The presentation describes what is Apache Solr, how it could be used. There is apache solr overview, performance tuning tips and advanced features description
The presentation is an advanced version about Oracle Result cache feature. It is rewritten presentation from HIGLOAD-2017. A lot of result cache internals under the cover.
The presentation is a deep analysis of Oracle JSON treatment feauture. It is considered to real-life experience and workarounds to defeat known json errors.
the presentation describes a lot of very technical details about row level security, possibble security breaches in relational databases like Oracle and Postgres. A lot of examples how to protect data is shown.
Oracle JSON treatment evolution - from 12.1 to 18 AOUG-2018Alexander Tokarev
The presentation was prepared for Austria Oracle User group 30 years. It tells us a lot of challenges which Oracle developers face with implementing high-load json processing pipelines.
The presentation describes various options for implementing row-level security in enterprise applications: database side, application server side, mixed approaches. we consider oracle virtual private database, different encription options and possible security breaches and their mitigation path.
Изначально будут раскрыты базовые причины, которые заставили появиться такой части механизма СУБД, как кэш результатов, и почему в ряде СУБД он есть или отсутствует.
Будут рассмотрены различные варианты кэширования результатов как sql-запросов, так и результатов хранимой в БД бизнес-логики. Произведено сравнение способов кэширования (программируемые вручную кэши, стандартный функционал) и даны рекомендации, когда и в каких случаях данные способы оптимальны, а порой опасны.
Для каждой из рекомендаций будут продемонстрированы как положительные так и отрицательные кейсы из опыта production-эксплуатации реальных систем, где используются разные варианты кэшей
The presentation is an advanced version about Oracle Result cache feature. It is rewritten presentation from HIGLOAD-2017. A lot of result cache internals under the cover.
The presentation is a deep analysis of Oracle JSON treatment feauture. It is considered to real-life experience and workarounds to defeat known json errors.
the presentation describes a lot of very technical details about row level security, possibble security breaches in relational databases like Oracle and Postgres. A lot of examples how to protect data is shown.
Oracle JSON treatment evolution - from 12.1 to 18 AOUG-2018Alexander Tokarev
The presentation was prepared for Austria Oracle User group 30 years. It tells us a lot of challenges which Oracle developers face with implementing high-load json processing pipelines.
The presentation describes various options for implementing row-level security in enterprise applications: database side, application server side, mixed approaches. we consider oracle virtual private database, different encription options and possible security breaches and their mitigation path.
Изначально будут раскрыты базовые причины, которые заставили появиться такой части механизма СУБД, как кэш результатов, и почему в ряде СУБД он есть или отсутствует.
Будут рассмотрены различные варианты кэширования результатов как sql-запросов, так и результатов хранимой в БД бизнес-логики. Произведено сравнение способов кэширования (программируемые вручную кэши, стандартный функционал) и даны рекомендации, когда и в каких случаях данные способы оптимальны, а порой опасны.
Для каждой из рекомендаций будут продемонстрированы как положительные так и отрицательные кейсы из опыта production-эксплуатации реальных систем, где используются разные варианты кэшей
15 Ways to Kill Your Mysql Application Performanceguest9912e5
Jay is the North American Community Relations Manager at MySQL. Author of Pro MySQL, Jay has also written articles for Linux Magazine and regularly assists software developers in identifying how to make the most effective use of MySQL. He has given sessions on performance tuning at the MySQL Users Conference, RedHat Summit, NY PHP Conference, OSCON and Ohio LinuxFest, among others.In his abundant free time, when not being pestered by his two needy cats and two noisy dogs, he daydreams in PHP code and ponders the ramifications of __clone().
This is a recording of my Advanced Oracle Troubleshooting seminar preparation session - where I showed how I set up my command line environment and some of the main performance scripts I use!
Building a near real time search engine & analytics for logs using solrlucenerevolution
Presented by Rahul Jain, System Analyst (Software Engineer), IVY Comptech Pvt Ltd
Consolidation and Indexing of logs to search them in real time poses an array of challenges when you have hundreds of servers producing terabytes of logs every day. Since the log events mostly have a small size of around 200 bytes to few KBs, makes it more difficult to handle because lesser the size of a log event, more the number of documents to index. In this session, we will discuss the challenges faced by us and solutions developed to overcome them. The list of items that will be covered in the talk are as follows.
Methods to collect logs in real time.
How Lucene was tuned to achieve an indexing rate of 1 GB in 46 seconds
Tips and techniques incorporated/used to manage distributed index generation and search on multiple shards
How choosing a layer based partition strategy helped us to bring down the search response times.
Log analysis and generation of analytics using Solr.
Design and architecture used to build the search platform.
NYJavaSIG - Big Data Microservices w/ SpeedmentSpeedment, Inc.
