Creating a sysimage


This section is for people who want to understand PackageCompiler.jl under the hood. It is not required reading to use the package.

Julia's compilation model and sysimages

Julia is a JIT-compiled language. More specifically, functions are compiled just before getting executed. A more suitable description of the Julia compilation model might, therefore, be Just-Ahead-of-Time (JAOT) compilation. The term JIT is sometimes used to describe the compilation model where code is dynamically recompiled based on runtime performance data, which Julia does not do. At the same time, Julia comes with a lot of built-in functionality including several standard libraries. If all this built-in functionality would need to be parsed, type inferred and compiled every time Julia started, the startup-time would be longer than reasonable. Therefore, Julia bundles something called a "sysimage" which is a shared library where (roughly) the state of a running Julia session has been stored (serialized). When Julia starts, this sysimage gets loaded, which is a quite quick process (50ms on the author's machine), and all the cached compiled code can immediately be used, without requiring any compilation.

Custom sysimages

There are cases where one wants to generate a custom sysimage for a similar reason as to why Julia bundles one: to reduce time from Julia start until the program is executing. The time from startup to execution is here denoted as "latency" and we want to minimize the latency of our program. A drawback of putting a package inside the sysimage is that it becomes "frozen" at the particular version it was, when it got put into the sysimage. In addition, all the dependencies of the package put into the sysimage will be frozen in the same manner. In particular, it will no longer be updated like normal packages when using the package manager. In some cases, other ways of reducing latency might be preferable, for example, using Revise.jl

Example workload

To have something concrete to work with, let's assume we have a small script that reads a CSV-file and computes some statistics on it. As an example, we will use a sample CSV file containing Florida insurance data, which can be downloaded from here.

One way of loading this file into Julia is by using the CSV.jl package. We can install CSV.jl using the Julia package manager Pkg as:

julia> import Pkg; Pkg.add("CSV")
 Resolving package versions...
  Updating `~/.julia/environments/v1.3/Project.toml`
  [336ed68f] + CSV v0.5.13
  Updating `~/.julia/environments/v1.3/Manifest.toml`
 [no changes]

When a package is loaded for the first time it gets "precompiled":

julia> @time using CSV
[ Info: Precompiling CSV [336ed68f-0bac-5ca0-87d4-7b16caf5d00b]
 13.321758 seconds (2.69 M allocations: 151.302 MiB, 0.05% gc time)

The term "precompiled" can be a bit misleading since there is no native compiled code cached in the precompilation file. Julia is dynamically typed so it is not obvious what types to compile the different methods for.

Even with CSV "precompiled", there is a still some loading time, but it is significantly lower:

julia> @time using CSV
  0.694224 seconds (1.90 M allocations: 114.210 MiB)

Let's load the sample CSV file:

julia> @time"FL_insurance_sample.csv");
9.264898 seconds (37.17 M allocations: 2.278 GiB, 3.90% gc time)1

That's is quite a long time to read a smallish CSV file. One way to check the compilation overhead is by running the function again:

julia> @time"FL_insurance_sample.csv");
  0.083543 seconds (423 allocations: 34.695 KiB)

So clearly, the first call to the function is dominated by compilation time. In many cases, this is not a problem in practice since often one wants to parse multiple CSV files such that the overhead will become negligible or one keeps a Julia session open for a longer time so that the compiled version of the function is still in memory.

However, since the end goal of this blog series is to create an executable that can be distributed we want to try to avoid as much runtime compilation (latency) as possible.

Creating a custom sysimage

If we time the loading of a standard library, it is clear that it is "cached" somehow since the time to load it is so short:

julia> @time using Dates
  0.000816 seconds (1.25 k allocations: 65.625 KiB)

Since Dates is a standard library it comes bundled in the system image. In fact, Dates is already "loaded" when starting Julia. The effect of running using Dates just makes the module available in the Main module namespace which is what the REPL evaluates in.

Delving into some internals, there is a dictionary in Base that keeps track of all loaded modules:

julia> Base.loaded_modules
Dict{Base.PkgId,Module} with 33 entries:
  SHA [ea8e919c-243c-51af-8825-aaa63cd721ce]              => SHA
  Profile [9abbd945-dff8-562f-b5e8-e1ebf5ef1b79]          => Profile
  Dates [ade2ca70-3891-5945-98fb-dc099432e06a]            => Dates
  Mmap [a63ad114-7e13-5084-954f-fe012c677804]             => Mmap

and we can here see the Dates module is there, even after restarting Julia. This means that Dates is in the sysimage itself and does not have to be loaded from anywhere external.

Creating and using a custom sysimage is done in three steps:

  1. Start Julia with the --output-o=sys.o custom_sysimage.jl where custom_sysimage.jl is a file that creates the state that we want the sysimage to contain and sys.o is the resulting object file that we will turn into a sysimage.
  2. Create a shared library from the object file by linking it with libjulia. This is the actual sysimage.
  3. Use the custom sysimage in Julia with the -Jpath/to/sysimage (or the longer, more descriptive --sysimage) flag.

