Static Compilation of Java Applications at Alibaba at Scale


Cloud computing aims to provide computing resources as a service and the core principle of cloud computing is to use only those resources that are necessary to run an application and scale when needed. To take advantage of the benefits of cloud computing, developers should architect and write applications according to this principle.

GraalVM native image at Alibaba

Over the years, Java has proliferated in Alibaba. Many applications are written in Java. Approximately 10,000 Java developers have written more than a billion lines of Java code! Alibaba has customized most of its Java software based on the vibrant open-source ecosystem. In Alibaba Cloud, these Java programs are developed for online trading, payments, and logistics operations. Many of them are developed as microservices running on top of Kubernetes native environment to service online requests.

How We Did That

GraalVM native image provides a great set of tools for developers to close the gap between traditional and statically compiled Java and provides a way to migrate from the former to the latter. In this section, we will focus on the challenges we faced and the approaches we developed at Alibaba for compiling Java applications into native images. We also contributed many of the solutions back to the GraalVM community.

class A {
static B b = new B();
class B {
static {
class C {
static long currentTime;
static {
static void dosomething(){…}

Slow GC performance

In the world of static compilation, garbage collection is still an indispensable component. The default garbage collector in native image is a pure ‘Copy’ GC, which divides the heap space into two parts: young and old spaces. Java threads keep allocating objects in the young space, and when young space is full, a ‘Young GC’ is performed by evacuating all the live objects from young space to old space. When the old space is full, a ‘Full GC’ is performed by compacting all the live objects in Java heap and release free spaces.

  • A background thread is used to un-map memory asynchronously. Native image uses memory chunks to hold Java objects. When it wants to release free chunks to the OS to lower the footprint, it just un-maps the chunk. We observed that for a typical Java application un-mapping memory might cost a long time, so we make this operation asynchronous and execute it outside the stop-the-world pause.
  • Image roots are scanned based on a card table in the young GC. For some specific workloads the final executable image may be large after static compilation, which usually holds a vast set of GC roots and has to be scanned thoroughly for any GC. In the existing design of the native image garbage collector this may cost much time. We added a card table for the image roots, and for young GC operations we only scan those references that got dirtied since last GC pause.

Performance Gains

Startup time speedup

After we made the changes to address the challenges of using native image, we collected the performance data for static compiled SOFABoot applications in our production environment. As shown in the Figure, the startup time decreased from 60 seconds to 3 seconds, i.e., 20X speedup in the starting up time of the Java app. In addition, the GC pause time was controlled to under 100 milliseconds.

Sofaboot Application Startup Time Comparison
Traditional Java Function vs Statically Compiled Function

GC performance

With the above enhancements we did in GC, we successfully reduced the p90 pause time of a typical Java microservice from 1.5+ seconds to around 100 milliseconds.

GC time improvements


If you’re exploring ways to develop a serverless application for the cloud it’s worth evaluating GraalVM native image, especially when you’re looking for the best startup performance and lower memory footprint.

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