The Hardware Dilemma. Stephanie Best, SGI Director Big Data Marketing Ray Morcos, SGI Big Data Engineering

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1 The Hardware Dilemma Stephanie Best, SGI Director Big Data Marketing Ray Morcos, SGI Big Data Engineering April 9, 2013

2 The Blurring of the Lines Business Applications and High Performance Computing Are Closer Today Than Ever Before Business Applications High Performance Computing Redundancy Scale & Speed SGI s leadership in High Performance Computing and extensive experience in Hadoop and other Big Data technologies make us perfectly poised to help you tackle the changing Big Data landscape. Slide 2

3 SGI Technical Computing Compute Intensive SGI Technical Computing Data Intensive Memory/IO intensive HPC Unstructured data SGI UV TM Very large memory SMP SGI ICE TM X High performance bladed cluster SGI Rackable TM High density Optimized cluster Slide 3

4 SGI DataRaptor with Engineered from the ground up to be: Easy to Deploy. Up and running in hours, not days or weeks. Single point of contact for sales and support. Provide Real-Time Big Data - Any Volume and Any Data Type Structured, unstructured, and semi-structured. Real-time, near-real-time, and batch. Massively Scalable and Flexible. Scale as data volume grows & business demands change. Add capacity quickly, easily, and cost-effectively. True Out-of-the-Box Deployment. Each unit undergoes rigorous testing before it leaves the factory. Configured and ready to go, every time! Slide 4

5 SGI DataRaptor with Engineered from the ground up to be: Roughly 50% the cost of competing solutions or building a comparable solution yourself! Slide 5

6 SGI DataRaptor with The fastest, most flexible, easiest, and most cost effective way to achieve real-time insight from your Big Data MarkLogic Software SGI Hardware: Compute, Storage, Networking, Management Software PERFORMANCE CONFIGURATION CAPACITY CONFIGURATION Full Rack 386 TB 638 TB Half Rack 184 TB 304 TB Quarter rack 92 TB 152 TB Slide 6

7 SGI DataRaptor with The core to the SGI DataRaptor is the Rackable ISS3112 or ISS3124 storage server (similar capabilities but different disk sub-chassis for different size disks). RP2 two-socket motherboard with the latest Intel Xeon E microprocessor 16 cores of computing power per server 384 GB of main memory Up to 1600 MHz in speed Six PCIe gen 3 x 8 slots, full-height, or two PCIe gen 3 x 8 slots, full-height, and two PCIe gen3 x 16 slots, double-widht Either twelve 3.5 inch disks or twenty-four 2.5 inch disks, SAS, SATA or SSD Slide 7

8 DataRaptor Configurations Full Rack Slide 8 Option #1A: Speed: More Disk Option #1B: Option #3: Speed: More Flash High Capacity Disk Uncompressed Disk Bandwidth Up to 47GB/s Up to 35GB/s Up to 26GB/s Uncompressed Flash Data bandwidth Database Disk IOPS Database Flash IOPS Up to 40GB/s Up to 80GB/s Up to 20GB/s Up to 144,000 8K read Up to 4,200,000 8K read Up to 120,000 8K read Up to 8,400,000 8K read Up to 32,500 8K read Up to 2,100,000 8K read Raw Disk Capacity (after RAID 6) 275 TiB 205 TiB 458 TiB Uncompressed Usable Capacity ~120 TiB ~90 TiB ~200 TiB Rack 46U/24 standard rack 46U/24 standard rack 46U/24 standard rack Nodes CPU 42 Intel E5 42 Intel E5 42 Intel E5 Cores Memory 2688 GB 2688 GB 2688 GB Disk / Flash Networking 420x900TB 10Krpm + 84x100GB SLC Dual, 10GigE for data, 1 GigE for Management 336x900TB 10Krpm + 168x100GB SLC Dual, 10GigE for data, 1 GigE for Management 210x3TB 7.2Krpm + 42x200GB SLC Dual, 10GigE for data, 1 GigE for Management

9 DataRaptor Configurations Node Schematics Intel Xeon Processor E Intel Xeon Processor E CPU Intel Xeon Processor E Intel Xeon Processor E CPU 128 GB Memory 128 GB Memory I I RAID I RAID SAS Expander SAS Expander DISKS DISKS 100GB SSDs 900GB SAS HDDs 200GB SSDs 3TB SATA HDDs RAID10 200GB RAID10 4TB RAID10 4TB RAID1 200GB RAID6 (8+2) 20TB High Performance Server: ISS3124-RP2 High Capacity Server: ISS3112-RP2 Slide 9

