A look at Cayley by Tony.
From the post:
Recently I took the time to check out Cayley, a graph database written in Go that’s been getting some good attention.
A great introduction to Cayley. Tony has some comparisons to Neo4j, but for beginners with graph databases, those comparisons may not be real useful. Come back for those comparisons once you have moved beyond example graphs.
Quick Play with Cayley Graph DB and Ordnance Survey Linked Data by John Goodwin.
From the post:
Earlier this month Google announced the release of the open source graph database/triplestore Cayley. This weekend I thought I would have a quick look at it, and try some simple queries using the Ordnance Survey Linked Data.
Just unpack to install.
Loading data is almost that easy, except that the examples are limited to n-triple format and the documentation doesn’t address importing other data types.
Has a Gremlin-“inspired” query language, which makes me wonder what shortcoming in Gremlin is addressed by this new query language?
If there is, that isn’t apparent from the documentation which is rather sparse at the moment.
It will be interesting to see if Cayley goes beyond the capabilities of the average graph db or not.
Cayley – An open-source graph database
From the webpage:
Cayley is an open-source graph inspired by the graph database behind Freebase and Google’s Knowledge Graph.
Its goal is to be a part of the developer’s toolbox where Linked Data and graph-shaped data (semantic webs, social networks, etc) in general are concerned.
Features
- Written in Go
- Easy to get running (3 or 4 commands, below)
- RESTful API
- or a REPL if you prefer
- Built-in query editor and visualizer
- Multiple query languages:
- Plays well with multiple backend stores:
- Modular design; easy to extend with new languages and backends
- Good test coverage
- Speed, where possible.
Rough performance testing shows that, on consumer hardware and an average disk, 134m triples in LevelDB is no problem and a multi-hop intersection query — films starring X and Y — takes ~150ms.
If you are seriously thinking about a graph database, see also these comments. Not everything you need to know but useful comments none the less.
I first saw this in a tweet from Hacker News.
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