How to Choose the Best In-‐Memory Solucons for Your Apps
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
JSON Processing in Apache Ignite As Cache of RDBMS
JSON processing in Apache Ignite as cache of RDBMS Lazarev Nikita, Borisenko Oleg ISP RAS Plan ● JSON processing in RDBMS ● Implementing of JSON processing in Apache Ignite ● Apache Ignite as cache to RDBMS ● Testing ● Results This work is funded by the Minobrnauki Russia (grant id RFMEFI60417X0199, grant number 14.604.21.0199) Introduction JSON documents: ● Describes objects with substructure ● Flexible scheme ● Used in WEB ● Supported in some RDBMS ● Not included in SQL standard Introduction 1. SQL tables may contain in the columns JSON type 2. Functions allowing developers generate JSON documents directly in SQL queries 3. Transformation of string data types to JSON and reverse operations 4. CAST-operator both into JSON and out of it 5. Operations to check correctness of the documents 6. Operations to work with documents directly in SQL 7. Indexing of JSON documents Introduction Feature Oracle MySQL MS SQL Server PostgreSQL 1 Stored in strings Yes Stored in strings Yes, Binary storage is possible 2 Incompletely Yes Yes Yes 3 Incompletely Yes Yes Yes 4 Incompletely Yes Incompletely Yes 5 Yes Yes Yes Yes 6 Yes Yes Incompletely Yes 7 Full text search No No For binary representation Introduction PostgreSQL provides: ● json, jsonb data types ● json and jsonb operators: ○ “->”, “->>”, “#>”, “#>>” - get document element as document or text ● jsonb operators: ○ “@>”, “<@” - comparison of documents ○ “?”, “?|”, “?&” - key existence ○ “||”, “-”, “#-” - document modifications ● Addition JSON processing functions ● Aggregate functions ● GIN Index for jsonb Introduction ● The cost of RAM decreases ● Increase of data processing performance ● Horizontal scaling ● CAP-theorem ● Power supply dependency Apache Ignite Apache Ignite is the open source version of GridGain Systems product. -
Beyond Relational Databases
EXPERT ANALYSIS BY MARCOS ALBE, SUPPORT ENGINEER, PERCONA Beyond Relational Databases: A Focus on Redis, MongoDB, and ClickHouse Many of us use and love relational databases… until we try and use them for purposes which aren’t their strong point. Queues, caches, catalogs, unstructured data, counters, and many other use cases, can be solved with relational databases, but are better served by alternative options. In this expert analysis, we examine the goals, pros and cons, and the good and bad use cases of the most popular alternatives on the market, and look into some modern open source implementations. Beyond Relational Databases Developers frequently choose the backend store for the applications they produce. Amidst dozens of options, buzzwords, industry preferences, and vendor offers, it’s not always easy to make the right choice… Even with a map! !# O# d# "# a# `# @R*7-# @94FA6)6 =F(*I-76#A4+)74/*2(:# ( JA$:+49>)# &-)6+16F-# (M#@E61>-#W6e6# &6EH#;)7-6<+# &6EH# J(7)(:X(78+# !"#$%&'( S-76I6)6#'4+)-:-7# A((E-N# ##@E61>-#;E678# ;)762(# .01.%2%+'.('.$%,3( @E61>-#;(F7# D((9F-#=F(*I## =(:c*-:)U@E61>-#W6e6# @F2+16F-# G*/(F-# @Q;# $%&## @R*7-## A6)6S(77-:)U@E61>-#@E-N# K4E-F4:-A%# A6)6E7(1# %49$:+49>)+# @E61>-#'*1-:-# @E61>-#;6<R6# L&H# A6)6#'68-# $%&#@:6F521+#M(7#@E61>-#;E678# .761F-#;)7-6<#LNEF(7-7# S-76I6)6#=F(*I# A6)6/7418+# @ !"#$%&'( ;H=JO# ;(\X67-#@D# M(7#J6I((E# .761F-#%49#A6)6#=F(*I# @ )*&+',"-.%/( S$%=.#;)7-6<%6+-# =F(*I-76# LF6+21+-671># ;G';)7-6<# LF6+21#[(*:I# @E61>-#;"# @E61>-#;)(7<# H618+E61-# *&'+,"#$%&'$#( .761F-#%49#A6)6#@EEF46:1-# -
High Performance with Distributed Caching
High Performance with Distributed Caching Key Requirements For Choosing The Right Solution High Performance with Distributed Caching: Key Requirements for Choosing the Right Solution Table of Contents Executive summary 3 Companies are choosing Couchbase for their caching layer, and much more 3 Memory-first 4 Persistence 4 Elastic scalability 4 Replication 5 More than caching 5 About this guide 5 Memcached and Oracle Coherence – two popular caching solutions 6 Oracle Coherence 6 Memcached 6 Why cache? Better performance, lower costs 6 Common caching use cases 7 Key requirements for an effective distributed caching solution 8 Problems with Oracle Coherence: