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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

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 9y ago
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. -- [1] https://thoughts.t37.net/how-we-reindexed-36-billions-documents-in-5-days-within-the-same-elasticsearch-cluster-cd9c054d1db8 https://thoughts.t37.net/how-we-reindexed-36-billions-docume... [2] http://docs.vespa.ai/documentation/vespa-quick-start.html http://docs.vespa.ai/documentation/vespa-quick-start.html
- toast0 9y ago
  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.

FractalNerve 9y ago

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. -- [1] https://thoughts.t37.net/how-we-reindexed-36-billions-documents-in-5-days-within-the-same-elasticsearch-cluster-cd9c054d1db8 https://thoughts.t37.net/how-we-reindexed-36-billions-docume... [2] http://docs.vespa.ai/documentation/vespa-quick-start.html http://docs.vespa.ai/documentation/vespa-quick-start.html

toast0 9y ago
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.