Oracle Exadata

Oracle Exadata
Original author(s) Oracle Corporation
Initial release October 2008
Operating system Oracle Linux
Platform Exadata Database Machine
License Commercial
Website ''

The Oracle Exadata Database Machine (Exadata[1]) is a computing platform that is specialized and optimized for running the Oracle Database. The goal of Exadata[2] is to achieve higher performance and availability at lower cost by moving database algorithms and intelligence into storage and networking, bypassing the traditional processing layers.[3]

Exadata integrates Oracle Corporation hardware and software, including scale-out compute servers, scale-out storage servers, InfiniBand networking and specialized Exadata Software[4], packaged in one or more hardware racks, in a wide range of shapes and price points. Exadata compute servers use Intel Xeon processors and the Oracle Linux operating system or Oracle Virtual Machine to run Oracle Database software. Exadata Storage Servers use Intel Xeon processors to perform block storage functions and run Exadata Software that offloads data-intensive database processing. State-of-the-art Flash technology in Exadata Storage Servers has exposed the importance of Exadata's offloading for realizing the full performance of Flash[5].

Exadata debuted in 2008 as the first in Oracle Corporation's family of Engineered Systems[6] for use in corporate data centers deployed as "private clouds". In October 2015, Exadata became available in the Oracle Cloud as a subscription service, known as the Exadata Cloud Service[7][8]. Oracle databases deployed in the Exadata Cloud Service are 100% compatible with databases deployed on Exadata on-premises, which enables customers to transition to the Oracle Cloud with no application changes. Oracle Corporation manages this service, including hardware, network, Linux software and Exadata software, while customers have complete ownership of their databases.

In early 2017, a third Exadata deployment choice became available. Exadata Cloud at Customer[9] is Exadata Cloud Service technology deployed on-premises (behind the corporate firewall) and managed by Oracle Cloud experts. Like the Exadata Cloud Service, Exadata Cloud at Customer is owned and managed by Oracle, and licensed through a pay-as-you-go subscription. The Oracle Cloud at Customer[10] program is intended to bring all the benefits of the Oracle public cloud while still satisfying security and regulatory constraints.

Exadata Use Cases

Exadata is designed to optimally run any Oracle Database workload or combination of workloads, such as an OLTP application running simultaneously with Analytics processing. The platform 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 sometimes shrink.

Historically, specialized database computing platforms were designed for a particular workload, such as Data Warehousing, and 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 also allow for prioritized allocation of system resources, such as always favoring workloads servicing interactive users over reporting and batch, even if they are accessing the same data.

Long running requests, characterized by Data Warehouses, reports, batch jobs and Analytics, are reputed to run many times faster compared to a conventional, non-Exadata database server.[11][12] Customer references often cite performance gains of 10x or greater. Analytics workloads can also use the Oracle Database In-Memory[13] option on Exadata for additional acceleration, and In-Memory Databases on Exadata have been extended to take advantage of Flash memory capacity, many times larger than the capacity of DRAM. Exadata’s Hybrid Columnar Compression[14] feature is intended to reduce the storage consumption of Data Warehouses and archival data as well as increase performance by reducing the amount of IO.

Transactional (OLTP) workloads on Exadata benefit from the incorporation of Flash memory into Exadata’s storage hierarchy, and the automatic "tiering" of data into memory, Flash or disk storage. Special Flash algorithms optimize 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.

Exadata Design Concepts

In order to understand the design of Exadata, it must be compared with a traditional database computing platform, made from separate hardware and software components that operate independently.

Traditional Computing Platforms are General-Purpose

The hardware components that make up a typical database computing platform are a compute server connected over a network to a storage array. The database software runs on the compute server and sends or receives database information to and from the storage array over the network. The hardware components use standard software protocols to "talk" to each other.

This separation via standard interfaces is what allows a computing platform to run a wide variety of software and hardware from different vendors. The hardware components don't know or care about the purpose of the software applications running on the compute server. The storage array, for example, just responds to requests to retrieve or store data, without regard for whether that data is part of an important financial transaction or a monthly report of minor value, or even an email message. All of the application logic and the processing of the data is performed on the compute server, to which all the data must be sent. With this approach, a computing platform can be used for a wide range of software applications, though it will not be optimized for any particular application.

Oracle Database is the Focus of Exadata

From the outset, the goal of Exadata[15][2][16] was to create the best Oracle Database platform by building a complete stack of software and hardware focused on the Oracle Database, that allowed processing to be moved to its optimal location. Oracle believed such a platform could be streamlined and optimized for significantly better results in terms of performance, database availability and security. In other words, if Exadata is only processing Oracle Database requests, and database functions can run on any piece of hardware, it can take advantage of that in all the software layers running on every hardware component. Furthermore, the hardware design itself can include elements that are most advantageous to Oracle Database applications, such as very fast InfiniBand networking and Flash memory. Given the importance of data storage to databases, Oracle was particularly focused on optimizing that aspect of the Exadata platform.

