# BaraDB

![BaraDB Logo](baabaaage.png)

**A multimodal database engine written in Nim — 100% native, zero dependencies.**

[![Version](https://img.shields.io/badge/version-1.1.8-blue.svg)](baradadb.nimble)
[![Documentation](https://img.shields.io/badge/docs-2_languages-blue.svg)](docs/index.md)
[![Stars](https://img.shields.io/github/stars/katehonz/barabaDB?style=social)](https://github.com/katehonz/barabaDB)

## Documentation

📖 **[Read the documentation in your language](docs/index.md)** — English, Български

BaraDB combines document, graph, vector, columnar, and full-text search storage
in a single engine with a unified query language (BaraQL). It compiles to a
single 3.3MB binary with no runtime dependencies.

⭐ Thank you to everyone who continues to star and support BaraDB on [GitHub](https://github.com/katehonz/barabaDB)!

> **Current Status:** BaraDB is a production-ready multimodal database engine.
> All core storage engines, query processing, and protocol layers are fully
> implemented and tested. See [Limitations](#current-limitations) below for
> details on remaining edge-case improvements.

## Why BaraDB?

| Feature | GEL/EdgeDB | BaraDB |
|---|---|---|
| Core language | Python + Cython + Rust | **100% Nim** |
| Storage backend | PostgreSQL only | **Native multi-engine** |
| Vector search | pgvector extension | **Built-in HNSW/IVF-PQ** |
| Hybrid RAG search | None | **Vector + FTS + RRF reranking** |
| Graph algorithms | None | **BFS, DFS, Dijkstra, PageRank, Louvain + Cypher** |
| Graph SQL integration | None | **CREATE GRAPH, GRAPH_TABLE(), SQL-native** |
| Full-text search | PG FTS extension | **Built-in BM25 + TF-IDF** |
| Unified Search Engine | None | **HNSW + inverted index + boolean + phrase + facets + stemmers** |
| AI Agents / NL→SQL | None | **Built-in `nl_to_sql()`, `schema_prompt()`** |
| MCP Server | None | **STDIO JSON-RPC for AI tools** |
| LangChain integration | External adapters | **Native Vector Store (Python + JS)** |
| Embedded mode | No | **Yes (SQLite-like)** |
| Binary size | ~50MB+ | **3.3MB** |
| Dependencies | PostgreSQL, Python, many libs | **Zero** |

## Architecture

```
┌─────────────────────────────────────────────────────────┐
│                    CLIENT LAYER                          │
│  Binary Protocol │ HTTP/REST │ WebSocket │ Embedded      │
├─────────────────────────────────────────────────────────┤
│                 QUERY LAYER (BaraQL)                     │
│  Lexer → Parser → AST → IR → Optimizer → Codegen        │
├─────────────────────────────────────────────────────────┤
│                EXECUTION ENGINE                          │
│  Document │ Graph │ Vector │ Columnar │ FTS              │
├─────────────────────────────────────────────────────────┤
│                    STORAGE                               │
│  LSM-Tree │ B-Tree │ WAL │ Bloom Filter │ mmap           │
├─────────────────────────────────────────────────────────┤
│                DISTRIBUTED                               │
│  Raft Consensus │ Sharding │ Replication                 │
└─────────────────────────────────────────────────────────┘
```

## Formal Verification

BaraDB core distributed algorithms are formally specified and model-checked
with **TLA+** and the TLC model checker. All specs run with **weak fairness**
(`WF_vars(Next)`) to ensure realistic execution:

| Algorithm | Spec | States | Properties Verified |
|-----------|------|--------|---------------------|
| **Raft Consensus** | `formal-verification/raft.tla` | 38,051,647 | ElectionSafety, LeaderAppendOnly, StateMachineSafety, CommittedIndexValid, LogMatching, LeaderHasSelfHeartbeat |
| **Two-Phase Commit** | `formal-verification/twopc.tla` | 22,855,681 | Atomicity, NoOrphanBlocks, CoordinatorConsistency, NoDecideWithoutConsensus, ParticipantStateValid, RecoveryConsistency |
| **MVCC** | `formal-verification/mvcc.tla` | 177,721 | NoDirtyReads, ReadOwnWrites, WriteWriteConflict, CommittedMustStart, CommittedVersionsUnique, NoWriteSkew, **CommitProgress** (liveness) |
| **Replication** | `formal-verification/replication.tla` | 3,687,939 | AcksRemovePending, PendingAreKnown, AppliedLteCurrent, MonotonicLsn (temporal) |
| **Gossip (SWIM)** | `formal-verification/gossip.tla` | 692,497 | AliveNotFalselyDead, IncarnationMonotonic, DeadConsistency |
| **Deadlock Detection** | `formal-verification/deadlock.tla` | 3,767,361 | GraphIntegrity, NoSelfLoops |
| **Sharding** | `formal-verification/sharding.tla` | 186,305 | VirtualNodeMapping, NodeAssignmentConsistency, VnodeOrdering |

Run all checks locally:

```bash
cd formal-verification
bash run_all.sh
```

Or run individual specs:

```bash
cd formal-verification
java -cp tla2tools.jar tlc2.TLC -workers auto -config models/raft.cfg raft.tla
java -cp tla2tools.jar tlc2.TLC -workers auto -config models/twopc.cfg twopc.tla
java -cp tla2tools.jar tlc2.TLC -workers auto -config models/mvcc.cfg mvcc.tla
```

## Quick Start

```bash
# Build
nimble build -d:release

# Run tests
nimble test

# Run benchmarks
nimble bench

# Start server
./build/baradadb
```

## BaraQL — Query Language

BaraQL implements partial SQL:2023 coverage with extensions for graph, vector, and document queries.

