Data Encoding and Compression Techniques in Distributed Systems

Problem Description
In distributed systems, data encoding and compression techniques are used to reduce storage space and network transmission overhead. How should common encoding methods (such as dictionary encoding, delta encoding, bitmap encoding, etc.) and compression algorithms (such as Snappy, LZ4, Zstandard) be chosen? What are their principles and applicable scenarios?

Key Knowledge Points and Problem-Solving Approach

1. Core Objectives

   • Reduce data volume and lower storage costs.
   • Increase network transmission speed and improve system throughput.
   • Balance compression ratio, compression/decompression speed, and CPU overhead.

2. Common Encoding Techniques

   • Dictionary Encoding:

     • Principle: Replace frequently occurring values (e.g., strings) with shorter unique identifiers (e.g., integer indices).
     • Example: Original data ["apple", "banana", "apple"] is encoded into dictionary {0: "apple", 1: "banana"} and sequence [0, 1, 0].
     • Applicable Scenarios: Text logs, data with high-frequency repetitions in key-value pairs.

   • Delta Encoding:

     • Principle: Store the differences between data points rather than the original values.
     • Example: Time series data [100, 101, 103, 106] is encoded as [100, +1, +2, +3].
     • Applicable Scenarios: Time-series databases (e.g., Prometheus), ordered ID sequences.

   • Bitmap Encoding:

     • Principle: Use bits to represent Boolean values or enumerated states, where one byte can store 8 states.
     • Example: User tag data [true, false, true, true] is encoded as binary 0b1101 (requiring only 1 byte).
     • Applicable Scenarios: Boolean fields, high-cardinality data of enumerated types (e.g., user profiles).

3. Compression Algorithm Classification and Selection

   • Lossless Compression: Guarantees complete data recovery, suitable for all scenarios.

     • Fast Algorithms (e.g., LZ4, Snappy):
       • Characteristics: Extremely fast compression/decompression speed, moderate compression ratio.
       • Applicable Scenarios: Real-time data processing, message queues (Kafka), in-memory caching.
     • High Compression Ratio Algorithms (e.g., Zstandard, Gzip):
       • Characteristics: Higher compression ratio, but with greater CPU overhead.
       • Applicable Scenarios: Cold data storage (HDFS), backup systems.

   • Lossy Compression: Allows for precision loss, mainly used for specific data types.

     • Example: Truncation of floating-point precision (e.g., retaining 3 decimal places), image/video transcoding.
     • Applicable Scenarios: Monitoring metrics (e.g., Prometheus), multimedia data.

4. Practical Strategies in Distributed Systems

   • Tiered Compression:
     • Use fast algorithms (e.g., LZ4) for hot data and high compression ratio algorithms (e.g., Zstd) for cold data.
   • Columnar Storage Encoding:
     • Combine encoding (e.g., dictionary encoding + RLE) and compression algorithms in columnar databases (e.g., Apache Parquet) to further improve efficiency.
   • Network Transmission Optimization:
     • Enable compression (e.g., Gzip) for repeated request content (e.g., HTTP responses) to reduce bandwidth usage.

Summary
When selecting encoding and compression schemes, it is necessary to evaluate:

• Data characteristics (repetitiveness, orderliness, data type).
• System requirements (latency-sensitive vs. storage-sensitive).
• Resource constraints (CPU, memory, network bandwidth).
  By combining encoding techniques and compression algorithms, efficient storage and transmission can be achieved in distributed systems.