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.
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
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 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.
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.
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.
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.
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
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 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.
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.
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.
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.
Escape from Hadoop: Ultra Fast Data Analysis with Spark & CassandraPiotr Kolaczkowski
We present the basic functionality of the official DataStax spark-cassandra connector - how to load cassandra tables as Spark RDDs and how to save Spark RDDs to Cassandra.
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.
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.
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.
C* Summit 2013: Real-time Analytics using Cassandra, Spark and Shark by Evan ...DataStax Academy
This session covers our experience with using the Spark and Shark frameworks for running real-time queries on top of Cassandra data.We will start by surveying the current Cassandra analytics landscape, including Hadoop and HIVE, and touch on the use of custom input formats to extract data from Cassandra. We will then dive into Spark and Shark, two memory-based cluster computing frameworks, and how they enable often dramatic improvements in query speed and productivity, over the standard solutions today.
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!
Maximum Overdrive: Tuning the Spark Cassandra Connector (Russell Spitzer, Dat...DataStax
Worried that you aren't taking full advantage of your Spark and Cassandra integration? Well worry no more! In this talk we'll take a deep dive into all of the available configuration options and see how they affect Cassandra and Spark performance. Concerned about throughput? Learn to adjust batching parameters and gain a boost in speed. Always running out of memory? We'll take a look at the various causes of OOM errors and how we can circumvent them. Want to take advantage of Cassandra's natural partitioning in Spark? Find out about the recent developments that let you perform shuffle-less joins on Cassandra-partitioned data! Come with your questions and problems and leave with answers and solutions!
About the Speaker
Russell Spitzer Software Engineer, DataStax
Russell Spitzer received a Ph.D in Bio-Informatics before finding his deep passion for distributed software. He found the perfect outlet for this passion at DataStax where he began on the Automation and Test Engineering team. He recently moved from finding bugs to making bugs as part of the Analytics team where he works on integration between Cassandra and Spark as well as other tools.
Lessons from Cassandra & Spark (Matthias Niehoff & Stephan Kepser, codecentri...DataStax
We built an application based on the principles of CQRS and Event Sourcing using Cassandra and Spark. During the project we encountered a number of challenges and problems with Cassandra and the Spark Connector.
In this talk we want to outline a few of those problems and our actions to solve them. While some problems are specific to CQRS and Event Sourcing applications most of them are use case independent.
About the Speakers
Matthias Niehoff IT-Consultant, codecentric AG
works as an IT-Consultant at codecentric AG in Germany. His focus is on big data & streaming applications with Apache Cassandra & Apache Spark. Yet he does not lose track of other tools in the area of big data. Matthias shares his experiences on conferences, meetups and usergroups.
Stephan Kepser Senior IT Consultant and Data Architect, codecentric AG
Dr. Stephan Kepser is an expert on cloud computing and big data. He wrote a couple of journal articles and blog posts on subjects of both fields. His interests reach from legal questions to questions of architecture and design of cloud computing and big data systems to technical details of NoSQL databases.
Cassandra Community Webinar: Apache Cassandra InternalsDataStax
Apache Cassandra solves many interesting problems to provide a scalable, distributed, fault tolerant database. Cluster wide operations track node membership, direct requests and implement consistency guarantees. At the node level, the Log Structured storage engine provides high performance reads and writes. All of this is implemented in a Java code base that has greatly matured over the past few years.
In this webinar Aaron Morton will step through read and write requests, automatic processes and manual maintenance tasks. He will also discuss the general approach to solving the problem and drill down to the code responsible for implementation.
Speaker: Aaron Morton, Apache Cassandra Committer
Aaron Morton is a Freelance Developer based in New Zealand, and a Committer on the Apache Cassandra project. In 2010 he gave up the RDBMS world for the scale and reliability of Cassandra. He now spends his time advancing the Cassandra project and helping others get the best out of it.
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.
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?
Feeding Cassandra with Spark-Streaming and KafkaDataStax Academy
In this session we will examine a sample application that simulates an IoT stream that is handled through Kafka, Spark Streaming, and into Cassandra. The session will discuss the implementation details including the Kafka design considerations, Spark Steaming functionality including working with windowing to achieve analytics and finally Cassandra Time series data model considerations. The example is based on OSS Kafka and Integrated Spark and Cassandra in DSE.
