Spark Streaming with Kafka allows processing streaming data from Kafka in real-time. There are two main approaches - receiver-based and direct. The receiver-based approach uses Spark receivers to read data from Kafka and write to write-ahead logs for fault tolerance. The direct approach reads Kafka offsets directly without a receiver for better performance but less fault tolerance. The document discusses using Spark Streaming to aggregate streaming data from Kafka in real-time, persisting aggregates to Cassandra and raw data to S3 for analysis. It also covers using stateful transformations to update Cassandra in real-time.
Fast and Simplified Streaming, Ad-Hoc and Batch Analytics with FiloDB and Spa...Helena Edelson
O'Reilly Webcast with Myself and Evan Chan on the new SNACK Stack (playoff of SMACK) with FIloDB: Scala, Spark Streaming, Akka, Cassandra, FiloDB and Kafka.
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.
Fast and Simplified Streaming, Ad-Hoc and Batch Analytics with FiloDB and Spa...Helena Edelson
O'Reilly Webcast with Myself and Evan Chan on the new SNACK Stack (playoff of SMACK) with FIloDB: Scala, Spark Streaming, Akka, Cassandra, FiloDB and Kafka.
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.
Building production spark streaming applicationsJoey Echeverria
Designing, implementing, and testing an Apache Spark Streaming application is necessary to deploy to production but is not sufficient for long term management and monitoring. Simply learning the Spark Streaming APIs only gets you part of the way there. In this talk, I’ll be focusing on everything that happens after you’ve implemented your application in the context of a real-time alerting system for IT operational data.
Apache Spark has emerged over the past year as the imminent successor to Hadoop MapReduce. Spark can process data in memory at very high speed, while still be able to spill to disk if required. Spark’s powerful, yet flexible API allows users to write complex applications very easily without worrying about the internal workings and how the data gets processed on the cluster.
Spark comes with an extremely powerful Streaming API to process data as it is ingested. Spark Streaming integrates with popular data ingest systems like Apache Flume, Apache Kafka, Amazon Kinesis etc. allowing users to process data as it comes in.
In this talk, Hari will discuss the basics of Spark Streaming, its API and its integration with Flume, Kafka and Kinesis. Hari will also discuss a real-world example of a Spark Streaming application, and how code can be shared between a Spark application and a Spark Streaming application. Each stage of the application execution will be presented, which can help understand practices while writing such an application. Hari will finally discuss how to write a custom application and a custom receiver to receive data from other systems.
Scalable Data Science with SparkR: Spark Summit East talk by Felix CheungSpark Summit
R is a very popular platform for Data Science. Apache Spark is a highly scalable data platform. How could we have the best of both worlds? How could a Data Scientist leverage the rich 9000+ packages on CRAN, and integrate Spark into their existing Data Science toolset?
In this talk we will walkthrough many examples how several new features in Apache Spark 2.x will enable this. We will also look at exciting changes in and coming next in Apache Spark 2.x releases.
A Tale of Two APIs: Using Spark Streaming In ProductionLightbend
Fast Data architectures are the answer to the increasing need for the enterprise to process and analyze continuous streams of data to accelerate decision making and become reactive to the particular characteristics of their market.
Apache Spark is a popular framework for data analytics. Its capabilities include SQL-based analytics, dataflow processing, graph analytics and a rich library of built-in machine learning algorithms. These libraries can be combined to address a wide range of requirements for large-scale data analytics.
To address Fast Data flows, Spark offers two API's: The mature Spark Streaming and its younger sibling, Structured Streaming. In this talk, we are going to introduce both APIs. Using practical examples, you will get a taste of each one and obtain guidance on how to choose the right one for your application.
Deep Dive with Spark Streaming - Tathagata Das - Spark Meetup 2013-06-17spark-project
Slides from Tathagata Das's talk at the Spark Meetup entitled "Deep Dive with Spark Streaming" on June 17, 2013 in Sunnyvale California at Plug and Play. Tathagata Das is the lead developer on Spark Streaming and a PhD student in computer science in the UC Berkeley AMPLab.
