Elasticsearch can be integrated with Hadoop and Hive to enable searching structured data stored in these frameworks. Elasticsearch indexes can be populated from Hadoop using MapReduce jobs where Elasticsearch is the output format, or data can be extracted from Elasticsearch to Hadoop. Similarly for Hive, external tables can be defined pointing to Elasticsearch indexes as the data source, or data can be loaded from Hive tables to Elasticsearch indexes. The document provides code examples for performing these types of Extract, Transform, Load operations between Elasticsearch, Hadoop and Hive.
ElasticES-Hadoop: Bridging the world of Hadoop and ElasticsearchMapR Technologies
In this talk, we will provide an overview of Elasticsearch for Apache Hadoop (ES-Hadoop), which includes integrations between the various Hadoop libraries, whether batch (Map/Reduce, Pig, Hive) or stream oriented (such as Apache Spark). We will also cover the YARN support and the HDFS snapshot/restore plugin available as part of ES-Hadoop. We will talk about the upcoming ES-Hadoop 2.1 GA release and near-term roadmap.
Node collaboration - Exported Resources and PuppetDBm_richardson
Node Collaboration - How can your servers share information with each other. Exploring Exported Resources, PuppetDB and other methods.
This talk was given at Sydney Puppet Users Meetup on 14/08/2014.
This document provides an introduction and overview of Elasticsearch. It discusses installing Elasticsearch and configuring it through the elasticsearch.yml file. It describes tools like Marvel and Sense that can be used for monitoring Elasticsearch. Key terms used in Elasticsearch like nodes, clusters, indices, and documents are explained. The document outlines how to index and retrieve data from Elasticsearch through its RESTful API using either search lite queries or the query DSL.
This document summarizes a presentation comparing Solr and Elasticsearch. It outlines the main topics covered, including documents, queries, mapping, indexing, aggregations, percolations, scaling, searches, and tools. Examples of specific features like bool queries, facets, nesting aggregations, and backups are demonstrated for both Solr and Elasticsearch. The presentation concludes by noting most projects work well with either system and to choose based on your use case.
This document provides an overview of using Elasticsearch for data analytics. It discusses various aggregation techniques in Elasticsearch like terms, min/max/avg/sum, cardinality, histogram, date_histogram, and nested aggregations. It also covers mappings, dynamic templates, and general tips for working with aggregations. The main takeaways are that aggregations in Elasticsearch provide insights into data distributions and relationships similarly to GROUP BY in SQL, and that mappings and templates can optimize how data is indexed for aggregation purposes.
Elasticsearch is a JSON document database that allows for powerful full-text search capabilities. It uses Lucene under the hood for indexing and search. Documents are stored in indexes and types which are analogous to tables in a relational database. Documents can be created, read, updated, and deleted via a RESTful API. Searches can be performed across multiple indexes and types. Elasticsearch offers advanced search features like facets, highlighting, and custom analyzers. Mappings allow for customization of how documents are indexed. Shards and replicas improve performance and availability. Multi-tenancy can be achieved through separate indexes or filters.
ElasticES-Hadoop: Bridging the world of Hadoop and ElasticsearchMapR Technologies
In this talk, we will provide an overview of Elasticsearch for Apache Hadoop (ES-Hadoop), which includes integrations between the various Hadoop libraries, whether batch (Map/Reduce, Pig, Hive) or stream oriented (such as Apache Spark). We will also cover the YARN support and the HDFS snapshot/restore plugin available as part of ES-Hadoop. We will talk about the upcoming ES-Hadoop 2.1 GA release and near-term roadmap.
Node collaboration - Exported Resources and PuppetDBm_richardson
Node Collaboration - How can your servers share information with each other. Exploring Exported Resources, PuppetDB and other methods.
This talk was given at Sydney Puppet Users Meetup on 14/08/2014.
This document provides an introduction and overview of Elasticsearch. It discusses installing Elasticsearch and configuring it through the elasticsearch.yml file. It describes tools like Marvel and Sense that can be used for monitoring Elasticsearch. Key terms used in Elasticsearch like nodes, clusters, indices, and documents are explained. The document outlines how to index and retrieve data from Elasticsearch through its RESTful API using either search lite queries or the query DSL.