Webinar - How to Build Data Pipelines for Real-Time Applications with SMACK &...DataStax
Data is being collected more and more every year. Cloud applications, including IoT, web, and mobile send torrents of bits at our data centers that have to be processed and stored. In addition, users expect an always-on experience, with little room for error. Numerous companies are successfully doing this every day. In this webinar, you will learn about the convergence of complementary technologies: Spark, Mesos, Akka, Cassandra and Kafka (SMACK), how Apache Kafka can help you get your data under control and the critical role Kafka plays in your data pipeline.
Webinar recording: https://youtu.be/uwYlwLyv-1s
Webinar Q&A will be posted shortly.
Exactly-once Stream Processing with Kafka StreamsGuozhang Wang
I will present the recent additions to Kafka to achieve exactly-once semantics (0.11.0) within its Streams API for stream processing use cases. This is achieved by leveraging the underlying idempotent and transactional client features. The main focus will be the specific semantics that Kafka distributed transactions enable in Streams and the underlying mechanics to let Streams scale efficiently.
Escape from Hadoop: Ultra Fast Data Analysis with Spark & CassandraPiotr Kolaczkowski
We present the basic functionality of the official DataStax spark-cassandra connector - how to load cassandra tables as Spark RDDs and how to save Spark RDDs to Cassandra.
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.
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.
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.
C* Summit 2013: Real-time Analytics using Cassandra, Spark and Shark by Evan ...DataStax Academy
This session covers our experience with using the Spark and Shark frameworks for running real-time queries on top of Cassandra data.We will start by surveying the current Cassandra analytics landscape, including Hadoop and HIVE, and touch on the use of custom input formats to extract data from Cassandra. We will then dive into Spark and Shark, two memory-based cluster computing frameworks, and how they enable often dramatic improvements in query speed and productivity, over the standard solutions today.
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!
Maximum Overdrive: Tuning the Spark Cassandra Connector (Russell Spitzer, Dat...DataStax
Worried that you aren't taking full advantage of your Spark and Cassandra integration? Well worry no more! In this talk we'll take a deep dive into all of the available configuration options and see how they affect Cassandra and Spark performance. Concerned about throughput? Learn to adjust batching parameters and gain a boost in speed. Always running out of memory? We'll take a look at the various causes of OOM errors and how we can circumvent them. Want to take advantage of Cassandra's natural partitioning in Spark? Find out about the recent developments that let you perform shuffle-less joins on Cassandra-partitioned data! Come with your questions and problems and leave with answers and solutions!
About the Speaker
Russell Spitzer Software Engineer, DataStax
Russell Spitzer received a Ph.D in Bio-Informatics before finding his deep passion for distributed software. He found the perfect outlet for this passion at DataStax where he began on the Automation and Test Engineering team. He recently moved from finding bugs to making bugs as part of the Analytics team where he works on integration between Cassandra and Spark as well as other tools.
Lessons from Cassandra & Spark (Matthias Niehoff & Stephan Kepser, codecentri...DataStax
We built an application based on the principles of CQRS and Event Sourcing using Cassandra and Spark. During the project we encountered a number of challenges and problems with Cassandra and the Spark Connector.
In this talk we want to outline a few of those problems and our actions to solve them. While some problems are specific to CQRS and Event Sourcing applications most of them are use case independent.
About the Speakers
Matthias Niehoff IT-Consultant, codecentric AG
works as an IT-Consultant at codecentric AG in Germany. His focus is on big data & streaming applications with Apache Cassandra & Apache Spark. Yet he does not lose track of other tools in the area of big data. Matthias shares his experiences on conferences, meetups and usergroups.
Stephan Kepser Senior IT Consultant and Data Architect, codecentric AG
Dr. Stephan Kepser is an expert on cloud computing and big data. He wrote a couple of journal articles and blog posts on subjects of both fields. His interests reach from legal questions to questions of architecture and design of cloud computing and big data systems to technical details of NoSQL databases.
Cassandra Community Webinar: Apache Cassandra InternalsDataStax
Apache Cassandra solves many interesting problems to provide a scalable, distributed, fault tolerant database. Cluster wide operations track node membership, direct requests and implement consistency guarantees. At the node level, the Log Structured storage engine provides high performance reads and writes. All of this is implemented in a Java code base that has greatly matured over the past few years.
In this webinar Aaron Morton will step through read and write requests, automatic processes and manual maintenance tasks. He will also discuss the general approach to solving the problem and drill down to the code responsible for implementation.
