ClickHouse and the Magic of Materialized Views, By Robert Hodges and Altinity...Altinity Ltd
Presented at the webinar, June 26, 2019
Materialized views are a killer feature of ClickHouse that can speed up queries 20X or more. Our webinar will teach you how to use this potent tool starting with how to create materialized views and load data. We'll then walk through cookbook examples to solve practical problems like deriving aggregates that outlive base data, answering last point queries, and using AggregateFunctions to handle problems like counting unique values, which is a special ClickHouse feature. There will be time for Q&A at the end. At that point you'll be a wizard of ClickHouse materialized views and able to cast spells of your own.
Webinar slides: MORE secrets of ClickHouse Query Performance. By Robert Hodge...Altinity Ltd
Webinar May 27, 2020
ClickHouse is famously fast, but a small amount of extra work makes it much faster. Join us for the latest version of our popular talk on single-node ClickHouse performance. We start by examining the system log to see what ClickHouse queries are doing. Then we introduce standard tricks to increase speed: adding CPUs, reducing I/O with filters, restructuring joins, adding indexes, and using materialized views, plus many more. In each case we show how to measure the results of your work. There will as usual be time for questions as well at the end. Sign up now to polish your ClickHouse performance skills!
ClickHouse Data Warehouse 101: The First Billion Rows, by Alexander Zaitsev a...Altinity Ltd
Columnar stores like ClickHouse enable users to pull insights from big data in seconds, but only if you set things up correctly. This talk will walk through how to implement a data warehouse that contains 1.3 billion rows using the famous NY Yellow Cab ride data. We'll start with basic data implementation including clustering and table definitions, then show how to load efficiently. Next, we'll discuss important features like dictionaries and materialized views, and how they improve query efficiency. We'll end by demonstrating typical queries to illustrate the kind of inferences you can draw rapidly from a well-designed data warehouse. It should be enough to get you started--the next billion rows is up to you!
ClickHouse and the Magic of Materialized Views, By Robert Hodges and Altinity...Altinity Ltd
Presented at the webinar, June 26, 2019
Materialized views are a killer feature of ClickHouse that can speed up queries 20X or more. Our webinar will teach you how to use this potent tool starting with how to create materialized views and load data. We'll then walk through cookbook examples to solve practical problems like deriving aggregates that outlive base data, answering last point queries, and using AggregateFunctions to handle problems like counting unique values, which is a special ClickHouse feature. There will be time for Q&A at the end. At that point you'll be a wizard of ClickHouse materialized views and able to cast spells of your own.
Webinar slides: MORE secrets of ClickHouse Query Performance. By Robert Hodge...Altinity Ltd
Webinar May 27, 2020
ClickHouse is famously fast, but a small amount of extra work makes it much faster. Join us for the latest version of our popular talk on single-node ClickHouse performance. We start by examining the system log to see what ClickHouse queries are doing. Then we introduce standard tricks to increase speed: adding CPUs, reducing I/O with filters, restructuring joins, adding indexes, and using materialized views, plus many more. In each case we show how to measure the results of your work. There will as usual be time for questions as well at the end. Sign up now to polish your ClickHouse performance skills!
ClickHouse Data Warehouse 101: The First Billion Rows, by Alexander Zaitsev a...Altinity Ltd
Columnar stores like ClickHouse enable users to pull insights from big data in seconds, but only if you set things up correctly. This talk will walk through how to implement a data warehouse that contains 1.3 billion rows using the famous NY Yellow Cab ride data. We'll start with basic data implementation including clustering and table definitions, then show how to load efficiently. Next, we'll discuss important features like dictionaries and materialized views, and how they improve query efficiency. We'll end by demonstrating typical queries to illustrate the kind of inferences you can draw rapidly from a well-designed data warehouse. It should be enough to get you started--the next billion rows is up to you!
