployment environment

✅ Python-First Pipelines

• Customize search with Python code
• Pre/post-processing hooks
• ML model integration (ONNX, TensorFlow, PyTorch)
• Built-in examples (synonym expansion, re-ranking, A/B testing)

✅ Cloud-Native

• Kubernetes operator
• StatefulSets for data nodes
• Auto-scaling coordination nodes
• S3/MinIO/Ceph integration

✅ Query Optimization

• Custom Go query planner (learning from Calcite principles)
• Cost-based optimization with logical plan representation
• Push-down filters, projections, and UDFs
• Hybrid inverted + columnar scans
• Multi-tiered UDFs: Expression Trees (80%) + WASM (15%) + Python (5%)

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Architecture Overview

┌──────────────────────────────────────────────────────────────┐
│                    CONJUGATE Cluster                          │
├──────────────────────────────────────────────────────────────┤
│                                                                │
│  ┌────────────────────────────────────────────────────────┐  │
│  │         API Layer (OpenSearch Compatible)               │  │
│  │   REST API | DSL | PPL | Python Pipelines              │  │
│  └────────────────────────────────────────────────────────┘  │
│                            ↓                                   │
│  ┌────────────────────────────────────────────────────────┐  │
│  │         Control Plane (Dual-Mode Support)              │  │
│  │   Mode 1: Master Nodes (Raft) - Bare metal/VMs/K8S    │  │
│  │   Mode 2: K8S Operator - K8S-native with CRDs          │  │
│  │   • Cluster state    • Shard allocation                │  │
│  │   • Index metadata   • Node discovery                  │  │
│  └────────────────────────────────────────────────────────┘  │
│                            ↓                                   │
│  ┌────────────────────────────────────────────────────────┐  │
│  │            Coordination Nodes (Query Planning)         │  │
│  │   • DSL/PPL parsing       • Custom Go query planner    │  │
│  │   • Python pipelines      • Result aggregation         │  │
│  └────────────────────────────────────────────────────────┘  │
│                            ↓                                   │
│  ┌────────────────────────────────────────────────────────┐  │
│  │              Data Nodes (Diagon Core)                  │  │
│  │   Inverted Index  │  Forward Index  │  Computation     │  │
│  │   • Text search   │  • Aggregations │  • Joins         │  │
│  │   • BM25 scoring  │  • Sorting      │  • ML inference  │  │
│  │   • SIMD-accelerated with skip indexes                 │  │
│  └────────────────────────────────────────────────────────┘  │
│                                                                │
└──────────────────────────────────────────────────────────────┘

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Distributed Search (Implemented ✅)

CONJUGATE now supports horizontal scaling across multiple physical DataNodes with automatic shard distribution and result aggregation.

Inter-Node Distributed Search Architecture

Client HTTP Request
    ↓
Coordination Node (REST API)
    ↓
QueryExecutor (Go)
    ├─ Get shard routing from Master
    ├─ Query all DataNodes in parallel (gRPC)
    │   ↓
    │   DataNode 1, 2, 3... (Go + C++)
    │       ↓
    │       Shard.Search() → Diagon C++ Engine (local)
    │       ↓
    │       Returns SearchResult with Aggregations
    ↓
Aggregate Results (Go)
    ├─ Merge hits (global ranking by score)
    ├─ Merge aggregations (all 14 types)
    └─ Apply global pagination
    ↓
Return SearchResult to Client

Key Features

✅ Parallel Query Distribution

• Coordination node queries all DataNodes concurrently via gRPC
• Each DataNode executes queries on local shards using Diagon C++ engine
• Connection pooling and automatic error handling

✅ Comprehensive Aggregation Support (14 types)

• Bucket: terms, histogram, date_histogram, range, filters
• Metric: stats, extended_stats, percentiles, cardinality
• Simple Metric: avg, min, max, sum, value_count
• 12/14 types maintain exactness across shards (85.7%)

✅ Continuous Auto-Discovery

• Coordination node polls master every 30 seconds for cluster state
• New DataNodes automatically discovered and registered
• Dynamic scaling: add nodes without restarts

✅ Graceful Degradation

• Queries succeed with partial results when some shards are unavailable
• No cascading failures
• Proportional degradation with node failures

✅ Global Result Ranking

• Hits sorted by score across all shards
• Global pagination (from/size parameters)
• No duplicate documents in results

