Cassandra Compaction Strategies for Faster Reads and Writes

Introduction to Cassandra Compaction Strategies

In Apache Cassandra, compaction is one of the most critical processes for maintaining database performance and efficiency. It’s the system’s way of merging and reorganizing SSTables (Sorted String Tables) to reduce redundancy, optimize disk space usage, and ensure fast read and write operations. Without proper compaction, the database could suffer from slower queries and ballooning storage requirements.

Cassandra offers multiple compaction strategies, each designed to address different workload patterns and data distribution needs. These include Size-Tiered Compaction Strategy (STCS), Leveled Compaction Strategy (LCS), Time-Window Compaction Strategy (TWCS), and the Unified Compaction Strategy (UCS). Selecting the right strategy for your workload can drastically improve performance and resource utilization.

This blog explores these compaction strategies in depth, offering guidance on their use cases, configurations, and practical tips for tuning them to meet your application needs.

Understanding Compaction in Cassandra

Before diving into the specific compaction strategies, let’s break down what compaction actually means in Cassandra and why it’s such a big deal.

What Are SSTables?

SSTables, or Sorted String Tables, are immutable files that store data in Cassandra. Every time data is written to the database, it first goes into a Memtable (in-memory structure) and is later flushed to disk as an SSTable. Over time, you can end up with multiple SSTables for a single table, leading to:

• Duplicate data: Old versions of records might still linger in different SSTables.
• Fragmentation: Related rows could be scattered across multiple files.
• Read inefficiency: The database has to look in several places to fetch the required data.

This is where compaction comes to the rescue.

Why Compaction Matters

Compaction is like tidying up your messy room—it combines smaller, fragmented SSTables into fewer, larger ones while discarding redundant data (like outdated versions of rows). This helps by:

• Reducing the number of SSTables to scan during reads
• Freeing up disk space by removing tombstones (markers for deleted data)
• Improving write amplification and disk I/O efficiency

But here’s the kicker: Not all workloads are the same. What works for one system might not work for another. That’s why Cassandra offers several compaction strategies, each tailored to specific use cases. Let’s explore them one by one.

Size-Tiered Compaction Strategy (STCS)

How It Works

Size-Tiered Compaction Strategy is Cassandra’s default compaction strategy and works by merging SSTables of similar sizes. When enough similarly sized SSTables accumulate (by default, four), Cassandra combines them into a larger SSTable.

Think of it like combining all the files in a folder once they reach a certain number—it’s efficient for write-heavy workloads.

Ideal Use Cases

STCS shines in scenarios where you have:

• High write throughput
• Workloads with fewer reads or workloads where latency isn’t critical
• Data with longer lifecycles that doesn’t change frequently

Benefits

• Handles write-heavy operations like a champ
• Simple and straightforward to manage
• Works well in systems where disk space is less of a concern

Drawbacks

• Can lead to read amplification since multiple SSTables might still need to be scanned for a query
• Not ideal for workloads with frequent updates or deletes, as tombstones may linger longer

Leveled Compaction Strategy (LCS)

If Size-Tiered Compaction Strategy (STCS) feels like the messy-but-effective option, Leveled Compaction Strategy (LCS) is the organized and methodical approach. It’s designed to reduce read amplification and keep your data neatly arranged.

How It Works

LCS divides data into levels, each with progressively larger SSTables. Here’s the gist:

1. New SSTables are written to Level 0.
2. Once a threshold is reached, these Level 0 SSTables are compacted into Level 1.
3. SSTables in each level are non-overlapping, meaning any row is guaranteed to exist in just one SSTable at that level.

This structured leveling means fewer SSTables need to be checked during reads, making it great for read-heavy workloads.

