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Exadata Goals Ideal Oracle Database Platform

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Presentation on theme: "Exadata Goals Ideal Oracle Database Platform"— Presentation transcript:

1 Exadata Database Machine Morana Kobal Butković Senior Sales Consultant Oracle Hrvatska

2 Exadata Goals Ideal Oracle Database Platform
Best Machine for Data Warehousing Best Machine for OLTP Best Machine for Database Consolidation Unique Architecture Makes it Fast and cost efficient Oracle has set the bar extremely high for Exadata. We are designing a single hardware platform that is best for any class of Database Workloads. The hardware is identical for all workload types since high compute capacity, high network bandwidth, low latency, high IO throughput, and Flash help any workload. The software features vary by workload. For example Oracle has many Warehouse specific software features such as Bitmap indexing, integrated OLAP and Data Mining, etc. The Unique capabilities of Exadata are highlighted in the presentation by being underlined in Red

3 Exadata in the Marketplace
Launched in Fall 2008 Seeing rapid adoption in all geographies and industries

4 Agenda Hardware Architecture Key Technologies Consolidation & Protection

5 The Products Exadata Storage Server & Database Machine
Storage Product Optimized for Oracle Database Extreme I/O and SQL Processing performance Combination of hardware and software Exadata Storage Server Software Exadata Database Machine Pre-Configured High Performance Balanced performance configuration Takes the guess work out of building an Oracle deployment Exadata Storage Server Software Oracle Database 11.2

6 Exadata Storage Server Building Block
Hardware by Sun Software by Oracle Uses high performance components 12 disks GB 15K RPM SAS 2.0, or 2TB 7200 RPM SATA 2 Xeon quad-core processors with PCI 2.0 Dual ported 40 Gb/sec InfiniBand 4 96 GB PCI Flash Cards Runs at full disk and flash bandwidth

7 Exadata Hardware Architecture
Scalable Grid of industry standard servers for Compute and Storage Eliminates long-standing tradeoff between Scalability, Availability, Cost Database Grid Storage Grid 8 compute servers (1U) 64 Intel cores 14 storage servers (2U) 112 Intel cores in storage 100 TB SAS disk, or 336 TB SATA disk 5 TB PCI Flash Data mirrored across storage servers Oracle believes that the Scaleable Grid architecture is the architecture of the future. It eliminates the long-standing tradeoff between high-end hardware platforms that are scaleable (up to a point) and available, but also very high cost due to low volume. Sometimes people think that because there is so much compute and storage in a database machine it must consume huge amounts of power. In fact the power consumption is not large. Maximum Power usage of a Full Rack Database Machine is 14KW, typical is 9.8 KW. A single high end SMP platform without storage or switches can consume well over 20 KW. InfiniBand Network Redundant 40Gb/s switches Unified server & storage net

8 Database Server Hardware
Dual-redundant, hot-swappable power supplies 72 GB DRAM (18 x 4GB) ILOM 4 x 2.5” 146GB Disk Drives 4 x 1GbE Interfaces 2 Quad-Core Intel® Xeon® E5540 Processors InfiniBand QDR (40Gb/s) dual port card Installed Software: Oracle Enterprise Linux Oracle Database 11.2 Software Drivers Disk Controller HBA with 512M battery backed cache

9 Start Small and Grow Field Upgradeable
Exadata allows easy expansion within and between racks. A quarter rack can be upgraded to a half rack. A half rack can be upgraded to a full rack. Two half racks can also be connected together to form the equivalent of a full rack. This is sometimes useful in data centers with weight or heat density restrictions. Once a full rack is deployed it can be increased in half rack size increments. For example a full rack grows to 1.5 racks, then to 2 racks, then to 2.5 racks. Equipment can be mixed across hardware generations. For example a half rack can be grown to a full rack using the next generation of servers to fill out the rack. Quarter Rack Half Rack Full Rack Balanced Incremental Scaling for OLTP and DW

10 Scales to 8 Racks by Just Adding Cables Full Bandwidth and Redundancy
Exadata has much more compute, storage, and interconnect capacity than other systems on the market. An 8 rack Exadata system is equivalent to at least 30 racks of leading conventional products, and costs much less. The user data capacity is much larger after compression. Scales beyond 8 racks by using external InfiniBand switches 10

