Apache AGE
RocksDB
Apache AGE | RocksDB | |
---|---|---|
31 | 44 | |
709 | 27,536 | |
- | 1.2% | |
8.5 | 9.8 | |
almost 2 years ago | about 18 hours ago | |
C | C++ | |
Apache License 2.0 | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Apache AGE
-
Alternatives to Neo4j Enterprise
What about the AGE extension for Postgres? https://age.apache.org/
-
Anyone Using Graph Databases in F#?
Waiting for Postgres to release theirs.
-
In MongoDB you can have duplicate items even if you have unique index
I think they are talking about the AGE extension https://age.apache.org
-
Age 1.0 – PostgreSQL extension for graph database
It's my understanding of the "incubation" period of Apache Software Foundation projects is to determine if they're able to actually execute the ASF process, and a bunch of other "project maturity metrics" (https://community.apache.org/apache-way/apache-project-matur...) of which AGE currently has some self-certification: https://age.apache.org/?l=maturity#
I recognize that's not exactly an answer to the question you asked, but I would be surprised if someone other than a project member knows a more forward-looking one
-
Looking for opinions: 95% of my Data fits extremely well in a Relational Database and 5% fits extremely well into a graph database. Should I consider splitting it between the two, or is that a silly idea?
Postgres has a graph extension: https://age.apache.org. This means you can keep all your data in PG and use both models.
-
Getting Started with Redis and RedisGraph
PostgreSQL with graph extension, developed by a team at Apache Software Foundation as Apache AGE. Apache AGE uses Gremlin.
-
Ask HN: Why are relational DBs are the standard instead of graph-based DBs?
The big thing that graph dbs provide is transitive traversals of join relationships.
The problem with graph dbs is trying to return something that is not a graph. Like a count. Or derived information. And which graph model do you use? There’s more than one. Lots of information is very poorly modeled in graph dbs. Temporal organization, for example.
Ultimately, graphs are a way to use relations. But relations allow you much more flexibility to associate information (subject to the issue of transitive relationship traversal).
Mixed graph-relational is perfectly reasonable. Reasonable start here: [https://age.apache.org/]
their actual landing page is actually better than the Github one. It's a translation layer(s) to allow querying Postgres using openCypher
-
Truth Behind Neo4j’s “Trillion” Relationship Graph
Depending on how one views "postgres", there are at least two extensions that allegedly do it: https://age.apache.org/ and the AgensGraph from which AGE derives
-
One table vs two table design
There's an extension to postgresql (I haven't used it, but I am familiar with node/edge tables in MSSQL) that allows you to do this: https://age.apache.org/
RocksDB
-
How to choose the right type of database
RocksDB: A high-performance embedded database optimized for multi-core CPUs and fast storage like SSDs. Its use of a log-structured merge-tree (LSM tree) makes it suitable for applications requiring high throughput and efficient storage, such as streaming data processing.
-
Fast persistent recoverable log and key-value store
[RocksDB](https://rocksdb.org/) isn’t a distributed storage system, fwiw. It’s an embedded KV engine similar to LevelDB, LMDB, or really sqlite (though that’s full SQL, not just KV)
-
The Hallucinated Rows Incident
To output the top 3 rocks, our engine has to first store all the rocks in some sorted way. To do this, we of course picked RocksDB, an embedded lexicographically sorted key-value store, which acts as the sorting operation's persistent state. In our RocksDB state, the diffs are keyed by the value of weight, and since RocksDB is sorted, our stored diffs are automatically sorted by their weight.
-
In-memory vs. disk-based databases: Why do you need a larger than memory architecture?
The in-memory version of Memgraph uses Delta storage to support multi-version concurrency control (MVCC). However, for larger-than-memory storage, we decided to use the Optimistic Concurrency Control Protocol (OCC) since we assumed conflicts would rarely happen, and we could make use of RocksDB’s transactions without dealing with the custom layer of complexity like in the case of Delta storage.
-
Local file non relational database with filter by value
I was looking at https://github.com/facebook/rocksdb/ but it seems to not allow queries by value, as my last requirmenet.
- Rocksdb over network
-
How RocksDB Works
Tuning RocksDB well is a very very hard challenge, and one that I am happy to not do day to day anymore. RocksDB is very powerful but it comes with other very sharp edges. Compaction is one of those, and all answers are likely workload dependent.
If you are worried about write amplification then leveled compactions are sub-optimal. I would try the universal compaction.
- https://github.com/facebook/rocksdb/wiki/Universal-Compactio...
-
What are the advantages of using Rust to develop KV databases?
It's fairly challenging to write a KV database, and takes several years of development to get the balance right between performance and reliability and avoiding data loss. Maybe read through the documentation for RocksDB https://github.com/facebook/rocksdb/wiki/RocksDB-Overview and watch the video on why it was developed and that may give you an impression of what is involved.
-
We’re the Meilisearch team! To celebrate v1.0 of our open-source search engine, Ask us Anything!
LMDB is much more sain in the sense that it supports real ACID transactions instead of savepoints for RocksDB. The latter is heavy and consumes a lot more memory for a lot less read throughput. However, RocksDB has a much better parallel and concurrent write story, where you can merge entries with merge functions and therefore write from multiple CPUs.
-
Google's OSS-Fuzz expands fuzz-reward program to $30000
https://github.com/facebook/rocksdb/issues?q=is%3Aissue+clic...
Here are some bugs in JeMalloc:
What are some alternatives?
Neo4j - Graphs for Everyone
LevelDB - LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
janusgraph - JanusGraph: an open-source, distributed graph database
LMDB - Read-only mirror of official repo on openldap.org. Issues and pull requests here are ignored. Use OpenLDAP ITS for issues.
RedisGraph - A graph database as a Redis module
SQLite - Unofficial git mirror of SQLite sources (see link for build instructions)
yugabyte-db - YugabyteDB - the cloud native distributed SQL database for mission-critical applications.
sled - the champagne of beta embedded databases
datalevin - A simple, fast and versatile Datalog database
ClickHouse - ClickHouse® is a real-time analytics DBMS
datahike - A durable Datalog implementation adaptable for distribution.
TileDB - The Universal Storage Engine