Speaker: Aaron Morton, Apache Cassandra Committer
Aaron Morton is a Freelance Developer based in New Zealand, and a Committer on the Apache Cassandra project. In 2010 he gave up the RDBMS world for the scale and reliability of Cassandra. He now spends his time advancing the Cassandra project and helping others get the best out of it.
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.
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?
Feeding Cassandra with Spark-Streaming and KafkaDataStax Academy
In this session we will examine a sample application that simulates an IoT stream that is handled through Kafka, Spark Streaming, and into Cassandra. The session will discuss the implementation details including the Kafka design considerations, Spark Steaming functionality including working with windowing to achieve analytics and finally Cassandra Time series data model considerations. The example is based on OSS Kafka and Integrated Spark and Cassandra in DSE.
Webinar - How to Build Data Pipelines for Real-Time Applications with SMACK &...DataStax
Data is being collected more and more every year. Cloud applications, including IoT, web, and mobile send torrents of bits at our data centers that have to be processed and stored. In addition, users expect an always-on experience, with little room for error. Numerous companies are successfully doing this every day. In this webinar, you will learn about the convergence of complementary technologies: Spark, Mesos, Akka, Cassandra and Kafka (SMACK), how Apache Kafka can help you get your data under control and the critical role Kafka plays in your data pipeline.
Webinar recording: https://youtu.be/uwYlwLyv-1s
Webinar Q&A will be posted shortly.
Exactly-once Stream Processing with Kafka StreamsGuozhang Wang
I will present the recent additions to Kafka to achieve exactly-once semantics (0.11.0) within its Streams API for stream processing use cases. This is achieved by leveraging the underlying idempotent and transactional client features. The main focus will be the specific semantics that Kafka distributed transactions enable in Streams and the underlying mechanics to let Streams scale efficiently.
Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, ScalaHelena Edelson
Scala Days, Amsterdam, 2015: Lambda Architecture - Batch and Streaming with Spark, Cassandra, Kafka, Akka and Scala; Fault Tolerance, Data Pipelines, Data Flows, Data Locality, Akka Actors, Spark, Spark Cassandra Connector, Big Data, Asynchronous data flows. Time series data, KillrWeather, Scalable Infrastructure, Partition For Scale, Replicate For Resiliency, Parallelism
Isolation, Data Locality, Location Transparency
Lambda Architecture with Spark, Spark Streaming, Kafka, Cassandra, Akka and S...Helena Edelson
Regardless of the meaning we are searching for over our vast amounts of data, whether we are in science, finance, technology, energy, health care…, we all share the same problems that must be solved: How do we achieve that? What technologies best support the requirements? This talk is about how to leverage fast access to historical data with real time streaming data for predictive modeling for lambda architecture with Spark Streaming, Kafka, Cassandra, Akka and Scala. Efficient Stream Computation, Composable Data Pipelines, Data Locality, Cassandra data model and low latency, Kafka producers and HTTP endpoints as akka actors...
Real-Time Stream Processing with KSQL and Apache Kafkaconfluent
Real Time Stream Processing with KSQL and Kafka
David Peterson, Confluent APAC
APIdays Melbourne 2018
Unordered, unbounded and massive datasets are increasingly common in day-to-day business. Using this to your advantage is incredibly difficult with current system designs. We are stuck in a model where we can only take advantage of this *after* it has happened. Many times, this is too late to be useful in the enterprise.
KSQL is a streaming SQL engine for Apache Kafka. KSQL lowers the entry bar to the world of stream processing, providing a simple and completely interactive SQL interface for processing data in Kafka. KSQL (like Kafka) is open-source, distributed, scalable, and reliable.
A real time Kafka platform moves your data up the stack, closer to the heart of your business, allowing you to build scalable, mission-critical services by quickly deploying SQL-like queries in a severless pattern.
This talk will highlight key use cases for real time data, and stream processing with KSQL: Real time analytics, security and anomaly detection, real time ETL / data integration, Internet of Things, application development, and deploying Machine Learning models with KSQ.
Real time data and stream processing means that Kafka is just as important to the disrupted as it is to the disruptors.
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.