11 Exadata Database Machine Product Family
Quarter Rack Half Rack Full Rack 2-8 Full Racks Database Servers 2 4 8 16-64 Exadata Storage Servers 3 7 14 28-112 Total Disk Capacity SAS 21 TB 50 TB 100 TB 200 – 800TB Total Disk Capacity SATA 70 TB 168 TB 336 TB 336 – 2688TB User Data (uncompressed SAS) 6 TB 14 TB 28 TB 56 – 224 TB I/O Throughput (disks SAS) 4.5 GB/sec 10.5 GB/sec 21 GB/sec GB/sec I/O Throughput (flash) 11 GB/sec 25 GB/sec 50 GB/sec GB/sec Flash IOPS 225,000 500,000 1,000,000 1M – 8M Racks 1 2-8

12 Standardized and Simple to Deploy
All Database Machines are the same Delivered Tested and Ready-to-Run Highly Optimized Highly Supportable No unique configuration issues Identical to config used by Oracle Engineering Runs existing OLTP and DW applications Full 30 years of Oracle DB capabilities No Exadata certification required Leverages Oracle ecosystem Skills, knowledge base, people, partners Ready- to-Run Eliminates the complexity of deploying a high performance database system. Database machines are tested in the factory and delivered ready to run. Because all database machines are the same, their characteristics and operations are well known and understood by Oracle field engineers and support. Each customer will not need to diagnose and resolve unique issues that only occur on their configuration. Performance tuning, and stress testing performed at Oracle is done on the exact same configuration that the customer has ensuring better performance and higher quality. Applications do not need to be certified against Exadata. Applications that are certified with Oracle Database 11.2 RAC will run against Exadata. Very few applications need to certify the storage subsystem underneath a database, and Exadata fundamentally is the Oracle Database with a very fast storage subsystem. Deploy in Days, Not Months

13 Agenda Hardware Architecture Key Technologies Consolidation & Protection

14 Keys to Speed and Cost Advantage
Exadata Hybrid Columnar Compression Exadata Intelligent Storage Grid Exadata Smart Flash Cache

15 Exadata Intelligent Storage Grid Most Scalable Data Processing
Data Intensive processing runs in Exadata Storage Grid Filter rows and columns as data streams from disks Example: How much product X sold last quarter Exadata Storage Reads 10TB from disk Exadata Storage Filters rows by Product & Date Sends 100GB of matching data to DB Servers Scale-out storage parallelizes execution and removes bottlenecks Exadata storage servers scan the data in parallel removing all central bottlenecks. Traditional storage has bottlenecks at the back-end disk loops, caches, controllers, front-end channels, and SAN links. In traditional storage, receiving Terabytes of data into the DB server and filtering it consumes large amount of CPU in the database hosts.

16 Simple Query Example SUM Exadata Storage Grid Oracle Database Grid
What were my sales yesterday? Optimizer Chooses Partitions to Access Exadata Storage Grid Oracle Database Grid Scan compressed blocks in partitions Retrieve sales amounts for Sept 24 Select sum(sales) where Date=’24-Sept’ SUM 10 TB scanned 100 GB returned to servers 16

17 Exadata Intelligent Storage
Exadata Intelligent Storage Grid Exadata storage servers also run more complex operations in storage Join filtering Incremental backup filtering I/O prioritization Storage Indexing Database level security Offloaded scans on encrypted data Data Mining Model Scoring Smart File Creation 10x reduction in data sent to DB servers is common

18 Exadata is Smart Storage
Storage Server is smart storage, not a DB node Storage remains an independent tier Database Servers Perform complex database processing such as joins, aggregation, etc. Exadata Storage Servers Search tables and indexes filtering out data that is not relevant to a query Cells serve data to multiple databases enabling OLTP and consolidation Simplicity, and robustness of storage appliance Compute and Memory Intensive Processing Data Intensive Processing

19 Exadata Hybrid Columnar Compression
Data is organized and compressed by column Dramatically better compression Speed Optimized Query Mode for Data Warehousing 10X compression typical Runs faster because of Exadata offload! Space Optimized Archival Mode for infrequently accessed data 15X to 50X compression typical Query Faster and Simpler Backup, DR, Caching, Reorg, Clone Benefits Multiply

