Django 3.2 - News on compressed fixtures and fixtures compression

© 2019 Paolo Melchiorre "Photo of a renaissance tower helical staircase in Palazzo Ducale - Urbino, Marche, Italy" instagram.com/pauloxnet

Management Commands¶

As reported in the documentation, the changes are related to the scope of the management commands.

• loaddata now supports fixtures stored in XZ archives (.xz) and LZMA archives (.lzma).

• dumpdata now can compress data in the bz2, gz, lzma, or xz formats.

loaddata¶

The loaddata command searches for and loads the contents of the named fixture into the database.

Compressed fixtures

In the Django 3.2 version was addes support for xz archives (.xz) and lzma archives (.lzma).

Fixtures may be compressed in zip, gz, bz2, lzma, or xz format.

For example $ django-admin loaddata mydata.json would look for any of mydata.json, mydata.json.zip, mydata.json.gz, mydata.json.bz2, mydata.json.lzma, or mydata.json.xz.

The first file contained within a compressed archive is used.

dumpdata¶

The dumpdata outputs all data in the database associated with some or installed applications. The output of dumpdata can be used as input for loaddata.

Fixtures compression

In the Django 3.2 version was addes support to dump data directly to a compressed file.

The output file can be compressed with one of the bz2, gz, lzma, or xz formats by ending the filename with the corresponding extension.

For example, to output the data as a compressed JSON file $ django-admin dumpdata -o mydata.json.gz

Benchmarks¶

After the development of the new fixtures compression function I carried out benchmarks for all supported file formats starting from different databases, from small projects to larger ones.

The benchmark were performed on my pc and are only examples of the relationship between time, file size, memory and cpu occupation that is needed to export data directly into different type of compressed files.

System info¶

import os
import platform

print(f"Architecture:\t{platform.architecture()[0]}")
print(f"Machine type:\t{platform.machine()}")
print(f"System glibc:\t{platform.libc_ver()[1]}")
print(f"System memory:\t{os.sysconf('SC_PAGE_SIZE') * os.sysconf('SC_PHYS_PAGES')}")
print(f"System release:\t{platform.release()}")
print(f"System type:\t{platform.system()}")
print(f"Python impl.:\t{platform.python_implementation()}")
print(f"Python version:\t{platform.python_version()}")

Architecture:   64bit
Machine type:   x86_64
System glibc:   2.32
System memory:  33402449920
System release: 5.8.0-48-generic
System type:    Linux
Python impl.:   CPython
Python version: 3.8.6

Benchmark 01

Benchmark 02

Benchmark 03

Benchmark 04

Benchmark 05

Benchmark 06

Benchmark 07

Benchmark 08

Conclusion¶

From the benchmarks carried out with various starting data in exporting data directly to compressed files, it is clear that:

• the xz format almost always produces the smallest files in the face of greater memory and cpu occupation
• the gz and bz2 formats almost always have execution times comparable to saving on simple and uncompressed text files in the face of a strong reduction in the space occupied
• the space gain in the generated compressed files compared to the uncompressed text file ranges from 55% to 98%
• export execution times for compressed files are in the worst case (xz) about double the export in an uncompressed file and in the best case (gz) a tenth faster

The export of fixtures directly to compressed files therefore allows a strong reduction of the space occupied in the face of a small increase in the time and resources required for creation.

In addition there is the possibility for the user to choose the best file type for their use case, opting for maximum compression (xz) or for greater portability (gz).

External links¶