Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Getting Started with Apache Ignite as a Distributed Database

In-Memory Computing Meetup Vol.1, Tokyo, May 23

  • Be the first to comment

Getting Started with Apache Ignite as a Distributed Database

  1. 1. Getting Started with Apache Ignite as a Distributed Database Stephen Leung Director of Solution Architect, Asia Pacific 2019 © GridGain Systems
  2. 2. 2018 © GridGain Systems Agenda • Why not standard RDBMS and NoSQL? • Apache Ignite Overview • Reference Case sharing • Q & A
  3. 3. 2018 © GridGain Systems Why not Standard RDBMS and NoSQL?
  4. 4. 2018 © GridGain Systems Existing Databases are Too Slow
  5. 5. 2018 © GridGain Systems Existing Databases Can't Scale
  6. 6. 2018 © GridGain Systems Existing Databases Can't Scale
  7. 7. 2018 © GridGain Systems NoSQL to the Rescue?
  8. 8. 2018 © GridGain Systems NoSQL to the Rescue? RDBMS Consistent, but not scalable NoSQL Scalable, but not consistent
  9. 9. 2018 © GridGain Systems Apache Ignite Consistent & Scalable
  10. 10. 2018 © GridGain Systems Ignite Data Warehouse Operational DB TransactionsAnalytics, ML, AI ETL Analytics, ML, AI & Transactions Real Time, Scalable, Available, Flexible ETL, Batch, Inflexible HTAP Architecture
  11. 11. 2018 © GridGain Systems In-Memory Computing Mainframe NoSQL Hadoop Data Layer RDBMS In-Memory Computing Financial Services Initiatives Private/Public Cloud/SaaS Regulatory Compliance Omnichannel Banking Security Fraud Risk Management Trading HPC
  12. 12. 2018 © GridGain Systems Current solution comparison Feature In-Memory Cache (Redis) IMDG (Hazelcast, GigaSpaces) IMDB (MemSQL, VoltDB) IMC Platform (GridGain) Scale Out and Availability ✓ ✓ ✓ ✓ In-Memory ✓ ✓ ✓ ✓ Complements 3rd Party DB X ✓ X ✓ Persistence as Extension ✓ X (copy of data in RAM) X (snapshots) ✓ Fast Restarts X X X ✓ Consistency and Transactions X ✓ ✓ ✓ SQL X X ✓ ✓ Collocated Processing X ✓ X ✓ Machine and Deep Learning ✓ X X ✓
  13. 13. 2018 © GridGain Systems Apache Ignite Overview
  14. 14. 2018 © GridGain Systems Memory-centric distributed database, caching, and processing platform In-memory computing platform based Apache Ignite adding enterprise features and support for mission critical deployments
  15. 15. 2018 © GridGain Systems 15 Among Top Apache Projects Top 5 Developer Mailing Lists 1. Beam 2. Ignite 3. Kafka 4. Tomcat 5. James Top 5 User Mailing Lists 1. Flink 2. Lucene 3. Ignite 4. Cassandra 5. Kafka Over 2M downloads per year
  16. 16. 2018 © GridGain Systems In-Memory Computing Benefits In-Memory Speeds 10-1,000x faster than systems built on disk-based databases Massive Scalability Scale out to petabytes of in-memory data Easy to Implement No rip-and-replace of existing databases
  17. 17. 2018 © GridGain Systems Comprehensive Solution • Slides In Between Existing Application and Data Layers • Works With RDBMS, NoSQL and Hadoop Databases • Multi-Language Support Including SQL, Java, .NET, PHP, Node.js, Scala and MapReduce • Deploy On-Premises, In the Cloud, or on Hybrid Environments
  18. 18. 2018 © GridGain Systems eCommerce, Retail & Travel Financial Services Software FinTech Pharma & Healthcare Ignite – Used by Leading Companies Worldwide IoT AdTech Telecom & Mobile Logistics & Transportation
  19. 19. 2018 © GridGain Systems Apache Ignite In-Memory Computing Platform Security&Auditing Monitoring&Management DataSnapshots&Recovery Memory-Centric Storage Scale to 1000s of Nodes & Store TBs of Data Ignite Native Persistence (Flash, SSD, Intel 3D XPoint) Third-Party Persistence Keep Your Own DB (RDBMS, HDFS, NoSQL) SQL Transactions Compute Services MLStreamingKey/Value IoTFinancial Services Pharma & Healthcare E-CommerceTravel & Logistics Telco DataCenterReplication