10 DataRaptor: Configurations Model with High Performance Drives MarkLogic DB Full Rack Half Rack Quarter Rack Base Chassis or Tray Size Intel Xeon Processor Rack Size Rackable 2U E series 46U # of Sockets per Node 2 Memory per Node 16 x 8GB 10 GigE Switch per Rack 2 Nodes per Rack Sockets per Rack Drives per Node 20x 900GB 10k rpm SAS drives + 4x 100GB SLC SSDs Raw Storage per Rack (TB) 386 TB 184 TB 92TB Slide 10

11 DataRaptor: Specifications Model with High Performance Drives ISS3124-RP2 with Intel Xeon Processor E Series as MarkLogic High Performance DB Servers: Full Rack: 386 TB raw capacity Half Rack: 184 TB raw capacity Quarter Rack: 92 TB raw capacity 10GigE Ingest, Search, Process, Analyze text and media Multi-Rack: Petabytes useable capacity 2U Full Depth chassis uses 20x GB 10k rpm SAS drives, 4x100GB Enterprise Class Flash SSDs in RAID 10 configuration Separate 80GB SSD for OS drive Intel Xeon Processors E (2.7GHz eight-core) 16x 8GB 1.5v 1600 MHz DIMMs (128GB Memory) Dual port 10 GigE An optimally balanced configuration of cores, drives and memory Affordable High Performance I/O subsystem. Slide 11

12 DataRaptor: Configurations Model with High Capacity Drives MarkLogic DB Full Rack Half Rack Quarter Rack Base Chassis or Tray Size Intel Xeon Processor Rack Size Rackable 2U E series 46U # of Sockets per Node 2 Memory per Node 16 x 8GB 10 GigE Switch per Rack 2 Nodes per Rack 10x 3TB 7.2k rpm SATA drives+ 2x200GB SLC SSDs Sockets per Rack Drives per Node Raw Storage per Rack (TB) 638TB 304TB 152 TB Slide 12

13 DataRaptor: Specifications Model with High Capacity Drives ISS3112-RP2 with Intel Xeon Processor E Series as MarkLogic High Capacity DB Servers: Full Rack: 638 TB raw capacity Half Rack: 304 TB raw capacity Quarter Rack: 152 TB raw capacity 10GigE Ingest, Search, Process, Analyze text and media Multi-Rack: Petabytes useable capacity 2U Full Depth chassis uses 10x3.5 3TB 7.2k rpm SATA drives in RAID 6, 2x200GB Enterprise Class SSDs in RAID 1 configuration Separate 80GB SSD for OS drive Intel Xeon Processors E (2.7GHz eight-core); 16x 8GB 1.5v 1600 MHz DIMMs (128GB Memory); Dual port 10 GigE An optimally balanced configuration of cores, drives and memory; Affordable dollar per terabyte. Slide 13

14 Adding Hardware is Simple! Hardware-Ready Feature Adding nodes to the cluster done at software level. Database never needs to be shutdown during hardware addition. Scripts are used to add entire nodes and racks. Slide 14

15 CPoX Benchmark: Overview Simulates Wikipedia workload search application, faceted navigation, and some document updates Data set consists of 61.8 million documents (4KB/doc) in 11 languages, consuming approx 1TB of disk space. Two Phases: 1. Loading Document Phase Loads in Wikipedia articles using multiple threads & indexes the document. Each thread is simulating a dedicated user that keeps on repeating the request without sleep time in between. Benchmark metric for this phase is documents-per-second (doc/s). 2. Running runtime Request Phase Simulates Wikipedia users Metric is the overall throughput in terms of requests-per-second (req/s). Each request could be a search, a view of an article, or an edit-confirm cycle for an article. Slide 15

16 CPoX Benchmark Summary At a Glance Ingestion Rate (documents/second) High Performance configuration is 11%-15% higher than High Capacity configuration Flash I/O performance in High Performance cluster configured with 4 SSDs in RAID10 is contributing to the higher ingestion rate, compared to the High Capacity cluster configured with 2 SSDs in RAID1. Query throughput (requests/s) Within a 500ms response time is similar on the High performance and High Capacity configurations. Linear Scalability Both ingestion and throughput scale linearly from 1 to 5 and 10 nodes in the high performance and high capacity appliance clusters Slide 16

