MImistrial9·Aug 3, 2018
https://github.com/thinkaurelius/titan/issues/1360
clarification welcome
In Show HN: HugeGraph – An open source fast and highly scalable graph databasethinkaurelius
titan
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thinkaurelius / titan
titan
Distributed Graph Database
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Observed mentions: 2012-08-07 01:23:23 UTC to 2018-08-03 17:40:33 UTC.
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| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2012 | 2012-01-01 00:00:00 UTC | 2013-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2013 | 2013-01-01 00:00:00 UTC | 2014-01-01 00:00:00 UTC | 13 | 3 | 10 |
| 2014 | 2014-01-01 00:00:00 UTC | 2015-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2015 | 2015-01-01 00:00:00 UTC | 2016-01-01 00:00:00 UTC | 3 | 0 | 3 |
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 4 | 2 | 2 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 1 | 0 | 1 |
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23 sourcesTitan Graph DB Downloads Redirecting to DataStax
1 points · Open discussionThere's quite a lot of activity on http://github.com/thinkaurelius/titan for a dead project. Admittedly, the issues are piling up, but the code seems to be actively worked on.
In DGraph – Scalable, Distributed, Low-Latency, High-Throughput Graph DatabaseTitan 0.4 [1] and Neo4j 1.9.4 [2] were the versions released in the Fall of 2013 so maybe that's when they did the work.
[1] https://github.com/thinkaurelius/titan/releases/tag/0.4.0
[2] https://github.com/neo4j/neo4j/releases/tag/1.9.4
In SQLGraph: An Efficient Relational-Based Property Graph Store (2015) [pdf]For those who, like me, were looking at the master branch and assumed the codebase was inactive, this is apparently the branch where development is happening:
https://github.com/thinkaurelius/titan/tree/titan09
This is version 0.9, and they are gearing up for a 1.0 release.
Anyone here using it who can summarize their experience? Production-readiness, performance, general usability as an alternative to other NoSQL databases?
In Titan: Distributed Graph DatabaseFrom what I understand, they weren't really pushing to become an Apache project (Tinkerpop did, though). That said, I thought activity was pretty much dead also until I took a look at the 0.9 branch: https://github.com/thinkaurelius/titan/tree/titan09
In Titan Graph Database Integration with DynamoDBThanks, our backend is based on node.js so we're considering to support the Rexster server (https://github.com/thinkaurelius/titan/wiki/Rexster-Graph-Se...), which provides a REST API to Gremlin. What do you think about it?
We're also eager to see any binary protocole to communicate with graph databases.
In Show HN: Linkurious, the first data visualization platform for graph databasesFor general graph DB tutorials, see...
* TinkerPop Book "Resources" section: http://www.tinkerpopbook.com/
* Marko's blog: http://markorodriguez.com/ -- start with the "On Graph Computing" post (http://markorodriguez.com/2013/01/09/on-graph-computing/).
* Aurelius Blog: http://thinkaurelius.com/blog/
For Python, see the Bulbs Docs: http://bulbflow.com/docs/
I've been meaning to update the Bulbs docs for Bulbs/Titan. It's essentially the same as Bulbs/Neo4jServer and Bulbs/Rexster, except Titan does indexing a bit different.
Here's a few pointers...
* Boutique Graph Data with Titan: http://thinkaurelius.com/2013/11/24/boutique-graph-data-with...
* Titan Overview: https://github.com/thinkaurelius/titan/wiki
* Download: https://github.com/thinkaurelius/titan/wiki/Downloads
* Titan Server: https://github.com/thinkaurelius/titan/wiki/Rexster-G
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raph-Se...
* Bulbs Titan Example: https://gist.github.com/espeed/3938820
Could anyone explain to me what it means "native" versus "non-native" graph processing in that slide show?
One of the leading native graph processing engines is GraphLab (http://graphlab.org/); however, the creator of GraphLab, Dr. Joey Gonzalez, is now focused on GraphX, which is essentially GraphLab built on Spark (http://spark.incubator.apache.org), which is a non-native analytics platform.
Building a graph-processing engine on a general processing system like Spark makes pre-processing and post-processing much easier.
