Apache Cassandra 2.0 is out - now there's no reason not to ditch that ol' legacy relational system for your important online applications. Cassandra 2.0 includes big impact features like Light Weight Transactions and Triggers. Do you know about the other new enhancements that got lost in the noise. Let's put the spotlight on all the things! Changes in memory management, file handling and internals. Low hype but they pack a big punch. While we were at it, we also did a bit of house cleaning.
Further discussion on Data Modeling with Apache Cassandra. Overview of formal data modeling techniques as well as practical. Real-world use cases and associated data models.
Functional data models are great, but how can you squeeze out more performance and make them awesome! Let's talk through some example models, go through the tuning steps and understand the tradeoffs. Many time's just a simple understanding of the underlying internals can make all the difference. I've helped some of the biggest companies in the world do this and I can help you. Do you feel the need for Cassandra 2.0 speed?
At this meetup Patrick McFadin, Solutions Architect at DataStax, will be discussing the most recently added features in Apache Cassandra 2.0, including: Lightweight transactions, eager retries, improved compaction, triggers, and CQL cursors. He'll also be touching on time series data with Apache Cassandra.
This summer, coming to a server near you, Cassandra 3.0! Contributors and committers have been working hard on what is the most ambitious release to date. It’s almost too much to talk about, but we will dig into some of the most important, ground breaking features that you’ll want to use. Indexing changes that will make your applications faster and spark jobs more efficient. Storage engine changes to get even more density and efficiency from your nodes. Developer focused features like full JSON support and User Defined Functions. And finally, one of the most requested features, Windows support, has made it’s arrival. There is more, but you’ll just have to some see for yourself. Get your front row seat and don’t miss it!
Storing time series data with Apache CassandraPatrick McFadin
If you are looking to collect and store time series data, it's probably not going to be small. Don't get caught without a plan! Apache Cassandra has proven itself as a solid choice now you can learn how to do it. We'll look at possible data models and the the choices you have to be successful. Then, let's open the hood and learn about how data is stored in Apache Cassandra. You don't need to be an expert in distributed systems to make this work and I'll show you how. I'll give you real-world examples and work through the steps. Give me an hour and I will upgrade your time series game.
Cassandra Day SV 2014: Fundamentals of Apache Cassandra Data ModelingDataStax Academy
You know you need Cassandra for it's uptime and scaling, but what about that data model? Let's bridge that gap and get you building your game changing app. We'll break down topics like storing objects and indexing for fast retrieval. You will see by understanding a few things about Cassandra internals, you can put your data model in the spotlight. The goal of this talk is to get you comfortable working with data in Cassandra throughout the application lifecycle. What are you waiting for? The cameras are waiting!
A lot has changed since I gave one of these talks and man, has it been good. 2.0 brought us a lot of new CQL features and now with 2.1 we get even more! Let me show you some real life data models and those new features taking developer productivity to an all new high. User Defined Types, New Counters, Paging, Static Columns. Exciting new ways of making your app truly killer!
Further discussion on Data Modeling with Apache Cassandra. Overview of formal data modeling techniques as well as practical. Real-world use cases and associated data models.
Functional data models are great, but how can you squeeze out more performance and make them awesome! Let's talk through some example models, go through the tuning steps and understand the tradeoffs. Many time's just a simple understanding of the underlying internals can make all the difference. I've helped some of the biggest companies in the world do this and I can help you. Do you feel the need for Cassandra 2.0 speed?
At this meetup Patrick McFadin, Solutions Architect at DataStax, will be discussing the most recently added features in Apache Cassandra 2.0, including: Lightweight transactions, eager retries, improved compaction, triggers, and CQL cursors. He'll also be touching on time series data with Apache Cassandra.
This summer, coming to a server near you, Cassandra 3.0! Contributors and committers have been working hard on what is the most ambitious release to date. It’s almost too much to talk about, but we will dig into some of the most important, ground breaking features that you’ll want to use. Indexing changes that will make your applications faster and spark jobs more efficient. Storage engine changes to get even more density and efficiency from your nodes. Developer focused features like full JSON support and User Defined Functions. And finally, one of the most requested features, Windows support, has made it’s arrival. There is more, but you’ll just have to some see for yourself. Get your front row seat and don’t miss it!