Adding Database Intelligence to Storage

At the time Exadata was conceived, Oracle had several decades of experience developing Oracle Database software, and was well aware of the limitations and performance bottlenecks imposed by traditional computing platforms[16]. To fulfill the Exadata mission, Oracle needed a storage layer that could easily scale out and parallelize Oracle Database requests. It also recognized the opportunity for storage to cooperate in the processing of database requests beyond just storing and shipping data. For example, rather than send an entire database table across the network to the compute server to find a small number of records, such data filtering could be done in storage and only the resulting records sent across the network.

The addition of Flash memory to Exadata Storage Servers with the second (V2) generation of Exadata also opened up a range of possibilities for optimizing performance in the storage layer. Over time, as the performance and capacity of Flash storage increased at a rapid rate, the network became a performance bottleneck for traditional database platforms and Exadata's offloading of database processing into Exadata Storage Servers avoided that problem.

In summary, Oracle recognized the need for a powerful server that could run intelligent database software and act as a storage array, with a modular design that could easily grow in capacity and performance as the database grew. A "database-aware" storage server that could cooperate with compute servers in the execution of database requests was a compelling and now possible development, enabled by focusing Exadata on what is best for the Oracle Database.

The foundation of Exadata is the Exadata Storage Server[17][18], invented by Oracle to replace the traditional storage array.

Optimizing Across the Full Stack

To maximize the effectiveness of Exadata, Oracle had to be in control of the software and hardware components of the platform so that coordinated improvements could be made anywhere at any time. Oracle already had a broad portfolio of software products when Exadata was conceived, covering most of the software layers that are required to run a database platform, such as the Oracle Linux operating system, storage management software, monitoring and administrative tools and virtual machine software, and, of course, Oracle Database and options software.

The initial 2008 release of Exadata (V1) was a joint development between Oracle (software) and Hewlett-Packard (hardware). The second generation (V2) of Exadata switched to hardware from Sun Microsystems, and shortly thereafter Oracle acquired Sun Microsystems and thus gained ownership of the main hardware components of Exadata.

At this point (2009) the ability for Oracle to develop an entire computing platform optimized around the Oracle Database was in place. A further benefit for customers was the ability to support the entire Exadata platform from one vendor; something impossible with a traditional computing platform of hardware and software components from multiple vendors.

Exadata Smart Software

With Oracle's control over all Exadata software and hardware components, Oracle embarked on a regular release schedule of new software capabilities married closely with leading-edge hardware components.

Using knowledge of the Oracle Database formats and algorithms, plus an understanding of the application workload, these "smart" software enhancements, running in the Oracle Database and all software and firmware in the platform, had the potential to significantly outperform the capabilities of a traditional database platform. The ability to run Oracle Database routines in the Exadata Storage Server software was a major enabler of this advantage[16].

For example, when an OLTP application commits a transaction to the database software, that request is viewed by Exadata as a critical operation and prioritized accordingly within the network and in the storage servers. The commit request will move in front of less important messages on the network and in the IO queue[19].

Another example is the use of Flash memory for caching data that is being accessed by Analytics applications. Because a columnar data format is more effective for Analytics workloads, when Exadata moves the data row from disk to Flash, it automatically reformats the data into a columnar format[20].

Both examples require the Oracle Database to understand the intent of the application and to cascade this understanding to the network and storage software, which behave accordingly.

Since Exadata's debut in 2008, there have been at least two significant Exadata software releases per year, delivering dozens of "smart" software enhancements. Most of these enhancements are based on a smaller number of technical foundations, as explained below.

Technical Foundations

  • Offloading to Storage - refers to the execution of data-intensive database operations within the Exadata Storage Servers, such as data scans, table joins and filtering of rows and columns. This reduces the amount of data that must ultimately be returned over the network to the compute servers, thus avoiding the network bottleneck. Offloading is possible because Exadata storage is built on first-class servers, capable of running database functions in coordination with the Oracle Database, simultaneous with storage IO. Over time, more database functions and more data types have been offloaded. In addition, "reverse offloading" will push an operation back to the compute servers if Exadata storage is too busy.
  • Storage Indexes - enable the avoidance of IO by tracking column values within small regions of storage. If the Storage Index confirms that an IO to a region will not find a match, that IO isn't performed, and a costly and unnecessary operation is avoided. Storage indexes are automatically maintained and kept in memory on Exadata Storage Servers. Initially Storage Indexes tracked value ranges within a small number of columns. Over time, more columns and more sophisticated value tracking have been added, so that additional IO operations can be avoided.
  • Flash Caching - delivers the low latency (fast response) of Flash, while preserving the lower cost of disk for storing large databases. The goal is the best IO performance at the lowest cost. In general, a small percentage of a database is active at any one time. If the active data is all held in Flash, the IO performance would be equal to all-Flash storage, at a much lower cost. Exadata Flash caching uses intelligence about the applications currently running to keep the most active data in Flash, in the optimal format. For instance, Exadata knows when an IO is part of a database backup, and not an indication of an active data block, whereas traditional platforms view any IO as a "hot" block. Flash caching will also reformat rows into columnar format in Flash if the data is being accessed for Analytics. Initially, Flash caching was only used for reading data, then extended to log writes and all other write IO. Most recently, Flash is used as an extension of Oracle's Database In-Memory columnar data store, for significantly larger in-memory databases than DRAM capacity alone.
  • Hybrid Columnar Compression (HCC) - reduces the amount of storage consumed by Data Warehouses and other infrequently updated data. Without compression, Data Warehouses can grow to enormous sizes. Exadata's Hybrid Columnar Compression benefits from the greater compressibility of columnar formats and avoids the performance pitfall of an all-columnar approach. Conventional data compression algorithms yield between 2x and 4x compression, whereas HCC averages between 10x and 15x compression. Such a large reduction in the amount of IO can also substantially improve performance. Initially, HCC tables did not support row-level locking, limiting their use with OLTP applications. In 2016, support for row-level locking was added to HCC on Exadata, improving the performance of mixed workloads with HCC data.
  • Resource Management - allocates Exadata system resources, such as CPU, IO and network bandwidth, to databases, applications or users based on priorities. When Exadata is used for consolidating many databases, Resource Management is usually employed to ensure appropriate quality of service. IO Resource Management debuted in V1 of Exadata. Network Resource Management was added in Exadata X4.
  • In-Memory Databases - offer exceptional performance for Analytics workloads, leveraging DRAM on compute servers, a complement to Exadata's emphasis on storage and networking. Oracle Database In-Memory became available in 2014 on Exadata, leveraging its fast InfiniBand network for in-memory Fault Tolerance. To support larger in-memory databases, Exadata Storage Servers now implement in-memory routines and data formats in Exadata Flash, as an extension of the same in-memory processing that occurs on compute servers.

