Explained: Cassandra Compaction Strategies

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We’ll explain all about Cassandra compaction, compaction strategies, and how to choose the best for your use-case. We’ll explain everything you need to know in this post. Apache Cassandra, a highly scalable and high-performance distributed database, owes much of its flexibility and efficiency to its compaction process. Compaction is a core element in the Cassandra write path analysis and plays a significant role in maintaining Cassandra’s performance and disk space utilization.

What is Compaction in Cassandra?

Compaction in Cassandra is a background process that consolidates and discards the redundant, obsolete data held in SSTables (Sorted String Tables), thereby saving disk space and improving read performance. When Cassandra writes data, it first writes to a commit log for durability, then writes the data to an in-memory structure called Memtable. Once the Memtable is full, Cassandra flushes it to an SSTable on disk. Over time, you could end up with many SSTables on disk, and the same piece of data (row or cell) could exist in multiple SSTables.

This is where the role of compaction becomes crucial. Compaction is a background process that merges these SSTables, resolves conflicts by picking the most recent data, and discards older or deleted data marked by tombstones. This process effectively reduces the number of SSTables and thus, the disk space utilization, making read operations more efficient since they have fewer SSTables to scan.

Cassandra provides different compaction strategies, including SizeTieredCompactionStrategy (STCS), LeveledCompactionStrategy (LCS), and TimeWindowCompactionStrategy (TWCS). Each of these strategies has different effects on Cassandra’s database structure, performance, and disk usage, and understanding these differences is essential for effective Cassandra compaction tuning.

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Cassandra Compaction Strategies

Cassandra offers three main types of compaction strategies: SizeTieredCompactionStrategy (STCS), LeveledCompactionStrategy (LCS), and TimeWindowCompactionStrategy (TWCS). Each has a different impact on Cassandra database structure, performance, and disk usage.

SizeTieredCompactionStrategy (STCS)

SizeTieredCompactionStrategy (STCS) is one of the primary compaction strategies offered by Apache Cassandra. It’s the default strategy, mostly suited for write-heavy workloads where write performance is more important than read performance.

In STCS, Cassandra compacts SSTables of similar sizes, aiming to minimize the number of SSTables a read operation needs to check. When a certain number of similar-sized SSTables exist, they’re compacted together into a larger one. This compaction process reduces the overall number of SSTables on the disk, leading to less I/O during read operations.

However, STCS might not be the best choice for all use cases due to its disk space requirements. During the compaction process, it might temporarily require additional disk space, up to 50% of the original data size, which can be a substantial amount for large data sets. Additionally, STCS can lead to larger SSTables that cover a wider range of partition keys, potentially affecting read performance for specific keys.

Therefore, STCS is best used when your Cassandra workload is primarily write-oriented, and you have sufficient disk space to handle the compaction process. If your use case is read-heavy, or your application requires more predictable read latencies, you might want to consider other compaction strategies like LeveledCompactionStrategy or TimeWindowCompactionStrategy. Always monitor and adjust your compaction strategy according to your application’s performance and needs.

LeveledCompactionStrategy (LCS)

LeveledCompactionStrategy (LCS) is a critical compaction strategy offered by Apache Cassandra, primarily optimized for read-heavy workloads, or where read and write operations are almost equal.

Unlike the SizeTieredCompactionStrategy (STCS) which consolidates SSTables of a similar size, LCS organizes SSTables into levels and manages them in a way that provides more predictable read performance. Each level is ten times as large as the previous one, and SSTables within each level are of approximately the same size (about 160MB by default). An SSTable moves from one level to a higher one only if it doesn’t overlap with more than ten SSTables in the higher level. This strategy minimizes the number of SSTables that need to be checked during a read operation, thereby reducing read latency and optimizing disk space utilization.

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However, while LCS has its advantages, it’s not without its trade-offs. LCS is more write-intensive than STCS. It results in more write amplification because it triggers compaction more frequently, which can increase write latency and cause more I/O operations.

Nevertheless, if your application has a balanced or read-intensive workload, LCS can offer significant benefits in terms of predictable read latency and improved space utilization. It’s essential to monitor the performance characteristics of your application and adjust the compaction strategy as needed to ensure the optimal performance and efficiency of your Cassandra cluster.

TimeWindowCompactionStrategy (TWCS)

TimeWindowCompactionStrategy (TWCS) is one of the most advanced Cassandra compaction strategies, specifically designed for handling time-series data. Time-series data is a sequence of data points indexed in time order – a common scenario in many modern applications like IoT sensor data, user activity logs, and performance metrics.

Unlike SizeTieredCompactionStrategy (STCS) and LeveledCompactionStrategy (LCS), TWCS groups SSTables into distinct windows based on data timestamps. Only SSTables with data that falls into the same time window are compacted together. This strategy significantly reduces the number of SSTables that need to be checked during a read operation, improving read performance and better managing disk space over time.

TWCS shines in situations where data expires and can be deleted after a certain period (Time to Live or TTL). The expired data, often marked with tombstones, gets efficiently removed during the compaction process, which further optimizes disk space usage.

TWCS also aids in reducing the impact of compaction on Cassandra’s performance. As compactions within a window are isolated from compactions in other windows, the chances of large, performance-impacting compactions are reduced.

If your Cassandra use case involves time-series data or data with TTL, TimeWindowCompactionStrategy should be your go-to choice among Cassandra compaction strategies. As always, continuous monitoring and tuning of your compaction strategy according to your application’s needs are essential to maintain optimal database performance.

