seqOp

SeqOp, short for sequential operation, is a functional parameter used in distributed data processing, most commonly in Apache Spark’s aggregation APIs such as aggregateByKey and aggregate. It defines how to update an accumulator with each input value while processing data within a partition. The operation is paired with a zeroValue, the initial accumulator, and with a separate combOp, which merges two accumulators from different partitions.

In Spark, seqOp has the signature (U, V) => U, where U is the type of the accumulator and V is the type of the input elements. During an aggregation, Spark traverses each partition and applies seqOp to update the local accumulator with incoming values, before the partial results are sent across the network to be combined by combOp. This design allows efficient, partially parallel computation.

A common use is to compute per-key sums with aggregateByKey. For a key-value RDD, one might supply zeroValue = 0, seqOp = (sum, x) => sum + x, and combOp = (a, b) => a + b. The seqOp updates the running total within each partition, while the combOp merges these partial totals into the final result. Other examples include counting elements, averaging, or constructing more complex aggregates, provided the combining function is compatible with the chosen accumulator type.

Good practice recommends using associative and commutative operations for seqOp and combOp to ensure deterministic results regardless of partitioning or data order. Side effects inside seqOp should be avoided. SeqOp is a core concept in Spark’s distributed reduction framework, enabling scalable aggregation over large datasets.