Storing time series data with Apache CassandraPatrick McFadin
If you are looking to collect and store time series data, it's probably not going to be small. Don't get caught without a plan! Apache Cassandra has proven itself as a solid choice now you can learn how to do it. We'll look at possible data models and the the choices you have to be successful. Then, let's open the hood and learn about how data is stored in Apache Cassandra. You don't need to be an expert in distributed systems to make this work and I'll show you how. I'll give you real-world examples and work through the steps. Give me an hour and I will upgrade your time series game.
Cassandra Day SV 2014: Fundamentals of Apache Cassandra Data ModelingDataStax Academy
You know you need Cassandra for it's uptime and scaling, but what about that data model? Let's bridge that gap and get you building your game changing app. We'll break down topics like storing objects and indexing for fast retrieval. You will see by understanding a few things about Cassandra internals, you can put your data model in the spotlight. The goal of this talk is to get you comfortable working with data in Cassandra throughout the application lifecycle. What are you waiting for? The cameras are waiting!
A lot has changed since I gave one of these talks and man, has it been good. 2.0 brought us a lot of new CQL features and now with 2.1 we get even more! Let me show you some real life data models and those new features taking developer productivity to an all new high. User Defined Types, New Counters, Paging, Static Columns. Exciting new ways of making your app truly killer!
Introduction to data modeling with apache cassandraPatrick McFadin
Are you using relational databases and wonder how to get started with data modeling and Apache Cassandra? Here is a starting tour of how to get started. Translating from the knowledge you already have to the knowledge you need to effective with Cassandra development. We cover patterns and anti-patterns. Get going today!
Cassandra Day Atlanta 2015: Building Your First Application with Apache Cassa...DataStax Academy
You’ve heard the talks, followed the tutorials, and done the research. You are a font of Cassandra knowledge. Now it’s time to change the world! (Or at least build something to make your boss happy). In this talk we’ll walk through the process of building KillrVideo, an open source video sharing website where users can upload and share videos, rate them, comment on them, and more. By looking at a real application, we’ll talk about architectural decisions, how the application drives the data model, some pro tips when using the DataStax drivers, and some lessons learned from mistakes made along the way. You’ll leave this session ready to start building your next application (world-changing or otherwise) with Cassandra.
DataStax: An Introduction to DataStax Enterprise SearchDataStax Academy
1) Why We Built DSE Search
2) Basics of the Read and Write Paths
3) Fault-tolerance and Adaptive Routing
4) Analytics with Search and Spark
5) Live Indexing
A brief, but action-packed introduction to DataStax Enterprise Search. In this deck, we'll get an overview of DSE Search's value proposition, see some example CQL search queries, and dive into the details of the indexing and query paths.
Cassandra Community Webinar | Become a Super ModelerDataStax
Sure you can do some time series modeling. Maybe some user profiles. What's going to make you a super modeler? Let's take a look at some great techniques taken from real world applications where we exploit the Cassandra big table model to it's fullest advantage. We'll cover some of the new features in CQL 3 as well as some tried and true methods. In particular, we will look at fast indexing techniques to get data faster at scale. You'll be jet setting through your data like a true super modeler in no time.
Speaker: Patrick McFadin, Principal Solutions Architect at DataStax
Time series with Apache Cassandra - Long versionPatrick McFadin
Apache Cassandra has proven to be one of the best solutions for storing and retrieving time series data. This talk will give you an overview of the many ways you can be successful. We will discuss how the storage model of Cassandra is well suited for this pattern and go over examples of how best to build data models.
Cassandra Community Webinar: Back to Basics with CQL3DataStax
Cassandra is a distributed, massively scalable, fault tolerant, columnar data store, and if you need the ability to make fast writes, the only thing faster than Cassandra is /dev/null! In this fast-paced presentation, we'll briefly describe big data, and the area of big data that Cassandra is designed to fill. We will cover Cassandra's unique, every-node-the-same architecture. We will reveal Cassandra's internal data structure and explain just why Cassandra is so darned fast. Finally, we'll wrap up with a discussion of data modeling using the new standard protocol: CQL (Cassandra Query Language).
