Binary Data Formats

While text-based formats shine in human-readable scenarios, binary formats take center stage when performance and compactness are important.

Think of them as the difference between sending a handwritten letter versus a compressed digital file – both convey information, but the latter is optimized for machine processing. However, the handwritten letter can be read easily upon receipt, while a compressed digital file must be uncompressed, then opened onto a viewer to be read.

Why Binary Formats are Used

1. Machine-friendly: Like a well-oiled assembly line, binary data requires minimal preprocessing.
2. Schema-driven: Metadata lives in a separate blueprint, streamlining the actual data.
3. Portable: Easily translatable across languages, provided they share the encoding/decoding rulebook.
4. Precision-focused: Capable of handling large numbers and floating-point values with high accuracy.
5. Standardized: Well-documented schemas promote consistency across implementations.

Many binary formats rely on schema documents to define data structure.

1. Protocol Buffers (Protobuf) & Apache Thrift

These formats share similar DNA, using numbered tags and type definitions. Here’s a taste of their syntax:

message Product {

    required int64 product_id = 1;

    optional string product_name = 2;

    repeated float price_history = 3;

}

Key Elements:

• Tags: Numerical identifiers (e.g., 1, 2, 3) replace field names for compactness.
• Labels: ‘required’, ‘optional’, and ‘repeated’ guide encoding/decoding behavior.
• Types: Explicit data types (int64, string, etc.) for each field.
• Names: Human-readable identifiers, separate from the encoded tags.

2. Apache Avro

Avro takes a different approach, focusing on value-only encoding and dynamic schema resolution.

{

  "type": "record",

  "name": "Product",

  "fields": [

    {"name": "product_id", "type": "long"},

    {"name": "product_name", "type": ["null", "string"]},

    {"name": "price_history", "type": {"type": "array", "items": "float"}}

  ]

}

Avro’s flexibility shines in its ability to handle schema evolution gracefully. When reader and writer schemas differ, Avro performs a clever dance of field matching and default value application.

Choosing the right format

While all binary formats offer speed and efficiency gains, they each have their sweet spots:

• JSON: Ideal for small-scale, JavaScript-centric systems prioritizing readability.
• XML: Best for complex, cross-system data structures requiring extensive markup.
• Avro: Excels with large files and frequent schema version changes.
• Protobuf/Thrift: Shine in low-latency, high-performance scenarios.

Feature Comparison

Remember, there’s no one-size-fits-all solution. The best format depends on your specific use case, balancing factors like performance, readability, and ease of implementation.

Use Cases

To truly grasp the impact of different binary formats, let’s examine their performance across various scenarios:

1. Small Data Payloads (< 1 KB)
   • Protobuf and Thrift often shine here due to their low overhead.
   • JSON might still be competitive for very small payloads.
2. Large Data Sets (> 100 MB)
   • Avro typically takes the lead, especially with homogeneous data.
   • Protobuf and Thrift maintain strong performance but may lag slightly.
3. Frequent Schema Changes
   • Avro’s dynamic schema resolution gives it a significant edge.
   • Protobuf and Thrift might require more careful version management.
4. Complex Nested Structures
   • All binary formats handle these well, but Protobuf’s repeated fields can be particularly efficient.
5. Cross-Language Interoperability
   • Thrift’s extensive language support might give it an advantage in polyglot environments.
6. Encoding/Decoding Speed
   • Protobuf often leads in pure serialization/deserialization speed.
   • Avro’s schema resolution might add slight overhead in some cases.
7. Memory Usage
   • Binary formats generally use less memory than JSON or XML.
   • Avro’s value-based encoding can lead to the most compact representations.
8. CPU Utilization
   • Binary formats typically require less CPU time for parsing compared to text-based formats.
   • The difference becomes more pronounced with larger data sets.