  20. 20. 2018 © GridGain Systems Distributed In-Memory Data Store In-Memory Data Store GridGain Server Cluster Predictable Memory Consumption Fully Transactional WAL (Write Ahead Log) Instantaneous Restarts Automatic Defragmentation Off-heap Removes Noticeable GC Pauses Stores Superset of Data Distributed Persistent Store In-Memory Data Store Persistent Store Server Node In-Memory Data Store Persistent Store Server Node In-Memory Data Store Persistent Store Server Node
  21. 21. 2018 © GridGain Systems Reference Case Sharing
  22. 22. 2018 © GridGain Systems The ING Group is a Dutch multinational banking and financial services corporation headquartered in Amsterdam. Its primary businesses are retail banking, direct banking, commercial banking, investment banking, asset management, and insurance services. Problem • To deliver new competitive customer services fast • High cost of running on mainframe infrastructure • Transaction consistency over multiple geo-locations GridGain Solution • Powers the core solution for delivering new services Data aggregation across multiple sources • Reduces infrastructure costs Front-End APIs Payments SecuritiesAccounts Credits Clients Multi-datacenter Infrastructure GridGain In-Memory Computing Platform - Next Generation Banking
  23. 23. 2018 © GridGain Systems Wellington - Next Generation, Real-time IBOR A top 20 worldwide asset management firm with over $1 trillion under management • Problem – Current systems no longer scaled to handle the volumes – Didn’t comply with new regulations following financial crisis – Needed to introduce new asset classes faster • GridGain Solution – Investment Book of Record (IBOR), a single real-time version of the truth for positions, exposure, valuations and performance for all customers, teams and trades. – 10x performance gains, linear horizontal scalability – Support for SQL and ACID transactions, and for existing systems and skillsets – Enabled transactions and analytics on a single platform – Collocated computing scales complex calculations, analytics Trading Systems GridGain In-Memory Computing Platform In-Memory Data Grid In-Memory Database Streaming Analytics Continuous Learning Framework Accounting System Other Back Office Portfolio Management Risk Management Regulatory & Compliance Investment Book of Record (IBOR) Oracle RAC
  24. 24. 2018 © GridGain Systems2019 © GridGain Systems Ping An – Accelerate the database performance 24 Ping An Insurance has over 1M sales people generating US$122.4B+ in revenue Front-End APIs SQL TXCompute Service Grid Key / Value Commission Calculation System GridGain In-Memory Computing Platform Distributed In-Memory Data Store Distributed Persistent Store In-Memory Data Store Persistent Store Server Node In-Memory Data Store Persistent Store Server Node In-Memory Data Store Persistent Store Server Node Problems • Calculating sales commissions is a complex matrix math challenge • Key requirements: accuracy and speed • Current platform: Oracle HW+SW for multi-field/range- based queries across 8 or more tables • Goal: achieve 5x improvement in calculation times— without errors GridGain Solution • Reduce cost – Move out from Oracle to GridGain • Improve the delivery performance – the commission calculation change from 8 hours to 1 hour. • Immediate restart from disk (no memory warm-up)
  25. 25. 2018 © GridGain Systems What is Ignite? - https://apacheignite.readme.io/docs/what-is-ignite Getting Start - https://apacheignite.readme.io/docs/getting-started - In-memory computing essentials: Part 1 - In-memory computing essentials: Part 2 Download Apache Ignite - https://ignite.apache.org/download.cgi Free 30-Day Ultimate, Enterprise or Professional Edition Trial - https://www.gridgain.com/resources/download Next Steps or Any Questions?

×