Parallel Zip on JVM

Zipping tons of files on one core in a multicore/SSD/cloud era is a massive waste of time.

A zip file is just an array of entries and a central directory at the end of a file.

We cannot write to a zip file in parallel, but we can compress data in parallel in memory.

Last but not least, nobody wants to reimplement zip logic from scratch or use an unsupported third-party zip library. We reuse standard java.util.zip.ZipOutputStream and java.util.zip.ZipInputStream in the presented approach.

Algorithm

1. Collect all zip entries and their bytes for each input file in parallel. For each input file:
   • Get a ByteArrayOutputStream and a ZipOutputStream on top of it
   • Write an entry to the zip stream skipping the central directory using SingleEntryZipOutputStream.
   • Get the bytes from the byte stream

varzipEntries = ConcurrentHashMap<ZipEntry, byte[]>();
// for each input file in parallel:varout = newByteArrayOutputStream();
varzipEntry = newZipEntry(filePathRelativeToZipRoot);
try (varzip = newSingleEntryZipOutputStream(out)) {
try (varfileStream = Files.newInputStream(filePath)) {
zip.putNextEntry(zipEntry);
fileStream.transferTo(zip);
zip.closeEntry();
}
}
zipEntries.put(zipEntry, out.toByteArray());

2. Write all entries and bytes sequentially to a target zip file:
   • Get a FileOutputStream and a ZipOutputStream on top of it
   • Write bytes of all entries to the file stream updating the zip stream state
   • Write the central directory by closing the zip stream as usual

try (varos = Files.newOutputStream(zipFile)) {
try (varzip = newZipOutputStream(os)) {
varoffset = 0L;
for (Map.Entry<ZipEntry, byte[]> o : zipEntries.entrySet()) {
varzipEntry = o.getKey();
varbytes = o.getValue();
zip.xEntries.add(newXEntry(zipEntry, offset)); // via reflectionos.write(bytes);
offset += bytes.length;
}
zip.offset = offset; // via reflection// write the central directory on close
}
}

Notes

1. Java Reflection is used to work around missing Java API. To avoid that in the future, we must request such an API

2. The algorithm takes roughly the same amount of memory as the target zip file. We can start writing to disk when new zip entries are ready, applying backpressure to control memory consumption

3. It's the compression that takes most of the time. We can generate already compressed data in parallel in various data generation tasks. Then, saving it to disk will take very little time

4. We merge zip files without repacking using the same technique

Results

Zipping 12.06 GB of 175,866 items to a 1.14 GB zip file on a MacBook M2 Max in seconds:

Mode	Seconds
Sequential	151
Parallel	18

A fully functional parallel zip in pure Java (source):

./gradlew runJava --args="<out.zip> <file-or-dir> .."

A fully functional parallel zip in Kotlin (source):

./gradlew runKotlin --args="<out.zip> <file-or-dir> .."

Sequential zipping for comparison in pure Java (source):

./gradlew runSequential --args="<out.zip> <file-or-dir> .."

FatJar and Native Image

Distributions	Type	Size
fatJar	Jar file with all deps	3 MB
GraalVM Native Image	Native executable	10 MB

# build the fatJar
./gradlew fatJar
# run the fatJar via java -jar
java -jar build/libs/parallel-zip.jar <out.zip><file-or-dir> ..
# build the GraalVM Native Image executable from the fatJar<GRAALVM_HOME>/bin/native-image -jar ./build/libs/parallel-zip.jar
# run the GraalVM Native Image executable
./parallel-zip <out.zip><file-or-dir> ..