In this session, Infinispan developer Galder Zamarreño will:
- Provide a brief introduction to peer-to-peer and client/server architectures.
- Describe the benefits of using Infinispan in a client/server mode, particularly in cloud-style environments.
- Introduce the audience to Infinispan’s selection of server modules that provide varied access methods: REST and WebSocket for HTTP access, Memcached protocol access and Hot Rod, Infinispan’s very own highly efficient binary protocol which supports smart-clients.
- Demonstrate an Infinispan client/server example showing how geographically separated Infinispan data grids could be linked together via Hot Rod client/server modules in order to provide different disaster recovery strategies.
Keeping Infinispan In Shape: Highly-Precise, Scalable Data EvictionGalder Zamarreño
Java Collections Framework represents one of the key building blocks of any Java application. Although the standard JDK devoted a lot of attention to developing a coherent and easy to use collections framework one important aspect remains overlooked – collection element eviction. Collection memory footprint can not grow indefinitely because we would eventually run out of memory; we either have to remove elements from a collection or somehow periodically evict certain elements according to a chosen eviction algorithm. Since day one eviction has been a key feature of Infinispan, and in the latest 4.1 release thanks to event update batching, Infinispan has reduced the eviction overhead to such an extent that it hardly affects application performance. On top of that, Infinispan implements LIRS, a more precise eviction algorithm compared to the traditional LRU, making it the first open source project to implement this revolutionary algorithm in the data grid space. In this session, Galder and Vladimir will present to the details behind these changes, performance measurements and third-party use case testimonies.
In an environment where cloud-scaling applications is becoming more and more important, client-server architectures paradigms, as shown by memcached, are back with vengeance. In this talk, Galder will talk about Hot Rod, Infinispan's new client/server binary protocol, explaining the key differences compared to memcached's binary protocol, such as the possibility of receiving cluster topology changes. Audience of this talk will learn of the importance of Hot Rod in 'cloud-scale' application server clustering, where stateless application server instances could use Infinispan Hot Rod clients to retrieve state from an elastic farm of Infinispan Hot Rod servers, improving capabilities to run application server instances as a PaaS. The talk will finish with a brief demo of a cluster of Infinispan Hot Rod servers running on EC2 being accessed from a non-Java client. The audience is expected to have an intermediate understanding of client-server software architectures and cloud deployments.
Apache conbigdata2015 christiantzolov-federated sql on hadoop and beyond- lev...Christian Tzolov
Slides from ApacheCon BigData 2015 HAWQ/GEODE talk: http://sched.co/3zut
In the space of Big Data, two powerful data processing tools compliment each other. Namely HAWQ and Geode. HAWQ is a scalable OLAP SQL-on-Hadoop system, while Geode is OLTP like, in-memory data grid and event processing system. This presentation will show different integration approaches that allow integration and data exchange between HAWQ and Geode. Presentation will walking you through the implementation of the different Integration strategies demonstrating the power of combining various OSS technologies for processing bit and fast data. Presentation will touch upon OSS technologies like HAWQ, Geode, SpringXD, Hadoop and Spring Boot.
Building Wall St Risk Systems with Apache GeodeAndre Langevin
In this talk from the 2016 Apache Geode Summit, I discuss how Geode forms the core of many Wall Street derivative risk solutions. By externalizing risk from trading systems, Geode-based solutions provide cross-product risk management at speeds suitable for automated hedging, while simultaneously eliminating the back office costs associated with traditional trading system based solutions.
Keeping Infinispan In Shape: Highly-Precise, Scalable Data EvictionGalder Zamarreño
Java Collections Framework represents one of the key building blocks of any Java application. Although the standard JDK devoted a lot of attention to developing a coherent and easy to use collections framework one important aspect remains overlooked – collection element eviction. Collection memory footprint can not grow indefinitely because we would eventually run out of memory; we either have to remove elements from a collection or somehow periodically evict certain elements according to a chosen eviction algorithm. Since day one eviction has been a key feature of Infinispan, and in the latest 4.1 release thanks to event update batching, Infinispan has reduced the eviction overhead to such an extent that it hardly affects application performance. On top of that, Infinispan implements LIRS, a more precise eviction algorithm compared to the traditional LRU, making it the first open source project to implement this revolutionary algorithm in the data grid space. In this session, Galder and Vladimir will present to the details behind these changes, performance measurements and third-party use case testimonies.
