Cassandra Day SV 2014: Netflix’s Astyanax Java Client Driver for Apache Cassandra
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Astyanax is the thrift protocol based C* driver widely used and open sourced by Netflix. It was recently integrated with the Java Driver released by DataStax. This talk focusses on the different options available with Astyanax and how it complements the Java Driver. About Puneet Oberai, Senior Software Engineer at Netflix Senior Software Engineer at Netflix and proud team member of Netflix CDE (Cloud Data Engineering).
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Understand key AWS Big Data services including S3, Amazon EMR, Kinesis, and Redshift
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Architects, developers and data scientists who are looking to start a Big Data initiative
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The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
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This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
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Cassandra Summit 2014: Astyanax — To Be or Not To Be
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February 2016 Webinar Series - Architectural Patterns for Big Data on AWS
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AWS re:Invent 2016: Big Data Architectural Patterns and Best Practices on AWS...
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Examples and snippets available here: https://github.com/lbialy/functionaljava8
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Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Pitangent Analytics & Technology Solutions Pvt. Ltd
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.AppSec PNW: Android and iOS Application Security with MobSF
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
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Cassandra Day SV 2014: Netflix’s Astyanax Java Client Driver for Apache Cassandra
- 1. Astyanax Puneet Oberai Ne1lix, Inc.
- 3. Useful features – Load balancing – ConnecDon pooling -‐ resilient with failover and retries – Integrates with external host discovery – Metrics (there are a lot!) – Highly configurable and pluggable
- 4. Fluent query syntax ColumnList<Integer> = keyspace .readQuery( “myCF” ) .getRow( “A” ) .withColumnSlice( new RangeBuilder() .greaterThan(11) .lessThanEquals(18) .build()) .execute() .getResult();
- 5. Recipes • All Rows Reader • Distributed Row Lock • Queue • Chunked Object Store
- 6. How does Ne1lix use it
- 7. Java Driver release in 2013 • Async interface! • Cursor support • Batching for mutaDons • Prepared statements • Flexible retry policies • And everything else in CQL ..
- 8. But what about thrie !!?? I’m sDll using it. Calm down, thrie is sDll supported J
- 9. Astyanax can use both drivers
- 10. Now the good stuff! What about performance? If you use prepared statements, you’re good!
- 11. Reads (about 2%)
- 12. Writes (a bit more 5%-‐10%)
- 13. Prepared Statements are a must
- 14. Prepared Statements • There is no magic here – You “prepare” – Then you re-‐use
- 15. Astyanax value add Structured queries naturally have some query signature keyspace.prepareQuery( myCF ) .withRow( myRowKey ) .withColumnSlice( start, end) .execute(); Translates to select * from ks.myCF where key=? and column1 >=? and column1 <=?;
- 16. Columns v/s Rows • Are columns really columns? • Are rows sDll rows? • Are columns really rows?
- 17. Simple schema Key validator – int Col comparator – int Default validator – u18
- 18. Rows are now transposed
- 19. Astyanax value add • Astyanax maintains backwards compaDbility with the api • Hence rows are sDll rows and columns are sDll columns
- 20. So what should I use? • Astyanax has recipes • Astyanax is good for structured queries • If all you want is CQL3, then use Java Driver
- 21. QuesDons?