But an engine does not actually have to scan the same data multiple times - it can combine multiple logical scans into a single physical one. This is not a mistake - self-joins are a common example.

Many queries refer to the same table multiple times. Even though our version running inside Azure Synapse today is a derivative of Apache Sparkв„ў 2.4.4, we compared it with the latest open-source release of Apache Sparkв„ў 3.0.1 and saw Azure Synapse was 2x faster in total runtime for the Test-DS comparison.ДЄlso, we observed up to 18x query performance improvement on Azure Synapse compared to the open-source Apache Sparkв„ў 3.0.1.Д«elow are some of the techniques and optimizations we implemented to achieve these results. To compare the performance, we derived queries from TPC-DS with 1TB scale and ran them on 8 nodes Azure E8V3 cluster (15 executors вЂ“ 28g memory, 4 cores). Indexing: Optimizing data representation in the data lake for the workload yields significant improvement.Intelligent caching: Using local SSDs, native code, and hardware-assisted parsing.Cluster optimizations: Picking the right VM types, rapid provisioning of VMs.Autoscaling: Automatically scaling clusters up and down with load, even as a job is running.

Query optimization: New query execution elements and better plan selection yielded major gains.

As Azure Synapse brings the worlds of data warehousing, big data, and data integration into a single unified analytics platform, we continue to invest in improving performance for customers that choose Azure Synapse for multiple types of analytical workloads-including big data workloads. Recently, Azure Synapse Analytics has made significant investments in the overall performance for Apache Spark workloads.

How to install spark pay on the surface