Smart Software Enhancements

A more detailed listing of smart software enhancements is below, grouped by their value to Analytics or OLTP workloads, or their impact on Availability. Similar enhancements cannot be duplicated on other platforms because they require software and API modifications and integration across database software, operating systems, networking and storage.

Refer to the Exadata documentation[21] and Data Sheet[1] for descriptions of these features

  • Automatically parallelize and offload data scans to storage
  • Filter rows in storage based on 'where' clause
  • Filter rows in storage based on columns selected
  • JSON and XML offload
  • Filter rows in storage based on join with other table
  • Hybrid Columnar Compression
  • IO Resource Management by user, query, service, DB
  • Automatic transformation to columnar format in Flash Cache
  • Smart Flash Cache for table scans
  • Offload index fast full scans
  • Offload scans on encrypted data, with FIPS compliance
  • Storage offload for LOBs and CLOBs
  • Storage offload for min/max operations
  • Data Mining offload
  • All ports active InfiniBand messaging
  • Reverse offload to DB servers if storage CPUs are busy
  • Database In-Memory automatic memory population/depopulation
  • In-Memory support for external tables
  • In-Memory optimized arithmetic
  • Instant detection of node or cell failure
  • In-Memory Fault Tolerance
  • Sub-second failover of IO on stuck disk or flash
  • Offload backups to storage servers
  • Exadata data validation (H.A.R.D.)
  • Instant data file creation
  • Prioritize rebalance of critical files
  • Automatic hard disk scrub and repair
  • Power cycle failed drives to eliminate false drive failures
  • Avoid reading predictive failed disks
  • Cell software transparent restart
  • Flash and disk life cycle management alert
  • Confinement of temporarily poor performing drives
  • Prevent shutdown if mirror server is down
  • Automatic Software Updates on an entire "fleet" of Exadata systems with one operation
  • Hot pluggable Flash cards
  • Keep standby database consistent when NO FORCE logging is used
  • Fast, secure eraser of disk and Flash
  • Database Aware PCI Flash
  • Exadata Smart Flash Logging
  • Write-back Flash Cache
  • IO Prioritization by DB, user, or workload to ensure QoS
  • Direct-to-Wire Protocol
  • Network Resource Management
  • EXAchk full-stack validation
  • Full-stack security scanning
  • NVMe flash interface for lowest latency IO
  • Active AWR includes storage stats for end to end monitoring
  • Database scoped security
  • Cell-to-cell rebalance preserving flash cache
  • In-Memory commit cache
  • Memory optimized OLTP and IoT lookups

Database Software

Exadata compute servers run the Oracle Linux 6 operating system and Oracle Database 11g Release 2 Enterprise Edition, Oracle Database 12c Enterprise Edition, or Oracle Database 18c Enterprise Edition. Exadata system resources can be optionally virtualized using the Xen-based Oracle VM. All Oracle Database options, such as Real Application Clusters, Multitenant, Database In-Memory, Advanced Compression, Advanced Security, Partitioning, Active Data Guard and others are optionally available with Exadata. Applications that are certified to a supported version of the Oracle Database are automatically compatible with Exadata. No additional modifications or certifications are required[15].

The same database software that runs on Exadata on-premises will run in the Exadata Cloud Service and Exadata Cloud at Customer. In addition, on-premises software licenses are eligible for the BYOL[22] (Bring Your Own License) transfer into the Oracle Cloud or Cloud at Customer.


Exadata provides high-speed networks for internal and external connectivity. A 40 Gigabits per second (40 Gb/sec) InfiniBand network is used for internal connectivity between compute and storage servers and 25, 10 and 1 Gb/sec Ethernet ports are included for data center connectivity. The InfiniBand network is also used as the cluster interconnect between compute servers.

