Hello all!
First of all, my apologies for the long silence. We need to do better in terms of communication. I'll try my best to send a monthly update from now on. Keep me honest, remind me if I fail.
First, we had a security incident at the end of December, which forced us to move from our Kafka based update stream back to the RecentChanges poller. The details are still private, but you will be able to get the full story soon on phabricator [1]. The RecentChange poller is less efficient and this is leading to high update lag again (just when we thought we had things slightly under control). We tried to mitigate this by improving the parallelism in the updater [2], which helped a bit, but not as much as we need.
Another attempt to get update lag under control is to apply back pressure on edits, by adding the WDQS update lag to the Wikdiata maxlag [6]. This is obviously less than ideal (at least as long as WDQS updates are lagging as often as they are), but does allow the service to recover from time to time. We probably need to iterate on this, provide better granularity, differentiate better between operations that have an impact on update lag and those which don't.
On the slightly better news side, we now have a much better understanding of the update process and of its shortcomings. The current process does a full diff between each updated entity and what we have in blazegraph. Even if a single triple needs to change, we still read tons of data from Blazegraph. While this approach is simple and robust, it is obviously not efficient. We need to rewrite the updater to take a more event streaming / reactive approach, and only work on the actual changes. This is a big chunk of work, almost a complete rewrite of the updater, and we need a new solution to stream changes with guaranteed ordering (something that our kafka queues don't offer). This is where we are focusing our energy at the moment, this looks like the best option to improve the situation in the medium term. This change will probably have some functional impacts [3].
Some misc things:
We have done some work to get better metrics and better understanding of what's going on. From collecting more metrics during the update [4] to loading RDF dumps into Hadoop for further analysis [5] and better logging of SPARQL requests. We are not focusing on this analysis until we are in a more stable situation regarding update lag.
We have a new team member working on WDQS. He is still ramping up, but we should have a bit more capacity from now on.
Some longer term thoughts:
Keeping all of Wikidata in a single graph is most probably not going to work long term. We have not found examples of public SPARQL endpoints with > 10 B triples and there is probably a good reason for that. We will probably need to split the graphs at some point. We don't know how yet (that's why we loaded the dumps into Hadoop, that might give us some more insight). We might expose a subgraph with only truthy statements. Or have language specific graphs, with only language specific labels. Or something completely different.
Keeping WDQS / Wikidata as open as they are at the moment might not be possible in the long term. We need to think if / how we want to implement some form of authentication and quotas. Potentially increasing quotas for some use cases, but keeping them strict for others. Again, we don't know how this will look like, but we're thinking about it.
What you can do to help:
Again, we're not sure. Of course, reducing the load (both in terms of edits on Wikidata and of reads on WDQS) will help. But not using those services makes them useless.
We suspect that some use cases are more expensive than others (a single property change to a large entity will require a comparatively insane amount of work to update it on the WDQS side). We'd like to have real data on the cost of various operations, but we only have guesses at this point.
If you've read this far, thanks a lot for your engagement!
Have fun!
Guillaume