SingleStore demonstrates high throughput performance for ad-hoc analytical queries, processing 1,280,625,752,550 rows per second on a cluster of 448 Intel Skylake cores. The query's success is attributed to careful code design, exploiting distributed execution, instruction-level and data-level parallelism. To achieve this performance, SingleStore uses pipelining and SIMD (Single Instruction, Multiple Data) techniques. Pipelining allows the processor to execute multiple instructions in parallel, while SIMD enables the processing of multiple rows simultaneously. The query's success also depends on the efficient organization of data, which is stored in a columnar format with a dictionary-based indexing system. This approach enables grouping by small integers and reduces write-write conflicts. Overall, SingleStore's performance is made possible by leveraging parallelism through distributed execution, pipelining, and SIMD, allowing for a significant increase in computing power to respond to queries within a short time frame.