Fast data in times of crisis with GPU accelerated database QikkDB | Business Breakfast | 23.4.2020
0 likes116 views
The Adastra Group hosted a webinar discussing advancements in data management and analysis using GPU technology, particularly in the context of crisis situations like COVID-19. The presentation highlighted their GPU-accelerated database, QikkDB, which improves processing speed and efficiency for complex data tasks compared to traditional databases. Emphasizing the benefits of real-time data story-telling, they showcased tools like TellStory for visualizing and interpreting large datasets swiftly.
![Data bases & GPU intro with QikkDB [45mins]
Intro into the deep-tech DB space
What are GPUs and how they accelerate HPC
Data story telling with TellStory [45mins]
Traditional BI vs. data story telling
Explaining Covid19 by creating a data story
Agenda for today
Let’s
GO](https://image.slidesharecdn.com/businessbreakfastqikkdbandtellstory202004230v1-200423135000/85/Fast-data-in-times-of-crisis-with-GPU-accelerated-database-QikkDB-Business-Breakfast-23-4-2020-5-320.jpg)
Fast data in times of crisis with GPU accelerated database QikkDB | Business Breakfast | 23.4.2020
- 2. Adastra Group Our Solution Portfolio Webinar: Fast data in times of crisis with the help of GPU2 One Focus: Data & Digitalization Advanced Analytics (Big) Data Engineering Data Governance Cloud Services Machine Learning & AI Digital Transformation
- 3. ADASTRA Group Adastra introduction 3 Adastra Group International consulting company that creates functional solutions in various sectors, facilitating the transition to the digital era. Cutting-edge software for data quality management, Master Data Management, and data governance. Solutions to complex business problems in risk management, sales, and process optimization. Specialist in mobile app development. Full-service creative agency based on a strong technological background. Recruitment for banks, financial institutions, telecoms and insurance companies, and many others, including Adastra. Artificial intelligence, machine learning and optimization services. Big data monetization solutions. Webinar: Fast data in times of crisis with the help of GPU
- 4. Adastra Group Technical & other details Webinar: Fast data in times of crisis with the help of GPU4 The panel Matej Misik QikkDB & TellStory product owner Ask questions & answer polls Get beta access to the tools we show Leave us with feedback Tomas Synek Moderator Martin Zahumensky TellStory power user
- 5. Data bases & GPU intro with QikkDB [45mins] Intro into the deep-tech DB space What are GPUs and how they accelerate HPC Data story telling with TellStory [45mins] Traditional BI vs. data story telling Explaining Covid19 by creating a data story Agenda for today Let’s GO
- 6. General intro into the problem and to DBs
- 7. Some of our challenges Real-time visitors reporting over stream of data 30k per second ~ 2.6 billion per day e.g. monitoring crowd during an event, targeted marketing
- 8. Some of our challenges Data science on large datasets Testing hypotheses and ad-hoc querying when indexing is not predictable Profiling new datasets Large flows of commuters above 500 SIM-cards
- 9. We were looking for solutions Tested different technologies Elastic, ClickHouse... not working for us very well for various reasons Came across GPU accelerated computing so? Why not? Elastic – slow on one node, slow data ingest Actian Vector – faster, but still not performing well on one node Clickhouse – much faster, no geo-spatial capabilities, only for linux MS-SQL – even when tuned not fast enough MapD (Omnisci) – considered but far too expensive
- 10. Types of databases By type of use: • Transactional • Batch • Real-time • Analytical • Streaming By using resources: • In-memory • Disk databases • Hardware accelerated (FPGA, GPU, Quantum) Relational Columnar Time-seriesGraph DocumentKey-value By stored data: ...