### Basic Queries

```sql
-- SELECT with WHERE, ORDER BY, LIMIT
SELECT name, age FROM users WHERE age > 18 ORDER BY name LIMIT 10;

-- INSERT
INSERT users { name := 'Alice', age := 30 };

-- UPDATE
UPDATE users SET age = 31 WHERE name = 'Alice';

-- DELETE
DELETE FROM users WHERE name = 'Alice';
```

### Aggregates and Grouping

```sql
-- GROUP BY with HAVING
SELECT department, count(*), avg(salary)
FROM employees
GROUP BY department
HAVING count(*) > 5;

-- Aggregates: count, sum, avg, min, max
SELECT count(*), sum(amount), avg(price) FROM orders;
```

### JOINs

```sql
-- INNER JOIN
SELECT u.name, o.total
FROM users u
INNER JOIN orders o ON u.id = o.user_id;

-- LEFT JOIN
SELECT u.name, o.total
FROM users u
LEFT JOIN orders o ON u.id = o.user_id;

-- Multiple JOINs
SELECT *
FROM orders o
JOIN users u ON o.user_id = u.id
JOIN products p ON o.product_id = p.id;
```

### CTEs (Common Table Expressions)

```sql
-- Single CTE
WITH active_users AS (
  SELECT * FROM users WHERE active = true
)
SELECT * FROM active_users;

-- Multiple CTEs
WITH
  recent AS (SELECT * FROM orders WHERE date > '2025-01-01'),
  totals AS (SELECT user_id, sum(amount) as total FROM recent GROUP BY user_id)
SELECT u.name, t.total FROM users u JOIN totals t ON u.id = t.user_id;
```

### Subqueries

```sql
-- Subquery in FROM
SELECT * FROM (SELECT id, name FROM users WHERE active = true) AS active;

-- EXISTS subquery
SELECT name FROM users WHERE EXISTS (SELECT 1 FROM orders WHERE orders.user_id = users.id);
```

### CASE Expressions

```sql
SELECT name,
  CASE
    WHEN age < 18 THEN 'minor'
    WHEN age < 65 THEN 'adult'
    ELSE 'senior'
  END AS category
FROM users;
```

### Schema Definition

```sql
-- Create type with properties and links
CREATE TYPE Person {
  name: str,
  age: int32
};

CREATE TYPE Movie {
  title: str,
  director: Person
};
```

### JSON & JSONB

```sql
-- Create table with JSON column
CREATE TABLE events (id INT PRIMARY KEY, payload JSON);

-- Insert valid JSON
INSERT INTO events (id, payload) VALUES (1, '{"action": "click"}');

-- JSON path operators
SELECT payload->'action' AS action_json,
       payload->>'action' AS action_text
FROM events;
```

### Full-Text Search (SQL)

```sql
-- Create FTS index
CREATE INDEX idx_fts ON articles(body) USING FTS;

-- Search with BM25 ranking
SELECT * FROM articles WHERE body @@ 'machine learning';
```

### Set Operations

```sql
SELECT name FROM customers
UNION ALL
SELECT name FROM suppliers;
```

### Point-in-Time Recovery

```sql
RECOVER TO TIMESTAMP '2026-05-07T12:00:00';
```

## Storage Engines

### LSM-Tree (Key-Value)

The primary storage engine with write-optimized append-only log structure.

```nim
import barabadb/storage/lsm

var db = newLSMTree("./data")
db.put("key1", cast[seq[byte]]("value1"))
let (found, value) = db.get("key1")
db.close()
```

Components:
- **MemTable** — in-memory sorted buffer
- **WAL** — write-ahead log for durability
- **SSTable** — sorted string tables on disk
- **Bloom Filter** — probabilistic set membership
- **Compaction** — size-tiered strategy with level management
- **Page Cache** — LRU cache with hit rate tracking

### B-Tree Index

Ordered index for range scans and point lookups.

```nim
import barabadb/storage/btree

var btree = newBTreeIndex[string, string]()
btree.insert("key1", "value1")
let values = btree.get("key1")
let range = btree.scan("key_a", "key_z")
```

### Vector Engine

Native HNSW and IVF-PQ indexes for similarity search with full SQL integration.

```sql
-- SQL vector search
CREATE TABLE items (id INT PRIMARY KEY, embedding VECTOR(768));
INSERT INTO items (id, embedding) VALUES (1, '[0.1, 0.2, 0.3, ...]');

-- Nearest neighbor search
SELECT id FROM items
ORDER BY cosine_distance(embedding, '[0.1, 0.2, 0.3, ...]') ASC
LIMIT 10;

-- With HNSW index
CREATE INDEX idx_vec ON items(embedding) USING hnsw;
```

Native Nim API:
```nim
import barabadb/vector/engine

var idx = newHNSWIndex(dimensions = 128)
idx.insert(1, @[1.0'f32, 0.0'f32, ...], {"category": "A"}.toTable)
let results = idx.search(queryVector, k = 10)

# With metadata filtering
let filtered = idx.searchWithFilter(queryVector, k = 10,
  filter = proc(meta: Table[string, string]): bool =
    return meta.getOrDefault("category") == "A")
```

Features:
- **SQL vector types** — `VECTOR(n)` with dimension validation
- **SQL distance functions** — `cosine_distance()`, `euclidean_distance()`, `inner_product()`, `l1_distance()`, `l2_distance()`
- **`<->` operator** — Euclidean distance nearest-neighbor shorthand
- **HNSW index** — `CREATE INDEX ... USING hnsw` with automatic maintenance
- **IVF-PQ** — inverted file index with product quantization
- **Distance metrics** — cosine, euclidean, dot product, Manhattan
- **Quantization** — scalar 8-bit/4-bit, product, binary
- **Metadata filtering** — filter results by key-value pairs

### Hybrid RAG Search

Combine vector similarity with full-text search and reciprocal rank fusion (RRF):

```sql
-- Hybrid search: vector + FTS reranked with RRF
SELECT hybrid_search('articles', 'embedding', 'body',
                     'machine learning', '[0.1, 0.2, ...]', 10) AS results;

-- With metadata pre-filtering (tenant isolation)
SELECT hybrid_search_filtered('articles', 'embedding', 'body',
                              'AI trends', '[0.1, 0.2, ...]', 10,
                              'tenant_id', 'company-a') AS results;

-- Re-rank existing results
SELECT rerank('machine learning', '[{"id":"1","score":"0.9"}, ...]') AS boosted;
```

Features:
- **Reciprocal Rank Fusion** — merges HNSW vector and BM25 FTS rankings
- **Metadata pre-filtering** — HNSW search with relational column filters
- **SQL functions** — `hybrid_search()`, `hybrid_search_ids()`, `hybrid_search_filtered()`, `rerank()`

### Graph Engine

Adjacency list storage with built-in algorithms.

```nim
import barabadb/graph/engine

var g = newGraph()
let alice = g.addNode("Person", {"name": "Alice"}.toTable)
let bob = g.addNode("Person", {"name": "Bob"}.toTable)
discard g.addEdge(alice, bob, "knows")

# Traversal
let bfs = g.bfs(alice)
let dfs = g.dfs(alice)
let path = g.shortestPath(alice, bob)
let ranks = g.pageRank()
```

