Julia v1.3.0-rc2 is now available

The second release candidate for Julia v1.3.0 is now available. Get binaries for Linux (i686, x86-64, ARMv7, AArch64), FreeBSD (x86-64), Windows (32-, 64-bit), and macOS at https://julialang.org/downloads. Check out the NEWS file to see what will be new in 1.3.0 and see the list of commits included since 1.3.0-rc1 here.

As a release candidate, this should not be considered production-ready; it’s intended to give users a chance to try out their code with 1.3.0 prior to a full release. Note that 1.3 on Travis AppVeyor, and Cirrus CI now points to 1.3.0-rc2.

The versioning for the 1.3 prereleases is a bit misleading (my fault), so even though this is labeled a release candidate, we know it won’t be the last RC; we’re already working on the next one. Apologies for the confusion, but do note that 1.3 is feature frozen, so any remaining changes in these RCs are simply bugfixes.

As usual, let us know in the issue tracker if you run into any issues.


I have a complex piece of code that fails on 1.3RC2. But I think I wait for RC3 before creating an issue. Simplifying this code to provide a MWE will be difficult.

Can you share the code with a Manifest to snapshot the versions of dependencies?

Created a bug report: https://github.com/JuliaLang/julia/issues/33358

This might be a little premature, but I noticed a substantial increase in the memory footprint of Julia v1.3 compared to v1.2/1.1.

In particular, I have some simple code that makes few thousands http requests, parse json responses and store the results in an array of DataFrames.
The same code performs similarly in Julia v1.1/1.2/1.3, with the exception of the RAM used, which in v1.3 ends up being about twice as much that of v1.1/1.2.
Is this expected?

Can you post a reproducer?

versioninfo() returns rc1 in the rc2 binaries:

julia> versioninfo()
Julia Version 1.3.0-rc1.0
Commit 768b25f6a8 (2019-08-18 00:04 UTC)
Platform Info:
  OS: Linux (x86_64-pc-linux-gnu)
  CPU: AMD Ryzen Threadripper 2950X 16-Core Processor
  LIBM: libopenlibm
  LLVM: libLLVM-6.0.1 (ORCJIT, znver1)

I get

julia> versioninfo()
Julia Version 1.3.0-rc2.0
Commit a04936e3e0 (2019-09-12 19:49 UTC)
1 Like

As an update, I tried to manually invoke GC.gc() and the difference in memory usage significantly decreased to around 20%.
So, I guess this is not really an issue but it has more to do with differences in how memory is allocated and garbage collected.