We will examine most of the features that this “Swiss knife” software provides. It is an in-memory fabric that fits between the database and the application layer. Apache Ignite is powered by the H2 engine. They have used it to create an in-memory distributed ACID, fully ANSI-99 complaint, Highly Available (HA) and scalable database. They have used a non-consensus (https://en.wikipedia.org/wiki/Rendezvous_hashing) clustering algorithm to be even more scalable compared to other NoSql solutions. This tool respects the relational data model that we have used for so many years and eliminates traditional problems like the “expensive joins” since it uses the RAM as the primary storage medium. We will see what this tool can do in action through hands-on examples.
Here are my slides from my Nike Tech Talk on an Introduction to Apache Ignite. https://niketechtalksdec2017.splashthat.com/ Abstract: Memory-first architectures are paradigm shifting for database backends. They can enhance performance dramatically but also allow for horizontal scale-out on distributed relational architectures. Even more, they can be put in front of various file systems, or NoSQL databases. Apache Ignite provides a caching layer between applications and the system of record, but additionally, it provides a peer to peer architecture for transacting data, performing computations, microservices, streaming, and much more.
During this session, we will do a deep-dive into the Apache Ignite architecture and discuss how it is being deployed around the globe. You will walk away knowing why and when to use Apache Ignite in your next data intensive application!
Apache Ignite vs Alluxio: Memory Speed Big Data AnalyticsDataWorks Summit
Apache Ignite vs Alluxio: Memory Speed Big Data Analytics - Apache Spark’s in memory capabilities catapulted it as the premier processing framework for Hadoop. Apache Ignite and Alluxio, both high-performance, integrated and distributed in-memory platform, takes Apache Spark to the next level by providing an even more powerful, faster and scalable platform to the most demanding data processing and analytic environments.
Speaker
Irfan Elahi, Consultant, Deloitte
This presentation gives an overview of the Apache Ignite project. It explains Ignite in relation to its architecture, scaleability, caching, datagrid and machine learning abilities.
Links for further information and connecting
http://www.amazon.com/Michael-Frampton/e/B00NIQDOOM/
https://nz.linkedin.com/pub/mike-frampton/20/630/385
https://open-source-systems.blogspot.com/
Here are my slides from my Nike Tech Talk on an Introduction to Apache Ignite. https://niketechtalksdec2017.splashthat.com/ Abstract: Memory-first architectures are paradigm shifting for database backends. They can enhance performance dramatically but also allow for horizontal scale-out on distributed relational architectures. Even more, they can be put in front of various file systems, or NoSQL databases. Apache Ignite provides a caching layer between applications and the system of record, but additionally, it provides a peer to peer architecture for transacting data, performing computations, microservices, streaming, and much more.
During this session, we will do a deep-dive into the Apache Ignite architecture and discuss how it is being deployed around the globe. You will walk away knowing why and when to use Apache Ignite in your next data intensive application!
Apache Ignite vs Alluxio: Memory Speed Big Data AnalyticsDataWorks Summit
Apache Ignite vs Alluxio: Memory Speed Big Data Analytics - Apache Spark’s in memory capabilities catapulted it as the premier processing framework for Hadoop. Apache Ignite and Alluxio, both high-performance, integrated and distributed in-memory platform, takes Apache Spark to the next level by providing an even more powerful, faster and scalable platform to the most demanding data processing and analytic environments.
Speaker
Irfan Elahi, Consultant, Deloitte
This presentation gives an overview of the Apache Ignite project. It explains Ignite in relation to its architecture, scaleability, caching, datagrid and machine learning abilities.
Links for further information and connecting
http://www.amazon.com/Michael-Frampton/e/B00NIQDOOM/
https://nz.linkedin.com/pub/mike-frampton/20/630/385
https://open-source-systems.blogspot.com/
SQL is a popular database language for modern applications, given its flexibility in modelling workloads and how widely it is understood by developers. However, most modern applications running in the clouds require fault tolerance, the ability to scale out and geographic data distribution of data. These are hard to achieve with traditional SQL databases, which is paving the way for distributed SQL databases.
Google Spanner is arguably the world's first truly distributed SQL database. Given its fully decentralized architecture, it delivers higher performance and availability for geo-distributed SQL workloads than other specialized transactional databases such as Amazon Aurora. Now, there are a number of open source derivatives of Google Spanner such as YugaByte DB, CockroachDB and TiDB. This talk will focus on the common architectural paradigms that these databases are built on (using YugaByte DB as an example). Learn about the concepts these databases leverage, how to evaluate if these will meet your needs and the questions to ask to differentiate among these databases.
