RedisGraph VS PostgreSQL

This approach reminds me of RedisGraph[1] (which is now unfortunately EoL).

"RedisGraph is the first queryable Property Graph database to use sparse matrices to represent the adjacency matrix in graphs and linear algebra to query the graph."

1. https://github.com/RedisGraph/RedisGraph

It's also my impression that redis-graph uses Gherkin (the language of Cucumber) while the redis server relies on Tcl for testing. So is Tcl is a valid choice for BDD in C++ ? (redis is C, but any such framework would immediately be transferable).

Redis - Graph

So that we don’t give an ex-inmate the option of crime, let’s get them reintegrated into society faster. Dating a person who has also been in the system could be a faster way to achieve that. To matchmake both entities, we need to consider their peculiar dating interests. The more data we have for these interests, the more we can give the perfect match. Usually, when building a social tool that involves complex relationships amongst entities, it is recommended to use a graph database such as RedisGraph. We may explore this later as we progress. But not to complicate things, for now, we would rather store these interests coming from various Omni-channels such as social media, sms, online forms, text, etc. in JSON. Often, this is the first step of the data analysis process known as data collection.

$ git clone https://github.com/RedisGraph/RedisGraph -b v2.4.11 --recurse-submodules -j8 Cloning into 'RedisGraph'... remote: Enumerating objects: 49063, done. remote: Counting objects: 100% (2906/2906), done. remote: Compressing objects: 100% (1082/1082), done. remote: Total 49063 (delta 1998), reused 2448 (delta 1736), pack-reused 46157 Receiving objects: 100% (49063/49063), 39.33 MiB | 114.00 KiB/s, done. Resolving deltas: 100% (38402/38402), done. Submodule 'deps/RediSearch' (https://github.com/RediSearch/RediSearch.git) registered for path 'deps/RediSearch' Submodule 'deps/googletest' (https://github.com/google/googletest.git) registered for path 'deps/googletest' Submodule 'deps/libcypher-parser' (https://github.com/RedisGraph/libcypher-parser.git) registered for path 'deps/libcypher-parser' Submodule 'deps/rax' (https://github.com/antirez/rax.git) registered for path 'deps/rax' Submodule 'deps/readies' (https://github.com/RedisLabsModules/readies.git) registered for path 'deps/readies' Submodule 'deps/xxHash' (https://github.com/Cyan4973/xxHash.git) registered for path 'deps/xxHash' Cloning to '/home/bpdp/master/postdoc-ugm/RedisGraph/deps/RediSearch'... remote: Enumerating objects: 34395, done. remote: Counting objects: 100% (1802/1802), done. remote: Compressing objects: 100% (1097/1097), done. remote: Total 34395 (delta 1150), reused 1182 (delta 696), pack-reused 32593 Receiving objects: 100% (34395/34395), 23.62 MiB | 71.00 KiB/s, done. Resolving deltas: 100% (25261/25261), done. Cloning to '/home/bpdp/master/postdoc-ugm/RedisGraph/deps/rax'... remote: Enumerating objects: 668, done. remote: Counting objects: 100% (25/25), done. remote: Compressing objects: 100% (14/14), done. remote: Total 668 (delta 12), reused 19 (delta 11), pack-reused 643 Receiving objects: 100% (668/668), 236.14 KiB | 1.41 MiB/s, done. Resolving deltas: 100% (414/414), done. Cloning to '/home/bpdp/master/postdoc-ugm/RedisGraph/deps/readies'... remote: Enumerating objects: 2354, done. remote: Counting objects: 100% (833/833), done. remote: Compressing objects: 100% (329/329), done. remote: Total 2354 (delta 608), reused 675 (delta 503), pack-reused 1521 Receiving objects: 100% (2354/2354), 390.69 KiB | 17.00 KiB/s, done. Resolving deltas: 100% (1577/1577), done. Cloning to '/home/bpdp/master/postdoc-ugm/RedisGraph/deps/libcypher-parser'... remote: Enumerating objects: 3250, done. remote: Counting objects: 100% (68/68), done. remote: Compressing objects: 100% (46/46), done. remote: Total 3250 (delta 42), reused 43 (delta 21), pack-reused 3182 Receiving objects: 100% (3250/3250), 2.10 MiB | 28.00 KiB/s, done. Resolving deltas: 100% (2488/2488), done. Cloning to '/home/bpdp/master/postdoc-ugm/RedisGraph/deps/xxHash'... remote: Enumerating objects: 4784, done. remote: Counting objects: 100% (345/345), done. remote: Compressing objects: 100% (188/188), done. remote: Total 4784 (delta 189), reused 255 (delta 143), pack-reused 4439 Receiving objects: 100% (4784/4784), 2.54 MiB | 27.00 KiB/s, done. Resolving deltas: 100% (2922/2922), done. Cloning to '/home/bpdp/master/postdoc-ugm/RedisGraph/deps/googletest'... remote: Enumerating objects: 23334, done. remote: Counting objects: 100% (234/234), done. remote: Compressing objects: 100% (142/142), done. remote: Total 23334 (delta 120), reused 146 (delta 81), pack-reused 23100 Receiving objects: 100% (23334/23334), 9.49 MiB | 44.00 KiB/s, done. Resolving deltas: 100% (17191/17191), done. Submodule path 'deps/RediSearch': checked out '68430b3c838374478dd9ffe4e361534f572b16ff' Submodule 'deps/googletest' (https://github.com/google/googletest.git) registered for path 'deps/RediSearch/deps/googletest' Submodule 'deps/readies' (https://github.com/RedisLabsModules/readies.git) registered for path 'deps/RediSearch/deps/readies' Cloning to '/home/bpdp/master/postdoc-ugm/RedisGraph/deps/RediSearch/deps/googletest'... remote: Enumerating objects: 23334, done. remote: Counting objects: 100% (234/234), done. remote: Compressing objects: 100% (148/148), done. remote: Total 23334 (delta 120), reused 141 (delta 75), pack-reused 23100 Receiving objects: 100% (23334/23334), 9.56 MiB | 1.05 MiB/s, done. Resolving deltas: 100% (17185/17185), done. Kloning ke '/home/bpdp/master/postdoc-ugm/RedisGraph/deps/RediSearch/deps/readies'... remote: Enumerating objects: 2354, done. remote: Counting objects: 100% (833/833), done. remote: Compressing objects: 100% (329/329), done. remote: Total 2354 (delta 608), reused 675 (delta 503), pack-reused 1521 Receiving objects: 100% (2354/2354), 390.69 KiB | 853.00 KiB/s, done. Resolving deltas: 100% (1577/1577), done. Submodule path 'deps/RediSearch/deps/googletest': checked out 'dea0216d0c6bc5e63cf5f6c8651cd268668032ec' Submodule path 'deps/RediSearch/deps/readies': checked out '89be267427c7dfcfaab4064942ef0f595f6b1fa3' Submodule path 'deps/googletest': checked out '565f1b848215b77c3732bca345fe76a0431d8b34' Submodule path 'deps/libcypher-parser': checked out '38cdee1867b18644616292c77fe2ac1f2b179537' Submodule path 'deps/rax': checked out 'ba4529f6c836c9ff1296cde12b8557329f5530b7' Submodule path 'deps/readies': checked out 'd59f3ad4e9b3d763eb41df07567111dc94c6ecac' Submodule path 'deps/xxHash': checked out '726c14000ca73886f6258a6998fb34dd567030e9' $

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