Exadata uses a custom-designed, database-oriented protocol over the InfiniBand network to achieve higher performance. It makes extensive use of Remote Direct Memory Access (RDMA) to improve efficiency by avoiding data copies when moving data between servers. Exadata also has a direct-to-wire protocol[23] that allows the database to "talk" directly to the InfiniBand hardware, bypassing the operating system.

Exadata also takes advantage of InfiniBand Lanes[24] in its Network Resource Management[19] feature to prioritize important traffic across the network. In this feature the Oracle Database software tags network messages that require low latency, such as transaction commits, lock messages and IO operations issued by interactive users, enabling them to bypass messages issued by less critical high-throughput workloads such as reporting and batch. The result is analogous to how an emergency vehicle with its siren on is able to move more quickly through heavy traffic - high-priority network messages are moved to the front of the server, network switch, and storage queues, bypassing lower-priority messages and resulting in shorter and more predictable response times.

Management Software

Oracle Enterprise Manager Cloud Control (EM) manages Oracle software and hardware, including the Exadata Database Machine. EM integrates with the built-in Exadata management tooling, as well as with customer’s existing systems management and helpdesk tools. The Exadata plug-in for EM provides an integrated view of compute servers, storage servers, switches, and topology. In addition, it also provides discovery, monitoring and alerting capability for Exadata systems management.

The Exadata Cloud Service and Exadata Cloud at Customer platforms are managed by Oracle, while customers control and manage the software and databases running on the compute servers. Exadata Cloud platforms include a web-based provisioning wizard through which customers can quickly provision their chosen Exadata system and subsequently their database instances. Customers also perform familiar database administration and OS administration tasks aided by cloud automation for backup, patching, and upgrades

Exadata Hardware

Exadata is available in two models: one based on two-socket compute servers and the other based on eight-socket compute servers. The two models differ only in the hardware used for the compute servers. The networking, storage servers and software are the same in both models.

The most recent Oracle Exadata Database Machine is the X7 generation, introduced in October 2017.

The X7-2 compute servers feature a small form factor, 1 RU (Rack Unit) in height. They employ 2-socket Intel Xeon processors; each socket with 24 compute cores for 48 total cores per compute server. Memory starts at 384 Gigabytes (GB) and can be expanded to 1.5 Terabytes (TB).

The Exadata Database Machine base configuration has 2 compute servers and 3 storage servers, referred to as a Quarter Rack. The same hardware is also available in an Eighth Rack configuration with half of the processing and half of the storage capacity. As the database workload and/or data size increases, additional compute and storage servers may be added to increase the volume of work performed in parallel, using Exadata's Elastic Configuration.

The X7-8 compute server uses eight-socket compute servers that consume 5 RU in height and have greater memory capacity than the X7-2. Whereas each X7-2 compute server contains 48 compute cores, the X7-8 server contains 192 compute cores. This allows large database workloads to easily scale-up within a compute server while still supporting Exadata’s scale-out expandability across multiple servers. The larger memory capacity of the X7-8 also favors In-Memory Databases and very large OLTP, consolidation, and Data Warehouse workloads. Like the X7-2, the Exadata X7-8 base configuration has 2 compute servers and 3 storage servers, but consumes a Half Rack of space. Additional compute and storage servers may be added until the rack is full.

Exadata Storage Servers

There are two choices for Exadata Storage Servers; Extreme Flash and High Capacity. The Extreme Flash Storage Server is all-flash storage containing 8 PCIe flash drives for a total of 51.2 TB of raw storage capacity in its current X7-2 configuration. The High Capacity storage server in its current X7-2 configuration contains 12 disks, 10 TB each, for a total of 120 TB of raw storage capacity. To improve IO response times, the High-Capacity storage server also employs 25.6 TB of PCIe flash to cache active data blocks. Exadata’s Smart Flash Cache, Smart Flash Log, Columnar Flash Cache and Write Back Flash Cache features determine how and when to use flash.

In addition to adding storage servers into an Exadata Database Machine base configuration, storage servers may also be acquired with or added to Exadata Storage Expansion[25] racks.

Storage performance specifications for a full rack Exadata configuration are as follows:

Exadata Storage ServerScan RateRead IOPSWrite IOPS
X7-2 Extreme Flash350 GB/sec5,970,0005,400,000
X7-2 High Capacity350 GB/sec4,776,0004,352,000

Note: Based on an elastic configuration of 12 storage servers, 10 compute servers.[26]

IOPS = 8K IO Operations per Second from SQL

Memory-Level Performance with Shared Storage[27]

Architects of traditional computing platforms have always had to cope with technology change affecting the design of their systems. The goal is to eliminate bottlenecks so that the output of storage moves through the network and is processed by compute servers without any slowdown. Solving an imbalance generally involves adding faster or more network connections or compute servers. This was before the advent of ultra-fast PCIe Flash memory and the NVMe Flash interface.