NOTE: This was converted to Powerpoint from Keynote. Slideshare does not play the embedded videos. You can download the powerpoint from slideshare and import it into keynote. The videos should work in the keynote.
Abstract:
In this presentation, we will describe the "Spark Kernel" which enables applications, such as end-user facing and interactive applications, to interface with Spark clusters. It provides a gateway to define and run Spark tasks and to collect results from a cluster without the friction associated with shipping jars and reading results from peripheral systems. Using the Spark Kernel as a proxy, applications can be hosted remotely from Spark.
Reactive app using actor model & apache sparkRahul Kumar
Developing Application with Big Data is really challenging work, scaling, fault tolerance and responsiveness some are the biggest challenge. Realtime bigdata application that have self healing feature is a dream these days. Apache Spark is a fast in-memory data processing system that gives a good backend for realtime application.In this talk I will show how to use reactive platform, Actor model and Apache Spark stack to develop a system that have responsiveness, resiliency, fault tolerance and message driven feature.
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
Being Ready for Apache Kafka - Apache: Big Data Europe 2015Michael Noll
These are the slides of my Kafka talk at Apache: Big Data Europe in Budapest, Hungary. Enjoy! --Michael
Apache Kafka is a high-throughput distributed messaging system that has become a mission-critical infrastructure component for modern data platforms. Kafka is used across a wide range of industries by thousands of companies such as Twitter, Netflix, Cisco, PayPal, and many others.
After a brief introduction to Kafka this talk will provide an update on the growth and status of the Kafka project community. Rest of the talk will focus on walking the audience through what's required to put Kafka in production. We’ll give an overview of the current ecosystem of Kafka, including: client libraries for creating your own apps; operational tools; peripheral components required for running Kafka in production and for integration with other systems like Hadoop. We will cover the upcoming project roadmap, which adds key features to make Kafka even more convenient to use and more robust in production.
700 Updatable Queries Per Second: Spark as a Real-Time Web ServiceEvan Chan
700 Updatable Queries Per Second: Spark as a Real-Time Web Service. Find out how to use Apache Spark with FiloDb for low-latency queries - something you never thought possible with Spark. Scale it down, not just scale it up!
Spark Streaming & Kafka-The Future of Stream ProcessingJack Gudenkauf
Hari Shreedharan/Cloudera @Playtika. With its easy to use interfaces and native integration with some of the most popular ingest tools, such as Kafka, Flume, Kinesis etc, Spark Streaming has become go-to tool for stream processing. Code sharing with Spark also makes it attractive. In this talk, we will discuss the latest features in Spark Streaming and how it integrates with Kafka natively with no data loss, and even do exactly once processing!
Build Real-Time Streaming ETL Pipelines With Akka Streams, Alpakka And Apache...Lightbend
Things were easier when all our data used to be offline, analyzed overnight in batches. Now our data is online, in motion, and generated constantly. For architects, developers and their businesses, this means that there is an urgent need for tools and applications that can deliver real-time (or near real-time) streaming ETL capabilities.
In this session by Konrad Malawski, author, speaker and Senior Akka Engineer at Lightbend, you will learn how to build these streaming ETL pipelines with Akka Streams, Alpakka and Apache Kafka, and why they matter to enterprises that are increasingly turning to streaming Fast Data applications.
Alpine academy apache spark series #1 introduction to cluster computing wit...Holden Karau
Alpine academy apache spark series #1 introduction to cluster computing with python & a wee bit of scala. This is the first in the series and is aimed at the intro level, the next one will cover MLLib & ML.
Robust and Scalable ETL over Cloud Storage with Apache SparkDatabricks
The majority of reported Spark deployments are now in the cloud. In such an environment, it is preferable for Spark to access data directly from services such as Amazon S3, thereby decoupling storage and compute. However, there are limitations to object stores such as S3. Chained or concurrent ETL jobs often run into issues on S3 due to inconsistent file listings and the lack of atomic rename support. Metadata performance also becomes an issue when running jobs over many thousands to millions of files.
