Overview
When creating a MongoDB cluster in ScaleGrid, users can select from three data compression algorithms: Snappy, Zlib, and Zstd. These algorithms are only supported on MongoDB clusters, and they optimize storage and performance based on workload requirements. This article explains each algorithm, best use case, and guidance on choosing the right one for your database cluster.
What Are Data Compression Algorithms?
Data compression algorithms reduce the size of stored data in a database, optimizing disk usage and potentially improving performance. In ScaleGrid, compression is applied at the storage engine level (that is MongoDB’s WiredTiger) during cluster creation. The three available options—Snappy, Zlib, and Zstd—offer different trade-offs between compression ratio, speed, and CPU usage.
1. Snappy
Snappy, developed by Google, is a fast, lossless compression algorithm designed for high-speed compression and decompression with minimal CPU overhead.
Best Use Case
Snappy is ideal for high-throughput, performance-critical applications, such as real-time analytics or Online Transaction Processing (OLTP) workloads in MongoDB, where low latency is prioritized over storage efficiency.
2. Zlib
Zlib, based on the DEFLATE algorithm, is a general-purpose, lossless compression library used in tools like gzip and PNG. It provides a strong compression ratio, making it suitable for storage optimization.
Best Use Case
Zlib is best for archival databases, data warehouses, or systems with infrequent data access where storage savings are more critical than query performance.
3. Zstd (Zstandard)
Zstd, developed by Facebook, is a modern, lossless compression algorithm that balances high compression ratios with fast performance.
Best Use Case
Zstd is ideal for mixed workloads (e.g., OLTP and OLAP) or modern database deployments requiring a balance of storage efficiency and performance.
Comparison Table
Algorithm
|
Compression Ratio
|
Speed (Comp/Decomp)
|
CPU Usage
|
MongoDB Version (Block/Network)
|
Best For
|
---|---|---|---|---|---|
Snappy
|
Low
|
Very Fast
|
Low
|
3.0+/3.4+
|
Latency-sensitive, high-throughput workloads
|
Zlib
|
High
|
Slow
|
High
|
3.0+/3.6+
|
Storage-constrained, less frequent access
|
Zstd
|
High
|
Fast
|
Medium
|
4.2+/4.2+
|
Balanced workloads, modern databases
|
Block vs. Network Compression
In MongoDB, compression can be applied in two contexts:
Block Compression: Compresses data stored on disk for collections and indexes in the WiredTiger storage engine, reducing storage requirements. It directly impacts disk usage and I/O performance.
Network Compression: Compresses data transmitted between MongoDB clients and servers, reducing bandwidth usage and improving network performance.
NOTE: Block compression for Snappy and Zlib is supported since MongoDB 3.0, while Zstd requires MongoDB 4.2. Network compression support began later: Snappy (3.4+), Zlib (3.6+), and Zstd (4.2+). Ensure your MongoDB version aligns with your compression needs, as older versions (e.g., 3.6) do not support Zstd.
Conclusion
The choice of compression algorithm—Snappy, Zlib, or Zstd—in ScaleGrid significantly impacts your database’s performance and storage efficiency. Snappy excels in high-speed, low-latency scenarios, Zlib optimizes for storage savings, and Zstd offers a versatile balance for modern workloads. By understanding your application’s needs and testing configurations, you can select the best algorithm to ensure optimal performance for your ScaleGrid database cluster. For further reference, please see the links below:
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