This document summarizes a presentation comparing Solr and Elasticsearch. It outlines the main topics covered, including documents, queries, mapping, indexing, aggregations, percolations, scaling, searches, and tools. Examples of specific features like bool queries, facets, nesting aggregations, and backups are demonstrated for both Solr and Elasticsearch. The presentation concludes by noting most projects work well with either system and to choose based on your use case.
This document provides an overview of using Elasticsearch for data analytics. It discusses various aggregation techniques in Elasticsearch like terms, min/max/avg/sum, cardinality, histogram, date_histogram, and nested aggregations. It also covers mappings, dynamic templates, and general tips for working with aggregations. The main takeaways are that aggregations in Elasticsearch provide insights into data distributions and relationships similarly to GROUP BY in SQL, and that mappings and templates can optimize how data is indexed for aggregation purposes.
Elasticsearch is a JSON document database that allows for powerful full-text search capabilities. It uses Lucene under the hood for indexing and search. Documents are stored in indexes and types which are analogous to tables in a relational database. Documents can be created, read, updated, and deleted via a RESTful API. Searches can be performed across multiple indexes and types. Elasticsearch offers advanced search features like facets, highlighting, and custom analyzers. Mappings allow for customization of how documents are indexed. Shards and replicas improve performance and availability. Multi-tenancy can be achieved through separate indexes or filters.
This document discusses using Apache Spark and Elasticsearch together to index streaming data in real-time and reduce network overhead. It provides an overview of Spark and Elasticsearch, demonstrates how to set up a Spark streaming job to index tweets in Elasticsearch in real-time, and describes a modification made to the Elasticsearch connector to write data directly to shards based on the Spark partition, avoiding unnecessary network hops. The document includes code samples and concludes with links for further information.
A presentation given at the Lucene/Solr Revolution 2014 conference to show Solr and Elasticsearch features side by side. The presentation time was only 30 minutes, so only the core usability features were compared. The full video is embedded on the last slide.
This document discusses debugging and testing Elasticsearch systems. It provides tips for debugging issues like typos in mappings, setting up a local environment for testing, useful commands like analyze and explain, tuning queries, and testing strategies using Java and Ruby. The document emphasizes the importance of testing representative queries to ensure expected results and the ability to tune queries without breaking other queries. It also recommends using Elasticsearch plugins like Head for visualizing clusters and indices.
How EverTrue is building a donor CRM on top of ElasticSearch. We cover some of the issues around scaling ElasticSearch and which aspects of ElasticSearch we are using to deliver value to our customers.
Hazelcast is an in-memory data grid that provides a distributed map for fast, reliable storage and access of data in a clustered environment. It offers features such as simple configuration, automatic data partitioning and replication, fail-safety, scalability, and integration with Java interfaces and Spring. Developers can use Hazelcast to store and query data, distribute work across a cluster, and publish and subscribe to cluster-wide events.
This document discusses using Elasticsearch for social media analytics and provides examples of common tasks. It introduces Elasticsearch basics like installation, indexing documents, and searching. It also covers more advanced topics like mapping types, facets for aggregations, analyzers, nested and parent/child relations between documents. The document concludes with recommendations on data design, suggesting indexing strategies for different use cases like per user, single index, or partitioning by time range.
Developing and Deploying Apps with the Postgres FDWJonathan Katz
This document summarizes Jonathan Katz's experience building a foreign data wrapper (FDW) between two PostgreSQL databases to enable an API for his company VenueBook. He created separate "app" and "api" databases, with the api database using FDWs to access tables in the app database. This allowed inserting and querying data across databases. However, he encountered permission errors and had to grant various privileges on the remote database to make it work properly, demonstrating the importance of permissions management with FDWs.
You're stuck on a basic Windows estate, you can't pull the data out, there's no SIEM, and you have 20GB of logs you've been tasked to turn into actionable intelligence. Powershell brings not just in-built tools for querying Windows event logs, but also extremely powerful text processing tools. This talk will give you a quick overview of these features and its notable quirks, allowing you to pull off tricks that are often thought to be only for *NIX environments.