Streaming Design Patterns Using Alpakka Kafka Connector (Sean Glover, Lightbe...confluent
Do you ever feel that your stream processor gets in the way of expressing business requirements? Most processors are frameworks, which are highly opinionated in the design and implementation of apps. Performing Complex Event Processing invariably leads to calling out to other technologies, but what if that integration didn’t require an RPC call or could be modeled into your stream itself? This talk will explore how to build rich domain, low latency, back-pressured, and stateful streaming applications that require very little infrastructure, using Akka Streams and the Alpakka Kafka connector.
We will explore how Alpakka Kafka maps to Kafka features in order to provide a comprehensive understanding of how to build a robust streaming platform. We’ll explore transactional message delivery, defensive consumer group rebalancing, stateful stages, and state durability/persistence. Akka Streams is built on top of Akka, an asynchronous messaging-driven middleware toolkit that can be used to build Erlang-like Actor Systems in Java or Scala. It is used as a JVM library to facilitate common streaming semantics within an existing or standalone application. It’s different from other stream processors in several ways. It natively supports back-pressure flow control inside a single JVM instance or across distributed systems to help prevent overloading downstream infrastructure. It’s perfect for modeling Complex Event Processing with its easy integration into existing apps and Akka Actor systems. Also, unlike most acyclic stream processors, Akka Streams can support sophisticated pipelines, or Graphs, by allowing the user to model cycles (loops) when there’s a need.
Kappa Architecture on Apache Kafka and Querona: datamass.ioPiotr Czarnas
Kappa architecture for event processing using Apache Kafka and Querona for managing data, joining external data sources and empowering data science teams.
Using Akka Persistence to build a configuration datastoreAnargyros Kiourkos
Akka is a library implementing the actor model on the JVM. It enables the development of distributed, concurrent, fault-tolerant and scalable applications. Akka persistence enables actors to persist their internal state so that it can be recovered where an actor is restarted. We demonstrate the practical application of Akka persistence to build a configuration datastore using CQRS and Event Sourcing
WattGo: Analyses temps-réél de series temporelles avec Spark et Solr (Français)DataStax Academy
Since two years, embracing new challenges as Smart Grid technologies emerge and IoT world grows, WattGo engages utility customers with personalized Smart Energy Analytics revealing the value of raw energy data.
During this session, we will show you how we are able to handle massive dataflow and perform real-time analysis on smart meters and IoT devices data using Spark Streaming.
Then we will describe some key features of our infrastructure and how we designed reactive data processing pipeline on top of Cassandra using core functionalities like Cassandra Triggers and DSE field transformers.
In the end, we will explain why we decide to move from ElasticSearch to Solr leveraging full power of DSE.
Why is My Spark Job Failing? by Sandy Ryza of ClouderaJack Gudenkauf
Abstract:
You are not a bad person. But your Apache Spark job is failing. It is running out of memory. It is stalled. It is complaining that no executors have registered or spitting out "Filesystem closed" exceptions with lines upon lines of $anon$1's or being consumed by a swarm of locusts the likes of which have not been seen since Moses crossed the Red Sea. Or it's completing -- 20 times as slow as it should reasonably take. Why? In this talk, you'll learn the internals of Spark jobs, the root causes of such ailments, and tuning strategies for avoiding them.
Why is My Spark Job Failing? by Sandy Ryza of ClouderaData Con LA
Abstract:
You are not a bad person. But your Apache Spark job is failing. It is running out of memory. It is stalled. It is complaining that no executors have registered or spitting out "Filesystem closed" exceptions with lines upon lines of $anon$1's or being consumed by a swarm of locusts the likes of which have not been seen since Moses crossed the Red Sea. Or it's completing -- 20 times as slow as it should reasonably take. Why? In this talk, you'll learn the internals of Spark jobs, the root causes of such ailments, and tuning strategies for avoiding them.
Bio:
Sandy Ryza is a data scientist at Cloudera, an Apache Hadoop committer, and a Spark contributor. Sandy is also the co-author of Advanced Analytics with Spark.
Building large-scale analytics platform with Storm, Kafka and Cassandra - NYC...Alexey Kharlamov
At Integral, we process heavy volumes of click-stream traffic. 50K QPS of ad impressions at peak and close to 200K QPS of all browser calls. We build analytics on this streams of data. There are two applications which require quite significant computational effort: 'sessionization' and fraud detection.
Sessionization implies linking a series of requests from same browser into single record. There can be 5 or more total requests spread over 15-30 minutes which we need to link to each other.
Fraud detection is a process looking at various signals in browser requests and at substantial historical evidence data classifying ad impression either as legitimate or as fraudulent.
