Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes with radius queries and streams. Redis has built-in replication, Lua scripting, LRU eviction, transactions and different levels of on-disk persistence, and provides high availability via Redis Sentinel and automatic partitioning with Redis Cluster.
Based on our record, Redis should be more popular than NetworkX. It has been mentiond 183 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
The page 404s for me currently and it does not seem to be archived by the wayback machine either: https://web.archive.org/web/20240000000000*/https://redis.io/news/121. - Source: Hacker News / 24 days ago
Redis - real time data storage with different data structures in a cache. - Source: dev.to / 26 days ago
Redis.io no longer mentions open source. They have still not changed meta description on their page. It still says it is open source ^^ view-source:https://redis.io/. - Source: Hacker News / about 1 month ago
Follow the steps below to install Redis:. - Source: dev.to / about 2 months ago
Redis: An open-source, in-memory data structure store supporting various data types. It offers persistence, replication, and clustering, making it ideal for more complex caching requirements and session storage. - Source: dev.to / 2 months ago
In the project we used Python lib networkx and a DiGraph object (Direct Graph). To detect a table reference in a Query, we use sqlglot, a SQL parser (among other things) that works well with Bigquery. - Source: dev.to / 4 months ago
If you program in Python, can use NetworkX for that. But it's probably a good idea to implement the basic algorithms yourself at least one time. Source: 5 months ago
For those wanting to play with graphs and ML I was browsing the arangodb docs recently and I saw that it includes integrations to various graph libraries and machine learning frameworks [1]. I also saw a few jupyter notebooks dealing with machine learning from graphs [2]. Integrations include: * NetworkX -- https://networkx.org/ * DeepGraphLibrary -- https://www.dgl.ai/ * cuGraph (Rapids.ai Graph) --... - Source: Hacker News / 7 months ago
Org-roam-ui is a great interactive visualization tool, but its main use is visualization. The hope of this library is that it could be part of a larger graph analysis pipeline. The demo provides an example graph visualization, but what you choose to do with the resulting graph certainly isn't limited to that. See for example networkx. Source: 12 months ago
Back in college, I had an assignment deadline coming up and I wanted to work on it in the train since I had an 8-hour journey ahead of me. It was about some analysis of graph data, which used a Python package called NetworkX. The train's WiFi didn't allow me to access their documentation because it apparently thought it was porn. Source: 12 months ago
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
neo4j - Meet Neo4j: The graph database platform powering today's mission-critical enterprise applications, including artificial intelligence, fraud detection and recommendations.
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
RedisGraph - A high-performance graph database implemented as a Redis module.
Apache Cassandra - The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.
PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.