ClickHouse Materialized Views: The Magic ContinuesAltinity Ltd
Slides for the webinar, presented on February 26, 2020
By Robert Hodges, Altinity CEO
Materialized views are the killer feature of ClickHouse, and the Altinity 2019 webinar on how they work was very popular. Join this updated webinar to learn how to use materialized views to speed up queries hundreds of times. We'll cover basic design, last point queries, using TTLs to drop source data, counting unique values, and other useful tricks. Finally, we'll cover recent improvements that make materialized views more useful than ever.
All About JSON and ClickHouse - Tips, Tricks and New Features-2022-07-26-FINA...Altinity Ltd
JSON is the king of data formats and ClickHouse has a plethora of features to handle it. This webinar covers JSON features from A to Z starting with traditional ways to load and represent JSON data in ClickHouse. Next, we’ll jump into the JSON data type: how it works, how to query data from it, and what works and doesn’t work. JSON data type is one of the most awaited features in the 2022 ClickHouse roadmap, so you won’t want to miss out. Finally, we’ll talk about Jedi master techniques like adding bloom filter indexing on JSON data.
A Day in the Life of a ClickHouse Query Webinar Slides Altinity Ltd
Why do queries run out of memory? How can I make my queries even faster? How should I size ClickHouse nodes for best cost-efficiency? The key to these questions and many others is knowing what happens inside ClickHouse when a query runs. This webinar is a gentle introduction to ClickHouse internals, focusing on topics that will help your applications run faster and more efficiently. We’ll discuss the basic flow of query execution, dig into how ClickHouse handles aggregation and joins, and show you how ClickHouse distributes processing within a single CPU as well as across many nodes in the network. After attending this webinar you’ll understand how to open up the black box and see what the parts are doing.
Webinar: Secrets of ClickHouse Query Performance, by Robert HodgesAltinity Ltd
From webinars September 11 and September 17, 2019
ClickHouse is famous for speed. That said, you can almost always make it faster! This webinar uses examples to teach you how to deduce what queries are actually doing by reading the system log and system tables. We'll then explore standard ways to increase query speed: data types and encodings, filtering, join reordering, skip indexes, materialized views, session parameters, to name just a few. In each case we'll circle back to query plans and system metrics to demonstrate changes in ClickHouse behavior that explain the boost in performance. We hope you'll enjoy the first step to becoming a ClickHouse performance guru!
Speaker Bio:
Robert Hodges is CEO of Altinity, which offers enterprise support for ClickHouse. He has over three decades of experience in data management spanning 20 different DBMS types. ClickHouse is his current favorite. ;)
Better than you think: Handling JSON data in ClickHouseAltinity Ltd
Robert Hodges shows how ClickHouse, a relational database with tables, can offer high-performance analysis of JSON data. This talk provides a cookbook of schema design, indexing, data loading, and query tricks we gave learned over years of helping users build analytical apps for servicds logs, observability data, financial transactions, and other types of semi-structured data. Robert Hodges is CEO of Altinity and a certified database geek.
https://altinity.com
https://www.meetup.com/San-Francisco-Bay-Area-ClickHouse-Meetup
The Future of Column-Oriented Data Processing With Apache Arrow and Apache Pa...Dremio Corporation
Essentially every successful analytical DBMS in the market today makes use of column-oriented data structures. In the Hadoop ecosystem, Apache Parquet (and Apache ORC) provide similar advantages in terms of processing and storage efficiency. Apache Arrow is the in-memory counterpart to these formats and has been been embraced by over a dozen open source projects as the de facto standard for in-memory processing. In this session the PMC Chair for Apache Arrow and the PMC Chair for Apache Parquet discuss the future of column-oriented processing.
