People tend to severely underestimate how fast modern machines are and overestimate how much you need to spend on hardware. Back in my last startup, I was doing a crypto market intelligence website that subscribed to full trade & order book feeds from the top 10 exchanges. It handled about 3K incoming messages/second (~260M per day), including all of the message parsing, order book update, processing, streaming to we…
What Andy giveth, Bill taketh away.[0] I'm more than a little annoyed that so much data engineering is still done in Scala Spark or PySpark. Both suffer from pretty high memory overhead, which leads to suboptimal resource utilization. I've worked with a few different systems that compile their queries into C/C++ (which is transparent to the developer). Those tend to be significantly faster or can use fewer nodes to p…
Furthermore, pyspark is by far the most popular and used spark, and it’s also got the absolute world-worst atrocious mechanical sympathy. Why?
Developer velocity trumps compute velocity any day?
(I want the niceness of python and the performance of eg firebolt. Why must I pick?)
(There is a general thing to get spark “off heap” and use generic query compute on the spark sql space, but it is miles behind those who start off there)