See "Introduction to GraphX - Presented by Joseph Gonzalez, Reynold Xin - UC Berkeley AmpLab 2013" (http://www.youtube.com/watch?v=mKEn9C5bRck)
Also, a bunch of advancements in graph processing are coming down the pipe, which will be released in a few months (see https://news.ycombinator.com/item?id=6786563).
Ditto for "
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native" versus "non-native" graph storage.
See this post by Dr. Matthias Broecheler, the creator of Titan (https://github.com/thinkaurelius/titan/wiki)...
"A Letter Regarding Native Graph Databases" (http://thinkaurelius.com/2013/11/01/a-letter-regarding-nativ...)
Their HBase implementation is very fast, but it involves using their whole Hadoop distribution...3rd party support isn't there. If you want to use anything you find on GitHub, get ready to add support for MapRFS, and revert the API version back to 0.21
The MapR team is sharp and responsive. It's founded by ex-Googlers from the search-infrastructure team (http://www.wired.com/wiredenterprise/2011/12/ex-google-man/).
M7 Tables are designed to be a drop in for HBase, with better performance and without the HBase complexity (it's much easier to run M7 Tables as the primary datastore for online apps).
M7 Tables support and certification for several key projects is coming along...
Spark 0.7 already works with MapR M7 Tables with the Hive images that were released with the Eco-1310 (http://doc.mapr.com/display/components/Hive+Release+Notes, http
Read the rest
://answers.mapr.com/questions/7803/shark-spark-over-mfs).
MapR is in the process of certifying Titan (https://github.com/thinkaurelius/titan/wiki), and it's going is going through final QA right now (https://groups.google.com/d/msg/aureliusgraphs/RTeFVssIvoI/m...).
It's cool to see graph DBs like Titan and Neo4j moving up the ranks.
There are some major advancements coming down the pipe in the world of open-source graph computing that will make working with big graph data accessible to anyone, not just the Googles, Facebooks, and Twitters of the world.
Here are some of the things coming down the pipe:
Titan 0.4 was just released https://github.com/thinkaurelius/titan), and the number of backend datastores Titan supports is growing. Datagrid support was just added for Hazelcast, and it can serve as the reference implementation if anyone wants to add support for Infinispan, Galaxy (http://puniverse.github.io/galaxy/), or one of the other datagrids.
MapR is in the final stages of certifying Titan on M7 Tables (https://groups.google.com/d/msg/aureliusgraphs/RTeFVssIvoI/m...), which will allow you to run
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Titan on HBase without all the HBase complexity. AWS (http://aws.amazon.com/elasticmapreduce/mapr/) and GCE (http://www.mapr.com/products/google-cloud-platform) already have direct support for M7 so it's easy to spin up a cluster.
TinkerPop3 is scheduled for release within the next six months (https://github.com/tinkerpop/tinkerpop3/wiki), and it will blur the lines between graph databases and graph processing engines.
Marko just released the first version of TinkerPop3's OLAPGraph -- this is Blueprints for graph-processing engines, which means that in addition to OLTP graph databases like Titan and Neo4j, TinkerPop3 will support OLAP engines like Giraph, HAMA, Faunus, GraphLab, and the new GraphX engine in Spark (http://amplab.github.io/graphx/, http://www.youtube.com/watch?v=mKEn9C5bRck&list=PLbDk7g7PotW...).
You'll be able to run Gremlin over any Blueprints-enabled graph-database or graph-processing engine, and Gremlin will be able to jump between the database and processing engine, depending on if it's a local or global graph algorithm (http://markorodriguez.com/2011/04/19/local-and-distributed-t...).
And Marko's recent breakthrough on swarm computing over derived graphs (https://groups.google.com/d/msg/gremlin-users/1KObZ8F2d00/CJ...) means you'll be able to run traditional graph algos over property graphs. This will pave the way for the community to construct a massive library of graph algorithms in Gremlin (https://github.com/tinkerpop/furnace/wiki).
All of this is coming together now. 2014 will see a major leap in open-source graph computing.
Yeah, FlockDB (https://github.com/twitter/flockdb) comes to mind. I think Titan (https://github.com/thinkaurelius/titan) should / will be able to handle this too.
In Why You Should Never Use MongoDBTitan (https://github.com/thinkaurelius/titan/wiki) persists its graph as records in a distributed Cassandra or HBase cluster.
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