Using Apache Arrow, Calcite, and Parquet to Build a Relational CacheDremio Corporation
From DataEngConf 2017 - Everybody wants to get to data faster. As we move from more general solution to specific optimization techniques, the level of performance impact grows. This talk will discuss how layering in-memory caching, columnar storage and relational caching can combine to provide a substantial improvement in overall data science and analytical workloads. It will include a detailed overview of how you can use Apache Arrow, Calcite and Parquet to achieve multiple magnitudes improvement in performance over what is currently possible.
Finite-State Queries in Lucene:
* Background, improvement/evolution of MultiTermQuery API in 2.9 and Flex
* Implementing existing Lucene queries with NFA/DFA for better performance: Wildcard, Regex, Fuzzy
* How you can use this Query programmatically to improve relevance (I'll use an English test collection/English examples)
Quick overview of other Lucene features in development, such as:
* Flexible Indexing
* "More-Flexible" Scoring: challenges/supporting BM25, more vector-space models, field-specific scoring, etc.
* Improvements to analysis
Bonus:
* Lucene / Solr merger explanation and future plans
About the presenter:
Robert Muir is a super-active Lucene developer. He works as a software developer for Abraxas Corporation. Robert received his MS in Computer Science from Johns Hopkins and BS in CS from Radford University. For the last few years Robert has been working on foreign language NLP problems - "I really enjoy working with Lucene, as it's always receptive to better int'l/language support, even though everyone seems to be a performance freak... such a weird combination!"
Have you ever wondered what the relative differences are between two of the more popular open source, in-memory data stores and caches? In this session, we will describe those differences and, more importantly, provide live demonstrations of the key capabilities that could have a major impact on your architectural Java application designs.
Zero Data Loss Recovery Applianceによるデータベース保護のアーキテクチャオラクルエンジニア通信
データ量の増大、業務の24時間化に伴い、従来のバックアップ・ソリューションではデータ保護のニーズをすべて満たせなくなってきています。これを解消すべくOracle Databaseの保護に特化して設計されたエンジニアド・システム、Zero Data Loss Recovery Applianceが登場しました。これからの時代のデータ保護テクノロジーに関して、アーキテクチャを中心に紹介します。
Apache ignite as in-memory computing platformSurinder Mehra
Apache ignite is one of the powerful horizontally scalable in-memory computing platforms which is capable to handling huge amount of data in memory/disk with quick cluster restart.
Apache Iceberg - A Table Format for Hige Analytic DatasetsAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Apache Iceberg - A Table Format for Hige Analytic Datasets
Speaker:
Ryan Blue, Netflix
For more Alluxio events: https://www.alluxio.io/events/
Azure + DataStax Enterprise (DSE) Powers Office365 Per User StoreDataStax Academy
We will present our Office 365 use case scenarios, why we chose Cassandra + Spark, and walk through the architecture we chose for running DSE on Azure.
The presentation will feature demos on how you too can build similar applications.
SQL is a popular database language for modern applications, given its flexibility in modelling workloads and how widely it is understood by developers. However, most modern applications running in the clouds require fault tolerance, the ability to scale out and geographic data distribution of data. These are hard to achieve with traditional SQL databases, which is paving the way for distributed SQL databases.
Google Spanner is arguably the world's first truly distributed SQL database. Given its fully decentralized architecture, it delivers higher performance and availability for geo-distributed SQL workloads than other specialized transactional databases such as Amazon Aurora. Now, there are a number of open source derivatives of Google Spanner such as YugaByte DB, CockroachDB and TiDB. This talk will focus on the common architectural paradigms that these databases are built on (using YugaByte DB as an example). Learn about the concepts these databases leverage, how to evaluate if these will meet your needs and the questions to ask to differentiate among these databases.
Using Apache Arrow, Calcite, and Parquet to Build a Relational CacheDremio Corporation
From DataEngConf 2017 - Everybody wants to get to data faster. As we move from more general solution to specific optimization techniques, the level of performance impact grows. This talk will discuss how layering in-memory caching, columnar storage and relational caching can combine to provide a substantial improvement in overall data science and analytical workloads. It will include a detailed overview of how you can use Apache Arrow, Calcite and Parquet to achieve multiple magnitudes improvement in performance over what is currently possible.
