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Improving Performance with the Kryo Serializer


Previous to Mule 3.7, Mule relied on plain old Java serialization (in which Java classes must implement or to store objects in files or to replicate them through a Mule cluster. Starting with the Mule 3.7 Enterprise Edition, the Kryo Serializer and the Community Edition Serialization API decouple Mule and its extensions from the actual serialization mechanism to be use, enabling you to configure the mechanism you want to use, or to create your own serializer.

Mule relies on serialization when:

  • You read from or write to a persistent ObjectStore

  • You read from or write to a persistent VM or JMS queue

  • An object distributes through a Mule cluster

  • You read from or write to an object from a file

Changing the serialization mechanism for these tasks can greatly improve functionality and performance.

Use Cases

  • Create a custom serializer using the Serialization API (CE)

  • Configure the Kryo Serializer (EE only)

Using the Serialization API

Community Edition - The open source Serialization API available in GitHub in the interface lets you serialize or deserialize objects into a byte array.

The Serialization API:

  • Is thread safe

  • Passes an OutputStream when serializing and streaming

  • Allows an InputStream as an input source

  • You can specify which classloader to use (the default is the current classloader)

  • When deserializing, if the obtained object implements the DeserializationPostInitialisable interface, the serializer is responsible for properly initializing the object before returning it

Configuring a Custom Serializer

Configure the default ObjectSerializer for the Mule application using the Mule <configuration> tag:

<?xml version="1.0" encoding="UTF-8"?>
<mule xmlns=""

    <spring:bean id = "customSerializer" class = "" />
  <configuration defaultObjectSerializer-ref = "customSerializer" />


If defaultObjectSerializer is not configured, then the default implementation uses plain old Java serialization. This means that from a user perspective, there’s no behavioral change unless you specifically set a different serializer.

The Batch module is the only component that does not have its behavior affected, because the module always uses serialization.

Obtaining a Configured ObjectSerializer

There are many ways to obtain an ObjectSerializer inside Java code. Generally, the recommended approach is through dependency injection. The following code example shows you how to get the currently configured default ObjectSerializer:

public class MyClass {
   private  ObjectSerializer objectSerializer;

If you want a specific named serializer (whether it’s the default or not), you can access the serializer by name:

public class MyClass {
    @Named ("mySerializer")
  private ObjectSerializer objectSerializer;

Finally, although dependency injection is preferred, you can always lookup a serializer from the muleContext:

// Returns the default object serializer

// Returns a named object serializer

Configuring the Kryo Serializer

Enterprise Edition - Mule provides an implementation of ObjectSerializer which relies on the Kryo framework. Using Kryo provides:

  • Better performance. Kryo is much faster than Java serialization

  • Support for a wider range on Java types. Kryo is not bounded by most of the limitations that Java serialization imposes like requiring to implement the Serializable interface, having a default constructor, etc

  • Support for compression: You can use either Deflate or GZip compression algorithms

The Kryo namespace let’s you configure this serializer inside your Mule application, without needing to define a custom Spring bean. This configuration example sets the default serializer to a Kryo based one:

<?xml version="1.0" encoding="UTF-8"?>
<mule xmlns=""

    <kryo:serializer name = "kryo" />
    <configuration defaultObjectSerializer-ref = "kryo" />


Additionally, you can also include the compressionMode XML attribute to configure compression:

<kryo:serializer name = "noCompression" compressionMode = "NONE" />
<kryo:serializer name = "deflate" compressionMode = "DEFLATE" />
<kryo:serializer name = "gzip" compressionMode = "GZIP" />

Performance Using Kryo

Using Kryo provides particular performance improvements over plain old Java serialization when you are using these components:

  • Persistent or clustered object stores

  • Persistent or distributed VM queues

  • JMS transport

The following illustrations determined by MuleSoft’s performance team compare the transactions per second (TPS) of persistent and distributed VM queues and ObjectStore using the plain old Java serializer vs. various Kryo settings.



