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oracle
graal
Java2016–2026
oracle / graal

graal

GraalVM compiles applications into native executables that start instantly, scale fast, and use fewer compute resources 🚀

94Distinct people
146Mentions
2Explicit recommenders
21.8KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

GraalVM compiles applications into native executables that start instantly, scale fast, and use fewer compute resources 🚀

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

"I love the promise of Graal native binaries. At the moment I'm unable to get it to work with my codebase." [2] "Building is a resource hog. I set up a VM with 2 CPUs, 100GB of…

Mawr used a recommendation phrase. 2 indexed comments match. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 12:03:30 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year146 dated mentions

Observed mentions: 2016-07-08 13:46:19 UTC to 2026-10-02 12:57:29 UTC.

01020302016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments20162017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 12 mentions; 5 submissions, 7 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 32 mentions; 4 submissions, 28 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 24 mentions; 3 submissions, 21 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 6 mentions; 0 submissions, 6 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 15 mentions; 1 submissions, 14 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 19 mentions; 3 submissions, 16 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 9 mentions; 0 submissions, 9 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 11 mentions; 1 submissions, 10 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 10 mentions; 2 submissions, 8 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 5 mentions; 1 submissions, 4 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC303
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC1257
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC32428
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC24321
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC606
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC15114
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC19316
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC909
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC11110
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC1028
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC514

What Hacker News says

11 sources
> TruffleRuby even had the extremely big brain idea of running a Truffle interpreter for C for native extensions […] TruffleC was a research project and the first attempt of running C code on Truffle that I'm aware of. It directly interpreted C source code and while that works for small self-contained programs, you quickly run into a lot of problems as soon as you want to run larger real world programs. You need everything including the C library available as pure C code and you have to deal with the fact that a lot of C code uses some UB/IB. In addition, your C parser has to fully adhere to the C standard and once you want to support C++ too because a lot of code is written in C++, you have to re-start from scratch. I don't know if TruffleC was ever released as open source. The next / current attempt is Sulong which uses LLVM to compile
Read the rest
C/C++/Rust/… to LLVM IR ("bitcode") and then directly interprets that bitcode. It's a lot better, because you don't have to write your own complete C/C++/… parser/compiler, but bitcode still has various limitations. Essentially as soon as the program uses handwritten assembler code somewhere, or if it does some low level things like setjmp/longjmp, things get hairy pretty quickly. Bitcode itself is also platform dependent (think of constants/macros/… that get expanded during compilation), you still need all code / libraries in bitcode, every language uses a just so slightly different set of IR nodes and requires a different runtime library so you have to explicitly support them, and even then you can't make it fully memory safe because typical programs will just break. In addition, the optimization level you choose when compiling the source program can result in very different bitcode with very different IR nodes, some of which were not supported for a long time (e.g., everything related to vectorization). Sulong can load libraries and expose them via the Truffle FFI, and it can be used for C extensions in GraalPython and TruffleRuby AFAIK. It's open source [1] and part of GraalVM, so you can play around with it. Another research project was then to directly interpret AMD64 machine code and emulate a Linux userspace environment, because that would solve all the problems with inline assembly and language compatibility. Although that works, it has an entirely different set of problems: Graal/Truffle is simply not made for this type of code and as a result the performance is significantly worse than Sulong. You also end up re-implementing the Linux syscall interface in your interpreter, you have to deal with all the low level memory features that are available on Linux like mmap/mprotect/... and they have to behave exactly as on a real Linux system, and you can't easily export subroutines via Truffle FFI in a way that they also work with foreign language objects. It does work with various guest languages like C/C++/Rust/Go/… without modifying the interpreter, as long as the program is available as native Linux/AMD64 executable and doesn't use any of the unimplemented features. This project is also available as open source [2], but its focus somewhat shifted to using the interpreter for execution trace based program analysis. Things that aren't supported by any of these projects AFAIK are full support for multithreading and multiprocessing, full support for IPC, and so on. Sulong partially solves it by calling into the native C library loaded in the VM for subroutines that aren't available as bitcode and aborting on certain unsupported calls like fork/clone, but then you obviously lose the advantage of having everything in the interpreter. The conclusion is, whatever you try to interpret C/C++/… code, get ready for a world of pain and incompatibilities if you intend to run real world programs. [1] https://github.com/oracle/graal/tree/master/sulong [2] https://github.com/pekd/tracer/tree/master/vmx86
In Writing memory safe JIT compilers
They are indeed scary, but one thing that can make them less so is writing them in a high level language. A nice code base to study if you're new to GCs is the GenScavange module of SubstrateVM, a JVM written entirely in Java. Start here: https://github.com/oracle/graal/blob/master/substratevm/src/... It gets compiled to native code for use. It's actually a bit of a dialect of Java because of course, you need low level memory and stack access. So you can see at parts there is use of special APIs that get compiled to things like stack allocations, so it can avoid allocating on the heap whilst working with it. Even so the "business logic" of a GC can be easily seen here, especially if you don't know C++.
In Garbage Collectors Are Scary
Contrary to what vocal Kotlin advocates might believe, Kotlin only matters on Android, and that is thanks to Google pushing it no matter what. https://spectrum.ieee.org/the-top-programming-languages-2023 https://snyk.io/reports/jvm-ecosystem-report-2021/ And even so, they had to conceed Android and Kotlin on their own, without the Java ecosystem aren't really much useful, thus ART is now updatable via Play Store, and currently supports OpenJDK 17 LTS on Android 12 and later devices. As for your question regarding numbers, mostly Java 74.6%, C++ 13.7%, on the OpenJDK, other JVM implementations differ, e.g. GraalVM is mostly Java 91.8%, C 3.6%. https://github.com/openjdk/jdk https://github.com/oracle/graal Two examples from many others, https://en.wikipedia.org/wiki/List_of_Java_virtual_machines
In Java 23: The New Features Are Officially Announced
That's pretty interesting. It's not as aggressive as Bee sounds, but the Espresso JVM is somewhat similar in concept. It's a full blown JVM written in Java with all the mod cons, which can either be compiled ahead of time down to memory-efficient native code giving something similar to a JVM written in C++, or run itself as a Java application on top of another JVM. In the latter mode it obviously doesn't achieve top-tier performance, but the advantage is you can easily hack on it using all the regular Java tools, including hotswapping using the debugger. When run like this, the bytecode interpreter, runtime system and JIT compiler are all regular Java that can be debugged, edited, explored in the IDE, recompiled quickly and so on. Only the GC is provided by the host system. If you compile it to native code, the GC is also written in Java
Read the rest
(with some special conventions to allow for convenient direct memory access). What's most interesting is that Espresso isn't a direct translation of what a classical C++ VM would look like. It's built on the Truffle framework, so the code is extremely high level compared to traditional VM code. Details like how exactly transitions between the interpreter/compiled code happen, how you communicate pointer maps to the GC and so on are all abstracted away. You don't even have to invoke the JIT compiler manually, that's done for you too. The only code Espresso really needs is that which defines the semantics of the Java bytecode language and associated tools like the JDWP debugger protocol. https://github.com/oracle/graal/tree/master/espresso This design makes it easy to experiment with new VM features that would be too difficult or expensive to implement otherwise. For example it implements full hotswap capability that lets you arbitrarily redefine code and data on the fly. Espresso can also fully self-host recursively without limit, meaning you can achieve something like what's described in the paper by running Espresso on top of Espresso.
In Live Objects All the Way Down: Removing the Barriers Between Apps and VMs

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