[elvinyung]

I think at a glance, it's basically a much more scalable version of something like Elasticsearch, optimized for very quick wide fanout to a large number of leaf nodes.

It's a datastore in its own right (just like ES), but I imagine that e.g. you wouldn't use it to handle transactions.

[FractalNerve]

So the upsides of Vespa over Elasticsearch are speeding up the rate at which it scales? Ah, that seems reasonable for a company this size, but is there something in there that's of use for Startups?

This blog post shows how Elasticsearch was used to reindex a 136TB dataset with 36B documents[1], so I'm unsure exactly where except for Google/Yahoo Scale companies Vespa is of use. I would like to understand howto utilize it though without adding an umnanagable complexity.

EDIT: Maybe a Vespa Cloud startup, that abstracts the management and makes "Scalability as a Service" by utilizing other Cloud providers.

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[1] https://thoughts.t37.net/how-we-reindexed-36-billions-docume...

[2] http://docs.vespa.ai/documentation/vespa-quick-start.html

[toast0]

In my experience running machines with Vespa (ended in 2011) and elastic search (which ended earlier this year), Vespa was a lot more stable, even though my elastic search had many times more hardware and fewer documents. At least once a month, elastic search would take a several minute break to do who knows what, even though there was not even any indexing or anything other than searching going on. In case it matters, I was running elastic as a single node cluster (actually several single node cluster), my production Vespa was multinode, but I think we had a single node (or fewer node anyway) cluster for dev/testing.

Anyway, I'm happy that we have more options in this space now.