The previous charts indicate that Kryo without compression is significantly faster than the plain old Java serializer in all cases. However, the compression mode only provides an actual improvement on the high availability (HA) cases.

For the compression to be worthy, the amount of time the CPU spends compressing and decompressing has to be significantly lower than the amount of I/O time saved by reducing the payload size. Because network operations are typically slower than disk operations and because HA clustering requires node replication, which translates to more traffic), only in the HA case the compression paid off.

This is not a universal constant. You might be running Mule on machines with slower disks or higher I/O demands in which compression might be worthy on any case. Also, these tests were performed with 1 MB payloads, but the larger the data stream, the more worthy becomes the compression.

Performance Summary

The following are the performance results:

Test VM Persistent OS Persistent VM HA OS HA






Kryo + Deflate





Kryo + GZip





The conclusions from the table are:

  • You can get up to a 77.13% improvement in performance when using distributed ObjectStores, 63.77% when using distributed VM queues and 64.71% when using local persistent VM queues.

  • Although local object stores don’t show much improvement. They are actually slower when using compression. There’s no use case in which you don’t get some level of gain when using Kryo.

Performance results are a guideline rather than an absolute fact. Depending on your application, environment, payload size, etc., the actual output may vary.

Limitations and Considerations

The following sections provide information you need to use serializers.

Changing Serializers Requires a Clean Slate

Serializers are not interoperable nor interchangeable. That means that if you decide to change the serializer your application uses, you need to make sure that all messages in VM and JMS queues have been consumed and that those queues are empty by the time the new serializer kicks in. This is because Kryo serializer won’t be able to read datagrams written by the Java serializer and vice-versa. The same thing applies to persistent ObjectStores. If you try to read an entry generated with a different serializer, you can’t read the entry.

Serialization in a Shared VM Connector

Domains provide a way to share resources between applications. For example, you can define a VM connector on a domain to allow inter-app communication through VM message queues. However, serializers can only be configured at an application level, they cannot be configured at a domain. If applications A and B communicate with each other through a VM connector defined on a domain to which both belong, but A serializes using Java and B using Kryo, it just works. Whenever either app tries to write to an endpoint which uses the shared connector, that particular message is not serialized with the application’s serializer but the one the VM connector uses. This is good from the point of view of the plug and play experience, but you won’t be able to tell that shared VM connector to use Kryo and get a performance improvement out of it.

Less Improvement for a Local Persistent ObjectStore

Unlike the other cases, the local persistent ObjectStore doesn’t show much improvement because of high contention on the ObjectStore implementation which pretty much absorbs all the gain.

No JMS Improvement Chart

Per the JMS API, the queues don’t work with raw payload objects. Instead, you have to provide an instance of the javax.jms.Message class. The broker client is then responsible for serializing it, not Mule. Therefore, the impact of Kryo in such an scenario is minimum. The only performance gain of using Kryo with JMS is that Mule serializes the MuleSession and puts it as a header in Base64 format. Serializing the MuleSession with Kryo can give you up to 10% performance speed, but we don’t consider it as an example use case since the big part of the serialization is up to the JMS broker instead of Mule.

Problematic Types

Although Kryo is capable is serializing objects that don’t implement the Serializable interface, setting Kryo as the default serializer doesn’t mean that components such as the VM transport, ObjectSerializer, or Cluster are able to handle objects which don’t implement such an interface. That’s because even though Kryo can deal with those objects, the Java APIs for those components still expect instances of Serializable in their method signatures.

Note: Plain old Java serialization fails with an object that implements the Serializable interface. However if serialization contains another object which doesn’t implement the Serializable interface, Kryo is likely (but not guaranteed) to succeed. A typical case is a POJO containing an org.apache.xerces.jaxp.datatype.XMLGregorianCalendarImpl, which is in use in the NetSuite or Microsoft Dynamics CRM connectors.