What is Google Snappy

Google Snappy is a fast and efficient data compression library created by Google. Unlike traditional compression tools that focus on achieving the smallest file size. Snappy prioritizes speed, enabling rapid compression and decompression which makes it ideal for real time applications and big data systems where processing time is important. It’s widely used in technologies like Apache Hadoop, Apache Spark and databases such as LevelDB for quick, lightweight data compression without sacrificing much performance.

Key Features

• High Speed Compression and Decompression: Snappy focuses on speed to ensure minimal CPU overhead during data compression and decompression.
• Moderate Compression Ratio: Snappy strikes a practical balance that reduces data size enough to save storage and bandwidth without slowing down processing.
• Simple API: The library provides an easy to use interface allowing developers to quickly implement compression in their software.
• Cross Platform Support: Snappy is available in several programming languages ensuring developers can use it regardless of their tech stack.

Basic Functions

1. snappy.compress(data)

This function compresses the input byte data using Snappy's fast compression algorithm. It returns a smaller byte string ideal for saving storage or transmitting data quickly.

import snappy

data = b"Snappy is fast!"
compressed = snappy.compress(data)
print("Compressed:", compressed)

Output:

Compressed: b'\x0f8Snappy is fast!'

2. snappy.uncompress(data)

This function decompresses previously compressed Snappy data. It restores the original byte content exactly as it was before compression.

import snappy

compressed = snappy.compress(b"Snappy is fast!")
decompressed = snappy.uncompress(compressed)
print("Decompressed:", decompressed)

Output:

Decompressed: b'Snappy is fast!'

3. snappy.StreamCompressor()

Used for compressing large or streaming data in multiple chunks instead of all at once. It helps manage memory efficiently during real time or batch data processing.

import snappy

sc = snappy.StreamCompressor()
chunk = sc.add_chunk(b"This is a stream of data.")
print("Compressed Stream Chunk:", chunk)

Output:

Compressed Stream Chunk: b'\xff\x06\x00\x00sNaPpY\x01\x1d\x00\x00\x85\x10\xf2\x98This is a stream of data.'

4. snappy.StreamDecompressor()

Allows you to decompress Snappy compressed data stream by stream. Useful when working with large files or network streams where the entire data isn't available at once.

import snappy

sc = snappy.StreamCompressor()
compressed_chunk = sc.add_chunk(b"Streaming works!")

sd = snappy.StreamDecompressor()
decompressed_chunk = sd.decompress(compressed_chunk)

print("Decompressed Stream Chunk:", decompressed_chunk)

Output:

Decompressed Stream Chunk: b'Streaming works!'

How do we use Google Snappy?

• Using Google Snappy is straightforward due to its simple API. First you include the Snappy library in your project available for languages like C++, Java and Python.
• Then to compress data you call the compression function on your input bytes or strings which quickly returns the compressed output.
• Similarly, for decompression you pass the compressed data to the decompression function to retrieve the original data as Snappy is optimized for speed it integrates well in applications where fast data processing is important.
• Overall its ease of use combined with high performance makes it a popular choice for developers needing lightweight compression.

Example

Step 1: Install Snappy

pip install python-snappy

Step 2: Perform Functions on Original Data

This code demonstrates how to use the snappy module to compress and decompress byte data. It first compresses a byte string using snappy.compress() then restores it back to the original using snappy.uncompress() and prints both results. This showcases Snappy’s fast and easy to use compression.

import snappy

# Original data
data = b"This is some data we want to compress quickly."

# Compress 
compressed_data = snappy.compress(data)
print("Compressed:", compressed_data)

# Decompress 
decompressed_data = snappy.uncompress(compressed_data)
print("Decompressed:", decompressed_data)

Output:

Compressed: b'.\xb4This is some data we want to compress quickly.'

Decompressed: b'This is some data we want to compress quickly.'

Applications

1. Big Data Processing: Frameworks like Apache Hadoop and Apache Spark integrate Snappy to compress intermediate data quickly, reducing storage needs and speeding up data shuffles across clusters.
2. Databases: Databases such as LevelDB and RocksDB use Snappy to compress data files improving disk space usage and speeding up read/write operations by reducing I/O overhead.
3. Distributed Systems: Snappy optimizes communication between distributed nodes by compressing messages fast, helping maintain low latency and high throughput in complex systems.
4. Network Protocols: Protocols like gRPC use Snappy to compress network payloads which reduces bandwidth usage and improves response times without adding heavy CPU costs.