This document summarizes how the Spark Cassandra Connector works to read and write data between Spark and Cassandra in a distributed manner. It discusses how the connector partitions Spark RDDs based on Cassandra token ranges and nodes, retrieves data from Cassandra in batches using CQL, and writes data back to Cassandra in batches grouped by partition key. Key classes and configuration parameters that control this distributed processing are also outlined.
.NET developers have a lot of options when it comes to databases these days. Apache Cassandra is a scalable, fault-tolerant database that has already found its way into more than 25% of the Fortune 100 and continues to grow in popularity. But what makes it different from the myriad of other options available? In this talk, we’ll take a deep dive into Cassandra and learn about:
- Cassandra’s internals and how it works
- CQL (the SQL-like query language for Cassandra)
- Data Modeling like a pro
- Tools available for developers
- Writing .NET code that talks to Cassandra
If there’s time and interest, we’ll finish up with how some companies are already using Cassandra to power services you probably interact with in your daily life. You’ll leave with all the tools you need to start build highly available .NET applications and services on top of Cassandra.
Cassandra Day NY 2014: Getting Started with the DataStax C# DriverDataStax Academy
So you’ve grabbed the latest 2.0 beta of DataStax C# driver from NuGet. Now what? In this talk, Luke will walk you through some of the basics of the C# driver--how to bootstrap the driver and connect to a cluster, execute statements, and retrieve result sets. Wondering what the difference between a PreparedStatement and a SimpleStatement is? Not sure what the appropriate lifetime for a Cluster or a Session object is and whether you should reuse one (from multiple threads)? What about ADO.NET and LINQ support? We’ll cover this and more, so that you can get on with building applications on top of Cassandra and .NET.
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.
Operations, Consistency, Failover for Multi-DC Clusters (Alexander Dejanovski...DataStax
Cassandra's support for multiple data centers can bring massive benefits to an organization, however it can also bring painful operational lessons. While there is no recipe for trouble free mutli DC clusters, the best approach is to understand why you are using one, what Cassandra supports, and how it does it. With this knowledge in your toolkit you will have a better chance of fixing the sort of gremlins that can trouble a globally distributed database.
In this talk Alexander Dejanovski, Consultant at The Last Pickle, will outline the motivations people typically have for running a multi DC cluster. He will also look at how multiple DC's are supported through all areas of the Cassandra, how it impacts your application and operations, and how you can always blame the network.
About the Speaker
Alexander DEJANOVSKI Consultant, The Last Pickle
Alexander has been working as a software developer for the last 18 years, mainly for the french leader of express shipments. He's been leading there the effort to build a Cassandra based architecture and migrate services to it from traditional RDBMS. He is involved in the Cassandra community through the development of a JDBC wrapper for the DataStax Java Driver. Recently, he joined The Last Pickle as a Cassandra consultant and now helps customers to get the best out of it.
.NET developers have a lot of options when it comes to databases these days. Apache Cassandra is a scalable, fault-tolerant database that has already found its way into more than 25% of the Fortune 100 and continues to grow in popularity. But what makes it different from the myriad of other options available? In this talk, we’ll take a deep dive into Cassandra and learn about:
- Cassandra’s internals and how it works
- CQL (the SQL-like query language for Cassandra)
- Data Modeling like a pro
- Tools available for developers
- Writing .NET code that talks to Cassandra
If there’s time and interest, we’ll finish up with how some companies are already using Cassandra to power services you probably interact with in your daily life. You’ll leave with all the tools you need to start build highly available .NET applications and services on top of Cassandra.
Cassandra Day NY 2014: Getting Started with the DataStax C# DriverDataStax Academy
So you’ve grabbed the latest 2.0 beta of DataStax C# driver from NuGet. Now what? In this talk, Luke will walk you through some of the basics of the C# driver--how to bootstrap the driver and connect to a cluster, execute statements, and retrieve result sets. Wondering what the difference between a PreparedStatement and a SimpleStatement is? Not sure what the appropriate lifetime for a Cluster or a Session object is and whether you should reuse one (from multiple threads)? What about ADO.NET and LINQ support? We’ll cover this and more, so that you can get on with building applications on top of Cassandra and .NET.
