, it builds a batch consisting of singleton insert statements, and then executes the batch via the prep/exec model. One can further example the Spark JDBC connector Since the load was taking longer than expected, we examined theĭMV while load was running, and saw that there was a fair amount of latch contention on various pages, which wouldn’t not be expected if data was being loaded via a bulk API.Įxamining the statements being executed, we saw that the JDBC driver usesįor each inserted row therefore, the operation is not a bulk insert. option("password", "M圜omplexPassword!001") option("url", "jdbc:sqlserver://.com databaseName=TestDB") Changing the batch size to 50,000 did not produce a material difference in performance.ĭ("overwrite").format("jdbc")
Here is a snippet of the code to write out the Data Frame when using the Spark JDBC connector. Performance in SQL on windows v/s SQL on Linux is comparable and for brevity we only depict results on SQL Server on Linux. In this blog, we will describe several experiments that demonstrate the major performance improvement provided by the SQL Spark connector.Īzure Blob Storage containing 50 parquet files.Ĩ+1 node cluster, each node is a DS3V2 Azure VM (4 cores, 17 GB RAM) , a financial industry customer, was able to achieve 15X performance improvements in their ETL pipeline, loading millions of rows into a columnstore table that is used to provide analytical insights through their application dashboards. However, compared to the SQL Spark connector, the JDBC connector isn’t optimized for data loading, and this can substantially affect data load throughput.Īs an example, utilizing the SQLBulkCopy API that the SQL Spark connector uses, , which gives the ability to connect to several relational databases. , access to SQL databases from Spark was implemented using the This data often lands in a database serving layer like SQL Server or Azure SQL Database, where it is consumed by dashboards and other reporting applications.
Spark is often used to transform, manipulate, and aggregate data. Apache Spark is a distributed processing framework commonly found in big data environments.
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