Finite-State Queries in Lucene:
* Background, improvement/evolution of MultiTermQuery API in 2.9 and Flex
* Implementing existing Lucene queries with NFA/DFA for better performance: Wildcard, Regex, Fuzzy
* How you can use this Query programmatically to improve relevance (I'll use an English test collection/English examples)
Quick overview of other Lucene features in development, such as:
* Flexible Indexing
* "More-Flexible" Scoring: challenges/supporting BM25, more vector-space models, field-specific scoring, etc.
* Improvements to analysis
Bonus:
* Lucene / Solr merger explanation and future plans
About the presenter:
Robert Muir is a super-active Lucene developer. He works as a software developer for Abraxas Corporation. Robert received his MS in Computer Science from Johns Hopkins and BS in CS from Radford University. For the last few years Robert has been working on foreign language NLP problems - "I really enjoy working with Lucene, as it's always receptive to better int'l/language support, even though everyone seems to be a performance freak... such a weird combination!"
Have you ever wondered what the relative differences are between two of the more popular open source, in-memory data stores and caches? In this session, we will describe those differences and, more importantly, provide live demonstrations of the key capabilities that could have a major impact on your architectural Java application designs.
Zero Data Loss Recovery Applianceによるデータベース保護のアーキテクチャオラクルエンジニア通信
データ量の増大、業務の24時間化に伴い、従来のバックアップ・ソリューションではデータ保護のニーズをすべて満たせなくなってきています。これを解消すべくOracle Databaseの保護に特化して設計されたエンジニアド・システム、Zero Data Loss Recovery Applianceが登場しました。これからの時代のデータ保護テクノロジーに関して、アーキテクチャを中心に紹介します。
Apache ignite as in-memory computing platformSurinder Mehra
Apache ignite is one of the powerful horizontally scalable in-memory computing platforms which is capable to handling huge amount of data in memory/disk with quick cluster restart.
Apache Iceberg - A Table Format for Hige Analytic DatasetsAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Apache Iceberg - A Table Format for Hige Analytic Datasets
Speaker:
Ryan Blue, Netflix
For more Alluxio events: https://www.alluxio.io/events/
Azure + DataStax Enterprise (DSE) Powers Office365 Per User StoreDataStax Academy
We will present our Office 365 use case scenarios, why we chose Cassandra + Spark, and walk through the architecture we chose for running DSE on Azure.
The presentation will feature demos on how you too can build similar applications.
HPC and cloud distributed computing, as a journeyPeter Clapham
Introducing an internal cloud brings new paradigms, tools and infrastructure management. When placed alongside traditional HPC the new opportunities are significant But getting to the new world with micro-services, autoscaling and autodialing is a journey that cannot be achieved in a single step.
SpringPeople - Introduction to Cloud ComputingSpringPeople
Cloud computing is no longer a fad that is going around. It is for real and is perhaps the most talked about subject. Various players in the cloud eco-system have provided a definition that is closely aligned to their sweet spot –let it be infrastructure, platforms or applications.
This presentation will provide an exposure of a variety of cloud computing techniques, architecture, technology options to the participants and in general will familiarize cloud fundamentals in a holistic manner spanning all dimensions such as cost, operations, technology etc
Scabi is a simple, light-weight Cluster Computing and Storage framework for BigData processing written purely in Java. Scabi provides high performance computing and storage with ease of use. Users can get started on using Scabi within a few minutes. Scabi is free of cost to use. https://www.github.com/dilshadmustafa/scabi
Data Lake and the rise of the microservicesBigstep
By simply looking at structured and unstructured data, Data Lakes enable companies to understand correlations between existing and new external data - such as social media - in ways traditional Business Intelligence tools cannot.
For this you need to find out the most efficient way to store and access structured or unstructured petabyte-sized data across your entire infrastructure.
In this meetup we’ll give answers on the next questions:
1. Why would someone use a Data Lake?
2. Is it hard to build a Data Lake?
3. What are the main features that a Data Lake should bring in?
4. What’s the role of the microservices in the big data world?
This is the presentation on clusters computing which includes information from other sources too including my own research and edition. I hope this will help everyone who required to know on this topic.
