SQL Server reads only the columns required for the result, as referenced by a SELECT statement, JOIN predicate, or filter predicate. When a query is executed in batch mode, and accesses data in columnstore indexes, the execution tree operators and child operators read multiple rows together in column segments. The result is better parallelism and faster performance. For more information, see Batch mode on rowstore.īatch mode processing operates on compressed data when possible, and eliminates the exchange operator used by row mode execution. However, starting with SQL Server 2019 (15.x) and in Azure SQL Database, batch mode execution no longer requires columnstore indexes. When it was first introduced, batch mode execution was closely integrated with, and optimized around, the columnstore storage format. Batch mode processing also uses algorithms that are optimized for the multi-core CPUs and increased memory throughput that are found on modern hardware.

Each column within a batch is stored as a vector in a separate area of memory, so batch mode processing is vector-based. Batch mode executionīatch mode execution is a query processing method used to process multiple rows together (hence the term batch). Row mode execution is very efficient for OLTP scenarios, but can be less efficient when scanning large amounts of data, for example in Data Warehousing scenarios. From each row that is read, SQL Server then retrieves the columns that are required for the result set, as referenced by a SELECT statement, JOIN predicate, or filter predicate. When a query is executed and accesses data in row store tables, the execution tree operators and child operators read each required row, across all the columns specified in the table schema. Row mode execution is a query processing method used with traditional RDBMS tables, where data is stored in row format. The SQL Server Database Engine can process Transact-SQL statements using two distinct processing modes: The following sections cover how SQL Server processes queries and optimizes query reuse through execution plan caching. The SQL Server Database Engine processes queries on various data storage architectures such as local tables, partitioned tables, and tables distributed across multiple servers.

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