How many writes per second can Cassandra handle?

Apache Cassandra is designed for immense scalability and high availability, capable of handling an exceptionally high volume of writes per second, often reaching into the millions. Its distributed architecture allows it to scale linearly with the addition of more nodes, making it a powerful choice for write-intensive applications.

Demonstrated Performance Benchmark

In a notable demonstration of its capabilities, a Cassandra deployment was able to sustain one million writes per second. This impressive throughput was achieved with a median latency of 10.3 milliseconds (ms), and 95% of these writes completed in under 23 ms.

The setup that achieved this benchmark consisted of:

This benchmark highlights Cassandra's ability to not only handle a massive number of writes but also to do so with low and predictable latency, crucial for many modern applications.

Factors Influencing Cassandra Write Performance

The actual number of writes Cassandra can handle in any given environment is not a fixed value, but rather depends on several critical factors:

• Cluster Size and Hardware: More nodes, coupled with robust CPU, sufficient RAM, fast I/O (SSDs are highly recommended), and optimized network interfaces, directly translate to higher write capacity.
• Data Model Design: A well-designed data model with appropriate primary keys and partitioning strategies is fundamental. Poorly designed tables can lead to hot spots (uneven data distribution), which severely limit write throughput.
• Consistency Levels: The chosen consistency level for writes (e.g., ONE, QUORUM, ALL) significantly impacts performance. Lower consistency levels (e.g., ONE) offer faster writes because fewer replicas need to acknowledge the write, while higher levels (ALL) ensure stronger consistency but incur higher latency.
• Workload Characteristics: The size of each write, the number of columns, the presence of secondary indexes, and the frequency of updates versus inserts all affect performance. Larger writes or more complex operations consume more resources.
• Network Latency: In distributed systems, network latency between nodes plays a crucial role. A high-speed, low-latency network is essential for optimal write performance.

Optimizing Cassandra for High Write Throughput

To maximize Cassandra's write performance, consider the following best practices:

• Tune JVM Settings: Allocate sufficient heap memory to the Java Virtual Machine (JVM) and optimize garbage collection settings.
• Monitor Disk I/O: Ensure your storage subsystem can handle the write load. Use monitoring tools to identify I/O bottlenecks.
• Distribute Data Evenly: Design your partition keys to ensure data is evenly distributed across all nodes in the cluster, preventing hot spots.
• Use Asynchronous Writes: Leverage asynchronous client drivers to send multiple write requests concurrently without waiting for each to complete.
• Batch Writes Strategically: While Cassandra supports batching, use it judiciously. Only batch writes that belong to the same partition key to avoid performance degradation.
• Choose Appropriate Consistency: Select the lowest consistency level that meets your application's durability and availability requirements. For most high-throughput write scenarios, ONE or LOCAL_ONE is often used, relying on Cassandra's eventually consistent nature for reads.

Real-World Applications

Cassandra's ability to handle massive write loads makes it ideal for use cases such as:

• Internet of Things (IoT): Ingesting vast streams of sensor data.
• Time-Series Data: Storing metrics, logs, and event data from various sources.
• User Activity Tracking: Recording user actions, clicks, and interactions on large-scale web applications.
• Financial Transactions: Processing high volumes of transactional data, ensuring data integrity across distributed systems.