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.
Operations, Consistency, Failover for Multi-DC Clusters (Alexander Dejanovski...DataStax
Cassandra's support for multiple data centers can bring massive benefits to an organization, however it can also bring painful operational lessons. While there is no recipe for trouble free mutli DC clusters, the best approach is to understand why you are using one, what Cassandra supports, and how it does it. With this knowledge in your toolkit you will have a better chance of fixing the sort of gremlins that can trouble a globally distributed database.
In this talk Alexander Dejanovski, Consultant at The Last Pickle, will outline the motivations people typically have for running a multi DC cluster. He will also look at how multiple DC's are supported through all areas of the Cassandra, how it impacts your application and operations, and how you can always blame the network.
About the Speaker
Alexander DEJANOVSKI Consultant, The Last Pickle
Alexander has been working as a software developer for the last 18 years, mainly for the french leader of express shipments. He's been leading there the effort to build a Cassandra based architecture and migrate services to it from traditional RDBMS. He is involved in the Cassandra community through the development of a JDBC wrapper for the DataStax Java Driver. Recently, he joined The Last Pickle as a Cassandra consultant and now helps customers to get the best out of it.
Optimizing Your Cluster with Coordinator Nodes (Eric Lubow, SimpleReach) | Ca...DataStax
With the addition of vnodes (Virtual Nodes), Cassandra users were able to gain a few benefits as a result of streaming when it came to bootstrapping and decommissioning nodes. On the flip side, having to route requests on larger clusters became a lot more intensive of a workload for all nodes that were then forced to act coordinator nodes. By setting up a tier of proxy nodes, we were able to have our cluster of 50 nodes perform with a 300% improvement on average in a mixed workload environment. This is an explanation of what we did, how we did it, and why it works.
About the Speaker
Eric Lubow CTO, SimpleReach
Eric Lubow is CTO of SimpleReach, where he builds highly-scalable distributed systems for processing analytics data. Eric is also a DataStax MVP for Cassandra, and co-author of Practical Cassandra. In his spare time, Eric is a skydiver, motorcycle rider, mixed martial artist, and dog dad.
Back-Pressure in Action: Handling High-Burst Workloads with Akka Streams & Ka...Reactivesummit
Akka Streams and its amazing handling of stream back-pressure should be no surprise to anyone. But it takes a couple of use cases to really see it in action - especially use cases where the amount of work increases as you process make you really value the back-pressure.
This talk takes a sample web crawler use case where each processing pass expands to a larger and larger workload to process, and discusses how we use the buffering capabilities in Kafka and the back-pressure with asynchronous processing in Akka Streams to handle such bursts.
In addition, we will also provide some constructive “rants” about the architectural components, the maturity, or immaturity you’ll expect, and tidbits and open source goodies like memory-mapped stream buffers that can be helpful in other Akka Streams and/or Kafka use cases.
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
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?
Paolo Alvarado Customer Support Engineer, Fastly at Altitude 2016
Customer Support Engineer Paolo Alvarado discusses various useful features of advanced Varnish Configuration Language (VCL).
Stupid Boot Tricks: using ipxe and chef to get to boot management blissmacslide
In this talk I will cover how I built a boot system using ipxe and chef's api to create a lightweight tool for managing install and firmware updating of hosts and network gear.
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.
The Best and Worst of Cassandra-stress Tool (Christopher Batey, The Last Pick...DataStax
Making sure your Data Model will work on the production cluster after 6 months as well as it does on your laptop is an important skill. It's one that we use every day with our clients at The Last Pickle, and one that relies on tools like the cassandra-stress. Knowing how the data model will perform under stress once it has been loaded with data can prevent expensive re-writes late in the project.
In this talk Christopher Batey, Consultant at The Last Pickle, will shed some light on how to use the cassandra-stress tool to test your own schema, graph the results and even how to extend the tool for your own use cases. While this may be called premature optimisation for a RDBS, a successful Cassandra project depends on it's data model.