cost, complexity, capabilities 8 Memcached: A simple, powerful open source cache 10 Lack of enterprise support, built-in management, and advanced features 10 Couchbase Server as a high-performance distributed cache 10 General-purpose NoSQL database with Memcached roots 10 Meets key requirements for distributed caching 11 Develop with agility 11 Perform at any scale 11 Manage with ease 12 Benchmarks: Couchbase performance under caching workloads 12 Simple migration from Oracle Coherence or Memcached to Couchbase 13 Drop-in replacement for Memcached: No code changes required 14 Migrating from Oracle Coherence to Couchbase Server 14 Beyond caching: Simplify IT infrastructure, reduce costs with Couchbase 14 About Couchbase 14 Caching has become Executive Summary a de facto technology to boost application For many web, mobile, and Internet of Things (IoT) applications that run in clustered performance as well or cloud environments, distributed caching is a key requirement, for reasons of both as reduce costs. performance and cost. By caching frequently accessed data in memory – rather than making round trips to the backend database – applications can deliver highly responsive experiences that today’s users expect. -
Apache Ignitetm In-Memory Data Fabric in Action Fast Data Meets Open Source DMITRIY SETRAKYAN Founder, PMC
Apache IgniteTM In-Memory Data Fabric In Action Fast Data Meets Open Source DMITRIY SETRAKYAN Founder, PMC https://ignite.apache.org @apacheignite @dsetrakyan Apache®, Apache Ignite, Ignite®, and the Apache Ignite logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. Coding Examples • Compute Grid • Data Grid • Streaming Grid • Service Grid Apache®, Apache Ignite, Ignite®, and the Apache Ignite logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. Join Us! Apache®, Apache Ignite, Ignite®, and the Apache Ignite logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. In-Memory Data Fabric: More Than Data Grid Apache®, Apache Ignite, Ignite®, and the Apache Ignite logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. Apache Ignite: Complete Cloud Support • Automatic Discovery – Simple Configuration – AWS/EC2/S3 – Google Compute Engine – Other Clouds with JClouds • Docker Support – Automatically Build and Deploy Apache®, Apache Ignite, Ignite®, and the Apache Ignite logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. In-Memory Compute Grid • MapReduce • ForkJoin • Zero Deployment • Cron-like Task Scheduling • State Checkpoints • Load Balancing • Automatic Failover • Full Cluster Management • Pluggable SPI Design Apache®, Apache Ignite, Ignite®, and the Apache Ignite logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. -
Concurrent and Distributed Cloudsim Simulations
Concurrent and Distributed CloudSim Simulations Pradeeban Kathiravelu Luis Veiga INESC-ID Lisboa INESC-ID Lisboa Instituto Superior Tecnico,´ Universidade de Lisboa Instituto Superior Tecnico,´ Universidade de Lisboa Lisbon, Portugal Lisbon, Portugal Email: [email protected] Email: [email protected] Abstract—Cloud Computing researches involve a tremendous CloudSim was further developed as a cloud simulator on its amount of entities such as users, applications, and virtual ma- own. Due to its modular architecture which facilitates cus- chines. Due to the limited access and often variable availabil- tomizations, it is extended into different simulation tools such ity of such resources, researchers have their prototypes tested as CloudAnalyst [15] and NetworkCloudSim [16]. Developed against the simulation environments, opposed to the real cloud in Java, CloudSim is portable. CloudSim can be easily modified environments. Existing cloud simulation environments such as by extending the classes, with a few changes to the CloudSim CloudSim and EmuSim are executed sequentially, where a more advanced cloud simulation tool could be created extending them, core. Its source code is open and maintained. Hence, CloudSim leveraging the latest technologies as well as the availability of was picked as the core module to build the distributed simulator. multi-core computers and the clusters in the research laboratories. In the remaining of the paper, we will discuss the pre- This research seeks to develop Cloud2Sim, a concurrent and liminary background information on CloudSim in section II. distributed cloud simulator, extending CloudSim while exploiting the features provided by Hazelcast, Infinispan and Hibernate Section III discusses design and implementation of Cloud2Sim, Search to distribute the storage and execution of the simulation. -