JAVA MICROSERVICES FOR BIG DATA WITH LOW LATENCY - Per-Ake Minborg, CTO Speedment
By leveraging on memory mapped files (eg. Hazelcast, ChronicleMaps etc.), Speedment supports large Java Maps that easily can exceed the size of your server’s RAM. Because the Java Maps are mapped onto files, these maps can be shared instantly between several microservice JVMs and new microservice instances can be added, removed or restarted very quickly. Data can be retrieved with predictable ultra-low latency for a wide range of operations. The solution can be synchronized with an underlying database so that your in-memory maps will be consistently “alive”. The mapped files can be terabytes which has been done in real world deployment cases and there can be a large number of microservices that shares these maps simultaneously.
Lucene 4.0 is on its way to deliver a tremendous amount of new features and improvements. Beside Real-Time Search & Flexible Indexing DocValues aka. Column Stride Fields is one of the "next generation" features
DocValues aka. Column Stride Fields in Lucene 4.0 - By Willnauer Simonlucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/revolution/2011
Lucene 4.0 is on its way to deliver a tremendous amount of new features and improvements. Beside
Real-Time Search & Flexible Indexing DocValues aka. Column Stride Fields is one of the “next
generation” features. DocValues enable Lucene to efficiently store and retrieve type-safe Document
& Value pairs in a column stride fashion either entirely memory resident random access or disk
resident iterator based without the need to un-invert fields. Its final goal is to provide a
independently update-able per document storage for scoring, sorting or even filtering. This talk will
introduce the current state of development, implementation details, its features and how DocValues
have been integrated into Lucene’s Codec API for full extendability.
Presented by Rafal Kuć, Consultant and Software engineer, , Sematext Group, Inc.
Even though Solr can run without causing any troubles for long periods of time it is very important to monitor and understand what is happening in your cluster. In this session you will learn how to use various tools to monitor how Solr is behaving at a high level, but also on Lucene, JVM, and operating system level. You'll see how to react to what you see and how to make changes to configuration, index structure and shards layout using Solr API. We will also discuss different performance metrics to which you ought to pay extra attention. Finally, you'll learn what to do when things go awry - we will share a few examples of troubleshooting and then dissect what was wrong and what had to be done to make things work again.
Determining the root cause of performance issues is a critical task for Operations. In this webinar, we'll show you the tools and techniques for diagnosing and tuning the performance of your MongoDB deployment. Whether you're running into problems or just want to optimize your performance, these skills will be useful.
Tempto is a product test framework that allows developers to write and execute tests for SQL databases running on Hadoop. Individual test requirements such as data generation, HDFS file copy/storage of generated data and schema creation are expressed declaratively and are automatically fulfilled by the framework. Developers can write tests using Java (using a TestNG like paradigm and AssertJ style assertion) or by providing query files with expected results. We will show how we use it for presto product tests.
Benchto is a benchmark framework that provides an easy and manageable way to define, run and analyze macro benchmarks in clustered environment. Understanding behavior of distributed systems is hard and requires good visibility intostate of the cluster and internals of tested system. This project was developed for repeatable benchmarking ofHadoop SQL engines, most importantly Presto.
15 Ways to Kill Your Mysql Application Performanceguest9912e5
Jay is the North American Community Relations Manager at MySQL. Author of Pro MySQL, Jay has also written articles for Linux Magazine and regularly assists software developers in identifying how to make the most effective use of MySQL. He has given sessions on performance tuning at the MySQL Users Conference, RedHat Summit, NY PHP Conference, OSCON and Ohio LinuxFest, among others.In his abundant free time, when not being pestered by his two needy cats and two noisy dogs, he daydreams in PHP code and ponders the ramifications of __clone().
This is a recording of my Advanced Oracle Troubleshooting seminar preparation session - where I showed how I set up my command line environment and some of the main performance scripts I use!
Building a near real time search engine & analytics for logs using solrlucenerevolution
Presented by Rahul Jain, System Analyst (Software Engineer), IVY Comptech Pvt Ltd
Consolidation and Indexing of logs to search them in real time poses an array of challenges when you have hundreds of servers producing terabytes of logs every day. Since the log events mostly have a small size of around 200 bytes to few KBs, makes it more difficult to handle because lesser the size of a log event, more the number of documents to index. In this session, we will discuss the challenges faced by us and solutions developed to overcome them. The list of items that will be covered in the talk are as follows.
Methods to collect logs in real time.
How Lucene was tuned to achieve an indexing rate of 1 GB in 46 seconds
Tips and techniques incorporated/used to manage distributed index generation and search on multiple shards
How choosing a layer based partition strategy helped us to bring down the search response times.
Log analysis and generation of analytics using Solr.
Design and architecture used to build the search platform.
