How to Install and Uninstall librust-parking-lot-core-0.4+deadlock-detection-dev Package on Ubuntu 20.04 LTS (Focal Fossa)

Last updated: November 26,2024

1. Install "librust-parking-lot-core-0.4+deadlock-detection-dev" package

Please follow the steps below to install librust-parking-lot-core-0.4+deadlock-detection-dev on Ubuntu 20.04 LTS (Focal Fossa)

$ sudo apt update $ sudo apt install librust-parking-lot-core-0.4+deadlock-detection-dev

2. Uninstall "librust-parking-lot-core-0.4+deadlock-detection-dev" package

Here is a brief guide to show you how to uninstall librust-parking-lot-core-0.4+deadlock-detection-dev on Ubuntu 20.04 LTS (Focal Fossa):

$ sudo apt remove librust-parking-lot-core-0.4+deadlock-detection-dev $ sudo apt autoclean && sudo apt autoremove

3. Information about the librust-parking-lot-core-0.4+deadlock-detection-dev package on Ubuntu 20.04 LTS (Focal Fossa)

Package: librust-parking-lot-core-0.4+deadlock-detection-dev
Architecture: amd64
Version: 0.4.0-4
Multi-Arch: same
Priority: optional
Section: universe/rust
Source: rust-parking-lot-core-0.4
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Rust Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 6
Provides: librust-parking-lot-core+deadlock-detection-dev (= 0.4.0-4), librust-parking-lot-core-0+deadlock-detection-dev (= 0.4.0-4), librust-parking-lot-core-0.4.0+deadlock-detection-dev (= 0.4.0-4)
Depends: librust-parking-lot-core-0.4-dev (= 0.4.0-4), librust-backtrace-0.3+default-dev (>= 0.3.2-~~), librust-petgraph-0.5+default-dev | librust-petgraph-0.4+default-dev (>= 0.4.5-~~), librust-thread-id-3+default-dev (>= 3.2.0-~~)
Filename: pool/universe/r/rust-parking-lot-core-0.4/librust-parking-lot-core-0.4+deadlock-detection-dev_0.4.0-4_amd64.deb
Size: 1168
MD5sum: ca01ee30d1a0c298a505117a107365cf
SHA1: 601e33ee165865b579cb4510be9dc668ba421e97
SHA256: b1ec365d9a0d70f42b9bf19ecf8633669369ca6b22f70d023cc0499289a80514
Description: API for creating synchronization primitives - feature "deadlock_detection"
Description-md5: dd3ba8e49d44aec2942cc7b23720b251