Choosing the Best Compaction Strategy

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Compaction is closely related to Cassandra’s tombstone management. Tombstones, markers for deleted data, are removed during the compaction process after a certain period (defined by gc_grace_seconds). Proper handling of tombstones through compaction is critical as a large number of tombstones can slow down queries and increase read latency.

Choosing the right compaction strategy involves understanding your workload and the read/write characteristics of your application. If your application is write-heavy, STCS might be the best option. For read-intensive or balanced read/write workloads, LCS can offer better performance. If your data has time-based characteristics, TWCS would be the best fit. It’s also essential to monitor your compaction performance and adjust your strategy as needed.

Compaction isn’t a set-it-and-forget-it operation. Regular monitoring and performance tuning are required to ensure optimal performance. Factors such as write and read amplification, disk space utilization, compaction throughput, and the handling of tombstones must be considered when optimizing your compaction strategy.

Determining if your Compaction Strategy is Causing Performance Issues

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Understanding the impact of your chosen compaction strategy on the performance of your Cassandra database is critical for maintaining optimal system efficiency. Compaction can affect various aspects of performance, including read and write speeds, disk space usage, and general system load. Here are some steps to help determine if your compaction strategy is affecting your Cassandra’s performance.

  1. Monitoring system metrics: Keep a close eye on system metrics, particularly disk I/O, CPU usage, and disk space utilization. A high disk I/O could indicate that the compaction process is causing extensive disk usage. Similarly, high CPU usage might suggest that your compaction strategy is computationally intensive, affecting overall system performance. Monitoring these metrics over time can provide insights into how your compaction strategy impacts system resources.
  2. Review SSTable count: One of the primary goals of compaction is to reduce the number of SSTables. An unusually high number of SSTables can lead to slower read operations, as more SSTables need to be merged to return a result. If you notice a consistently high number of SSTables despite regular compactions, this might indicate a problem with your current compaction strategy.
  3. Evaluate read and write latency: Compaction can impact both read and write latencies. If write latency is high, it could be because compaction is causing significant write amplification. Read latency, on the other hand, can increase if the compaction strategy results in a large number of SSTables to scan during read operations. Continuous monitoring of read and write latencies can provide valuable insights into the performance implications of your compaction strategy.
  4. Observe tombstone behavior: Compaction plays a crucial role in removing tombstones, markers for deleted data. A large number of tombstones can slow down queries and increase read latency. If you observe slow queries despite having a low number of SSTables, it could be due to a large number of tombstones, indicating that your compaction strategy might not be handling tombstone removal effectively.
  5. Analyze compaction logs: Cassandra’s logs provide detailed information about the compaction process. Regularly reviewing these logs can help identify issues related to compaction that could be affecting performance.
  6. Test different compaction strategies: Finally, one of the best ways to determine the impact of a compaction strategy on performance is to test different strategies under controlled conditions. Using a staging environment that mirrors your production setup, apply different compaction strategies and measure their impact on key performance metrics. This can give you a clear idea of how different strategies could affect your production performance.
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Choosing the right compaction strategy and tuning it according to your application’s specific needs is as much an art as a science. It involves continuous monitoring, testing, and fine-tuning. Be proactive in monitoring your database performance and be ready to adjust your compaction strategy as needed to ensure optimal performance.

Frequently Asked Questions (FAQ)

What is a Memtable in Cassandra?

Memtable is an in-memory data structure where Cassandra writes data initially. When a write operation occurs, Cassandra records it in a commit log on disk (for durability) and simultaneously writes the data into the Memtable. The Memtable holds data in sorted order until it reaches a certain size threshold. At this point, the data in the Memtable is flushed to disk as an SSTable (Sorted String Table), a persistent, immutable data file. Memtables and SSTables together contribute to Cassandra’s high write performance and durability, while also allowing efficient retrieval of data. It’s crucial to note that while data in the Memtable is susceptible to loss in case of a system crash, the commit log ensures data durability by providing a means to recover any data not yet written to SSTables.

What are Cassandra SSTables?

SSTables (Sorted String Tables) are immutable data files stored on disk. They are created when the data in the Memtable is flushed to disk once it reaches a certain size threshold or when a commit log is replayed. SSTables are organized by keys in a sorted order, which allows Cassandra to quickly locate and read data during a query operation. Each SSTable comprises data, primary index, bloom filter, compression information, and statistics. SSTables are designed to be append-only structures, ensuring high write throughput and efficiency. Despite their name, SSTables store binary data, not strings. They are a crucial part of Cassandra’s architecture, working alongside Memtables and commit logs to deliver high performance, efficient storage, and data durability. Due to their immutability, multiple versions of an item can exist across SSTables, and a compaction process is employed to reconcile and remove redundant data.

Does my data model (schema) affect compaction?

The data model in Cassandra significantly impacts compaction because it influences how frequently and intensively compaction occurs. If your data model involves frequent updates or deletions, compaction will be more frequent to manage tombstones and updated data. Write-heavy models can lead to more SSTables, necessitating regular compaction. Time-series data models can benefit from time-window compaction to efficiently manage data expiration. The partition key selection also plays a crucial role, as hot partitions could lead to compaction issues. Thus, a well-designed data model that aligns with your chosen compaction strategy can significantly improve overall Cassandra performance.

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