Relaxed Operator Fusion for In-Memory Databases: Making Compilation, Vectorization, and Prefetching Work Together At Last
Relaxed Operator Fusion for In-Memory Databases: Making Compilation, Vectorization, and Prefetching Work Together At Last
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DOI：
10.14778/3151113.3151114
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发表时间：
2017-09
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通讯作者：
Prashanth Menon;Andrew Pavlo;T. Mowry
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作者：
Prashanth Menon;Andrew Pavlo;T. Mowry
内存数据库管理系统（DBMS）是现代在线分析处理（OLAP）应用程序的关键组成部分，因为它们提供了对大量数据的低延迟访问权限。 ，设计查询执行引擎的重点已转移以优化CPU性能。解释计划的最新汇编是在查询计划中融合的，以有效地传递零件，以使我们的经验分析有效地传递零件。我们提出了一个称为“宽松的操作员融合”的查询处理模型，该模型允许DBMS在查询计划中引入分期，其中中间结果是临时实现的DBM使用预摘要和SIMD矢量化的组合来利用计划中固有的核心并行性，以支持超过CPU级速度缓存大小的数据集的更快查询执行。与其他内存DBMS相比，OLAP查询的最高可达2.2倍，并且具有1.8倍的性能。
In-memory database management systems (DBMSs) are a key component of modern on-line analytic processing (OLAP) applications, since they provide low-latency access to large volumes of data. Because disk accesses are no longer the principle bottleneck in such systems, the focus in designing query execution engines has shifted to optimizing CPU performance. Recent systems have revived an older technique of using just-in-time (JIT) compilation to execute queries as native code instead of interpreting a plan. The state-of-the-art in query compilation is to fuse operators together in a query plan to minimize materialization overhead by passing tuples efficiently between operators. Our empirical analysis shows, however, that more tactful materialization yields better performance. We present a query processing model called "relaxed operator fusion" that allows the DBMS to introduce staging points in the query plan where intermediate results are temporarily materialized. This allows the DBMS to take advantage of inter-tuple parallelism inherent in the plan using a combination of prefetching and SIMD vectorization to support faster query execution on data sets that exceed the size of CPU-level caches. Our evaluation shows that our approach reduces the execution time of OLAP queries by up to 2.2× and achieves up to 1.8× better performance compared to other in-memory DBMSs.