- 11. The technological edge – Why GPU? GPUs for HPC (high performance computing) ~10x higher performance in single hardware unit Great effectiveness (cheaper computations) Power growing exponentially vs linear CPU Image processing Tsunami simulation DNA analyses Generic commodi ty HW Available in Cloud AWS, Azure
- 12. Lot of processors for parallel computing Intel® Xeon® Platinum 8253 has 16 cores NVDIA Tesla V100 has 5120 cores and is data center focused Rediscovery of Columnar Data Storage Utilizing GPUs computation power requires different approach to storing data. The most suitable database architecture that works well with parallel processing is columnar storage. In contrast to conventional relational databases which store data in row-based format, columnar databases store data in separate columns. In context of parallel processing, GPUs love long vectors of the same data type FIgure 1: GPUs have thousands of arithmetic logic units (ALUs) in one piece of hardware. CPU GPU GPUs help to accelerate compute-intensive use-cases “1 GPU node replaces up to 54 CPU nodes” (NVIDIA) New cards to be announced 2020 with approx. 8000 cores & 40% faster
- 13. Inserting a GPU into the machine is not enough Need to parallelize programs = hard CUDA programming model since 2007 by Nvidia Algorithms must be Embarrassingly parallel
- 14. Multi-GPU How the computation is spread onto cores GPU CUDA core A B C Logical conditions Records meeting the condition Result after reconstruction A>= B A < 5 Final AND mask 1st 1 5 Apple 0 1 0 - Orange 2 4 Grapes 0 1 0 - Lemon 3 3 Orange 1 1 1 Orange - 2nd 4 2 Lemon 1 1 1 Lemon - 5 1 Banana 1 0 0 - - nth ... Transfer data CPU RAM to GPU GPU memory – no transfers GPU to CPU RAM SELECT C FROM FRUIT_TABLE WHERE A >= B AND A < 5 Parallel execution 1 2 n 1 n
- 15. Where is Spatio-temporal different? Polygon Operations
- 16. Crucial requirements for the database system Fast insert Fast processing Scalability & high availability Limit pre-aggregations Standardized access and common syntax
- 17. Deep-tech based on real science Google Protocol Buffers Processing data on GPU is written in CUDA 10 (direct commands to HW on single core level) Database core is written in low level language C++ 17 (memory management, control of instructions…) Libraries for specific modules (networking, building, parsing…) Created in cooperation with Slovak Technical University top talents
- 18. What is qikkDB for? Filtering and aggregations over single flat huge table Spatio-temporal data processing Complex polygon operations (contains, intersect, union) Numeric and datetime data Incremental data which are growing over time Network utilization & analysis, Risk scoring, Dynamic pricing, Real-time Analytics, Hypothesis verification, Profiling of big data, Machine learning, etc. Logs Polygons IoT GPSNetwork Events Auto motive Maps
- 19. So how fast is it? 1.2B data rows in 7 columns Average execution time was obtained based on 200 query runs Biggest datasets tested at 400GB, limited by Memory, can be cached from disk for bigger datasets, benchmarks to come soon
- 20. Execution Times Results 1. QikkDB 2. GiraffeDB leading GPU database 3. CatDB leading columnar database 4. RacoonDB tuned leading relational database CPU machine(c5d.9xlarge)36 CPU cores We use codenames for well known databases because for legal reasons we can’t tell you who these slow guys are. GPU machine(p3.8xlarge)4x Tesla V100 Compared to Other DBs (results in ms) Query qikkDB @ p3.8xl qikkDB @ g4dn.12xl GiraffeDB @ p3.8xl CatDB @ c5d.9xl RacoonDB @ c5d.9xl Elastic (tuned) Spark 21x m3xl Spark i3.8xl #1 22 37 25 435 22 810 2362 22000 #2 37 82 235 1061 964 1818 3559 25000 #3 228 925 231 1630 3491 n/a 4019 27000 #4 283 1105 417 2174 3996 n/a 20412 65000 Avg 143 537 227 1325 2118 n/a 7588 34750 10 to 100x quicker
- 21. The blazing speed Same HW, 1.2bn data points, 2 databases www.tellstory.cloud Both running on AWS g4dn.12xlarge 48vCPU 192GB RAM, 4x Tesla T4 GPU Deployed beta platform with data exploration front-end
- 22. QikkDB demo on smart meter data
- 23. Persisted data on disk (compressed) Pre-loaded data on RAM Relevant columns go to the GPU Data on GPU RAM (decompressed) Result set PCI-E Filters & aggregations CUDA kernels When inserting new data a column is automatically created ~ “schema less”, good for IoT and similar Whats going on in the background? Data storage & flow