About the Speaker
Christopher Batey Consultant / Software Engineer, The Last Pickle
Christopher (@chbatey) is a part time consultant at The Last Pickle where he works with clients to help them succeed with Apache Cassandra as well as a freelance software engineer working in London. Likes: Scala, Haskell, Java, the JVM, Akka, distributed databases, XP, TDD, Pairing. Hates: Untested software, code ownership. You can checkout his blog at: http://www.batey.info
Caching the Uncacheable: Leveraging Your CDN to Cache Dynamic ContentFastly
June 25, 2014. Hooman Beheshti, VP Technology at Fastly, discusses how using a real-time, modern CDN that provides instant cache invalidation and real-time analytics allows for instantaneous control over dynamic content caching. In this session, he looks at the challenges CDNs face with dynamic content and how you can use programmatic means to fully integrate your applications with your CDN.
Learn from Fastly veteran Cassandra Dixon on some of the most common customer issues we see — such as why things aren’t caching, misconfigured origins, issues with intermediary proxies, and VCL snafus — and the best ways to resolve them. We’ll also discuss our unique approach to debugging — using seemingly mundane tools to diagnose issues in creative ways — and how you can apply these methods to your own organization to get the most out of Fastly’s offerings.
Optimizing Your Cluster with Coordinator Nodes (Eric Lubow, SimpleReach) | Ca...DataStax
With the addition of vnodes (Virtual Nodes), Cassandra users were able to gain a few benefits as a result of streaming when it came to bootstrapping and decommissioning nodes. On the flip side, having to route requests on larger clusters became a lot more intensive of a workload for all nodes that were then forced to act coordinator nodes. By setting up a tier of proxy nodes, we were able to have our cluster of 50 nodes perform with a 300% improvement on average in a mixed workload environment. This is an explanation of what we did, how we did it, and why it works.
About the Speaker
Eric Lubow CTO, SimpleReach
Eric Lubow is CTO of SimpleReach, where he builds highly-scalable distributed systems for processing analytics data. Eric is also a DataStax MVP for Cassandra, and co-author of Practical Cassandra. In his spare time, Eric is a skydiver, motorcycle rider, mixed martial artist, and dog dad.
Back-Pressure in Action: Handling High-Burst Workloads with Akka Streams & Ka...Reactivesummit
Akka Streams and its amazing handling of stream back-pressure should be no surprise to anyone. But it takes a couple of use cases to really see it in action - especially use cases where the amount of work increases as you process make you really value the back-pressure.
This talk takes a sample web crawler use case where each processing pass expands to a larger and larger workload to process, and discusses how we use the buffering capabilities in Kafka and the back-pressure with asynchronous processing in Akka Streams to handle such bursts.
In addition, we will also provide some constructive “rants” about the architectural components, the maturity, or immaturity you’ll expect, and tidbits and open source goodies like memory-mapped stream buffers that can be helpful in other Akka Streams and/or Kafka use cases.
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
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?
Paolo Alvarado Customer Support Engineer, Fastly at Altitude 2016
Customer Support Engineer Paolo Alvarado discusses various useful features of advanced Varnish Configuration Language (VCL).
Stupid Boot Tricks: using ipxe and chef to get to boot management blissmacslide
In this talk I will cover how I built a boot system using ipxe and chef's api to create a lightweight tool for managing install and firmware updating of hosts and network gear.
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.
The Best and Worst of Cassandra-stress Tool (Christopher Batey, The Last Pick...DataStax
Making sure your Data Model will work on the production cluster after 6 months as well as it does on your laptop is an important skill. It's one that we use every day with our clients at The Last Pickle, and one that relies on tools like the cassandra-stress. Knowing how the data model will perform under stress once it has been loaded with data can prevent expensive re-writes late in the project.
In this talk Christopher Batey, Consultant at The Last Pickle, will shed some light on how to use the cassandra-stress tool to test your own schema, graph the results and even how to extend the tool for your own use cases. While this may be called premature optimisation for a RDBS, a successful Cassandra project depends on it's data model.