Speaker: Eric Liang
This talk was originally presented at Spark Summit East 2017.
Building production spark streaming applicationsJoey Echeverria
Designing, implementing, and testing an Apache Spark Streaming application is necessary to deploy to production but is not sufficient for long term management and monitoring. Simply learning the Spark Streaming APIs only gets you part of the way there. In this talk, I’ll be focusing on everything that happens after you’ve implemented your application in the context of a real-time alerting system for IT operational data.
Apache Spark has emerged over the past year as the imminent successor to Hadoop MapReduce. Spark can process data in memory at very high speed, while still be able to spill to disk if required. Spark’s powerful, yet flexible API allows users to write complex applications very easily without worrying about the internal workings and how the data gets processed on the cluster.
Spark comes with an extremely powerful Streaming API to process data as it is ingested. Spark Streaming integrates with popular data ingest systems like Apache Flume, Apache Kafka, Amazon Kinesis etc. allowing users to process data as it comes in.
In this talk, Hari will discuss the basics of Spark Streaming, its API and its integration with Flume, Kafka and Kinesis. Hari will also discuss a real-world example of a Spark Streaming application, and how code can be shared between a Spark application and a Spark Streaming application. Each stage of the application execution will be presented, which can help understand practices while writing such an application. Hari will finally discuss how to write a custom application and a custom receiver to receive data from other systems.
Scalable Data Science with SparkR: Spark Summit East talk by Felix CheungSpark Summit
R is a very popular platform for Data Science. Apache Spark is a highly scalable data platform. How could we have the best of both worlds? How could a Data Scientist leverage the rich 9000+ packages on CRAN, and integrate Spark into their existing Data Science toolset?
In this talk we will walkthrough many examples how several new features in Apache Spark 2.x will enable this. We will also look at exciting changes in and coming next in Apache Spark 2.x releases.
A Tale of Two APIs: Using Spark Streaming In ProductionLightbend
Fast Data architectures are the answer to the increasing need for the enterprise to process and analyze continuous streams of data to accelerate decision making and become reactive to the particular characteristics of their market.
Apache Spark is a popular framework for data analytics. Its capabilities include SQL-based analytics, dataflow processing, graph analytics and a rich library of built-in machine learning algorithms. These libraries can be combined to address a wide range of requirements for large-scale data analytics.
To address Fast Data flows, Spark offers two API's: The mature Spark Streaming and its younger sibling, Structured Streaming. In this talk, we are going to introduce both APIs. Using practical examples, you will get a taste of each one and obtain guidance on how to choose the right one for your application.
Deep Dive with Spark Streaming - Tathagata Das - Spark Meetup 2013-06-17spark-project
Slides from Tathagata Das's talk at the Spark Meetup entitled "Deep Dive with Spark Streaming" on June 17, 2013 in Sunnyvale California at Plug and Play. Tathagata Das is the lead developer on Spark Streaming and a PhD student in computer science in the UC Berkeley AMPLab.
NOTE: This was converted to Powerpoint from Keynote. Slideshare does not play the embedded videos. You can download the powerpoint from slideshare and import it into keynote. The videos should work in the keynote.
Abstract:
In this presentation, we will describe the "Spark Kernel" which enables applications, such as end-user facing and interactive applications, to interface with Spark clusters. It provides a gateway to define and run Spark tasks and to collect results from a cluster without the friction associated with shipping jars and reading results from peripheral systems. Using the Spark Kernel as a proxy, applications can be hosted remotely from Spark.
Reactive app using actor model & apache sparkRahul Kumar
Developing Application with Big Data is really challenging work, scaling, fault tolerance and responsiveness some are the biggest challenge. Realtime bigdata application that have self healing feature is a dream these days. Apache Spark is a fast in-memory data processing system that gives a good backend for realtime application.In this talk I will show how to use reactive platform, Actor model and Apache Spark stack to develop a system that have responsiveness, resiliency, fault tolerance and message driven feature.