A Gridgain Systems In-Memory Computing White Paper
WHITE PAPER A GridGain Systems In-Memory Computing White Paper February 2017 © 2017 GridGain Systems, Inc. All Rights Reserved. GRIDGAIN.COM WHITE PAPER Accelerate MySQL for Demanding OLAP and OLTP Use Cases with Apache Ignite Contents Five Limitations of MySQL ............................................................................................................................. 2 Delivering Hot Data ................................................................................................................................... 2 Dealing with Highly Volatile Data ............................................................................................................. 3 Handling Large Data Volumes ................................................................................................................... 3 Providing Analytics .................................................................................................................................... 4 Powering Full Text Searches ..................................................................................................................... 4 When Two Trends Converge ......................................................................................................................... 4 The Speed and Power of Apache Ignite ........................................................................................................ 5 Apache Ignite Tames a Raging River of Data ............................................................................................... -
An Analysis of Network-Partitioning Failures in Cloud Systems
An Analysis of Network-Partitioning Failures in Cloud Systems Ahmed Alquraan, Hatem Takruri, Mohammed Alfatafta, and Samer Al-Kiswany, University of Waterloo https://www.usenix.org/conference/osdi18/presentation/alquraan This paper is included in the Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’18). October 8–10, 2018 • Carlsbad, CA, USA ISBN 978-1-939133-08-3 Open access to the Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation is sponsored by USENIX. An Analysis of Network-Partitioning Failures in Cloud Systems Ahmed Alquraan, Hatem Takruri, Mohammed Alfatafta, Samer Al-Kiswany University of Waterloo, Canada Abstract production networks, network-partitioning faults occur We present a comprehensive study of 136 system as frequently as once a week and take from tens of failures attributed to network-partitioning faults from minutes to hours to repair. 25 widely used distributed systems. We found that the Given that network-partitioning fault tolerance is a majority of the failures led to catastrophic effects, such well-studied problem [13, 14, 17, 20], this raises as data loss, reappearance of deleted data, broken locks, questions about how these faults sill lead to system and system crashes. The majority of the failures can failures. What is the impact of these failures? What are easily manifest once a network partition occurs: They the characteristics of the sequence of events that lead to require little to no client input, can be triggered by a system failure? What are the characteristics of the isolating a single node, and are deterministic. However, network-partitioning faults? And, foremost, how can we the number of test cases that one must consider is improve system resilience to these faults? To help answer these questions, we conducted a extremely large. -
White Paper Using Hazelcast with Microservices