NYJavaSIG - Big Data Microservices w/ SpeedmentSpeedment, Inc.
JAVA MICROSERVICES FOR BIG DATA WITH LOW LATENCY - Per-Ake Minborg, CTO Speedment
By leveraging on memory mapped files (eg. Hazelcast, ChronicleMaps etc.), Speedment supports large Java Maps that easily can exceed the size of your server’s RAM. Because the Java Maps are mapped onto files, these maps can be shared instantly between several microservice JVMs and new microservice instances can be added, removed or restarted very quickly. Data can be retrieved with predictable ultra-low latency for a wide range of operations. The solution can be synchronized with an underlying database so that your in-memory maps will be consistently “alive”. The mapped files can be terabytes which has been done in real world deployment cases and there can be a large number of microservices that shares these maps simultaneously.
Lucene 4.0 is on its way to deliver a tremendous amount of new features and improvements. Beside Real-Time Search & Flexible Indexing DocValues aka. Column Stride Fields is one of the "next generation" features
DocValues aka. Column Stride Fields in Lucene 4.0 - By Willnauer Simonlucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/revolution/2011
Lucene 4.0 is on its way to deliver a tremendous amount of new features and improvements. Beside
Real-Time Search & Flexible Indexing DocValues aka. Column Stride Fields is one of the “next
generation” features. DocValues enable Lucene to efficiently store and retrieve type-safe Document
& Value pairs in a column stride fashion either entirely memory resident random access or disk
resident iterator based without the need to un-invert fields. Its final goal is to provide a
independently update-able per document storage for scoring, sorting or even filtering. This talk will
introduce the current state of development, implementation details, its features and how DocValues
have been integrated into Lucene’s Codec API for full extendability.
Presented by Rafal Kuć, Consultant and Software engineer, , Sematext Group, Inc.
Even though Solr can run without causing any troubles for long periods of time it is very important to monitor and understand what is happening in your cluster. In this session you will learn how to use various tools to monitor how Solr is behaving at a high level, but also on Lucene, JVM, and operating system level. You'll see how to react to what you see and how to make changes to configuration, index structure and shards layout using Solr API. We will also discuss different performance metrics to which you ought to pay extra attention. Finally, you'll learn what to do when things go awry - we will share a few examples of troubleshooting and then dissect what was wrong and what had to be done to make things work again.
Determining the root cause of performance issues is a critical task for Operations. In this webinar, we'll show you the tools and techniques for diagnosing and tuning the performance of your MongoDB deployment. Whether you're running into problems or just want to optimize your performance, these skills will be useful.
Tempto is a product test framework that allows developers to write and execute tests for SQL databases running on Hadoop. Individual test requirements such as data generation, HDFS file copy/storage of generated data and schema creation are expressed declaratively and are automatically fulfilled by the framework. Developers can write tests using Java (using a TestNG like paradigm and AssertJ style assertion) or by providing query files with expected results. We will show how we use it for presto product tests.
Benchto is a benchmark framework that provides an easy and manageable way to define, run and analyze macro benchmarks in clustered environment. Understanding behavior of distributed systems is hard and requires good visibility intostate of the cluster and internals of tested system. This project was developed for repeatable benchmarking ofHadoop SQL engines, most importantly Presto.
Introduction to Solr. A brief introduction to Solr for the resources who wants to get trained on Solr.
1. Introduction to Solr
2. Solr Terminologies
3.Installation and Configuration
4. Configuration files schema.xml and solrconfig.xml
5. Features of SOLR
a. Hit Highlighting
Auto Complete / Suggester
Stop words
Synonyms
SpellCheck
Geo Spatial Search
Result Grouping
Query Syntax
Query Boosting
Content Spotlighting
Block Record / Remove URL Feature
Content Spotlighting / Merchandising / Banner / Elevate
Block Record / Remove URL Feature
6. Indexing the Data
7. Search Queries
8. DataImportHandler - DIH
9. Plugins to index various types of Data (XML, CSV, DB, Filesystem)
10. Solr Client APIs
11. Overview of SOLRJ API
12. Running Solr on Tomcat
13. Enabling SSL on Solr
14. Zookeeper Configuration
15. Solr Cloud Deployment
16. Production Indexing Architecture
17. Production Serving Architecture
18. Solr Upgradation
19. References
Assamese search engine using SOLR by Moinuddin Ahmed ( moin )'Moinuddin Ahmed
Its a search engine i developed for my mother tongue, Assamese. I used Nutch-Lucene-Solr to make this possible. I'm open for comments and suggestions.
Email: moinz.lair@gmail.com
OpenCms 8.5 integrates Apache Solr. And not only for full text search, but as a powerful query engine as well.
