Memgraph VS QEMU

Memgraph | Staff C++ Database Engineer | REMOTE (Central/Western Europe, LatAm, or North America) https://memgraph.com/

Memgraph is a Seed stage, open source graph database vendor. Graph DBs are a great solution for GenAI, logistics, cybersecurity and fintech so we are looking to grow aggressively this year.

We're looking for a staff-level engineer to set technical direction, mentor junior team members, and solve some very difficult problems.

Either DM me (the hiring manager) or apply here: https://join.com/companies/memgraph/10684850-staff-software-...

It's not possible to escape tradeoffs. To deal with tradeoffs, focus is important. API to tradeoffs is also important.

I bet somebody will raise a similar question in a few years time when the list under https://db-engines.com/en/ranking/graph+dbms will be bigger.

DISCLAIMER: Coming from https://github.com/memgraph/memgraph

Albeit the significant engineering endeavor, the larger-than-memory architecture is a super valuable asset to Memgraph users since it allows them to store large amounts of data cheaply on disk without sacrificing the performance of in-memory computation. We are actively working on resolving issues introduced with the new storage mode, so feel free to ask, open an issue, or pull a request. We will be more than happy to help. Until next time 🫡

NoSQL databases are non-relational databases with flexible schema designed for high performance at a massive scale. Unlike traditional relational databases, which use tables and predefined schemas, NoSQL databases use a variety of data models. There are 4 main types of NoSQL databases - document, graph, key-value, and column-oriented databases. NoSQL databases generally are well-suited for unstructured data, large-scale applications, and agile development processes. The most popular examples of NoSQL databases are MongoDB (document), Memgraph (graph), Redis (key-value store) and Apache HBase (column-oriented).

Whether it's about identifying similar user profiles in a social network, detecting similar patterns in a communication network, or classifying nodes in a semantic network, cosine similarity contributes valuable insights. Combined with a powerful graph database system, such as Memgraph, it gives a better understanding of complex networks. Memgraph is an open-source in-memory graph database built to handle real-time use cases at an enterprise scale. Memgraph supports strongly-consistent ACID transactions and uses the standardized Cypher query language for structuring, manipulating, and exploring data.

It should be noted this article is on the blog of a project which advertises itself as open source, under a BSL license that puts limitations on distribution and use.

At the moment, benchgraph is a project under Memgraph repository (previously Mgbench). It consists of Python scripts and a C++ client. Python scripts are used to manage the benchmark execution by preparing the workload, configurations, and so on, while the C++ client actually executes the benchmark.

These five steps will result in something similar to this simplified version of demo.py example:

Take a look at this blog post about choosing the optimal index. It focuses on Memgraph graph database but it offers a theoretical background that is not vendor related.

In this tutorial, we will utilize betweenness centrality for identifying essential proteins. For this task, we are using Memgraph, a graph analytics platform, which can perform complex graph analysis on all sorts of networks. Even though we will use betweenness centrality, other graph algorithms can also be applied to the protein-protein interaction network, such as other centrality measures or the PageRank algorithm.

My most-wanted QEMU feature: https://github.com/qemu/qemu/commit/a2260983c6553

Using `gic-version=3` on macOS you can now use more than 8 cores on ARM chips.

A better solution is just to write a plain ass shell script that tests if various C snippets compile.

https://github.com/oilshell/oil/blob/master/configure

https://github.com/oilshell/oil/blob/master/build/detect-pwe...

Not an unholy mix of m4, shell, and C, all in the same file.

---

These are the same style as a the configure scripts that Fabrice Bellard wrote for tcc and QEMU.

They are plain ass shell scripts, because he actually understands the code he writes.

https://github.com/qemu/qemu/blob/master/configure

https://github.com/TinyCC/tinycc/blob/mob/configure

OCaml’s configure script is also “normal”.

You don’t have to copy and paste thousands of lines of GNU stuff that you don’t understand.

(copy of lobste.rs comment)

Related:

A fast Pascal (Delphi) WebAssembly interpreter:

https://github.com/marat1961/wasm

WASM-4:

https://github.com/aduros/wasm4

Curated list of awesome things regarding WebAssembly (wasm) ecosystem:

https://github.com/mbasso/awesome-wasm

Also, it would be nice if there was a WASM (soft) CPU for QEMU, which (if it existed!) would go here:

https://github.com/qemu/qemu/tree/master/target

> architectural registers are always updated

In tiny code, the guest registers (global TCG variables) are stored in the host's registers until you either call an helper which can access the CPU state or you return (`git grep la_global_sync`). This is the reason why QEMU is not so terribly slow.

But after a check, this also happens when you access the guest memory address space! https://github.com/qemu/qemu/blob/master/include/tcg/tcg-opc... (TCG_OPF_SIDE_EFFECTS is what matters)

But still, in the end, it's the same problem. What QEMU does, can be done in LLVM too. You could probably be more efficient in LLVM by using the exception handling mechanism (invoke and friends) to only serialize back to memory when there's an actual exception, at the cost of higher register pressure. More or less what we do here: https://rev.ng/downloads/bar-2019-paper.pdf

Um, in case you don't know, UTM (based on QEMU) is out for quite a while.

Some of these tools include Oracle VM VirtualBox (that I've used since before the acquisition of Sun Microsystems by Oracle), VMWare Workstation Player, and QEMU, but last year, I found out about Multipass.

For C/C++ projects that use meson as the build system, there is an excellent way to manage dependencies:

https://mesonbuild.com/Wrapdb-projects.html

https://mesonbuild.com/Wrap-dependency-system-manual.html

meson will download and build the libraries automatically and give you a variable which you pass as a regular dependency into the built target:

https://github.com/qemu/qemu/tree/005ad32358f12fe9313a4a0191...

https://github.com/harfbuzz/harfbuzz/tree/main/subprojects

https://github.com/harfbuzz/harfbuzz/blob/37457412b3212463c5...

Or, if you're using proper operating systems, they're managed by the usual package manager, just like everything else.

For all the users of the Linux platform, QEMU is the VM that you should go for. This software comes without any price tag and works as an emulator of various machines with utmost ease and completion; the software uses dynamic translations to emulate hardware peripherals and enhances its overall performance. If you are using QEMU as a virtualizer, then it will function exactly like the host system (provided you have the right set of hardware).

In this tutorial, we set up macOS and Windows virtual machines on UTM, a macOS application that provides a GUI wrapper for QEMU, a powerful open-source emulator and virtualizer. UTM allows you to easily manage and run virtual machines without memorizing complex commands. It also has special handling for macOS, making it simpler to install compared to other virtual machine software.