This document compares the performance and scalability of Elasticsearch and Solr for two use cases: product search and log analytics. For product search, both products performed well at high query volumes, but Elasticsearch handled the larger video dataset faster. For logs, Elasticsearch performed better by using time-based indices across hot and cold nodes to isolate newer and older data. In general, configuration was found to impact performance more than differences between the products. Proper testing with one's own data is recommended before making conclusions.
The document provides information about Hive and Pig, two frameworks for analyzing large datasets using Hadoop. It compares Hive and Pig, noting that Hive uses a SQL-like language called HiveQL to manipulate data, while Pig uses Pig Latin scripts and operates on data flows. The document also includes code examples demonstrating how to use basic operations in Hive and Pig like loading data, performing word counts, joins, and outer joins on sample datasets.
The document discusses the percolator feature in Elasticsearch. It begins by explaining what a percolator is and how it works at a high level. It then provides more technical details on how to index queries, perform percolation searches, and the benefits of the redesigned percolator. Key points covered include how the percolator works in distributed environments, examples of how percolator can be used, and new features like filtering, sorting, scoring, and highlighting.
Part 2 of a three part presentation showing how nutch and solr may be used to crawl the web, extract data and prepare it for loading into a data warehouse.
This document discusses OpenStack deployments using Puppet and provides an overview of OpenStack components and architecture. It describes how Puppet modules correspond to OpenStack projects and can be used to build and manage OpenStack deployments. It includes examples of Puppet profiles and classes to configure common OpenStack services like Keystone, RabbitMQ, MySQL, and Nova.
This document provides an overview and instructions for installing and using Elasticsearch. It describes how Elasticsearch is schema-free, distributed, uses JSON documents and the Lucene search engine. It also provides examples of indexing, searching, and configuring documents in Elasticsearch including shards, replicas, node names and master/data nodes.
This document discusses Elasticsearch, including understanding how it works and optimizing performance. It covers Elasticsearch concepts like clusters, indexes, shards and nodes. It also discusses installing and configuring Elasticsearch, modeling data, indexing and querying optimizations. Lastly it discusses integrating Elasticsearch with Hadoop and using SQL on Elasticsearch.
Elasticsearch is a powerful open source search and analytics engine. It allows for full text search capabilities as well as powerful analytics functions. Elasticsearch can be used as both a search engine and as a NoSQL data store. It is easy to set up, use, scale, and maintain. The document provides examples of using Elasticsearch with Rails applications and discusses advanced features such as fuzzy search, autocomplete, and geospatial search.
This document summarizes how Elasticsearch can be used for scaling analytics applications. Elasticsearch is an open source, distributed search and analytics engine that can index large volumes of data. It automatically shards and replicates data across nodes for redundancy and high availability. Analytics queries like date histograms, statistical facets, and geospatial searches can retrieve insightful results from large datasets very quickly. The document provides an example of using Elasticsearch to perform sentiment analysis, location tagging, and analytical queries on over 100 million social media documents.
Spark with Elasticsearch - umd version 2014Holden Karau
Holden Karau gave a talk on using Apache Spark and Elasticsearch. The talk covered indexing data from Spark to Elasticsearch both online using Spark Streaming and offline. It showed how to customize the Elasticsearch connector to write indexed data directly to shards based on partitions to reduce network overhead. It also demonstrated querying Elasticsearch from Spark, extracting top tags from tweets, and reindexing data from Twitter to Elasticsearch.
Amazon Elastic MapReduce (EMR) allows users to run Hadoop MapReduce jobs on the AWS cloud infrastructure. It provides elasticity, ease of use, reliability, integration with other AWS services, and security. EMR is ideal for prototyping and creating repeatable environments without having to configure or deploy clusters manually. MRUnit makes it easy to write and read unit tests for mappers and reducers. Logging in Hadoop uses Log4j. The Java heap size can be configured using a bootstrap action. The DistributedCache can be used to access files from the mapper or reducer. EMR can interact with other AWS services and MongoDB.
Testing multi outputformat based mapreduceAshok Agarwal
The document describes using a MultiOutputFormat in MapReduce to generate separate output files for each stock price based on input that contains stock price data. It includes code for a mapper that extracts the stock name and price from each input record and a reducer that writes these values to individual files for each stock name. Unit tests are also described to test the reducer by mocking the MultipleOutputs class and validating that the output files contain the expected stock price values.