Imagine you want to show a list of "all resources of type news, that have changed since yesterday, where property X has the value Y" on your web page. Sure, there are API methods in OpenCms to load resources based on the type, on the date of change, or on the value of a specific property. But for many common use case combinations, there is no single API call. This means if you create a collector, you often end up sorting out the results of the initial API query in code.
In this session, Rüdiger will show how Apache Solr has been integrated in OpenCms 8.5. He will explain how to create improved front-end full text search functions with advanced options like faceting and spell check suggestions. And he will explain how to use Solr to directly read resources from the OpenCms VFS, allowing query combinations that combine resource attributes, properties and content in a powerful new way.
In this On-Demand Webinar, Erik Hatcher, co-founder of Lucid Imagination, co-author of Lucene in Action, and Lucene/Solr PMC member and committer, presents and discusess key features and innovations of Apache Solr 1.4
Apache Solr serves search requests at the enterprises and the largest companies around the world. Built on top of the top-notch Apache Lucene library, Solr makes indexing and searching integration into your applications straightforward.
Solr provides faceted navigation, spell checking, highlighting, clustering, grouping, and other search features. Solr also scales query volume with replication and collection size with distributed capabilities. Solr can index rich documents such as PDF, Word, HTML, and other file types.
Search Engine Building with Lucene and Solr (So Code Camp San Diego 2014)Kai Chan
Slides for my presentation at SoCal Code Camp, June 29, 2014
(http://www.socalcodecamp.com/socalcodecamp/session.aspx?sid=6337660f-37de-4d6e-a5bc-46ba54478e5e)
In this talk, Solr's built-in query parsers will be detailed included when and how to use them. Solr has nested query parsing capability, allowing for multiple query parsers to be used to generate a single query. The nested query parsing feature will be described and demonstrated. In many domains, e-commerce in particular, parsing queries often means interpreting which entities (e.g. products, categories, vehicles) the user likely means; this talk will conclude with techniques to achieve richer query interpretation.
Search engines, and Apache Solr in particular, are quickly shifting the focus away from “big data” systems storing massive amounts of raw (but largely unharnessed) content, to “smart data” systems where the most relevant and actionable content is quickly surfaced instead. Apache Solr is the blazing-fast and fault-tolerant distributed search engine leveraged by 90% of Fortune 500 companies. As a community-driven open source project, Solr brings in diverse contributions from many of the top companies in the world, particularly those for whom returning the most relevant results is mission critical.
Out of the box, Solr includes advanced capabilities like learning to rank (machine-learned ranking), graph queries and distributed graph traversals, job scheduling for processing batch and streaming data workloads, the ability to build and deploy machine learning models, and a wide variety of query parsers and functions allowing you to very easily build highly relevant and domain-specific semantic search, recommendations, or personalized search experiences. These days, Solr even enables you to run SQL queries directly against it, mixing and matching the full power of Solr’s free-text, geospatial, and other search capabilities with the a prominent query language already known by most developers (and which many external systems can use to query Solr directly).
Due to the community-oriented nature of Solr, the ecosystem of capabilities also spans well beyond just the core project. In this talk, we’ll also cover several other projects within the larger Apache Lucene/Solr ecosystem that further enhance Solr’s smart data capabilities: bi-directional integration of Apache Spark and Solr’s capabilities, large-scale entity extraction, semantic knowledge graphs for discovering, traversing, and scoring meaningful relationships within your data, auto-generation of domain-specific ontologies, running SPARQL queries against Solr on RDF triples, probabilistic identification of key phrases within a query or document, conceptual search leveraging Word2Vec, and even Lucidworks’ own Fusion project which extends Solr to provide an enterprise-ready smart data platform out of the box.
We’ll dive into how all of these capabilities can fit within your data science toolbox, and you’ll come away with a really good feel for how to build highly relevant “smart data” applications leveraging these key technologies.
Solr search engine with multiple table relationJay Bharat
Here you can learn how to use solr search engine and implement in your application like in PHP/MYSQL.
I am introducing how to handle multiple table data handling in SOLR.
The presentation explains how to setup rate limits, how to work with 429 code, how rate limits are implemented in kubernetes, cni, loadbalancer and so on
the presentation is about federated GraphQL in huge enterprises. I explain why and what for big enterprises need distributed GraphQL and classic one does not work.
The majority of cloud-based DWH provides a wide range of migration tools from in-house DWH. However, I believe that cloud migration success is based not only on reducing infrastructure maintenance costs, but also on additional performance profit inherited from tailored data model.
I am going to prove that copying star or snowflake schemas as is will not lead to maximum performance boost in such DWH as Amazon Redshift and Google BigQuery. Moreover, this approach may cause additional cloud expenses.
We will discuss why data models should be different for each particular database, and how to get maximum performance from database peculiarities.
