Accelerating analytics with dynamic in-memory expressions

Abstract

Oracle Database In-Memory (DBIM) accelerates analytic workload performance by orders of magnitude through an inmemory columnar format utilizing techniques such as SIMD vector processing, in-memory storage indexes, and optimized predicate evaluation and aggregation. With Oracle Database 12.2, Database In-Memory is further enhanced to accelerate analytic processing through a novel lightweight mechanism known as Dynamic In-Memory Expressions (DIMEs). The DIME mechanism automatically detects frequently occurring expressions in a query workload, and then creates highly optimized, transactionally consistent, in-memory columnar representations of these expression results. At runtime, queries can directly access these DIMEs, thus avoiding costly expression evaluations. Furthermore, all the optimizations introduced in DBIM can apply directly to DIMEs. Since DIMEs are purely in-memory structures, no changes are required to the underlying tables. We show that DIMEs can reduce query elapsed times by several orders of magnitude without the need for costly pre-computed structures such as computed columns or materialized views or cubes.

UDBMS: Road to Unification for Multi-model Data Management

“…Building up just-in-time multi-model data structure is probably another interface that the original OR-DBMS misses. For relational data, the traditional database vendors have developed in-memory database products such as Oracle Database In-Memory [20], IBM DB2 BLU Acceleration [21] and SQL Server In-Memory Columnstore [22]. For other data model, such as JSON, XML, graph and key-value, we also expect in-memory solutions to speed up query [12].…”