Download as PDF, PPTX
![Full-Text Search
“Rain Tire” ~5Kinetica includes powerful text search functionality,
including :
• Exact Phrases
• Boolean – AND / OR
• Wildcards
• Grouping
• Fuzzy Search (Damerau-Levenshtein optimal string alignment algorithm)
• N-Gram Term Proximity Search
• Term Boosting Relevance Prioritization
"Union Tranquility"~10
[100 TO 200]
22](https://image.slidesharecdn.com/howtoachievereal-timeanalyticsonadatalakeusinggpus-170620170154/75/How-To-Achieve-Real-Time-Analytics-On-A-Data-Lake-Using-GPUs-24-2048.jpg)
Uploaded byKinetica
How To Achieve Real-Time Analytics On A Data Lake Using GPUs
The document discusses methods for achieving real-time analytics on data lakes using GPUs, addressing challenges such as large data volumes and minimal latency. It presents various architectural approaches, including lambda and kappa architectures, and highlights the advantages of GPU-accelerated databases like Kinetica for processing and analyzing big data efficiently. Additionally, it includes use cases in logistics and financial sectors, showcasing the performance benefits of rapid data ingestion and analytics capabilities.
More Related Content
Similar to How To Achieve Real-Time Analytics On A Data Lake Using GPUs
How To Achieve Real-Time Analytics On A Data Lake Using GPUs
- 1.
- 2. The Challenge: How to maintain analytic performance while dealing with: • Larger data volumes • Streaming data with minimal end-to-end latency •Ad-hoc drill down (you can’t pre-aggregate everything) 2
- 3. Architectural and Design Approaches 1. One database to rule them all 2. SQL on Hadoop (or directly on the Data Lake) 3.Data Lake + NoSQL + Spark + Search + Cache +… 4. Lambda Architecture 5. Kappa Architecture 6. Next generation hardware acceleration 3
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10. Kappa Architecture Credit: Jay Kreps https://www.oreilly.com/ideas/questioning-the-lambda-architecture 8 Stream processing systemsalready have a notion of parallelism; why not just handle reprocessing by increasing the parallelism and replaying history very, very fast?
- 11. Next Generation Hardware Acceleration Credit: Jay Kreps https://www.oreilly.com/ideas/questioning-the-lambda-architecture 8 Consider a system with these characteristics: • Horizontally Scalable • Low end-to-end latency •Powerful enough to not require pre-aggregation This is now possible…
- 12. GPU Accelerated Compute 12 DATA WAREHOUSE RDBMS & Data Warehouse technologies enable organizations to store and analyze growing volumes of data on high performance machines, but at high cost. DISTRIBUTED STORAGE Hadoop and MapReduce enables distributed storage and processing across multiple machines. Storing massive volumes of data becomes more affordable, but performance is slow AFFORDABLE MEMORY Affordable memory allows for faster data read and write. HANA, MemSQL, & Exadata provide faster analytics. 1990 - 2000’s 2005…2010… 2017… AT SCALE PROCESSING BECOMES THE BOTTLENECK GPU ACCELERATED COMPUTE GPU cores bulk process tasks in parallel - far more efficient for many data-intensive tasks than CPUs which process those tasks linearly.
- 13. Kinetica: Core 13 ANALYTICS DATABASE ACCELERATED BY GPUs KINETICA Commodity Hardware w/ GPUs Disk A1 B1 C1 A2B2 C2 A3 B3 C3 A4 B4 C4 GPU Accelerated Columnar In-memory Database HTTP Head Node Columnar in-memory database Data available much like a traditional RDBMS… rows, columns Data held in-memory; persisted to disk Interact with Kinetica through its native REST API, Java, Python, JavaScript, NodeJS, C++, SQL, etc… as well as with various connectors Native GIS & IP address object support VERY FAST: Ideal for OLAP workloads Typical hardware setup: 256GB - 1TB memory with 2-4 GPUs per node.
- 14. Multi-Head Ingest and Scale-Out Architecture ON-DEMAND SCALE OUT Commodity Hardware w/ GPUs Disk A1 B1 C1 A2B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Head Node + Commodity Hardware w/ GPUs Disk A1 B1 C1 A2 B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Head Node Commodity Hardware w/ GPUs Disk A1 B1 C1 A2 B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Head Node MULTI-HEAD INGEST 19
- 15. Real-Time Data Handlers for Structured & Unstructured Data VISUALIZATION via ODBC/JDBCAPIs Java API JavaScript API REST API C++ API Node.js API Python API OPEN SOURCE INTEGRATION Apache NiFi Apache Kafka Apache Spark Apache Storm GEOSPATIAL CAPABILITIES Geometric Objects Tracks Geospatial Endpoints WMS WKT KINETICA CLUSTER On-Demand Scale Commodity Hardware w/ GPUs Disk A1 B1 C1 A2B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Head Node Commodity Hardware w/ GPUs Disk A1 B1 C1 A2 B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Head Node Commodity Hardware w/ GPUs Disk A1 B1 C1 A2 B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Head Node Commodity Hardware w/ GPUs Disk A1 B1 C1 A2 B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Head Node OTHER INTEGRATION Message Queues ETL Tools Streaming Tools 20
- 16.