A Cassandra + Solr + Spark Love Triangle Using DataStax EnterprisePatrick McFadin
Wait! Back away from the Cassandra 2ndary index. It’s ok for some use cases, but it’s not an easy button. "But I need to search through a bunch of columns to look for the data and I want to do some regression analysis… and I can’t model that in C*, even after watching all of Patrick McFadins videos. What do I do?” The answer, dear developer, is in DSE Search and Analytics. With it’s easy Solr API and Spark integration so you can search and analyze data stored in your Cassandra database until your heart’s content. Take our hand. WE will show you how.
Cassandra Day Chicago 2015: Advanced Data ModelingDataStax Academy
Speaker(s): Tim Berglund, Global Director of Training at DataStax
You know you need Cassandra for its uptime and scaling, but what about that data model? Let's bridge that gap and get you building your game changing app. We'll break down topics like storing objects and indexing for fast retrieval. You will see by understanding a few things about Cassandra internals, you can put your data model in the spotlight. The goal of this talk is to get you comfortable working with data in Cassandra throughout the application lifecycle. What are you waiting for?
Introduction to data modeling with apache cassandraPatrick McFadin
Are you using relational databases and wonder how to get started with data modeling and Apache Cassandra? Here is a starting tour of how to get started. Translating from the knowledge you already have to the knowledge you need to effective with Cassandra development. We cover patterns and anti-patterns. Get going today!
Cassandra Day Atlanta 2015: Building Your First Application with Apache Cassa...DataStax Academy
You’ve heard the talks, followed the tutorials, and done the research. You are a font of Cassandra knowledge. Now it’s time to change the world! (Or at least build something to make your boss happy). In this talk we’ll walk through the process of building KillrVideo, an open source video sharing website where users can upload and share videos, rate them, comment on them, and more. By looking at a real application, we’ll talk about architectural decisions, how the application drives the data model, some pro tips when using the DataStax drivers, and some lessons learned from mistakes made along the way. You’ll leave this session ready to start building your next application (world-changing or otherwise) with Cassandra.
DataStax: An Introduction to DataStax Enterprise SearchDataStax Academy
1) Why We Built DSE Search
2) Basics of the Read and Write Paths
3) Fault-tolerance and Adaptive Routing
4) Analytics with Search and Spark
5) Live Indexing
A brief, but action-packed introduction to DataStax Enterprise Search. In this deck, we'll get an overview of DSE Search's value proposition, see some example CQL search queries, and dive into the details of the indexing and query paths.
Cassandra Community Webinar | Become a Super ModelerDataStax
Sure you can do some time series modeling. Maybe some user profiles. What's going to make you a super modeler? Let's take a look at some great techniques taken from real world applications where we exploit the Cassandra big table model to it's fullest advantage. We'll cover some of the new features in CQL 3 as well as some tried and true methods. In particular, we will look at fast indexing techniques to get data faster at scale. You'll be jet setting through your data like a true super modeler in no time.
Speaker: Patrick McFadin, Principal Solutions Architect at DataStax
Time series with Apache Cassandra - Long versionPatrick McFadin
Apache Cassandra has proven to be one of the best solutions for storing and retrieving time series data. This talk will give you an overview of the many ways you can be successful. We will discuss how the storage model of Cassandra is well suited for this pattern and go over examples of how best to build data models.
Cassandra Community Webinar: Back to Basics with CQL3DataStax
Cassandra is a distributed, massively scalable, fault tolerant, columnar data store, and if you need the ability to make fast writes, the only thing faster than Cassandra is /dev/null! In this fast-paced presentation, we'll briefly describe big data, and the area of big data that Cassandra is designed to fill. We will cover Cassandra's unique, every-node-the-same architecture. We will reveal Cassandra's internal data structure and explain just why Cassandra is so darned fast. Finally, we'll wrap up with a discussion of data modeling using the new standard protocol: CQL (Cassandra Query Language).