Flash Outruns the Fastest Networks

Flash storage started to became mainstream in corporate computing around 2010, as a cache in front of hard disks or as a replacement for disks entirely. Every year thereafter Flash capacity and performance increased significantly. In 2017, leading-edge Flash performance crossed a threshold, where the most advanced networks were unable to match the performance of Flash, and became a substantial bottleneck.

For example, a single state-of-the-art Flash card in 2017 produced 5.5 GB/sec of data throughput, compared to around 5 GB/sec (i.e. 40 Gigabits/sec) for very fast network cards. Flash storage systems can support hundreds of Flash cards, easily overwhelming the more limited number of network connections possible. A popular all-Flash storage system with 480 Flash cards was rated at only 38 GB/sec of data throughput, whereas without a network bottleneck, that many Flash cards should produce over 2,600 GB/sec of data throughput. Plugging Flash directly into a compute server removes the network bottleneck, but also removes the ability to share storage with multiple compute servers.

Compare this example with the prior table showing the performance of Exadata Storage Servers. At 350 GB/sec, the performance of Exadata Flash approaches the performance of memory (DRAM) at 800 GB/sec for a full-rack Exadata system. There is no network bottleneck, even with shared storage, because Exadata is able to offload database functions to storage[5].

Exadata Hardware Specifications

Exadata Generation


Date introducedSep-2008Sep-2009Sep-2010Sep-2012Nov-2013Jan-2015Apr-2016Oct-2017
SpecificationsN/A Datasheet[28] Datasheet[29] Datasheet[30] Datasheet[31] Datasheet[32] Datasheet[33] Datasheet[1]
Operating systemLinuxLinuxLinuxLinuxLinuxLinuxLinuxLinux
Disk storage (raw TB)168336504504672134413441680
Flash cache (raw TB)N/A5.35.322.444.889.6179.2358.4
Extreme flash (raw TB)N/AN/AN/AN/AN/A179.2358.4716.8
Compute cores646496128192288352384
Max memory (Gio)2565761,1522,0484,0966,14412,28812,288
Ethernet (Gb/s)824184400400400400800
Exadata Generation


Date introducedN/AN/ASep-2010Sep-2012Jul-2014Nov-2015Apr-2016Oct-2017
SpecificationsN/AN/A Datasheet[34] Datasheet[35] Datasheet[36] Datasheet[37] Datasheet[38] Datasheet[39]
Operating systemN/AN/ALinuxLinuxLinuxLinuxLinuxLinux
Disk storage (raw TB)N/AN/A504504672134413441680
Flash cache (raw TB)N/AN/A5.322.489.689.6179.2358.4
Extreme flash (raw TB)N/AN/AN/AN/A179.2179.2358.4716.8
Compute coresN/AN/A96160240288288384
Max memory (TB)N/AN/A4412121212
Ethernet (Gb/s)N/AN/A176176180180180560

* Per 1 full rack with 8 compute servers and 14 storage servers

** Per 1 full rack with 2 compute servers and 14 storage servers

Elastic Configurations

Prior to the X5-2 generation, Exadata systems were only available in fixed-size configurations of Eighth, Quarter, Half and Full Rack sizes. With the X5-2 Exadata release in January, 2015, elastic configurations were introduced. An elastic configuration has a customer-specified combination of database servers and storage servers. Elastic configurations allow individual storage or compute servers to be added to a base configuration until the physical rack is full. For example, an Exadata system optimized for in-memory database processing could be created by adding many compute servers, each with maximum memory. Conversely, an Exadata system optimized for a large data warehouse could be configured by adding many High-Capacity storage servers. The ratio of compute to storage servers can vary, depending on the characteristics of the intended workload. Elastic configurations may also be used to scale out earlier generation Exadata systems using X7-2 servers. In addition, Exadata Database Machines have always been able to span multiple racks using the built-in InfiniBand network connections. Thus, Exadata’s scale-out extends beyond a single physical rack.

The Evolution of Exadata

Oracle Corporation releases a new generation of Exadata every twelve to eighteen months[40][41][42][43][44][45][46][47]. At each release, Oracle refreshes most hardware components to the latest Intel Xeon processors, memory, disk, flash and networking. The hardware refreshes in themselves result in performance increases with every release. Exadata software is also refreshed with each generation and periodically in between, enhancing some combination of performance, availability, security, management and workload consolidation. In October 2015, features to support the Oracle Cloud were introduced[48].

The emphasis of each Exadata generation is described below.

Exadata V1[40], released in 2008, focused on accelerating Data Warehousing by delivering the full throughput of storage to the database. Per Oracle, Exadata achieved this by moving database filtering operations into storage, instead of sending all data to the compute servers and filtering it there. Oracle refers to this capability as Exadata Smart Scan[49][50]. Exadata V1 also supported a consolidation feature for allocating IO bandwidth between databases or workloads, called IORM (IO Resource Manager)[51].

Exadata V1 was available in Full Rack or Half Rack sizes, and the choice of High Performance or High Capacity storage servers.

Exadata V2[41][52][53], released in 2009, added a Quarter Rack configuration and support for OLTP workloads via Flash storage and database-aware Flash Caching.[54]

Exadata V2 also introduced Hybrid Columnar Compression[14] to reduce the amount of storage consumed by large Data Warehousing tables.