About the Speaker
Christopher Batey Consultant / Software Engineer, The Last Pickle
Christopher (@chbatey) is a part time consultant at The Last Pickle where he works with clients to help them succeed with Apache Cassandra as well as a freelance software engineer working in London. Likes: Scala, Haskell, Java, the JVM, Akka, distributed databases, XP, TDD, Pairing. Hates: Untested software, code ownership. You can checkout his blog at: http://www.batey.info
Caching the Uncacheable: Leveraging Your CDN to Cache Dynamic ContentFastly
June 25, 2014. Hooman Beheshti, VP Technology at Fastly, discusses how using a real-time, modern CDN that provides instant cache invalidation and real-time analytics allows for instantaneous control over dynamic content caching. In this session, he looks at the challenges CDNs face with dynamic content and how you can use programmatic means to fully integrate your applications with your CDN.
Learn from Fastly veteran Cassandra Dixon on some of the most common customer issues we see — such as why things aren’t caching, misconfigured origins, issues with intermediary proxies, and VCL snafus — and the best ways to resolve them. We’ll also discuss our unique approach to debugging — using seemingly mundane tools to diagnose issues in creative ways — and how you can apply these methods to your own organization to get the most out of Fastly’s offerings.
This presentation will walk through some of the key considerations for planning and running load test to ensure your Cassandra application will meet you expected scaling requirements. We will also walk through some examples of using the cassandra-stress tool to construct load test for real-life application scenarios.
About the Speaker
Ben Slater Chief Product Officer, Instaclustr
Instaclustr provides Cassandra and Spark as a managed service in the cloud. As Chief Product Officer, Ben is charged with steering Instaclustr's development roadmap, managing product engineering and overseeing the production support and consulting teams. Ben has over 20 years experience in systems development including previously as lead architect for the product that is now Oracle Policy Automation and over 10 years as a solution architect and project manager for Accenture.
Spark and Cassandra with the Datastax Spark Cassandra Connector
How it works and how to use it!
Missed Spark Summit but Still want to see some slides?
This slide deck is for you!
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.
Building a High-Performance Database with Scala, Akka, and SparkEvan Chan
Here is my talk at Scala by the Bay 2016, Building a High-Performance Database with Scala, Akka, and Spark. Covers integration of Akka and Spark, when to use actors and futures, back pressure, reactive monitoring with Kamon, and more.
Beyond the Query – Bringing Complex Access Patterns to NoSQL with DataStax - ...StampedeCon
Learn how to model beyond traditional direct access in Apache Cassandra. Utilizing the DataStax platform to harness the power of Spark and Solr to perform search, analytics, and complex operations in place on your Cassandra data!
Beyond the Query: A Cassandra + Solr + Spark Love Triangle Using Datastax Ent...DataStax Academy
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.
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.
Analyzing Time-Series Data with Apache Spark and Cassandra - StampedeCon 2016StampedeCon
Have you ever wanted to analyze sensor data that arrives every second from across the world? Or maybe your want to analyze intra-day trading prices of millions of financial instruments? Or take all the page views from Wikipedia and compare the hourly statistics? To do this or any other similar analysis, you will need to analyze large sequences of measurements over time. And what better way to do this then with Apache Spark? In this session we will dig into how to consume data, and analyze it with Spark, and then store the results in Apache Cassandra.
DataSource V2 and Cassandra – A Whole New WorldDatabricks
Data Source V2 has arrived for the Spark Cassandra Connector, but what does this mean for you? Speed, Flexibility and Usability improvements abound and we’ll walk you through some of the biggest highlights and how you can take advantage of them today.
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.
Delivering Meaning In Near-Real Time At High Velocity In Massive Scale with A...Helena Edelson
Streaming Big Data: Delivering Meaning In Near-Real Time At High Velocity At Massive Scale with Apache Spark, Apache Kafka, Apache Cassandra, Akka and the Spark Cassandra Connector. Why this pairing of technologies and How easy it is to implement. Example application: https://github.com/killrweather/killrweather
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
In the ever-evolving landscape of technology, enterprise software development is undergoing a significant transformation. Traditional coding methods are being challenged by innovative no-code solutions, which promise to streamline and democratize the software development process.
