For applications where the Reduce stage is not a bottleneck, which is the case for SparkBLAST, it is demonstrated in the literature that Spark is much faster than Hadoop. In , those authors state that, for this class of application, MapReduce Hadoop is much slower than Spark in task initialization and is less efficient in memory management. Indeed, the supplementary document “Execution Measurements of SparkBLAST and CloudBLAST”, available in the online version of this paper, presents several measurements performed during SparkBLAST and CloudBLAST executions on the Microsoft Azure Cloud. These measurements show that task initialization in SparkBLAST is considerably faster than CloudBLAST. It is also shown that SparkBLAST is more efficient in memory management than CloudBLAST. The effect of SparkBLAST’s more efficient memory management can be observed in Additional file 1: Figures S5 and S6 of the supplementary information document. These figures show that Hadoop needs to use more memory than Spark, while Spark can maintain a larger cache and less swap to execute. Both factors - task initialization and memory management - are determinant for the improved scalability of SparkBLAST.