Real-time Data Ingestion from Kafka to ClickHouse with Deterministic Re-tries...HostedbyConfluent
In a real-time data ingestion pipeline for analytical processing, efficient and fast data loading to a columnar database such as ClickHouse favors large blocks over individual rows. Therefore, applications often rely on some buffering mechanism such as Kafka to store data temporarily, and having a message processing engine to aggregate Kafka messages into large blocks which then get loaded to the backend database. Due to various failures in this pipeline, a naive block aggregator that forms blocks without additional measures, would cause data duplication or data loss. We have developed a solution to avoid these issues, thereby achieving exactly-once delivery from Kafka to ClickHouse. Our solution utilizes Kafka’s metadata to keep track of blocks that we intend to send to ClickHouse, and later uses this metadata information to deterministically re-produce ClickHouse blocks for re-tries in case of failures. The identical blocks are guaranteed to be deduplicated by ClickHouse. We have also developed a run-time verification tool that monitors Kafka’s internal metadata topic, and raises alerts when the required invariants for exactly-once delivery are violated. Our solution has been developed and deployed to the production clusters that span multiple datacenters at eBay.
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use work load management.
ClickHouse Materialized Views: The Magic ContinuesAltinity Ltd
Slides for the webinar, presented on February 26, 2020
By Robert Hodges, Altinity CEO
Materialized views are the killer feature of ClickHouse, and the Altinity 2019 webinar on how they work was very popular. Join this updated webinar to learn how to use materialized views to speed up queries hundreds of times. We'll cover basic design, last point queries, using TTLs to drop source data, counting unique values, and other useful tricks. Finally, we'll cover recent improvements that make materialized views more useful than ever.
All About JSON and ClickHouse - Tips, Tricks and New Features-2022-07-26-FINA...Altinity Ltd
JSON is the king of data formats and ClickHouse has a plethora of features to handle it. This webinar covers JSON features from A to Z starting with traditional ways to load and represent JSON data in ClickHouse. Next, we’ll jump into the JSON data type: how it works, how to query data from it, and what works and doesn’t work. JSON data type is one of the most awaited features in the 2022 ClickHouse roadmap, so you won’t want to miss out. Finally, we’ll talk about Jedi master techniques like adding bloom filter indexing on JSON data.
A Day in the Life of a ClickHouse Query Webinar Slides Altinity Ltd
Why do queries run out of memory? How can I make my queries even faster? How should I size ClickHouse nodes for best cost-efficiency? The key to these questions and many others is knowing what happens inside ClickHouse when a query runs. This webinar is a gentle introduction to ClickHouse internals, focusing on topics that will help your applications run faster and more efficiently. We’ll discuss the basic flow of query execution, dig into how ClickHouse handles aggregation and joins, and show you how ClickHouse distributes processing within a single CPU as well as across many nodes in the network. After attending this webinar you’ll understand how to open up the black box and see what the parts are doing.
Webinar: Secrets of ClickHouse Query Performance, by Robert HodgesAltinity Ltd
From webinars September 11 and September 17, 2019
ClickHouse is famous for speed. That said, you can almost always make it faster! This webinar uses examples to teach you how to deduce what queries are actually doing by reading the system log and system tables. We'll then explore standard ways to increase query speed: data types and encodings, filtering, join reordering, skip indexes, materialized views, session parameters, to name just a few. In each case we'll circle back to query plans and system metrics to demonstrate changes in ClickHouse behavior that explain the boost in performance. We hope you'll enjoy the first step to becoming a ClickHouse performance guru!
Speaker Bio:
Robert Hodges is CEO of Altinity, which offers enterprise support for ClickHouse. He has over three decades of experience in data management spanning 20 different DBMS types. ClickHouse is his current favorite. ;)
Better than you think: Handling JSON data in ClickHouseAltinity Ltd
Robert Hodges shows how ClickHouse, a relational database with tables, can offer high-performance analysis of JSON data. This talk provides a cookbook of schema design, indexing, data loading, and query tricks we gave learned over years of helping users build analytical apps for servicds logs, observability data, financial transactions, and other types of semi-structured data. Robert Hodges is CEO of Altinity and a certified database geek.
https://altinity.com
https://www.meetup.com/San-Francisco-Bay-Area-ClickHouse-Meetup
The Future of Column-Oriented Data Processing With Apache Arrow and Apache Pa...Dremio Corporation
Essentially every successful analytical DBMS in the market today makes use of column-oriented data structures. In the Hadoop ecosystem, Apache Parquet (and Apache ORC) provide similar advantages in terms of processing and storage efficiency. Apache Arrow is the in-memory counterpart to these formats and has been been embraced by over a dozen open source projects as the de facto standard for in-memory processing. In this session the PMC Chair for Apache Arrow and the PMC Chair for Apache Parquet discuss the future of column-oriented processing.