This shift is particularly impactful for enterprises, which require robust, scalable, and efficient software to manage their operations. In this article, we will explore the various facets of enterprise software development with no-code solutions, examining their benefits, challenges, and the future potential they hold.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Top 7 Unique WhatsApp API Benefits | Saudi ArabiaYara Milbes
Discover the transformative power of the WhatsApp API in our latest SlideShare presentation, "Top 7 Unique WhatsApp API Benefits." In today's fast-paced digital era, effective communication is crucial for both personal and professional success. Whether you're a small business looking to enhance customer interactions or an individual seeking seamless communication with loved ones, the WhatsApp API offers robust capabilities that can significantly elevate your experience.
In this presentation, we delve into the top 7 distinctive benefits of the WhatsApp API, provided by the leading WhatsApp API service provider in Saudi Arabia. Learn how to streamline customer support, automate notifications, leverage rich media messaging, run scalable marketing campaigns, integrate secure payments, synchronize with CRM systems, and ensure enhanced security and privacy.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
2. @chbatey
Who am I?
• Maintainer of Stubbed Cassandra
• Other OS projects: akka-persistence,
wiremock
• Advocate for Apache Cassandra
• Part time consultant
3. @chbatey
Agenda
• Why - running Spark + C*
• How - Spark partitions are built up
• Example - KillrWeather
13. @chbatey
RDD Operations
• Transformations - Similar to Scala collections API
• Produce new RDDs
• filter, flatmap, map, distinct, groupBy, union, zip, reduceByKey, subtract
• Actions
• Require materialization of the records to generate a value
• collect: Array[T], count, fold, reduce..
14. Spark RDDs
Represent a Large
Amount of Data
Partitioned into Chunks
RDD
1 2 3
4 5 6
7 8 9Worker 2
Worker 1 Worker 3
Worker 4
15. Worker 2
Worker 1
Spark RDDs
Represent a Large
Amount of Data
Partitioned into Chunks
RDD
2
346
7 8 9
Worker 3
Worker 4
1 5
19. Cassandra Data is Distributed By Token Range
0
500
Node 1
Node 2
Node 3
Node 4
20. Cassandra Data is Distributed By Token Range
0
500
Node 1
Node 2
Node 3
Node 4
Without vnodes
21. Cassandra Data is Distributed By Token Range
0
500
Node 1
Node 2
Node 3
Node 4
With vnodes
22. @chbatey
Replication strategy
• NetworkTopology
- Every Cassandra node knows its DC and Rack
- Replicas won’t be put on the same rack unless Replication Factor > # of racks
- Unfortunately Cassandra can’t create servers and racks on the fly to fix this :(
25. @chbatey
Goals
• Spark partitions made up of token ranges on the same
node
• Tasks to be executed on workers co-located with that
node
• Same(ish) amount of data in each Spark partition
41. 4
spark.cassandra.input.fetch.size_in_rows 50
Data is Retrieved Using the DataStax Java Driver
0-50
780-830
Node 1
SELECT * FROM keyspace.table WHERE
token(pk) > 780 and token(pk) <= 830
SELECT * FROM keyspace.table WHERE
token(pk) > 0 and token(pk) <= 50
42. 4
spark.cassandra.input.fetch.size_in_rows 50
Data is Retrieved Using the DataStax Java Driver
0-50
780-830
Node 1
SELECT * FROM keyspace.table WHERE
token(pk) > 780 and token(pk) <= 830
SELECT * FROM keyspace.table WHERE
token(pk) > 0 and token(pk) <= 50
43. 4
spark.cassandra.input.fetch.size_in_rows 50
Data is Retrieved Using the DataStax Java Driver
0-50
780-830
Node 1
SELECT * FROM keyspace.table WHERE
token(pk) > 780 and token(pk) <= 830
SELECT * FROM keyspace.table WHERE
token(pk) > 0 and token(pk) <= 50
50 CQL Rows
44. 4
spark.cassandra.input.fetch.size_in_rows 50
Data is Retrieved Using the DataStax Java Driver
0-50
780-830
Node 1
SELECT * FROM keyspace.table WHERE
token(pk) > 780 and token(pk) <= 830