Maginatics @ SDC 2013: Architecting An Enterprise Storage Platform Using Obje...Maginatics
How did Maginatics build a strongly consistent and secure distributed file system? Niraj Tolia, Chief Architect at Maginatics, gave this presentation on the design of MagFS at the Storage Developer Conference on September 16, 2013.
For more information about MagFS—The File System for the Cloud, visit maginatics.com or contact us directly at info@maginatics.com.
Sanger, upcoming Openstack for Bio-informaticiansPeter Clapham
Delivery of a new Bio-informatics infrastructure at the Wellcome Trust Sanger Center. We include how to programatically create, manage and provide providence for images used both at Sanger and elsewhere using open source tools and continuous integration.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
Launch Your Streaming Platforms in MinutesRoshan Dwivedi
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
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#AIFusionBuddyforBeginners,
#AIFusionBuddyBenefits,
#AIFusionBuddyComparison,
#AIFusionBuddyInstallation,
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#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptx
Apache ignite v1.3
1. æHow to make your DBMS
1000x faster
19/12/2017 –City College
2. Presentation’s Overview
Part 1 – Theory
• What is the problem with RDBMS and NoSQL solutions
• What is Apache Ignite
• Main Features of Apache Ignite
• Use cases and Integrations
• Supported platforms
Part 2 – Examples from Apache Ignite’s repository
3. Scaling relational Databases is hard
• RDBMS mainly scale up / vertically (bigger/faster
machines)
• Limited scalability compared with Big Data requirements
• Shared all approach
• Same data files are available to all nodes (instances)
• Distributed locks required
• Distributed network search is required when the data
of an instance is not yet persisted into the file system.
In these cases a network search is required for recent
committed values. This approach is not scalable.
Source: http://www.marklogic.com/blog/relational-databases-scale/
4. NoSQL databases as a solution
• Provide horizontal scalability with the use of shared
nothing architectures and partitioning.
• But the following functionalities are not easily supported
in most NoSQL platforms:
• Joins
• Set operations (union/interest/minus)
• Transactions
• Full ANSI SQL support
• Constraints as we know from the RDBMS
5. What is Apache Ignite
Apache Ignite is the in-memory computing platform
that is durable, strongly consistent, and highly available
with powerful SQL, key-value and processing APIs Durable Memory
Ignite Persistence
ACID Compliance
Complete SQL Support
Key-Value
Collocated Processing
Scalability and
Durability
6. What you can do with Apache Ignite?
Apache Ignite, is an in-memory computing platform that
enables you to dramatically accelerate and scale out your
existing data-intensive applications without ripping and
replacing your existing databases. It can reduce query
times by 1,000x versus disk-based systems. You can scale
out by adding new nodes to your cluster, which can handle
hundreds of terabytes of data from multiple databases.
7. What you can do with Apache Ignite? (cont.)
You can modernize your existing data-intensive
architecture by inserting Apache Ignite between your
existing application and database layers. Apache Ignite
integrates seamlessly with RDBMS, NoSQL and Apache®
Hadoop™ databases. It features a unified API which
supports SQL, C++, .NET, PHP, MapReduce,
JAVA/Scala/Groovy, and Node.js protocols for the
application layer. Your Apache Ignite cluster, applications,
and databases can run on premise, in a hybrid
environment, or on a cloud platform such as AWS® or
Microsoft Azure.
8. Nikita Ivanov
Founder and CTO at GridGain systems
“You can buy a 10-blade server that has a terabyte of RAM for less than
$25,000 (~year 2015). RAM does push up the initial price but because RAM’s
lower power and cooling costs, and no moving parts to break, analysts say that
the TCO (Total Cost of Ownership) for using RAM instead of rotating or solid-
state storage as primary storage breaks even in about three years. And that's
just looking at TCO, not including the delivered value from getting much faster
processing performance.”