17 Response Time (ms) Requests/sec CPoX Throughput and Response Time 5 Nodes (High Performance) Throughput Scalability with users: 5 Nodes (80c) High Performance MarkLogic DB (Total 61.8 Million Docs) Key Points: Higher the better Scales almost linearly from 50% to 100% of the system size Delivers 189 requests/sec with a total of 80 physical cores or concurrent user threads Slide Number of Concurrent Threads/Users Response Time with users: 5 Nodes (80c) High Performance MarkLogic DB (Total 61.8 Million Docs) Lower the better 362 Response Time Threshold Number of Concurrent Threads/Users 762 Hyperthreading provides a 10% improvement in throughput over 80 physical threads, with a steep increase in response time beyond a threshold of ~500 ms. With no sleep time between thread initiations, the CPU usage peaks to 90% at 80 threads, the total system size.

18 Response Time (ms) Requests/sec CPoX Throughput and Response Time 10 Nodes (High Performance) Throughput Scalability with users: 10 Nodes (160c) High Performance MarkLogic DB (Total 61.8 Million Docs) Slide 18 Higher the better Number of Concurrent Threads/Users Response Time with users: 10 Nodes (160c) High Performance MarkLogic DB (Total 61.8 Million Docs) Lower the better 357 Response Time Threshold Number of Concurrent Threads/Users 843 Key Points: Scales almost linearly from 50% to 100% of the system size Delivered 358 requests/sec with a total of 160 physical cores or concurrent user threads. Hyperthreading provides only a 5% improvement in throughput over 160 physical threads, with a steep increase in response time beyond a threshold of ~500 ms. With no sleep time between thread initiations, the CPU usage peaks to 90% at 160 threads, the total system size.

19 Docs/sec Requests/sec CPoX Throughput and Response Time Multi-Node Scalability (High Performance Cluster) Throughput Scalability across nodes (High Performance) MarkLogic DB (Total 61.8 Million Docs) Higher the better (16 cores) 5 (80 cores) 10 (160 cores) Number of Nodes in the cluster Ingestion Rate Scalability across nodes (High Performance) MarkLogic DB (Total 61.8 Million Docs) Key Points: Scales linearly from 1 to 5 &10 nodes, deliversing up to 358 requests/sec on a ½ Rack. Ingestion rate using the xqsync load utility scales pretty linearly from 1 to 5 and 10 nodes in the cluster, delivering up to 15,470 docs/sec on a ½ Rack Slide 19 Higher the better 8,268 15,470 1,853 1 (16 cores) 5 (80 cores) 10 (160 cores) Number of Nodes in the cluster

20 How to Size Your SGI DataRaptor with MarkLogic Appliance Slide 20

21 Step 1: Determine configuration based on daily database loading need. A. Not Important B. High Performance: 72Tb/day C. High Capacity: 48Tb/day Step 2: Determine which configuration you are going to need. A. Not Important B. High Performance: Recommended for complex calculation queries Recommended when your customer application usage >10,000 users/hr C. High Capacity: Recommended when data size is expected to double after 1yr Slide 21

22 Step 3: Based on the previous two steps, determine the following: A. If output of steps 1 and 2 are both B : Select Performance Configuration B. If output of steps 1 and 2 is anything else: Select Capacity Configuration Step 4: Questions to answer in order to determine the data and rack-type required A. What is the expected % data growth over 5 years B. How many users per hour do you expect the appliance to service? Now complete the calculations on the following page. Slide 22

23 A. What is the expected % data growth over the next 5 years (i.e. 50% increase = 1.5) B. Enter your current data size here C. Multiply A x B to get DATA_SIZE D. Enter expected users qty/hr E. Divide above by 19,200 F. Select the ideal rack configuration based on the following: If value ~0.5 +/- 0.2 choose Quarter If value ~1 +/- 0.3 choose Half If value >1.3 choose Full If value <0.4 choose Node Slide 23