SELECT * FROM keyspace.table WHERE
token(pk) > 0 and token(pk) <= 50
50 CQL Rows
45. 4
spark.cassandra.input.fetch.size_in_rows 50
Data is Retrieved Using the DataStax Java Driver
0-50
780-830
Node 1
SELECT * FROM keyspace.table WHERE
token(pk) > 780 and token(pk) <= 830
SELECT * FROM keyspace.table WHERE
token(pk) > 0 and token(pk) <= 50
50 CQL Rows
50 CQL Rows
46. 4
spark.cassandra.input.fetch.size_in_rows 50
Data is Retrieved Using the DataStax Java Driver
0-50
780-830
Node 1
SELECT * FROM keyspace.table WHERE
token(pk) > 780 and token(pk) <= 830
SELECT * FROM keyspace.table WHERE
token(pk) > 0 and token(pk) <= 50
50 CQL Rows50 CQL Rows
47. 4
spark.cassandra.input.fetch.size_in_rows 50
Data is Retrieved Using the DataStax Java Driver
0-50
780-830
Node 1
SELECT * FROM keyspace.table WHERE
token(pk) > 780 and token(pk) <= 830
SELECT * FROM keyspace.table WHERE
token(pk) > 0 and token(pk) <= 50
50 CQL Rows50 CQL Rows
50 CQL Rows
48. 4
spark.cassandra.input.fetch.size_in_rows 50
Data is Retrieved Using the DataStax Java Driver
0-50
780-830
Node 1
SELECT * FROM keyspace.table WHERE
token(pk) > 780 and token(pk) <= 830
SELECT * FROM keyspace.table WHERE
token(pk) > 0 and token(pk) <= 50
50 CQL Rows50 CQL Rows
50 CQL Rows
49. 4
spark.cassandra.input.fetch.size_in_rows 50
Data is Retrieved Using the DataStax Java Driver
0-50
780-830
Node 1
SELECT * FROM keyspace.table WHERE
token(pk) > 780 and token(pk) <= 830
SELECT * FROM keyspace.table WHERE
token(pk) > 0 and token(pk) <= 50
50 CQL Rows50 CQL Rows
50 CQL Rows
50 CQL Rows
50. 4
spark.cassandra.input.page.row.size 50
Data is Retrieved Using the DataStax Java Driver
0-50
780-830
Node 1
SELECT * FROM keyspace.table WHERE
token(pk) > 0 and token(pk) <= 50
50 CQL Rows50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
51. 4
spark.cassandra.input.page.row.size 50
Data is Retrieved Using the DataStax Java Driver
0-50
780-830
Node 1
SELECT * FROM keyspace.table WHERE
token(pk) > 0 and token(pk) <= 50
50 CQL Rows50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
52. 4
spark.cassandra.input.page.row.size 50
Data is Retrieved Using the DataStax Java Driver
0-50
780-830
Node 1
SELECT * FROM keyspace.table WHERE
token(pk) > 0 and token(pk) <= 50
50 CQL Rows50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
50 CQL Rows
55. @chbatey
Then we’re into Spark land
• Spark partitions are made up of C* partitions that exist
on the same node
• C* connector tells Spark which workers to use via
information from the C* driver
57. Node 2
Node 1
RDD
2
346
7 8 9
Node 3
Node 4
1 5
The Spark Cassandra
Connector saveToCassandra
method can be called on
almost all RDDs
rdd.saveToCassandra("Keyspace","Table")
80. Weather Station Analysis
• Weather station collects data
• Cassandra stores in sequence
• Spark rolls up data into new
tables
Windsor California
July 1, 2014
High: 73.4F
Low : 51.4F
81. raw_weather_data
CREATE TABLE raw_weather_data (
weather_station text, // Composite of Air Force Datsav3 station number and NCDC WBAN numbe
year int, // Year collected
month int, // Month collected
day int, // Day collected
hour int, // Hour collected
temperature double, // Air temperature (degrees Celsius)
dewpoint double, // Dew point temperature (degrees Celsius)
pressure double, // Sea level pressure (hectopascals)
wind_direction int, // Wind direction in degrees. 0-359
wind_speed double, // Wind speed (meters per second)
sky_condition int, // Total cloud cover (coded, see format documentation)
sky_condition_text text, // Non-coded sky conditions
one_hour_precip double, // One-hour accumulated liquid precipitation (millimeters)
six_hour_precip double, // Six-hour accumulated liquid precipitation (millimeters)
PRIMARY KEY ((weather_station), year, month, day, hour)
) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC);
Reverses data in the storage engine.