In an environment where cloud-scaling applications is becoming more and more important, client-server architectures paradigms, as shown by memcached, are back with vengeance. In this talk, Galder will talk about Hot Rod, Infinispan's new client/server binary protocol, explaining the key differences compared to memcached's binary protocol, such as the possibility of receiving cluster topology changes. Audience of this talk will learn of the importance of Hot Rod in 'cloud-scale' application server clustering, where stateless application server instances could use Infinispan Hot Rod clients to retrieve state from an elastic farm of Infinispan Hot Rod servers, improving capabilities to run application server instances as a PaaS. The talk will finish with a brief demo of a cluster of Infinispan Hot Rod servers running on EC2 being accessed from a non-Java client. The audience is expected to have an intermediate understanding of client-server software architectures and cloud deployments.
Apache conbigdata2015 christiantzolov-federated sql on hadoop and beyond- lev...Christian Tzolov
Slides from ApacheCon BigData 2015 HAWQ/GEODE talk: http://sched.co/3zut
In the space of Big Data, two powerful data processing tools compliment each other. Namely HAWQ and Geode. HAWQ is a scalable OLAP SQL-on-Hadoop system, while Geode is OLTP like, in-memory data grid and event processing system. This presentation will show different integration approaches that allow integration and data exchange between HAWQ and Geode. Presentation will walking you through the implementation of the different Integration strategies demonstrating the power of combining various OSS technologies for processing bit and fast data. Presentation will touch upon OSS technologies like HAWQ, Geode, SpringXD, Hadoop and Spring Boot.
Building Wall St Risk Systems with Apache GeodeAndre Langevin
In this talk from the 2016 Apache Geode Summit, I discuss how Geode forms the core of many Wall Street derivative risk solutions. By externalizing risk from trading systems, Geode-based solutions provide cross-product risk management at speeds suitable for automated hedging, while simultaneously eliminating the back office costs associated with traditional trading system based solutions.
In April 2015, Apache Geode (incubating) was born from Pivotal’s GemFire, the distributed in-memory database. However, the donation of over 1M LOC was just the beginning of the journey. In this talk we discuss how the GemFire engineering team has adapted their development infrastructure, processes, and culture to embrace the “Apache Way". We present lessons learned and best practices for new and incubating open source projects in areas of initial code submission, IP clearance, governance policies, code review, and community building. We discuss the challenges the team faced and how we changed internal communication and software design processes to a community-driven model. In particular, we highlight effective strategies for growing a project community and embracing new members. Finally, we show how changing to the open source model has increased both productivity and quality.
Apache Geode provides a database-like consistency model, reliable transaction processing and a shared-nothing architecture to maintain very low latency performance with high concurrency processing.
How to use the WAN Gateway feature of Apache Geode to implement multi-site and active-active failover, disaster recovery, and global scale applications.
Building Apps with Distributed In-Memory Computing Using Apache GeodePivotalOpenSourceHub
Slides from the Meetup Monday March 7, 2016 just before the beginning of #GeodeSummit, where we cover an introduction of the technology and community that is Apache Geode, the in-memory data grid.
An Introduction to Apache Geode (incubating)Anthony Baker
Geode is a data management platform that provides real-time, consistent access to data-intensive applications throughout widely distributed cloud architectures.
Geode pools memory (along with CPU, network and optionally local disk) across multiple processes to manage application objects and behavior. It uses dynamic replication and data partitioning techniques for high availability, improved performance, scalability, and fault tolerance. Geode is both a distributed data container and an in-memory data management system providing reliable asynchronous event notifications and guaranteed message delivery.
Pivotal GemFire has had a long and winding journey, starting in 2002, winding through VMware, Pivotal, and finding it's way to Apache in 2015. Companies using GemFire have deployed it in some of the most mission critical latency sensitive applications in their enterprises, making sure tickets are purchased in a timely fashion, hotel rooms are booked, trades are made, and credit card transactions are cleared. This presentation discusses:
- A brief history of GemFire
- Architecture and use cases
- Why we are taking GemFire Open Source
- Design philosophy and principles
But most importantly: how you can join this exciting community to work on the bleeding edge in-memory platform.
In April 2015, Apache Geode (incubating) was born from Pivotal’s GemFire, the distributed in-memory database. However, the donation of over 1M LOC was just the beginning of the journey. In this talk we discuss how the GemFire engineering team has adapted their development infrastructure, processes, and culture to embrace the “Apache Way". We present lessons learned and best practices for new and incubating open source projects in areas of initial code submission, IP clearance, governance policies, code review, and community building. We discuss the challenges the team faced and how we changed internal communication and software design processes to a community-driven model. In particular, we highlight effective strategies for growing a project community and embracing new members. Finally, we show how changing to the open source model has increased both productivity and quality.
Apache Geode provides a database-like consistency model, reliable transaction processing and a shared-nothing architecture to maintain very low latency performance with high concurrency processing.
How to use the WAN Gateway feature of Apache Geode to implement multi-site and active-active failover, disaster recovery, and global scale applications.