Source: https://www.linux.com/news/gridgain-memory-data-fabric-becomes-apache-
ignite
9. Source: https://gist.github.com/jboner/2841832
Latency Comparison Numbers
--------------------------
L1 cache reference 0.5 ns
Branch mispredict 5 ns
L2 cache reference 7 ns 14x L1 cache
Mutex lock/unlock 25 ns
Main memory reference 100 ns 20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy 3,000 ns 3 us
Send 1K bytes over 1 Gbps network 10,000 ns 10 us
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD
Read 1 MB sequentially from memory 250,000 ns 250 us
Round trip within same datacenter 500,000 ns 500 us
Read 1 MB sequentially from SSD* 1,000,000 ns 1,000 us 1 ms ~1GB/sec SSD, 4X
memory
Disk seek 10,000,000 ns 10,000 us 10 ms 20x datacenter roundtrip
Read 1 MB sequentially from disk 20,000,000 ns 20,000 us 20 ms 80x memory, 20X SSD
Send packet CA->Netherlands->CA 150,000,000 ns 150,000 us 150 ms
11. In-Memory Database (IMDB)
Apache Ignite can be used as a distributed and
horizontally scalable in-memory database
(IMDB) that supports ACID transactions and can
be used with SQL, key-value, compute, machine
learning and other data processing APIs.
One of the distinguishing characteristics of
Ignite SQL is the support for distributed SQL
JOINs, which works in both, collocated and non-
collocated fashions.
When collocated, the JOINs are executed on the local data available on each node
without having to move large data sets across the network. Such collocated
approach provides the best scalability and performance in distributed clusters.
More information about the IMDB can be found here.
12. Quiz 1
1. How many times is RAM faster than an SSD disk for
1MB sequential read?
1. 4
2. 10
3. 8
2. How many times is RAM faster than a typical HDD
for 1MB sequential read?
1. 80
2. 100
3. 1000
14. Distributed SQL Database
Apache Ignite is fully complaint with ANSI-
99 compliant, horizontally scalable and
fault-tolerant distributed SQL database.
The distribution is provided either by
partitioning the data across cluster nodes
or by full replication, depending on the
use case.
You can interact with Ignite as you would
with any other SQL storage, using standard
JDBC or ODBC connectivity. Ignite also
provides native SQL APIs for Java, .NET
and C++ developers for better
performance.
More information about the Distributed SQl DBs can be found here.
Distributed Collocated SQL Query
15. In-Memory Data Grid (IMDG) – Key-Value store
The data grid has been built from
the ground up to linearly scale to
hundreds of nodes with strong
semantics for data locality and
affinity data routing to reduce
redundant data noise.
It can be viewed as a distributed partitioned hash map with every cluster
node owning a portion of the overall data. This way the more cluster nodes
we add, the more data we can cache.
More information about the IMDG can be found here.
16. Compute Grid
Distributed computations are performed in
parallel fashion to gain high performance,
low latency, and linear scalability.
Ignite compute grid provides a set of
simple APIs that allow users distribute
computations and data processing across
multiple computers in the cluster.
Distributed parallel processing is based on
the ability to take any computation and
execute it on any set of cluster nodes and
return the results back.
More information about the Compute grid can be found here.
17. • Continuous availability of deployed services regardless of topology changes or crashes.
• Automatically deploy any number of distributed service instances in the cluster.
• Automatically deploy singletons, including cluster-singleton, node-singleton, or key-affinity-
singleton.
• Automatically deploy distributed services on node start-up by specifying them in the configuration.
• Undeploy any of the deployed services.
• Get information about service deployment topology within the cluster.
• Create service proxy for accessing remotely deployed distributed services
Service Grid
Service Grid allows for deployments
of arbitrary user-defined services on
the cluster. You can implement and
deploy any service, such as custom
counters, ID generators, hierarchical
maps, etc.
More information about the Service grid can be found here.
18. Distributed Data Structures
Ignite allows for most of the data structures
from java.util.concurrent framework to be
used in a distributed fashion.
Ignite gives you the capability to take a data
structure you are familiar with and use it in a
clustered fashion.
For example, you can take
java.util.concurrent.BlockingDeque and add
something to it on one node and poll it from
another node.
Or have a distributed primary key generator
which would guarantee uniqueness on all
nodes.
More information about the Distributed data Strucutres can be found here.
• Queue and Set
• Atomic Types
• CountDownLatch
• ID Generator
• Semaphore
19. Quiz 3
1. Apache Ignite is able to scale up and down by simply
adding/removing nodes from the cluster?
1. Yes
2. No
2. Does Apache Ignite has the concept of servers and
clients?
1. Yes
2. No
3. Is it possible to manage an Apache Ignite cluster
remotely?
1. Yes
2. No
20. Data Streamers
1. Client nodes inject finite or
continuous streams of data into
Ignite caches using Ignite Data
Streamers.