Ideal Use Cases

LCS is a go-to for:

• Read-heavy applications where low-latency queries are critical
• Scenarios where minimizing read amplification is more important than optimizing for write efficiency
• Workloads with frequent updates and deletes

Benefits

• Minimizes read amplification by organizing data neatly into levels
• Ensures that tombstones (data marked for deletion) are cleared quickly
• Ideal for systems with high read-to-write ratios

Drawbacks

• High write amplification: Each SSTable may be rewritten multiple times as it progresses through levels.
• Disk usage: Requires more disk space compared to other strategies because of the write amplification.

Time-Window Compaction Strategy (TWCS)

TWCS is the solution for time-series data, where older data becomes less relevant over time. Instead of endlessly compacting SSTables, it groups them into fixed time windows, like daily or hourly chunks.

How It Works

1. Data is grouped into SSTables based on the timestamp of its creation.
2. Compaction happens within each time window, leaving older SSTables untouched.

Imagine you’re organizing photos by year and only rearranging the current year’s folder. This approach saves effort while keeping things tidy.

Ideal Use Cases

TWCS is perfect for time-series use cases like:

• IoT sensor data
• Logging systems
• Metrics and analytics dashboards

Benefits

• Reduces the overhead of compacting older data
• Optimized for workloads where older data is rarely accessed
• Improves efficiency for time-series queries by clustering data by time

Drawbacks

• Older data is never re-compacted, which can lead to inefficiency if accessed later
• Tombstones may linger if they exist across time windows

Unified Compaction Strategy (UCS)

UCS is the newest addition to Cassandra’s arsenal of compaction strategies. It combines the best features of STCS, LCS, and TWCS to provide a unified approach that adapts to various workloads.

How It Works

UCS dynamically adjusts its behavior based on the data distribution and workload. It applies different techniques—like size-tiered for heavy writes, leveled for critical reads, or time-window for time-series data—depending on the situation.

Think of it as a “smart” compaction strategy that doesn’t make you choose just one method.

Ideal Use Cases

UCS is a jack-of-all-trades and works well for:

• Mixed workloads that don’t fit neatly into one category
• Systems where workloads vary over time
• Applications requiring both high write throughput and low read latency

Benefits

• Adapts to changing workloads without manual intervention
• Balances disk usage and performance across use cases
• Simplifies configuration since you don’t need to commit to a single strategy

Drawbacks

• Complexity in implementation and understanding
• Still evolving and may not be as mature as other strategies

Comparative Analysis of Compaction Strategies

To make things easier, here’s a quick comparison of the four strategies:

Choosing the Right Compaction Strategy for Your Workload

Selecting the right compaction strategy boils down to understanding your workload. Here are some tips to guide you:

1. Analyze Your Read-Write Ratio:
   • If writes dominate, STCS or UCS might be a better fit.
   • For read-heavy workloads, LCS can reduce latency.
2. Consider Data Lifespan:
   • If your data becomes irrelevant over time, TWCS is ideal.
   • For data with long-term value, LCS or UCS could work better.
3. Monitor Resource Usage:
   • Disk space constraints may push you towards LCS or UCS.
   • For write-intensive workloads, STCS is less resource-intensive.

Pro Tips

• Start with UCS if you’re unsure—it offers flexibility.
• Regularly monitor performance and adjust as your workload evolves.
• Don’t be afraid to experiment with strategies in a test environment.

Configuring Compaction Strategies in Cassandra

Now that we’ve covered the theory, let’s get practical. Setting up a compaction strategy in Cassandra is straightforward with CQL (Cassandra Query Language). Here’s a step-by-step guide to help you configure compaction strategies in your database.

Step 1: Understand the compaction_strategy Option

The compaction_strategy parameter in Cassandra defines which strategy to use for a given table. You can set it when creating or altering a table.

