Apache Cassandra vs ScyllaDB

Apache Cassandra and ScyllaDB are both NoSQL databases designed for high availability and scalability, but they have different architectures and performance characteristics. Here’s a detailed comparison between the two:

Architecture

Apache Cassandra:

- Developer: Apache Software Foundation

- License: Open-source

- Architecture:

?- Peer-to-Peer: All nodes in the cluster are equal, avoiding single points of failure.

?- Write Path: Writes go to a commit log and then to an in-memory structure called a memtable. Once the memtable is full, data is flushed to SSTables on disk.

?- Read Path: Data is read from the memtable and SSTables, with Bloom filters and partition key caches used to optimize reads.

?- Consistency: Tunable consistency; users can configure consistency levels per operation (e.g., ONE, QUORUM, ALL).

ScyllaDB:

- Developer: ScyllaDB Inc.

- License: Open-source (with proprietary enterprise options)

- Architecture:

?- Seastar Framework: Uses a shared-nothing, asynchronous programming model for improved performance.

?- Write Path: Similar to Cassandra, but optimized with an asynchronous, lock-free approach.

?- Read Path: Enhanced with more efficient caching and read-ahead mechanisms.

?- Consistency: Also tunable, similar to Cassandra.

Performance

Read Performance:

- Cassandra: Can handle high read loads, but performance can degrade with increasing read volume and larger datasets. Optimizations like read-repair and speculative retries help maintain performance.

- ScyllaDB: Designed to outperform Cassandra in read operations due to its more efficient architecture and use of the Seastar framework. ScyllaDB claims to offer significantly lower read latencies.

Write Performance:

- Cassandra: Excels at write-heavy workloads due to its log-structured merge-tree (LSM) storage engine. Writes are fast because they are sequentially written to disk.

- ScyllaDB: Generally offers better write performance than Cassandra, benefiting from optimizations in its asynchronous architecture and better resource utilization.

Latency:

- Cassandra: Typically exhibits higher latencies compared to ScyllaDB due to its architecture and Java-based implementation.

- ScyllaDB: Known for lower latencies, often in the single-digit millisecond range, due to its more efficient C++ implementation and advanced asynchronous design.

Throughput:

- Cassandra: Scales well horizontally, but can be less efficient than ScyllaDB at scale.

- ScyllaDB: Scales more efficiently and often achieves higher throughput with lower hardware requirements due to better resource management.

?Features

Cassandra:

- Maturity: Well-established and widely used with a large community.

- Ecosystem: Strong ecosystem with various tools and integrations (e.g., Spark, Hadoop).

- Flexibility: Highly tunable consistency and fault tolerance options.

- Data Model: Supports wide-column store with flexible schema design.

ScyllaDB:

- Performance: Superior performance in most benchmarks, particularly for read and write operations.

- Compatibility: Compatible with Cassandra’s API, making migration relatively straightforward.

- Resource Efficiency: Better utilization of modern multi-core CPUs and large memory, leading to cost savings.

- Data Model: Similar to Cassandra, supports wide-column store with flexible schema design.

?Use Cases

Cassandra:

- High Availability: Ideal for applications requiring high availability and fault tolerance.

- Write-Heavy Workloads: Suitable for write-intensive applications such as logging, time-series data, and IoT.

- Wide Adoption: Beneficial for organizations leveraging its mature ecosystem and extensive community support.

ScyllaDB:

- Performance-Critical Applications: Best for applications requiring low latency and high throughput.

- Cost Efficiency: Suitable for organizations looking to reduce hardware costs while maintaining performance.

- Real-Time Analytics: Good for use cases requiring fast reads and writes, such as real-time analytics, recommendation engines, and high-frequency trading.

Why read and writes are faster in ScyallaDB compared to Cassandra ?