Most of performance tuning techniques for cloud-based DWH are about adding extra nodes to cluster, but it may lead to performance degradation in some cases, as well as extra costs burden. Sometimes, this approach allows to get maximum speed from current hardware configuration, may be even less expensive servers.
I will show some examples from production projects with extra performance using lower hardware, and edge cases like huge wide fact table with fully denormalized dimensions instead of classical star schema.
The presentation describes how to design robust solution for tagging search, how to use tagging for faceted search. Various architecture and data patterns are considered. We discuss relational databases like Oracle, full text search servers like Apache Solr. We will see how Oracle 18c features permit to use embedded faceted search.
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!
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
In the ever-evolving landscape of technology, enterprise software development is undergoing a significant transformation. Traditional coding methods are being challenged by innovative no-code solutions, which promise to streamline and democratize the software development process.
This shift is particularly impactful for enterprises, which require robust, scalable, and efficient software to manage their operations. In this article, we will explore the various facets of enterprise software development with no-code solutions, examining their benefits, challenges, and the future potential they hold.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
AI Genie Review: World’s First Open AI WordPress Website CreatorGoogle
AI Genie Review: World’s First Open AI WordPress Website Creator
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-genie-review
AI Genie Review: Key Features
✅Creates Limitless Real-Time Unique Content, auto-publishing Posts, Pages & Images directly from Chat GPT & Open AI on WordPress in any Niche
✅First & Only Google Bard Approved Software That Publishes 100% Original, SEO Friendly Content using Open AI
✅Publish Automated Posts and Pages using AI Genie directly on Your website
✅50 DFY Websites Included Without Adding Any Images, Content Or Doing Anything Yourself
✅Integrated Chat GPT Bot gives Instant Answers on Your Website to Visitors
✅Just Enter the title, and your Content for Pages and Posts will be ready on your website
✅Automatically insert visually appealing images into posts based on keywords and titles.
✅Choose the temperature of the content and control its randomness.
✅Control the length of the content to be generated.
✅Never Worry About Paying Huge Money Monthly To Top Content Creation Platforms
✅100% Easy-to-Use, Newbie-Friendly Technology
✅30-Days Money-Back Guarantee
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIGenieApp #AIGenieBonus #AIGenieBonuses #AIGenieDemo #AIGenieDownload #AIGenieLegit #AIGenieLiveDemo #AIGenieOTO #AIGeniePreview #AIGenieReview #AIGenieReviewandBonus #AIGenieScamorLegit #AIGenieSoftware #AIGenieUpgrades #AIGenieUpsells #HowDoesAlGenie #HowtoBuyAIGenie #HowtoMakeMoneywithAIGenie #MakeMoneyOnline #MakeMoneywithAIGenie
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
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3. FTS solutions attributes
1. Search by content of documents rather than by attributes
2. Read-oriented
3. Flexible data structure
4. 1 dedicated tailored index used further for search
5. index contains unique terms and their position in all documents
6. Indexer takes into account language-specific nuances like stop words, stemming,
shingling (word-grams, common-grams)
10. Solr
• True open source (under Apache) full text search engine
• Built over Lucene
• Multi-language support
• Rich document parsing (rtf, pdf, …)
• Various client APIs
• Versatile query language
• Scalable
• Full of additional features
13. Client access
1. Main REST API
• Common operations
• Schema API
• Rebalance/collection API
• Search API
• Faceted API
2. Native JAVA client SolrJ
3. Client bindings like Ruby, .Net, Python, PHP, Scala – see
https://wiki.apache.org/solr/IntegratingSolr +
https://wiki.apache.org/solr/SolPython
4. Parallel SQL (via REST and JDBC)
15. Index modeling
Choose Solr mode:
1. Schema
2. Schema-less
Define field attributes:
1. Indexed (query, sort, facet, group by, provide query suggestions for, execute
function)
2. Stored – all fields which are intended to be shown in a response
3. Mandatory
4. Data type
5. Multivalued
6. Copy field (calculated)
Choose a field for UniqueIdentifier
16. Field data types
1. Dates
2. Strings
3. Numeric
4. Guid
5. Spatial
6. Boolean
7. Currency and etc
22. Transaction management
