May 2022 brings QuestDB 6.3, a release focused on performance and usability based on the vital feedback of our user community and customers. Here's a roundup of changes that have just landed in the latest, greatest version yet.
This blog post is the outcome of collaboration between Isha Terdal from Cube, Andrey Pechkurov from QuestDB and Yitaek Hwang, a QuestDB contributor.
Time series data has now become a critical part of the data applications landscape. In this blog, we'll take a look at how QuestDB and Cube work together to provide a time series data pipeline that is fast, consistent, and reliable.
At QuestDB we are all about performance. To showcase querying capabilities of the database we have been running a live demo of historical taxi rides in NYC with 1.6 billion rows [1] and a geospatial dataset that contains the locations of 250k unique ships [2] moving over time. You can analyze this dataset with SQL on our live instance and see how fast each query is processed. Today, we introduce a new dataset on the same demo instance: crypto market data ingested in real-time from the Coinbase Exchange. For ingestion, we use a convenient Python library Cryptofeed, a cryptocurrency exchange feed handler that supports QuestDB. And for visualization, we use Grafana to create interactive live charts, which refresh every 5 seconds.
There is a lot of Ukraine in QuestDB: Our co-founder Vlad and two of our engineers, Alex and Eugene, are both Ukrainians. This is our response and our call on all our users to join us in helping the people of Ukraine.
We've just published 6.2 and it includes a lot of changes, such as SQL JIT compiler, JDK 17 support, SQL and ILP improvements, settings to improve the memory footprint when used with Grafana, autocomplete in the Web Console, improved ILP stability, and more. Here's a roundup of changes that have just landed in the latest and greatest version!
QuestDB 6.2.0 brings a brand new JIT (Just-in-Time) compiler as a part of the SQL engine. The compiler aims to significantly improve execution times for queries with simple arithmetic expressions used to filter the data. It took us 11K lines of code, 250+ commits, and plenty of coffee to ship it, and we'd like to share the story with you.
Since founding QuestDB, we've raised over $15 million in venture capital to fund the development of the fastest open source time series database. This blog post describes the two-year journey to raise our Series A, what we learned along the way and the pitch deck we used for the raise.
We've just published 6.1.3 and it includes Prometheus Alertmanager support, new counters in the Prometheus endpoint for memory info, automatic query timeout, Monaco as the new SQL editor for QuestDB's web interface, and more UI additions. Here's a roundup of changes that have just landed in the latest and greatest version!
This post was written by Miguel Arregui, who describes how he developed a passion for computing early on, his experience in research at CERN and the ESA, and eventually working at QuestDB. Miguel works as a software engineer in the core database team, improving upon the internals of the fastest open source time series database.
Inter-thread messaging is a fundamental part of any asynchronous system. It is the component responsible for the transportation of data between threads. Messaging forms the infrastructure, scaffolding multi-threaded applications, and just like real-world transport infrastructure, we want it to be inexpensive, fast, reliable, and clean. For QuestDB, we wrote our own messaging system, and this post is about how it works and how fast it is.