Architecture Deep Dive
Understand how SynergyDB unifies relational, document, graph, vector, and time-series data in a single Rust-based engine -- routing queries from 16+ wire protocols through one optimized storage layer.
System Architecture
16+ protocol front-ends, one execution path, one storage engine. Every layer is purpose-built for multi-model operation including vector search and graph traversal.
Protocol Translation Pipeline
From connection handshake to response serialization -- every query follows the same six-stage pipeline, regardless of protocol.
Connection
Your application connects using its native database driver -- psycopg2 for Python/Postgres, pymongo for MongoDB, redis-py for Redis, or any standard driver. No special SDK required.
Protocol Detection
SynergyDB inspects the initial handshake bytes to identify the wire protocol. PostgreSQL starts with a startup message, MongoDB with OP_MSG, Redis with RESP, and Neo4j with Bolt. Detection completes in under 50 microseconds.
Query Parsing
A protocol-specific parser converts the incoming query into an Abstract Query Tree (AQT). SQL is parsed with a full grammar, MQL with BSON expression evaluation, Cypher with a pattern-matching compiler, and SynergyQL with its unified GRAPH_MATCH + vector_distance() syntax.
Unified Query Plan
The cross-protocol optimizer analyzes the AQT, evaluates available indexes and storage formats, and produces a cost-optimal execution plan. A Postgres SELECT and a Mongo find() for the same data yield the same plan.
Execution
The storage engine executes the plan using the optimal access pattern -- B+Tree for point lookups, HNSW for vector similarity, Tantivy for full-text, graph adjacency for traversals. All from the same underlying LSM-tree data.
Response
Results are serialized back into the original wire protocol format. A Postgres client receives a RowDescription + DataRow sequence, a MongoDB client receives a BSON document, Redis gets a RESP bulk string.
Explore Each Layer
Click on any layer to reveal the engineering details beneath the surface.
Protocol Layer
Speaks 16+ native wire protocols: PostgreSQL, MySQL, MongoDB, Neo4j Bolt, Redis, Cassandra, Elasticsearch, InfluxDB, gRPC, REST, and more. Your applications connect with existing drivers -- zero changes required.
Query Planner
A cross-protocol query optimizer that understands SQL, MQL, Cypher, GraphQL, SynergyQL, and key-value semantics -- then generates a single unified execution plan.
Storage Engine
An LSM-tree storage engine with 7+ index types: B+Tree, HNSW vector, GIN inverted, full-text (Tantivy/BM25), graph adjacency, hash, and persistent B+Tree.
Replication Layer
Raft-based consensus with MVCC transactions, 4 isolation levels, deadlock detection, and distributed 2-phase commit.
One Dataset, Every Protocol
The same patient record accessed four different ways. Same data, same cluster, same millisecond-level latency -- no synchronization required.
SELECT * FROM patients WHERE id = 'P-1001';db.patients.findOne({ _id: "P-1001" })GET patient:P-1001MATCH (p:Patient {id: 'P-1001'})-[:HAS_VISIT]->(v) RETURN p, vSELECT * FROM patients ORDER BY vector_distance(embedding, $query, 'cosine') LIMIT 5Patient P-1001's record is stored once. Each protocol accesses the single source of truth through SynergyDB's unified storage engine.
SynergyDB vs. Traditional Multi-DB Stack
Running PostgreSQL + MongoDB + Neo4j + Pinecone + InfluxDB separately means 5x the operational burden. SynergyDB collapses that into one.
| Metric | Traditional (4 Databases) | SynergyDB |
|---|---|---|
| Infrastructure Cost | 5+ servers, 5+ licenses | 1 unified cluster |
| Operational Complexity | 5+ backup strategies, 5+ monitoring configs | Unified management plane |
| Data Consistency | CDC pipelines, eventual consistency | Full ACID across all data models |
| Cross-Model Queries | Application-level joins across databases | Native SynergyQL: SQL + Graph + Vector in one query |
| AI/Vector Search | Separate vector DB + sync pipeline | Built-in HNSW index with embedding API |
| Compliance Surface | 5+ systems to audit & certify | 1 system, 1 audit (HIPAA, SOC 2, GDPR, PCI DSS) |
Performance Benchmarks
Measured on a 3-node cluster (AWS r6g.2xlarge) with 10M records. Compared against dedicated single-protocol databases on equivalent hardware.
End-to-End Latency
Lower is better. Measured across all supported protocols.
Queries per Second
Higher is better. 64 concurrent clients per protocol.
Live Migration Ingest Rate
Higher is better. Zero-downtime live migration throughput.
Methodology: Benchmarks conducted using a standardized workload generator with reproducible configurations. Traditional stack runs each database on dedicated r6g.2xlarge instances. SynergyDB runs a 3-node cluster on equivalent total compute. All tests use the same dataset, same query patterns, and same client concurrency. Full benchmark suite and reproduction scripts available in our documentation.
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