Real-time Data Ingestion from Kafka to ClickHouse with Deterministic Re-tries...HostedbyConfluent
In a real-time data ingestion pipeline for analytical processing, efficient and fast data loading to a columnar database such as ClickHouse favors large blocks over individual rows. Therefore, applications often rely on some buffering mechanism such as Kafka to store data temporarily, and having a message processing engine to aggregate Kafka messages into large blocks which then get loaded to the backend database. Due to various failures in this pipeline, a naive block aggregator that forms blocks without additional measures, would cause data duplication or data loss. We have developed a solution to avoid these issues, thereby achieving exactly-once delivery from Kafka to ClickHouse. Our solution utilizes Kafka’s metadata to keep track of blocks that we intend to send to ClickHouse, and later uses this metadata information to deterministically re-produce ClickHouse blocks for re-tries in case of failures. The identical blocks are guaranteed to be deduplicated by ClickHouse. We have also developed a run-time verification tool that monitors Kafka’s internal metadata topic, and raises alerts when the required invariants for exactly-once delivery are violated. Our solution has been developed and deployed to the production clusters that span multiple datacenters at eBay.
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use work load management.
Tutorial given at ICWE'13, Aalborg, Denmark on 08.07.2013
Abstract:
Web crawling, a process of collecting web pages in an automated manner, is the primary and ubiquitous operation used by a large number of web systems and agents starting from a simple program for website backup to a major web search engine. Due to an astronomical amount of data already published on the Web and ongoing exponential growth of web content, any party that want to take advantage of massive-scale web data faces a high barrier to entry. In this tutorial, we will introduce the audience to five topics: architecture and implementation of high-performance web crawler, collaborative web crawling, crawling the deep Web, crawling multimedia content and future directions in web crawling research.
To cite this tutorial:
Please refer to http://dx.doi.org/10.1007/978-3-642-39200-9_49
Scaling GIS Data in Non-relational Data StoresMike Malone
As the amount of GIS data we need to keep track of increases, the amount of devices accessing it increases, and the amount of GIS writes increase, we’re finding that, much like real-time web applications, normal RDBMS’s are not well suited to scaling. This talk covers why GIS data is hard to scale in a normal RDBMS, what nonrelational stores exist out there, and some basic examples of how to do spatial queries within a nonrelational store.
This presentation describes how a web browser works. From the user request, request processing, getting to DNS server, generating the HTTP request, receiving the HTTP response and finally rendering the HTML page and displaying the webpage to the user as requested, this presentation encompasses the entire process of a web browser fetching a webpage.
PS: Pl download to see the entire presentation with the animation involved. Without the animation few slides may seem to be confusing.
Thank you.
Large Scale Crawling with Apache Nutch and Friendslucenerevolution
Presented by Julien Nioche, Director, DigitalPebble
This session will give an overview of Apache Nutch. I will describe its main components and how it fits with other Apache projects such as Hadoop, SOLR, Tika or HBase. The second part of the presentation will be focused on the latest developments in Nutch, the differences between the 1.x and 2.x branch and what we can expect to see in Nutch in the future. This session will cover many practical aspects and should be a good starting point to crawling on a large scale with Apache Nutch and SOLR.
Large Scale Crawling with Apache Nutch and FriendsJulien Nioche
This session will give an overview of Apache Nutch. I will describe its main components and how it fits with other Apache projects such as Hadoop, SOLR, Tika or HBase. The second part of the presentation will be focused on the latest developments in Nutch, the differences between the 1.x and 2.x branch and what we can expect to see in Nutch in the future. This session will cover many practical aspects and should be a good starting point to crawling on a large scale with Apache Nutch and SOLR.