Multi-Node Deployment Example

# Start 3-node distributed cluster
kubectl apply -f - <<EOF
apiVersion: conjugate.io/v1
kind: ConjugateCluster
metadata:
  name: conjugate-prod
spec:
  version: "1.0.0"
  master:
    replicas: 3  # Raft quorum
  coordination:
    replicas: 2
  data:
    replicas: 3  # Horizontal scaling
    storage:
      size: "100Gi"
EOF

# Create index with 6 shards (distributed across 3 DataNodes)
curl -X PUT "http://localhost:9200/products" \
  -H 'Content-Type: application/json' \
  -d '{
    "settings": {
      "number_of_shards": 6,
      "number_of_replicas": 1
    }
  }'

# Index 100K documents (auto-distributed via consistent hashing)
# ... bulk indexing ...

# Search across all nodes with aggregations
curl -X GET "http://localhost:9200/products/_search" \
  -H 'Content-Type: application/json' \
  -d '{
    "query": {"match_all": {}},
    "size": 10,
    "aggs": {
      "categories": {
        "terms": {"field": "category", "size": 10}
      },
      "price_ranges": {
        "range": {
          "field": "price",
          "ranges": [
            {"key": "low", "to": 50},
            {"key": "medium", "from": 50, "to": 200},
            {"key": "high", "from": 200}
          ]
        }
      },
      "price_stats": {
        "stats": {"field": "price"}
      }
    }
  }'

# Response: Results merged from all 3 DataNodes
# - Global hit ranking by score
# - Aggregations merged correctly
# - Total hits: sum across all shards

Performance Characteristics

Query Latency (measured on 116M docs, single shard):

• 0.40ms P50 warm across all 42 Big5 query types
• 2x-388x faster than OpenSearch 2.11 on the same hardware

Scalability:

• Zero degradation from 10M to 116M docs (OS degrades 3-10x)
• Linear throughput scaling: 2x nodes ≈ 2x QPS
• Aggregation merge overhead: <10% vs single-node

Reliability:

• Partial shard failure: Query succeeds with available data
• Master failover: New leader elected within 5 seconds (Raft)
• Auto-recovery: Failed nodes rejoin automatically

Architecture Principles

🎯 Clean Separation: Network layer (Go) separate from search engine (C++)

• C++ Diagon engine queries LOCAL shards only (no network I/O)
• Go QueryExecutor handles inter-node distribution and result aggregation

🎯 Fault Tolerance: Built-in resilience

• Partial results when some nodes fail
• Timeout handling per shard
• Circuit breaker patterns

🎯 Auto-Discovery: Zero-configuration scaling

• Coordination nodes automatically discover DataNodes via Master
• No manual client registration
• Polling interval: 30 seconds (configurable)

---

Quick Start

One-Command Deploy 🚀

# Clone repository
git clone https://github.com/yourorg/conjugate.git
cd conjugate

# Deploy to Kubernetes (auto-detects control plane mode)
./scripts/deploy-k8s.sh --profile dev

# Get endpoint
kubectl get svc conjugate-coordination -n conjugate

That's it! Your distributed search cluster is running.

📖 Detailed Guide: QUICKSTART_K8S.md

Deployment Modes

CONJUGATE supports two control plane modes:

K8S-Native (Auto-selected for K8S)

./scripts/deploy-k8s.sh --mode k8s --profile dev

• Uses Kubernetes Operator + CRDs
• Leverages K8S etcd (Raft built-in)
• Cost: ~$40/month (AWS EKS)

Traditional Raft (For multi-environment)

./scripts/deploy-k8s.sh --mode raft --profile prod

• Dedicated master nodes with Raft
• Works on K8S, VMs, bare metal
• Cost: ~$162/month (AWS EKS)

Auto-Detect (Default)

./scripts/deploy-k8s.sh --mode auto

• K8S → Uses K8S-native
• Non-K8S → Uses Raft

Index & Search

# Create index
curl -X PUT "http://localhost:9200/my-index" \
  -H 'Content-Type: application/json' \
  -d '{
    "settings": {"number_of_shards": 1},
    "mappings": {
      "properties": {
        "title": {"type": "text"},
        "price": {"type": "float"}
      }
    }
  }'

# Index document
curl -X PUT "http://localhost:9200/my-index/_doc/1" \
  -H 'Content-Type: application/json' \
  -d '{
    "title": "CONJUGATE Search Engine",
    "price": 99.99
  }'

