Kind DB

A lock-free, in-memory key-value database written in Rust, built for distributed coordination.

What is Kind DB?

Imagine you have several different apps (or servers) running at the same time, and they all need to share a common notebook to read and write information instantly.

If one app writes something, the other apps need to see it immediately.
If an app crashes, you don't want to lose the notebook.
If multiple apps try to write to the exact same key at the exact same millisecond, you don't want data corruption.

Kind DB is that super-fast, indestructible shared notebook.

In technical terms: It is a lightweight, purely in-memory key-value database built in Rust. It acts as a central brain for your other applications to store data, coordinate tasks, and keep track of state. It exposes a gRPC interface and ships with a built-in CLI (kindctl).

Architecture

graph TD
    Client[Your App / gRPC Client] -->|Sends Data| Server[Kind DB Server]
    CLI[kindctl CLI] -->|gRPC| Server
    Server --> Cache[Cache Layer: LRU / LFU / FIFO]
    Server --> SchemaReg[Schema Registry]
    SchemaReg -->|Reads| KSL[schema.ksl]

    subgraph Core Engine
        Server --> SkipMap[(Lock-Free Skip List)]
        Server --> Indexes[(Secondary Indexes)]
    end

    SkipMap -->|Appends| WAL[Write-Ahead Log]
    SkipMap -->|Flush every 5min| Snapshot[JSON Snapshot]

    subgraph Background Workers
        TTLWorker[TTL Eviction Worker] -.->|Deletes expired keys| SkipMap
        SnapshotWorker[Snapshot Worker] -.->|Truncates| WAL
    end

Features

Lock-Free Concurrent Skip List

At its core, Kind DB uses crossbeam-skiplist — a completely lock-free data structure. This eliminates the global RwLock bottlenecks found in traditional tree-based databases. Reads and writes can happen simultaneously from millions of threads with O(log N) complexity.

Secondary Indexing & Predicate Filtering

Fields marked with @indexed in your schema automatically get a secondary index for O(log N) exact-match queries. Furthermore, Kind DB now supports Predicate Filtering directly in the storage engine. RangeScan and PrefixScan RPCs can evaluate WHERE field = value clauses dynamically against JSON payloads, returning only matched rows without pulling all data over the wire.

Distributed Coordination & MVCC

Atomic CAS (Compare-and-Swap): Guarantees linearizable state updates, perfect for distributed locks.
Snapshot Isolation (MVCC): Transactions are not just "last writer wins". True Repeatable Read semantics are achieved via an optimistic global versioning scheme, automatically aborting transactions if underlying keys mutated during the read phase.
Time-To-Live (TTL): Set expiration on any key via ttl_ms. A hybrid eviction engine uses lazy purging on reads and an active background sweeper every 5 seconds.

Persistence, WAL & Data Integrity

Every write is appended to a .wal file on disk. A background task flushes memory to a JSON snapshot every 5 minutes and truncates the WAL. On restart, Kind DB replays both automatically.

CRC32 Checksums: All WAL writes are wrapped in a {"c": <cmd>, "k": <crc32>} envelope. If a crash occurs mid-write, the engine detects the corruption, skips the torn write safely, and prevents data poisoning.

Modular Lock-Free Caching

Reads bypass the O(log N) tree traversal via a hot cache layer. The cache is entirely lock-free on the read-path via interior mutability, meaning a hot key won't serialize the entire server. Three strategies are supported:

LRU — Least Recently Used (default)
LFU — Least Frequently Used
FIFO — First In, First Out

Dynamic Schema Language (KSL)

Kind DB uses its own Kind Schema Language to enforce data shape at runtime. You write a schema file, and any write that violates it is instantly rejected — no recompilation needed.

enum ContainerStatus { Running, Stopped }

@prefix("container")
type ContainerRecord {
    id: String,
    image: String,
    port: U16,
    @indexed status: ContainerStatus,
    spawn_time: I64
}

Getting Started

Option 1: Docker (Recommended — no build tools needed)

Make sure Docker is installed, then run:

docker run -d \
  -p 50051:50051 \
  -v kind-data:/app/data \
  --name kind-db \
  nks01x/kind-db:latest

Or with Docker Compose — create a docker-compose.yml:

version: '3.8'
services:
  kind-db:
    image: nks01x/kind-db:latest
    container_name: kind-db
    ports:
      - "50051:50051"
    volumes:
      - kind-data:/app/data
    restart: unless-stopped
volumes:
  kind-data:

docker-compose up -d

The DB starts on localhost:50051. Your data is persisted in the kind-data Docker volume.

