Google Test VS PostgreSQL

cmake_minimum_required(VERSION 3.5) project(knn_cpp CXX) include(FetchContent) FetchContent_Declare( googletest GIT_REPOSITORY https://github.com/google/googletest.git GIT_TAG release-1.11.0 ) FetchContent_MakeAvailable(googletest) FetchContent_Declare(matplotplusplus GIT_REPOSITORY https://github.com/alandefreitas/matplotplusplus GIT_TAG origin/master) FetchContent_GetProperties(matplotplusplus) if(NOT matplotplusplus_POPULATED) FetchContent_Populate(matplotplusplus) add_subdirectory(${matplotplusplus_SOURCE_DIR} ${matplotplusplus_BINARY_DIR} EXCLUDE_FROM_ALL) endif() function(knn_cpp_test TEST_NAME TEST_SOURCE) add_executable(${TEST_NAME} ${TEST_SOURCE}) target_link_libraries(${TEST_NAME} PUBLIC matplot) aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/../lib LIB_SOURCES) target_link_libraries(${TEST_NAME} PRIVATE gtest gtest_main gmock gmock_main) target_include_directories(${TEST_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_SOURCE_DIR}/../) target_sources(${TEST_NAME} PRIVATE ${LIB_SOURCES} ) include(GoogleTest) gtest_discover_tests(${TEST_NAME}) endfunction() knn_cpp_test(LinearAlgebraTest la_test.cc) knn_cpp_test(KnnTest knn_test.cc) knn_cpp_test(UtilsTest utils_test.cc)

Okay, time to start unit tests!!! We will use Unity Test Framework to do unit testing. It is one of widely used testing frameworks alongside with Check, Google Test etc. Just downloading source code, and putting it to the project folder is enough to make it work (that is also why it is portable).

Updating GCC (it happened to GoogleTest).

Roy's project uses Google Test, a C++ testing framework. His testing setup is similar to mine as we both keep source files in one directory and tests in another. The key difference is that I can run the tests using the Visual Studios run button. It was fairly easy to write the new tests as there were existing ones that I could reference to check the syntax!

The Google Test Documentation provides a primer for first-time users. The primer introduces some basic concepts and terminology, some of which I've been able to learn for this lab exercise.

> git submodule add https://github.com/google/googletest.git > git submodule update --init --recursive

cmake_minimum_required(VERSION 3.14) project(my_project) # GoogleTest requires at least C++14 set(CMAKE_CXX_STANDARD 14) set(CMAKE_CXX_STANDARD_REQUIRED ON) include(FetchContent) FetchContent_Declare( googletest URL https://github.com/google/googletest/archive/03597a01ee50ed33e9dfd640b249b4be3799d395.zip ) # For Windows: Prevent overriding the parent project's compiler/linker settings set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) FetchContent_MakeAvailable(googletest) enable_testing() add_executable( hello_test hello_test.cpp ) target_link_libraries( hello_test GTest::gtest_main ) include(GoogleTest) gtest_discover_tests(hello_test)

FetchContent\_Declare(googletestGIT\_REPOSITORY [[email protected]](mailto:[email protected]):googletest.git [https://github.com/google/googletest.git](https://github.com/google/googletest.git)GIT\_TAG release-1.12.1)FetchContent\_MakeAvailable(googletest)

Not sure about CppUnit but I can speak to my previous experience using the googletest framework which compiles your tests to an executable, and since it's a very simple framework we were able to cross-compile and run directly on our device. We just had to hook up a device to the server that was running the CI so it could flash it when needed. That basically meant that our process was:

PostgreSQL

The LMS Moodle Operator serves as a meta-operator, orchestrating the deployment and management of Moodle instances in Kubernetes. It handles the entire stack required to run Moodle, including components like Postgres, Keydb, NFS-Ganesha, and Moodle itself. Each of these components has its own Kubernetes Operator, ensuring seamless integration and management.

Another key feature of txtai is being able to quickly move from prototyping to production. This article will demonstrate how txtai can integrate with Postgres, a powerful, production-ready and open source object-relational database system. After txtai persists content to Postgres, we'll show it can be directly queried with SQL from any Postgres client

SQL (Structured Query Language) databases are relational databases. They organize data into tables with rows and columns, and they use SQL for querying and managing data. Examples include MySQL, PostgreSQL, and SQLite.

Node.js, MySQL and PostgreSQL servers installed on your machine

Each app’s front end is built with Qwik and uses Tailwind for styling. The server-side is powered by Qwik City (Qwik’s official meta-framework) and runs on Node.js hosted on a shared Linode VPS. The apps also use PM2 for process management and Caddy as a reverse proxy and SSL provisioner. The data is stored in a PostgreSQL database that also runs on a shared Linode VPS. The apps interact with the database using Drizzle, an Object-Relational Mapper (ORM) for JavaScript. The entire infrastructure for both apps is managed with Terraform using the Terraform Linode provider, which was new to me, but made provisioning and destroying infrastructure really fast and easy (once I learned how it all worked).

I've used MySQL for years. But recently, I found myself working PostgreSQL and simple things like dumping and restoring a database are different enough that I decided to document the process. It's straightforward enough once I knew how.

A running Rails application needs a database to connect to. You may already have your database of choice installed, but if not, I recommend PostgreSQL, or Postgres for short. On a Mac, probably the easiest way to install it is with Posrgres.app. Another option, the one I prefer, is to use Homebrew. With Homebrew installed, this command will install PostgreSQL version 16 along with libpq:

PostgreSQL