WHITE PAPER Using Hazelcast with Microservices By Nick Pratt Vertex Integration June 2016 Using Hazelcast with Microservices Vertex Integration & Hazelcast WHITE PAPER Using Hazelcast with Microservices TABLE OF CONTENTS 1. Introduction 3 1.1 What is a Microservice 3 2. Our experience using Hazelcast with Microservices 3 2.1 Deployment 3 2.1.1 Embedded 4 2.2 Discovery 5 2.3 Solving Common Microservice Needs with Hazelcast 5 2.3.1 Multi-Language Microservices 5 2.3.2 Service Registry 5 2.4 Complexity and Isolation 6 2.4.1 Data Storage and Isolation 6 2.4.2 Security 7 2.4.3 Service Discovery 7 2.4.4 Inter-Process Communication 7 2.4.5 Event Store 8 2.4.6 Command Query Responsibility Segregation (CQRS) 8 3. Conclusion 8 TABLE OF FIGURES Figure 1 Microservices deployed as HZ Clients (recommended) 4 Figure 2 Microservices deployed with embedded HZ Server 4 Figure 3 Separate and isolated data store per Service 6 ABOUT THE AUTHOR Nick Pratt is Managing Partner at Vertex Integration LLC. Vertex Integration develops and maintains software solutions for data flow, data management, or automation challenges, either for a single user or an entire industry. The business world today demands that every business run at maximum efficiency.T hat means reducing errors, increasing response time, and improving the integrity of the underlying data. We can create a product that does all those things and that is specifically tailored to your needs. If your business needs a better way to collect, analyze, report, or share data to maximize your profitability, we can help. -
Alfresco Enterprise on AWS: Reference Architecture October 2013
Amazon Web Services – Alfresco Enterprise on AWS: Reference Architecture October 2013 Alfresco Enterprise on AWS: Reference Architecture October 2013 (Please consult http://aws.amazon.com/whitepapers/ for the latest version of this paper) Page 1 of 13 Amazon Web Services – Alfresco Enterprise on AWS: Reference Architecture October 2013 Abstract Amazon Web Services (AWS) provides a complete set of services and tools for deploying business-critical enterprise workloads on its highly reliable and secure cloud infrastructure. Alfresco is an enterprise content management system (ECM) useful for document and case management, project collaboration, web content publishing and compliant records management. Few classes of business-critical applications touch more enterprise users than enterprise content management (ECM) and collaboration systems. This whitepaper provides IT infrastructure decision-makers and system administrators with specific technical guidance on how to configure, deploy, and run an Alfresco server cluster on AWS. We outline a reference architecture for an Alfresco deployment (version 4.1) that addresses common scalability, high availability, and security requirements, and we include an implementation guide and an AWS CloudFormation template that you can use to easily and quickly create a working Alfresco cluster in AWS. Introduction Enterprises need to grow and manage their global computing infrastructures rapidly and efficiently while simultaneously optimizing and managing capital costs and expenses. The computing and storage services from AWS meet this need by providing a global computing infrastructure as well as services that simplify managing infrastructure, storage, and databases. With the AWS infrastructure, companies can rapidly provision compute capacity or quickly and flexibly extend existing on-premises infrastructure into the cloud. -
Sexy Technology Like Schema on the Fly
Sexy Technology Like Schema On The Fly Sergei never change-overs any Botswana depolymerize speedfully, is Apollo acquitted and broadcast enough? Sometimes limiest Jarvis misdoes her sinfonia provokingly, but bespectacled Layton understudies belive or prognosticated hooly. Drearisome and mystifying Taylor prosper her enthrallment mechanizes or waver successfully. Id that much greater numbers in place to already instrumented with schema on technology the sexy like Anil has contain a technical contributor for various blogs such as IBM Watson. This method of keeping the live application up-to-date is called Hot Module. Monitor the dot database vulnerable to identify hot spots in future data. Worry throughput can be provisioned on the legislation without any downtime. Apache HBase Reference Guide. Convolutional neural networks we want to collected, for you to have to face development literature on the events on other technologies are generated automatically spins up and adults. Is OLAP Dead Senturus. As their name implies schemaless does not allege a schema. The Analyze Vacuum schema utility helps you automate the table. New scripts are hollow-deployed inside your already running Flink job as described in accept next section. Roles and schemas in part has their observation of characters in video. Amazon Timestream Is Finally Released Is inventory Worth Your. In making deal a large volume big data various databases technologies emerged adopting SQL like paradigmApache Hive is one cherish them. Using Infinispan as taking database replacement using Hibernate OGM you can. Find himself right schemas for your military and your thread on Schemaorg. Agents and Ambient Intelligence Achievements and Challenges. Gender Development Research for Sex Roles Historical. -