2. Data is automatically partitioned
between Ignite data nodes, and each
node gets equal amount of data.
3. Streamed data can be concurrently
processed directly on the Ignite data
nodes in collocated fashion.
4. Clients can also perform concurrent
SQL queries on the streamed data.
More information about the Data Streamers can be found here.
21. Integration with major streaming technologies
Apache Ignite integrates with major streaming technologies and frameworks
in order to bring even more advanced streaming capabilities to Ignite-based
architectures:
1. Kafka Streamer
2. Camel Streamer
3. JMS Streamer
4. MQTT Streamer
5. Storm Streamer
6. Flink streamer
7. Twitter Streamer
8. Flume Streamer
9. Zero MQ
10. Rocket MQ
More information about Integrating Ignite with Data
Streamers can be found here.
22. Messaging & Events
Exchange custom messages between nodes across the cluster.
Ignite distributed messaging allows for topic based cluster-wide
communication between all nodes.
Messages with a specified message topic can be distributed to all or sub-
group of nodes that have subscribed to that topic.
Ignite messaging is based on publish-subscribe paradigm where publishers
and subscribers are connected together by a common topic.
When one of the nodes sends a message A for topic T, it is published on all
nodes that have subscribed to T.
More information about Messaging & Events can be found here.
23. Sliding Windows
More information about Sliding Windows can be found here.
Sliding windows are configured as Ignite cache eviction policies,
and can be:
• Time-based sliding windows
• FIFO sliding windows
• LRU sliding windows
• Querying sliding windows
24. Web Session clustering
More information about Web Session Clustering can be found here.
Ignite In-Memory Data Fabric is capable of
caching web sessions of all Java Servlet
containers that follow Java Servlet 3.0
Specification, including Apache Tomcat,
Eclipse Jetty, Oracle WebLogic, and others.
• No need for sticky sessions provided by
the Load Balancer.
25. Hibernate L2 Cache
More information about Hibernate L2 cache can be found here.
Ignite In-Memory Data Fabric can be used as
Hibernate Second-Level cache (or L2 cache),
which can significantly speed-up the
persistence layer of your application.
Hibernate is a well-known and widely used
framework for Object-Relational Mapping
(ORM). While interacting closely with an SQL
database, it performs caching of retrieved
data to minimize expensive database requests
26. Spring Caching
More information about Spring Cache can be found here.
Ignite is shipped with SpringCacheManager - an implementation of Spring
Cache Abstraction. It provides an annotation-based way to enable caching
for Java methods so that the result of a method execution is stored in the
Ignite cache. Later, if the same method is called with the same set of
parameter values, the result will be retrieved from the cache instead of
actually executing the method.
Example:
private JdbcTemplate jdbc;
@Cacheable("averageSalary")
public long averageSalary(int organizationId) {
String sql = "SELECT AVG(e.salary) " + "FROM Employee e " + "WHERE e.organizationId = ?";
return jdbc.queryForObject(sql, Long.class, organizationId);
}
27. Spring Data
More information about Spring Data can be found here.
Spring Data Framework provides a unified and
widely used API that allows abstracting an
underlying data storage from the application
layer.
Spring Data helps you avoid locking to a specific
database vendor, making it easy to switch from
one database to another with minimal efforts.
Apache Ignite implements Spring Data
CrudRepository interface that not only supports
basic CRUD operations but also provides access
to the Apache Ignite SQL Grid via the unified
Spring Data API.
@RepositoryConfig(cacheName = "PersonCache")
public interface PersonRepository extends IgniteRepository
<Person, Long> {
/**
* Gets all the persons with the given name.
* @param name Person name.
* @return A list of Persons with the given first name.
*/
public List<Person> findByFirstName(String name);
/**
* Returns top Person with the specified surname.
* @param name Person surname.
* @return Person that satisfy the query.
*/
public Cache.Entry<Long, Person> findTopByLastNameLike
(String name);
}
28. Apache Spark
More information about Ignite for Spark can be found here.
Apache Ignite provides an implementation of
Spark RDD (Resilient Distributed Datasets)
abstraction which allows to easily share
state in memory across Spark jobs. The main
difference between native Spark RDD and
IgniteRDD is that Ignite RDD provides a
shared in-memory view on data across
different Spark jobs, workers, or
applications, while native Spark RDD cannot
be seen by other Spark jobs or applications.
29. Other integrations
More information about integrations can be found here.