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
Being Ready for Apache Kafka - Apache: Big Data Europe 2015Michael Noll
These are the slides of my Kafka talk at Apache: Big Data Europe in Budapest, Hungary. Enjoy! --Michael
Apache Kafka is a high-throughput distributed messaging system that has become a mission-critical infrastructure component for modern data platforms. Kafka is used across a wide range of industries by thousands of companies such as Twitter, Netflix, Cisco, PayPal, and many others.
After a brief introduction to Kafka this talk will provide an update on the growth and status of the Kafka project community. Rest of the talk will focus on walking the audience through what's required to put Kafka in production. We’ll give an overview of the current ecosystem of Kafka, including: client libraries for creating your own apps; operational tools; peripheral components required for running Kafka in production and for integration with other systems like Hadoop. We will cover the upcoming project roadmap, which adds key features to make Kafka even more convenient to use and more robust in production.
700 Updatable Queries Per Second: Spark as a Real-Time Web ServiceEvan Chan
700 Updatable Queries Per Second: Spark as a Real-Time Web Service. Find out how to use Apache Spark with FiloDb for low-latency queries - something you never thought possible with Spark. Scale it down, not just scale it up!
Spark Streaming & Kafka-The Future of Stream ProcessingJack Gudenkauf
Hari Shreedharan/Cloudera @Playtika. With its easy to use interfaces and native integration with some of the most popular ingest tools, such as Kafka, Flume, Kinesis etc, Spark Streaming has become go-to tool for stream processing. Code sharing with Spark also makes it attractive. In this talk, we will discuss the latest features in Spark Streaming and how it integrates with Kafka natively with no data loss, and even do exactly once processing!
Build Real-Time Streaming ETL Pipelines With Akka Streams, Alpakka And Apache...Lightbend
Things were easier when all our data used to be offline, analyzed overnight in batches. Now our data is online, in motion, and generated constantly. For architects, developers and their businesses, this means that there is an urgent need for tools and applications that can deliver real-time (or near real-time) streaming ETL capabilities.
In this session by Konrad Malawski, author, speaker and Senior Akka Engineer at Lightbend, you will learn how to build these streaming ETL pipelines with Akka Streams, Alpakka and Apache Kafka, and why they matter to enterprises that are increasingly turning to streaming Fast Data applications.
Alpine academy apache spark series #1 introduction to cluster computing wit...Holden Karau
Alpine academy apache spark series #1 introduction to cluster computing with python & a wee bit of scala. This is the first in the series and is aimed at the intro level, the next one will cover MLLib & ML.
Robust and Scalable ETL over Cloud Storage with Apache SparkDatabricks
The majority of reported Spark deployments are now in the cloud. In such an environment, it is preferable for Spark to access data directly from services such as Amazon S3, thereby decoupling storage and compute. However, there are limitations to object stores such as S3. Chained or concurrent ETL jobs often run into issues on S3 due to inconsistent file listings and the lack of atomic rename support. Metadata performance also becomes an issue when running jobs over many thousands to millions of files.
Speaker: Eric Liang
This talk was originally presented at Spark Summit East 2017.
Apache Spark 2.0: A Deep Dive Into Structured Streaming - by Tathagata Das Databricks
“In Spark 2.0, we have extended DataFrames and Datasets to handle real time streaming data. This not only provides a single programming abstraction for batch and streaming data, it also brings support for event-time based processing, out-or-order/delayed data, sessionization and tight integration with non-streaming data sources and sinks. In this talk, I will take a deep dive into the concepts and the API and show how this simplifies building complex “Continuous Applications”.” - T.D.
Databricks Blog: "Structured Streaming In Apache Spark 2.0: A new high-level API for streaming"
https://databricks.com/blog/2016/07/28/structured-streaming-in-apache-spark.html
// About the Presenter //
Tathagata Das is an Apache Spark Committer and a member of the PMC. He’s the lead developer behind Spark Streaming, and is currently employed at Databricks. Before Databricks, you could find him at the AMPLab of UC Berkeley, researching datacenter frameworks and networks with professors Scott Shenker and Ion Stoica.