We've been doing both (as well as all other analytics) in batch mode once an hour at best. Both processes, and, in particular, fraud detection, are time sensitive and much more meaningful if done in near-real-time.
This talk would be about our experience migrating a once-per-day offline batch processing of impression data using hadoop to in-memory stream processing using Kafka, Storm and Cassandra. We will touch upon our choices and our reasoning for selecting the products used for this solution.
Hadoop is no longer the only or always preferred option in Big Data space. In-memory stream processing may be more effective for time series data preparation and aggregation. Ability to scale at a significantly lower cost means more customers, better accuracy and better business practices: since only in-stream processing allows for low-latency data and insight delivery it opens entirely new opportunities. However, transitioning of non-trivial data pipelines raises a number of questions hidden previously within the offline nature of batch processing. How will you join several data feeds? How will you implement failure recovery? In addition to handling terabytes of data per day our streaming system has to be guided by the following considerations:
• Recovery time
• Time relativity and continuity
• Geographical distribution of data sources
• Limit on data loss
• Maintainability
The system produces complex cross-correlational analysis of several data feeds and aggregation for client analytics with input feed frequency of up to 100K msg/sec.
This presentation will benefit anyone interested in learning an alternate approach for big data analytics, especially the process of joining multiple streams in memory using Cassandra. Presentation will also highlight certain optimization patterns used those can be useful in similar situations.
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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.
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38. Kafka
Producer Consumer
Collection
API
Temperature
Processor
Precipitation
Processor
Topic = Temperature
Tem
1
Temp
2
Tem
3
Temp
4
Temp
5
Topic = Precipitation
Precip
1
Precip
2
Precip
3
Precip
4
Precip
5
Broker
Partition 0
Partition 0
Tem
1
Temp
2
Tem
3
Temp
4
Temp
5
Partition 1
Temperature
Processor
Topic = Temperature
Tem
1
Temp
2
Tem
3
Temp
4
Temp
5
Topic = Precipitation
Precip
1
Precip
2
Precip
3
Precip
4
Precip
5
Broker
Partition 0
Partition 0
Tem
1
Temp
2
Tem
3
Temp
4
Temp
5
Partition 1
Topic Temperature
Replication Factor = 2
Topic Precipitation
Replication Factor = 2
39. Kafka
Producer
Consumer
Collection
API
Temperature
Processor
Precipitation
Processor
Topic = Temperature
Tem
1
Temp
2
Tem
3
Temp
4
Temp
5
Topic = Precipitation
Precip
1
Precip
2
Precip
3
Precip
4
Precip
5
Broker
Partition 0
Partition 0
Tem
1
Temp
2
Tem
3
Temp
4
Temp
5
Partition 1 Temperature
Processor
Topic = Temperature
Tem
1
Temp
2
Tem
3
Temp
4
Temp
5
Topic = Precipitation
Precip
1
Precip
2
Precip
3
Precip
4
Precip
5
Broker
Partition 0
Partition 0
Tem
1
Temp
2
Tem
3
Temp
4
Temp
5
Partition 1
Temperature
Processor
Temperature
Processor
Precipitation
Processor
Topic Temperature
Replication Factor = 2
Topic Precipitation
Replication Factor = 2
40. Guarantees
Order
•Messages are ordered as they are sent by the
producer
•Consumers see messages in the order they were
inserted by the producer
Durability
•Messages are delivered at least once
•With a Replication Factor N up to N-1 server failures
can be tolerated without losing committed messages
43. Akka in a nutshell
• Highly concurrent
• Reactive
• Fully distributed
• Completely elastic and resilient
Actor
Mailbox
Actor
Mailbox
Actor
Mailbox
Actor
Mailbox
46. TemperatureActor
class TemperatureActor(sc: SparkContext, settings: WeatherSettings)
extends WeatherActor with ActorLogging {
def receive : Actor.Receive = {
case e: GetDailyTemperature => daily(e.day, sender)
case e: DailyTemperature => store(e)
case e: GetMonthlyHiLowTemperature => highLow(e, sender)
}
47. TemperatureActor
/** Computes and sends the daily aggregation to the `requester` actor.
* We aggregate this data on-demand versus in the stream.
*
* For the given day of the year, aggregates 0 - 23 temp values to statistics:
* high, low, mean, std, etc., and persists to Cassandra daily temperature table
* by weather station, automatically sorted by most recent - due to our cassandra schema -
* you don't need to do a sort in spark.
