Big Data LDN (London) is UK’s leading data and analytics conference and exhibition. This year, Javier Ramirez, Developer Advocate at QuestDB, delivered a talk on "Ingesting A Million Time Series Per Second On A Single Instance".
Big Data LDN is the largest event focusing on data in the UK, with two days of talks in parallel tracks, plus dozens of vendors. This was a great opportunity to learn about the latest trends and engage with data-minded folks. We asked Javier to tell us more about the experience.
A good mix of data engineers, data analysts, decision makers, data vendors, and some students. There were some well-known developer tools and databases, such as Confluent (Kafka), OLAP database ClickHouse, InfluxData (the parent company of InfluxDB), MongoDB, and Fivetran, to name a few.
We actually met a few QuestDB users, which was an absolute pleasure. We also met a lot of companies with interesting near real time challenges looking for solutions, and we found some time to talk to other vendors, explore collaborations, or simply have a friendly talk about QuestDB's data analytics capacities. Users were from many different backgrounds: companies managing wireless networks producing thousands of events per millisecond, hedge funds overseeing a wide variety of assets, and cryptocurrency exchanges. We even had a great time speaking with a F1 team looking for a time series database. We hope to see them all soon in our community slack channel!
Mostly about fast and big data, but also quite a bit about the internals of
QuestDB and what makes us stand out from the rest. In particular, our latest
developments on optimizing imports using
io_uring, or the fact that we use
JAVA with near zero Garbage Collection were popular topics. But we mostly talked
about real use cases and constraints, and how QuestDB could help.
- Is QuestDB built from scratch or on top of some other engine?
- How are you going to monetize an open source database?
- Are you really a team with less than 30 people?
The title of my presentation was "Ingesting A Million Time Series Per Second On A Single Instance". The title was really a click-bait, as the interesting part was not that we could ingest over a million events per second, but that we could actually run fast queries while sustaining heavy writes. The talk was first about how to identify if there was a time series problem, followed by why and how we built QuestDB to make it the fastest open source time series database out there.
We received some questions about horizontal scalability (coming soon), and about how much work it was to move from thinking in a relational database model to a time series one, which in QuestDB's case should be an easy switch thanks to our SQL layer.
Not really. The cool thing when you were presenting about open source, was that you were not perceived as much as someone trying to sell, but rather as someone trying to help. We had a live demo, and we were very confident it wouldn't break as it was basically running some queries using our demo site.