Break a large tar.gz file into multiple smaller tar.gz files - linux

Question

I get OutOfMemoryError when processing tar.gz files greater than 1gb in spark.
To get past this error I have tried splitting the tar.gz into multiple parts using the 'split' command only to find out that each split is not a tar.gz on its own and so cannot be processed as such.
dir=/dbfs/mnt/data/temp
b=524288000
for file in /dbfs/mnt/data/*.tar.gz;
do
a=$(stat -c%s "$file");
if [[ "$a" -gt "$b" ]] ; then
split -b 500M -d --additional-suffix=.tar.gz $file "${file%%.*}_part"
mv $file $dir
fi
done
Error when trying to process the split files
Caused by: java.io.EOFException
at org.apache.commons.compress.compressors.gzip.GzipCompressorInputStream.read(GzipCompressorInputStream.java:281)
at java.io.BufferedInputStream.read1(BufferedInputStream.java:284)
at java.io.BufferedInputStream.read(BufferedInputStream.java:345)
at org.apache.commons.compress.archivers.tar.TarArchiveInputStream.read(TarArchiveInputStream.java:590)
at org.apache.commons.io.input.ProxyInputStream.read(ProxyInputStream.java:98)
at sun.nio.cs.StreamDecoder.readBytes(StreamDecoder.java:284)
at sun.nio.cs.StreamDecoder.implRead(StreamDecoder.java:326)
at sun.nio.cs.StreamDecoder.read(StreamDecoder.java:178)
at java.io.InputStreamReader.read(InputStreamReader.java:184)
at java.io.Reader.read(Reader.java:140)
at org.apache.commons.io.IOUtils.copyLarge(IOUtils.java:2001)
at org.apache.commons.io.IOUtils.copyLarge(IOUtils.java:1980)
at org.apache.commons.io.IOUtils.copy(IOUtils.java:1957)
at org.apache.commons.io.IOUtils.copy(IOUtils.java:1907)
at org.apache.commons.io.IOUtils.toString(IOUtils.java:778)
at org.apache.commons.io.IOUtils.toString(IOUtils.java:803)
at linea3796c25fa964697ba042965141ff28825.$read$$iw$$iw$$iw$$iw$$iw$$iw$Unpacker$$anonfun$apply$1.apply(command-2152765781429277:33)
at linea3796c25fa964697ba042965141ff28825.$read$$iw$$iw$$iw$$iw$$iw$$iw$Unpacker$$anonfun$apply$1.apply(command-2152765781429277:31)
at scala.collection.immutable.Stream$$anonfun$map$1.apply(Stream.scala:418)
at scala.collection.immutable.Stream$$anonfun$map$1.apply(Stream.scala:418)
at scala.collection.immutable.Stream$Cons.tail(Stream.scala:1233)
at scala.collection.immutable.Stream$Cons.tail(Stream.scala:1223)
at scala.collection.immutable.Stream.foreach(Stream.scala:595)
at scala.collection.TraversableOnce$class.toMap(TraversableOnce.scala:316)
at scala.collection.AbstractTraversable.toMap(Traversable.scala:104)
at linea3796c25fa964697ba042965141ff28825.$read$$iw$$iw$$iw$$iw$$iw$$iw$Unpacker$.apply(command-2152765781429277:34)
at linea3796c25fa964697ba042965141ff28827.$read$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(command-2152765781429278:3)
at linea3796c25fa964697ba042965141ff28827.$read$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(command-2152765781429278:3)
I have tar.gz files that go upto 4gb in size and each of these can contain upto 7000 json documents whose sizes vary from 1mb to 50mb.
If I want to divide the large tar.gz files into smaller tar.gz files is my only option to decompress and somehow then recompress based on file size or file count? - "is this the way?"

Answer 1

Normal gzip files are not splittable.
GZip Tar archives are harder to deal with.
Spark can handle gzipped json files, but not gzipped tar files and not tar files.
Spark can handle binary files up to about 2GB each.
Spark can handle JSON that's been concatenated together
I would recommend using a Pandas UDF or a .pipe() operator to process each tar gzipped file (one per worker). Each worker would unzip, untar and process each JSON document in a streaming fashion, never filling memory. Hopefully you have enough source files to run this in parallel and see a speed up.
You might want to explore streaming approaches for delivering your compressed JSON files incrementally to ADLS Gen 2 / S3 and using Databricks Auto Loader features to load and process the files as soon as they arrive.
Also answers to this question How to load tar.gz files in streaming datasets? appear promising.