*
* Because the gov. data is not by interval (window/slide) but by specific date/time
* we look for historic data for hours 0-23 that may or may not already exist yet
* and create stats on does exist at the time of request.
*/
def daily(day: Day, requester: ActorRef): Unit =
(for {
aggregate <- sc.cassandraTable[Double](keyspace, rawtable)
.select("temperature").where("wsid = ? AND year = ? AND month = ? AND day = ?",
day.wsid, day.year, day.month, day.day)
.collectAsync()
} yield forDay(day, aggregate)) pipeTo requester
48. TemperatureActor
/**
* Would only be handling handles 0-23 small items or fewer.
*/
private def forDay(key: Day, temps: Seq[Double]): WeatherAggregate =
if (temps.nonEmpty) {
val stats = StatCounter(temps)
val data = DailyTemperature(
key.wsid, key.year, key.month, key.day,
high = stats.max, low = stats.min,
mean = stats.mean, variance = stats.variance, stdev = stats.stdev)
self ! data
data
} else NoDataAvailable(key.wsid, key.year, classOf[DailyTemperature])
49. TemperatureActor
class TemperatureActor(sc: SparkContext, settings: WeatherSettings)
extends WeatherActor with ActorLogging {
def receive : Actor.Receive = {
case e: GetDailyTemperature => daily(e.day, sender)
case e: DailyTemperature => store(e)
case e: GetMonthlyHiLowTemperature => highLow(e, sender)
}
50. TemperatureActor
/** Stores the daily temperature aggregates asynchronously which are triggered
* by on-demand requests during the `forDay` function's `self ! data`
* to the daily temperature aggregation table.
*/
private def store(e: DailyTemperature): Unit =
sc.parallelize(Seq(e)).saveToCassandra(keyspace, dailytable)
54. Token
Server
•Consistent hash between 2-63
and 264
•Each node owns a range of those
values
•The token is the beginning of that
range to the next node’s token value
•Virtual Nodes break these down
further
Data
Token Range
0 …
58. Table
CREATE TABLE weather_station (
id text,
name text,
country_code text,
state_code text,
call_sign text,
lat double,
long double,
elevation double,
PRIMARY KEY(id)
);
Table Name
Column Name
Column CQL Type
Primary Key Designation Partition Key
59. Queries supported
CREATE TABLE raw_weather_data (
wsid text,
year int,
month int,
day int,
hour int,
temperature double,
dewpoint double,
pressure double,
wind_direction int,
wind_speed double,
sky_condition int,
sky_condition_text text,
one_hour_precip double,
six_hour_precip double,
PRIMARY KEY ((wsid), year, month, day, hour)
) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC);
Get weather data given
•Weather Station ID
•Weather Station ID and Time
•Weather Station ID and Range of Time
64. Consistency level
Consistency Level Number of Nodes Acknowledged
One One - Read repair triggered
Local One One - Read repair in local DC
Quorum 51%
Local Quorum 51% in local DC
81. Simple example
/** keyspace & table */
val tableRDD = sc.cassandraTable("isd_weather_data", "raw_weather_data")
/** get a simple count of all the rows in the raw_weather_data table */
val rowCount = tableRDD.count()
println(s"Total Rows in Raw Weather Table: $rowCount")
sc.stop()
Executer
SELECT *
FROM isd_weather_data.raw_weather_data
Spark RDD
Spark Partition
Spark Connector
82. Saving back the weather data
val cc = new CassandraSQLContext(sc)
cc.setKeyspace("isd_weather_data")
cc.sql("""
SELECT wsid, year, month, day, max(temperature) high, min(temperature) low
FROM raw_weather_data
WHERE month = 6
AND temperature !=0.0
GROUP BY wsid, year, month, day;
""")
.map{row => (row.getString(0), row.getInt(1), row.getInt(2), row.getInt(3), row.getDouble(4), row.getDouble(5))}
.saveToCassandra("isd_weather_data", "daily_aggregate_temperature")
92. Kafka on Mesos example
Scheduler
• Provides the operational automation for a Kafka Cluster
• Manages the changes to the broker's configuration
• Exposes a REST API for the CLI to use or any other client
• Runs on Marathon for high availability
Executor
• The executor interacts with the kafka broker as an
intermediary to the scheduler