Storage Indexes[55] in Exadata V2 increased performance by eliminating the need to read entire regions of storage, based on knowledge of the data contained in the region.

Exadata X2-2[29], the third generation, was released in 2010 and a second model of Exadata, Exadata X2-8[42], was introduced. The X2-8 and subsequent “8 socket” Exadata models feature Intel processors targeted at large memory, scale-up workloads. The use of Flash storage beyond caching began in this release with a Smart Flash Logging[56][57] feature. Support for 10 Gigabit per second (Gb/sec) Ethernet connectivity was also added.

Data security through encryption was encouraged with the incorporation of hardware decryption[58] in Exadata X2-2, largely eliminating the performance overhead compared to software decryption.

A Storage Expansion Rack[59] based on Exadata X2-2 was added in 2011 to accommodate large, fast-growing Data Warehouses and archival databases. All subsequent 2-socket Exadata generations have included a new Storage Expansion Rack.

Exadata X3-2[43][16][30] and X3-8[35] were released in 2012, including a new Eighth Rack X3-2 entry-level configuration. Flash storage capacity quadrupled and OLTP write throughput reportedly increased by 20x via the Write-Back Flash Cache[60] feature.

A number of availability enhancements were added, bypassing slow or failed storage media[61], reducing the duration of storage server brownouts and simplifying replacement of failed disks.

Exadata X4-2[44][31] was released in 2013. Flash capacity doubled and Flash compression was added, effectively doubling capacity again. Network Resource Management[19] was introduced, automatically prioritizing critical messages. InfiniBand bandwidth doubled with support for active/active connections.

Exadata X4-8[36] released in 2014, plus Capacity on Demand[62] licensing, IO latency capping and timeout thresholds.

Exadata X5-2[45][32] and X5-8[37] were released in 2015 with a major set of enhancements. Flash and disk capacity doubled. Elastic configurations[63] were introduced to enable expansion one server at a time. Virtualization was added as an option to Exadata along with Trusted Partitions[64] for flexible licensing within a virtual machine. Database snapshots[65] on Exadata storage enabled efficient development and testing. Oracle Database In-Memory on Exadata included Fault Tolerant [66][67]redundancy. The High Performance Exadata storage servers were replaced with all-Flash (Extreme Performance) storage servers and Exadata became the first major vendor to adopt the NVMe Flash interface. Columnar Flash cache was introduced to automatically reformat analytics data into row format in Flash. IPv6 support was completed. Exadata Cloud Service[48][68] was launched on the Oracle Cloud.

Exadata X6-2[46][33] and X6-8[38] were released in 2016. Flash capacity doubled. Exafusion Direct-to-Wire protocol[69] reduced messaging overhead in a cluster and Smart Fusion Block Transfer[70] eliminated log write delays for OLTP applications in a cluster. Exadata Cloud at Customer[71][9] debuted, enabling Oracle Cloud benefits within corporate data centers.

Exadata X7-2[47] and X7-8 were released in 2017[72]. Flash capacity doubled. Flash cards became Hot pluggable for online replacement. 10 Terabyte (TB) disk drives debuted along with 25 Gb/sec Ethernet connectivity. Oracle Database In-Memory processing was extended into Flash storage, and storage server DRAM was utilized for faster OLTP.