1. Creating the object file

For now, the goal is to put CSV in the sysimage (in the same way as the standard library Dates is in it). We therefore initially simply create a file called custom_sysimage.jl with the content.

using CSV

in a custom_sysimage.jl file. Let's try using the flag --output-o (and disabling using the startup file) and running the file:

julia --startup-file=no --output-o=sys.o -- custom_sysimage.jl
ERROR: could not open file boot.jl

That did not work well. It turns out that when using the --output-o option one has to explicitly give a sysimage path (due to this line). Since we do not have a custom sysimage yet we just want to give the path to the default sysimage which we can get the path to via:

julia> unsafe_string(Base.JLOptions().image_file)

Let's try again, specifying the default sysimage path with the -J flag:

julia --startup-file=no --output-o sys.o -J"/home/kc/julia/lib/julia/" custom_sysimage.jl
signal (11): Segmentation fault
in expression starting at none:0
uv_write2 at /workspace/srcdir/libuv/src/unix/stream.c:1397
uv_write at /workspace/srcdir/libuv/src/unix/stream.c:1492
jl_uv_write at /buildworker/worker/package_linux64/build/src/jl_uv.c:476
uv_write_async at ./stream.jl:967
uv_write at ./stream.jl:924

Failure again! Another caveat when using --output-o is that modules __init__() functions do not end up getting called, which is what normally happens when a module is loaded. The reason for this is that often the state that gets defined in __init__ is not something that you want to serialize to a file. In this particular case, some parts of the IO system have not been initialized so Julia crashes while trying to print an error. The magic incantation to make IO work properly is Base.reinit_stdio(). To figure out the actual problem we modify the custom_sysimage.jl file to look like:

using CSV

and rerun the julia-command:

julia --startup-file=no --output-o sys.o -J"/home/kc/julia/lib/julia/" custom_sysimage.jl
ERROR: LoadError: ArgumentError: Package CSV not found in current path:
- Run `import Pkg; Pkg.add("CSV")` to install the CSV package.

 [1] require(::Module, ::Symbol) at ./loading.jl:887
 [2] include at ./boot.jl:328 [inlined]
 [3] include_relative(::Module, ::String) at ./loading.jl:1105
 [4] include(::Module, ::String) at ./Base.jl:31
 [5] exec_options(::Base.JLOptions) at ./client.jl:295
 [6] _start() at ./client.jl:468
in expression starting at /home/kc/custom_sysimage.jl:2

Okay, now we can see the error. Julia can not find the CSV package. Package-loading in Julia is based on the two arrays LOAD_PATH and DEPOT_PATH. Adding @show LOAD_PATH and @show DEPOT_PATH to the custom_sysimage.jl file and rerunning the command above prints:

LOAD_PATH = String[]
DEPOT_PATH = String[]

Again, we have an initialization problem. Looking at what Julia itself does before including the standard libraries, we can see that the functions initializing these variables are explicitly called. Let us do the same by updating the custom_sysimage.jl file to:


using CSV


and running

julia --startup-file=no --output-o sys.o -J"/home/kc/julia/lib/julia/" custom_sysimage.jl

This time, after some waiting (2 min on the authors quite beefy computer) we do end up with a sys.o file.

2. Creating the sysimage shared library from the object file

The goal in this part is to take the object file, link it with libjulia to finally produce a shared library which is our sysimage. For this, we need to use a C-compiler e.g. gcc. We need to link with libjulia so we need to give the compiler the path to where the julia library resides which can be gotten by:

julia> abspath(Sys.BINDIR, Base.LIBDIR)

We tell gcc that we want a shared library with the -shared flag and to keep all symbols into the library by passing the --whole-archive to the linker (this is on Linux, see the later section for platform differences). The final gcc invocation ends up as:

gcc -shared -o -Wl,--whole-archive sys.o -Wl,--no-whole-archive -L"/home/kc/julia/lib" -ljulia

which creates the sysimage

We can compare the size of the new sysimage versus the default one and see that the new is a bit larger due to the extra packages it contains:

julia> stat("").size / (1024*1024)

julia> stat(unsafe_string(Base.JLOptions().image_file)).size / (1024*1024)

Platform differences


On macOS the linker flag -Wl,--whole-archive is instead written as -Wl,-all_load so the command would be

gcc -shared -o sys.dylib -Wl,-all_load sys.o -L"/home/kc/Applications/julia-1.3.0-rc4/lib" -ljulia

Note that the extension has been changed from so to dylib which is the convention for shared libraries on macOS.


Getting a compiler toolchain on Windows that works well with Julia is a bit trickier than on Linux or macOS. One quite simple way is to follow the same process as needed to compile Julia on windows as outlined here and then use the x86_64-w64-mingw32-gcc compiler in Cygwin instead of gcc. Alternatively, a mingw compiler can be downloaded from here The libjulia is also in a different location on Windows. Instead of the lib folder it is in the bin folder. Other than that, the same flags as for Linux should work to produce the sysimage shared library.

