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Kinetica master chug_9.12

September 12 CHUG Meetup w Kinetica GPU enabled In Memory Database

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Kinetica – Industry’s Fastest Analytics Database 1
About	Me
• Engineering	Background	- AppDev
• Open	source	Contributor
• Hadoop	– 10	years
• HWX	Principle	Solution	Engineer
• Director,	Solutions	Engineering	@	Kinetica
• Kinetica Local	Contact	Information
• Sunile Manjee,	Director	Solutions	Engineering,	smanjee@kinetica.com
• Phil	Zacharia,	Director	Central	Region,	pzacharia@kinetica.com
2
The image part with relationship ID rId2 was not found in the file.
What	is	Kinetica?
3
Patented
In	Memory
Columnar
Distributed
GPU	Accelerated
Database
The image part with relationship ID rId2 was not found in the file.
Developed	to	Identify	Terroristic	Threats	in	Real-
Time
4
Kinetica incubated as a massively parallel
computational engine for US Army INSCOM
Ingests 200+ sources of streaming data –
mobile devices, drones, social media, cyber data
200B new records per hour
Incorporates geospatial and temporal data
Real-time, actionable threat intelligence
First high-performance database leveraging GPUs
4
The image part with relationship ID rId2 was not found in the file.
Who	is	Kinetica?
2009
‘HPC Research Project’
incubated by US military
2010
2011
Patent # US8373710
B1 issued to GPUdb
2012
US Army deploys
GPUdb
2013
GPUdb commercially
available
2014
IDC HPC innovation
excellence award
Army
GPUdb goes
into production
at USPS
2015
Iron Net selects
GPUdb for Cyber
Defense
2015
PG&E selects GPUdb
for electric grid
analysis
IDC HPC innovation
excellence award
USPS
2016
Rebrand to
The image part with relationship ID rId3 was
not found in the file.
4
2012
Confidential Information
Confidential Information
6
Current Data Architectures Can’t Keep Up | Complex, Rigid, Agility
Challenges
• Infrastructure complexity, costs – stitch together multiple
tools – separate tools for BI, ML, OLAP cubes, databases
• High Latency – can’t handle big data’s volume, variety,
velocity
• Data needs to be pre-aggregated and transformed to cubes
• Processing is batch and not real-time
• Rigid – can’t handle changing requirements, changing data
• Dashboard slowness pains
• Datamarts in Tableau, caching, very complex query
• Difficult to simultaneously ingest and analyze at scale
• Limited Agility – admin overhead, resources, skills
Tableau
EDW
(Teradata, Oracle)
Star schema – facts & dimensions
DATA
3rd partyERP, CRM, SFA Databases Flat files
MSTR SAS
Data Integration (INFA, Talend)
Others
Hadoop
(Horton,
Cloudera)
DATA
MARTS
OLAP
CUBES
INDICES
SUMMARY
Tables
NiFi,
Kafka
Ad