1. Solr doesn’t expose immediately new data as well as not remove deleted
2. Commit/rollback should be issued
Commit types:
1. Soft
Data indexed in memory
1. Hard
It moves data to hard-drive
Risks:
1. Commits are slow
2. Many simultaneous commits could lead to Solr exceptions (too many commits)
<h2>HTTP ERROR: 503</h2>
<pre>Error opening new searcher. exceeded limit of maxWarmingSearchers=2, try again later.</pre>
3. Commit command works on instance level – not on user one
23. Transaction log
Intention:
1. recovery/durability
2. Nearly-Real-Time (NRT) update
3. Replication for Solr cloud
4. Atomic document update, in-place update (syntax is different)
5. Optimistic concurrency
Transaction log could be enabled in solrconfig.xml
<updateLog>
<str name="dir">${solr.ulog.dir:}</str>
</updateLog>
Atomic update example:
{"id":"mydoc",
"price":{"set":99},
"popularity":{"inc":20},
"categories":{"add":["toys","games"]},
"promo_ids":{"remove":"a123x"},
"tags":{"remove":["free_to_try"," on_sale"]}
}
24. Data modification Rest API
Rest API accepts:
1. Json objects
2. Xml-update
3. CSV
Solr UPDATE = UPSERT if schema.xml has <UniqueIdentifier>
26. Post utility
1. Java-written utility
2. Intended to load files
3. Works extremely fast
4. Loads csv, json
5. Loads files by mask of file-by-file
bin/post -c http://localhost:8983/cloud tags*.json
ISSUE: doesn’t work with Solr Cloud
27. Data import handler
1. Solr loads data itself
2. DIH could access to JDBC, ATOM/RSS, HTTP, XML, SMTP datasource
3. Delta approach could be implemented (statements for new, updated and deleted
data)
4. Loading progress could be tracked
5. Various transformation could be done inside (regexp, conversion, javascript)
6. Own datasource loaders could be implemented via Java
7. Web console to run/monitor/modify
28. Data import handler
How to implement:
1. Create data config
<dataConfig>
<dataSource name="jdbc" driver="org.postgresql.Driver"
url="jdbc:postgresql://localhost/db"
user="admin" readOnly="true" autoCommit="false" />
<document>
<entity name="artist" dataSource="jdbc" pk="id"
query="select *from artist a"
transformer="DateFormatTransformer"
>
<field column="id" name="id"/>
<field column="department_code" name="department_code"/>
<field column="department_name" name="department_name"/>
<field column = "begin_date" dateTimeFormat="yyyy-MM-dd" />
</entity>
</document>
</dataConfig>
2. Publish in solrconfig.xml
<requestHandler name="/jdbc"
class="org.apache.solr.handler.dataimport.DataImportHandler ">
<lst name=“default">
<str name="jdbc.xml</str>
</lst>
</requestHandler>
DIH could be started via REST call
curl http://localhost:8983/cloud/jdbc -F command=full-import
29. Data import handler
In process:
<?xml version="1.0" encoding="UTF-8"?>
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">0</int>
</lst>
<lst name="initArgs">
<lst name="defaults">
<str name="config">jdbc.xml</str>
</lst>
</lst>
<str name="status">busy</str>
<str name="importResponse">A command is still running...</str>
<lst name="statusMessages">
<str name="Time Elapsed">0:1:15.460</str>
<str name="Total Requests made to DataSource">39547</str>
<str name="Total Rows Fetched">59319</str>
<str name="Total Documents Processed">19772</ str>
<str name="Total Documents Skipped">0</str>
<str name="Full Dump Started">2010-10-03 14:28:00</str>
</lst>
<str name="WARNING">This response format is experimental. It is likely to change in the future.</ str>
</response>
30. Data import handler
After import:
<?xml version="1.0" encoding="UTF-8"?>
<response>
<lst name="responseHeader">
<int name="status">0</int>
<int name="QTime">0</int>
</lst>
<lst name="initArgs">
<lst name="defaults">
<str name="config">jdbc.xml</str>
</lst>
</lst>
<str name="status">idle</str>
<str name="importResponse"/>
<lst name="statusMessages">
<str name="Total Requests made to DataSource">2118645</str>
<str name="Total Rows Fetched">3177966</str>
<str name="Total Documents Skipped">0</str>
<str name="Full Dump Started">2010-10-03 14:28:00</str>
<str name="">Indexing completed. Added/Updated: 1059322 documents. Deleted 0
documents.</str>
<str name="Committed">2010-10-03 14:55:20</str>
<str name="Optimized">2010-10-03 14:55:20</str>
<str name="Total Documents Processed">1059322</str>
<str name="Time taken ">0:27:20.325</str>
</lst>
<str name="WARNING">This response format is experimental. It is likely to change in the
future.</str>
</response>
33. Search typesFuzzy
Developer~ Developer~1 Developer~4
It matches developer, developers, development and etc.
Proximity
“solr search developer”~ “solr search developer”~1
It matches: solr search developer, solr senior developer
Wildcard
Deal* Com*n C??t
Need *xed? Add ReversedWildcardFilterFactory.
