Case Research: Sequoia Capital — Why We Moved from Elasticsearch to Rockset

Sequoia Capital is a enterprise capital agency that invests in a broad vary of client and enterprise start-ups. To maintain up with all the info round potential funding alternatives, they created a set of inner knowledge purposes a number of years in the past to raised assist their funding groups. Extra lately, they transitioned their inner apps from Elasticsearch to Rockset. We spoke with Sequoia’s head of engineering, Jake Quist, and VP of knowledge science, Hem Wadhar, about their causes for doing so.

Inform us in regards to the inner instruments you construct and handle at Sequoia

Sequoia makes use of a mix of inner and exterior knowledge to tell our decision-making course of. Now we have funding professionals and knowledge scientists, and we would like our customers to have the ability to get the info they want for his or her work.

Over time, we’ve constructed a lot of inner apps to floor knowledge to our customers. From a handful of customers early on, we now have half our agency utilizing our apps in some type. Half of our apps require transactional consistency, so that they use Postgres or DynamoDB. The opposite half—about 15 instruments—use Rockset for search and analytics. We had initially constructed them on Elasticsearch however switched to Rockset a yr in the past. We additionally use Retool for the front-end for our apps.

Why did you progress search and analytics from Elasticsearch to Rockset?

There are two major causes we most popular Rockset to Elasticsearch for the analytical apps we have been constructing: the flexibility to make use of SQL and shorter indexing occasions.

Rockset lets us write SQL in opposition to our knowledge. SQL is a greater match for what we’re doing in bringing collectively a number of knowledge units to create a map of the start-up universe during which we function. The power to do relational algebra in Rockset is basically useful.

SQL permits extra individuals to work together with the info. Our engineers and knowledge scientists are way more productive writing queries in SQL. Every part was that a lot tougher when utilizing Elasticsearch DSL. Previous to shifting to Rockset, we averted Elasticsearch DSL syntax if we may, typically performing duties in Spark as a substitute. We’re continually iterating on our queries, and we’re capable of decide correctness extra shortly due to our familiarity with SQL. When issues do break, it’s simpler to examine what broke if we’re utilizing SQL.

We use knowledge from many alternative sources in our evaluation. We recurrently obtain knowledge recordsdata from our distributors that we have to ingest from S3. Elasticsearch and Rockset each index the info to speed up question efficiency, however the indexing time is way shorter with Rockset. This permits us to question the latest model of the info as shortly as attainable, with out compromising on efficiency.

What options did you take into account?

Given the challenges with Elasticsearch, there’s a superb probability we’d have moved off Elasticsearch anyway, even when Rockset weren’t an possibility. Up to now, we’ve thought of utilizing Postgres as a substitute, however we’d have needed to be extra selective in regards to the knowledge we put into Postgres, probably limiting the info units we convey into our apps. Snowflake and Amazon Athena have been different SQL choices, and we do use Snowflake at Sequoia, however Rockset is approach quicker for powering apps.

We’ve additionally experimented with different NoSQL databases, however SQL is simply a lot simpler to make use of. All of the NoSQL options required studying one thing completely different from SQL. In the end, there’s loads of worth in with the ability to question utilizing SQL however not having to specify the schema, and Rockset provides us that means.

What did you obtain by making the change from Elasticsearch to Rockset?

Our staff doesn’t use Elasticsearch anymore. We’ve moved our inner apps over to Rockset for search and analytics.

We received the flexibility to do joins. Elasticsearch doesn’t assist joins, so we have been continually denormalizing our knowledge to get round this. It may well take per week to arrange a Spark job to denormalize every knowledge set, and due to the info we cope with, we’d expertise vital area amplification because of denormalization. Knowledge that may occupy 1 TB in Elasticsearch now takes up 10 GB in Rockset, roughly a 100x distinction from not having to denormalize as a way to be part of knowledge.

We shortened the time it takes to index our knowledge. With Elasticsearch, it might take 4-5 hours to index our largest knowledge set. We’re doing that in 15-Half-hour with Rockset. We’re making knowledge usable extra shortly now, and we now not have to expend effort monitoring longer-running ingestion on Elasticsearch.

We are able to transfer and iterate quicker with Rockset. Our knowledge mannequin is consistently in flux, and we don’t anticipate it can ever get to a gentle state, so it’s essential to have the ability to iterate shortly on our queries and apps. The schema exploration functionality in Rockset is basically useful in understanding the construction of the info we obtain. Constructing and debugging queries utilizing SQL in Rockset is trivial for us. We might typically take 15-Half-hour to assemble the equal queries in Elasticsearch, and it might nonetheless not be 100% sure that we’d appropriately specified the question we meant. Transferring to Rockset permits us to be extra environment friendly because of our familiarity with SQL. Rockset’s Question Lambdas (named, parameterized SQL queries saved in Rockset that may be executed from a devoted REST endpoint) function a useful abstraction layer on which we construct our inner apps.

We now not have to handle and keep a cluster. We beforehand used an Elasticsearch managed cloud service, however it nonetheless wanted loads of effective tuning from our engineers and would possibly go down for a few hours each month. Rockset is a upkeep delight. We don’t have to consider it and may merely concentrate on constructing our apps on high of it.

Total, we’ve improved the underlying knowledge infrastructure for our apps with this transition from Elasticsearch to Rockset. The variety of apps we construct and the info we make use of in our evaluation will proceed to develop, and we’re trying ahead to extra Rockset options and integrations to assist us on the best way.