Algorithms:
- **BFS/DFS** — breadth-first and depth-first traversal
- **Dijkstra** — shortest weighted path
- **PageRank** — node importance ranking
- **Louvain** — community detection
- **Pattern matching** — subgraph isomorphism search
- **Similarity** — Jaccard / Adamic-Adar node similarity
- **node2vec** — random-walk graph embeddings

### Graph SQL Integration

Graph data is queryable directly through BaraQL with `CREATE GRAPH`, `GRAPH_TABLE()`, and Cypher translation:

```sql
-- Create a native graph
CREATE GRAPH social_network;

-- Query via GRAPH_TABLE with algorithms
SELECT * FROM GRAPH_TABLE(
  social_network,
  MATCH (u:User)-[:KNOWS]->(f:User)
  ALGORITHM BFS
  START u.id = 1
  MAXDEPTH 3
);

-- Translate Cypher to BaraQL SQL
SELECT cypher('MATCH (u:User)-[:KNOWS]->(f) RETURN f.name') AS result;
```

Features:
- **Native graph DDL** — `CREATE GRAPH` / `DROP GRAPH`
- **SQL GRAPH_TABLE** — `MATCH`, `ALGORITHM`, `START`, `END`, `MAXDEPTH`
- **Auto-sync** — INSERT into `_nodes` / `_edges` syncs adjacency lists
- **Cypher layer** — `cypher()` SQL function translates `MATCH...RETURN` to BaraQL

## AI-Native Data Platform

BaraDB is the first database engine with built-in AI primitives — not bolted-on, but native to the query engine. RAG pipelines, LLM integration, and AI agent tools run inside the database with full multi-tenant RLS isolation.

### Natural Language → SQL

Ask questions in plain English (or any language) and get executable BaraQL:

```sql
-- Generate SQL from natural language
SELECT nl_to_sql('Show me the top 5 customers by total orders') AS query;

-- Schema-aware prompt for LLM context
SELECT schema_prompt('orders') AS context;
```

Features:
- **Schema-aware** — includes table definitions, indexes, RLS policies in the prompt
- **Validation layer** — wraps generated SQL in `LIMIT 0` to verify syntax before returning
- **Self-correction** — on error, feeds the error back to the LLM for an automatic fix
- **Tenant-aware** — respects `app.tenant_id` session variables
- **OpenAI + Ollama** — configurable via `BARADB_LLM_ENDPOINT`, `BARADB_LLM_MODEL`, `BARADB_LLM_API_KEY`

### Text Chunking & Auto-Embedding

Built-in text chunking and embedding generation for RAG pipelines:

```sql
-- Chunk text into overlapping pieces
SELECT chunk(long_article, 1024, 128) AS chunks;

-- Generate embeddings via external API (OpenAI / Ollama)
SELECT embed_text('Hello world') AS vector;
```

Features:
- **chunk() SQL function** — recursive splitting by paragraph, sentence, or fixed size
- **embed_text() SQL function** — HTTP embedding client with configurable endpoint
- **Auto-embedding on INSERT** — when a `VECTOR` column is NULL but `TEXT` is present, embeddings generate automatically
- **Configurable** via `BARADB_EMBED_ENDPOINT`, `BARADB_EMBED_MODEL`, `BARADB_EMBED_API_KEY`

### MCP Server (Model Context Protocol)

BaraDB exposes an MCP server over STDIO for AI agent integration:

```bash
./build/baramcp
```

Tools available to AI agents:
- **query** — execute parameterized BaraQL with RLS isolation
- **vector_search** — semantic HNSW search with metadata filtering
- **schema_inspect** — explore tables, columns, indexes, and RLS policies

```json
{
  "name": "vector_search",
  "arguments": {
    "table": "docs",
    "column": "embedding",
    "query_vector": [0.1, 0.2, ...],
    "k": 10,
    "tenant_id": "company-a"
  }
}
```

### LangChain Integration

Native Vector Store implementations for Python and JavaScript:

**Python:**
```python
from baradb.langchain_store import BaraDBStore

store = BaraDBStore(
    client=client,
    table="docs",
    embedding_function=OpenAIEmbeddings().embed_query,
    tenant_id="company-a"
)
await store.add_texts(["hello world", "quick brown fox"])
results = await store.similarity_search("hello", k=5)
```

**JavaScript:**
```javascript
const { BaraDBStore } = require('./baradb_langchain');

const store = new BaraDBStore({
  client,
  table: 'docs',
  embeddingFunction: async (text) => [...],
  tenantId: 'company-a'
});
await store.addDocuments([{ pageContent: 'hello world' }]);
const results = await store.similaritySearch('hello', 5);
```

Features:
- **Hybrid search** — uses `hybrid_search()` / `hybrid_search_filtered()` under the hood
- **MMR reranking** — `max_marginal_relevance_search()` for diverse results
- **Multi-tenant** — respects `tenant_id` with RLS isolation
- **Metadata filters** — pre-filter vector search by relational columns

### Chat Message History

Store conversation threads in BaraDB with RLS isolation:

```python
from baradb.chat_history import BaraDBChatHistory

history = BaraDBChatHistory(
    client=client,
    session_id="session-123",
    tenant_id="company-a",
    user_id="user-42"
)
history.add_user_message("Hello, AI!")
history.add_ai_message("Hello, how can I help?")
messages = history.messages
```

### Full-Text Search

Inverted index with BM25 and TF-IDF ranking.

```nim
import barabadb/fts/engine

var idx = newInvertedIndex()
idx.addDocument(1, "Nim is a fast programming language")
idx.addDocument(2, "Python is popular for data science")

# BM25 search
let results = idx.search("programming language")

# TF-IDF search
let tfidf = idx.searchTfidf("programming language")

# Fuzzy search (typo tolerance)
let fuzzy = idx.fuzzySearch("programing", maxDistance = 2)

# Wildcard search
let wild = idx.regexSearch("prog*")
```

### Unified Search Engine

A high-performance search module combining heap-optimized HNSW, segment-based
inverted indexing, boolean queries, phrase/proximity search, n-gram fuzzy
matching, faceted search, and multilingual stemming into a single
`UnifiedSearchEngine` API.

```nim
import barabadb/search/engine

var se = newUnifiedSearchEngine()

# Index documents with fields and facets
se.addDocument(1, "Introduction to Machine Learning",
  fields = {"category": "AI", "lang": "en"}.toTable)
se.addDocument(2, "Deep Learning with Neural Networks",
  fields = {"category": "AI", "lang": "en"}.toTable)
se.addDocument(3, "Nim Programming Language Guide",
  fields = {"category": "programming", "lang": "en"}.toTable)

# Boolean query (AND / OR / NOT / ranges)
let boolResults = se.booleanSearch("machine AND learning")

# Phrase search with proximity
let phraseResults = se.phraseSearch("deep learning", slop = 2)

# N-gram fuzzy search (typo-tolerant)
let fuzzyResults = se.ngramSearch("machne lerning", n = 3)

# Faceted search — filter and aggregate by field values
let facetResults = se.facetedSearch("learning",
  facetFields = @["category", "lang"])

# Stemming in multiple languages (Porter2: EN, BG, DE, FR, RU)
let stemmed = se.search("running", stemmer = porter2EN)
```

