According to the Amazon Web Services, Amazon Aurora Parallel Query provides faster analytical queries over transactional data that can speed up queries by up to 2 orders of magnitude, while maintaining high throughput for core transactional workloads. They have enhanced the query time performance of its Aurora relational database service, to leverage the benefits of cloud computing.
Amazon Aurora is compatible with PostgreSQL and MySQL databases and includes the availability/performance of high-end commercial databases along with the ease and cost-effectiveness of open source offerings at the same time being faster than both.
According to AWS, those faster analytical queries occur without the need to copy transactional data into a separate system, benefitting from a new take on parallelized queries. Aurora data is distributed through hundreds of storage nodes in several locations in the product's storage layer, and Amazon Parallel Query puts the processing power of all those nodes to work to speed up queries. The fully managed RDBMS service, is now even faster with new functionality leveraging cloud features like scalability and distributed processing.
In the analytical database model, parallelism breakdowns a query into many pieces and spreads them over many CPUs, which produces as much as 100-times faster querying speeds. Aurora database is designed for simple queries that quickly summarize the results. Parallel processing is typically reserved for data warehouse platforms based on analytical databases, like AWS' own Redshift data warehousing service and those from Teradata, Microsoft and Oracle.
Parallel queries boost the performance of over 200 types of single-table predicates and hash joins. The Aurora query optimizer will automatically choose whether to use Parallel Query based on the size of the table and the amount of table data that is already in memory.
Because this new model decreases network, CPU, and buffer pool contention, users can run a mix of transactional and analytical queries simultaneously on the same table while maintaining high throughput.
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