Hadoop Online Training : kelly technologies is the bestHadoop online Training Institutes in Bangalore. ProvidingHadoop online Training by real time faculty in Bangalore.
Storm-Crawler is a collection of resources for building low-latency, large scale web crawlers on Apache Storm. We will compare with similar projects like Apache Nutch and present several use cases where the storm-crawler is being used. In particular we will see how the Storm-crawler can be used with ElasticSearch and Kibana for crawling and indexing web pages.
Web Crawling Using Location Aware Techniqueijsrd.com
Most of the modern search engines are based in the well-known web crawling model. A centralized crawler or a farm of parallel crawlers is dedicated to the process of downloading the web pages and updating the database. However, the model fails to keep pace with the size of the web, and the frequency of changes in the web documents. For this reason, there have been some recent proposals for distributed web crawling, trying to alleviate the bottlenecks in the search engines and scale with the web . In this work, facilitating the flexibility and extensibility of Mobile Agent , we consider adding location awareness to distributed web crawling, so that each web page is crawled from the web crawler logically most near to it, the crawler that could download it the faster. We evaluate the location aware approach and show that it can reduce the time demanded for downloading to one order of magnitude.
Spring 2007 SharePoint Connections Oleson Advanced Administration and Plannin...Joel Oleson
Advanced Administration the 2nd part in a 2 part series on Administration topics for SharePoint Server by Joel Oleson. SharePoint Connections Spring 2007 in Orlando,
Smart Crawler: A Two Stage Crawler for Concept Based Semantic Search Engine.iosrjce
The internet is a vast collection of billions of web pages containing terabytes of information
arranged in thousands of servers using HTML. The size of this collection itself is a formidable obstacle in
retrieving necessary and relevant information. This made search engines an important part of our lives. Search
engines strive to retrieve information as relevant as possible. One of the building blocks of search engines is the
Web Crawler. We tend to propose a two - stage framework, specifically two smart Crawler, for efficient
gathering deep net interfaces. Within the first stage, smart Crawler, performs site-based sorting out centre
pages with the assistance of search engines, avoiding visiting an oversized variety of pages. To realize
additional correct results for a targeted crawl, smart Crawler, ranks websites to order extremely relevant ones
for a given topic. Within the second stage, smart Crawler, achieves quick in – site looking by excavating most
relevant links with associate degree accommodative link -ranking
Topics covered:
1. Generating a new Remix project
2. Conventional files
3. Routes (including the nested variety)
4. Styling
5. Database interactions (via sqlite and prisma)
6. Mutations, Validation, and Authentication
7. Error handling
8. SEO with Meta Tags and much more
This talk will give an overview of Apache Nutch, its main components, how it fits with other Apache projects and its latest developments.
Apache Nutch was started exactly 10 years ago and was the starting point for what later became Apache Hadoop and also Apache Tika. Nutch is nowadays the tool of reference for large scale web crawling.
In this talk I will give an overview of Apache Nutch and describe its main components and how Nutch fits with other Apache projects such as Hadoop, SOLR or Tika.
The second part of the presentation will be focused on the latest developments in Nutch and the changes introduced by the 2.x branch with the use of Apache GORA as a front end to various NoSQL datastores.
Similar to Design and Implementation of a High- Performance Distributed Web Crawler (20)
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
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Design and Implementation of a High- Performance Distributed Web Crawler
1. Design and Implementation of a High-
Performance Distributed Web Crawler
Vladislav Shkapenyuk* Torsten Suel
CIS Department
Polytechnic University
Brooklyn, NY 11201
* Currently at AT&T Research Labs, Florham Park
2. Overview
:
1. Introduction
2. PolyBot System Architecture
3. Data Structures and Performance
4. Experimental Results
5. Discussion and Open Problems
3. 1. Introduction
Web Crawler: (also called spider or robot)
tool for data acquisition in search engines
large engines need high-performance
crawlers
need to parallelize crawling task
PolyBot: a parallel/distributed web crawler
cluster vs. wide-area distributed
4. Basic structure of a search engine:
indexin
g
Crawle
r Index
disk
s
Search.com look
Query: “computer” up
5. Crawler
Crawle
r
disk
fetches pages from the web s
starts at set of “seed pages”
parses fetched pages for
hyperlinks
then follows those links (e.g., BFS)
variations:
- recrawling
- focused crawling
- random walks
6. Breadth-First Crawl:
Basic idea:
- start at a set of known URLs
- explore in “concentric circles” around these URLs
start
pages
distance-one
pages
distance-two
pages
used by broad web search engines
balances load between servers
7. Crawling Strategy and Download Rate:
crawling strategy: “What page to download next?”