A Cassandra + Solr + Spark Love Triangle Using DataStax EnterprisePatrick McFadin
Wait! Back away from the Cassandra 2ndary index. It’s ok for some use cases, but it’s not an easy button. "But I need to search through a bunch of columns to look for the data and I want to do some regression analysis… and I can’t model that in C*, even after watching all of Patrick McFadins videos. What do I do?” The answer, dear developer, is in DSE Search and Analytics. With it’s easy Solr API and Spark integration so you can search and analyze data stored in your Cassandra database until your heart’s content. Take our hand. WE will show you how.
Cassandra Day Chicago 2015: Advanced Data ModelingDataStax Academy
Speaker(s): Tim Berglund, Global Director of Training at DataStax
You know you need Cassandra for its uptime and scaling, but what about that data model? Let's bridge that gap and get you building your game changing app. We'll break down topics like storing objects and indexing for fast retrieval. You will see by understanding a few things about Cassandra internals, you can put your data model in the spotlight. The goal of this talk is to get you comfortable working with data in Cassandra throughout the application lifecycle. What are you waiting for?
Building Antifragile Applications with Apache CassandraPatrick McFadin
Even with the best infrastructure, failures will occur without warning and are almost guaranteed. Building applications that can resist this fact of life can be both art and science. In this talk, I'll try to eliminate the art portion and focus more on the science. Starting at high level architecture decisions, I will take you through each layer and finally down to actual application code. Using Cassandra as the back end database, we can build layers of fault tolerance that will leave end users completely unaware of the underlying chaos that could be occurring. With a little planning, we can say goodbye to the Fail Whale and the fragility of the traditional RDBMS. Topics will include:
- Application strategies to utilize active-active, diverse, datacenters
- Replicating data with the highest integrity and maximum resilience
- Utilizing Cassandra's built-in fault tolerance
- Architecture of private, cloud or hybrid based applications
- Application driver techniques when using Cassandra
A 30 minute talk I did at Cassandra Dublin and Cassandra London. Just some things I've learned along the way as I've helped some of the largest users of Cassandra be successful. Learn form other peoples mistakes!
Owning time series with team apache Strata San Jose 2015Patrick McFadin
Break out your laptops for this hands-on tutorial is geared around understanding the basics of how Apache Cassandra stores and access time series data. We’ll start with an overview of how Cassandra works and how that can be a perfect fit for time series. Then we will add in Apache Spark as a perfect analytics companion. There will be coding as a part of the hands on tutorial. The goal will be to take a example application and code through the different aspects of working with this unique data pattern. The final section will cover the building of an end-to-end data pipeline to ingest, process and store high speed, time series data.
Datadog: a Real-Time Metrics Database for One Quadrillion Points/DayC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2mAKgJi.
Ian Nowland and Joel Barciauskas talk about the challenges Datadog faces as the company has grown its real-time metrics systems that collect, process, and visualize data to the point they now handle trillions of points per day. They also talk about how the architecture has evolved, and what they are looking to in the future as they architect for a quadrillion points per day. Filmed at qconnewyork.com.
Ian Nowland is the VP Engineering Metrics and Alerting at Datadog. Joel Barciauskas currently leads Datadog's distribution metrics team, providing accurate, low latency percentile measures for customers across their infrastructure.
"But It Worked In Development!" - 3 Hard SQL Server ProblemsBrent Ozar
Warning: this is not an introductory session. These are going to be tough problems.
You've been performance tuning queries and indexes for a few years, but lately, you've been running into problems you can't explain. Could it be RESOURCE_SEMAPHORE, THREADPOOL, or lock escalation? These problems only pop up under heavy load or concurrency, so they're very hard to detect in a development environment.
In a very fast-paced session, I'll show these three performance problems pop up under load. I won't be able to teach you how to fix them for good - not inside the span of 75 minutes - but at least you'll be able to recognize the symptoms when they strike, and I'll show you where to go to learn more.
Rod Anderson
For the small business support person being able to provide PostgreSQL hosting for a small set of specific applications without having to build and support several Pg installations is necessary. By building a multi-tenant Pg cluster with one tenant per database and each application in it's own schema maintenance and support is much simpler. The issues that present themselves are how to provide and control dba and user access to the database and get the applications into their own schema. With this comes need to make logging in to the database (pg_hba.conf) as non-complex as possible.
