Apache Spark SQL articles

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Shuffle join in Spark SQL

Shuffle consists on moving data with the same key to the one executor in order to execute some specific processing on it. We could think that it concerns only *ByKey operations but it's not necessarily true. Continue Reading →

Broadcast join in Spark SQL

Joining DataFrames can be a performance-sensitive task. After all, it involves matching data from two data sources and keeping matched results in a single place. As you can deduce, the first thinking goes towards shuffle join operation. However, it's not the single strategy implemented in Spark SQL. For some specific use cases another type called broadcast join can be preferred. Continue Reading →

Loading data from RDBMS

Structured data processing takes more and more place in Apache Spark project. Structured streaming is one of the proofs. But how does Spark SQL work - and particularly, how does it load data from sources of structured data as RDMBS ? Continue Reading →

Schema projection

Even if it's always better to explicit things, in programming we have often the possibility to let the computer to guess. Spark SQL also has this level of intelligence, for example during schema resolving. Continue Reading →


Spark SQL - even if the SQL suffix makes automatically think about RDBMS - works well with other data sources, as even plain CSVs or JSON files. This relation would be difficult to achieve without the concept of schema. Continue Reading →