The Unified Compaction Strategy in Cassandra 5

Summary

This video explores various database compaction strategies, focusing on the Unified Compaction Strategy (UCS). It compares UCS to other methods like LCS and STCS, showcasing UCS's superior performance for read-heavy workloads. The speaker highlights UCS’s ability to manage large data volumes, prevent performance degradation, and adapt dynamically without requiring extensive re-compaction. Additionally, potential future features like time-based levels for efficient data management are discussed. Finally, the importance of using Cassandra 5's latest settings to fully leverage UCS and other improvements is emphasized.

Takeaways

• 😀 UCS (Unified Compaction Strategy) improves performance for read-heavy workloads, especially those involving wide partitions.
• 😀 UCS is better than LCS and STCS for managing write-heavy and time-series data workloads.
• 😀 The switch to UCS from LCS/STCS resulted in immediate performance improvements without complications or additional work.
• 😀 UCS can perform whole table exploration, making it suitable for time-series workloads and efficient in compacting old and new SSTables separately.
• 😀 One feature of UCS is the ability to prioritize compactions across different levels, ensuring no level gets neglected even under heavy data influx.
• 😀 UCS can avoid poor performance scenarios by keeping compaction in sync across all levels, unlike other strategies that may stall for long periods.
• 😀 UCS allows users to adjust compaction strategies with minimal impact, avoiding complex recomputations seen in older compaction strategies.
• 😀 The adaptability of UCS enables potential future automation for adjusting compactions to better optimize read latencies dynamically.
• 😀 A future enhancement for UCS includes introducing time-based compaction levels to handle data aging, e.g., performing full compaction after data reaches a certain age.
• 😀 Netflix has expressed interest in introducing time-based compaction features, which would help manage data more efficiently and improve the handling of tombstones.
• 😀 Cassandra 5 introduces new settings that activate the latest features, offering significantly better performance if used, with results showing up to twice as fast when enabled.

Q & A

What is UCS, and how does it relate to other compaction strategies like STCS and LCS?

UCS (Unified Compaction Strategy) is a compaction strategy designed to handle various workload types effectively, particularly read-heavy workloads. Unlike STCS (Size-Tiered Compaction Strategy) and LCS (Leveled Compaction Strategy), which are more suited for different use cases, UCS performs better in scenarios involving a write-heavy partition workload, such as time-series data.

Why does UCS perform better than STCS and LCS in a read-heavy workload?

UCS is optimized for read-heavy workloads because it minimizes unnecessary compaction and reduces the risk of mixing old SSTables with new ones. It also avoids inefficient processes by focusing on full table exploration, ensuring better performance under high read demands.

What happens when you switch to UCS after a certain period of time?

When switching to UCS, as shown in the example at 20,000 seconds, the performance improves immediately without additional work or complications. This highlights the ease of upgrading to UCS and its efficiency in managing workloads after the transition.

How does UCS handle compaction at different levels to maintain performance?

UCS handles compaction by prioritizing tasks at various levels of the hierarchy, ensuring that no level is neglected. It distributes the compaction workload evenly, which prevents scenarios where compaction lags and causes poor performance, unlike other strategies that might leave levels behind.

What is the role of time-based levels in UCS, and why are they being considered?

Time-based levels in UCS would allow for better management of old data by setting time-based rules for full compaction. This feature could address issues such as tombstone accumulation, which is particularly useful for time-series data. Although this feature is in development, it promises more efficient data management.

What is adaptive compaction in UCS, and how can it be used?

Adaptive compaction in UCS refers to the ability to adjust compaction strategies dynamically based on the current workload and performance needs. This feature allows for fine-tuning of compaction parameters, such as optimizing for read latencies without triggering full compactions, making UCS more flexible and efficient.

Can UCS be easily adjusted without disrupting performance?

Yes, UCS allows for easy adjustments of compaction parameters without triggering complex or full compactions. This flexibility enables users to optimize for specific workloads, such as improving read latencies, without negatively impacting system performance.

How does UCS compare to LCS in terms of efficiency for read-heavy workloads?

UCS generally outperforms LCS in read-heavy workloads because LCS can struggle with the increased number of compactions required for such scenarios. UCS, on the other hand, avoids mixing new and old SSTables, which allows it to maintain better performance, especially in environments with high read demands.

What are the benefits of using the latest settings in Cassandra 5?

Using the latest settings in Cassandra 5 enables new features and optimizations, which can significantly improve performance. For example, using the proper settings can result in performance improvements of over 2x, whereas the default conservative settings might not fully leverage the capabilities of Cassandra 5.

What is the potential impact of introducing time-based compaction strategies in UCS?

Introducing time-based compaction strategies in UCS could help manage aging data more effectively, ensuring old data is compacted at regular intervals, such as every few weeks. This could help address issues like tombstone accumulation and improve overall system performance by focusing compaction on specific time windows.