download rate: “How many pages per second?”
different scenarios require different strategies
lots of recent work on crawling strategy
little published work on optimizing download rate
(main exception: Mercator from DEC/Compaq/HP?)
somewhat separate issues
building a slow crawler is (fairly) easy ...
9. System Requirements:
flexibility (different crawling strategies)
scalabilty (sustainable high performance at low cost)
robustness
(odd server content/behavior, crashes)
crawling etiquette and speed control
(robot exclusion, 30 second intervals, domain level
throttling, speed control for other users)
manageable and reconfigurable
(interface for statistics and control, system setup)
10. Details: (lots of ‘em)
robot exclusion
- robots.txt file and meta tags
- robot exclusion adds overhead
handling filetypes
(exclude some extensions, and use mime types)
URL extensions and CGI scripts
(to strip or not to strip? Ignore?)
frames, imagemaps
black holes (robot traps)
(limit maximum depth of a site)
different names for same site?
(could check IP address, but no perfect solution)
11. Crawling courtesy
minimize load on crawled server
no more than one outstanding request per
site
better: wait 30 seconds between accesses to
site
(this number is not fixed)
problems:
- one server may have many sites
- one site may be on many servers
- 3 years to crawl a 3-million page site!
give contact info for large crawls
12. Contributions:
distributed architecture based on collection of services
- separation of concerns
- efficient interfaces
I/O efficient techniques for URL handling
- lazy URL -seen structure
- manager data structures
scheduling policies
- manager scheduling and shuffling
resulting system limited by network and parsing
performane
detailed description and how-to (limited experiments)
13. 2. PolyBot System
Architecture
Structure:
separation of crawling strategy and basic
system
collection of scalable distributed services
(DNS, downloading, scheduling, strategy)
for clusters and wide-area distributed
optimized per-node performance
no random disk accesses (no per-page access)
14. Basic Architecture (ctd):
application issues
requests to manager
manager does DNS
and robot exclusion
manager schedules
URL on downloader
downloader gets
file
and puts it on disk
application is
notified
of new files
application parses
new
files for hyperlinks
application sends
data
to storage component
(indexing done later)
15. System components:
downloader: optimized HTTP client written in Python
(everything else in C++)
DNS resolver: uses asynchronous DNS library
manager uses Berkeley DB and STL for external and
internal data structures
manager does robot exclusion by generating requests
to downloaders and parsing files
application does parsing and handling of URLs
(has this page already been downloaded?)
16. Scaling the system:
small system on previous pages:
3-5 workstations and 250-400 pages/sec peak
can scale up by adding downloaders and DNS
resolvers
at 400-600 pages/sec, application becomes bottleneck
at 8 downloaders manager becomes bottleneck
need to replicate application and manager
hash-based technique (Internet Archive crawler)
partitions URLs and hosts among application parts
data transfer batched and via file system (NFS)
17. Scaling
up:
20
machines
1500
pages/s?
depends on
crawl strategy
hash to
nodes
based on site
(b/c robot ex)
18. 3. Data Structures and Techniques
Crawling
Application pcre
parsing using library
NFS eventually bottleneck
URL-seen problem:
- need to check if file has been parsed or downloaded before
- after 20 million pages, we have “seen” over 100 million URLs
- each URL is 50 to 75 bytes on average
Options: compress URLs in main memory, or use disk
- prefix+huffman coding (DEC, JY01) or Bloom Filter (Archive)
- disk access with caching (Mercator)
- we use lazy/bulk operations on disk
19. Implementation of URL-seen check:
- while less than a few million URLs seen, keep in main memory
- then write URLs to file in alphabetic, prefix-compressed order
- collect new URLs in memory and periodically reform bulk
check by merging new URLs into the file on disk
When is a newly a parsed URL downloaded?