Apache Ignite and Gridgain Enterprise Data Fabric Training Gridgain Brings Apache Ignite to the Enterprise
Apache Ignite and GridGain Enterprise Data Fabric Training GridGain brings Apache Ignite to the enterprise. Apache Ignite is a high-performance, integrated and distributed in-memory platform for computing and transacting on large- scale data sets in real-time, orders of magnitude faster than possible with traditional disk- based or flash technologies. C123-401 This training is designed to provide you with the knowledge required to build high throughput, low latency applications for scaling with Apache Ignite and GridGain Enterprise™ Data Fabric. GridGain Enterprise™ consists of Apache Ignite™ plus premium features to enhance functionality for enterprises. By completing this training you will learn and gain experience about GridGain Enterprise and Apache Ignite and what they are used for, Learn where and when to deploy Apache Ignite and GridGain Enterprise, Learn how to build applications that run on Apache Ignite and GridGain Enterprise and take advantage of the benefits and Make the right development decisions while using Apache Ignite and GridGain Enterprise. AUDIENCE Developers Project Managers SI Architects KNOWLEDGE REQUIREMENTS Java working knowledge IntelliJ IDE knowledge is a plus LENGTH 3 Days BONUS Plenty of hands-on lab sessions on modifying the Custom applications SYLLABUS GridGain and Apache Ignite Apache Ignite/GridGain API (Day 2) Apache Ignite/GridGain API (Day 3) Introduction (Day 1) 7. Data Modeling 12. Compute Grid 1. Course Introduction 8. SQL Grid Query 13. Continuous Queries 2. GridGain Introduction 9. SQL Grid DML 14. Service Grid 3. Clustering and Caching 10. Transactions 15. Additional API Concepts 11. Persistence 16. Administration Concerns 4. Order It Demo Application 17. -
Evaluation of SQL Benchmark for Distributed In-Memory Database Management Systems
IJCSNS International Journal of Computer Science and Network Security, VOL.18 No.10, October 2018 59 Evaluation of SQL benchmark for distributed in-memory Database Management Systems Oleg Borisenko† and David Badalyan†† Ivannikov Institute for System Programming of the RAS, Moscow, Russia Summary Requirements for modern DBMS in terms of speed are growing every day. As an alternative to traditional relational DBMS 2. Overview distributed, in-memory DBMS are proposed. In this paper, we investigate capabilities of Apache Ignite and VoltDB from the This chapter discusses the features of Apache Ignite and point of view of relational operations and compare them to VoltDB. Both DBMS support data replication in distributed PostgreSQL using our implementation of TPC-H like workload. mode. However, the paper will consider only the case of Key words: data distribution without replication. Apache Ignite, VoltDB, PostgreSQL, In-memory Computing, distributed systems. 2.1 Apache Ignite 1. Introduction Apache Ignite is a distributed in-memory DBMS [3] [4] written in the Java programming language. It is a caching Today, most applications have to handle large amounts of and data processing platform designed for managing large data. Architects of complex systems face a problem of amounts of data using large number of compute nodes. choosing a DBMS corresponding to reliability, scalability Despite original key-value nature of the system, the and usability requirements. Traditionally used RDBMS developers declare ACID compliance and full support for have several advantages, such as integrity control, data the SQL:1999 standard. consistency, matureness. However, the centralized data H2 Database is used as a subsystem to process SQL queries.