Technical analyses of system internals, runtime behavior, and implementation details. These articles examine how things actually work in practice, especially under scale and operational pressure.
How Spark’s internal rewriting framework, Catalyst, exposes an embedded DSL with a public extension surface, the same one Delta Lake and Iceberg use to plug into the optimizer pipeline.
Partition pruning and data skipping are invisible by default. delta-explain reads the Delta log directly and shows, step by step, how a WHERE predicate narrows down candidate files, with no engine required.
Six concrete anti-patterns I encountered building a real Catalyst extension: from the wrong rule type for throws, to mutable state under AQE and Spark Connect, to JVM bootstrap traps in PySpark.
Why calling Spark ’lazy’ is technically reductive, and how thinking of it as a dataflow compiler changes the way you design pipelines.
In large-scale Spark pipelines, skew can occur when a single key carries a disproportionately large nested payload. Asymmetric salting offers a targeted solution: explode, salt, join in parallel, and optionally re-aggregate.
Parsing arithmetic expressions looks simple… until precedence enters the picture. Two classic algorithms — Dijkstra’s Shunting Yard and Pratt’s Top-Down Operator Precedence — provide radically different answers that reveal the same underlying intuition.