This document discusses using Apache Spark and Elasticsearch together to index streaming data in real-time and reduce network overhead. It provides an overview of Spark and Elasticsearch, demonstrates how to set up a Spark streaming job to index tweets in Elasticsearch in real-time, and describes a modification made to the Elasticsearch connector to write data directly to shards based on the Spark partition, avoiding unnecessary network hops. The document includes code samples and concludes with links for further information.
A presentation given at the Lucene/Solr Revolution 2014 conference to show Solr and Elasticsearch features side by side. The presentation time was only 30 minutes, so only the core usability features were compared. The full video is embedded on the last slide.
This document discusses debugging and testing Elasticsearch systems. It provides tips for debugging issues like typos in mappings, setting up a local environment for testing, useful commands like analyze and explain, tuning queries, and testing strategies using Java and Ruby. The document emphasizes the importance of testing representative queries to ensure expected results and the ability to tune queries without breaking other queries. It also recommends using Elasticsearch plugins like Head for visualizing clusters and indices.
How EverTrue is building a donor CRM on top of ElasticSearch. We cover some of the issues around scaling ElasticSearch and which aspects of ElasticSearch we are using to deliver value to our customers.
Hazelcast is an in-memory data grid that provides a distributed map for fast, reliable storage and access of data in a clustered environment. It offers features such as simple configuration, automatic data partitioning and replication, fail-safety, scalability, and integration with Java interfaces and Spring. Developers can use Hazelcast to store and query data, distribute work across a cluster, and publish and subscribe to cluster-wide events.
This document discusses using Elasticsearch for social media analytics and provides examples of common tasks. It introduces Elasticsearch basics like installation, indexing documents, and searching. It also covers more advanced topics like mapping types, facets for aggregations, analyzers, nested and parent/child relations between documents. The document concludes with recommendations on data design, suggesting indexing strategies for different use cases like per user, single index, or partitioning by time range.
Developing and Deploying Apps with the Postgres FDWJonathan Katz
This document summarizes Jonathan Katz's experience building a foreign data wrapper (FDW) between two PostgreSQL databases to enable an API for his company VenueBook. He created separate "app" and "api" databases, with the api database using FDWs to access tables in the app database. This allowed inserting and querying data across databases. However, he encountered permission errors and had to grant various privileges on the remote database to make it work properly, demonstrating the importance of permissions management with FDWs.
You're stuck on a basic Windows estate, you can't pull the data out, there's no SIEM, and you have 20GB of logs you've been tasked to turn into actionable intelligence. Powershell brings not just in-built tools for querying Windows event logs, but also extremely powerful text processing tools. This talk will give you a quick overview of these features and its notable quirks, allowing you to pull off tricks that are often thought to be only for *NIX environments.
This document compares the performance and scalability of Elasticsearch and Solr for two use cases: product search and log analytics. For product search, both products performed well at high query volumes, but Elasticsearch handled the larger video dataset faster. For logs, Elasticsearch performed better by using time-based indices across hot and cold nodes to isolate newer and older data. In general, configuration was found to impact performance more than differences between the products. Proper testing with one's own data is recommended before making conclusions.
The document provides information about Hive and Pig, two frameworks for analyzing large datasets using Hadoop. It compares Hive and Pig, noting that Hive uses a SQL-like language called HiveQL to manipulate data, while Pig uses Pig Latin scripts and operates on data flows. The document also includes code examples demonstrating how to use basic operations in Hive and Pig like loading data, performing word counts, joins, and outer joins on sample datasets.
The document discusses the percolator feature in Elasticsearch. It begins by explaining what a percolator is and how it works at a high level. It then provides more technical details on how to index queries, perform percolation searches, and the benefits of the redesigned percolator. Key points covered include how the percolator works in distributed environments, examples of how percolator can be used, and new features like filtering, sorting, scoring, and highlighting.
Part 2 of a three part presentation showing how nutch and solr may be used to crawl the web, extract data and prepare it for loading into a data warehouse.
This document discusses OpenStack deployments using Puppet and provides an overview of OpenStack components and architecture. It describes how Puppet modules correspond to OpenStack projects and can be used to build and manage OpenStack deployments. It includes examples of Puppet profiles and classes to configure common OpenStack services like Keystone, RabbitMQ, MySQL, and Nova.