20 Exadata Hybrid Columnar Compression How it works
Compression Unit Tables are organized into sets of a few thousand rows Compression Units (CUs) Within CU, data is organized by column, then compressed Column organization brings similar values close together, enhancing compression Useful for data that is bulk loaded and queried Update activity is light Exadata servers offload filtering, projection, etc. for scans on compressed data Return compressed blocks to database so buffer cache benefits from compression Column 2 Column 1 Column 3 Reduces Table Size 4x to 40x 4x to 50x Reduction

21 Compression Ratio of Real-World Data
Compression Ratio varies by customer and table Trials were run on largest table at 10 large companies Average Query Compression ratio was 13x On top of Oracle’s already highly efficient format This slide shows the compression ratio achieved on the largest table in Exadata trials at 10 ultra large companies. We chose to report the ratio for the largest table since that gives a much better idea of the effective size reduction than averaging ratios across large and small tables. Compressing small tables generally does not provide a huge net space savings. These tables generally have many billions of rows. Compression ratio means how many times smaller the final data was. For example a 1TB table that compresses to 100GB would have a 10x compression ratio. Note that some vendors quote compression ratios starting from highly verbose flat file formats. This allows them to inflate their compression ratios by 50% to 100%. Oracle compression ratios are based on data that is already in Oracle’s highly efficient data format that uses compact variable length representations for all data types. Customers typically find that Oracle’s uncompressed data representation uses 30% to 50% less space than other database vendors. 21

22 Hybrid Columnar Comparisons
Uncompressed Uncompressed Pure Columnar Cliff OLTP Compress OLTP Hybrid Hybrid Pure Column Pure Column Table Size Scan Time Row Lookup Time Exadata Hybrid Columnar Compression is a second generation columnar technology combining the best of row and column formats Best compression – matching full columnar Excellent scan time – Compression provides 10x speedup After 10x I/O reduction, most queries become CPU bound Good single row lookup – no full columnar “cliff” Full columnar format falls off a cliff for single row lookup operations Must do 100 I/Os for a 100 column table to “stitch” a row back together - 100x slowdown. Often missing any secondary indexing capabilities. If full scan is only access method then slowdown is 1000x or more. Many systems that are considered Warehouses have extensive row lookups and trickle feeds. Pure columnar is a niche technology that is not useful in OLTP, and is useful in only some warehouses. Hybrid columnar gains almost all the compression advantages of full columnar compression, as well as the CPU reductions from column at a time processing. Because of the high compression factors achieved by hybrid and full columnar technologies, queries often become CPU bound instead of I/O bound, or the full data set fits in memory. In those cases there is NO advantage to full columnar. 93% statement is based on 1000 GB scan which becomes 100 GB scan with hybrid and 30 GB Scan with full columnar (assuming 33% of columns referenced). Hybrid gives 90% reduction compared to 97% reduction for full columnar. 90/97=93%. 22

23 Exadata Smart Flash Cache Breaks the Disk Random I/O Bottleneck
Trade-off between traditional disks drives and Flash Disk drives are cheap, high capacity but low I/Os per second Flash is expensive, lower capacity but can support tens of thousands of I/Os per second Ideal Solution - Exadata Smart Flash Cache Keep most data on disk for low cost Transparently move hot data to flash Use flash cards instead of flash disks to avoid disk controller limitations Flash cards in Exadata storage High bandwidth, low latency interconnect 300 I/O per Sec Tens of Thousands of I/O’s per Second

24 Sun FlashFire in Exadata Sun Flash Accelerator F20 PCIe Card
Cell cache on the storage cells Write-though cache, transparently used to accelerate reads 4 x Cards (384GB/cell) used to create a cache on the cell Database Machine has 5 TB of flash storage Able to pull 3.6GB/sec total bandwidth from each storage cell, for Full Rack: 50GB/sec total from flash 21GB/sec from SAS disk, 12Gb/sec SATA disk Up to 50,000 Disk IOPS SAS or Up to 20,000 Disk IOPS SATA Up to 1,000,000 Flash IOPS

25 Exadata Smart Flash Cache
Performance Use PCIe cards instead of SSDs to avoid slow disk interface Capacity Efficient Compression increases effective performance and capacity by up to 10X Smart Caching Caches data intelligently to maximize Flash usage for frequently read data Automatically skips caching of infrequently read objects or avoid caching data that will not fit in the cache Database awareness enables caching only data likely to be accessed again User can further optimize caching policies by specifying whether or not to cache specific database objects Understands different types of I/Os from database Skips caching I/Os to mirror copies Skips caching backups I/Os due to ASM rebalance operations not cached Skips caching data pump I/O Skips caching tablespace formatting Resistant to table scans Control File Reads and Writes are cached File header reads and writes are cached Data Blocks and Index blocks are cached DBA can enforce that an object is kept in flash cache ALTER TABLE calldetail STORAGE (CELL_FLASH_CACHE KEEP) Can be set like other storage clause values At table level, partition level, during creation time etc.