- 24. How can it scale? Multi-GPU (vertical) scalability single-node (up to 8 GPUs) • Accelerating computations • Enabling multiple session Multi-level caching • GPU RAM cache • CPU RAM cache On roadmap • Multi-nodes (horizontal) scalability • High-availability • Data lazy loading Not limited to data size ~ Best performance when data fit GPU mem, but can load from disk on demand
- 25. Why not just index? Traditional databases use indexing for faster processing resulting in slow insert qikkDB does not need indexing (but they are available anyway) Data are just appended GPU takes care of fast processing
- 26. Integration with your environment on standards you know Kafka connector ODBC/JDBC Adapters C#, Java, Python Streaming data Visualizationtools (PowerBI…) Customapplications,data analysis… Speed up your BI tools, applications or use TellStory for fast analysis TellStory Exploration & analysis FE Data story tellingwith real-timedata exploration
- 27. GPU AWS 12USD/hour GPU HW ~50k USD Expensive hardware? QikkDB can handle the queries in a fraction of the time of traditional databases, so you can do more with your hardware allocation in the same time. It also means that to do the same amount of work you need a lot less hardware and therefore saving on costs. “1 GPU node replaces up to 54 CPU nodes” (NVIDIA)
- 28. v In short: Interactive analytics on massive data sets GPU acceleration § Billions of data points in milliseconds Great for spatio-temporal data § Finding & understanding links between data points in space & time Standard SQL syntax § Easy to start using & integrate into the data science environment Efficiency & speed § GPUs becoming commodity HW and thanks to their efficiency cost per 10k queries on par with CPU approaches GPU Columnar DB Real-time queries in millisecs API, ODBC, JDBC, connects to everything SQL standard Spatio- temporal data processing Cloud or on-prem
- 29. Data bases & GPU intro with QikkDB Intro into the deep-tech DB space What are GPUs and how they accelerate HPC Data story telling with TellStory Traditional BI vs. data story telling Explaining Covid19 by creating a data story Live stories and fast data TellStory Roadmap Q&A Part 2! Let’s GO
- 30. Martin is ex-Instarea CEO now working in Ataccama as Head of Product Strategy Martin created https://qikk.ly/c ovid19 story and will lead you through how he did it
- 31. Interpreted data, easy to understand, with new facts brought to reader And once they have the story they can start to sell it to other parties Animated video playing Story telling
- 32. A story is about being visual Cool Visualization Plugins & animations Newspaper like reading & interactive Interesting facts
- 33. 1 2 3 Creating the Covid-19 story Live
- 34. When you want to have the story live, you must have the data live, and when you work with billions big data sets you need Fast Database Animated video playing LIVE story Live
- 35. More features to come in Phase 2, Let AI create your Story is in progress TellStory Roadmap Beta release JUNE Find interesting facts Minute by Minute updates (be notified when something interesting happens) Animated visualizations (timeline charts, maps) Share as Video (Instagram upload, Youtube livestream) Google sheets integration Auto update data (scheduled refresh) Embed sections (embedding only parts of story will be possible)
- 36. Value proposition for Adastra services with these tools Quick pilots for hands on experience § GPU data acceleration: 2 month pilot to deliver real-time processing of vast streaming data (e.g. 5G, smart meters, transactions) § Data story telling: 1 month pilot to provide customers with live & interactive intelligence and insights § Data story telling: 1 month pilot to give management the minute by minute data they need
- 38. Useful links More info § https://qikk.ly – product web with basic information § https://qikk.ly/downloads/qikkDB_white_pa per.pdf – White paper § https://docs.qikk.ly/ – Documentation & Installation instructions § https://support.qikk.ly/ – Issues & Features reporting portal § https://tellstory.cloud – Front-end for data visualization, SQL console on AWS § https://tellstory.ai – Find out more about TellStory