This document provides an overview and instructions for installing and using Elasticsearch. It describes how Elasticsearch is schema-free, distributed, uses JSON documents and the Lucene search engine. It also provides examples of indexing, searching, and configuring documents in Elasticsearch including shards, replicas, node names and master/data nodes.
This document discusses Elasticsearch, including understanding how it works and optimizing performance. It covers Elasticsearch concepts like clusters, indexes, shards and nodes. It also discusses installing and configuring Elasticsearch, modeling data, indexing and querying optimizations. Lastly it discusses integrating Elasticsearch with Hadoop and using SQL on Elasticsearch.
Elasticsearch is a powerful open source search and analytics engine. It allows for full text search capabilities as well as powerful analytics functions. Elasticsearch can be used as both a search engine and as a NoSQL data store. It is easy to set up, use, scale, and maintain. The document provides examples of using Elasticsearch with Rails applications and discusses advanced features such as fuzzy search, autocomplete, and geospatial search.
This document summarizes how Elasticsearch can be used for scaling analytics applications. Elasticsearch is an open source, distributed search and analytics engine that can index large volumes of data. It automatically shards and replicates data across nodes for redundancy and high availability. Analytics queries like date histograms, statistical facets, and geospatial searches can retrieve insightful results from large datasets very quickly. The document provides an example of using Elasticsearch to perform sentiment analysis, location tagging, and analytical queries on over 100 million social media documents.
Spark with Elasticsearch - umd version 2014Holden Karau
Holden Karau gave a talk on using Apache Spark and Elasticsearch. The talk covered indexing data from Spark to Elasticsearch both online using Spark Streaming and offline. It showed how to customize the Elasticsearch connector to write indexed data directly to shards based on partitions to reduce network overhead. It also demonstrated querying Elasticsearch from Spark, extracting top tags from tweets, and reindexing data from Twitter to Elasticsearch.
Amazon Elastic MapReduce (EMR) allows users to run Hadoop MapReduce jobs on the AWS cloud infrastructure. It provides elasticity, ease of use, reliability, integration with other AWS services, and security. EMR is ideal for prototyping and creating repeatable environments without having to configure or deploy clusters manually. MRUnit makes it easy to write and read unit tests for mappers and reducers. Logging in Hadoop uses Log4j. The Java heap size can be configured using a bootstrap action. The DistributedCache can be used to access files from the mapper or reducer. EMR can interact with other AWS services and MongoDB.
Testing multi outputformat based mapreduceAshok Agarwal
The document describes using a MultiOutputFormat in MapReduce to generate separate output files for each stock price based on input that contains stock price data. It includes code for a mapper that extracts the stock name and price from each input record and a reducer that writes these values to individual files for each stock name. Unit tests are also described to test the reducer by mocking the MultipleOutputs class and validating that the output files contain the expected stock price values.
Elasticsearch is a distributed, open source search and analytics engine. It allows storing and searching of documents of any schema in real-time. Documents are organized into indices which can contain multiple types of documents. Indices are partitioned into shards and replicas to allow horizontal scaling and high availability. The document consists of a JSON object which is indexed and can be queried using a RESTful API.
Cascading provides a simpler way to write MapReduce programs through data flows. It uses a pipe and tap metaphor where data flows through pipes and is read from or written to taps. This allows assembling MapReduce jobs as data flow graphs in a more logical way compared to the traditional MapReduce API.
GwtQuery is a rewrite of the jQuery popular library with has brought to the GWT world its sexy API and its simplicity for doing complex things.
In this session Manuel will provide an overview of the fundamentals of gQuery, how to setup and use it, and how to write code which being laborious in GWT can be simplified using gQuery.
Elasticsearch is a highly scalable open-source full-text search and analytics engine. It allows you to store, search, and analyze big volumes of data quickly and in near real time.
Javascript Continues Integration in Jenkins with AngularJSLadislav Prskavec
The document describes a ToDo application built with AngularJS that uses MongoDB hosted on MongoHQ. It retrieves and saves ToDo items to the MongoDB database via a PHP proxy. It also discusses testing the application using tools like PhantomJS, Jasmine, JSCoverage, JSDoc, and continuous integration with Jenkins.