  1. 1 2 3 "Data Sheet: Oracle Exadata Database Machine X7-2" (PDF). Oracle Corporation. 2017. Retrieved July 27, 2018.
  2. 1 2 Loaiza; et al. (August 2, 2011). "Oracle Exadata: World's Fastest Database Machine". YouTube. Retrieved July 27, 2018.
  3. Umamageswaran and Goindi, Kothanda and Gurmeet (October 2, 2017). "Exadata Technical Deep Dive: Architecture and Internals" (PDF). Retrieved July 27, 2018.
  4. Stern, Janet (2018). Oracle Exadata System Software User's Guide 18c (18.1). Oracle Corporation.
  5. 1 2 Loaiza, Juan (2017). "Video: Oracle's Next Generation Exadata Database Machine". Oracle Corporation. Retrieved August 9, 2018.
  6. Hollis, Chuck (September 9, 2015). "Chuck's Blog: Grown-up IT for Grown-Up Applications". Typepad. Retrieved July 27, 2018.
  7. Various authors. "Oracle Database Exadata Cloud Service Technical Data Sheet" (PDF). Oracle Corporatoin. Retrieved July 27, 2018.
  8. Spendolini, Brian (2018). Oracle Database Exadata Cloud Service: A Beginner's Guide. McGraw-Hill Education. ISBN 978-1260120875.
  9. 1 2 Various authors. "Oracle Database Exadata Cloud at Customer Technical Data Sheet" (PDF). Oracle Corporation.
  10. Various authors. "Oracle Cloud at Customer Resources: Data Sheet et al". Oracle Corporation. Retrieved July 27, 2018.
  11. "Exadata Customer Success Videos". Retrieved July 16, 2018.
  12. Various. "Gartner Peer Insights: Oracle Exadata Database Machine". Retrieved August 8, 2018.
  13. Various (2018). "Data Sheet: Oracle Database In-Memory" (PDF). Oracle Technology Network. Retrieved August 8, 2018.
  14. 1 2 "Hybrid Columnar Compression (HCC) on Oracle Database 18c" (PDF). Oracle Technology Network. June 1, 2018. Retrieved August 8, 2018.
  15. 1 2 Various authors (2018). "Oracle Exadata Statement of Direction" (PDF). Retrieved July 27, 2018.
  16. 1 2 3 4 Gorbachev, Alex (May 1, 2013). "A Brief History of Exadata Time" (PDF). NoCOUG Journal. Retrieved August 5, 2018.
  17. Weiss, Ronald (2012). "A Technical Overview of the Oracle Exadata Database Machine and Exadata Storage Server" (PDF). Oracle Corporation. Retrieved August 2, 2018.
  18. Various authors (2018). Oracle Exadata Storage Server X7-2 EF and HC Service Manual. Oracle Corporation. pp. 15–23.
  19. 1 2 3 Loaiza, Juan (July 19, 2016). "Guaranteeing Fast Response for OLTP Messages". YouTube. Retrieved August 5, 2018.
  20. Rivenes, Andy (October 30, 2017). "Columnar Formats in Exadata Flash Cache". Oracle Corporation blogs. Retrieved August 6, 2018.
  21. Stern, Janet (2018). Oracle Exadata Database Machine Documentation Release 18c (18.1). Oracle Corporation.
  22. "Bring Your Own License and Universal Credits" (PDF). Oracle Corporation. 2018. Retrieved August 7, 2018.
  23. Loaiza, Juan. "Direct Access from Database to Network for Much Faster OLTP". Oracle Corporation. Retrieved August 8, 2018.
  24. "InfiniBand FAQ" (PDF). December 22, 2014. Retrieved August 8, 2018.
  25. "Data Sheet: Oracle Exadata Storage Expansion Rack X7-2" (PDF). Oracle Corporation. 2017. Retrieved August 8, 2018.
  26. "Oracle Exadata Configuration Assistant (OECA)". Oracle.
  27. "New Oracle Exadata X7 Delivers In-Memory Performance from Shared Storage". Oracle. Retrieved 2017-10-25.
  28. "Data Sheet: Sun Oracle Database Machine" (PDF). Oracle Corporation. 2009. Retrieved August 13, 2018.
  29. 1 2 "Data Sheet: Oracle Exadata Database Machine X2-2" (PDF). Oracle Corporation. 2012. Retrieved August 8, 2018.
  30. 1 2 "Data Sheet: Oracle Exadata Database Machine X3-2" (PDF). Oracle Corporation. 2013. Retrieved August 8, 2018.
  31. 1 2 "Data Sheet: Oracle Exadata Database Machine X4-2" (PDF). AAMRA Technologies. 2013. Retrieved August 8, 2018.
  32. 1 2 "Data Sheet: Oracle Exadata Database Machine X5-2" (PDF). Dynamic Systems Inc. 2015. Retrieved August 8, 2018.
  33. 1 2 "Data Sheet: Exadata Database Machine X6-2" (PDF). Oracle Corporation. 2017. Retrieved August 8, 2018.
  34. "Data Sheet: Oracle Exadata Database Machine X2-8" (PDF). Oracle Corporation. 2012. Retrieved August 8, 2018.
  35. 1 2 "Data Sheet: Oracle Exadata Database Machine X3-8" (PDF). Oracle Corporation. 2012. Retrieved August 8, 2018.
  36. 1 2 "Data Sheet: Oracle Exadata Database Machine X4-8" (PDF). Oracle Corporation. 2014. Retrieved August 8, 2018.
  37. 1 2 "Data Sheet: Oracle Exadata Database Machine X5-8" (PDF). Oracle Corporation. 2015. Retrieved August 8, 2018.
  38. 1 2 "Data Sheet: Oracle Exadata Database Machine X6-8" (PDF). Oracle Corporation. 2017. Retrieved August 8, 2018.
  39. "Data Sheet: Oracle Exadata Database Machine X7-8" (PDF). Oracle Corporation. 2017. Retrieved August 8, 2018.
  40. 1 2 Ledbetter, Letty (September 24, 2008). "Oracle Press Release: Oracle Introduces the HP Oracle Database Machine". Oracle Corporation. Retrieved July 27, 2018.