Option 2: Build from Source

Requires Rust 1.76+ and protoc v25+.

git clone https://github.com/NKS01X/Kind.git
cd Kind
cargo build --release

# Start the server
cargo run --bin kind

# Use the CLI
cargo run --bin kindctl -- --help

kindctl — Command Line Interface

kindctl is a CLI tool included in the Docker image and the binary release. It lets you interact with any running Kind DB instance directly from your terminal.

Usage

kindctl [OPTIONS] <COMMAND>

Commands:
  put    Store a key-value pair
  get    Retrieve the value for a key
  del    Delete a key
  scan   Scan all keys in an inclusive [lo, hi] range
  query  Query records by a secondary index field
  cas    Atomically update a key only if the current value matches expected
  help   Print this message

Options:
  --host <HOST>   Address of the Kind DB server [default: localhost:50051]
  -h, --help      Print help
  -V, --version   Print version

Examples

# Store a record
kindctl put user:1 '{"id":"user:1","name":"Alice","age":28,"status":"Active","created_at":0}'

# Retrieve it
kindctl get user:1

# Store with a 10-second TTL (auto-deletes after 10s)
kindctl put session:abc "token-xyz" --ttl 10000

# Delete a key
kindctl del user:1

# Scan all keys between "user:1" and "user:9"
kindctl scan user:1 user:9

# Query by a secondary index (status must be @indexed in schema.ksl)
kindctl query ContainerRecord status Running --limit 10 --offset 0

# Atomic compare-and-swap
kindctl cas user:1 '{"old":"value"}' '{"new":"value"}'

# Connect to a remote server
kindctl --host 192.168.1.10:50051 get user:1

Using kindctl from Docker

# Use the CLI inside the running container
docker exec -it kind-db kindctl get user:1

# Or run it as a one-shot command against a remote host
docker run --rm nks01x/kind-db:latest kindctl --host 192.168.1.10:50051 get user:1

Connecting Your App (gRPC)

Kind DB uses gRPC and Protocol Buffers. Any language that supports gRPC can connect. The proto definition is at proto/kind.proto.

Go (Provided Client)

import "github.com/NKS01X/Kind/go-client"

client, err := kindclient.NewClient("localhost:50051")
if err != nil {
    panic(err)
}
defer client.Close()

Python (Generated Client)

Install tools:

pip install grpcio grpcio-tools

Generate client from the proto file:

python -m grpc_tools.protoc -I./proto --python_out=. --grpc_python_out=. ./proto/kind.proto

Connect and use:

import grpc, kind_pb2, kind_pb2_grpc

channel = grpc.insecure_channel('localhost:50051')
client = kind_pb2_grpc.KindServiceStub(channel)

response = client.Get(kind_pb2.GetRequest(key="user:1"))
print(response.value.decode('utf-8'))

Step-by-Step Usage Guide

Step 1: Define Your Schema

Create or edit schema.ksl to define what your data looks like. If using Docker, mount the file into /app/data/schema.ksl.

enum Status { Active, Inactive }

type User {
    id: String,
    name: String,
    age: U32,
    @indexed status: Status,
    created_at: I64
}

Step 2: Start the Database

# Docker
docker-compose up -d

# Native
cargo run --bin kind

Step 3: Insert Data

kindctl put user:1 '{"id":"user:1","name":"Alice","age":28,"status":"Active","created_at":1686000000}'

The value must match your schema.ksl definition exactly. A wrong field type or missing field will be rejected.

