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Comment by lobster_johnson | Hacker News Reader

So is Titan considered alive and actively maintained now?

Back when DataStax acquired Aurelius, they announced [1] that they would stop developing Titan, and for a while it looked like it was completely dead. It seems DataStax are maintaining it somewhat, but there have been only 74 commits this year, all/almost of it from DataStax, and there's not a lot of meat on those commits. Plenty of open issues in the Github tracker.

For example, I noticed that Wikidata was considering Titan but dropped it [2] after the Aurelius announcement, and ended up with a fairly obscure database called BlazeGraph instead.

[1] https://groups.google.com/forum/#!topic/aureliusgraphs/c07WE...

[2] https://lists.wikimedia.org/pipermail/wikidata-tech/2015-Mar...

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> .. stop developing Titan ..

From your [1] we find the follow-up [1a] which clearly says work will not stop.

[1a]: https://groups.google.com/forum/#!topic/aureliusgraphs/WTNYY...

Thanks, didn't see that one. That said, it seems work has slowed down to a trickle, and I'm not seeing much activity on the Apache end.

From 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

I think the wikidata was the right decision :) SPARQL really is lovely when dealing with public databases over http. But then I am biased by the success of sparql.uniprot.org :)

On the other hand the usecase of wikidata and uniprot are quite different from most graph database deployments who have only internal or controlled access via APIs.

Still UniProt is a graph with 3 billion nodes and 15 billion edges so not tiny but not humongous either. Wikidata is a bit smaller if I recall.

Titan seems to have a different use case, much more orientated to graph traversal than analytics on graph modelled data. So I can understand that many systems need something like titan.

For my part I'm evaluating graph databases for content storage, as backing store for webapps, as an alternative to relational databases. Number of nodes less than a million, so very small datasets by most standards, and I do need fast ad-hoc queries (what you call graph traversal, not analytics), sharding and transaction support.

Other people may convince you otherwise, but I believe there are no mature graph databases.

You would use them if you want to experience the bleeding edge of databases and exploring uncharted territory excites you, and not if you want to get webapps built.

I'm curious, what is lacking from Neo4j to qualify it as a mature graph database?

We've worked on it for over a decade, it's used in production by thousands of community users, hundreds of customers and 75+ Global 2000 companies (see http://neo4j.com/customers). For many of those Neo4j is used in business critical use cases, i.e. they require Neo4j to be up and running every minute of every day or it'll show up in their next earnings call. If you've shopped online or in a US retail store this week for example, it's very likely that you've used Neo4j. There's rich support for pretty much any programming language and framework out there, an ecosystem of consulting partners whose sole business it is to do Neo4j implementations, 10+ books written specifically about Neo4j, rich online training, formal enterprise support backed by a global commercial organization, an active community. What's missing?

I'm not trying to be facetious -- I'm genuinely curious as to what you feel is missing to consider it mature.

The last time I tried Neo4j was in 2010 or 2011, when I was trying to build ConceptNet 5 (http://conceptnet5.media.mit.edu) on it.

It had showstopping security problems when bound to anything but 127.0.0.1, so I came up with a software firewall to put around it and hoped for the best. It promised Lucene search but its implementation was full of Lucene injections, unless I escaped every special character I could think of like a freaking PHP programmer. There was no way to get data in faster than a slow trickle, unless that data was somehow already in another Neo4j database. Doing any interesting graph operations led to interesting messages about running out of "PermGen". And before I could even get all the data in, it had consumed enough resources to blow my academic AWS budget for months.

I was on the mailing list looking for support, and found it pretty lacking. The best I ever got was a bunch of Java code to try (my code is in Python).

I use SQLite now. It doesn't do very much, but it does what it's supposed to, and that's great.

If Neo4J has improved significantly since then, forgive me that I'm not rushing back to try it again.

That sucks. :( Sorry about that. Neo4j isn't perfect today and it certainly wasn't perfect 4-5 years ago. We're working hard on it tho!

And thanks for being specific (amazed that you remember specific issues from five years ago!). I don't remember the 127.0.0.1 security problems, but I don't hear anything about them so my guess is they've been addressed. We have a lot of finance and government customers that have high requirements on security. As for your Lucene issues, we did a complete overhaul of our search and indexing story in Neo4j 2.0 (released late 2013). We've continuously improved import performance (which has traditionally been a weak spot) and Neo4j 2.2 includes a batch importer which injects >1M records / sec sustained pace at scale (10s of billions of records) on commodity hardware. As for the memory management issues, we like many other data products written in Java struggled with GC for a long time, and like many others we ultimately concluded that we had to move a lot of the critical parts off heap / manage the memory ourselves, which significantly improved memory utilization.

I understand that you got stung historically and therefore hesitate to check us out again. And if SQLite is working well for you, there's no need to! But Neo4j and the graph space has matured a LOT since 2010 and fortunately I don't think your "bleeding edge" experience from 4-5 years ago will be replicated anymore for someone coming new into the space.

Thanks for the feedback.

4-5 was a very long time ago in graph db time :) neo4j and its competitors have changed the lot!!

While neo4j has it's proponents. The lack of standards support means that as a data provider it's hard to support.

Check out http://tinkerpop.com. Apache TinkerPop 3.0.0 was released in June 2015 and it is a quantum leap forward. Not only is it now apart of the Apache Software Foundation, but the Gremlin3 query language has advanced significantly since Gremlin2. The language is much cleaner, provides declarative graph pattern matching constructs, and it supports both OLTP graph databases (e.g. Titan, Neo4j, OrientDB) and OLAP graph processors (e.g. Spark, Giraph). With most every graph vendor providing TinkerPop-connectivity, this should make it easier for developers as they don't have to learn a new query language for each graph system and developers are less prone to experience vendor lock-in as their code (like JDBC/SQL) can just move to another underlying graph system.

Its more about data interchange support, i.e. we could support GraphML instead/next to the RDF varieties. But this would be difficult for us to generate in a streaming way.

Then for our end users, we would need to hack in a namespace convention to avoid issues when integrating our data.

Then TinkerPop misses the SERVICE concept for federated querying in SPARQL1.1, which is essential for our endusers who do knowledge discovery (i.e. small biology labs without the inhouse capability of running their own large databases).

You use SQlite instead of Neo4J? May I ask what's your use case? I don't see these overlapping in any scenario...

Why do you need sharding on a dataset so small?

I should perhaps have said replicas (ie., redundant shards).

what store sparql.uniprot.org uses? Virtuoso?

Yes and it has gotten much better in recent releases. On the other hand switching to an other sparql impl takes about 2 weeks of work in evaluation and deployment. We can currently switch to GraphDB or oracle sem net without further effort. Blazegraph stardog are easy to switch too. Others would require a bit more work.

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