Features:
- **Heap-optimized HNSW** — priority-queue-based graph traversal, 2.4x faster than baseline
- **Segment-based inverted index** — partitioned posting lists for concurrent indexing
- **Phrase and proximity search** — ordered phrase matching with configurable slop
- **Boolean query parser** — AND, OR, NOT, range expressions (`price:[10 TO 100]`)
- **N-gram fuzzy search** — character n-gram index for typo-tolerant retrieval
- **Faceted search** — filter results and aggregate counts by field values
- **Porter2 stemmers** — English, Bulgarian, German, French, Russian
- **UnifiedSearchEngine API** — single entry point combining all search modes
- **Search benchmarks** — `benchmarks/bench_search.nim` for reproducible measurement

### Columnar Engine

Column-oriented storage for analytical queries.

```nim
import barabadb/core/columnar

var batch = newColumnBatch()
var ageCol = batch.addInt64Col("age")
var nameCol = batch.addStringCol("name")
ageCol.appendInt64(25)
nameCol.appendString("Alice")

# Aggregates
echo ageCol.sumInt64()
echo ageCol.avgInt64()

# Encoding
let rle = rleEncode(@[1'i64, 1, 1, 2, 2, 3])
let dict = dictEncode(@["apple", "banana", "apple"])
```

## Transactions

MVCC with snapshot isolation and deadlock detection.

```nim
import barabadb/core/mvcc

var tm = newTxnManager()
let txn = tm.beginTxn()
discard tm.write(txn, "key1", cast[seq[byte]]("value1"))
discard tm.write(txn, "key2", cast[seq[byte]]("value2"))

# Savepoint
tm.savepoint(txn)
discard tm.write(txn, "key3", cast[seq[byte]]("value3"))
discard tm.rollbackToSavepoint(txn)  # undo key3

discard tm.commit(txn)
```

## Protocol

### Binary Wire Protocol

16 message types with big-endian serialization.

```nim
import barabadb/protocol/wire

let msg = makeQueryMessage(1, "SELECT * FROM users")
let ready = makeReadyMessage(1)
let error = makeErrorMessage(1, 42, "Syntax error")
```

### HTTP/REST API

```nim
import barabadb/protocol/http

var router = newHttpRouter(port = 9470)
router.get("/api/users", proc(req: Request): Future[JsonNode] {.async.} =
  return %*[{"id": 1, "name": "Alice"}])
```

### WebSocket Streaming

```nim
import barabadb/protocol/websocket

var server = newWsServer(port = 9471)
server.onMessage = proc(ws: WebSocket, data: seq[byte]) {.gcsafe.} =
  echo "Received: ", cast[string](data)
asyncCheck server.run()
```

### Authentication

```nim
import barabadb/protocol/auth

var am = newAuthManager("secret-key")
let token = am.createToken(JWTClaims(sub: "user1", role: "admin"))
let result = am.validateCredentials(AuthCredentials(authMethod: amToken, payload: token))
```

### Rate Limiting

```nim
import barabadb/protocol/ratelimit

var rl = newRateLimiter(rlaTokenBucket, globalRate = 1000, perClientRate = 100)
if rl.allowRequest("client-123"):
  echo "Request allowed"
```

## Schema System

```nim
import barabadb/schema/schema

var s = newSchema()

let person = newType("Person")
person.addProperty("name", "str", required = true)
person.addProperty("age", "int32")
s.addType("default", person)

# Inheritance
let employee = newType("Employee")
employee.setBases(@["Person"])
employee.addProperty("department", "str")
s.addType("default", employee)

# Resolve inheritance — Employee gets name, age, department
let resolved = s.resolveInheritance(employee)

# Diff schemas
let diff = s.diff(oldSchema, newSchema)
```

## Distributed

### Raft Consensus

```nim
import barabadb/core/raft

var cluster = newRaftCluster()
cluster.addNode("node1")
cluster.addNode("node2")
cluster.addNode("node3")

let n1 = cluster.nodes["n1"]
n1.becomeCandidate()
n1.becomeLeader()
let entry = n1.appendLog("SET key1 value1")
```

### Sharding

```nim
import barabadb/core/sharding

var router = newShardRouter(ShardConfig(numShards: 4, replicas: 2, strategy: ssHash))
router.rebalance(@["node1", "node2", "node3"])
let shard = router.getShard("user_123")
```

### Replication

```nim
import barabadb/core/replication

var rm = newReplicationManager(rmSync)
rm.addReplica(newReplica("r1", "10.0.0.1", 9472))
rm.connectReplica("r1")
let lsn = rm.writeLsn(@[1'u8, 2, 3])
rm.ackLsn("r1", lsn)  # blocks until acked
```

## User Defined Functions

```nim
import barabadb/query/udf

var reg = newUDFRegistry()
reg.registerStdlib()  # abs, sqrt, pow, lower, upper, len, trim, substr, toString, toInt

# Custom function
reg.register("greet", @[UDFParam(name: "name", typeName: "str")],
  "str", proc(args: seq[Value]): Value =
    return Value(kind: vkString, strVal: "Hello, " & args[0].strVal & "!"))
```

## Performance Benchmarks

BaraDB is optimized for high throughput across all storage engines. Below are
**real measured results** on AMD Ryzen 9 5900X, NVMe SSD:

### BaraDB Standalone

| Engine | Operation | Throughput | Latency |
|--------|-----------|------------|---------|
| **LSM-Tree** | Write 100K keys | ~32.2K ops/s | 31.0 µs/op |
| **LSM-Tree** | Read 100K keys | ~4.0M ops/s | 0.25 µs/op |
| **B-Tree** | Insert 100K keys | ~2.5M ops/s | 0.40 µs/op |
| **B-Tree** | Point lookup 100K | ~2.3M ops/s | 0.43 µs/op |
| **Vector (HNSW)** | Insert 10K vectors (dim=128) | ~543 ops/s | 1.8 ms/op |
| **Vector (HNSW)** | Search top-10 | ~2.6 ms/query | — |
| **Vector (SIMD)** | Cosine distance (dim=768, n=10K) | ~1.17M ops/s | 0.85 µs/op |
| **FTS** | Index 10K documents | ~120K docs/s | 8.3 µs/doc |
| **FTS** | BM25 search (1K queries) | ~1.36K queries/s | 0.73 ms/query |
| **Graph** | Add 1K nodes | ~931K nodes/s | 1.1 µs/node |
| **Graph** | BFS traversal (100×) | ~5.6K traversals/s | 179 µs/traversal |
| **Graph** | PageRank (1K nodes, 5K edges) | ~1.6K graphs/s | 6.1 ms/graph |

### BaraDB vs PostgreSQL (Real Comparison)

| Test | PostgreSQL | BaraDB | Speedup |
|------|-----------|--------|---------|
| KV Write (100K) | 16.82K/s | 33.24K/s | **2.0x** |
| KV Read (100K) | 15.08K/s | 3.88M/s | **257.0x** |
| BTree Insert (100K) | 17.66K/s | 2.50M/s | **141.6x** |
| BTree Get (100K) | 14.50K/s | 2.64M/s | **182.3x** |
| BTree Scan (1K ranges) | 2.39K/s | 7.97M/s | **3340.9x** |
| FTS Index (10K docs) | 17.98K/s | 123.65K/s | **6.9x** |
| FTS Search (1K queries) | 784.12/s | 1.34K/s | **1.7x** |

**Overall:** BaraDB is **6.8x faster** for in-process/embedded workloads.
*(Note: PostgreSQL includes network round-trip overhead. BaraDB now outperforms PostgreSQL on all tested metrics including FTS after optimizations.)*

Run benchmarks yourself:

```bash
nim c -d:ssl -d:release -r benchmarks/bench_all.nim
```

## Docker Deployment

### Quick Start

```bash
docker build -t baradb:latest .
docker compose up -d
```

### Docker Files

| File | Purpose |
|------|---------|
| `Dockerfile` | Production-ready image (pre-built binary) |
| `Dockerfile.source` | Build from source |
| `docker-compose.yml` | Development |
| `docker-compose.prod.yml` | Production with TLS, backups, resource limits |
| `docker-entrypoint.sh` | Container initialization |

### Production

```bash
docker compose -f docker-compose.prod.yml up -d
```

See [docs/en/docker.md](docs/en/docker.md) for full Docker documentation.

### Ports

| Port | Description |
|------|-------------|
| `9472` | TCP binary protocol |
| `9912` | HTTP/REST API (TCP port + 440) |
| `9913` | WebSocket (TCP port + 441) |

### Environment Variables

| Variable | Default | Description |
|----------|---------|-------------|
| `BARADB_ADDRESS` | `0.0.0.0` | Bind address |
| `BARADB_PORT` | `9472` | TCP binary protocol port |
| `BARADB_DATA_DIR` | `/data` | Data directory |
| `BARADB_LOG_LEVEL` | `info` | Log level |
| `BARADB_TLS_ENABLED` | `false` | Enable TLS |
| `BARADB_CERT_FILE` | — | TLS certificate path |
| `BARADB_KEY_FILE` | — | TLS private key path |

## Built with BaraDB

### NodeBara

**[NodeBara](https://codeberg.org/baraDB/nodebara)** is the first large-scale application running on BaraDB — a modern forum platform forked from NodeBB and fully adapted for BaraDB's native multimodal engine.

- **Concurrent query safety** — TCP request queue in the JS client handles NodeBara's parallel startup queries without frame corruption
- **Numeric accuracy** — Big-endian float serialization guarantees correct zset scores, timestamps, and rankings across platforms
- **Non-blocking cluster gossip** — Async UDP sockets keep the event loop free under load

```bash
git clone https://codeberg.org/baraDB/nodebara
cd nodebara
npm install
npm run setup   # uses BaraDB as the default database
```

## Client SDKs

BaraDB provides official clients for multiple languages:

### JavaScript/TypeScript

```bash
npm install baradb
```

```javascript
import { Client } from 'baradb';
const client = new Client('localhost', 9472);
await client.connect();
const result = await client.query("SELECT name FROM users WHERE age > 18");
console.log(result.rows);
await client.close();
```

### Python

```bash
pip install baradb
```

```python
from baradb import Client
client = Client("localhost", 9472)
client.connect()
result = client.query("SELECT name FROM users WHERE age > 18")
print(result.rows)
client.close()
```

### Nim (Embedded)

```nim
import barabadb

var db = newLSMTree("./data")
db.put("key", cast[seq[byte]]("value"))
let (found, val) = db.get("key")
db.close()
```

### Rust

```toml
[dependencies]
baradb = "0.1"
```

```rust
use baradb::Client;
let mut client = Client::connect("localhost:9472").await?;
let result = client.query("SELECT name FROM users").await?;
```

## Security

### TLS/SSL

BaraDB supports TLS out of the box. If no certificate is provided, it auto-generates
a self-signed one on startup:

```bash
# With custom certificates
BARADB_TLS_ENABLED=true \
  BARADB_CERT_FILE=/etc/baradb/server.crt \
  BARADB_KEY_FILE=/etc/baradb/server.key \
  ./build/baradadb
```

### Authentication

JWT-based authentication with role-based access control:

```nim
import barabadb/protocol/auth

var am = newAuthManager("secret-key")
let token = am.createToken(JWTClaims(sub: "user1", role: "admin"))
let result = am.validateCredentials(...)
```

### Rate Limiting

Token-bucket rate limiting per client and globally:

```nim
var rl = newRateLimiter(rlaTokenBucket, globalRate = 10000, perClientRate = 1000)
```

## Configuration

BaraDB can be configured via environment variables or a config file:

```bash
# Environment variables
export BARADB_PORT=9472
export BARADB_HTTP_PORT=9470
export BARADB_DATA_DIR=/var/lib/baradb
export BARADB_LOG_LEVEL=info
export BARADB_COMPACTION_INTERVAL=60000

# Or create baradb.conf
port = 9472
http_port = 9470
data_dir = "/var/lib/baradb"
log_level = "info"
compaction_interval_ms = 60000
```

## Monitoring & Observability

### Built-in Metrics

BaraDB exposes operational metrics via the HTTP API:

```bash
curl http://localhost:9470/metrics
```

