Python Choose Compression Levels and Measure File Sizes

Measure compression tradeoffs using representative inputs and elapsed time.

How It Works

Higher compression effort may yield smaller files at the cost of CPU and memory. Compressibility depends on input; repeated plain text is unlike an already-compressed JPEG. Compare sizes and timings under the same conditions before changing a production backup policy.

Compress once per identical payload, and use repeat measurements before comparing very short timings.

Step-by-Step Example

import gzip
from time import perf_counter

payload = (b"daily metric,100\n" * 500)
for level in [1, 6, 9]:
    started = perf_counter()
    compressed = gzip.compress(payload, compresslevel=level)
    print(level, len(compressed), perf_counter() - started >= 0)

Understanding the Example

• The expression '(b"daily metric,100\\n" * 500)' supplies the value stored as 'payload'. This value is then used or checked by the later statements.
• The for loop processes one item per iteration rather than requiring a separate statement for each value.

A successful run of this example prints the following output:

1 117 True
6 78 True
9 78 True

Where This Fits in a Real Program

A practical archive workflow has separate decisions for selection, compression, extraction and verification. When files come from other people, inspect paths, member counts and uncompressed sizes before writing anything; a successful decompression does not establish that its contents are trustworthy.

Practice: Try It Yourself

Repeat the measurement with random-looking bytes and compare the sizes and run times.

For this exercise, predict the result before running the changed code. Then explain the difference between the original and modified cases using the actual values, not just whether the program completed.

Further Reading

Read the official Python documentation for complete parameters, return types and exceptions.

Common Beginner Mistakes

• Concluding that the highest compression level is always the best choice for every workload.
• Using a tiny synthetic test as proof of performance on large mixed production datasets.

Key Takeaways

• Measure compression tradeoffs using representative inputs and elapsed time.
• Higher compression effort may yield smaller files at the cost of CPU and memory.
• Compress once per identical payload, and use repeat measurements before comparing very short timings.