Embedded Key-Value Stores
Custom embedded stores for desktop apps, mobile apps, and IoT devices — single-file, crash-safe, optimised for your exact read/write ratios and footprint constraints.
Custom Database Engine Development
Bespoke Storage Engines for Workloads Off-the-Shelf Cannot Handle
Off-the-shelf databases fit 90% of cases. We engineer the other 10% — bespoke storage engines, custom indexes, query planners, embedded key-value stores, time-series engines, and vector databases — when standard databases hit a wall in throughput, latency, footprint, or workload shape.
Crash-safe · Fuzz-tested · Benchmark-driven · Senior systems engineers
C++Rust · Zig · Go
LSMB-Tree · Columnar
µsLatency targets
IoTEdge-ready
Database Engines We Build
From embedded single-file stores to columnar time-series engines and custom vector indexes — engineered when off-the-shelf hits a wall.
Time-Series Engines
Columnar time-series stores for telemetry, IoT, and financial tick data — with downsampling, retention policies, and query layers optimised for range aggregations.
Vector Databases
Custom approximate-nearest-neighbour indexes (HNSW, IVF) tuned to your embedding dimensionality, recall targets, and update cadence — outside the constraints of off-the-shelf stores.
In-Memory Caches
Bespoke in-memory caches and session stores when Redis or Memcached are not a fit — domain-aware eviction, sharding, replication, and persistence.
Custom Indexes & Query Planners
Custom secondary indexes, full-text engines, and query planners on top of existing databases — for workloads where Postgres or MySQL planners pick the wrong plan.
Storage Engine Migrations
Migrate from off-the-shelf engines to custom or alternative engines (Postgres to Citus, MySQL to TiDB, RocksDB tuning) with rigorous benchmarking and zero data loss.
Industries We Serve
DeepTech, financial trading, IoT, healthcare imaging, and geospatial workloads where standard databases struggle.
FinTech & HFT
Manufacturing & IoT
Logistics
Healthcare Imaging
EdTech
DeepTech
Retail Analytics
Geospatial
Stack & Storage Primitives
Systems-grade languages, modern storage primitives, and battle-tested consensus libraries.
C++17/20/23Core Language
RustCore Language
ZigCore Language
GoService Language
LSM-TreeStorage
B+TreeStorage
Columnar LayoutStorage
HNSW / IVFVector Index
Apache ArrowFormat
Parquet / ORCFormat
RocksDBLibrary
Raft / PaxosConsensus
Our Engine Development Process
A six-step rhythm engineered so we benchmark off-the-shelf first, prototype openly, and ship engines you can defend in an architecture review.
Workload Profiling
We profile your actual workload — read/write ratios, key distributions, value sizes, query shapes, latency tails — and benchmark against Postgres, MySQL, RocksDB, and SQLite first.
Architecture & Trade-offs
We design the storage layout, indexing strategy, consistency model, and recovery story — and document the trade-offs against off-the-shelf alternatives in plain English.
Prototype & Benchmark
A working prototype with end-to-end benchmarks against your real workload — published as a technical memo so you can challenge assumptions before full build.
Production Build
Crash-safe write paths, durable WAL, recovery tests, fuzzing, ASAN/UBSAN/TSAN runs, and exhaustive property-based tests covering the failure modes that matter.
Integration & Migration
SDK bindings (C, C++, Rust, Go, Python, Java), migration tooling from your current database, and dual-write validation before cutover.
Long-Term Support
Versioned formats, backwards-compatible on-disk layouts, observability (metrics, traces), and a retainer for performance tuning and new feature additions.
Why Choose Hendoi for Database Engines
Six commitments that decide whether you ship a database engine that earns trust — or one that corrupts data on a Tuesday afternoon.
Systems Engineering Depth
Custom databases are unforgiving — bugs corrupt data silently. We bring deep systems engineering: WAL design, crash safety, fuzzing, deterministic replay, and rigorous benchmarking.
Honest Build-or-Buy Advice
We will tell you when Postgres, RocksDB, ClickHouse, or DuckDB would fit better than a custom engine. Building one is expensive; we make sure it is the right call.
Crash Safety & Durability
Durable WAL, atomic commits, crash-recovery tests, power-loss simulation — your data survives every failure mode we can throw at it.
Benchmark-Driven Design
Every design decision is validated against your real workload, not synthetic benchmarks. Latency tails, throughput, and footprint all measured before commit.
