Overview
This article discusses advancements in MySQL compression techniques at Pinterest, specifically focusing on improving the compression ratio of Pin data stored as JSON blobs. The author details the transition from a basic compression system to a more efficient column compression method, achieving a compression ratio increase from 3:1 to 3.47:1.
What You'll Learn
1
How to optimize MySQL compression using predefined dictionaries
2
Why using a larger dataset improves compression ratios
3
When to implement column compression for JSON data in MySQL
Prerequisites & Requirements
- Understanding of MySQL and compression algorithms
- Familiarity with Zlib and its compression techniques(optional)
- Experience with programming in C(optional)
Key Questions Answered
How did Pinterest improve MySQL compression for JSON blobs?
Pinterest improved MySQL compression by implementing a predefined dictionary for the LZ77 compression stage, which allowed for better reuse of common substrings across multiple Pin objects. This change increased the compression ratio from 3:1 to 3.47:1, demonstrating significant space savings.
What compression library does MySQL use and how does it work?
MySQL uses the Zlib compression library, which employs the DEFLATE algorithm. This algorithm consists of two main stages: LZ77 compression, which replaces recurring strings with pointers, and Huffman encoding, which optimizes the resulting data. This two-stage process is crucial for effective data compression.
What were the benchmarks achieved after implementing the new compression technique?
The benchmarks showed that the new predefined dictionary achieved over 10% space savings compared to using a single Pin as the dictionary and 40% savings over the existing InnoDB Page Compression. This highlights the effectiveness of the new approach in optimizing data storage.
Key Statistics & Figures
Initial compression ratio
3:1
This was the compression ratio achieved before implementing the new predefined dictionary.
Final compression ratio
3.47:1
This is the improved compression ratio after applying the new techniques discussed in the article.
Space savings over existing InnoDB Page Compression
40%
The new predefined dictionary method provided significant savings compared to the previous compression method.
Technologies & Tools
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Database
Mysql
Used for storing Pin data as JSON blobs.
Compression Library
Zlib
Utilized for implementing the DEFLATE compression algorithm in MySQL.
Key Actionable Insights
1Utilizing a predefined dictionary can significantly enhance compression ratios in MySQL.By analyzing common substrings across multiple data entries, you can create a more effective compression strategy, which is particularly beneficial for datasets with shared characteristics, such as JSON blobs in Pinterest's case.
2Testing with larger datasets can yield better compression results.The article demonstrates that by compressing a larger number of Pins, the author was able to identify more common substrings, leading to improved compression ratios. This approach can be applied in various scenarios where data patterns are similar.
3Maintaining persistent state in long-running processes is crucial for efficiency.The author implemented a system to keep track of completed batches, allowing for recovery in case of failures. This practice can save time and resources in similar long-running data processing tasks.
Common Pitfalls
1
Relying on a single data object for compression can limit efficiency.
Using only one Pin object for the predefined dictionary does not leverage the shared characteristics of multiple entries, which can lead to suboptimal compression ratios. It's essential to analyze a broader dataset to identify common patterns.
Related Concepts
Data Compression Techniques
Mysql Optimization Strategies
Zlib Compression Library
JSON Data Storage Best Practices