A data-engineering team stores click-stream events as Parquet files in HDFS. The files are currently written with gzip compression, and CPU profiling shows that nearly 40% of interactive Spark-SQL query time is spent in the decompression step. Storage cost is acceptable; the primary goal is to lower query latency by switching to a different compressed format that is natively supported by Hadoop. Which codec should the team adopt to minimize decompression overhead while still providing a reasonable compression ratio?
LZ4 is designed for raw speed. Benchmarks on a modern x86-64 core show decoder throughput close to 5 GB/s, far higher than Snappy (approximately 2 GB/s) and an order of magnitude above gzip or bzip2. Because the performance bottleneck is CPU time spent decompressing, choosing LZ4 will shorten scan times even though its compression ratio is slightly lower than gzip. Bzip2 would actually slow things down, as its compression and decompression are both much slower. Snappy would help but not as much as LZ4. Leaving the data in gzip format leaves the existing bottleneck unresolved.
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What is the advantage of LZ4 over gzip in this scenario?
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How does Snappy compression compare to LZ4 in terms of performance?
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Why is bzip2 not suitable for reducing query latency?