Example response:

```json
{
  "queries_total": 152340,
  "queries_per_second": 1240,
  "storage_lsm_size_bytes": 2147483648,
  "storage_sstables": 12,
  "cache_hit_rate": 0.94,
  "active_connections": 42,
  "txns_active": 7,
  "txns_committed": 89123,
  "txns_rolled_back": 12
}
```

### OpenTelemetry Tracing

Built-in lightweight tracing with OTLP/HTTP export:

```nim
import barabadb/core/tracing

defaultTracer.enable()
let span = defaultTracer.beginSpan("SELECT users")
# ... query execution ...
defaultTracer.endSpan(span)

# Export to Jaeger/OTLP collector
discard defaultTracer.exportOtlp("http://localhost:4318/v1/traces")
```

### Health Check

```bash
curl http://localhost:9470/health
```

### Logging

Structured logging with configurable levels (`debug`, `info`, `warn`, `error`):

```bash
BARADB_LOG_LEVEL=debug ./build/baradadb
```

## Backup & Recovery

BaraDB includes a built-in backup manager that creates compressed tar.gz
snapshots of your data directory. The manager supports **online backups**
(server does not need to stop), **integrity verification**, **retention policies**,
**dry-run restore previews**, **automatic rollback protection**, and a full
**restore history log**.

### Quick Reference

| Command | Purpose |
|---------|---------|
| `backup backup` | Create a new snapshot |
| `backup restore` | Restore data from a snapshot (auto-verifies first) |
| `backup list` | Show all snapshots |
| `backup verify` | Check archive integrity without extracting |
| `backup cleanup` | Delete old snapshots, keep N most recent |
| `backup history` | Show log of all restore operations |
| `backup help` | Show full help text |

### Build the Backup Tool

```bash
nim c -o:build/backup src/barabadb/core/backup.nim
```

For production use, compile with release optimizations:

```bash
nim c -d:release -o:build/backup src/barabadb/core/backup.nim
```

### Creating Backups

**Basic backup** — creates `backup_<timestamp>.tar.gz` in the current directory:

```bash
./build/backup backup
```

**Custom output path**:

```bash
./build/backup backup --output=/backups/prod_$(date +%F).tar.gz
```

**Maximum compression** (slower, smaller file):

```bash
./build/backup backup --level=9
```

**Exclude WAL logs and temporary files**:

```bash
./build/backup backup \
  --exclude="*.log" \
  --exclude="wal/*" \
  --exclude="tmp/*"
```

**Verbose output** (shows tar command and progress):

```bash
./build/backup backup --verbose
```

### Listing Backups

```bash
./build/backup list
```

Example output:

```
Found 3 backup(s):
--------------------------------------------------------------------------------
#   Timestamp            Size        Path
--------------------------------------------------------------------------------
1   2026-05-06 23:04:56  12.45 MB    backup_1715011200.tar.gz
2   2026-05-05 12:30:00  11.20 MB    backup_1714921800.tar.gz
3   2026-05-04 08:15:22  10.89 MB    backup_1714834522.tar.gz
--------------------------------------------------------------------------------
```

### Verifying Backups

Always verify a snapshot before restoring, especially after transferring it
over the network. The restore command does this automatically, but you can
also check manually:

```bash
./build/backup verify --input=backup_1715011200.tar.gz
```

A valid archive prints:

```
Archive is valid: backup_1715011200.tar.gz (12.45 MB)
```

A corrupted archive prints an error and exits with code 1.

### Restoring from Backup

The restore command follows a **safe restore workflow**:

1. **Verify** archive integrity automatically
2. **Prompt** for confirmation (unless `--force` is used)
3. **Move** existing data to `data/server.old_<timestamp>`
4. **Extract** the archive
5. **Rollback** automatically if extraction fails
6. **Log** the operation to `backup_history.log`

> ⚠️ **WARNING:** Restore replaces the existing data directory. The old data
> is automatically moved to `data/server.old_<timestamp>` before extraction.
> If extraction fails, the tool attempts an automatic rollback to the old data.

**Interactive restore** (asks for confirmation):

```bash
./build/backup restore --input=backup_1715011200.tar.gz
```

You will be prompted:

```
Verifying archive before restore...
Archive is valid: backup_1715011200.tar.gz (12.45 MB)
WARNING: This will REPLACE the data in: data/server
Continue? [y/N]
```

**Force restore** — skip confirmation (for scripts and automation):

```bash
./build/backup restore --input=backup.tar.gz --force
```

**Dry-run restore** — preview what would happen without making changes:

```bash
./build/backup restore --input=backup.tar.gz --dry-run
```

Output:

```
DRY-RUN: The following actions would be performed:
  1. Verify archive integrity: backup.tar.gz
  2. Move existing data to:    data/server.old_1778099200
  3. Extract archive to:       data/server
  Archive size: 12.45 MB
  Free space:   45.20 GB
```

**Restore to a different data directory**:

```bash
./build/backup restore --input=backup.tar.gz --data-dir=data/recovered
```

**Verbose restore** (shows all steps and disk space check):

```bash
./build/backup restore --input=backup.tar.gz --verbose
```

### Restore History

Every restore operation is logged to `backup_history.log` in the current
directory. View the history:

```bash
./build/backup history
```

Example output:

```
Restore history:
--------------------------------------------------------------------------------
[2026-05-06 23:15:00] SUCCESS restore from /backups/backup_1715011200.tar.gz to /opt/baradb/data/server
[2026-05-06 22:30:15] FAILED  restore from /backups/backup_1715007000.tar.gz to /opt/baradb/data/server
[2026-05-05 08:00:00] DRY-RUN restore from /backups/backup_1714900000.tar.gz to /opt/baradb/data/server
--------------------------------------------------------------------------------
```

### Cleanup & Retention

Delete old snapshots automatically, keeping only the N most recent:

```bash
# Keep last 5 snapshots (default)
./build/backup cleanup

# Keep last 3 snapshots
./build/backup cleanup --keep=3

# Verbose — shows which files are deleted
./build/backup cleanup --keep=3 --verbose
```

### Automated Backups with Cron

Add to your crontab for daily backups at 2 AM:

```bash
# Edit crontab
crontab -e

# Add this line for daily backups
0 2 * * * cd /opt/baradb && ./build/backup backup --output=/backups/baradb_$(date +\%F).tar.gz --level=6 >> /var/log/baradb-backup.log 2>&1

# Weekly cleanup — keep last 7 snapshots
0 3 * * 0 cd /opt/baradb && ./build/backup cleanup --keep=7 >> /var/log/baradb-backup.log 2>&1
```

### Disaster Recovery Best Practices

1. **3-2-1 Rule:** Keep 3 copies, on 2 different media, with 1 offsite.
2. **Verify regularly:** Run `backup verify` on archived snapshots monthly.
3. **Test restores:** Perform a dry-run restore (`--dry-run`) weekly and a
   full test restore to a staging environment monthly.
4. **Monitor disk space:** The restore command warns if free space is less
   than 2× the archive size.
5. **Keep old data:** After restore, the previous data is preserved as
   `data/server.old_<timestamp>`. Only delete it after confirming the new
   data works.
6. **Log audit trail:** Use `backup history` to track all restore operations.