- 17.
- 18.
- 19. Advanced In-Database Analytics 1. User-defined functions (UDFs) can receive table data, do arbitrary computations, and save output to a separate table in a distributed manner. 2. UDFs have direct access to CUDA APIs –enables compute-to-grid analytics for logic deployed within Kinetica. 3. Works with custom code, or packaged code. Opens the way for machine learning/artificial intelligence libraries such as TensorFlow, BIDMach, Caffe and Torch to work on data directly within Kinetica. 4. Available now with C++ & Java bindings. 19 ORCHESTRATION LAYER WITH USER-DEFINED FUNCTIONS (UDFs) PHYSICAL / VIRTUAL SERVER Table A Table n GPU UDFs exposed from RESTful endpoint Data returned to output table for further analysis CUDA Libraries n number of Kinetica servers Table B Table C Proc Server UDF_A UDF_B UDF_n /exec/proc/UDF_A/
- 20.
- 21. 21 AI & BI on One GPU-Accelerated Database HIGH PERFORMANCE ANALYTICS DATABASE UDF UDF UDF ODBC / JDBCNative REST API WMS BUSINESS INTELLIGENCE CUSTOM APPLICATIONS HIGH FIDELITY GEOSPATIAL PIPELINE MACHINE LEARNING & DEEP LEARNING GPU-ACCELERATED DATA SCIENCE PREDICTIVE MODELS e.g. Risk Management, Sales Volume, Fraud. BIDMach SQL DATA SCIENTISTS / DEVELOPERS BUSINESS USERS
- 22. 50-100x Faster on Queries with Large Datasets • Large retailer tested complex SQL queries on 3 years of retail data (150bn rows) • 10 node Kinetica cluster against 30TB+ cluster from next best alternative •GPU is able to perform many instructions in parallel. Huge performance gains on aggregations, group bys, joins, etc. • Kinetica sustained ingest of 1.3bn objects/minute with 70 attributes per row 22 WHEN COMPARED TO LEADING IN-MEMORY ALTERNATIVES SUM (Q1) GROUP BY (Q5) SELECT (Q10) 0 5 10 15 20 25 30 35 40 45 50 Kinetica Leading In-Memory DB More Details
- 23. 23 Distributed Geospatial Pipeline 23 • NATIVE VISUALIZATION IS DESIGNED FOR FAST MOVING, LOCATION-BASED DATA Native Geospatial Object Types • Points, Shapes, Tracks, Labels Native Geospatial Functions •Filters (by area, by series, by geometry, etc.) • Aggregation (histograms) • Geofencing - triggers • Video generation (based on dates/times) Generate Map Overlay Imagery (via WMS) • Rasterize points • Style based on attributes (class-break) • Heat maps
- 24. Full-Text Search “Rain Tire” ~5Kinetica includes powerful text search functionality, including : •Exact Phrases • Boolean – AND / OR • Wildcards • Grouping • Fuzzy Search (Damerau-Levenshtein optimal string alignment algorithm) • N-Gram Term Proximity Search • Term Boosting Relevance Prioritization "Union Tranquility"~10 [100 TO 200] 22
- 25.
- 26.
- 27. LOGISTICS & FLEET MANAGEMENT 27 Kinetica enables agile tracking of shipments to assist store managers for tracking of inventory and arrival times. • Visibility and tracking of deliveries & trucks for store managers • ETA & Notifications –Provide estimated time of delivery, notifications and custom location based alerting • Route Optimization based on truck size, and if cargo is perishable or contains hazardous materials. LARGE RETAILER CASE STUDY : LOCATION BASED ANALYTICS
- 28. RISK MANAGEMENT 28 Large financial institution moves counterparty risk analysis from overnight to real-time. • Data collected by XVA library which computes risk metrics for each trade • Risk computations are becoming more complex and computationally heavy. xVAanalysis needs to project years into the future. • Kinetica enables banks to move from batch/overnight analysis to a streaming/real-time system for flexible real-time monitoring by traders, auditors and management. MULTINATIONAL BANK CASE STUDY : ADVANCED IN-DATABASE ANALYTICS
- 29.
- 30.