Over the years, I’ve been using MacOS at work and Ubuntu at home for my development tasks. However, my Lenovo P1 Gen 3 laptop didn’t work well with Linux, leading to frequent issues with the camera and graphics (screen flickering, I’m looking at you, and it hurts). I’ve triend Windows Subsystem for Linux (WSL) but it was quite bad to be honest. But as I’ve heard of WSL2 and WSLg, I decided to give it another shot. Read more

Exploring databricks SQL usage At my company, we adopted databricks SQL for most of our users. Some users have developed applications that use the JDBC connector, some users have built their dashboards, and some users write plain ad-hoc queries. We wanted to know what they queried, so we tried to use Unity Catalog’s insights, but it wasn’t enough for our case. We work with IOT and we are interested in what filters they apply within our tables. Read more

Well, I’m facing a huge interesting case. I’m working at Wallbox where we need to deal with billions of rows every day. Now we need to use Spark for some Kafka filtering and publish the results into different topics according to some rules. I won’t dig deep into the logic except for performance-related stuff, let’s try to increase the processing speed. When reading from Kafka you usually get 1 task per partition, so if you have 6 partitions and 48 cores you are not using 87. Read more

Long time without writing! On highlights: I left my job at Schwarz It in December last year, and now I’m a full-time employee at Wallbox! I’m really happy with my new job, and I’ve experienced interesting stuff. This one was just one of these strange cases where you start doubting the compiler. Context One of my main tables represents sensor measures from our chargers with millisecond precision. The numbers are quite high, we are talking over 2 billion rows per day. Read more

Últimamente me he centrado en mejorar mis habilidades con Spark y he aprovechado para hacer algunos trainings de databricks. (Que por cierto ha sacado Beacons, un programa de reconocimiento para sus colaboradores y ha mencionado algunos nombres muy grandes por ahí). Y en estos cursos está optimizing spark, que simplifica y explica de una forma bastante sencilla los problemas de rendimientos que ocurren en el mundo de big data. A estos problemas se les denomina las 5s: Read more

Continuando con la lista de optimizaciones en spark tneemos el spill. Hacer spill no es más que persistir un rdd en disco, ya que, sus datos no caben en memoria. Existen varias causas, la más sencilla de pensar es hacer un explode un array donde nuestras columnas crecen de forma exponencial. Cuando el spill ocurre se puede identificar por dos valores que siempre van de la mano: Spill (Memory) Spill (Disk) (Estas columnas solo aparecen en la spark ui si hay spill). Read more

I was a bit skeptical about photon since I realized that it cost about double the amount of DBU, required specifically optimized machines and did not support UDFs (it was my main target). From the Databricks Official Docs: Limitations Does not support Spark Structured Streaming. Does not support UDFs. Does not support RDD APIs. Not expected to improve short-running queries (<2 seconds), for example, queries against small amounts of data. Photon runtime Read more

For the last few months, I’ve been into ETL optimization. Most of the changes were as dramatic as moving tables from ORC to delta revamping the partition strategy to some as simple as upgrading the runtime version to 10.4 so the ETL starts using low-shuffle merge. But at my job, we have a lot of jobs. Each ETL can be easily launched at *30 with different parameters so I wanted to dig into the most effective strategy for it. Read more