  41. 1 2 Ledbetter, Letty (September 15, 2009). "Oracle Press Release: Oracle Unveils Exadata Version 2: The First Database Machine for OLTP". Oracle Corporation. Retrieved July 27, 2018.
  42. 1 2 Ledbetter, Letty (September 20, 2010). "Oracle Press Release: Oracle Introduces Exadata Database Machine X2-8". Oracle Corporation. Retrieved July 27, 2018.
  43. 1 2 Ledbetter, Letty (October 1, 2012). "Oracle Press Release: Oracle Announces Oracle Exadata X3 Database In-Memory Machine". Oracle Corporation. Retrieved July 27, 2018.
  44. 1 2 Ledbetter, Letty (December 11, 2013). "Oracle Press Release: Oracle Launches Oracle Exadata Database Machine X4". Oracle Corporation. Retrieved July 27, 2018.
  45. 1 2 Whitaker, Teri (January 21, 2015). "Oracle Press Release: Oracle Tackles Data Center Cost and Complexity with Next Generation Engineered Systems". Oracle Corporation. Retrieved July 27, 2018.
  46. 1 2 Maloney, Nicole (April 5, 2016). "Oracle Press Release: New Oracle Exadata X6 Database Machine Delivers Next-Generation Flash Performance, Massive Storage Capacity, and Real-Time OLTP". Oracle Corporation. Retrieved July 27, 2018.
  47. 1 2 Maloney, Nicole (October 2, 2017). "Oracle Press Release: New Oracle Exadata X7 Delivers In-Memory Performance from Shared Storage". Oracle Corporation. Retrieved July 27, 2018.
  48. 1 2 Whitaker, Teri (October 26, 2015). "Oracle Press Release: Oracle Expands Database Cloud Portfolio with Unprecedented Enterprise-Class Capabilities". Oracle Corporation. Retrieved July 27, 2018.
  49. Loaiza, Juan (July 19, 2018). "Exadata Smart Scan: Moving Queries to Data is Required to Realize Flash Performance". YouTube. Retrieved August 5, 2018.
  50. Osborne, Kerry (August 3, 2011). "Understanding Exadata Offloading" (PDF). Kerry Osborne's Oracle Blog. Retrieved July 27, 2018.
  51. Stern, Janet (2018). Oracle Exadata System Software User's Guide, 18c (18.1). Oracle Corporation. p. 12.
  52. "Data Sheet: Sun Oracle Exadata Storage Server" (PDF). Oracle Corporation. 2010. Retrieved August 8, 2018.
  53. "Sun Oracle Database Machine and Exadata Storage Server". Oracle Corporation. 2009. Retrieved August 8, 2018.
  54. Subramaniam, Mahesh (December 1, 2013). "White Paper: Exadata Smart Flash Cache Features and the Oracle Exadata Database Machine" (PDF). Oracle Corporation. Retrieved July 27, 2018.
  55. Loaiza, Juan (July 19, 2016). "Storage Indexes: By Understanding Data, Exadata Can Radically Reduce I/O". YouTube. Retrieved August 5, 2018.
  56. Loaiza, Juan (July 19, 2016). "Smart Logging Enables Fast OLTP on Flash". YouTube. Retrieved August 5, 2018.
  57. Fusek, Peter (November 20, 2011). "Video: Oracle Exadata Smart Flash Log". YouTube. Retrieved August 3, 2018.
  58. Nguyen, Khang T (December 26, 2012). "Securing Databases with Intel Advanced Encryption Standard New Instructions (AES-NI)". Intel. Retrieved August 3, 2018.
  59. Various authors (2017). "Data Sheet: Oracle Exadata Storage Expansion Rack X7-2" (PDF). Oracle Corporation. Retrieved August 3, 2018.
  60. Gomez, Deiby. "Oracle Exadata Database Machine - Write-Back Flash Cache". Oracle Corporation. Retrieved August 3, 2018.
  61. Loaiza, Juan (July 19, 2016). "Instant Server Failure Detection". YouTube. Retrieved August 5, 2018.
  62. Stern, Janet (2018). Exadata Database Machine Licensing Information User's Guide, 18c. Oracle Corporation. pp. 5–6.
  63. Stern, Janet (2018). Exadata Database Machine System Overview. Oracle Corporation. pp. 14–17.
  64. Goindi, Gurmeet (February 19, 2015). "Controlling Software Costs on Exadata". Oracle Blogs. Retrieved August 3, 2018.
  65. Shah, Manish (March 1, 2015). "White Paper: Storage Efficient Database Copy Options with Exadata" (PDF). Oracle Technology Network. Retrieved August 3, 2018.
  66. Loaiza, Juan (July 19, 2016). "Duplicating In-Memory Data Assures Availability". YouTube. Retrieved August 5, 2018.
  67. Colgan, Maria (March 10, 2017). "Oracle Database In-Memory Fault Tolerance Explained". YouTube. Retrieved August 3, 2018.
  68. "Data Sheet: Oracle Databade Exadata Cloud Service" (PDF). Oracle Corporation. 2017. Retrieved August 8, 2018.
  69. Loaiza, Juan (July 19, 2016). "Direct Access From Database to Network for Much Faster OLTP". YouTube. Retrieved August 5, 2018.
  70. Loaiza, Juan (July 19, 2016). "Non-Blocking Transfers of OLTP Data on Clusters". YouTube. Retrieved August 5, 2018.
  71. Maloney, Nicole (February 27, 2017). "Oracle Press Release: Oracle Expands Oracle Cloud at Customer Portfolio to Database Workloads with Oracle Exadata Cloud Machine". Oracle Corporation. Retrieved July 27, 2018.
  72. Loaiza, Juan (2017). "Video: Oracle's Next Generation Exadata Database Machine X7". Oracle Corporation. Retrieved August 2, 2018.
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