ScyllaDB achieves faster read and write performance compared to Apache Cassandra due to several key architectural differences and optimizations:

1. Programming Language and Framework

ScyllaDB:

- C++ and Seastar Framework: ScyllaDB is written in C++ and uses the Seastar framework, which is designed for high-performance server applications. Seastar allows for efficient, asynchronous, shared-nothing programming, enabling ScyllaDB to take full advantage of modern multi-core CPUs.

Cassandra:

- Java: Cassandra is written in Java, which involves garbage collection overhead and context switching. This can lead to higher latencies and less efficient CPU utilization compared to C++.

2. Thread Per Core Architecture

ScyllaDB:

- Thread Per Core: ScyllaDB uses a "thread per core" model where each CPU core has its own memory and queues, minimizing locking and context switching. This design maximizes CPU cache efficiency and reduces contention, leading to better performance.

Cassandra:

- Thread Pool Model: Cassandra uses a traditional thread pool model, which can suffer from thread contention, locking overhead, and less efficient CPU utilization, particularly under high concurrency.

3. Asynchronous I/O

ScyllaDB:

- Fully Asynchronous I/O: ScyllaDB leverages fully asynchronous I/O operations, allowing it to handle multiple read and write requests simultaneously without blocking. This results in lower latency and higher throughput.

Cassandra:

- Partial Asynchronous I/O: While Cassandra has some asynchronous operations, it is not fully asynchronous, leading to potential bottlenecks and higher latencies under heavy load.

4. Memory Management

ScyllaDB:

- Custom Memory Management: ScyllaDB employs a custom memory allocator tailored for its needs, avoiding the overhead of garbage collection and improving memory efficiency.

Cassandra:

- Garbage Collection: Being a Java application, Cassandra relies on the JVM’s garbage collector, which can introduce unpredictable pauses and degrade performance, especially under high memory pressure.

5. Storage Engine Optimizations

ScyllaDB:

- Optimized Storage Engine: ScyllaDB’s storage engine is optimized for modern hardware, including SSDs and NVMe storage, providing faster read and write operations. It uses advanced techniques like read-ahead and better caching mechanisms.

Cassandra:

- Traditional Storage Engine: Cassandra’s storage engine, while robust, is less optimized for the latest hardware capabilities, leading to relatively slower performance compared to ScyllaDB.

6. Load Balancing and Resource Utilization

ScyllaDB:

- Efficient Load Balancing: ScyllaDB has efficient load balancing mechanisms that ensure even distribution of workload across all nodes and cores, maximizing resource utilization and avoiding hotspots.

Cassandra:

- Load Balancing Challenges: While Cassandra has load balancing mechanisms, they are less efficient, sometimes leading to uneven workload distribution and suboptimal resource utilization.

7. Tunable Consistency and Repair Mechanisms

ScyllaDB:

- Efficient Repair Mechanisms: ScyllaDB uses efficient repair mechanisms that reduce the overhead associated with maintaining consistency across nodes, which can improve both read and write performance.

Cassandra:

- Repair Overhead: Cassandra’s repair mechanisms can be resource-intensive, potentially impacting performance, especially during large-scale repairs.

Summary

ScyllaDB’s faster read and write performance compared to Cassandra is primarily due to its modern architecture, which includes the use of C++ and the Seastar framework, a thread-per-core model, fully asynchronous I/O, custom memory management, optimized storage engine, efficient load balancing, and advanced repair mechanisms. These design choices enable ScyllaDB to better utilize modern hardware and handle high concurrency with lower latency and higher throughput than Cassandra.

What is Thread Contention

Thread contention occurs when multiple threads compete for the same resources, leading to performance degradation due to waiting times and overhead associated with managing access to shared resources. In the context of Apache Cassandra, which uses a traditional thread pool model, thread contention can arise in several scenarios, particularly under high concurrency. Here’s an explanation with an example:

Thread Pool Model in Cassandra

Cassandra employs a thread pool model for handling read and write requests. This involves creating a pool of worker threads that process incoming requests. When a request arrives, it is assigned to a thread from the pool. If all threads are busy, the request has to wait in a queue until a thread becomes available.

