rippled VS Memgraph

The bug occurred when a multi-path payment interacted with an AMM synthetic offer, and the synthetic offer required to fulfill the payment exceeded the AMM pool size. In this case, the payment engine attempted to use the single-path strategy to back-out the maximum liquidity available to swap against the pool. Although this condition was properly detected, there was an error on how the payment engine resized the synthetic offer to consume liquidity only up to the AMM size. Instead of using the actual swap rate, which represents the cost of fully swapping one side of an AMM pool, the new synthetic offer’s exchange rate was incorrectly set to the spot exchange rate of the AMM (that is, the rate it takes to execute an infinitesimally sized order). Consequently, an AMM operation that should have been expensive became relatively cheap, effectively violating the constant-product invariant of the pool. In this state, another user could deposit funds into the drained side of the pool, generating a large number of LP tokens and taking ownership of the pool. Now that the fix amendment is active, the team has opened a PR with a unit test demonstrating this behavior.

Explore the XRPL Developers Discord, the XRP Ledger Learning Portal, and the extensive documentation available on XRPL.org. You can also participate in XRPL Community Events and Hackathons to engage with like-minded individuals and stay abreast of the latest developments in the XRPL ecosystem.

fixDisallowIncomingV1: Fixes an issue that occurs with authorized trustlines and the “lsfDisallowIncomingTrustline” flag.

It’s good news for the builders who have been working round the clock. Tools such as AI chatbots will enable developers to quickly receive answers to their queries, speeding up the process from concept to application. AI Chatbots have already become a part of RippleX’s commitment to making blockchain development on the XRP Ledger more accessible and less time-consuming, especially for those new to the field. This approach will not only foster increased innovation on blockchains but could also enhance financial inclusion, making tools more accessible globally.

Currently Rippled 2.0.0 is scheduled to release December 18th, 2023: https://github.com/XRPLF/rippled/milestone/8

The XRPL Accelerator, a program dedicated to nurturing innovation and development on the XRP Ledger, is back with its second cohort of groundbreaking projects. After the success of the inaugural cohort, the Accelerator continues to support entrepreneurs and builders looking to scale their projects on the XRP Ledger.

The third annual Apex Developer Summit was the biggest yet. Held in collaboration with the XRP Ledger Foundation, this event brought together an impressive gathering of over 400 blockchain developers, validators, and XRPL community members in the heart of Amsterdam at the Leonardo Royal Hotel.

Explore the XRPL Developers Discord, the XRP Ledger Learning Portal, and the extensive documentation available on xrpl.org. You can also participate in XRPL Community Events and Hackathons to engage with like-minded individuals and stay abreast of the latest developments in the XRPL ecosystem.

As developers and contributors to the decentralized, open-source XRP Ledger (XRPL), the RippleX engineering team is excited to announce a major breakthrough in the XRPL ecosystem that will revolutionize the way developers build decentralized applications (dApps).

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.