3. Running Julia with the new sysimage

We start Julia with the flag to load the new custom sysimage (or sys.dylib, sys.dll on macOS and Windows respecitively) and indeed loading CSV is now very fast:

julia> @time using CSV
  0.000432 seconds (665 allocations: 32.656 KiB)

In fact, restarting Julia and looking at Base.loaded_modules we can see that, just like the standard libraries, CSV and its dependencies are already loaded when Julia is started:

julia> Base.loaded_modules
Dict{Base.PkgId,Module} with 52 entries:
   Parsers [69de0a69-1ddd-5017-9359-2bf0b02dc9f0] => Parsers
   CSV [336ed68f-0bac-5ca0-87d4-7b16caf5d00b]     => CSV

However, remember that a large part of the latency was not loading the package but to compile the functions used by CSV the first time. Let's try it with the custom sysimage:

julia> @time using CSV
  0.001487 seconds (711 allocations: 35.203 KiB)

julia> @time"FL_insurance_sample.csv");
  3.609626 seconds (16.34 M allocations: 795.619 MiB, 5.88% gc time)

julia> @time"FL_insurance_sample.csv");
  0.026917 seconds (423 allocations: 34.695 KiB)

Reading the CSV file is significantly faster than before but still a lot slower than the second time. As previously mentioned, the native code for the functions in CSV is not compiled just by loading the package. This means that even though CSV is in the sysimage the functions in CSV still need to be compiled. The reason why the first call is faster at all is likely that loading packages can invalidate other methods and they thus have to be recompiled. With CSV in the sysimage, these invalidations have already been resolved.

Recording precompile statements

We are now at the stage where we have CSV in the sysimage, but we still suffer some latency because of compilation. Note that Julia is a dynamically typed language, it is therefore not known statically what types will be used in functions. Therefore, in order to be able to compile code one needs to know what types functions should be compiled for. One way to do this is to run some representative workload and record what types functions end up getting called with. This is a little bit like Profile Guide Optimization (PGO) while it here being something more like Profile Guided Compilation..

There is indeed a way for Julia to record what functions are getting compiled. We can save these and then when building the sysimage tell Julia to compile and store the native code for these functions.

We create a file called generate_csv_precompile.jl containing some "training code" that we will use as a base to figure out what functions end up getting compiled:

using CSV"FL_insurance_sample.csv")

We then make julia run this code but we add the --trace-compile flag to output "precompilation statements" to a file:

julia --startup-file=no --trace-compile=csv_precompile.jl generate_csv_precompile.jl

Looking at csv_precompile.jl we can see hundreds of functions that end up getting compiled. For example, the line

precompile(Tuple{typeof(CSV.getsource), String, Bool})

instructs julia to compile the function CSV.getsource for the arguments of type String and Bool.

Note that some of the symbols in the list of precompile statements have a bit of a weird syntax containing Symbol(#...), e.g:

precompile(Tuple{typeof(, getfield(CSV, Symbol("##4#5")), Base.SubString{String}})

These are symbols that were not explicitly named in the source code but that Julia automatically gave an internal name to refer to. These symbols are not necessarily consistent between different Julia versions or even Julia built for different operating systems. It is possible to make the precompile statements more portable by filtering out any symbols starting with # but that naturally leaves some latency on the table since these now have to be compiled during runtime.

The way we make Julia cache the compilation of the functions in the list is simply by executing the statement on each line when the sysimage is created. It , unfortunately, isn't as simple as just adding an include("csv_precompile") to our custom_precompile.jl file. Firstly, all the modules used in the precompilation statements (like DataFrames) are not defined in the Main namespace. Secondly, due to some bugs in the way Julia export precompile statements running a precompile statement can fail. The solution to these issues is to load all modules in the sysimage by looping through Base.loaded_modules and to use a try-catch for each precompile statement. In addition, we evaluate everything in an anonymous module to not pollute the Main module with a bunch of symbols.

The end result is a custom_sysimage.jl file looking like:


using CSV

@eval Module() begin
    for (pkgid, mod) in Base.loaded_modules
        if !( in ("Main", "Core", "Base"))
            eval(@__MODULE__, :(const $(Symbol(mod)) = $mod))
    for statement in readlines("csv_precompile.jl")
            Base.include_string(@__MODULE__, statement)
            # See julia issue #28808
            @info "failed to compile statement: $statement"
end # module


After repeating the process of creating the object file and using a compiler to create the shared library sysimage, we are in a position to time again:

julia> @time using CSV
  0.000408 seconds (665 allocations: 32.656 KiB)

julia> @time"FL_insurance_sample.csv");
  0.031504 seconds (441 allocations: 37.383 KiB)

julia> @time"FL_insurance_sample.csv");
  0.021355 seconds (423 allocations: 34.695 KiB)

And finally, our first time for parsing the CSV-file is close to the second time.