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Convergence of HPC, Databases, and Analytics

Convergence of HPC, Databases, and Analytics. Tirthankar Lahiri Senior Director, Oracle TimesTen In-Memory Database. Enabling The Real-Time World. Financial Services. Social Media. Real-Time Analytics . Telecommunications. eCommerce. Next Generation DBMSs. High-Speed Networks.

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Convergence of HPC, Databases, and Analytics

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  1. Convergence of HPC, Databases, and Analytics TirthankarLahiri Senior Director, Oracle TimesTen In-Memory Database

  2. Enabling The Real-Time World Financial Services Social Media Real-Time Analytics Telecommunications eCommerce Next Generation DBMSs High-Speed Networks Multicore 64-bit Processors SSD/Persistent Memory Massive DRAM capacity

  3. Analytics Requirements • Intuitive interfaces • Instantaneous response time • Real-time reporting • Structured / Unstructured data • Extreme concurrency

  4. Hardware Trends • Processor throughput • Cores per socket: 5x in last 5 years • Clock frequency: Flat in last 2 years • Memory capacity 2x every 2 years • High-speed networking: Infiniband, RapidIO, 10GigE, etc. • Persistent memory technologies • Flash replacing disks for online storage • PC-RAM, MRAM : extension of RAM, or superfast disks for hot data

  5. Database Design Trends • Emerging Industry Trend • Exadata, Exalytics, Big-Data Appliance • Balanced Compute/Capacity/Power • Co-developed components • Built-in scale up, scale out • Built-in interoperability • Pushdown functionality into hardware • Exadata Smart Storage • Integrated Appliances

  6. Example: Oracle ExalyticsIn-Memory Machine TimesTen for Exalytics 1 TB RAM 40 Processing Cores High Speed Networking Memory Optimized Essbase Adaptive In-Memory Tools Optimized Oracle Business Intelligence Foundation Suite In-Memory Analytics Software In-Memory Analytics Hardware

  7. Database Design Trends • CPU core performance is flat • Parallelize (don’t paralyze) • Coarse-grained parallelism • Exploit workload parallelism • Parallelize query execution • Parallelize maintenance operations (backup/restore) • Exploit high speed communication primitives (e.g. Infiniband RDS) • Fine-grained parallelism • Vector execution • Multi-threading of low-level primitives • Parallelism Everywhere

  8. Database Design Trends • Use all the available tools: • In-memory storage when applicable • Column storage for sequential accesses • Row storage for random accesses • Advanced compression techniques • More bang for your storage buck • !!Beware of NUMA !! • NUMA locality awareness • Lock free or well partitioned data structures • Avoid global updates to shared memory • Storage Management

  9. Database Design Trends Where’s that %^@#$ plan?? • Enhance for analytics • Analytic functions, data mining models, graph models, etc • Cache-friendly access methods • Sequential scans better than random access • Cost modeling for modern hardware • Disk IOs are no longer the dominant cost • Cache Misses / Memory References • CPU cycles, execution time • Query Optimization

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