Follow T.D. on -
Twitter: https://twitter.com/tathadas
LinkedIn: https://www.linkedin.com/in/tathadas
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...
Big data challenges are common : we are all doing aggregations , machine learning , anomaly detection, OLAP ...
This presentation describe how InnerActive answer those requirements
Intro to Big Data Analytics using Apache Spark and Apache ZeppelinAlex Zeltov
This workshop will provide an introduction to Big Data Analytics using Apache Spark and Apache Zeppelin.
https://github.com/zeltovhorton/intro_spark_zeppelin_meetup
There will be a short lecture that includes an introduction to Spark, the Spark components.
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
The lecture will be followed by demo . There will be a short lecture on Hadoop and how Spark and Hadoop interact and compliment each other. You will learn how to move data into HDFS using Spark APIs, create Hive table, explore the data with Spark and SQL, transform the data and then issue some SQL queries. We will be using Scala and/or PySpark for labs.
"Microservices" is one of the hottest buzzwords and, as usual, everyone wants them, but few know how to build them. In this talk we will offer our interpretation of microservice architecture, and show how we are implementing these ideas: using Scala, Akka, sbt and Docker, we modularized Akka applications, Spark jobs and Play servers.
In the talk we will discuss design trade-offs and challenges that we faced in the process, and how we have overcome them. The focus is not on particular features of Scala language or a library, but on building modern applications using the Typesafe stack and other open-source tools.
Strata NYC 2015: What's new in Spark StreamingDatabricks
As the adoption of Spark Streaming in the industry is increasing, so is the community’s demand for more features. Since the beginning of this year, we have made significant improvements in performance, usability, and semantic guarantees. In particular, some of these features are:
- New Kafka integration for exactly-once guarantees
- Improved Kinesis integration for stronger guarantees
- Addition of more sources to the Python API
Significantly improved UI for greater monitoring and debuggability.
In this talk, I am going to discuss these improvements as well as the plethora of features we plan to add in the near future.
The need to handle increasingly large volumes of data, to quickly drive decisions (via streaming technologies and machine learning algorithms), to scale systems effectively, to guarantee the right level of continuity, to float data across systems efficiently and others are becoming critical and challenging requirements. During this talk we’ll demonstrate how to design reactive, resilient, message driven and elastic applications by combining technologies such as Akka, Kakfa, Cassandra and Spark along with architectural patterns like CQRS, ES, etc. in order to achieve the previously mentioned needs.
Writing Continuous Applications with Structured Streaming Python APIs in Apac...Databricks
Description:
We are amidst the Big Data Zeitgeist era in which data comes at us fast, in myriad forms and formats at intermittent intervals or in a continuous stream, and we need to respond to streaming data immediately. This need has created a notion of writing a streaming application that’s continuous, reacts and interacts with data in real-time. We call this continuous application, which we will discuss.
Abstract:
We are amidst the Big Data Zeitgeist era in which data comes at us fast, in myriad forms and formats at intermittent intervals or in a continuous stream, and we need to respond to streaming data immediately. This need has created a notion of writing a streaming application that’s continuous, reacts and interacts with data in real-time. We call this continuous application.
In this talk we will explore the concepts and motivations behind the continuous application, how Structured Streaming Python APIs in Apache Spark 2.x enables writing continuous applications, examine the programming model behind Structured Streaming, and look at the APIs that support them.
Through a short demo and code examples, I will demonstrate how to write an end-to-end Structured Streaming application that reacts and interacts with both real-time and historical data to perform advanced analytics using Spark SQL, DataFrames and Datasets APIs.
You’ll walk away with an understanding of what’s a continuous application, appreciate the easy-to-use Structured Streaming APIs, and why Structured Streaming in Apache Spark 2.x is a step forward in developing new kinds of streaming applications.