Range
[1 TO25] {23 TO50} {23 TO90]
34. Search characteristics
1. Similarity
2. Term frequency
Similarity could be changed via boosting:
q=title:(solr for developers)^2.5 AND description:(professional)
q=title:(java)^0.5 AND description:(professional)^3
35. Search result customization
Field list
/query?=&fl=id, genre /query?=&fl=*,score
Sort
/query?=&fl=id, name&sort=date, score desc
Paging
select?q=*:*&sort=id&fl=id&rows=5&start=5
Transformers
[docid] [shard]
Debuging
/query?=&fl=id&debug=true
Format
/query?=&fl=id&wt=json /query?=&fl=id&wt=xml
39. Advanced Solr
1. Streaming language
Special language tailored mostly for Solr Cloud, parallel processing, map-reduce style
approach. The idea is to process and return big datasets.
Commands like: search, jdbc, intersect, parallel, or, and
2. Parallel query
JDBC/REST to process data in SQL style. Works on many Solr nodes in MPP style.
curl --data-urlencode 'stmt=SELECT to, count(*) FROM collection4 GROUP BY to ORDER BY count(*) desc LIMIT 10'
http://localhost:8983/solr/cloud/sql
3. Graph functions
Graph traversal, aggregations, cycle detection, export to GraphML format
4. Spatial queries
There is field datatype Location. It permits to deal with spatial conditions like filtering by distance (circle,
square, sphere)
and etc.
&q=*:*&fq=(state:"FL" AND city:"Jacksonville")&sort=geodist()+asc
5. Spellchecking
It could be based on a current index, another index, file or using word breaks. Many options what to
return: most similar,
more popular etc
http://localhost:8983/solr/cloud/spell?df=text&spellcheck.q=delll+ultra+sharp&spellcheck=true
6. Suggestions
http://localhost:8983/solr/cloud/a_term_suggest?q=sma&wt=json
7. Highlighter
Marks fragments in found document
http://localhost:8983/solr/cloud/select?hl=on&q=apple
8. Facets
Arrangement of search results into categories based on indexed terms with statistics. Could be done by
values, range, dates, interval, heatmap
40. Performance tuning Cache
Be aware of Solr cache types:
1. Filter cache
Holds unordered document identifiers associated with filter queries that have
been executed (only if fq query parameter is used)
2. Query result cache
Holds ordered document identifiers resulting from queries that have been
executed
3. Document cache
Holds Lucene document instances for access to fields marked as stored
Identify most suitable cache class
1. LRUCache – last recently used are evicted first, track time
2. FastLRUCache – the same but works in separate thread
3. LFUCache – least frequently used are evicted first, track usage count
Play with auto-warm
<filterCache class="solr.FastLRUCache" size="512“ initialSize=“100" autowarmCount=“10"/>
Be aware how auto-warm works internally – doesn’t delete data, repopulated
completely
41. Performance tuning Memory
Care about OS memory for disk caching
Estimate properly Java heap size for Solr – use
https://svn.apache.org/repos/asf/lucene/dev/tags/lucene_solr_4_2_0/dev -tools/size-estimator-lucene-solr.xls
42. Performance tuning Schema design
1. Try to decrease number of stored fields mark as indexed only
2. If fields are used only to be returned in search results – use stored only
43. Performance tuning Ingestion
1. Use bulk sending data rather than per-document
2. If you use SolrJ use ConcurentUpdateSolrServer class
3. Disable ID uniqueness checking
4. Identify proper mergeFactor + maxSegments for Lucene segment merge
5. Issue OPTIMIZE after huge bulk loadings
6. If you use DIH try to not use transformers – pass them to DB level in SQL
7. Configure AUTOCOMMIT properly
44. Performance tuning Search
1. Choose appropriate query parser based on use case
2. Use Solr pagination to return data without waiting for a long time
3. If you return huge data set use Solr cursors rather than pagination
4. Use fq clause to speed up queries with one equal condition – time for scoring
isn’t used + results are put in cache
5. If you have a lot of stored fields but queries don’t show all of them use field lazy
loading
<enableLazyFieldLoading>true</enableLazyFieldLoading>
6. Use shingling to make phrasal search faster
<filter class="solr.ShingleFilterFactory“ maxShingleSize="2" outputUnigrams="true"/>
<filter class="solr.CommonGramsQueryFilterFactory“ words="commongrams.txt" ignoreCase="true""/>
Hello. My name is Alex and today we are going to tell you about full text search solutions. The project I’m currently working doesn’t use dedicated search server and it leads to some issues. We decided to check how we could address it using tailored software. In order to create POC we chosen Apache Solr and would like to share our experience with you. Alex on behalf of devops team will show us how to achieve fault tolerance and scalability.