This document describes Vocanic Map Reduce Lite, a PHP-based map reduce framework for processing moderately sized datasets between 50k-500k rows from MySQL. It aims to provide an alternative to Hadoop as maintaining Hadoop clusters is time consuming and AWS Hadoop is too costly. The framework exports MySQL data to files, runs map and reduce functions on the data in parallel, and provides implementations for common operations like object counting and CSV output. It uses S3 for temporary storage and SQLite for summary tables. Innovations include using S3 for exporting/importing, running tasks as a service, incremental runs, and asynchronous processing with callbacks. The framework is currently in alpha but implemented for an internal data processing project.
PigSPARQL: A SPARQL Query Processing Baseline for Big DataAlexander Schätzle
In this paper we discuss PigSPARQL, a competitive yet easy to use SPARQL query processing system on MapReduce that allows ad-hoc SPARQL query processing on large RDF graphs out of the box. Instead of a direct mapping, PigSPARQL uses the query language of Pig, a data analysis platform on top of Hadoop MapReduce, as an intermediate layer between SPARQL and MapReduce. This additional level of abstraction makes our approach independent of the actual Hadoop version and thus ensures the compatibility to future changes of the Hadoop framework as they will be covered by the underlying Pig layer. We revisit PigSPARQL and demonstrate the performance improvement when simply switching the underlying version of Pig from 0.5.0 to 0.11.0 without any changes to PigSPARQL itself. Because of this sustainability, PigSPARQL is an attractive long-term baseline for comparing various MapReduce based SPARQL implementations which is also underpinned by its competitiveness with existing systems, e.g. HadoopRDF.
The document discusses cloud-native JavaScript applications and projects like Cumulus and Pangeo. It provides a case study of DigitalGlobe's satellite imagery pipeline that ingests 80 TB of data per day and stores 100 PB of data. It also discusses topics like microservices architecture, stability, security, affordability, and devops practices for cloud applications.
This presentation was given at the Boston Django meetup on November 16, and surveyed several leading PaaS providers including Stackato, Dotcloud, OpenShift and Heroku.
For each PaaS provider, I documented the steps necessary to deploy Mezzanine, a popular Django-based CMS and blogging platform.
At the end of the presentation, I do a wrap-up of the different providers and provide a comparison matrix showing which providers have which features. This matrix is likely to go out-of-date quickly because these providers are adding new features all the time.
Recent developments in Hadoop version 2 are pushing the system from the traditional, batch oriented, computational model based on MapRecuce towards becoming a multi paradigm, general purpose, platform. In the first part of this talk we will review and contrast three popular processing frameworks. In the second part we will look at how the ecosystem (eg. Hive, Mahout, Spark) is making use of these new advancements. Finally, we will illustrate "use cases" of batch, interactive and streaming architectures to power traditional and "advanced" analytics applications.
This document summarizes Nuxeo's Release 8.1 including new tools for launching performance tests on Nuxeo clusters, an instant share feature for temporarily granting access without account creation, Live Connect integration for Box file sharing, and expanded Elasticsearch integration. It also discusses Nuxeo Docker images, a Nuxeo code generator, a Polymer sample app, updated REST and automation clients, and upcoming branch management features.
CouchDB Mobile - From Couch to 5K in 1 HourPeter Friese
This document provides an overview of CouchDB, a NoSQL database that uses JSON documents with a flexible schema. It demonstrates CouchDB's features like replication, MapReduce, and filtering. The presentation then shows how to build a mobile running app called Couch25K that tracks locations using CouchDB and syncs data between phones and a server. Code examples are provided in Objective-C, Java, and JavaScript for creating databases, saving documents, querying, and syncing.
The document provides an overview of the Android infrastructure and development environment. It discusses:
- The layers of an Android application including presentation, application logic, and domain layers.
- Key aspects of the Android runtime including the Dalvik VM, app lifecycle, resources and context handling.
- Libraries that help with common tasks like compatibility, fragments, networking and dependency injection including the Android Support Library, ActionBarSherlock, Retrofit, Dagger and RoboGuice.
- Alternatives for data storage like SQLite and ORM libraries like ORMLite and GreenDAO.
- Options for testing Android apps using the DVM, JVM, Robotium and Robolectric.