Example Scenario of Thread Contention

Scenario: High Concurrency Write Operations

Imagine a Cassandra cluster where numerous clients are performing high concurrency write operations.

1. Write Request Handling:

- Each incoming write request is assigned to a thread from the pool.

- The thread processes the request by writing to the commit log and updating the in-memory memtable.

- When the memtable reaches a certain threshold, it is flushed to disk as an SSTable.

2. Resource Sharing and Contention:

- Commit Log: Multiple threads need to write to the commit log, which involves I/O operations. If the commit log is a single file, concurrent access can lead to I/O contention, where threads are waiting for access to the log file.

- Memtable: Threads update the in-memory structure (memtable). Accessing and modifying shared memory structures can require synchronization mechanisms like locks or concurrent data structures, which introduce locking overhead.

- Disk I/O: Flushing memtables to disk involves writing SSTables. Disk I/O contention can occur if multiple threads try to flush data simultaneously, leading to a bottleneck.

3. Locks and Synchronization:

- To ensure data consistency and integrity, Cassandra might use locks or other synchronization mechanisms for access to shared resources (commit log, memtable).

- When one thread holds a lock, other threads trying to access the same resource are blocked until the lock is released. This waiting time adds to the contention.

Example of Contention:

Consider a scenario where the commit log is the bottleneck:

- Thread A: Attempts to write to the commit log.

- Thread B: Also tries to write to the commit log at the same time.

- Lock Acquisition: Thread A acquires the lock on the commit log.

- Thread B Waits: Thread B has to wait until Thread A finishes writing and releases the lock.

- Performance Impact: As the number of concurrent write requests increases, more threads end up waiting, leading to increased latency and reduced throughput.

In this example, Thread 1 holds the lock on the commit log, causing Thread 2 and Thread 3 to wait, leading to thread contention.

Impact of Thread Contention

- Increased Latency: Threads waiting for access to shared resources increases the overall latency of requests.

- Reduced Throughput: The effective throughput of the system decreases because threads spend time waiting rather than processing requests.

- CPU Underutilization: Even if CPU resources are available, they may remain underutilized because threads are blocked waiting for locks or I/O operations to complete.

How ScyllaDB Mitigates Thread Contention

ScyllaDB addresses these issues with its architecture:

- Thread Per Core Model: Each CPU core has its own dedicated memory and I/O queues, reducing the need for locking and synchronization.

- Asynchronous I/O: ScyllaDB uses asynchronous, non-blocking I/O operations, allowing threads to handle other tasks while waiting for I/O to complete, thereby improving resource utilization and reducing contention.

- Seastar Framework: The Seastar framework used by ScyllaDB is designed to avoid traditional locking mechanisms, instead leveraging message passing and other concurrency techniques to minimize contention.

By avoiding the pitfalls of a traditional thread pool model and implementing a more efficient concurrency model, ScyllaDB achieves lower latencies and higher throughput, especially under high concurrency workloads.

Conclusion

When to Choose Cassandra:

- If you need a mature, widely-adopted database with a large ecosystem and community support.

- If your workload is write-heavy and you need a robust, battle-tested solution.

- If your team is already familiar with Cassandra and you prefer a more established platform.

When to Choose ScyllaDB:

- If you need superior performance in terms of read/write latency and throughput.

- If you want to maximize hardware utilization and reduce costs.

- If your application requires low-latency responses and you can benefit from ScyllaDB’s architectural advantages.

Ultimately, the choice between Cassandra and ScyllaDB will depend on your specific requirements, including performance needs, cost considerations, and existing infrastructure. Both databases offer robust solutions, but ScyllaDB’s modern architecture provides significant performance benefits for demanding applications.

In upcoming posts I'll share architecture diagrams, benchmarking details, how we can tune these for best performance etc.