# Search
curl -X GET "http://localhost:9200/my-index/_search" \
  -H 'Content-Type: application/json' \
  -d '{
    "query": {
      "bool": {
        "must": {"match": {"title": "search"}},
        "filter": {"range": {"price": {"lte": 100}}}
      }
    }
  }'

---

Documentation

Core Documentation

📖 Architecture Overview - Complete system design

• Node types and responsibilities
• API compatibility (100% DSL, 90% PPL)
• Query processing pipeline
• Storage architecture
• Distributed coordination

📖 Implementation Roadmap - 18-month plan

• OpenSearch API compatibility matrix
• 6 implementation phases
• Team structure (8-10 people)
• Timeline and milestones
• Risk assessment

📖 Kubernetes Deployment - Cloud-native guide

• Operator installation
• Cluster configuration
• Storage and networking
• Monitoring and security
• Backup and restore

📖 Python Pipeline Guide - Customize search

• Pipeline architecture
• Processor types
• API reference
• Examples (synonym expansion, ML re-ranking, A/B testing)
• Testing and deployment

Control Plane Architecture

📖 Dual-Mode Control Plane - Flexible architecture design ⭐

• Support for BOTH traditional (Raft) and K8S-native modes
• Pluggable control plane interface
• Complete implementation for both modes
• Auto-detection of deployment environment
• Migration paths between modes
• Unified configuration format

📖 Master Node Architecture - Traditional Raft control plane

• Master node responsibilities and Raft consensus
• Bandwidth allocation analysis (16 KB/sec total)
• Traditional deployment patterns
• Cost analysis and recommendations
• Key finding: 3 master nodes can handle 1000+ data nodes

📖 Kubernetes Deployment Guide - K8S deployment patterns

• Complete manifests (StatefulSets, Deployments, Services)
• Traditional masters vs K8S-native control plane
• Production patterns (multi-zone, node selectors, PDBs)
• Cost analysis ($162/month for 3 masters vs $40/month for operator)
• Migration strategies and Helm charts

📖 K8S-Native Deep Dive - Cloud-native architecture analysis

• Why K8S-native should be considered for K8S deployments
• K8S already provides Raft (via etcd/strong consistency)
• Operator pattern as 2026 standard (Vitess, TiDB, Strimzi)
• Complete CRD and Controller implementation examples
• Cost/latency/complexity trade-off analysis

📖 K8S-Native Summary - Quick architectural decision guide

• Decision framework for choosing control plane architecture
• Trade-offs comparison (Traditional vs K8S-Native)
• When to use each mode

Diagon Core

🔗 Diagon Project - Underlying search engine

• Lucene-style inverted index
• ClickHouse columnar storage
• SIMD-accelerated BM25
• Comprehensive design docs (100% complete)

---

Use Cases

1. Log Analytics (Replacing OpenSearch)

# High-throughput log ingestion
indices: logs-*
settings:
  number_of_shards: 10
  codec: "diagon_best_compression"
  refresh_interval: "5s"

Benefits:

• 2-289x faster range queries (measured on 116M docs)
• 2-388x faster sort queries
• Sub-millisecond warm latency across all query types

---

2. E-Commerce Search

# Python pipeline for ML re-ranking
class PersonalizedRankingProcessor(Processor):
    def process_response(self, response, request):
        user_id = request.user.user_id
        user_profile = self.get_user_profile(user_id)

        # Re-rank with personalization model
        features = self.extract_features(response.hits, user_profile)
        scores = self.model.predict(features)

        for hit, score in zip(response.hits.hits, scores):
            hit._score = score

        response.hits.hits.sort(key=lambda h: h._score, reverse=True)
        return response

Benefits:

• Customizable ranking with Python
• ML model integration (ONNX)
• A/B testing framework

---

3. Real-Time Analytics (PPL - Planned Phase 4)

-- PPL query for time-series analytics (coming in Phase 4)
source=metrics
| where timestamp > now() - 1h
| stats avg(cpu_usage), max(memory_usage) by host, span(1m)
| where avg(cpu_usage) > 80
| sort -avg(cpu_usage)

Benefits (when implemented):

• SQL-like syntax (90% OpenSearch PPL compatible)
• Query planner-optimized execution
• Skip indexes for fast granule pruning

---

Deployment Modes

Single-Process (Development)

# All roles in one process
node:
  roles: [master, coordination, inverted_index, forward_index, computation]

Use Cases:

• Local development
• Integration testing
• Small deployments (<1M documents)

---

Distributed (Production)

# Specialized nodes
master:
  replicas: 3
  resources: {memory: "8Gi", cpu: "4"}

coordination:
  replicas: 5-20  # Auto-scaling
  python: {enabled: true}

data:
  replicas: 10-1000+
  storage: {class: "nvme", size: "1Ti"}
  roles: [inverted_index, forward_index, computation]

Use Cases:

• Production deployments
• Multi-tenant SaaS
• Large-scale analytics

---

Python Pipelines

Customize search behavior with Python:

Example: Synonym Expansion

from conjugate.pipeline import Processor

class SynonymExpansionProcessor(Processor):
    def __init__(self):
        self.synonyms = {
            "search": ["find", "query", "lookup"],
            "fast": ["quick", "rapid", "speedy"]
        }

    def process_request(self, request):
        # Expand query with synonyms
        if "match" in request.query:
            field, text = next(iter(request.query["match"].items()))
            terms = text.split()

            expanded = []
            for term in terms:
                expanded.append(term)
                expanded.extend(self.synonyms.get(term.lower(), []))

            request.query = {
                "bool": {
                    "should": [
                        {"match": {field: text}},
                        {"match": {field: " ".join(expanded)}}
                    ]
                }
            }

        return request

Deploy:

conjugate pipeline deploy --cluster prod --package my-pipeline.tar.gz

Use:

curl -X POST "http://localhost:9200/my-index/_search?pipeline=my-pipeline" \
  -d '{"query": {"match": {"title": "fast search"}}}'

---

Benchmark: CONJUGATE vs OpenSearch 2.11

Measured on 116,000,000 documents (Big5 workload), single shard, same machine, same data. 10 iterations, 3 warmup rounds, size=0. Full report: BIG5_BENCHMARK_REPORT_V10.md.

Query Latency (P50, warm)

Category	Queries	CONJ P50	OS P50	Speedup
Text Querying	6	0.40ms	1.02ms	1.9-3.7x
Sorting	13	0.39ms	26.03ms	2.0-387.8x
Date Histogram	4	0.40ms	1.23ms	2.3-3.7x
Range Queries	10	0.41ms	16.04ms	2.1-288.8x
Terms Aggregation	9	0.39ms	0.99ms	2.1-3.0x
Total	42	0.40ms	9.06ms	1.9-387.8x

Result: CONJ wins 41/42 queries. OS ERROR on 1 (sort-keyword-can-match). OS wins 0.

Scaling Behavior (10M to 116M docs)

Query	OS 10M P50	OS 116M P50	OS degradation	CONJ 116M P50
sort-numeric-desc-with-match	43ms	141ms	3.3x slower	0.38ms
range-conjunction-big-range-big-term	12ms	116ms	9.7x slower	0.40ms
range-numeric	3ms	21ms	7.0x slower	0.40ms
desc-sort-timestamp	2ms	9ms	4.5x slower	0.40ms

CONJ shows zero scaling degradation from 10M to 116M.

Aggregation Parity (116M docs)

Check	Result
Range agg bucket counts (4 buckets)	EXACT
Date histogram (336 hourly buckets)	EXACT
Cardinality low (50 unique values)	EXACT
Terms agg (50 keys, 116M total)	Same keys, ±4% per-key variance
Cardinality high (~116M unique)	3.6% diff (different estimation algorithms)
Range sub-agg avg	<0.3% diff (NDV 200K sample vs full scan)

Indexing Throughput (116M docs)

Engine	Throughput	Notes
CONJ	~25,800 docs/sec	Stable, no degradation
OS 2.11	~29,700 docs/sec	~15% faster

Comparison

Feature	OpenSearch	CONJUGATE
API Compatibility	100% (reference)	100% (DSL), 90% (PPL)
Core Engine	Lucene (Java)	Diagon (C++, Lucene-compatible)
Query Latency (116M docs)	9.06ms avg P50	0.40ms avg P50
Sorting (worst case)	141ms P50	0.38ms P50
Range Queries (worst case)	116ms P50	0.40ms P50
Indexing	~29,700 docs/sec	~25,800 docs/sec
Scaling (10M→116M)	3-10x degradation	No degradation
Columnar Storage	Limited	Native (NumericDocValues)
SIMD Acceleration	No	AVX2/NEON
Cloud-Native	Helm charts	K8S operator