The document provides an overview of the Android infrastructure and key concepts:
(1) It describes the layers of an Android application including the presentation layer, application logic layer, and domain layer.
(2) It explains important Android concepts such as the Android runtime environment, Dalvik virtual machine, application lifecycle and activities, and use of contexts.
(3) It discusses alternatives for common tasks like dependency injection with RoboGuice and Dagger, handling resources and views with ButterKnife and AndroidAnnotations, and accessing data with SQLite and ORMLite.
(4) It also briefly covers testing approaches on the DVM and JVM using AndroidTestCase, Robotium, and Robolectric
Similar to Elastic search integration with hadoop leveragebigdata (20)
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!
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
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/
Microservice Teams - How the cloud changes the way we workSven Peters
A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
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
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UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
Odoo ERP software
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Elastic search integration with hadoop leveragebigdata
1. 11/23/2014 Elastic Search integration with Hadoop | leveragebigdata
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Elastic Search integration with Hadoop
28 Saturday Jun 2014
POSTED BY LEVERAGEBIGDATA IN UNCATEGORIZED
Elastic Search, Hadoop, Hive, MapReduce
Elastic is open source distributed search engine, based on lucene framework with Rest API. You
can download the elastic search using the URL
http://www.elasticsearch.org/overview/elkdownloads/. Unzip the downloaded zip or tar file and
then start one instance or node of elastic search by running the script ‘elasticsearch-
1.2.1/bin/elasticsearch’ as shown below:
Installing plugin:
We can install plugins for enhance feature like elasticsearch-head provide the web interface to
interact with its cluster. Use the command ‘elasticsearch-1.2.1/bin/plugin -install
mobz/elasticsearch-head’ as shown below:
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And, Elastic Search web interface can be using url: http://localhost:9200/_plugin/head/
Creating the index:
(You can skip this step) In Search domain, index is like relational database. By default number of
shared created is ’5′ and replication factor “1″ which can be changed on creation depending on
your requirement. We can increase the number of replication factor but not number of shards.
1 curl -XPUT "http://localhost:9200/movies/" -d '{"settings" : {"number_of_shards" Create Elastic Search Index
Loading data to Elastic Search:
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If we put data to the search domain it will automatically create the index.
Load data using -XPUT
We need to specify the id (1) as shown below:
1 curl -XPUT "http://localhost:9200/movies/movie/1" -d '{"title": "Men with Wings", 1 curl -XPOST "http://localhost:9200/movies/movie" -d' { "title": "Lawrence of Arabia", 1 curl -XPOST "http://localhost:9200/_search" -d' { "query": { "query_string": { Note: movies->index, movie->index type, 1->id
Elastic Search -XPUT
Load data using -XPOST
The id will be automatically generated as shown below:
Elastic Search -XPOST
Note: _id: U2oQjN5LRQCW8PWBF9vipA is automatically generated.
The _search endpoint
The index document can be searched using below query:
ES Search Result
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Integrating with Map Reduce (Hadoop 1.2.1)
To integrate Elastic Search with Map Reduce follow the below steps:
Add a dependency to pom.xml:
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<dependency>
<groupId>org.elasticsearch</groupId>
<artifactId>elasticsearch-hadoop</artifactId>
<version>2.0.0</version>
</dependency>
or Download and add elasticSearch-hadoop.jar file to classpath.