26 Exadata is Architected for Flash
Traditional storage arrays now offer optional flash disks Exadata Flash Architecture Scale-Out Storage No bottlenecks to scaling flash I/O InfiniBand Highest throughput, lowest latency Intelligent Storage Key to using full flash bandwidth Even InfiniBand can’t send 50GB/sec PCI Flash Avoids disk controller bottlenecks. Cards in storage enable HA, RAC Compression Multiply flash capacity 10x Also multiplies data scan rates Flash Cache Speed of flash, cost of disk Optionally specify table placement

27 Exadata Storage Index Transparent I/O Elimination with No Overhead
Table Index Exadata Storage Indexes maintain summary information about table data in memory Store MIN and MAX values of columns Typically one index entry for every MB of disk Eliminates disk I/Os if MIN and MAX can never match “where” clause of a query Completely automatic and transparent A B C D 1 3 5 8 Min B = 1 Max B =5 Min B = 3 Max B =8 Select * from Table where B< Only first set of rows can match 27

28 Benefits Multiply Example
10 TB of user data Normally 10 TB of IO 1 TB with 10x compression 100 GB with partition pruning Seconds On Exadata 50 GB with Storage Indexes Scan 50GB in Flash 10 GB returned after Exadata Filtering Data is 10x Smaller, Scan is 2000x faster

29 Agenda Hardware Architecture Key Technologies Consolidation & Protection

30 Unified Hardware, Specialized Software
Exadata enables a single hardware architecture for all database needs Massively parallel hardware, InfiniBand, & Flash for all DB applications and workloads Enables Strategic building block approach to IT Domain specialization is in software, not hardware Analytics OLAP, Statistics, Spatial, Data Mining, etc. Warehousing Flexible Partitioning, Bitmap Indexing, Join indexing, Materialized Views, Result Cache Data Relational, XML, Objects, Secure Files OLTP, Security, HA OLAP ETL Others argue that a specialized hardware or software platform is needed for each application or class of work. Biggest driver of ongoing cost is system sprawl Hardware configurations are isolated and customized to specific applications Special systems for high performance, fault tolerance, low cost, data warehousing Database machine enables database consolidation Better performance than high-end systems With fault tolerance built in At high volume price Runs any combination of workloads with extreme performance Warehouse oriented bulk data processing OLTP oriented random updates Real time BI against transactional data Data Mining

31 Platform for Database Consolidation
ERP CRM Warehouse Data Mart HR Consolidation is key to reducing costs Administration, hardware, software, data center Many databases can be consolidated on Exadata Multiple small databases within a node Large databases can span nodes using RAC Exadata serves as farm/cloud for databases Exadata delivers performance for complex workloads that mix OLTP and DW Complex OLTP with batch and reporting Complex Warehousing with thousands of users Multiple databases running different applications

32 Consolidate Database Storage
ERP CRM Warehouse Data Mart HR Exadata and ASM allow all storage servers to be shared across databases Shared Configuration Advanced ASM data striping spreads every database across all storage servers Eliminates hot-spots and captive unused space Full storage grid performance available to all databases Predictable Performance Exadata I/O resource manager prioritizes I/Os to ensure predictable performance At user, job, application, or database level No need for isolated storage islands

33 Consolidate Database Servers
Many databases can run on Database Machine servers Shared Configuration Applications connect to a database service that runs on one or more database servers Services can grow, shrink, & move dynamically Large databases can span nodes using RAC Multiple small databases can run on a single node Predictable performance Instance caging provides predictable CPU resources when multiple databases run on the same node Restricts a database to subset of processors ERP CRM Warehouse Data Mart HR


35 Resources
Oracle Exadata Technology Portal on OTN:

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