Reordering request stream
- want to space ot requests from same subdomain
- needed due to load on small domains and due to security tools
- sort URLs with hostname reversed (e.g., com.amazon.www),
and then “unshuffle” the stream provable load balance
20. Crawling Manager
large stream of incoming URL request files
goal: schedule URLs roughly in the order that they
come, while observing time-out rule (30 seconds)
and maintaining high speed
must do DNS and robot excl. “right before”download
keep requests on disk as long as possible!
- otherwise, structures grow too large after few million pages
(performance killer)
22. URL Loading
Policy new request file from disk whenever less
read
than x hosts in ready queue
choose x > speed * timeout (e.g., 100 pages/s * 30s)
# of current host data structures is
x + speed * timeout + n_down + n_transit
which is usually < 2x
nice behavior for BDB caching policy
performs reordering only when necessary!
23. 4. Experimental Results
crawl of 120 million pages over 19 days
161 million HTTP request
16 million robots.txt requests
138 million successful non-robots requests
17 million HTTP errors (401, 403, 404 etc)
121 million pages retrieved
slow during day, fast at night
peak about 300 pages/s over T3
many downtimes due to attacks, crashes, revisions
“slow tail” of requests at the end (4 days)
lots of things happen
24. Experimental Results ctd.
bytes in bytes out frames out
Poly T3 connection over 24 hours of 5/28/01
(courtesy of AppliedTheory)
25. Experimental Results ctd.
sustaining performance:
- will find out when data structures hit disk
- I/O-efficiency vital
speed control tricky
- vary number of connections based on feedback
- also upper bound on connections
- complicated interactions in system
- not clear what we should want
other configuration: 140 pages/sec sustained
on 2 Ultra10 with 60GB EIDE and 1GB/768MB
similar for Linux on Intel
26. More Detailed Evaluation (to be done)
Problems
- cannot get commercial crawlers
- need simulation systen to find system bottlenecks
- often not much of a tradeoff (get it right!)
Example: manager data structures
- with our loading policy, manager can feed several
downloaders
- naïve policy: disk access per page
parallel communication overhead
- low for limited number of nodes (URL exchange)
- wide-area distributed: where do yu want the data?
- more relevant for highly distributed systems
27. 5. Discussion and Open Problems
Related work
Mercator (Heydon/Najork from DEC/Compaq)
- used in altaVista
- centralized system (2-CPU Alpha with RAID disks)
- URL-seen test by fast disk access and caching
- one thread per HTTP connection
- completely in Java, with pluggable components
Atrax: very recent distributed extension to Mercator
- combines several Mercators
- URL hashing, and off-line URL check (as we do)
28. Related work (ctd.)
early Internet Archive crawler (circa 96)
- uses hashing to partition URLs between crawlers
- bloom filter for “URL seen” structure
early Google crawler (1998)
P2P crawlers (grub.org and others)
Cho/Garcia-Molina (WWW 2002)
- study of overhead/quality tradeoff in parallel crawlers
- difference: we scale services separately, and focus on
single-node performance
- in our experience, parallel overhead low
29. Open Problems:
Measuring and tuning peak performance
- need simulation environment
- eventually reduces to parsing and network
- to be improved: space, fault-tolerance (Xactions?)
Highly Distributed crawling
- highly distributed (e.g., grub.org) ? (maybe)
- bybrid? (different services)
- few high-performance sites? (several Universities)
Recrawling and focused crawling strategies
- what strategies?
- how to express?
- how to implement?