Avoiding Java Serialization to increase performance

Overview

Many frameworks for storing objects in an off-line or cached manner, use standard Java Serialization to encode the object as bytes which can be turned back into the original object.

Java Serialization is generic and can serialise just about any type of object.

Why avoid it

The main problem with Java Serialization is performance and efficiency. Java serialization is much slower than using in memory stores and tends to significantly expand the size of the object. Java Serialization also creates a lot of garbage.

Access performance

Say you have a collection and you want to update a field of many elements. Something like

for (MutableTypes mt : mts) {
   mt.setInt(mt.getInt());
}

If you update one million elements for about five seconds how long does each one take.

CollectionFast PCLabtop
Huge Collection5.1 ns33 ns
List<JavaBean>6.5 ns54 ns
List<byte[]> with Externalizable5,841 ns17,508 ns
List<byte[]> with Serializable23,217 ns60,947 ns

If you update ten million elements for five seconds or more

CollectionFast PCLabtop
Huge Collection5.4 ns33 ns
List<JavaBean>6.6 ns60 ns
List<byte[]> with Externalizable6,073 ns71,691 ns
List<byte[]> with Serializable22,943 nsfailed

* Fast PC - 3.8 GHz i7 with 24 GB of memory.
* Labtop - 2.3 GHz Core Duo with 4 GB of memory.

Huge Collection stores information in a column based based, so accessing just one field is much more CPU cache efficient than using JavaBeans. If you were to update every field, it would be about 2x or more times slower.

Using an optimised Externalizable is much faster than the default Serializable, however is it 400x slower than using a a JavaBean

Memory efficiency

The per object memory used is also important as it impacts how many object you can store and the performance of accessing those objects.

Collection typeHeap used
per million
Direct memory
per million
Garbage produced
per million
Huge Collection0.09 MB34 MB80 bytes
List<JavaBean>68 MBnone30 bytes
List<byte[]> using Externalizable140 MBnone5,941 MB
List<byte[]>506 MBnone16,746 MB
This test was performed on a collection of one million elements.

To test the amount of garbage produced I set the Eden size target greater than 17 GB so no GC would be performed.
-mx22g -XX:NewSize=20g -XX:-UseTLAB -verbosegc

Conclusion

Having an optimised readExternal/writeExternal can improve performance and the size of a serialised object by 2-4 times, however if you need to maximise performance and efficiency you can gain much more by not using it.

Related Links

Collections Library for millions of elements

Ehcache BigMemory performance typical latency is around 200 μs for huge caches, indicating that serialization might not be the only bottleneck.

HugeArrayVsSerializationTest.java The test code

MutableTypes.java The data type used for testing different implementations.

Comments

  1. Peter,

    the link to HugeArrayVsSerializationTest.java is wrong.

    regards

    ReplyDelete
  2. @Zeljko K, Thank you, fixed it now.

    ReplyDelete
  3. What is the format that serializable actually creates? Does it create a field listing as well?

    ReplyDelete
  4. @steven, by default the Serializable format includes field listing (the first like an object of a type is encoded) It also lists the class and its Serializable parents and their fields the first time. However if each object is Serialized individually (in its own byte[]) this is inefficient.

    ReplyDelete

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