Step 2: Create or Alter a Table

To specify a compaction strategy, you’ll use the WITH clause in your CQL statements. For example:

Setting Size-Tiered Compaction:

CREATE TABLE my_table (
    id UUID PRIMARY KEY,
    value TEXT
) WITH compaction = {
    'class': 'SizeTieredCompactionStrategy'
};

Switching to Leveled Compaction:

ALTER TABLE my_table
WITH compaction = {
    'class': 'LeveledCompactionStrategy',
    'sstable_size_in_mb': '160'
};

Step 3: Fine-Tune Parameters

Each compaction strategy comes with specific parameters you can tweak to optimize performance. Here are a few commonly used options:

• min_threshold and max_threshold: Define how many SSTables are compacted at once. Lower thresholds speed up compaction but increase disk usage.
• tombstone_compaction_interval: Controls how often tombstones are compacted, reducing unnecessary storage.
• time_window_size: (For TWCS) Sets the size of time windows (e.g., hours, days).

Step 4: Verify Your Configuration

After applying the settings, confirm that the strategy is correctly configured:

DESCRIBE TABLE my_table;

This will show you the compaction settings for your table.

Tips for Configuring Compaction

• Always test your configuration on a non-production environment first.
• Monitor disk usage and query performance after making changes.
• Use metrics to determine if the selected strategy aligns with your workload.

Monitoring and Tuning Compaction Performance

Once you’ve set up your compaction strategy, the next step is keeping an eye on its performance. Monitoring and tuning are essential to ensure your database operates smoothly.

Key Metrics to Monitor

Use tools like nodetool or third-party monitoring solutions to track the following metrics:

• Compaction Throughput: Measures how fast compactions are happening.
• Pending Tasks: Keeps tabs on compactions waiting in the queue.
• Tombstone Removal Rate: Tracks how quickly tombstones are being cleared.
• Disk Usage: Monitors how much space your SSTables are consuming.

Tools for Monitoring

1. Nodetool: Run nodetool compactionstats to see active and pending compactions.
2. Metrics Collectors: Tools like Prometheus and Grafana can provide visual insights into compaction activity.
3. Cassandra Logs: Check logs for warnings about compaction backlogs or disk usage spikes.

Tuning Tips

• Adjust Thread Settings: Increase concurrent_compactors in the Cassandra configuration file to parallelize compactions.
• Leverage TTLs: Use Time-To-Live settings for temporary data to reduce unnecessary compaction.
• Split Large SSTables: If compactions are slow, consider breaking up oversized SSTables into smaller chunks.

Compaction isn’t a “set it and forget it” feature. Regular tuning based on metrics is the key to maintaining top-notch performance.

Common Challenges and Troubleshooting Tips

Even with the best planning, issues can arise. Here are some common challenges with Cassandra compaction and how to address them.

1. High Disk Usage

Compaction temporarily increases disk usage, especially with LCS and UCS. If you’re running out of space:

• Reduce the min_threshold to compact fewer SSTables at a time.
• Schedule compaction during low-traffic hours.
• Add more disk capacity or move old data to a cold storage solution.

2. Compaction Backlogs

Backlogs occur when compaction can’t keep up with the data being written. Fix this by:

• Increasing the concurrent_compactors value in your configuration.
• Scaling out your cluster to reduce the write load per node.

3. Tombstones Not Being Removed

If tombstones linger, they can slow down queries. To resolve this:

• Check your gc_grace_seconds setting. Lowering it can speed up tombstone removal but risks data resurrection in multi-data center setups.
• Use nodetool compact to force compaction and clean up tombstones.

4. Read Latency Issues

If read latencies are spiking, it might be due to too many SSTables. To address this:

• Switch to a strategy like LCS for better read performance.
• Compact older SSTables manually using nodetool.

Conclusion

Cassandra’s compaction strategies are like tools in a toolbox—each is designed for a specific job. Whether you’re optimizing for high writes, low reads, or time-series data, there’s a strategy that fits your needs. Here’s a quick recap:

• STCS for write-heavy workloads
• LCS for read-heavy scenarios
• TWCS for time-series data
• UCS for mixed or unpredictable workloads

The key is to monitor your system, experiment in test environments, and make adjustments as needed. With the right strategy in place, your Cassandra database will hum along smoothly, no matter what you throw at it.

Frequently Asked Questions (FAQ)