Senior Systems Engineers
CEO-led architecture. The engineers who scope your database engine are the ones who write the WAL, the indexes, and the recovery code — no juniors near critical paths.
Transparent Memos
Every design choice is captured in a written memo — read it, challenge it, sign off. No black-box engineering on something this critical.
Engagement Models
Pick the shape that matches whether you are exploring, committed, or in long-term care.
Discovery & Prototype
A 4-6 week sprint to profile your workload, benchmark off-the-shelf alternatives, build a prototype if needed, and deliver a written build-or-buy recommendation.
- Workload profiling
- Off-the-shelf benchmarking
- Prototype + written memo
Production Engine Build
A coordinated programme — engine, indexes, SDKs, migration tooling, benchmarks, and observability — delivered in milestones with weekly demos and benchmark reports.
- Engine + SDK + tooling
- Benchmark-driven milestones
- Crash-safety + fuzzing
Engine Maintenance Retainer
Predictable monthly retainer covering performance tuning, format upgrades, new feature additions, security patches, and on-call response to production incidents.
- Performance tuning
- New features & upgrades
- On-call incident response
Real-World Use Cases
Representative custom engines and storage projects engineered across IoT, trading, AI, gaming, GIS, and audit-grade workloads.
IoT Edge Telemetry Store
Embedded time-series store for gateway devices in a manufacturing plant — 4MB binary, persists rolling 30 days of sensor data with downsampling, syncs to cloud over flaky links.
HFT Tick Database
In-memory columnar tick store for a proprietary trading desk — microsecond reads, durable WAL to NVMe, hot/cold tiering, and Python/C++ SDKs for research and execution.
Vector Search for Legal AI
Custom HNSW vector index over 100M legal documents — recall-tuned, memory-bounded, and integrated with a Postgres-stored metadata layer for hybrid retrieval.
Game Save Storage Engine
Single-file embedded engine for a game studio's save system — corruption-resistant, versioned format, and 50ms cold-start on consumer SSDs.
GIS Tile Cache Engine
Custom tile cache engine for a logistics company's mapping stack — LRU eviction tuned to fleet routing patterns, with replication across edge POPs.
Audit Log Storage Engine
Append-only audit log engine for a regulated NBFC — tamper-evident, cryptographically chained, with retention policies and structured query API for inspections.
Frequently Asked Questions
Common questions architects and CTOs ask before committing to a custom database engine.
When should I build a custom database engine versus using Postgres, RocksDB, or DuckDB?
Use off-the-shelf whenever possible. Custom engines pay off only when you have unusual workload shapes (extreme write ratios, microsecond latency, tiny footprint, exotic indexes) that off-the-shelf cannot meet — even after tuning. We benchmark off-the-shelf first and document the gap honestly before recommending a custom build.
In which languages do you build database engines?
C++ (17/20/23), Rust, and Zig for the core engine. Go for service layers. We pick based on the integration target — Rust and Zig for new greenfield engines, C++ when integrating with legacy systems, Go when the engine is a network service.
How do you ensure data is never lost?
Durable Write-Ahead Log with fsync semantics tuned per workload, atomic commit protocols, crash-recovery tests, power-loss simulation, fuzzing, and property-based tests covering rare failure modes. We also build dual-write migration tools so cutover from existing databases is reversible.
What about consensus and replication?
Raft is our default for new replicated engines (well-understood, well-tested implementations available). We integrate with battle-tested Raft libraries rather than rolling our own consensus — that is where serious databases break, and we treat it with appropriate respect.
How long does a custom database engine take to build?
A prototype with benchmarks: 4-6 weeks. A production-ready engine with SDKs, migration tooling, and crash-safety tests: 4-9 months depending on scope. We share milestones with weekly benchmark reports.
What does a custom database engine cost?
Significant — six-figure budgets in INR for production-grade engines. We make sure the build/buy maths actually pencil out before recommending a custom path. Many projects end up choosing tuned off-the-shelf databases, which is often the right answer.
Will I get the source code?
Yes — full source, build system, test suite, technical memos, and benchmark methodology. You own everything. We can also provide ongoing maintenance under a retainer, but you are never locked in.
Can you help us tune existing databases instead?
Absolutely — often that is the right call. We tune RocksDB, Postgres, ClickHouse, MySQL, and DuckDB deployments for workloads that are misbehaving. Cheaper than a custom build, and we will be honest about whether tuning is enough.