Apache Ignite integrates with:
• Hadoop
• Apache Cassandra
• PHP PDO – Data Objects
• MyBatis L2 Cache
• OSGi
30. Ignite Native Persistence
Ignite native persistence is a distributed,
ACID, and SQL-compliant disk store that
transparently integrates with Ignite's durable
memory. Ignite persistence is optional and can
be turned on and off. When turned off Ignite
becomes a pure in-memory store.
With the native persistence enabled, Ignite always stores a superset of data
on disk, and as much as possible in RAM. For example, if there are 100 entries
and RAM has the capacity to store only 20, then all 100 will be stored on disk
and only 20 will be cached in RAM for better performance.
More information about the Ignite Native Persistence can be found here.
31. 3rd Party Persistence
JCache specification comes with APIs for
javax.cache.integration.CacheLoader and
javax.cache.integration.CacheWriter which are used for write-through
and read-through to and from an underlying persistent storage
respectively (e.g. an RDBMS database like Oracle or MySQL, or NoSQL
database like MongoDB or Couchbase).
It supports:
• Read/Write Through
• Write-Behind
More information about the 3rd Party Persistence can be found here.
32. Supported platforms & protocols
• Java
• .NET
• C++
• REST API
• Memcached
• Redis
• PHP
More information about the Platforms & Protocols can be found here.
Apache Ignite has a rich set of APIs that
are covered throughout the
documentation.
The APIs are implemented in the form of
native libraries that support major
languages such as Java, .NET and C++, as
well as a variety of protocols like REST,
Memcached, and Redis
34. SqlLine with version 2.3.0
More information about the sqlline tool can can be found here.
35. Typical deployment for Apache Ignite
More information can be found here.
1. One or more applications connect to the Apache Ignite cluster in
order to manipulate the data in memory.
2. The application never communicates directly with the database.
3. Apache Ignite is responsible to synchronise the data.
36. Legacy systems?
More information can be found here.
1. Existing legacy systems updating a database.
2. New systems that rely on Apache Ignite with 3rd Party
Persistence enabled.
3. How we guarantee that stale data won’t reside on Ignite
cluster for a long time and will be updated as soon as the
database receives updated from the legacy application?
37. Legacy systems. Possible solution 1.
More information can be found here.
Connect the legacy system to the Ignite cluster directly.
1. Development is required in order
to make the transition from the
existing DB to Apache Ignite.
2. Complex PL/SQL stored
procedures needs rewrite.
3. Many legacy applications.
1. Simple solution.
2. No added costs.
38. Legacy systems. Possible solution 2 (Push).
Custom logic on the third party database that would propagate the
committed changes back to the Apache Ignite cluster.
1. Development cost.1. The data are being
replicated on time
39. Legacy systems. Possible solution 3 (Gridgain
and Oracle GoldenGate Integrator).
Use Gridgain cluster and Oracle GoldenGate Integrator.
1. Licenses cost ($).1. No need to develop complex
code
More information can be found here.
40. 1. Startup a cluster
2. Run the JdbcExample/modified and show the console online
3. CacheTransactionExample
4. CacheQueryExample
5. CacheDataStreamerExample
6. CacheContinuousQueryExample (Show partitions from the web console)
7. CacheAffinityExample (java 8)
8. ComputeClosureExample (java 8)
9. IgniteAtomicSequenceExample
10. MessagingExample
11. PersistenceStoreExample
Examples from Apache Ignite’s github repo
sqlline.bat --color=true --verbose=true -u jdbc:ignite:thin://127.0.0.1/
create table city(id long primary key, name varchar) with "template=replicated";
create table person (id long, name varchar, city_id long, primary key(id, city_id)) with "backups=1, affinityKey=city_id";
create index idx_city_name on city(name);
create index idx_person_name on Person(name);
!tables
insert into city (id, name) values(1, 'Forest Hill');
insert into city (id, name) values(2, 'Denver');
insert into city (id, name) values(3, 'St. Petersburg');
insert into person (id, name, city_id) values (1, 'John Doe', 3);
insert into person (id, name, city_id) values (2, 'Rob Chen', 2);
insert into person (id, name, city_id) values (3, 'Mary Davis', 1);
insert into person (id, name, city_id) values (4, 'Richard Miles', 2);
select p.name, c.name from Person p, city c where p.city_id = c.id and c.name='Denver';