Debugging Skynet: A Machine Learning Approach to Log Analysis - Ianir Ideses,...DevOpsDays Tel Aviv
This talk will introduce a machine-learning system for classifying log data that is built for high scalability and Big Data processing. The system uses state of-the-art ML algorithms such as SVMs and Deep Learning principles to classify log events. The training is based on user-behavior patterns and crowdsourcing knowledge bases. The results indicate the possibility of harnessing ML in DevOps.
What We Learned About Cassandra While Building go90 (Christopher Webster & Th...DataStax
Go90 is a mobile entertainment platform offering access to live and on demand videos. We built the web services platform and social features like activity feed for go90 by making heavy use of Cassandra and Scala, and would like to share what we learned during development and while operating go90. In this presentation, we cover our data model evolution from the initial prototypes to the current production version and the significant performance gain by using a better data model. We will explain how we apply time series data modeling and the benefits of using expiring columns with DateTieredCompactionStrategy. We will also talk about interesting experiences related to table modifications, tombstones and table pagination. On the operations side, we will discuss our findings on java driver usage, performance, monitoring, cluster maintenance, version upgrade, 2-way ssl and many more. We hope you can learn from our mistakes instead of making them yourself!
About the Speakers
Christopher Webster Software Engineer, AOL
Christopher Webster works on the web services platform for the go90 AOL project. Previously he was a Computer Scientist for the Mission Control Technologies project at NASA Ames Center. Chris worked as a senior staff engineer at Sun Microsystems for Project zembly, the cloud development and deployment environment as well as technical lead in many NetBeans projects. Chris is an author of the NetBeans Field Guide and Assemble the Social Web With Zembly.
Thomas Ng Software Engineer, AOL
Thomas Ng is a software engineer at AOL, building web services for the go90 mobile entertainment platform using Cassandra, Scala and Kafka.
Managing large volumes of data isn’t trivial and needs a plan. Fast Data is how we describe the nature of data in a heavily consumer-driven world. Fast in. Fast out. Is your data infrastructure ready? You will learn some important reference architectures for large-scale data problems. The three main areas are covered:
Organize - Manage the incoming data stream and ensure it is processed correctly and on time. No data left behind.
Process - Analyze volumes of data you receive in near real-time or in a batch. Be ready for fast serving in your application.
Store - Reliably store data in the data models to support your application. Never accept downtime or slow response times.
If you’re involved in open source work in or around a business, you will inevitably have the discussion, “Is this open source or proprietary?” Do not take this moment lightly. This seemingly easy question is met with strong opinions on both sides. Friendships have been lost. Companies have suffered. It’s as close to religious warfare as we can get in the tech world.
It’s time to call a truce.
There are plenty of valid arguments on both sides. Patrick McFadin outlines the pros and cons of each. Using example scenarios of projects that must decide whether or not they’ll be open source, Patrick explores objective ways to make a decision without descending into chaos and name calling. Even without a completely objective picture, understanding both sides of the argument can help keep you on track and civil. Patrick has been involved in OSS for more years than he likes to admit and would love for his past mistakes to benefit you.
Topics include:
- Key questions to ask to help guide your decision
- Reasons for choosing OSS
- Reasons for staying strictly proprietary
- Considerations for mixing OSS and proprietary models
- Transitioning from one model to the other
An Introduction to time series with Team ApachePatrick McFadin
We as an industry are collecting more data every year. IoT, web, and mobile applications send torrents of bits to our data centers that have to be processed and stored, even as users expect an always-on experience—leaving little room for error. Patrick McFadin explores how successful companies do this every day using the powerful Team Apache: Apache Kafka, Spark, and Cassandra.
Patrick walks you through organizing a stream of data into an efficient queue using Apache Kafka, processing the data in flight using Apache Spark Streaming, storing the data in a highly scaling and fault-tolerant database using Apache Cassandra, and transforming and finding insights in volumes of stored data using Apache Spark.