### Nim API

You can also use the backup module programmatically:

```nim
import barabadb/core/backup

# Create a snapshot
let ok = backupDataDir("data/server", "snapshot.tar.gz")
if not ok:
  echo "Backup failed"

# List existing snapshots
let backups = listBackups("data/server")
for b in backups:
  echo b.path, " → ", formatBytes(b.size)

# Verify without extracting
let valid = verifyArchive("snapshot.tar.gz")

# Restore with rollback protection
let restored = restoreDataDir("snapshot.tar.gz", "data/server")

# Dry-run restore — preview without changes
let preview = restoreDataDir("snapshot.tar.gz", "data/server", dryRun = true)

# Cleanup retention
cleanupOldBackups("data/server", keepLast = 5)

# Read restore history
for entry in readHistory():
  echo entry
```

### Full Option Reference

| Option | Short | Default | Description |
|--------|-------|---------|-------------|
| `--data-dir` | `-d` | `data/server` | Path to the data directory |
| `--output` | `-o` | auto-generated | Destination path for new backup |
| `--input` | `-i` | — | Source archive for restore/verify |
| `--keep` | `-k` | `5` | Number of snapshots to retain |
| `--exclude` | `-e` | — | Exclude pattern (repeatable) |
| `--level` | `-l` | `6` | Gzip compression 0-9 |
| `--dry-run` | — | off | Preview restore without changes |
| `--force` | `-f` | off | Skip confirmation prompts |
| `--verbose` | `-v` | off | Detailed progress output |
| `--help` | `-h` | — | Show help text |

### Exit Codes

| Code | Meaning |
|------|---------|
| `0` | Success |
| `1` | Error (invalid args, missing files, verification or extraction failure) |

### Point-in-Time Recovery (WAL)

For fine-grained recovery, replay the WAL from a checkpoint:

```bash
./build/baradadb --recover --wal-dir=./wal --checkpoint=/backup/snapshot.tar.gz
```

### Cross-Modal Queries

One of BaraDB's unique strengths is querying across storage engines in a single
BaraQL statement:

```sql
-- Find articles about "machine learning" similar to a vector
SELECT a.title, a.score
FROM articles a
WHERE MATCH(a.body) AGAINST('machine learning')
ORDER BY cosine_distance(a.embedding, [0.1, 0.2, ...])
LIMIT 10;

-- Graph + vector: find friends with similar taste
MATCH (u:User)-[:KNOWS]->(friend:User)
WHERE u.name = 'Alice'
ORDER BY cosine_distance(friend.taste_vector, u.taste_vector)
RETURN friend.name;

-- Full-text + aggregate: top departments by article count
SELECT department, count(*) as articles
FROM docs
WHERE MATCH(content) AGAINST('Nim programming')
GROUP BY department
ORDER BY articles DESC;
```

## Troubleshooting

### Port Already in Use

```
Error: unhandled exception: Address already in use
```

**Fix:** Change the port or kill the existing process:

```bash
BARADB_PORT=5433 ./build/baradadb
# or
lsof -ti:9472 | xargs kill -9
```

### SSL Compilation Error

```
Error: BaraDB requires SSL support. Compile with -d:ssl
```

**Fix:** Always compile with `-d:ssl`:

```bash
nim c -d:ssl -d:release -o:build/baradadb src/baradadb.nim
```

### Permission Denied on Data Directory

**Fix:** Ensure the data directory exists and is writable:

```bash
mkdir -p ./data && chmod 755 ./data
```

### High Memory Usage

**Fix:** Tune the MemTable size and page cache:

```bash
export BARADB_MEMTABLE_SIZE_MB=64
export BARADB_CACHE_SIZE_MB=256
```

## Project Structure

```
src/barabadb/
├── core/
│   ├── types.nim         # Type system (17 native types)
│   ├── config.nim        # Configuration loader (env + file)
│   ├── server.nim        # Async TCP wire-protocol server
│   ├── httpserver.nim    # Multi-threaded HTTP/REST server
│   ├── websocket.nim     # WebSocket streaming server
│   ├── mvcc.nim          # Multi-version concurrency control
│   ├── deadlock.nim      # Wait-for graph deadlock detection
│   ├── raft.nim          # Raft consensus (leader election + log replication)
│   ├── sharding.nim      # Hash / range / consistent-hash sharding
│   ├── replication.nim   # Sync / async / semi-sync replication
│   ├── gossip.nim        # SWIM-like membership & failure detection
│   ├── disttxn.nim       # Two-phase commit distributed transactions
│   ├── crossmodal.nim    # Cross-engine query federation
│   ├── columnar.nim      # Columnar storage + RLE/dict encoding
│   ├── backup.nim        # Online snapshot & point-in-time recovery
│   ├── recovery.nim      # WAL replay & crash recovery
│   ├── logging.nim       # Structured logging
│   └── fileops.nim       # Async file I/O utilities
├── storage/
│   ├── lsm.nim           # LSM-Tree storage engine (MemTable + SSTable)
│   ├── btree.nim         # B-Tree ordered index
│   ├── wal.nim           # Write-ahead log for durability
│   ├── bloom.nim         # Bloom filter for SSTable skip
│   ├── compaction.nim    # Size-tiered compaction + LRU page cache
│   └── mmap.nim          # Memory-mapped file I/O
├── query/
│   ├── lexer.nim         # Tokenizer (80+ token types)
│   ├── parser.nim        # Recursive descent BaraQL parser
│   ├── ast.nim           # Abstract syntax tree (25+ node kinds)
│   ├── ir.nim            # Intermediate representation & execution plans
│   ├── codegen.nim       # IR → storage-engine code generation
│   ├── executor.nim      # Query execution engine
│   ├── adaptive.nim      # Adaptive query optimization
│   └── udf.nim           # User-defined function registry
├── vector/
│   ├── engine.nim        # HNSW + IVF-PQ index implementations
│   ├── quant.nim         # Scalar / product / binary quantization
│   └── simd.nim          # SIMD-optimized distance functions
├── graph/
│   ├── engine.nim        # Adjacency-list graph + BFS/DFS/Dijkstra/PageRank
│   ├── community.nim     # Louvain community detection
│   └── cypher.nim        # Cypher-to-SQL translator + query parser
├── ai/
│   ├── llm.nim           # LLM client for NL→SQL (OpenAI / Ollama)
│   ├── chunk.nim         # Text chunking for RAG pipelines
│   └── embed.nim         # HTTP embedding client (OpenAI / Ollama)
├── mcp/
│   └── server.nim        # MCP STDIO server (JSON-RPC 2.0 AI tools)
├── fts/
│   ├── engine.nim        # Inverted index + BM25 + TF-IDF
│   └── multilang.nim     # Tokenizers for EN, BG, DE, FR, RU
├── search/
│   ├── engine.nim        # UnifiedSearchEngine — single entry point
│   ├── hnsw_opt.nim      # Heap-optimized HNSW (priority-queue traversal)
│   ├── inverted.nim      # Segment-based inverted index
│   ├── phrase.nim        # Phrase and proximity search
│   ├── boolean.nim       # Boolean query parser (AND/OR/NOT/ranges)
│   ├── ngram.nim         # N-gram fuzzy search
│   ├── facet.nim         # Faceted search (field filtering + aggregation)
│   ├── stemmer.nim       # Porter2 stemmers (EN/BG/DE/FR/RU)
│   └── priority_queue.nim # Min-heap priority queue for HNSW candidates
├── protocol/
│   ├── wire.nim          # Binary wire protocol (16 message types)
│   ├── http.nim          # HTTP/REST JSON router
│   ├── websocket.nim     # WebSocket frame handler
│   ├── pool.nim          # Connection pool
│   ├── auth.nim          # JWT + HMAC authentication
│   ├── ratelimit.nim     # Token-bucket rate limiter
│   ├── ssl.nim           # TLS/SSL certificate management
│   └── zerocopy.nim      # Zero-copy buffer management
├── schema/
│   └── schema.nim        # Strong types, links, inheritance, migrations
├── client/
│   ├── client.nim        # Nim binary-protocol client
│   └── fileops.nim       # Client-side file helpers
└── cli/
    └── shell.nim         # Interactive BaraQL REPL
```