24 Step 5: Based on project requirements in Step 3, and the sizing in Step 4, identify your ideal SGI DataRaptor with MarkLogic configuration: Rack Capacity Configuration Performance Configuration Full 638 Tb 386 Tb Half 304 Tb 184 Tb Quarter 152 Tb 92 Tb Node < 4 Tb < 10 Tb Slide 24

25 Sizing Example 1 The Customer Requirements at a Glance Needs complex calculations Loads greater than 72Tb/day Current data size is 400Tb Database size is expected to grow by 25% over the next 5 years Maximum number of users per hour for customer is 30,000. Slide 25

26 Sizing Example 1 Step 1: Determine configuration based on daily database loading need. A. Not Important B. High Performance: 72Tb/day C. High Capacity: 48Tb/day Step 2: Determine which configuration you are going to need. A. Not Important B. High Performance: Recommended for complex calculation queries Recommended when your customer application usage >10,000 users/hr C. High Capacity: Recommended when data size is expected to double after 1yr Slide 26

27 Sizing Example 1 Step 3: Based on the previous two steps, determine the following: A. If output of steps 1 & 2 are both B : Select Performance Configuration B. If output of steps 1 and 2 are anything else: Select Capacity Configuration Step 4: Questions to answer in order to determine the data and rack-type required A. What is the expected % data growth over 5 years B. How many users per hour do you expect the appliance to service?. 25% over the next 5 years 30,000 users per hour Slide 27

28 Sizing Example 1 A. What is the expected % data growth over the next 5 years (i.e. 50% increase = 1.5) 1.25 B. Enter your current data size here 400 C. Multiply A x B to get DATA_SIZE 500 D. Enter expected users qty/hr 30,000 E. Divide above by 19, F. Select the ideal rack configuration based on the following: If value ~0.5 +/- 0.2 choose Quarter If value ~1 +/- 0.3 choose Half If value >1.3 choose Full If value <0.4 choose Node Slide 28

29 Sizing Example 1 Step 5: Based on project requirements in Step 3, and the sizing in Step 4, identify your ideal SGI DataRaptor with MarkLogic configuration: Rack Capacity Configuration Performance Configuration Full 638 Tb 386 Tb Half 304 Tb 184 Tb Quarter 152 Tb 92 Tb Node < 4 Tb < 10 Tb Slide 29

30 Sizing Example 2 The Customer Requirements at a Glance Does not care about daily loading Believes that their database is not likely to grow beyond 25% in the next year Current database size is 50Tb The database size is expected grow by 10% in the next five years. The maximum number of users per hour is 8,000. Slide 30

31 Sizing Example 2 Step 1: Determine configuration based on daily database loading need. A. Not Important B. High Performance: 72Tb/day C. High Capacity: 48Tb/day Step 2: Determine which configuration you are going to need. A. Not Important B. High Performance: Recommended for complex calculation queries Recommended when your customer application usage >10,000 users/hr C. High Capacity: Recommended when data size is expected to double after 1yr Slide 31

32 Sizing Example 2 Step 3: Based on the previous two steps, determine the following: A. If output of steps 1 & 2 are both B : Select Performance Configuration B. If output of steps 1 and 2 are anything else: Select Capacity Configuration Step 4: Questions to answer in order to determine the data and rack-type required A. What is the expected % data growth over 5 years 10% over the next 5 years B. How many users per hour do you expect the appliance to service? 10,000 users per hour Slide 32

33 Sizing Example 2 A. What is the expected % data growth over the next 5 years (i.e. 50% increase = 1.5) 1.10 B. Enter your current data size here 50 C. Multiply A x B to get DATA_SIZE 55 D. Enter expected users qty/hr 8,000 E. Divide above by 19, F. Select the ideal rack configuration based on the following: If value ~0.5 +/- 0.2 choose Quarter If value ~1 +/- 0.3 choose Half If value >1.3 choose Full If value <0.4 choose Node Slide 33

34 Sizing Example 2 Step 5: Based on project requirements in Step 3, and the sizing in Step 4, identify your ideal SGI DataRaptor with MarkLogic configuration: Rack Capacity Configuration Performance Configuration Full 638 Tb 386 Tb Half 304 Tb 184 Tb Quarter 152 Tb 92 Tb Node < 4 Tb < 10 Tb Slide 34

35 Any Questions? Slide 35

36 For More Information Stephanie Best Ray Morcos Slide 36

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