Topics include:
- Understanding the right use case
- Considerations when deploying Apache Kafka
- Processing streams with Apache Spark Streaming
- A deep dive into how Apache Cassandra stores data
- Integration between Cassandra and Spark
- Data models for time series
- Postprocessing without ETL using Apache Spark on Cassandra
You’ve heard all of the hype, but how can SMACK work for you? In this all-star lineup, you will learn how to create a reactive, scaling, resilient and performant data processing powerhouse. We will go through the basics of Akka, Kafka and Mesos and then deep dive into putting them together in an end2end (and back again) distrubuted transaction. Distributed transactions mean producers waiting for one or more of consumers to respond. On the backend, you will see how Apache Cassandra and Spark can be combined to add the incredibly scaling storage and data analysis needed for fast data pipelines. With these technologies as a foundation, you have the assurance that scale is never a problem and uptime is default.
Help! I want to contribute to an Open Source project but my boss says no.Patrick McFadin
You love using Open Source Software. It's done right by you and now you want to contribute back. You get your patch all ready and… the boss says no! Don't feel alone. Enterprises everywhere are trying to figure this out. I'll walk you through what actually risks exist to businesses and how you can help manage them. Maybe armed with some information your boss will say... yes!
Analyzing Time Series Data with Apache Spark and CassandraPatrick McFadin
You have collected a lot of time series data so now what? It's not going to be useful unless you can analyze what you have. Apache Spark has become the heir apparent to Map Reduce but did you know you don't need Hadoop? Apache Cassandra is a great data source for Spark jobs! Let me show you how it works, how to get useful information and the best part, storing analyzed data back into Cassandra. That's right. Kiss your ETL jobs goodbye and let's get to analyzing. This is going to be an action packed hour of theory, code and examples so caffeine up and let's go.
Apache cassandra and spark. you got the the lighter, let's start the firePatrick McFadin
Introduction to analyzing Apache Cassandra data using Apache Spark. This includes data models, operations topics and the internal on how Spark interfaces with Cassandra.
Nike Tech Talk: Double Down on Apache Cassandra and SparkPatrick McFadin
Apache Cassandra has proven to be one of the best solutions for storing and retrieving time series data at high velocity and high volume. This talk will give you an overview of the many ways you can be successful by introducing Apache Cassandra concepts. We will discuss how the storage model of Cassandra is well suited for this pattern and go over examples of how best to build data models. There will also be examples of how you can use Apache Spark along with Apache Cassandra to create a real time data analytics platform. It’s so easy, you will be shocked and ready to try it yourself.
Apache Cassandra is a popular choice for a wide variety of application persistence needs. There are many design choices that can effect uptime and performance. In this talk we'll look at some of the many things to consider from a single server to multiple data centers. Basic understanding of Cassandra features coupled with client driver features can be a very powerful combination. This talk will be an introduction but will deep dive into the technical details of how Cassandra works.
Making money with open source and not losing your soul: A practical guidePatrick McFadin
We now live in a world where Open Source Software is as generally accepted as any commercial software. This doesn’t mean that there are lack of commercial aspects for OSS, because I’m here to tell you, Open Source is a perfectly viable business model. Don't worry! You don't have to sell your soul to the suits on Wall Street and give up on the core values of open source to make it work. I'm employed by a company that (hopefully) embodies these values with a lot of success. I’ve also interviewed many business leaders in Open Source companies. Let me share some of what I’ve learned so you too can be successful. The topics I will be covering:
- Picking the right open source license
- Business models for monetizing open source
- Engaging the community in a mutually beneficial way
- Competing with commercial alternatives
- The selling process (yes, we have to talk about that)
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.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
4. SELECT * FROM users
WHERE username = ’jbellis’
[empty resultset]
Session 1
SELECT * FROM users
WHERE username = ’jbellis’
[empty resultset]
Session 2
Lightweight transactions: the problem
INSERT INTO users
(username,password)
VALUES (’jbellis’,‘xdg44hh’)
INSERT INTO users
(userName,password)
VALUES (’jbellis’,‘8dhh43k’)
It’s a Race!
Who wins?