## Tests

```bash
# Run all tests (448 tests, 60+ suites)
nim c --path:src -r tests/test_all.nim

# Run benchmarks
nim c -d:release -r benchmarks/bench_all.nim
```

## Roadmap Progress

| Phase | Status | Progress | Since |
|-------|--------|----------|-------|
| Core (LSM + B-Tree + compaction + cache + mmap) | ✅ | 100% | v1.0.0 |
| BaraQL (GROUP BY + JOIN + CTE + aggregates + codegen + UDF) | ✅ | 100% | v1.0.0 |
| Multimodal storage (KV + graph + vector + columnar + FTS) | ✅ | 100% | v1.0.0 |
| Transactions (MVCC + deadlock + WAL + savepoints) | ✅ | 100% | v1.0.0 |
| Protocol (binary + HTTP + WS + pool + auth + ratelimit) | ✅ | 100% | v1.0.0 |
| Schema (inheritance + computed + migrations) | ✅ | 100% | v1.0.0 |
| Vector engine (HNSW + IVF-PQ + quant + SIMD + metadata) | ✅ | 100% | v1.0.0 |
| Vector SQL Integration (VECTOR type, distance functions, <->, HNSW indexes) | ✅ | 100% | v1.1.6 |
| Graph engine (all algorithms + pattern matching) | ✅ | 100% | v1.0.0 |
| FTS (BM25 + TF-IDF + fuzzy + regex + multi-language) | ✅ | 100% | v1.0.0 |
| CLI shell | ✅ | 100% | v1.0.0 |
| Cluster (Raft + sharding + replication + gossip) | ✅ | 100% | v1.0.0 |
| Cross-modal queries | ✅ | 100% | v1.0.0 |
| Backup & Recovery | ✅ | 100% | v1.0.0 |
| Client SDKs (JS, Python, Nim, Rust) | ✅ | 100% | v1.0.0 |
| Graph SQL Integration (CREATE GRAPH, GRAPH_TABLE, Cypher) | ✅ | 100% | v1.1.6 |
| Hybrid RAG Search (vector + FTS + RRF reranking) | ✅ | 100% | v1.1.6 |
| AI Chunking & Auto-Embedding (`chunk()`, `embed_text()`) | ✅ | 100% | v1.1.6 |
| NL→SQL (`nl_to_sql()`, `schema_prompt()`) | ✅ | 100% | v1.1.6 |
| MCP Server (STDIO JSON-RPC for AI agents) | ✅ | 100% | v1.1.6 |
| LangChain Vector Store (Python + JS) | ✅ | 100% | v1.1.6 |
| Production Hardening (prop tests, fuzz tests, thread safety) | ✅ | 100% | v1.1.6 |
| Unified Search Engine (HNSW-opt + inverted + boolean + phrase + n-gram + facets + stemmers) | ✅ | 100% | v1.2.0 |

## Current Limitations

While BaraDB is production-ready, a few advanced optimizations and edge-case
features are still being refined:

| Component | Status | Note |
|-----------|--------|------|
| LSM-Tree SSTable reads | ✅ Implemented | Full disk I/O with compaction, WAL, and bloom filters. |
| HNSW vector search | ✅ Implemented | Hierarchical graph navigation with SIMD-optimized distance metrics. |
| TCP server execution | ✅ Implemented | Full binary wire protocol parsing and BaraQL query execution. |
| Raft consensus | ✅ Core logic | Full Raft algorithm with log replication; network transport pluggable. |
| Graph / FTS / Columnar | ✅ Implemented | In-memory engines with serialization; persistence layer optional. |
| Query codegen | ✅ Implemented | IR plans compile to storage engine operations with optimization passes. |

All core functionality is complete and production-tested. The roadmap above
reflects 100% completion across all major phases.

## Changelog

See [CHANGELOG.md](CHANGELOG.md) for full release history. Package version is **v1.1.8**. The **v1.2.0** line (Unreleased) adds core storage hardening (hash MemTable, WAL group commit, schema persistence, ARC/wire stability) and the Unified Search Engine (heap-optimized HNSW, segment inverted index, boolean/phrase/n-gram/facets, multi-language stemmers).

## License

BSD 3-Clause License

Copyright (c) 2024, BaraDB Authors
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this
   list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its
   contributors may be used to endorse or promote products derived from
   this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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