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Kinetica master chug_9.12

  • 1. Kinetica – Industry’s Fastest Analytics Database 1
  • 2. About Me • Engineering Background - AppDev • Open source Contributor • Hadoop – 10 years • HWX Principle Solution Engineer • Director, Solutions Engineering @ Kinetica • Kinetica Local Contact Information • Sunile Manjee, Director Solutions Engineering, smanjee@kinetica.com • Phil Zacharia, Director Central Region, pzacharia@kinetica.com 2
  • 3. The image part with relationship ID rId2 was not found in the file. What is Kinetica? 3 Patented In Memory Columnar Distributed GPU Accelerated Database
  • 4. The image part with relationship ID rId2 was not found in the file. Developed to Identify Terroristic Threats in Real- Time 4 Kinetica incubated as a massively parallel computational engine for US Army INSCOM Ingests 200+ sources of streaming data – mobile devices, drones, social media, cyber data 200B new records per hour Incorporates geospatial and temporal data Real-time, actionable threat intelligence First high-performance database leveraging GPUs 4
  • 5. The image part with relationship ID rId2 was not found in the file. Who is Kinetica? 2009 ‘HPC Research Project’ incubated by US military 2010 2011 Patent # US8373710 B1 issued to GPUdb 2012 US Army deploys GPUdb 2013 GPUdb commercially available 2014 IDC HPC innovation excellence award Army GPUdb goes into production at USPS 2015 Iron Net selects GPUdb for Cyber Defense 2015 PG&E selects GPUdb for electric grid analysis IDC HPC innovation excellence award USPS 2016 Rebrand to The image part with relationship ID rId3 was not found in the file. 4 2012 Confidential Information
  • 6. Confidential Information 6 Current Data Architectures Can’t Keep Up | Complex, Rigid, Agility Challenges • Infrastructure complexity, costs – stitch together multiple tools – separate tools for BI, ML, OLAP cubes, databases • High Latency – can’t handle big data’s volume, variety, velocity • Data needs to be pre-aggregated and transformed to cubes • Processing is batch and not real-time • Rigid – can’t handle changing requirements, changing data • Dashboard slowness pains • Datamarts in Tableau, caching, very complex query • Difficult to simultaneously ingest and analyze at scale • Limited Agility – admin overhead, resources, skills Tableau EDW (Teradata, Oracle) Star schema – facts & dimensions DATA 3rd partyERP, CRM, SFA Databases Flat files MSTR SAS Data Integration (INFA, Talend) Others Hadoop (Horton, Cloudera) DATA MARTS OLAP CUBES INDICES SUMMARY Tables NiFi, Kafka
  • 7. Confidential Information 7 Kinetica Database | Real-Time, Flexible, Simple Data and Analytics Tableau EDW (Teradata, Oracle) DATA 3rd partyERP, CRM, SFA Databases Flat files MSTR SAS Data Integration (INFA, Talend) Others Hadoop (HDP, CDH, MapR) Kinetica NiFi, Kafka Solution • Low Latency – millisecond response time • Real-time at scale – simultaneously ingest and analyze • Full data provisioning – ingest, manage, analyze, visualize • Flexible – handle changing requirements, changing data, minimize aggregates, indexes, cubes • Simplicity – minimize admin overhead, resources, skills Plus • Converge AI and BI • Location-based Analytics • Deploy on commodity hardware on-prem, cloud
  • 8. The image part with relationship ID rId2 was not found in the file. Confidential Information Kinetica : Unique Strengths & Capabilities Fast, Distributed, In-Memory Analytics Engine for Fast Moving, Large Scale Data Kinetica is designed to take advantage of the parallel processing nature of the GPU. It delivers low-latency, high performance analytics on large data sets, and makes streaming data available for query in real-time. 8 OLAP Performance, Scalability, Stability Geospatial Processing & Visualization API for GPU Powered Data & Compute Orchestration Converged AI and BI User Defined Functions (UDFs) and orchestration of data in a distributed manner enable Kinetica to offer low- level customizations for machine learning and AI workloads Native Geospatial & Visualization Pipeline Native visualization pipeline makes it easier to work with large geospatial data sets. Ideal for IoT use-cases, and powering geospatial applications Sonic Layer (Fast/True Real time Analytics) Historic and Predictive Insights Interactive Location-Based Analytics
  • 9. c c 9 CUDA SELECT a*x+y FROM TABLE SQL Python import gpudb h_db = gpudb.GPUdb(encoding = 'BINARY', host = '127.0.0.1', port = '9191’) response = h_db.get_records_by_column(’TABLE', ["(a*x+y)"], 0, 10, 'json', {}) Make/Build Cuda Abstraction, SaxPy Example https://devblogs.nvidia.com/parallelforall/easy-introduction-cuda-c-and-c/ Confidential Information
  • 10. Kinetica | Reference Architecture
  • 11. Kinetica Architecture 7 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/ GPU’s Disk A1 B1 C1 A2 B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Head Node Commodity Hardware W/ GPU’s Disk A1 B1 C1 A2 B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Head Node Commodity Hardware W/ GPU’s Disk A1 B1 C1 A2 B2 C2 A3 B3 C3 A4 B4 C4 Columnar In-memory HTTP Head Node Commodity Hardware W/ GPU’s 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 • Reliable, Available and Scalable • Disk based persistence • Add nodes on demand • Data replication for high availability • Scale up and/or out • Performance • GPU Accelerated (1000’s Cores per GPU) • Ingest Billions of records in minutes • Ultra low latency query performance • Massive Data Sizes • 100’s of Terabytes Scale • Billions of entries • Connectors • ODBC/JDBC • Restful Endpoints • Rich API’s • Standard Geospatial Capabilities • Run Anywhere • On premise, Amazon, Azure, Google Cloud, Nimbix, SoftLayer • Hardware Partners • IBM, Dell, Cisco, HP
  • 12. The image part with relationship ID rId2 was not found in the file. Core Design & Architecture 12 GPU SHARD Chunk Logical Node Chunk Chunk SHARD Chunk Chunk Chunk SHARD Chunk Chunk Chunk SHARD Chunk Chunk Chunk SHARD Chunk Chunk SHARD Chunk Chunk SHARD Chunk Chunk SHARD Chunk Chunk GPU Logical Node GPU SHARD Chunk Logical Node CPU Socket Chunk Chunk SHARD Chunk Chunk Chunk SHARD Chunk Chunk Chunk SHARD Chunk Chunk Chunk SHARD Chunk Chunk SHARD Chunk Chunk SHARD Chunk Chunk SHARD Chunk Chunk GPU Logical Node System Memory (RAM) ChunkChunk ChunkChunk ChunkChunk Chunk Chunk Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Table:Column:Data Map to Persist CPU Socket Confidential Information
  • 13. The image part with relationship ID rId2 was not found in the file. Kinetica UDF 13 GPU SHARD Chunk Logical Node CPU Socket Chunk Chunk SHARD Chunk Chunk Chunk SHARD Chunk Chunk Chunk SHARD Chunk Chunk Chunk SHARD Chunk Chunk SHARD Chunk Chunk SHARD Chunk Chunk SHARD Chunk Chunk Logical Node GPU SHARD Chunk Logical Node CPU Socket Chunk Chunk SHARD Chunk Chunk Chunk SHARD Chunk Chunk Chunk SHARD Chunk Chunk Chunk SHARD Chunk Chunk SHARD Chunk Chunk SHARD Chunk Chunk SHARD Chunk Chunk Logical Node System Memory (RAM) ChunkChunk ChunkChunk ChunkChunk Chunk Chunk The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. GPU GPU The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. The image part with relationship ID rId3 was not found in the file. Confidential Information
  • 14. The image part with relationship ID rId2 was not found in the file. CPU Bound "Real Time” Architectures 14 Data Stream Buy/Add More Nodes Concurrent Ingest & Analytics Confidential Information
  • 15. The image part with relationship ID rId2 was not found in the file. Kinetica Real Time Analytics Architecture 15 Data Stream Concurrent Ingest & Analytics GPU Confidential Information
  • 16. The image part with relationship ID rId2 was not found in the file. Demo 16