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This repository contains code to measure the worst-case pauses observable from of a specific workflow in many languages.

The workflow (allocating N 1Kio strings with only W kept in memory at any time, and the oldest string deallocated) comes from James Fischer's blog post Low latency, large working set, and GHC’s garbage collector: pick two of three, May 2016, who identified it as a situation in which the GHC garbage collector (Haskell) exhibits unpleasant latencies.

The following languages are currently included: Haskell, OCaml, Racket, Java, Go and D.

How to contribute

Merge requests to implement support for a new language are welcome. You may also send git patchsets (as produced for example by git format-patch origin/trunk --stdout > contrib.patch) as email attachment.

Please remember to edit the Makefile to add the recipes to run and analyze the benchmark for your program: run-foo should run the benchmark for the language foo and store results in a .log file, and analyze-foo must extract a latency measurement from the .log file.

If you use non-standard settings or had to be careful about how you set-up the Makefile rules, please explain why in the comments. People that scrutinize a benchmark will want explanations for your choices.

If you want to test different compilation/monitoring techniques, you can have set of rules per language -- for example Java has java rules using the default JVM parameters and java-g1 rules to use the low-latency G1 collector.

How to measure worst-case latency

The benchmark is essentially a loop where each iteration allocates a new string and adds it to the message set, and (if the maximum window size W is reached) also removes the oldest message in the set.

Runtime instrumentation vs. manual measurement

There are two ways to measure worst-case pause time. One, "instrumentation", is to activate some sort of runtime monitoring/instrumentation that is specific to the language implementation, and get its worst-case-pause number. Another, "manual measure" is to simply measure time at each iteration, and compute the maximal difference.

We recommend trying both ways (it's good to build knowledge of how to measure GC latencies, and having a Makefile full of instructions for many languages is useful). One has to be careful with instrumentation, as it may be incomplete (not account for certain sources of pauses); if the two measures disagrees, we consider the manual measure to be the reference.

Message payloads

The fact that the messages themselves take some space is an essential aspect of the benchmark: without it, GHC's garbage collector does an excellent job. Please ensure that your implementation actually allocates 1Kio of memory for each message (no copy-on-write, etc.). (It's fine if the GC knows that this memory doesn't need to be traversed). You should also avoid using less-compact string representations (UTF16, or linked lists of bytes, etc.).

Message set structure

The message set is an associative data structure where each message is indexed by the time it was inserted.

We are not trying to measure latency caused by the specific choice of associative data-structure. For the initial tests Haskell, OCaml and Racket, using an array, a balanced search tree or a hashtable makes no difference. For a new language, feel free to choose whichever gives the best results; but if one of them creates large latencies, you may want to understand why -- there were bugs in Go's maps that made latencies much higher, and some of them were since fixed. For GC-ed languages it may be the case that contiguous arrays are actually worse than structures with more pointer indirections, if the GC doesn't incrementally scan arrays; then use another data structure.

Links and reference

Gabriel Scherer wrote a blog post on measuring the latency of the OCaml benchmark through GC instrumentation, and of how Racket developers advised to tune the Racket benchmark and decided to make small changes to their runtime: Measuring GC latencies in Haskell, OCaml, Racket.

Will Sewell, working at the same company as Jame Fisher's, has a follow-up blog post on this work where Go latencies are discussed: Golang’s Real-time GC in Theory and Practice.

Gorgi Kosev runs another latency-copmarison benchmark, also inspired by the same blog post, but also involving HTTP requests, at

Santeri Hiltunen has a nice blog post with other measurements, in particular some information on tuning the Java benchmark, and a Javascript benchmark with similar explanations, and data on the importance of graph-of-pointers over contiguous data structures to reduce latencies.