ZeptoDB vs ClickHouse

Overview

ClickHouse excels at analytical queries over large datasets. ZeptoDB is purpose-built for real-time time-series where microsecond latency and temporal operations (ASOF JOIN, EMA) are critical.

---

Feature Comparison

	ZeptoDB	ClickHouse
Primary Use	Real-time time-series	OLAP analytics
Query Latency	272μs (1M rows)	~5ms
Ingestion	5.52M events/sec	~100K events/sec
Storage Model	In-memory + Parquet HDB	Disk-based columnar (MergeTree)
ASOF JOIN	✓ (native)	✗
Window JOIN	✓	✗
EMA / VWAP	✓ (built-in)	UDF required
xbar (time bucketing)	✓	toStartOfInterval()
Python Zero-Copy	522ns	— (requires serialization)
JIT Compilation	LLVM JIT	Partial
SIMD	Highway (AVX2/512, NEON)	SSE4.2/AVX2
Real-Time Ingestion	Lock-free ring buffer	Async inserts + merge
Feed Handlers	FIX, ITCH, Binance, Kafka	Kafka (via connector)
Clustering	Multi-node auto-sharding	ReplicatedMergeTree + ZooKeeper
License	Apache 2.0	Apache 2.0

---

When to Choose ZeptoDB

• Sub-millisecond query latency is a requirement
• You need ASOF JOIN, Window JOIN, or temporal analytics
• Real-time streaming ingestion (millions of events/sec)
• Python zero-copy for ML/quant workflows
• Financial market data or high-frequency sensor data

When ClickHouse May Be Better

• Large-scale batch analytics (billions of rows, seconds-acceptable latency)
• Complex OLAP queries with many JOINs on dimension tables
• Existing ClickHouse ecosystem and tooling investment
• Data warehouse use cases where disk-based storage is preferred

---

Performance Comparison

Workload	ZeptoDB	ClickHouse
Point query (1M rows)	272μs	~5ms
Aggregation (1M rows)	185μs	~3ms
ASOF JOIN	410μs	N/A
Ingestion (single stream)	5.52M/sec	~100K/sec
Python result access	522ns	~1ms (serialization)

Get started with the Quick Start Guide.