Building Apps with Distributed In-Memory Computing Using Apache GeodePivotalOpenSourceHub
Slides from the Meetup Monday March 7, 2016 just before the beginning of #GeodeSummit, where we cover an introduction of the technology and community that is Apache Geode, the in-memory data grid.
An Introduction to Apache Geode (incubating)Anthony Baker
Geode is a data management platform that provides real-time, consistent access to data-intensive applications throughout widely distributed cloud architectures.
Geode pools memory (along with CPU, network and optionally local disk) across multiple processes to manage application objects and behavior. It uses dynamic replication and data partitioning techniques for high availability, improved performance, scalability, and fault tolerance. Geode is both a distributed data container and an in-memory data management system providing reliable asynchronous event notifications and guaranteed message delivery.
Pivotal GemFire has had a long and winding journey, starting in 2002, winding through VMware, Pivotal, and finding it's way to Apache in 2015. Companies using GemFire have deployed it in some of the most mission critical latency sensitive applications in their enterprises, making sure tickets are purchased in a timely fashion, hotel rooms are booked, trades are made, and credit card transactions are cleared. This presentation discusses:
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- Why we are taking GemFire Open Source
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2. Infinispan Servers:
Beyond Peer-to-Peer
Data Grids
Galder Zamarreño
Senior Engineer, Red Hat
7th October 2010, JUDCon - Berlin
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
3. Who is Galder?
• R&D engineer (Red Hat Inc):
• Infinispan developer
• JBoss Cache developer
• Contributor and committer:
• JBoss AS, Hibernate, JGroups, JBoss Portal,...etc
• Blog: zamarreno.com
• Twitter: @galderz
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
4. Agenda
• Introduction to Infinispan
• Peer-to-peer (P2P) data grids vs client-server
• Infinispan server comparison
• The path ahead for Infinispan servers
• Demo
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
5. What is Infinispan?
• A data grid platform which is:
• Open Source (LGPL)
• In-memory
• Distributed
• Elastic
• Highly Available
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
6. Brief feature overview
• Seamless data distribution, linearly scalable data structures
• Implicit eviction
• Write through and write behind to persistent storage
• JTA and XA transactions
• Listeners and notifications
• Querying and indexing
• Alternative JPA-like API
• JMX management and GUI console
• Multiple endpoints for remote invocations...
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
11. Data Grid per Application?
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
12. Shared Data Grid
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
13. Independent Tier Management
• Independently deploy new
app version
• Security
• Incompatible JVM tuning
requirements
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
14. Infinispan Server Modules
• Protocols supported in 4.1 :
• REST
• Memcached
• Hot Rod
• Websocket
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
15. REST Server
• RESTful HTTP access
• HTTP PUT/POST to store
• HTTP GET to retrieve
• Available since 4.0
• In ‘all’ distribution
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
16. REST Server via Python
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
17. Memcached not good enough
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
19. Routing not so smart
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
20. Essential Information
• To run, execute:
• startServer.sh -r memcached
• New in 4.1
• Only text protocol supported
• Works with any Memcached client
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
21. Hot Rod to the rescue!
• What is Hot Rod?
• Wire protocol for client-server
communications
• Open and language independent
• Built-in dynamic failover and load
balancing
• Smart routing
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
22. Dynamic routing with Hot Rod
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
23. Smart routing with Hot Rod
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
24. Essential Information
• To run:
• startServer.sh -r hotrod
• New in 4.1
• Only a Java client available:
• Supports smart routing and
dynamic load balancing
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
25. Java Hot Rod Client
//API entry point, by default it connects to localhost:11311
CacheContainer cacheContainer = new RemoteCacheManager();
//obtain a handle to the remote default cache
Cache<String, String> cache = cacheContainer.getCache();
//now add something to the cache and make sure it is there
cache.put("car", "bmw");
assert cache.get("car").equals("bmw");
//remove the data
cache.remove("car");
assert !cache.containsKey("car") : "Value must have been removed!";
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
26. Websocket Server
• Exposes Infinispan Cache
instance over Websocket
• To run it:
• startServer.sh -r websocket
• Accessible via Javascript API
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
27. Server Comparison
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
28. The path ahead
• Hot Rod improvements:
• Remote querying
• Event handling
• Submit Hot Rod protocol to standards body (maybe)
• Others:
• Memcached binary protocol won’t be implemented
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
29. Prototype Hot Standby Demo
Demo available in:
github.com/galderz/infinispan-intersite-demo
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010
30. Summary
• Accessing data grids in client-server mode makes sense
• Infinispan 4.1 comes with a range of server modules
• Each server fits one type of use case
• We need your help to build more Hot Rod clients!
galder@jboss.org | twitter.com/galderz | zamarreno.com
Thursday, October 7, 2010