I plan to have intermediary breaks for small q&a sessions
What distinguishes fts solutions from others databases. Do you know what stemming is? It is word normalization i.e. drive, drove and driven will be written as drive
Consider the text "The quick brown fox jumped over the lazy dog". The use of shingling in a typical configuration would yield the indexed terms (shingles) "the quick", "quick brown", "brown fox", "fox jumped", "jumped over", "over the", "the lazy", and "lazy dog" in addition to all of the original nine terms.
Common-grams is a more selective variation of shingling that only shingles when one of the consecutive words is in a configured list. Given the preceding sentence using an English stop word list, the indexed terms would be "the quick", "over the", "the lazy", and the original nine terms.
There are 2 common approaches: fts index is created inside main database and dedicated FTS server. Which solution is better? It depends from your tasks, performance and scalability requirements. What is obvious FTS servers suggest reach function set but requires hardware, administration and development overhead. We will concentrate on dedicated FTS server.
In spite of FTS solutions looks like intended for content search only the spectrum of their usage patterns is rather big.
Pay attention that figures are calculated by faceted search engine
Suggestions could be made tailored for a particular user
All these patterns are done via FTS API which permits to reuse them without wasting time
Please pay attention that Lucene and Xapian are set of libraries. For instance Elasticsearch and Solr are based on Lucene
Full text search is rather sophisticated stuff throughout enterprise due it affects all aspects.
We will have a look into some of these aspects during last part of our presentation.
Any questions before we move to Apache Solr world?
It is worth mentioning that initially it was full text search engine – now I would rather name it Search engine
SOLR is j2ee application which as I mentioned uses Lucene library.
Storage stores metadata and inverted index in a file store. Solr could be configured to be stored for hdfs storage
Container
Lucene
DIH – export data from external sources
Velocity template – UI of Solr admin tool
RH – what’s process user requests: search, schema management, et.c
SOLR has REST API for main operations like search, indexing. Solr developers stated there are some groups of API.
Main idea was Solr api should be transparent enough to work without any additional payload – only by URI (in opposite of Elastic) but queries become more complicated and URI looks unreadable
SolrJ is included in Solr distributive
It is main structure. Please pay attention that stemming and stopwords aren’t used.
As you could see it stores the position as well. It is done for phrase queries like “New Car”
Data types
show real schema
Let’s have a look into ideal reverse index content
Ascif remove e akstegu
The first one removes continuous letters like cofeeeeee
Why synonym isn’t linked – it actually done on query time rather than on indexing
Let’s have a look into ideal reverse index content
Rollback + nrt + soft/hard commit + indexes – what is new index handler
p. 3 – it means if an user issue commit changes of others users will be committed as well
There is Autocommit and CommitWIthin – it mention dataframe
p. 4 – update only small part of the document rather than reindex it at all. Without it all document should be loaded for update.
In-place – only for dovValues
p. 5 is based in mandatory _Version field.
Ordinal, json, xml, csv, rtf, csv
My lovely feature
Data import handler
Data import handler
Data import handler
Query parsers
Pay attention to searcher – it reads read-only snapshot of Lucen index. once we commit the search is reopening which leads to cache invalidation.
Searcher uses query parser. There are 3 of them but we will concentrate on mostly used Lucene query parser.
~ - number of replacements. So named edit distance
Proximity the same as Fuzzu but edit distance in terms of words
Please pay attention that we don’t consider function usage, cross index and cross document joins, faceting
About boosting, relevancy, similarity
Fields are returned only for stored fields
To load huge datasets so named cursors are used – out of the scope
Pay attention to score – it is search relevancy measure. You could manage it via boosting
We will have a look more examples in demo + with debug
These features are shown in my own interest range
Solr has some advanced features which are out of the presentation but should be mentioned
Streams is tailored lightweight json format for decent volumes of data (source, decorator, evaluator)
p. 2 and 3 are based on p 1
p. 3 is used for recommendation engines
p. 8 is the most complicated stuff, 2 api, a lot of performance tricks
the Administration Console reports (Plugin/Stats | Cache)
There are additional caches which are out of control – field cache and field value cache. There is also an interface to implement own caching strategy as well as warming up.
sizing of document cache is to be larger than the max results * max concurrent queries being executed by Solr to prevent documents from being re-fetched during a query.
ConcurentUpdate uses many threads to connect to Solr as well as a compression to deliver documents faster
remove the QueryElevationComponent from solrconfig.xml
the more static your content is (that is, the less frequent you need to commit data), the lower the merge factor you want.
number of segments on the Overview screen's Statistics section.
No term vector, docvalues and e.t.c
ConcurentUpdate uses many threads to connect to Solr as well as a compression to deliver documents faster
remove the QueryElevationComponent from solrconfig.xml
the more static your content is (that is, the less frequent you need to commit data), the lower the merge factor you want.
number of segments on the Overview screen's Statistics section.
No term vector, docvalues and e.t.c