89. Query patterns
• Range queries
• “Slice” operation on disk
SELECT weatherstation,hour,temperature
FROM raw_weather_data
WHERE weatherstation_id=‘10010:99999'
AND year = 2005 AND month = 12 AND day = 1
AND hour >= 7 AND hour <= 10;
Single seek on disk
2005:12:1:12
-5.4
2005:12:1:11
-4.9
2005:12:1:7
-5.6-5.1
2005:12:1:8
-4.9
2005:12:1:9
10010:99999
-5.3
2005:12:1:10
Partition key for locality
90. Query patterns
• Range queries
• “Slice” operation on disk
Programmers like this
Sorted by event_time
2005:12:1:7
-5.6
2005:12:1:8
-5.1
2005:12:1:9
-4.9
10010:99999
10010:99999
10010:99999
weather_station hour temperature
2005:12:1:10
-5.3
10010:99999
SELECT weatherstation,hour,temperature
FROM raw_weather_data
WHERE weatherstation_id=‘10010:99999'
AND year = 2005 AND month = 12 AND day = 1
AND hour >= 7 AND hour <= 10;
91. weather_station
CREATE TABLE weather_station (
id text PRIMARY KEY, // Composite of Air Force Datsav3 station number and NCDC WBAN number
name text, // Name of reporting station
country_code text, // 2 letter ISO Country ID
state_code text, // 2 letter state code for US stations
call_sign text, // International station call sign
lat double, // Latitude in decimal degrees
long double, // Longitude in decimal degrees
elevation double // Elevation in meters
);
Lookup table
92. daily_aggregate_temperature
CREATE TABLE daily_aggregate_temperature (
weather_station text,
year int,
month int,
day int,
high double,
low double,
mean double,
variance double,
stdev double,
PRIMARY KEY ((weather_station), year, month, day)
) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC);
SELECT high, low FROM daily_aggregate_temperature
WHERE weather_station='010010:99999'
AND year=2005 AND month=12 AND day=3;
high | low
------+------
1.8 | -1.5
93. daily_aggregate_precip
CREATE TABLE daily_aggregate_precip (
weather_station text,
year int,
month int,
day int,
precipitation counter,
PRIMARY KEY ((weather_station), year, month, day)
) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC);
SELECT precipitation FROM daily_aggregate_precip
WHERE weather_station='010010:99999'
AND year=2005 AND month=12 AND day>=1 AND day <= 7;
0
10
20
30
40
1 2 3 4 5 6 7
17
26
2
0
33
12
0
94. Weather Station Stream Analysis
• Weather station collects data
• Data processed in stream
• Data stored in Cassandra
Windsor California
Today
Rainfall total: 1.2cm
High: 73.4F
Low : 51.4F
95. Incoming data from Kafka
725030:14732,2008,01,01,00,5.0,-3.9,1020.4,270,4.6,2,0.0,0.0
98. @chbatey
Building an aggregate
CREATE TABLE daily_aggregate_precip (
weather_station text,
year int,
month int,
day int,
precipitation counter,
PRIMARY KEY ((weather_station), year, month, day)
) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC);
CQL Counter