Variable Int Encoding: Optimizing Storage Efficiency in Cassandra

• Thu, Aug 15, 2024
• 4-minute read

When evaluating a database for your application, it’s essential to consider the total cost of ownership. This includes the obvious expenses like team salaries and hardware costs, but also extends to more subtle factors like storage efficiency and network bandwidth usage. In my years of working with Cassandra clusters, I’ve found that optimizing how data is encoded can have a surprising impact on both performance and operating costs.

Why Encoding Matters for Database Efficiency

Storage efficiency directly affects several aspects of database operations:

• Disk Usage: Less space means lower storage costs
• Network Bandwidth: Smaller data sizes mean faster transfers between nodes
• Memory Utilization: Efficient encoding can reduce memory pressure
• I/O Performance: Less data to read/write often translates to better throughput

For distributed databases like Cassandra, these benefits are multiplied across the entire cluster, making encoding optimization particularly valuable.

Variable Integer Encoding in Cassandra

One of the clever techniques Cassandra uses to optimize storage is variable integer encoding. Rather than storing all integers using a fixed number of bytes (typically 4 bytes for a 32-bit integer), Cassandra can store smaller values using fewer bytes.

In this post, I’ll explore how Cassandra implements this through the VIntCoding.java class. This approach is similar to what you might find in Protocol Buffers and other efficient serialization formats.

How Variable Int Encoding Works

The basic concept is straightforward: use fewer bytes for smaller numbers. Let’s break down the implementation:

Unsigned Integers

For unsigned integers, the encoding works as follows:

1. If the value fits in 7 bits (0-127), store it in a single byte
2. For larger values, use the high bit of each byte as a continuation flag
3. Store the bytes in a reversed order for efficient processing

public static int computeUnsignedVIntSize(long value) {
    int size = 1;
    while ((value & ~0x7FL) != 0L) {
        value >>>= 7;
        size++;
    }
    return size;
}

This means a value like 42 requires just one byte, while a value like 130 would need two bytes.

Signed Integers

For signed integers, we need a way to handle negative numbers efficiently. This is where Zig-Zag encoding comes in:

1. Map signed integers to unsigned integers in a way that maintains small values
2. Specifically: (value << 1) ^ (value >> 63) for longs
3. Then apply the same variable-length encoding as for unsigned integers

public static long encodeZigZag64(long value) {
    return (value << 1) ^ (value >> 63);
}

With Zig-Zag encoding, small negative numbers like -1 also use minimal storage space.

Real-World Impact

In my experience with large Cassandra clusters, this encoding technique can reduce storage requirements by 20-30% for integer-heavy schemas. One client I worked with was able to reduce their cluster size from 30 nodes to 24 nodes after optimizing their schema to take advantage of variable encoding, resulting in significant cost savings.

Here’s a simple comparison of storage requirements for different integer values:

Practical Applications

To leverage variable int encoding in your Cassandra clusters:

1. Choose appropriate types: Use types like VARINT for values that might vary greatly in size
2. Prefer counters for small increments: Counter values are stored efficiently
3. Consider decomposition: Break large integers into components if they have logical structure
4. Monitor metrics: Track storage usage per column to identify optimization opportunities

Implementation Considerations

While variable encoding offers significant benefits, it does come with trade-offs:

• CPU Usage: Encoding/decoding requires extra CPU cycles
• Complexity: More complex code than fixed-width storage
• Predictability: Storage requirements become less predictable

In practice, modern CPUs are fast enough that the encoding/decoding overhead is negligible compared to the benefits of reduced I/O.

Conclusion

Variable integer encoding is just one of many techniques used by Cassandra to optimize storage efficiency. By understanding how these mechanisms work, you can design more efficient schemas and potentially reduce your infrastructure costs significantly.

In future posts, I’ll explore other encoding optimizations in Cassandra, including collection types and custom serialization approaches. If you’re working on optimizing a large Cassandra deployment, I’d love to hear about your experiences in the comments below.

References

• Cassandra VIntCoding.java
• Protocol Buffers Encoding
• Zig-Zag Encoding Example
• Google Guava VarInt