EXADATA ARCHITECTURE IN ORACLE

OVERVIEW : 

              You can use Exadata to consolidate your storage environment Using Exadata multiple
 databases can use storage from a single pool. Exadata uses Oracle Automatic Storage Management (ASM) to evenly distribute the storage load for every database across
every available disk in the storage pool. Every database can use all the available disks
to maximize performance. Exadata requires the use of Oracle Database 11g Release 2.
Exadata works equally well with single-instance or Oracle Real Application Clusters
(RAC) databases. Users and database administrators use the same tools and
knowledge they are already familiar with. Being based on industry-standard components
and technologies, Exadata is inexpensive to deploy. In addition, tight integration with the
full suite of Oracle Database high-availability features, ensures that the reliability and
integrity needs of mission-critical environments are met.

             A key advantage of Exadata is the ability to offload some database processing to
Exadata servers. With Exadata, the database can offload single table scan predicate
filters and projections, join processing based on bloom filters, along with CPU-intensive
decompression and decryption operations. This ability is known as SQL processing

offload or Smart Scan.


        In addition to Smart Scan, Exadata has other smart storage capabilities including the
ability to offload incremental backup optimizations, file creation operations, and more. This
approach yields substantial CPU, memory, and I/O bandwidth savings in the database
server resulting in potentially massive performance improvements.
• Exadata includes Exadata Hybrid Columnar Compression. This feature provides very high
levels of data compression implemented inside Exadata. Exadata Hybrid Columnar
Compression allows the database to reduce the number of I/Os required to scan a table.
For example, for data with a compression ratio of 10 to 1, the I/Os required to scan the
data are reduced from 10 to 1 as well.
• Exadata ensures that I/O resources are made available whenever, and to whichever,
database needs them based on priorities and policies that you can define. The Database
Resource Manager (DBRM) and Exadata I/O Resource Management (IORM) work
together to manage intradatabase and interdatabase I/O resource usage to ensure that
your defined service-level agreements (SLAs) are met when multiple applications and
databases share Exadata storage.
• Finally, even for queries that do not use Smart Scan, Exadata has many advantages over
conventional storage. Exadata is highly optimized for fast processing of large queries. It
has been carefully architected to ensure no bottlenecks in the controller or in other
components inside the storage server. It makes intelligent use of high-performance flash
memory to boost performance and also uses a state-of-the-art InfiniBand network that has
much higher throughput than conventional storage networks.








Exadata is a self-contained storage platform that houses disk storage and runs the Exadata
Storage Server Software provided by Oracle A single Exadata server is also called a cell A
 cell is the building block for a storage grid. More cells provide greater capacity and I/O bandwidth. Databases are typically deployed across multiple cells, and multiple databases
can share a single cell. The databases and cells communicate with each other via a highperformance InfiniBand network.
         Each cell is a purely dedicated storage platform for Oracle Database files although you can use Database File System (DBFS), a feature of Oracle Database, to store your business files
inside the database. 
         Like other storage arrays, each cell is a computer with CPUs, memory, a bus, disks, network adapters, and the other components normally found in a server. It also runs an operating system (OS), which in the case of Exadata is Linux. The Oracle-provided software resident in the Exadata cell runs under this operating system. The OS is accessible in a restricted mode to administer and manage Exadata.


                

Classic Database I/O and SQL Processing Model
With traditional storage, all the database intelligence resides in the software on the database
server To illustrate how SQL processing is performed in this architecture an example of a
 table scan is shown in the graphic in the slide.
1. The client issues a SELECT statement with a predicate to filter a table and return only
the rows of interest to the user.
2. The database kernel maps this request to the file and extents containing the table.
3. The database kernel issues the I/Os to read all the table blocks.
4. All the blocks for the table being queried are read into memory.
5. SQL processing is conducted against the data blocks searching for the rows that satisfy
the predicate.
6. The required rows are returned to the client.
As is often the case with the large queries, the predicate filters out most of the rows in the
table. Yet all the blocks from the table need to be read, transferred across the storage
network, and copied into memory. Many more rows are read into memory than required to
complete the requested SQL operation. This generates a large amount of unproductive I/O,

which wastefully consumes resources and impacts application throughput and response tim







Exadata Smart Scan Model
Using Exadata, database operations are handled differently. Queries that perform table scans
can be processed within Exadata and return only the required subset of data to the database
server. Row filtering, column filtering, some join processing, and other functions can be
performed within Exadata. Exadata uses a special direct-read mechanism for Smart Scan
processing. The above graphic illustrates how a table scan operates with Exadata:
1. The client issues a SELECT statement to return some rows of interest.
2. The database kernel determines that Exadata is available and constructs an iDB
command representing the SQL command and sends it to the Exadata cells. iDB is a
unique Oracle data transfer protocol that is used for Exadata storage communications.
3 3. The Exadata server software scans the data blocks to extract the relevant rows and
columns which satisfy the SQL command.
4. Exadata returns to the database instance an iDB message containing the requested
rows and columns of data. These results are not block images, so they are not stored in
the buffer cache.
5. The database kernel consolidates the result sets from across all the Exadata cells. This
is similar to how the results from a parallel query operation are consolidated.
6 The rows are returned to the client

Moving SQL processing off the database server frees server CPU cycles and eliminates a
massive amount of unproductive I/O transfers. These resources are free to better service

other requests. Queries run faster, and more of them can be processed.


Exadata Smart Storage Capabilities

• Predicate filtering:
– Only the rows requested are returned to the database server
rather than all the rows in a table.
• Column filtering:
– Only the columns requested are returned to the database server rather than all the columns in a table.
.

                                     Exadata Smart Storage Capabilities

The following database functions are integrated within Exadata:
              • Exadata enables predicate filtering for table scans Rather than returning all the rows for scans. the database to evaluate, Exadata returns only the rows that match the filter condition. The conditional operators that are supported include =, !=, <, >, <=, >=, IS [NOT] NULL, LIKE, [NOT] BETWEEN, [NOT] IN, EXISTS, IS OF type, NOT, AND, OR. In addition, many
common SQL functions are evaluated by Exadata during predicate filtering. For a full list
of functions that can be offloaded to Exadata, use the following query:
SELECT * FROM v$sqlfn_metadata WHERE offloadable = 'YES';
            • Exadata provides column filtering, also called column projection, for table scans. Only
the requested columns are returned to the database server rather than all columns in a
table. For tables with many columns, or columns containing LOBs, the I/O bandwidth
saved by column filtering can be very large.
When used together, the combination of predicate and column filtering dramatically improves
performance and reduces I/O bandwidth consumption. For example, when processing the
following query, Exadata returns only the employee names that are longer than five
characters:

              SELECT name FROM employees WHERE LENGTH(name) > 5;

Without predicate and column filtering, the storage subsystem would need to send all the
rows and columns of the employees table to the database to evaluate.



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