Thursday, October 3, 13
9. Paxos
• Consensus algorithm
• All operations are quorum-based
• Each replica sends information about unfinished operations to the leader
during prepare
• Paxos made Simple
Thursday, October 3, 13
10. LWT: details
• 4 round trips vs 1 for normal updates
• Paxos state is durable
• Immediate consistency with no leader election or failover
• ConsistencyLevel.SERIAL
• http://www.datastax.com/dev/blog/lightweight-transactions-in-
cassandra-2-0
Thursday, October 3, 13
11. LWT: Use with caution
• Great for 1% of your application
• Eventual consistency is your friend
• http://www.slideshare.net/planetcassandra/c-summit-2013-eventual-consistency-
hopeful-consistency-by-christos-kalantzis
Thursday, October 3, 13
12. UPDATE USERS
SET email = ’jonathan@datastax.com’, ...
WHERE username = ’jbellis’
IF email = ’jbellis@datastax.com’;
INSERT INTO USERS (username, email, ...)
VALUES (‘jbellis’, ‘jbellis@datastax.com’, ... )
IF NOT EXISTS;
Using LWT
• Don’t overwrite an existing record
• Only update record if condition is met
Thursday, October 3, 13
13. Triggers
CREATE TRIGGER <name> ON <table> USING <classname>;
DROP TRIGGER <name> ON [<keyspace>.]<table>;
• Executed on the coordinator before mutation
• Takes original mutation and adds any new
• Jars deployed per server
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14. Trigger implementation
class MyTrigger implements ITrigger
{
public Collection<RowMutation> augment(ByteBuffer key, ColumnFamily update)
{
...
}
}
• You have to implement your own ITrigger (for now)
• Compile and deploy to each server
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15. Experimental!
• Relies on internal RowMutation, ColumnFamily classes
• Not sandboxed. Be careful!
• Expect changes in 2.1
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16. CQL Improvements
• ALTER DROP
• Remove a field from a CQL table.
• Conditional schema changes
• Only execute if condition met
CREATE KEYSPACE IF NOT EXISTS ks
WITH replication = { 'class': 'SimpleStrategy','replication_factor' :
3 };
CREATE TABLE IF NOT EXISTS test (k int PRIMARY KEY);
DROP KEYSPACE IF EXISTS ks;
ALTER TABLE users DROP address3;
Thursday, October 3, 13
17. CQL Improvements
• Aliases in SELECT
• Limit and TTL in prepared statements
SELECT event_id, dateOf(created_at) AS creation_date,
blobAsText(content) AS content
FROM timeline;
event_id | creation_date | content
-------------------------+--------------------------+----------------------
550e8400-e29b-41d4-a716 | 2013-07-26 10:44:33+0200 | Something happened!?
SELECT * FROM myTable LIMIT ?;
UPDATE myTable USING TTL ? SET v = 2 WHERE k = 'foo';
Thursday, October 3, 13
19. Query performance
• Hint when reading time series data
• Time series slices find data faster
• Hybrid approach to Leveled Compaction under stress
• Use size tiered until we catch up
• Reduce read latency impact
• Off-heap memory speedup
• Bytes moved on and off 10x faster
• Removal of row-level bloom filters
Thursday, October 3, 13
20. Server performance
• Single pass compaction
• No more incremental compaction for large storage rows
• LMAX Disruptor on Thrift interface
• Crazy fast and efficient concurrent threads. Faster HSHA
• Support for pluggable off-heap memory allocators
• JEMalloc support to start. Faster memory access.
• Bigger Level 0 file size
• 5M was just too small. Now 160M
Thursday, October 3, 13
21. Removed features
• SuperColumns are gone!
• Not the API just the underlying implementation
• On-heap row cache
• Row cache is no longer an option in the JVM
• Memory pressure relief valves - Gone from yaml
• flush_largest_memtables_at
• reduce_cache_sizes_at
• reduce_cache_sizes_to
Thursday, October 3, 13
22. Operation Changes
• JDK 7 now required
• Vnodes are default
• Streaming overhaul
• Control. Streams are grouped and broken into plans
• Traceability. Each stream has an ID. Monitor each stream.
• Performance. Streams are now pipelined. No waiting for ACK
Thursday, October 3, 13
23. Thank you!
Apache Cassandra 2.0 - Data model on fire
Next talk in my data model series!
Thursday, October 3, 13