Elastic Search as source & HDFS as sink:
In Map Reduce job, you specify the index/index type of search engine from where you need to
fetch data in hdfs file system. And input format type as ‘EsInputFormat’ (This format type is
defined in elasticsearch-hadoop jar). In org.apache.hadoop.conf.Configuration set elastic search
index type using field ‘es.resource’ and any search query using field ‘es.query’ and also set
InputFormatClass as ‘EsInputFormat’ as shown below:
ElasticSourceHadoopSinkJob.java
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import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.MapWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.elasticsearch.hadoop.mr.EsInputFormat;
public class ElasticSourceHadoopSinkJob {
public static void main(String arg[]) throws IOException, ClassNotFoundException, Configuration conf = new Configuration();
conf.set("es.resource", "movies/movie");
//conf.set("es.query", "?q=kill");
final Job job = new Job(conf,
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"Get information from elasticSearch.");
job.setJarByClass(ElasticSourceHadoopSinkJob.class);
job.setMapperClass(ElasticSourceHadoopSinkMapper.class);
job.setInputFormatClass(EsInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
job.setNumReduceTasks(0);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(MapWritable.class);
FileOutputFormat.setOutputPath(job, new Path(arg[0]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
ElasticSourceHadoopSinkMapper.java
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import java.io.IOException;
import org.apache.hadoop.io.MapWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class ElasticSourceHadoopSinkMapper extends Mapper<Object, MapWritable, @Override
protected void map(Object key, MapWritable value,
Context context)
throws IOException, InterruptedException {
context.write(new Text(key.toString()), value);
}
}
HDFS as source & Elastic Search as sink:
In Map Reduce job, specify the index/index type of search engine from where you need to load
data from hdfs file system. And input format type as ‘EsOutputFormat’ (This format type is
defined in elasticsearch-hadoop jar). ElasticSinkHadoopSourceJob.java
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import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.MapWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.elasticsearch.hadoop.mr.EsOutputFormat;
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public class ElasticSinkHadoopSourceJob {
public static void main(String str[]) throws IOException, ClassNotFoundException, Configuration conf = new Configuration();
conf.set("es.resource", "movies/movie");
final Job job = new Job(conf,
"Get information from elasticSearch.");
job.setJarByClass(ElasticSinkHadoopSourceJob.class);
job.setMapperClass(ElasticSinkHadoopSourceMapper.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(EsOutputFormat.class);
job.setNumReduceTasks(0);
job.setMapOutputKeyClass(NullWritable.class);
job.setMapOutputValueClass(MapWritable.class);
FileInputFormat.setInputPaths(job, new Path("data/ElasticSearchData"));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
ElasticSinkHadoopSourceMapper.java
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import java.io.IOException;
import org.apache.hadoop.io.ArrayWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.MapWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class ElasticSinkHadoopSourceMapper extends Mapper<LongWritable, Text, @Override
protected void map(LongWritable key, Text value,
Context context)
throws IOException, InterruptedException {
String[] splitValue=value.toString().split(",");
MapWritable doc = new MapWritable();
doc.put(new Text("year"), new IntWritable(Integer.parseInt(splitValue[0])));
doc.put(new Text("title"), new Text(splitValue[1]));
doc.put(new Text("director"), new Text(splitValue[2]));
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String genres=splitValue[3];
if(genres!=null){
String[] splitGenres=genres.split("$");
ArrayWritable genresList=new ArrayWritable(splitGenres);
doc.put(new Text("genres"), genresList);
}
context.write(NullWritable.get(), doc);
}
}
Integrate with Hive:
Download elasticsearch-hadoop.jar file and include it in path using hive.aux.jars.path as shown
below: bin/hive –hiveconf hive.aux.jars.path=<path-of-jar>/elasticsearch-hadoop-2.0.0.jar or ADD
elasticsearch-hadoop-2.0.0.jar to <hive-home>/lib and <hadoop-home>/lib
Elastic Search as source & Hive as sink:
Now, create external table to load data from Elastic search as shown below:
1 CREATE EXTERNAL TABLE movie (id BIGINT, title STRING, director STRING, year BIGINT, 1 CREATE TABLE movie_internal (title STRING, id BIGINT, director STRING, year BIGINT, You need to specify the elastic search index type using ‘es.resource’ and can specify query using
‘es.query’.
Load data from Elastic Search to Hive
Elastic Search as sink & Hive as source:
Create an internal table in hive like ‘movie_internal’ and load data to it. Then load data from
internal table to elastic search as shown below:
Create internal table:
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Load data to internal table:
1 LOAD DATA LOCAL INPATH '<path>/hiveElastic.txt' OVERWRITE INTO TABLE movie_internal;
hiveElastic.txt
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Title1,1,dire1,2003,Action$Crime$Thriller
Title2,2,dire2,2007,Biography$Crime$Drama
Load data from hive internal table to ElasticSearch :
1 INSERT OVERWRITE TABLE movie SELECT NULL, m.title, m.director, m.year, m.genres Load data from Hive to Elastic Search
Verify inserted data from Elastic Search query
References:
1. ElasticSearch
2. Apache Hadoop
3. Apache Hbase
4. Apache Spark
5. JBKSoft Technologies
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