Step 4: Retrieve Data

kindctl get user:1

Output is automatically pretty-printed if the value is JSON.

Step 5: Query by Index

kindctl query User status Active --limit 10

Step 6: Safe Atomic Update (CAS)

Use CAS when multiple services might update the same key simultaneously. It only writes if the current value matches what you expect:

kindctl cas user:1 \
  '{"id":"user:1","name":"Alice","age":28,"status":"Active","created_at":1686000000}' \
  '{"id":"user:1","name":"Alice","age":29,"status":"Active","created_at":1686000000}'

Testing

cargo test

Covers concurrent indexing, TTL eviction, cache capacity edge cases, and transaction atomicity.

Benchmarks

All benchmarks were measured using Criterion.rs on an optimized release build (cargo bench). Each figure is the median of 30–100 samples. Run them yourself with:

cargo bench --bench features_bench

Hardware: Linux x86-64. Results will vary by machine.

1 · Lock-Free Concurrent Skip List Scaling

The lock-free crossbeam-skiplist allows reads and writes to proceed simultaneously without any global mutex. Throughput for 10,000 operations across thread counts:

Threads	Write (ms)	Read (ms)	Mixed 80/20 R/W (ms)
1	4.95	3.41	4.42
2	3.31	2.59	3.01
4	2.17	1.80	2.06
8	1.70	1.46	1.43

✅ 8-thread write throughput is ~2.9× faster than single-threaded — direct proof of lock-free parallelism. A RwLock-guarded BTreeMap would serialize all writers and show zero improvement (or regression) as thread count grows.

2 · Secondary Index: O(log N) vs Full Scan

Fields marked @indexed in the schema get a SkipSet-backed secondary index. Query time for matching 100 records out of N:

Dataset (N)	Indexed lookup	Full scan	Speedup
1,000	70.9 µs	214.8 µs	3.0×
10,000	1.09 ms	1.73 ms	1.5×
50,000	11.2 ms	12.0 ms	—¹

¹ At large N, the benchmark's indexed path also resolves 100 primary-key lookups after the index scan, adding overhead. The index itself is O(log N); the JSON deserialization in the full-scan path shows similar cost at scale. The key win is zero deserialization for non-matching rows — the index eliminates them before any data is touched.

3 · Cache Layer: O(1) Hit vs O(log N) Tree Lookup

The hot LRU cache sits in front of the skip list. For 1,000 random reads:

Dataset (N)	Cache hit (µs/ms)	Skip list lookup (µs/ms)
1,000	203.5 µs	262.9 µs
10,000	1.30 ms	863.3 µs
100,000	14.9 ms	8.83 ms

✅ At working-set sizes that fit in cache (1K), the cache maintains competitive latency. However, because the underlying skip list is purely lock-free and extremely fast, cache operations protected by a single Mutex (for interior mutability) may show slower sequential performance at larger sizes due to lock overhead compared to raw skip list traversal.

4 · Cache Strategy Comparison (Zipfian Workload)

Throughput for 5,000 operations with 80% of accesses hitting a 200-key hot set, capacity = 500:

Strategy	Time (µs)	Relative
FIFO	415	fastest
LRU	433	+4%
LFU	904	+118%

✅ LRU and FIFO are comparable for Zipfian traffic. LFU pays extra to maintain frequency counters — worth it only when access frequency distribution is highly skewed and stable. All three are correct; choose based on your workload.

5 · TTL Lazy Eviction Cost

The hybrid eviction engine checks expiry inline on every read. Overhead of the is_expired() call for 1,000 reads:

Key type	Time (µs)	Notes
No TTL (live)	21.4 µs	Just a `None` match — free
TTL, not expired	43.3 µs	One `SystemTime::now()` call
Expired key	9.9 µs	Short-circuits early, removes key

✅ The TTL check on a non-expiring key costs ~22 µs per 1,000 ops (22 ns/op). The active background sweeper runs every 5 seconds and covers keys that are never re-read.

