ARROW-12224: [Rust] Use stable rust for no default test, clean up CI tests

# Rationale

1. As @jorgecarleitao noted on https://github.com/apache/arrow/pull/9889#discussion_r607720790, we should be running the check if arrow compiles with stable rust as that is what we target for the arrow crate
2. I noticed that there were several redundant (and inconsistent) settings of `RUSTFLAGS`
3. The titles of many of the tests are confusing (to me) as they have a lot of detailed architecture / rust version information before the description of what they are testing

![Screen Shot 2021-04-06 at 6 39 19 AM](https://user-images.githubusercontent.com/490673/113700398-ce026b00-96a4-11eb-8261-a5ef8745ebc1.png)

# Changes
1. Use rust stable for the check that ensures the crate builds without default features
2. Remove redundant `RUSTFLAGS`
3. Change titles of the jobs to consistently start with a description of what they do

# Note
This could be three individual PRs, but I wanted to avoid the overhead of three separate JIRA tickets and juggling several concurrent potentially conflicting PRs. I will break it into three individual ones however if reviewers want.

Closes #9904 from alamb/alamb/cleanup_rust_ci

Authored-by: Andrew Lamb <andrew@nerdnetworks.org>
Signed-off-by: Andrew Lamb <andrew@nerdnetworks.org>
1 file changed
tree: 46107476c592b88924cb6ae7dd56d9cd3fea9fba
  1. .github/
  2. ci/
  3. dev/
  4. format/
  5. rust/
  6. .asf.yaml
  7. .clang-format
  8. .clang-tidy
  9. .clang-tidy-ignore
  10. .dir-locals.el
  11. .dockerignore
  12. .env
  13. .gitattributes
  14. .gitignore
  15. .gitmodules
  16. .hadolint.yaml
  17. .pre-commit-config.yaml
  18. .readthedocs.yml
  19. .travis.yml
  20. appveyor.yml
  21. CHANGELOG.md
  22. cmake-format.py
  23. CODE_OF_CONDUCT.md
  24. CONTRIBUTING.md
  25. docker-compose.yml
  26. header
  27. LICENSE.txt
  28. NOTICE.txt
  29. README.md
  30. run-cmake-format.py
README.md

Apache Arrow

Build Status Coverage Status Fuzzing Status License Twitter Follow

Powering In-Memory Analytics

Apache Arrow is a development platform for in-memory analytics. It contains a set of technologies that enable big data systems to process and move data fast.

Major components of the project include:

Arrow is an Apache Software Foundation project. Learn more at arrow.apache.org.

What's in the Arrow libraries?

The reference Arrow libraries contain many distinct software components:

  • Columnar vector and table-like containers (similar to data frames) supporting flat or nested types
  • Fast, language agnostic metadata messaging layer (using Google's Flatbuffers library)
  • Reference-counted off-heap buffer memory management, for zero-copy memory sharing and handling memory-mapped files
  • IO interfaces to local and remote filesystems
  • Self-describing binary wire formats (streaming and batch/file-like) for remote procedure calls (RPC) and interprocess communication (IPC)
  • Integration tests for verifying binary compatibility between the implementations (e.g. sending data from Java to C++)
  • Conversions to and from other in-memory data structures
  • Readers and writers for various widely-used file formats (such as Parquet, CSV)

Implementation status

The official Arrow libraries in this repository are in different stages of implementing the Arrow format and related features. See our current feature matrix on git master.

How to Contribute

Please read our latest project contribution guide.

Getting involved

Even if you do not plan to contribute to Apache Arrow itself or Arrow integrations in other projects, we'd be happy to have you involved: