Apache ignite Datagrid

Oct 1, 2016Download as PPTX, PDF5 likes1,101 views

This document provides an overview of Apache Ignite 1.5, an in-memory data fabric for high performance computing. It discusses key components including the distributed in-memory data grid, caching APIs, distributed SQL queries, off-heap storage, eviction policies, persistence, data rebalancing, and different types of cache queries. The data grid implements JCache and allows for distributed transactions on large datasets in real-time. It provides a unified architecture for distributed caching, processing, and analytics.

S U R I N D E R
2 9 T H J U L Y , 2 0 1 6
In-Memory Data Fabric
Apache Ignite 1.5

Introduction
 A collection of independent, well-integrated, in-
memory components
 high-performance, integrated and distributed in-
memory platform
 transacting on large-scale data sets in real-time
 faster than possible with traditional disk-based
or flash technologies

Data Grid
 Distributed In-Memory
Caching
 Implements JCache (JSR
107)
 Distributed In-Memory
Transactions
 Data Consistency
 Tiered Off-Heap Storage
 Distributed ANSI-99
SQL Queries with
support for Joins

Keep required backup
Everyone knows
everything
Cache Modes

Cache Queries…
 Scan Query : return data matching BiPredicate
 Predicate sent to each node,
 Node scan its cache
 Data consolidated by requested node
 Sql Query : load data based on sql given
 Needs indexing to be enabled
 Registering indexing in config
 Annotations for fields visibility
 H2 Console for debugging
 Text Query : Query cache object on given value
 TextQuery txt = new TextQuery(Person.class, "Master Degree");

Cache Queries contd..
 Continuous Query : listens to change in data that fall
into filter
 Initial Query(any of fro previous slide)
 Remote Filter : evaluate the key on primary node to notify
local listener about the changes
 Local Listener : Perform the intended task when it get
notification about the change in data.

Off Heap Memory
 data can be stored and moved between on-
heap, off-heap, and swap space
 On Heap Tiered : stores on heap, evict to off heap
and then may evict to swap space(default mode)
 Off Heap Tiered : bypass on heap, may evict to swap
space
 Off Heap Values : Keys On heap and values off
Heap.
 Useful when keys are small and values are huge
 Swap Space : Disk space to evict data exceeding on
heap and off heap

Eviction Policies
 LRU : recommended when in doubt
 FIFO : it ignores the element access order
 Random
 Randomly evict any element
 Used for debugging
 Not beneficial when data fits in memory

Persistent Store
 CacheStore implements cacheLoader and
cacheWriter from JCache
 Read through
 Write through
 Write behind
 Works behind the cache API’s

Data Rebalancing
 Used when new node join the grid
 Possibly more backups than configured in such scenarios
 Rebalance Modes
 SYNC: cache calls blocked until rebalancing is completed
 ASYNC: rebalancing happen in background. Cache respond immediately
 NONE : No rebalancing, cache loaded on demand when required or
explicitly loading

This talk will cover utilizing native Hadoop storage policies and types to effectively archive and tier data in your existing Hadoop infrastructure. Key focus areas are: 1. Why use heterogeneous storage (tiering)? 2. Identifying key metrics for successful archiving of Hadoop data 3. Automation requirements at scale 4. Current limitations and gotchas The impact of successful archive provides Hadoop users better performance, lower hardware cost, and lower software costs. This session will cover the techniques and tools available to unlock this powerful capability in native Hadoop. Speakers: Peter Kisich works with multiple large scale Hadoop customers successfully tiering and optimizing Hadoop infrastructure. He co-founded FactorData to bring enterprise storage features and control to open Hadoop environments. Previously, Mr. Kisich served as a global subject matter expert in Big Data and Cloud computing for IBM including speaking at several global conferences and events.

Lessons Learned from Dockerizing Spark Workloads: Spark Summit East talk by T...Spark Summit

Many initiatives for running applications inside containers have been scoped to run on a single host. Using Docker containers for large-scale production environments poses interesting challenges, especially when deploying distributed Big Data applications like Apache Spark. Some of these challenges include container lifecycle management, smart scheduling for optimal resource utilization, network configuration and security, and performance. At BlueData, we’re “all in” on Docker containers – with a specific focus on Spark applications. We’ve learned first-hand how to address these challenges for Fortune 500 enterprises and government organizations that want to deploy Big Data workloads using Docker. In this session, you’ll learn about networking Docker containers across multiple hosts securely. We’ll discuss ways to achieve high availability across distributed Big Data applications and hosts in your data center. And since we’re talking about very large volumes of data, performance is a key factor. So we’ll discuss some of the storage options we explored and implemented at BlueData to achieve near bare-metal I/O performance for Spark using Docker. We’ll share our lessons learned as well as some tips and tricks on how to Dockerize your Big Data applications in a reliable, scalable, and high-performance environment.

Application Architectures with Hadoophadooparchbook

This document discusses application architectures using Hadoop. It provides an example case study of clickstream analysis. It covers challenges of Hadoop implementation and various architectural considerations for data storage and modeling, data ingestion, and data processing. For data processing, it discusses different processing engines like MapReduce, Pig, Hive, Spark and Impala. It also discusses what specific processing needs to be done for the clickstream data like sessionization and filtering.

August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...Yahoo Developer Network

Spark and Ignite are two of the most popular open source projects in the area of high-performance Big Data and Fast Data. But did you know that one of the best ways to boost performance for your next generation real-time applications is to use them together? In this session, Dmitriy Setrakyan, Apache Ignite Project Management Committee Chairman and co-founder and CPO at GridGain will explain in detail how IgniteRDD — an implementation of native Spark RDD and DataFrame APIs — shares the state of the RDD across other Spark jobs, applications and workers. Dmitriy will also demonstrate how IgniteRDD, with its advanced in-memory indexing capabilities, allows execution of SQL queries many times faster than native Spark RDDs or Data Frames. Don't miss this opportunity to learn from one of the experts how to use Spark and Ignite better together in your projects. Speakers: Dmitriy Setrakyan, is a founder and CPO at GridGain Systems. Dmitriy has been working with distributed architectures for over 15 years and has expertise in the development of various middleware platforms, financial trading systems, CRM applications and similar systems. Prior to GridGain, Dmitriy worked at eBay where he was responsible for the architecture of an add-serving system processing several billion hits a day. Currently Dmitriy also acts as PMC chair of Apache Ignite project.

Introduction to Apache KuduShravan (Sean) Pabba

Apache Kudu is a storage layer for Apache Hadoop that provides low-latency queries and high throughput for fast data access use cases like real-time analytics. It was designed to address gaps in HDFS and HBase by providing both efficient scanning of large amounts of data as well as efficient lookups of individual rows. Kudu tables store data in a columnar format and use a distributed architecture with tablets and masters to enable high performance and scalability for workloads involving both sequential and random access of data.

Introducing Kudu, Big Data Warehousing MeetupCaserta

Not just an SQL interface or file system, Kudu - the new, updating column store for Hadoop, is changing the storage landscape. It's easy to operate and makes new data immediately available for analytics or operations. At the Caserta Concepts Big Data Warehousing Meetup, our guests from Cloudera outlined the functionality of Kudu and talked about why it will become an integral component in big data warehousing on Hadoop. To learn more about what Caserta Concepts has to offer, visit http://casertaconcepts.com/

HPE Hadoop Solutions - From use cases to proposalDataWorks Summit

Hadoop is now doing a lot more than just storage and Map/Reduce and always improving and innovating. It brings near real time, interactive and cost efficient features to do Big Data. Join us to hear about solutions based on Hadoop, how they responds to specific customer needs, with what component(s) from the Hadoop ecosystem, based on what HPE Reference Architecture(s) for the platform. Hadoop solutions like, ETL offloading, Predictive Analytics, Ad hoc query, Complex Event processing, Stream processing, Search, Machine learning, Deep learning, … Based on software components like, Spark, Hive, HBase, Kafka, Storm, Flume, Impala and Elastic Search. Speaker John Osborn, SA, Hewlett Packard Enterprise

Introduction to Kudu: Hadoop Storage for Fast Analytics on Fast Data - Rüdige...Dataconomy Media

Kudu is a new column-oriented storage system for Apache Hadoop that is designed to enable fast analytics on fast, changing data. It aims to provide high throughput for large scans and low latency for random access simultaneously. Kudu is optimized for workloads that involve high volumes of incoming data and require real-time analytics through inserts, updates, scans and lookups. It simplifies architectures for real-time analytics compared to hybrid approaches using HBase and HDFS. Currently, Kudu is in beta and provides APIs for Java, C++, Impala, MapReduce and Spark.

Data Engineering Quick GuideAsim Jalis

Introducing KuduJeremy Beard

Jeremy Beard, a senior solutions architect at Cloudera, introduces Kudu, a new column-oriented storage system for Apache Hadoop designed for fast analytics on fast changing data. Kudu is meant to fill gaps in HDFS and HBase by providing efficient scanning, finding and writing capabilities simultaneously. It uses a relational data model with ACID transactions and integrates with common Hadoop tools like Impala, Spark and MapReduce. Kudu aims to simplify real-time analytics use cases by allowing data to be directly updated without complex ETL processes.

Using Kafka and Kudu for fast, low-latency SQL analytics on streaming dataMike Percy

The document discusses using Kafka and Kudu for low-latency SQL analytics on streaming data. It describes the challenges of supporting both streaming and batch workloads simultaneously using traditional solutions. The authors propose using Kafka to ingest data and Kudu for structured storage and querying. They demonstrate how this allows for stream processing, batch processing, and querying of up-to-second data with low complexity. Case studies from Xiaomi and TPC-H benchmarks show the advantages of this approach over alternatives.

HUG August 2010: Best practicesHadoop User Group

•Arun Murthy, from the Hadoop team at Yahoo! will introduce compendium of best practices for applications running on Apache Hadoop. In fact, we introduce the notion of a Grid Pattern which, similar to Design Pattern, represents a general reusable solution for applications running on the Grid. He will even cover the anti-patterns of applications running on the Apache Hadoop clusters. Arun will enumerate characteristics of well-behaved applications and provide guidance on appropriate uses of various features and capabilities of the Hadoop framework. It is largely prescriptive in its nature; a useful way to look at the presention is to understand that applications that follow, in spirit, the best practices prescribed here are very likely to be efficient, well-behaved in the multi-tenant environment of the Apache Hadoop clusters and unlikely to fall afoul of most policies and limits.

Introduction to Apache KuduJeff Holoman

Kudu is a storage engine for Hadoop designed to address gaps in Hadoop's ability to handle workloads that require both high-throughput data ingestion and low-latency random access. It is a columnar storage engine that uses a log-structured merge tree to store data and provides APIs for NoSQL and SQL access. Kudu aims to provide high performance for both scans and random access through its columnar design and tablet architecture that partitions data across servers.

Solving Real Problems with Apache Spark: Archiving, E-Discovery, and Supervis...Spark Summit

Today there are several compliance use cases — archiving, e-discovery, supervision + surveillance, to name a few — that appear naturally suited as Hadoop workloads but haven’t seen wide adoption. In this talk, we’ll discuss common limitations, how Apache Spark helps, and propose some new blueprints as to how to modernize this architecture and disrupt existing solutions. Additionally, we’ll discuss the rising role of Apache Spark in this ecosystem; leveraging machine learning and advanced analytics in a space that has traditionally been restricted to fairly rote reporting.

A brave new world in mutable big data relational storage (Strata NYC 2017)Todd Lipcon

The ever-increasing interest in running fast analytic scans on constantly updating data is stretching the capabilities of HDFS and NoSQL storage. Users want the fast online updates and serving of real-time data that NoSQL offers, as well as the fast scans, analytics, and processing of HDFS. Additionally, users are demanding that big data storage systems integrate natively with their existing BI and analytic technology investments, which typically use SQL as the standard query language of choice. This demand has led big data back to a familiar friend: relationally structured data storage systems. Todd Lipcon explores the advantages of relational storage and reviews new developments, including Google Cloud Spanner and Apache Kudu, which provide a scalable relational solution for users who have too much data for a legacy high-performance analytic system. Todd explains how to address use cases that fall between HDFS and NoSQL with technologies like Apache Kudu or Google Cloud Spanner and how the combination of relational data models, SQL query support, and native API-based access enables the next generation of big data applications. Along the way, he also covers suggested architectures, the performance characteristics of Kudu and Spanner, and the deployment flexibility each option provides.

Disaster Recovery and Cloud Migration for your Apache Hive WarehouseDataWorks Summit

As Apache Hadoop clusters become central to an organization’s operations, they have clusters in more than one data center. Historically, this has been largely driven by requirements of business continuity planning or geo localization. It has also recently been gaining a lot of interest from a hybrid cloud perspective, i.e. wherein people are trying to augment their traditional on-prem setup with cloud-based additions as well. A robust replication solution is a fundamental requirement in such cases. The Apache Hive community has been working on new capabilities for efficient and fault tolerant replication of data in the Hive warehouse. In this talk, we will discuss these new capabilities, how it works, what replication at Hive-scale looks like, what challenges it poses, what we have done to solve those issues. We will also focus on what we need to be aware of in our use case that might make replication optimal. Speaker Sankar Hariappan, Senior Software Engineer, Hortonworks

Exponea - Kafka and Hadoop as components of architectureMartinStrycek

Kafka and Hadoop were introduced at Exponea to address several issues: - The in-memory database was very fast but limited by memory constraints. Customers wanted the freedom to analyze all their data. - Processing large volumes of streaming data was problematic. - HDFS does not support appending new data files. Kafka was introduced to stream data for storage in Hadoop. - The new technologies introduced monitoring challenges for the expanded data stack.

Building Effective Near-Real-Time Analytics with Spark Streaming and KuduJeremy Beard

This document discusses building near-real-time analytics pipelines using Apache Spark Streaming and Apache Kudu on the Cloudera platform. It defines near-real-time analytics, describes the relevant components of the Cloudera stack (Kafka, Spark, Kudu, Impala), and how they can work together. The document then outlines the typical stages involved in implementing a Spark Streaming to Kudu pipeline, including sourcing from a queue, translating data, deriving storage records, planning mutations, and storing the data. It provides performance considerations and introduces Envelope, a Spark Streaming application on Cloudera Labs that implements these stages through configurable pipelines.

Intro to Apache Kudu (short) - Big Data Application MeetupMike Percy

Spark and Object Stores —What You Need to Know: Spark Summit East talk by Ste...Spark Summit

If you are running Apache Spark in cloud environments, Object Stores —such as Amazon S3 or Azure WASB— are a core part of your system. What you can’t do is treat them like “just another filesystem” —do that and things will, eventually, go horribly wrong. This talk looks at the object stores in the cloud infrastructures, including underlying architectures., compares them to what a “real filesystem” is expected to do and shows how to use object stores efficiently and safely as sources of and destinations of data. It goes into depth on recent “S3a” work, showing how including improvements in performance, security, functionality and measurement —and demonstrating how to use make best use of it from a spark application. If you are planning to deploy Spark in cloud, or doing so today: this is information you need to understand. The performance of you code and integrity of your data depends on it.

SQL Engines for Hadoop - The case for Impalamarkgrover

HPE Keynote Hadoop Summit San Jose 2016DataWorks Summit/Hadoop Summit

This document discusses enterprise-grade big data solutions from HPE. It outlines HPE's reference architecture for big data workloads including components like data lakes, data warehouses, archival storage, event processing, and in-memory analytics. It also discusses HPE's investments in Hortonworks and collaboration to optimize Hadoop for performance. The document promotes attending an HPE session at the Hadoop Summit on modernizing data warehouses and visiting the HPE booth for demos and a trivia game.

Interactive SQL-on-Hadoop and JethroDataOfir Manor

Introduction to HadoopDr. C.V. Suresh Babu

Dancing elephants - efficiently working with object stores from Apache Spark ...DataWorks Summit

As Hadoop applications move into cloud deployments, object stores become more and more the source and destination of data. But object stores are not filesystems: sometimes they are slower; security is different, What are the secret settings to get maximum performance from queries against data living in cloud object stores? That's at the filesystem client, the file format and the query engine layers? It's even how you lay out the files —the directory structure and the names you give them. We know these things, from our work in all these layers, from the benchmarking we've done —and the support calls we get when people have problems. And now: we'll show you. This talk will start from the ground up "why isn't an object store a filesystem?" issue, showing how that breaks fundamental assumptions in code, and so causes performance issues which you don't get when working with HDFS. We'll look at the ways to get Apache Hive and Spark to work better, looking at optimizations which have been done to enable this —and what work is ongoing. Finally, we'll consider what your own code needs to do in order to adapt to cloud execution.

Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks

This document discusses best practices for optimizing Apache Spark applications. It covers techniques for speeding up file loading, optimizing file storage and layout, identifying bottlenecks in queries, dealing with many partitions, using datasource tables, managing schema inference, file types and compression, partitioning and bucketing files, managing shuffle partitions with adaptive execution, optimizing unions, using the cost-based optimizer, and leveraging the data skipping index. The presentation aims to help Spark developers apply these techniques to improve performance.

Apache Kudu Fast Analytics on Fast Data （Hadoop / Spark Conference Japan 2016...Hadoop / Spark Conference Japan

High concurrency, Low latency analytics using Spark/KuduChris George

Fast, In-Memory SQL on Apache Cassandra with Apache Ignite (Rachel Pedreschi,...DataStax

This document discusses using Apache Ignite to enable in-memory SQL on Apache Cassandra. It provides an overview of GridGain's enterprise and open source strategies, with Ignite being based on the open source version. It then discusses EPAM's engineering capabilities. The remainder discusses Ignite's capabilities for scalable SQL queries with ACID transactions on Cassandra and provides a demo comparing performance of OLTP and OLAP queries between Cassandra and Ignite. Contact information and URLs for more information on Ignite and using it with Cassandra are also provided.

IMC Summit 2016 Breakout - Roman Shtykh - Apache Ignite as a Data Processing HubIn-Memory Computing Summit

Today, many companies are faced with a huge quantity of data and a wide variety of tools with which to process it. This potentially allows for great opportunities to satisfy customers’ needs and bring user experience to the next level. However, in order to achieve this and provide a competitive solution, sophisticated and complex data processing is needed. Such processing can rarely be done with one tool or framework — a number of tools are often involved, each having prowess in a particular field of the processing pipeline. In this session, we will see the latest endeavors of Apache Ignite to integrate with other big data platforms and provide its in-memory computing strengths for data processing pipelines. In particular we will have a closer look at how it can be integrated and used with Apache Kafka and/or Flume, and outline several use scenarios.

More Related Content

What's hot (20)

Data Engineering Quick GuideAsim Jalis

Introducing KuduJeremy Beard

Using Kafka and Kudu for fast, low-latency SQL analytics on streaming dataMike Percy

HUG August 2010: Best practicesHadoop User Group

Introduction to Apache KuduJeff Holoman

Solving Real Problems with Apache Spark: Archiving, E-Discovery, and Supervis...Spark Summit

A brave new world in mutable big data relational storage (Strata NYC 2017)Todd Lipcon

Disaster Recovery and Cloud Migration for your Apache Hive WarehouseDataWorks Summit

Exponea - Kafka and Hadoop as components of architectureMartinStrycek

Building Effective Near-Real-Time Analytics with Spark Streaming and KuduJeremy Beard

Intro to Apache Kudu (short) - Big Data Application MeetupMike Percy

Spark and Object Stores —What You Need to Know: Spark Summit East talk by Ste...Spark Summit

SQL Engines for Hadoop - The case for Impalamarkgrover

HPE Keynote Hadoop Summit San Jose 2016DataWorks Summit/Hadoop Summit

Interactive SQL-on-Hadoop and JethroDataOfir Manor

Introduction to HadoopDr. C.V. Suresh Babu

Dancing elephants - efficiently working with object stores from Apache Spark ...DataWorks Summit

Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks

Apache Kudu Fast Analytics on Fast Data （Hadoop / Spark Conference Japan 2016...Hadoop / Spark Conference Japan

High concurrency, Low latency analytics using Spark/KuduChris George

Data Engineering Quick GuideAsim Jalis

Introducing KuduJeremy Beard

Using Kafka and Kudu for fast, low-latency SQL analytics on streaming dataMike Percy

HUG August 2010: Best practicesHadoop User Group

Introduction to Apache KuduJeff Holoman

Solving Real Problems with Apache Spark: Archiving, E-Discovery, and Supervis...Spark Summit

A brave new world in mutable big data relational storage (Strata NYC 2017)Todd Lipcon

Disaster Recovery and Cloud Migration for your Apache Hive WarehouseDataWorks Summit

Exponea - Kafka and Hadoop as components of architectureMartinStrycek

Building Effective Near-Real-Time Analytics with Spark Streaming and KuduJeremy Beard

Intro to Apache Kudu (short) - Big Data Application MeetupMike Percy

Spark and Object Stores —What You Need to Know: Spark Summit East talk by Ste...Spark Summit

SQL Engines for Hadoop - The case for Impalamarkgrover

HPE Keynote Hadoop Summit San Jose 2016DataWorks Summit/Hadoop Summit

Interactive SQL-on-Hadoop and JethroDataOfir Manor

Introduction to HadoopDr. C.V. Suresh Babu

Dancing elephants - efficiently working with object stores from Apache Spark ...DataWorks Summit

Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks

Apache Kudu Fast Analytics on Fast Data （Hadoop / Spark Conference Japan 2016...Hadoop / Spark Conference Japan

High concurrency, Low latency analytics using Spark/KuduChris George

Viewers also liked (20)

Fast, In-Memory SQL on Apache Cassandra with Apache Ignite (Rachel Pedreschi,...DataStax

IMC Summit 2016 Breakout - Roman Shtykh - Apache Ignite as a Data Processing HubIn-Memory Computing Summit

Processamento em Big DataLuiz Henrique Zambom Santana

Data Grids and Data CachingGalder Zamarreño

Galder Zamarreño from Red Hat presented on Infinispan, an open source in-memory data grid platform. Infinispan can be used as a local cache, clustered cache, or as a data grid. As a data grid, it provides a highly available, distributed, and elastic data store. Infinispan also enables users to build their own data-as-a-service solutions in private clouds by virtualizing data and making it accessible in an elastic and scalable manner. Major companies use Infinispan both as a cache (e.g. for Hibernate) and as a data grid for applications requiring real-time access to distributed data.

From cache to in-memory data grid. Introduction to Hazelcast.Taras Matyashovsky

scale14x-bigtop-overview-roadmapNate D'Amico

This document provides an overview and agenda for the SCALE14x 2016 conference. It summarizes the Apache Bigtop project, which aims to standardize testing, packaging, and integration of major big data components like Hadoop, Spark, and Kafka. Bigtop addresses the "dependency hell" that can occur when combining these components by providing a unified build of materials (BOM) and packaging them together as deb/rpm files. The document outlines Bigtop's origins at Yahoo in 2010 and transition to the Apache foundation. It discusses Bigtop's focus on enhancing operations, deployment tutorials, and adding more modern and realistic test data and components going forward.

In Memory Data Grids, Demystified! Uri Cohen

This document provides an overview of in-memory data grids (IMDGs), including their history, how they work, and use cases. IMDGs evolved from local caches to distributed caches to provide a partitioned, highly available system of record with querying and transaction capabilities. They use consistent hashing to distribute data across nodes and provide availability through techniques like single master replication or quorum-based consensus. IMDGs are well-suited for fast, transactional access and real-time stream processing due to memory's speed advantage over disk. The document discusses data models, placement, consistency models, and other challenges IMDGs address.

In memory grids IMDGPrateek Jain

1. In Memory Grids break problems into parts that can be solved using multiple resources on a network, using main memory instead of disk for faster file I/O. 2. In Memory Compute Grids allow computation tasks to be split and executed in parallel across grid nodes, while In Memory Data Grids provide applications with the ability to keep frequently accessed data in memory across multiple JVMs for high availability and low latency access. 3. Reference architectures show how In Memory Grids distribute data, computation tasks, and resources across a cluster for real-time processing of large datasets.

Data Grids vs DatabasesGalder Zamarreño

This document summarizes a presentation about data grids versus databases. It discusses how data grids provide extremely fast in-memory access to distributed data and can be used for caching to improve database performance. While data grids offer benefits like scalability, their use requires a different programming model than databases. They may replace databases for some use cases like analytics but databases will remain important for their maturity and existing implementations. Data grids are best viewed as complementing rather than replacing databases.

Apache Cassandra Ignite PresentationJared Winick

This document discusses scaling NoSQL databases and describes how Google BigTable and Amazon Dynamo influenced the development of NoSQL databases. It explains BigTable's column family data model and how it allows for multi-dimensional scaling and schema-free sparse columns. The document also notes that BigTable and Dynamo helped drive the need to consider data modeling and query requirements when designing NoSQL databases.

IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...In-Memory Computing Summit

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.

Infinispan, Data Grids, NoSQL, Cloud Storage and JSR 347Manik Surtani

Manik Surtani is the founder and project lead of Infinispan, an open source data grid platform. He discussed data grids, NoSQL, and their role in cloud storage. Data grids evolved from distributed caches to provide features like querying, task execution, and co-location control. NoSQL systems are alternative data storage that is scalable and distributed but lacks relational structure. JSR 347 aims to standardize data grid APIs for the Java platform. Infinispan implements JSR 107 and will support JSR 347, acting as the reference backend for Hibernate OGM.

JBoss Community Introductionjbugkorea

The document introduces the JBoss Community, which was started in 1999 by Marc Fluery with a focus on middleware. It grew popular as the EJB container when Java grew and now has over 100 open source projects focused on Java standards and middleware development. Red Hat acquired JBoss Community in 2006 and supports enterprise middleware subscriptions. The community's principles are standards-driven innovation and rapid technology adoption seen in technologies like parallel loading in JBoss 7. Major projects include Hibernate and Drools.

Архитектура Apache Ignite .NETMikhail Shcherbakov

Building Wall St Risk Systems with Apache GeodeAndre Langevin

Infinspan: In-memory data grid meets NoSQLManik Surtani

Hacking Infinispan: the new open source data grid meets NoSQLCodemotion

Apache Geode - The First Six MonthsAnthony Baker

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.

Infinispan Servers: Beyond peer-to-peer data gridsGalder Zamarreño

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.

Fast, In-Memory SQL on Apache Cassandra with Apache Ignite (Rachel Pedreschi,...DataStax

IMC Summit 2016 Breakout - Roman Shtykh - Apache Ignite as a Data Processing HubIn-Memory Computing Summit

Processamento em Big DataLuiz Henrique Zambom Santana

Data Grids and Data CachingGalder Zamarreño

From cache to in-memory data grid. Introduction to Hazelcast.Taras Matyashovsky

scale14x-bigtop-overview-roadmapNate D'Amico

In Memory Data Grids, Demystified! Uri Cohen

In memory grids IMDGPrateek Jain

Data Grids vs DatabasesGalder Zamarreño

Apache Cassandra Ignite PresentationJared Winick

IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...In-Memory Computing Summit

Apache conbigdata2015 christiantzolov-federated sql on hadoop and beyond- lev...Christian Tzolov

Infinispan, Data Grids, NoSQL, Cloud Storage and JSR 347Manik Surtani

JBoss Community Introductionjbugkorea

Архитектура Apache Ignite .NETMikhail Shcherbakov

Building Wall St Risk Systems with Apache GeodeAndre Langevin

Infinspan: In-memory data grid meets NoSQLManik Surtani

Hacking Infinispan: the new open source data grid meets NoSQLCodemotion

Apache Geode - The First Six MonthsAnthony Baker

Infinispan Servers: Beyond peer-to-peer data gridsGalder Zamarreño

Similar to Apache ignite Datagrid (20)

Apache ignite as in-memory computing platformSurinder Mehra

Less is More: 2X Storage Efficiency with HDFS Erasure CodingZhe Zhang

Ever since its creation, HDFS has been relying on data replication to shield against most failure scenarios. However, with the explosive growth in data volume, replication is getting quite expensive: the default 3x replication scheme incurs a 200% overhead in storage space and other resources (e.g., network bandwidth when writing the data). Erasure coding (EC) uses far less storage space while still providing the same level of fault tolerance. Under typical configurations, EC reduces the storage cost by ~50% compared with 3x replication.

5266732.ppthothyfa

This document provides an overview of Cassandra, a decentralized structured storage model. Some key points: - Cassandra is a distributed database designed to handle large amounts of data across commodity servers. It provides high availability with no single point of failure. - Cassandra's data model is based on Dynamo and BigTable, with data distributed across nodes through consistent hashing. It uses a column-based data structure with rows, columns, column families and supercolumns. - Cassandra was originally developed at Facebook to address issues of high write throughput and latency for their inbox search feature, which now stores over 50TB of data across 150 nodes. - Other large companies using Cassandra include Netflix, eBay

SUE 2018 - Migrating a 130TB Cluster from Elasticsearch 2 to 5 in 20 Hours Wi...Fred de Villamil

JCache Using JCache日本Javaユーザーグループ

Using JCache This document discusses Java Caching (JCache), the Java standard for caching APIs specified in JSR-107. It introduces caching concepts and benefits, describes the key interfaces and classes in JCache like CacheManager and Cache, and demonstrates features like entry processors. It also discusses JCache implementations like Hazelcast and annotations for method-level caching. The future of JCache is outlined, with plans for JCache 1.1, 2.0 aligned with Java EE 8, and 3.0 aligned with future Java versions. The document ends with information on Hazelcast's JCache support.

Performance and predictabilityRichardWarburton

These days fast code needs to operate in harmony with its environment. At the deepest level this means working well with hardware: RAM, disks and SSDs. A unifying theme is treating memory access patterns in a uniform and predictable that is sympathetic to the underlying hardware. For example writing to and reading from RAM and Hard Disks can be significantly sped up by operating sequentially on the device, rather than randomly accessing the data. In this talk we’ll cover why access patterns are important, what kind of speed gain you can get and how you can write simple high level code which works well with these kind of patterns.

Slide 2 collecting, storing and analyzing big dataTrieu Nguyen

This document discusses the process of collecting, storing, processing and analyzing big data. It covers the key concepts and technologies for collecting data using tools like Apache Sqoop and Kafka, storing data using clusters, file systems, NoSQL databases and concepts like sharding and replication. It also discusses processing data using parallel and distributed processing with Hadoop, and analyzing data using Apache Phoenix which provides a SQL interface to query HBase databases.

Cpu caching concepts mr maheshFaridabad

The document discusses CPU caching concepts. It explains that caches are faster but smaller memories that store copies of frequently accessed data from main memory, due to principles of locality of reference and the speed gap between CPUs and memory. The document outlines cache hierarchy levels, organization, mapping techniques, handling cache misses through replacement policies, updating policies for writes, issues of stale data, and modern research areas like cache coherence for multicore CPUs.

Real time analytics using Hadoop and ElasticsearchAbhishek Andhavarapu

This document discusses using Hadoop and Elasticsearch for real-time analytics. It provides an overview of Elasticsearch, including how it is document-oriented, schema-free, distributed and fast. It also demonstrates indexing, retrieving, updating and deleting documents from Elasticsearch. The demo portion involves extracting data from a SQL database using Hive, transforming it with Hadoop/Hive, and loading it into Elasticsearch to run queries. Lessons learned focus on concurrency, filtering, field data caching and JVM memory usage.

Vault2016Dan Lambright

This document discusses algorithms and indexes for tracking objects in storage tier caches. It provides case studies on DM-cache (LVM cache), Ceph tiering, and Gluster tiering. It describes different types of cache indexes including hash tables, balanced trees, and Bloom filters. Key topics include memory overhead, lookup time, growth/shrinkage issues, and metadata approaches for different tiering systems.

Debunking the Myths of HDFS Erasure Coding Performance DataWorks Summit/Hadoop Summit

The document discusses erasure coding as an alternative to replication in distributed storage systems like HDFS. It notes that while replication provides high durability, it has high storage overhead, and erasure coding can provide similar durability with half the storage overhead but slower recovery. The document outlines how major companies like Facebook, Windows Azure Storage, and Google use erasure coding. It then provides details on HDFS-EC, including its architecture, use of hardware acceleration, and performance evaluation showing its benefits over replication.

Cassandra - Research Paper Overviewsameiralk

Cassandra is a decentralized structured storage system developed at Facebook to handle large amounts of structured data across many servers. It uses a distributed architecture with no single point of failure and dynamically replicates data across nodes for high availability. Cassandra uses a column-oriented data model and supports operations like insert, get, and delete. It partitions and distributes data using consistent hashing and handles failures through gossip-based cluster membership and an anti-entropy protocol.

What is Object storage ?Nabil Kassi

Very Large Data Files, Object Stores, and Deep Learning—Lessons Learned While...Databricks

In this session, IBM will present details on advanced Apache Spark analytics currently being performed through a collaborative project with the SETI Institute, NASA, Swinburne University, Stanford University and IBM. The Allen Telescope Array in northern California has been continuously scanning the skies for over two decades, generating data archives with over 200 million signal events. Come and learn how astronomers and researchers are using Apache Spark, in conjunction with assets such as IBM’s Cognitive Compute Cluster with over 700 GPUs, to train neural net models for signal classification, and to perform computationally intensive Spark workloads on multi-terabyte binary signal files. The speakers will also share details on one of the key components of this implementation: Stocator, an open source (Apache License 2.0) object store connector for Hadoop and Apache Spark, specifically designed to optimize their performance with object stores. Learn how Stocator works, and see how it was able to greatly improve performance and reduce the quantity of resources used, both for ground-to-cloud uploads of very large signal files, and for subsequent access of radio data for analysis using Spark.

Hadoop and Netezza - Co-existence or Competition?Krishnan Parasuraman

Hadoop is rapidly emerging as a viable platform for big data analytics. Thanks to early adoption by organizations like Yahoo and Facebook, and an active open source community, we have seen significant innovation around this platform. With support of relational constructs and a SQL-like query interface, many experts believe that Hadoop will subsume some of the data warehousing tasks at some point in the future. Even though Hadoop and parallel databases have some architectural similarities, they are designed to solve different problems. In this presentation, you will get introduced to Hadoop architecture, its salient differences from Netezza and typical use cases. You will learn about common co-existence deployment models that have been put into practice by Netezza's customers who have leveraged benefits from both these technologies. You will also understand Netezza's current support for Hadoop and future strategy.

Zero ETL analytics with LLAP in Azure HDInsightAshish Thapliyal

This document discusses HDInsight interactive query architecture and performance. It summarizes that: 1. HDInsight uses LLAP (Low Latency Analytical Processing) clusters to serve queries directly from Azure blob storage and data lake store for fast performance on text data. 2. Testing showed LLAP had high query concurrency and interactive query speed compared to Spark SQL and Presto. 3. The document also outlines HDInsight's logging architecture where the OMS agent collects logs and metrics from HDInsight clusters and sends them to Log Analytics for analysis.

Repository performance tuningJukka Zitting

This document provides an overview of performance tuning for a content repository. It discusses identifying performance issues, investigating potential causes related to hardware, the repository, applications or clients. Possible solutions include changing content, configuration, code or upgrading hardware. The document also summarizes key aspects of the repository internals like the data store, persistence manager, query index and clustering. Specific tips are provided for basic content access, batch processing and query performance.

Netapp Deduplication conceptsSaroj Sahu

NetApp deduplication provides block-level deduplication within flexible volumes to reduce storage capacity needs. It works at the 4KB block level on active file systems and can be configured to run automatically or manually. Deduplication uses fingerprints that are unique digital signatures for blocks. During deduplication, fingerprints are compared and any duplicate blocks are replaced with a pointer to the existing data block to save space. Up to 4% additional space may be needed in a volume and up to 3% in an aggregate for deduplication metadata.

Big data with HDFS and Mapreducesenthil0809

This document discusses Hadoop Distributed File System (HDFS) and MapReduce. It begins by explaining HDFS architecture, including the NameNode and DataNodes. It then discusses how HDFS is used to store large files reliably across commodity hardware. The document also provides steps to install Hadoop in single node cluster and describes core Hadoop services like JobTracker and TaskTracker. It concludes by discussing HDFS commands and a quiz about Hadoop components.

No sql databasesAshish Kumar Thakur

The document discusses massively parallel cloud data storage systems and NoSQL databases. It describes why these systems were developed due to the large data needs of social media and web companies. It then covers key aspects of NoSQL databases like their flexible schemas, distributed nature, and focus on high availability over consistency through eventual consistency. Common NoSQL systems and their architectures are also outlined.

Apache ignite as in-memory computing platformSurinder Mehra

Less is More: 2X Storage Efficiency with HDFS Erasure CodingZhe Zhang

5266732.ppthothyfa

SUE 2018 - Migrating a 130TB Cluster from Elasticsearch 2 to 5 in 20 Hours Wi...Fred de Villamil

JCache Using JCache日本Javaユーザーグループ

Performance and predictabilityRichardWarburton

Slide 2 collecting, storing and analyzing big dataTrieu Nguyen

Cpu caching concepts mr maheshFaridabad

Real time analytics using Hadoop and ElasticsearchAbhishek Andhavarapu

Vault2016Dan Lambright

Debunking the Myths of HDFS Erasure Coding Performance DataWorks Summit/Hadoop Summit

Cassandra - Research Paper Overviewsameiralk

What is Object storage ?Nabil Kassi

Very Large Data Files, Object Stores, and Deep Learning—Lessons Learned While...Databricks

Hadoop and Netezza - Co-existence or Competition?Krishnan Parasuraman

Zero ETL analytics with LLAP in Azure HDInsightAshish Thapliyal

Repository performance tuningJukka Zitting

Netapp Deduplication conceptsSaroj Sahu

Big data with HDFS and Mapreducesenthil0809

No sql databasesAshish Kumar Thakur

Recently uploaded (20)

LEARN SEO AND INCREASE YOUR KNOWLDGE IN SOFTWARE INDUSTRYNidaFarooq10

Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi

Exploring Code Comprehension in Scientific Programming: Preliminary Insight...University of Hawai‘i at Mānoa

This presentation explores code comprehension challenges in scientific programming based on a survey of 57 research scientists. It reveals that 57.9% of scientists have no formal training in writing readable code. Key findings highlight a "documentation paradox" where documentation is both the most common readability practice and the biggest challenge scientists face. The study identifies critical issues with naming conventions and code organization, noting that 100% of scientists agree readable code is essential for reproducible research. The research concludes with four key recommendations: expanding programming education for scientists, conducting targeted research on scientific code quality, developing specialized tools, and establishing clearer documentation guidelines for scientific software. Presented at: The 33rd International Conference on Program Comprehension (ICPC '25) Date of Conference: April 2025 Conference Location: Ottawa, Ontario, Canada Preprint: https://arxiv.org/abs/2501.10037

Microsoft AI Nonprofit Use Cases and Live Demo_2025.04.30.pdfTechSoup

In this webinar we will dive into the essentials of generative AI, address key AI concerns, and demonstrate how nonprofits can benefit from using Microsoft’s AI assistant, Copilot, to achieve their goals. This event series to help nonprofits obtain Copilot skills is made possible by generous support from Microsoft. What You’ll Learn in Part 2: Explore real-world nonprofit use cases and success stories. Participate in live demonstrations and a hands-on activity to see how you can use Microsoft 365 Copilot in your own work!

The Significance of Hardware in Information Systems.pdfdrewplanas10

Kubernetes_101_Zero_to_Platform_Engineer.pptxCloudScouts

Explaining GitHub Actions Failures with Large Language Models Challenges, In...ssuserb14185

GitHub Actions (GA) has become the de facto tool that developers use to automate software workflows, seamlessly building, testing, and deploying code. Yet when GA fails, it disrupts development, causing delays and driving up costs. Diagnosing failures becomes especially challenging because error logs are often long, complex and unstructured. Given these difficulties, this study explores the potential of large language models (LLMs) to generate correct, clear, concise, and actionable contextual descriptions (or summaries) for GA failures, focusing on developers’ perceptions of their feasibility and usefulness. Our results show that over 80% of developers rated LLM explanations positively in terms of correctness for simpler/small logs. Overall, our findings suggest that LLMs can feasibly assist developers in understanding common GA errors, thus, potentially reducing manual analysis. However, we also found that improved reasoning abilities are needed to support more complex CI/CD scenarios. For instance, less experienced developers tend to be more positive on the described context, while seasoned developers prefer concise summaries. Overall, our work offers key insights for researchers enhancing LLM reasoning, particularly in adapting explanations to user expertise. https://arxiv.org/abs/2501.16495

Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...Eric D. Schabell

It's time you stopped letting your telemetry data pressure your budgets and get in the way of solving issues with agility! No more I say! Take back control of your telemetry data as we guide you through the open source project Fluent Bit. Learn how to manage your telemetry data from source to destination using the pipeline phases covering collection, parsing, aggregation, transformation, and forwarding from any source to any destination. Buckle up for a fun ride as you learn by exploring how telemetry pipelines work, how to set up your first pipeline, and exploring several common use cases that Fluent Bit helps solve. All this backed by a self-paced, hands-on workshop that attendees can pursue at home after this session (https://o11y-workshops.gitlab.io/workshop-fluentbit).

Avast Premium Security Crack FREE Latest Version 2025mu394968

🌍📱👉COPY LINK & PASTE ON GOOGLE https://dr-kain-geera.info/👈🌍 Avast Premium Security is a paid subscription service that provides comprehensive online security and privacy protection for multiple devices. It includes features like antivirus, firewall, ransomware protection, and website scanning, all designed to safeguard against a wide range of online threats, according to Avast. Key features of Avast Premium Security: Antivirus: Protects against viruses, malware, and other malicious software, according to Avast. Firewall: Controls network traffic and blocks unauthorized access to your devices, as noted by All About Cookies. Ransomware protection: Helps prevent ransomware attacks, which can encrypt your files and hold them hostage. Website scanning: Checks websites for malicious content before you visit them, according to Avast. Email Guardian: Scans your emails for suspicious attachments and phishing attempts. Multi-device protection: Covers up to 10 devices, including Windows, Mac, Android, and iOS, as stated by 2GO Software. Privacy features: Helps protect your personal data and online privacy. In essence, Avast Premium Security provides a robust suite of tools to keep your devices and online activity safe and secure, according to Avast.

Why Orangescrum Is a Game Changer for Construction Companies in 2025Orangescrum

EASEUS Partition Master Crack + License Codeaneelaramzan63

Interactive odoo dashboards for sales, CRM , Inventory, Invoice, Purchase, Pr...AxisTechnolabs

Interactive Odoo Dashboard for various business needs can provide users with dynamic, visually appealing dashboards tailored to their specific requirements. such a module that could support multiple dashboards for different aspects of a business ✅Visit And Buy Now : https://bit.ly/3VojWza ✅This Interactive Odoo dashboard module allow user to create their own odoo interactive dashboards for various purpose. App download now : Odoo 18 : https://bit.ly/3VojWza Odoo 17 : https://bit.ly/4h9Z47G Odoo 16 : https://bit.ly/3FJTEA4 Odoo 15 : https://bit.ly/3W7tsEB Odoo 14 : https://bit.ly/3BqZDHg Odoo 13 : https://bit.ly/3uNMF2t Try Our website appointment booking odoo app : https://bit.ly/3SvNvgU 👉Want a Demo ?📧 [email protected] ➡️Contact us for Odoo ERP Set up : 091066 49361 👉Explore more apps: https://bit.ly/3oFIOCF 👉Want to know more : 🌐 https://www.axistechnolabs.com/ #odoo #odoo18 #odoo17 #odoo16 #odoo15 #odooapps #dashboards #dashboardsoftware #odooerp #odooimplementation #odoodashboardapp #bestodoodashboard #dashboardapp #odoodashboard #dashboardmodule #interactivedashboard #bestdashboard #dashboard #odootag #odooservices #odoonewfeatures #newappfeatures #odoodashboardapp #dynamicdashboard #odooapp #odooappstore #TopOdooApps #odooapp #odooexperience #odoodevelopment #businessdashboard #allinonedashboard #odooproducts

Adobe Lightroom Classic Crack FREE Latest link 2025kashifyounis067

🌍📱👉COPY LINK & PASTE ON GOOGLE http://drfiles.net/ 👈🌍 Adobe Lightroom Classic is a desktop-based software application for editing and managing digital photos. It focuses on providing users with a powerful and comprehensive set of tools for organizing, editing, and processing their images on their computer. Unlike the newer Lightroom, which is cloud-based, Lightroom Classic stores photos locally on your computer and offers a more traditional workflow for professional photographers. Here's a more detailed breakdown: Key Features and Functions: Organization: Lightroom Classic provides robust tools for organizing your photos, including creating collections, using keywords, flags, and color labels. Editing: It offers a wide range of editing tools for making adjustments to color, tone, and more. Processing: Lightroom Classic can process RAW files, allowing for significant adjustments and fine-tuning of images. Desktop-Focused: The application is designed to be used on a computer, with the original photos stored locally on the hard drive. Non-Destructive Editing: Edits are applied to the original photos in a non-destructive way, meaning the original files remain untouched. Key Differences from Lightroom (Cloud-Based): Storage Location: Lightroom Classic stores photos locally on your computer, while Lightroom stores them in the cloud. Workflow: Lightroom Classic is designed for a desktop workflow, while Lightroom is designed for a cloud-based workflow. Connectivity: Lightroom Classic can be used offline, while Lightroom requires an internet connection to sync and access photos. Organization: Lightroom Classic offers more advanced organization features like Collections and Keywords. Who is it for? Professional Photographers: PCMag notes that Lightroom Classic is a popular choice among professional photographers who need the flexibility and control of a desktop-based application. Users with Large Collections: Those with extensive photo collections may prefer Lightroom Classic's local storage and robust organization features. Users who prefer a traditional workflow: Users who prefer a more traditional desktop workflow, with their original photos stored on their computer, will find Lightroom Classic a good fit.

Top 10 Client Portal Software Solutions for 2025.docxPortli

Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak

As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.

How to Batch Export Lotus Notes NSF Emails to Outlook PST Easily?steaveroggers

Migrating from Lotus Notes to Outlook can be a complex and time-consuming task, especially when dealing with large volumes of NSF emails. This presentation provides a complete guide on how to batch export Lotus Notes NSF emails to Outlook PST format quickly and securely. It highlights the challenges of manual methods, the benefits of using an automated tool, and introduces eSoftTools NSF to PST Converter Software — a reliable solution designed to handle bulk email migrations efficiently. Learn about the software’s key features, step-by-step export process, system requirements, and how it ensures 100% data accuracy and folder structure preservation during migration. Make your email transition smoother, safer, and faster with the right approach. Read More:- https://www.esofttools.com/nsf-to-pst-converter.html

TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora

Unittest and pytest are the most popular testing frameworks in Python. Overall, pytest provides some advantages, including simpler assertion, reuse of fixtures, and interoperability. Due to such benefits, multiple projects in the Python ecosystem have migrated from unittest to pytest. To facilitate the migration, pytest can also run unittest tests, thus, the migration can happen gradually over time. However, the migration can be timeconsuming and take a long time to conclude. In this context, projects would benefit from automated solutions to support the migration process. In this paper, we propose TestMigrationsInPy, a dataset of test migrations from unittest to pytest. TestMigrationsInPy contains 923 real-world migrations performed by developers. Future research proposing novel solutions to migrate frameworks in Python can rely on TestMigrationsInPy as a ground truth. Moreover, as TestMigrationsInPy includes information about the migration type (e.g., changes in assertions or fixtures), our dataset enables novel solutions to be verified effectively, for instance, from simpler assertion migrations to more complex fixture migrations. TestMigrationsInPy is publicly available at: https://github.com/altinoalvesjunior/TestMigrationsInPy.

PDF Reader Pro Crack Latest Version FREE Download 2025mu394968

🌍📱👉COPY LINK & PASTE ON GOOGLE https://dr-kain-geera.info/👈🌍 PDF Reader Pro is a software application, often referred to as an AI-powered PDF editor and converter, designed for viewing, editing, annotating, and managing PDF files. It supports various PDF functionalities like merging, splitting, converting, and protecting PDFs. Additionally, it can handle tasks such as creating fillable forms, adding digital signatures, and performing optical character recognition (OCR).

Meet the Agents: How AI Is Learning to Think, Plan, and CollaborateMaxim Salnikov

Expand your AI adoption with AgentExchangeFexle Services Pvt. Ltd.

AgentExchange is Salesforce’s latest innovation, expanding upon the foundation of AppExchange by offering a centralized marketplace for AI-powered digital labor. Designed for Agentblazers, developers, and Salesforce admins, this platform enables the rapid development and deployment of AI agents across industries. Email: [email protected] Phone: +1(630) 349 2411 Website: https://www.fexle.com/blogs/agentexchange-an-ultimate-guide-for-salesforce-consultants-businesses/?utm_source=slideshare&utm_medium=pptNg

LEARN SEO AND INCREASE YOUR KNOWLDGE IN SOFTWARE INDUSTRYNidaFarooq10

Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi

Exploring Code Comprehension in Scientific Programming: Preliminary Insight...University of Hawai‘i at Mānoa

Microsoft AI Nonprofit Use Cases and Live Demo_2025.04.30.pdfTechSoup

The Significance of Hardware in Information Systems.pdfdrewplanas10

Kubernetes_101_Zero_to_Platform_Engineer.pptxCloudScouts

Explaining GitHub Actions Failures with Large Language Models Challenges, In...ssuserb14185

Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...Eric D. Schabell

Avast Premium Security Crack FREE Latest Version 2025mu394968

Why Orangescrum Is a Game Changer for Construction Companies in 2025Orangescrum

EASEUS Partition Master Crack + License Codeaneelaramzan63

Interactive odoo dashboards for sales, CRM , Inventory, Invoice, Purchase, Pr...AxisTechnolabs

Adobe Lightroom Classic Crack FREE Latest link 2025kashifyounis067

Top 10 Client Portal Software Solutions for 2025.docxPortli

Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak

How to Batch Export Lotus Notes NSF Emails to Outlook PST Easily?steaveroggers

TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora

PDF Reader Pro Crack Latest Version FREE Download 2025mu394968

Meet the Agents: How AI Is Learning to Think, Plan, and CollaborateMaxim Salnikov

Expand your AI adoption with AgentExchangeFexle Services Pvt. Ltd.

Apache ignite Datagrid

1. S U R I N D E R 2 9 T H J U L Y , 2 0 1 6 In-Memory Data Fabric Apache Ignite 1.5

2. Data Fabric

3. Introduction  A collection of independent, well-integrated, in- memory components  high-performance, integrated and distributed in- memory platform  transacting on large-scale data sets in real-time  faster than possible with traditional disk-based or flash technologies

4. Data Grid  Distributed In-Memory Caching  Implements JCache (JSR 107)  Distributed In-Memory Transactions  Data Consistency  Tiered Off-Heap Storage  Distributed ANSI-99 SQL Queries with support for Joins

5. Keep required backup Everyone knows everything Cache Modes

6. Cache Queries…  Scan Query : return data matching BiPredicate  Predicate sent to each node,  Node scan its cache  Data consolidated by requested node  Sql Query : load data based on sql given  Needs indexing to be enabled  Registering indexing in config  Annotations for fields visibility  H2 Console for debugging  Text Query : Query cache object on given value  TextQuery txt = new TextQuery(Person.class, "Master Degree");

7. Cache Queries contd..  Continuous Query : listens to change in data that fall into filter  Initial Query(any of fro previous slide)  Remote Filter : evaluate the key on primary node to notify local listener about the changes  Local Listener : Perform the intended task when it get notification about the change in data.

8. Off Heap Memory  data can be stored and moved between on- heap, off-heap, and swap space  On Heap Tiered : stores on heap, evict to off heap and then may evict to swap space(default mode)  Off Heap Tiered : bypass on heap, may evict to swap space  Off Heap Values : Keys On heap and values off Heap.  Useful when keys are small and values are huge  Swap Space : Disk space to evict data exceeding on heap and off heap

9. Eviction Policies  LRU : recommended when in doubt  FIFO : it ignores the element access order  Random  Randomly evict any element  Used for debugging  Not beneficial when data fits in memory

10. Persistent Store  CacheStore implements cacheLoader and cacheWriter from JCache  Read through  Write through  Write behind  Works behind the cache API’s

11. Data Rebalancing  Used when new node join the grid  Possibly more backups than configured in such scenarios  Rebalance Modes  SYNC: cache calls blocked until rebalancing is completed  ASYNC: rebalancing happen in background. Cache respond immediately  NONE : No rebalancing, cache loaded on demand when required or explicitly loading

12. Questions

Editor's Notes

#5: 1. ability to add nodes on demand in real-time. 2. supports local, replicated, and partitioned data sets and allows to freely cross query between these data sets using standard SQL syntax 3. As long as your cluster is alive, Ignite will guarantee that the data between different cluster nodes will always remain consistent regardless of crashes or topology changes
#6: LocalMode : useful when data is not distributed to other caches. Ideal where data can be periodically refreshed at expiration frequency. By default, backups are turned off for better performance <property name="cacheMode" value="PARTITIONED"/

Apache ignite Datagrid

Recommended

More Related Content

What's hot (20)

Viewers also liked (20)

Similar to Apache ignite Datagrid (20)

Recently uploaded (20)

Apache ignite Datagrid

Editor's Notes