Accelerating Real Time Analytics with Spark Streaming and FPGAaaS with Prabha...Databricks
As Real Time Analytics built on Spark Streaming framework gains increasing adoption and deployment in the data center, the need for hardware accelerators to scale for performance and offer lower latency for certain applications is becoming increasingly apparent. Recently reconfigurable accelerators based on FPGAs (Field Programmable Gate Arrays) have been proposed to accelerate the analytics workloads with support for low-latency streaming. This is becoming a feasible and attractive option now as major CSPs (Cloud Service Providers) announce deployment of FPGAs- as-a-Service (FPGAaaS).
However there are certain challenges in using FPGA accelerators in the cloud that first must be overcome: (1) Development of FPGA accelerators is challenging because of the lack of mature high level language support and standard accelerator interfaces; (2) Using hardware accelerators from high level application frameworks like Spark require special consideration for the efficient transfer of data to and from the application; and (3) Managing the FPGA accelerators for sharing across different applications is limited by the lack of a common resource sharing paradigm.
In this session, we’ll present a runtime framework that addresses these challenges. The framework will present highly optimized FPGA based accelerated functions as a service (AFaaS) to the application. The framework will demonstrate seamless integration of accelerators into existing Big Data Frameworks using Java, Scala, Pyton or other high level language APIs. The framework will also support the transfer of streaming data to/from the FPGA to the application and the sharing of accelerator libraries across different applications. Key takeaways:
This paper demonstrates the feasibility of accelerating Real Time Analytics applications using Spark Streaming with accelerated functions based on FPGAaaS to deliver higher performance efficiencies at lower latencies.
The paper will provide performance data for Spark Streaming applications using our solution compared to current native software implementations.
Spark (Structured) Streaming vs. Kafka Streams - two stream processing platfo...Guido Schmutz
Independent of the source of data, the integration and analysis of event streams gets more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analyzed, often with many consumers or systems interested in all or part of the events. In this session we compare two popular Streaming Analytics solutions: Spark Streaming and Kafka Streams.
Spark is fast and general engine for large-scale data processing and has been designed to provide a more efficient alternative to Hadoop MapReduce. Spark Streaming brings Spark's language-integrated API to stream processing, letting you write streaming applications the same way you write batch jobs. It supports both Java and Scala.
Kafka Streams is the stream processing solution which is part of Kafka. It is provided as a Java library and by that can be easily integrated with any Java application.
Writing Continuous Applications with Structured Streaming in PySparkDatabricks
We are in the midst of a Big Data Zeitgeist in which data comes at us fast, in myriad forms and formats at intermittent intervals or in a continuous stream, and we need to respond to streaming data immediately. This need has created a notion of writing a streaming application that reacts and interacts with data in real-time. We call this a continuous application. In this talk we will explore the concepts and motivations behind continuous applications and how Structured Streaming Python APIs in Apache Spark 2.x enables writing them. We also will examine the programming model behind Structured Streaming and the APIs that support them. Through a short demo and code examples, Jules will demonstrate how to write an end-to-end Structured Streaming application that reacts and interacts with both real-time and historical data to perform advanced analytics using Spark SQL, DataFrames, and Datasets APIs.
Getting Started With Spark Structured Streaming With Dustin Vannoy | Current ...HostedbyConfluent
Getting Started With Spark Structured Streaming With Dustin Vannoy | Current 2022
Many data pipelines still default to processing data nightly or hourly, but information is created all the time and should be available much sooner. While the move to stream processing adds complexity, Spark Structured Streaming makes it achievable for teams of any size to switch to streaming.
