Hi Guillaume,
On 06.06.19 21:32, Guillaume Lederrey wrote:
Hello all!
There has been a number of concerns raised about the performance and scaling of Wikdata Query Service. We share those concerns and we are doing our best to address them. Here is some info about what is going on:
In an ideal world, WDQS should:
- scale in terms of data size
- scale in terms of number of edits
- have low update latency
- expose a SPARQL endpoint for queries
- allow anyone to run any queries on the public WDQS endpoint
- provide great query performance
- provide a high level of availability
Scaling graph databases is a "known hard problem", and we are reaching a scale where there are no obvious easy solutions to address all the above constraints. At this point, just "throwing hardware at the problem" is not an option anymore. We need to go deeper into the details and potentially make major changes to the current architecture. Some scaling considerations are discussed in [1]. This is going to take time.
I am not sure how to evaluate this correctly. Scaling databases in general is a "known hard problem" and graph databases a sub-field of it, which are optimized for graph-like queries as opposed to column stores or relational databases. If you say that "throwing hardware at the problem" does not help, you are admitting that Blazegraph does not scale for what is needed by Wikidata.
From [1]:
At the moment, each WDQS cluster is a group of independent servers, sharing nothing, with each server independently updated and each server holding a full data set.
Then it is not a "cluster" in the sense of databases. It is more a redundancy architecture like RAID 1. Is this really how BlazeGraph does it? Don't they have a proper cluster solution, where they repartition data across servers? Or is this independent servers a wikimedia staff homebuild?
Some info here:
- We evaluated some stores according to their performance: http://www.semantic-web-journal.net/content/evaluation-metadata-representati... "Evaluation of Metadata Representations in RDF stores"
- Virtuoso has proven quite useful. I don't want to advertise here, but the thing they have going for DBpedia uses ridiculous hardware, i.e. 64GB RAM and it is also the OS version, not the professional with clustering and repartition capability. So we are playing the game since ten years now: Everybody tries other databases, but then most people come back to virtuoso. I have to admit that OpenLink is maintaining the hosting for DBpedia themselves, so they know how to optimise. They normally do large banks as customers with millions of write transactions per hour. In LOD2 they also implemented column store features with MonetDB and repartitioning in clusters.
- I recently heard a presentation from Arango-DB and they had a good cluster concept as well, although I don't know anybody who tried it. The slides seemed to make sense.
All the best,
Sebastian
Reasonably, addressing all of the above constraints is unlikely to ever happen. Some of the constraints are non negotiable: if we can't keep up with Wikidata in term of data size or number of edits, it does not make sense to address query performance. On some constraints, we will probably need to compromise.
For example, the update process is asynchronous. It is by nature expected to lag. In the best case, this lag is measured in minutes, but can climb to hours occasionally. This is a case of prioritizing stability and correctness (ingesting all edits) over update latency. And while we can work to reduce the maximum latency, this will still be an asynchronous process and needs to be considered as such.
We currently have one Blazegraph expert working with us to address a number of performance and stability issues. We are planning to hire an additional engineer to help us support the service in the long term. You can follow our current work in phabricator [2].
If anyone has experience with scaling large graph databases, please reach out to us, we're always happy to share ideas!
Thanks all for your patience!
Guillaume[1] https://wikitech.wikimedia.org/wiki/Wikidata_query_service/ScalingStrategy [2] https://phabricator.wikimedia.org/project/view/1239/