This post is an introduction to a holistic approach to Exadata: addressing what Exadata is and why we made it, and is based on this full technical brief (PDF). It complements the many resources that cover Exadata from a range of perspectives, from Exadata's technology innovations to its role as a pillar to IT solutions delivering both technology and business value.
The technical brief is also a guide to decision makers in their evaluation of the Exadata family of products and cloud services, backed by a comprehensive list of references. It covers Exadata Use Cases and Design Concepts (e.g., adding database intelligence to storage), Technical Foundations (e.g., in-memory databases), plus how Networking, Hardware, and Database Software play together in Exadata. It concludes with a full summary of Exadata generations that ties together all the innovations into a coherent evolution story. The rest of this post outlines Exadata use cases; see the paper for the full list of references.
Exadata is designed to optimally run any Oracle Database workload or combination of workloads; for example, an OLTP application running simultaneously with analytics processing. Exadata is frequently used to consolidate many databases that were previously running on dedicated database servers. Exadata's scale-out architecture is naturally suited to running in the Oracle Cloud, where computing requirements can dynamically grow and shrink.
Historically, specialized database computing platforms were designed for a particular workload, such as data warehousing, and were poor or unusable for other workloads, such as OLTP. Exadata has optimizations for all database workloads, implemented such that mixed workloads share system resources fairly. Resource management features allow for prioritized allocation of system resources. For example, Exadata can favor workloads servicing interactive users over reporting and batch, even if they are accessing the same data.
Long running requests, typical of data warehouses, reports, batch jobs and analytics, run many times faster compared to a conventional, non-Exadata database server[a]. Customer references often cite performance gains of 10x or greater. Analytics workloads can also use the Oracle Database In-Memory[b] option on Exadata for additional acceleration, and in-memory databases on Exadata have been extended to take advantage of Flash memory, whose capacity is many times larger than the capacity of DRAM. Exadata’s Hybrid Columnar Compression[c] feature is intended to reduce the storage consumption of data warehouses and archival data as well as increase performance by reducing the amount of I/O.
Transactional (OLTP) workloads on Exadata benefit from the incorporation of persistent memory and Flash memory into Exadata’s storage hierarchy, and the automatic tiering of data into DRAM, persistent memory, Flash or disk storage. Special algorithms optimize persistent memory and Flash for response time sensitive database operations such as log writes. For the most demanding OLTP, all-Flash storage eliminates the latency of disk media completely.
See Oracle Exadata: A guide for decision makers for more details.