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Roadmap

Phase 0: Foundation (Months 1-2) ✅

• Complete Diagon core essentials
• SIMD, compression, advanced queries

Phase 1: Distributed (Months 3-5) ✅ 99% COMPLETE

• ✅ Master node with Raft consensus
• ✅ Data node with Diagon C++ engine (5,000 lines)
• ✅ Coordination node with REST API
• ✅ All nodes start and communicate
• ⏳ Shard allocation integration (7 hours remaining)
• Status: All code complete, needs integration glue (see E2E_TEST_RESULTS.md)

Phase 2: Query Planning (Months 6-8) ⏳

• OpenSearch DSL support
• Custom Go query planner (learning from Calcite principles)
• Expression Trees + WASM UDF framework
• Query optimization

Phase 3: Python Integration (Months 9-10) ⏳

• Python runtime
• Pipeline framework
• Example pipelines

Phase 4: Production Features (Months 11-13) ⏳

• Aggregations, highlighting
• PPL support (90%)
• Security, observability

Phase 5: Cloud-Native (Months 14-16) ⏳

• Kubernetes operator
• Storage tiering (Hot/Warm/Cold)
• Backup & disaster recovery

Phase 6: Optimization (Months 17-18) ⏳

• Performance tuning
• Large-scale validation (1000+ nodes)
• Cost optimization

Target: 1.0 Release in Month 18

---

Contributing

We welcome contributions! This project is in the design phase.

How to Help

• Design Review: Review architecture docs and provide feedback
• Prototype: Build proof-of-concept for key components
• Diagon: Contribute to the Diagon core
• Documentation: Improve guides and examples

Getting Started

# Clone repository
git clone https://github.com/yourusername/conjugate.git
cd conjugate

# Read design documents
ls -la *.md

# Set up development environment (coming soon)
# make dev-setup

---

Team

Core Team (Target: 8-10 people)

• Tech Lead (1): Go/C++, architecture
• Backend Engineers (3): Go, master/coordination nodes
• Systems Engineers (2): C++, Diagon core
• DevOps Engineer (1): Kubernetes, CI/CD
• SRE Engineer (1): Reliability, operations
• Product Manager (1): Requirements, roadmap
• Security Engineer (1): Auth, compliance (Phase 4+)
• Technical Writer (1): Docs (Phase 5+)

Join us! See IMPLEMENTATION_ROADMAP.md for details.

---

Technology Stack

Component	Technology	Reason
Master Nodes	Go	Distributed systems, Raft consensus
Coordination Nodes	Go + Python	Orchestration (Go), Pipelines (Python)
Data Nodes	C++ (Diagon)	Performance, SIMD, existing codebase
Query Planner	Go	Custom planner learning from Calcite principles
Pipelines	Python	ML/NLP ecosystem
Orchestration	Kubernetes	Cloud-native, auto-scaling
Storage	S3/MinIO/Ceph	Object storage for cold tier
Monitoring	Prometheus + Grafana	Metrics and dashboards
Tracing	OpenTelemetry	Distributed tracing

---

Name Explanation

CONJUGATE is a backronym that captures our core capabilities:

• Cloud-native - Kubernetes deployment, microservices architecture
• Observability - Built-in monitoring, tracing, and telemetry
• Natural-language - Advanced NLP and semantic search capabilities
• Joint - Collaborative distributed processing
• Understanding - Deep semantic comprehension of queries and documents
• Granular - Fine-grained control and precision in search
• Analytics - Comprehensive data analytics and aggregations
• Tunable - Highly configurable and optimizable performance
• Engine - High-performance search engine core

The name reflects the harmony between components - just as mathematical conjugates work in pairs to achieve balance, CONJUGATE components work together seamlessly. The system combines cloud-native observability with natural language understanding through granular analytics in a highly tunable engine.

See NAMING.md for detailed naming rationale and migration from the previous name.

---

License

Apache License 2.0 - See LICENSE for details.

---

Acknowledgments

CONJUGATE is built upon the foundational work of:

• Apache Lucene - Inverted index design
• ClickHouse - Columnar storage architecture
• OpenSearch - API specification
• Apache Calcite - Query optimizer design principles
• Diagon Project - High-performance search engine core

---

Contact & Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Project Docs

---

Last Updated: 2026-03-14

Latest Benchmark: 116M docs, 42 queries, CONJ wins 41/42 — Full Report

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Made with ❤️ by the CONJUGATE team