Can Cassandra Be Choice for Ultra Low Latency Systems ?

Cassandra is fast because of its append-only writes, peer-to-peer architecture, and tunable consistency. However, it is not ideal for ultra-low-latency (sub-millisecond) workloads without heavy tuning

Apache Cassandra is not typically the first choice for ultra-low latency systems like high-frequency trading (HFT), but it can be fast and suitable for certain low-latency, high-throughput use cases—if used correctly.

Why Cassandra Can Be Fast?

1. High Write Throughput (Log-Structured Storage)

• Cassandra uses Log-Structured Merge Trees (LSM Trees).

• Writes are appended in memory (memtables) and flushed to disk later—no read-modify-write.

• It can handle millions of writes per second with low write latency.

2. No Master Node (Peer-to-Peer Architecture)

• All nodes are equal (no single point of failure or bottleneck).

• Data is partitioned using consistent hashing, which enables horizontal scalability and parallelism.

3. Tunable Consistency

• You can configure read/write consistency levels (e.g., QUORUM, ONE, LOCAL_QUORUM).

• Using LOCAL_ONE or ONE gives very low latency at the cost of stronger consistency.

4. In-Memory Reads (Row Cache + Bloom Filters)

• Cassandra uses Bloom filters, partition summaries, and caches to avoid disk I/O.

• Frequently accessed data can be served directly from memory.

5. Efficient Writes Without Locks

• Writes are designed to be lock-free and fast.

• here's no need to read before writing, which is ideal for append-heavy workloads.

Tradeoffs and Limitations:

• Cassandra is optimized for eventual consistency and high availability, not strict consistency or microsecond-level latency.

• Latency spikes can happen due to compaction, garbage collection (JVM), or SSTable reads.

• Query flexibility is limited—designing the right schema (denormalized, query-based) is essential to keep reads fast.

• Best for append-only or time-series-like data, not transactional workloads.

When Can Cassandra Be Used in Low-Latency Systems?

• Market data ingestion or tick data logging

• Real-time analytics dashboards

• Write-heavy telemetry and monitoring

• Precomputed lookups with caching on top

In HFT systems, though, developers often use:

• In-memory stores (e.g. Redis, Aerospike, ChronicleMap)

• Custom memory-mapped structures

• Flat files + memory indexing for deterministic access times