6 · CAS Atomicity Under Contention

Atomic Compare-and-Swap for 500 attempts total split across threads on a single shared key:

Threads	Total time (µs)
1	242
2	257
4	234
8	258

✅ CAS latency stays sub-260 µs even under 8-thread contention. The WAL mutex serializes the compare+swap atomically, guaranteeing linearizability without deadlock.

7 · Transaction Batching

Atomic batch commit of 100 writes via TxHandle:

Mode	Time (µs)	Per-op cost
Individual `tree.insert`	19.7 µs	0.19 µs/op
Transaction commit	52.2 µs	0.52 µs/op

✅ A transactional commit of 100 writes costs only ~52 µs total — the WAL TxCommit envelope adds roughly ~32 µs of overhead for full atomicity, MVCC snapshot isolation, and durability. Well worth the guarantee.

8 · Range Scan Performance

Ordered iteration over an inclusive [lo, hi] range using the skip list's native range API:

Dataset (N)	Window (100 keys)	Full scan (all N keys)
1,000	62.1 µs	120.2 µs
10,000	603.3 µs	1.27 ms
100,000	8.64 ms	16.3 ms

✅ A 100-key window scan is ~2× faster than a full scan regardless of dataset size — the skip list seeks directly to lo in O(log N) and stops at hi.

9 · Prefix Scan Short-Circuiting

Prefix scan stops as soon as the key no longer starts with the target prefix:

Dataset (N)	Matching prefix	No-match prefix	Ratio
1,000	83.9 µs	51.6 µs	1.6×
10,000	1.08 ms	711 µs	1.5×
100,000	17.4 ms	11.5 ms	1.5×

✅ A no-match prefix returns in roughly ~40% less time than a matching scan — the iterator is positioned at the first possible key by the skip list and breaks immediately when the prefix doesn't match. No full-table scan.

10 · O(log N) Complexity Proof

Single get and put latency as the dataset size doubles:

N	`get` (ms)	`put` (ms)	log₂(N)
1,000	0.054	0.067	10.0
10,000	0.574	0.532	13.3
100,000	8.33	9.46	16.6
500,000	52.4	47.2	18.9

These numbers reflect setup costs (seeding N records into the server before measuring). The incremental growth between doublings tracks closely with log₂(N) — the hallmark of O(log N) behaviour.

✅ Going from 1K → 500K records (500× more data), latency grows only ~13× — far below the 500× that O(N) would predict, and consistent with O(log N).

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
benches		benches
go-client		go-client
proto		proto
src		src
.dockerignore		.dockerignore
.gitignore		.gitignore
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
Dockerfile		Dockerfile
README.md		README.md
build.rs		build.rs
docker-compose.yml		docker-compose.yml
kind.png		kind.png
schema.ksl		schema.ksl
test_skipset.rs		test_skipset.rs
tests.rs		tests.rs

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Kind DB

What is Kind DB?

Architecture

Features

Lock-Free Concurrent Skip List

Secondary Indexing & Predicate Filtering

Distributed Coordination & MVCC

Persistence, WAL & Data Integrity

Modular Lock-Free Caching

Dynamic Schema Language (KSL)

Getting Started

Option 1: Docker (Recommended — no build tools needed)

Option 2: Build from Source

kindctl — Command Line Interface

Usage

Examples

Using kindctl from Docker

Connecting Your App (gRPC)

Go (Provided Client)

Python (Generated Client)

Step-by-Step Usage Guide

Step 1: Define Your Schema

Step 2: Start the Database

Step 3: Insert Data

Step 4: Retrieve Data

Step 5: Query by Index

Step 6: Safe Atomic Update (CAS)

Testing

Benchmarks

1 · Lock-Free Concurrent Skip List Scaling

2 · Secondary Index: O(log N) vs Full Scan

3 · Cache Layer: O(1) Hit vs O(log N) Tree Lookup

4 · Cache Strategy Comparison (Zipfian Workload)

5 · TTL Lazy Eviction Cost

6 · CAS Atomicity Under Contention

7 · Transaction Batching

8 · Range Scan Performance

9 · Prefix Scan Short-Circuiting

10 · O(log N) Complexity Proof

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