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Building a High Performance Analytics Platform
The document discusses using flash memory to build a high performance data platform. It notes that flash memory is faster than disk storage and cheaper than RAM. The platform utilizes NVMe flash drives connected via PCIe for high speed performance. This allows it to provide in-memory database speeds at the cost and density of solid state drives. It can scale independently by adding compute nodes or storage nodes. The platform offers a unified database for both real-time and analytical workloads through common APIs.
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- 1. A H ig h Pe r f o r m a n c e D a t a P l a t f o r m U s i n g F l a s h M A R C H 2 0 1 8 S u p e r C o m p u t i n g A s i a
- 2. • Edit Mastertext styles • Second level • Third level • Fourth level • Fifth level $48M in Funding20+ Deployments Industrial IoT Financial Services Telecommunications Cyber Security 2 100+ Employees NA APAC EMEA iguazio
- 3. • Disk andNetwork were still slow • Network was in general slower than disk When Hadoop started
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- 5. • Disk isfaster than Network Compute is where data is Compute Data StorageDistribute compute
- 6. • Scalable butinherently batch and slow • Disk and Network I/O (Map, reduce, shuffle … ) • Runs on JVM • Not suitable for all type of workloads (real-time, interactive, iterative M/L) • Cannot scale compute independent of storage and vice versa Big Data Workloads – Performance Characteristics
- 7. Big Data Workloads– Performance Characteristics Big Data Workload Disk I/O Network I/O Compute Iterative Machine Learning Jobs Interactive Analytics at scale Lambda & Kappa Architecture at scale Typically Disk or Network maxes out during a massively parallel job, before Compute High Medium Low
- 8. Big Data Workloads– Performance Characteristics Big Data Workload Disk I/O Network I/O Compute Iterative Machine Learning Jobs Interactive Analytics at scale Lambda & Kappa Architecture at scale Traditional Database Workload Compare this to traditional RDBBS – CPU or Disk I/O maxes out Typically Disk or Network maxes out during a massively parallel job, before Compute High Medium Low
- 9. Big Data Workloads– Performance Characteristics Big Data Workload Disk I/O Network I/O Compute Iterative Machine Learning Jobs Interactive Analytics at scale Lambda & Kappa Architecture at scale Traditional Database Workload in-memory and high-speed networking ? High Medium Low
- 10. • L1 cachereference 0.5 ns • Branch mispredict 5 ns • L2 cache reference 7 ns • Mutex lock/unlock 100 ns • Main memory reference 100 ns • Send 2K bytes over 1 Gbps network 20,000 ns • SSD seek 80,000 ns • Read 1 MB sequentially from memory 250,000 ns • Round trip within same datacenter 500,000 ns • Disk seek 10,000,000 ns • Read 1 MB sequentially from network 10,000,000 ns • Read 1 MB sequentially from disk 30,000,000 ns • Send packet CA->Netherlands->CA 150,000,000 ns Designs, Lessons and Advice from Building Large Distributed Systems by Dr Jeff Dean of Google Source http://www.slideshare.net/ikewu83/dean- keynoteladis2009-4885081 Optimizing Big Data Performance
- 11. RAM is 10-20xmore expensive than Flash in $Cost / GB RAM is expensive
- 12. SSDs prices arefalling
- 13. • L1 cachereference 0.5 ns • Branch mispredict 5 ns • L2 cache reference 7 ns • Mutex lock/unlock 100 ns • Main memory reference 100 ns • Send 2K bytes over 1 Gbps network 20,000 ns • SSD seek 80,000 ns • Read 1 MB sequentially from memory 250,000 ns • Round trip within same datacenter 500,000 ns • Disk seek 10,000,000 ns • Read 1 MB sequentially from network 10,000,000 ns • Read 1 MB sequentially from disk 30,000,000 ns • Send packet CA->Netherlands->CA 150,000,000 ns Designs, Lessons and Advice from Building Large Distributed Systems by Dr Jeff Dean of Google Source http://www.slideshare.net/ikewu83/dean- keynoteladis2009-4885081 Optimizing Big Data Performance
- 14. 0 1 2 3 4 5 6 7 8 9 DRAM NVMe SSDSATA SSD SAS HDD 8.5 1.2 0.85 0.04 Cost in $ per GB (Raw) Cost in $ per GB (Raw) Price of Memory Technology 2016/17
- 15. • NVMe isa software based standard that was specifically optimized for SSDs connected through the PCIe interface. What is NVMe?
- 16. • Shorter hardwaredata access path – directly connected via PCIe, Faster compared to SATA • NVMe Completely redesigned software - bypasses conventional block layer request queue – • Asynchronous Submission Queue for requests • Asynchronous Completion Queue Why is NVMe faster (than SATA) ? Source http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=2CA58A08BFB2821F59A7996DAC8742F1?doi=10.1.1.697.1493&rep=rep1&type=pdf
- 17. 17 Micro-Services & Serverless Functions UnifiedDB for Real-Time & Analytics EXTERNAL EVENTS AND SOURCES IMMEDIATE INSIGHTS & ACTIONS EXTERNAL DATA Enrich Reinforce Learning iguazio • Combined fresh data • and historical data • Immediate Insights • Rapid time to production • Supporting common APIs • Deployment Everywhere Unified DB for Real-Time & Analytics
- 18. FUNCTIONS & MICRO-SERVICES SQL &TS QUERY APIS ANALYTICS & AI TOOLS AWS APIS iguazio Data platform for real time and analytics Single platform All in One S3 Kinesis DynamoDB Secure Intelligent Multi-model UNIFIED & REAL-TIME DATABASE ENGINE EXTERNAL DATA LAKES & CLOUDS Architecture • In-mem DB performance with Flash economies and density • Access data concurrently through multiple standard APIs • Fully integrated PaaS
- 19. 19 LOCAL NVMe AND OSBYPASS Tr a d i t i o n a l L a y e r e d A p p r o a c h i g u a z i o How to take full advantage of NVMe?
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- 21. iguazio © 2016 21 Thetests were performed using the Yahoo! Cloud Serving Benchmark (YCSB), an open source framework for evaluating and comparing the performance of multiple types of NoSQL database management systems, the de facto industry standard for this purpose. Performance Results
- 22. Independently scaling compute andstorage iguazio Scale Outx2 Storage Data nodes (Processing) CPU/Drive ratio Performance Capacity Smart Rebuild Scale Out System CPU/Drive ratio Performance Capacity Smart Rebuild Processing + Storage Scale Up System Processing CPU/Drive ratio Performance Capacity Smart Rebuild Storage
- 23. iguazio © 2016 24 Whyiguazio? Very high ingestion rate Low latency for faster dashboards – in memory speed at the cost of SSD Real time analytics on both fresh and historical data Scale out architecture – enabling real time analytics on large data sets Platform as a service providing cloud experience
- 24. iguazio © 2016 25 Businessbenefits - what’s in it for me? Increase operational efficiency Reduce TCO Faster time to market for new services Reduce cost of traditional data center operations while constraining growth of expensive cloud services such as Amazon DynamoDB, Kinesis , S3 , redshift and EMR Bring new services on-line in less time with greater reliability & security Optimize business processes Increase data engineering efficiency Simplify the overall data pipe-line helping engineering to focus on building applications
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