This session shares techniques for data engineers who are new to building streaming pipelines with Spark Structured Streaming. It covers how to implement real-time stream processes with Apache Spark and Apache Kafka. We will discuss general concepts for Spark Structured Streaming along with introductory code examples. We will also look at important streaming concepts like triggers, windows, and state. To connect it all we will walk through a complete pipeline, including a demo using PySpark, Apache Kafka, and Delta Lake tables
Spark Streaming has quickly established itself as one of the more popular Streaming Engines running on the Hadoop Ecosystem. Not only does it provide integration with many type of message brokers and stream sources, but it also provides the ability to leverage other major modules in Spark like Spark SQL and MLib in conjunction. This allows for businesses and developers to make use out of data in ways they couldn’t hope to do in the past.
However, while building a Spark Streaming pipeline, it’s not sufficient to only know how to express your business logic. Operationalizing these pipelines and running the application with high uptime and continuous monitoring has a lot of operational challenges. Fortunately, Spark Streaming makes all that easy as well. In this talk, we’ll go over some of the main steps you’ll need to take to get your Spark Streaming application ready for production, specifically in conjunction with Kafka. This includes steps to gracefully shutdown your application, steps to perform upgrades, monitoring, various useful spark configurations and more.
The slides I prepared for https://www.meetup.com/Paris-Apache-Kafka-Meetup/events/268164461/ about Apache Kafka integration in Apache Spark Structured Streaming.
Writing Continuous Applications with Structured Streaming PySpark APIDatabricks
"We're amidst the Big Data Zeitgeist era in which data comes at us fast, in myriad forms and formats at intermittent intervals or in a continuous stream, and we need to respond to streaming data immediately. This need has created a notion of writing a streaming application that’s continuous, reacts and interacts with data in real-time. We call this continuous application.
In this tutorial we'll explore the concepts and motivations behind the continuous application, how Structured Streaming Python APIs in Apache Spark™ enable writing continuous applications, examine the programming model behind Structured Streaming, and look at the APIs that support them.
Through presentation, code examples, and notebooks, I will demonstrate how to write an end-to-end Structured Streaming application that reacts and interacts with both real-time and historical data to perform advanced analytics using Spark SQL, DataFrames and Datasets APIs.
You’ll walk away with an understanding of what’s a continuous application, appreciate the easy-to-use Structured Streaming APIs, and why Structured Streaming in Apache Spark is a step forward in developing new kinds of streaming applications.
This tutorial will be both instructor-led and hands-on interactive session. Instructions in how to get tutorial materials will be covered in class.
WHAT YOU’LL LEARN:
– Understand the concepts and motivations behind Structured Streaming
– How to use DataFrame APIs
– How to use Spark SQL and create tables on streaming data
– How to write a simple end-to-end continuous application
PREREQUISITES
– A fully-charged laptop (8-16GB memory) with Chrome or Firefox
–Pre-register for Databricks Community Edition"
Speaker: Jules Damji
"Structured Streaming was a new streaming API introduced to Spark over 2 years ago in Spark 2.0, and was announced GA as of Spark 2.2. Databricks customers have processed over a hundred trillion rows in production using Structured Streaming. We received dozens of questions on how to best develop, monitor, test, deploy and upgrade these jobs. In this talk, we aim to share best practices around what has worked and what hasn't across our customer base.
We will tackle questions around how to plan ahead, what kind of code changes are safe for structured streaming jobs, how to architect streaming pipelines which can give you the most flexibility without sacrificing performance by using tools like Databricks Delta, how to best monitor your streaming jobs and alert if your streams are falling behind or are actually failing, as well as how to best test your code."
These are the slides for the Productionizing your Streaming Jobs webinar on 5/26/2016.
Apache Spark Streaming is one of the most popular stream processing framework that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. In this talk, we will focus on the following aspects of Spark streaming:
- Motivation and most common use cases for Spark Streaming
- Common design patterns that emerge from these use cases and tips to avoid common pitfalls while implementing these design patterns
- Performance Optimization Techniques
Spark Streaming Programming Techniques You Should Know with Gerard MaasSpark Summit
At its heart, Spark Streaming is a scheduling framework, able to efficiently collect and deliver data to Spark for further processing. While the DStream abstraction provides high-level functions to process streams, several operations also grant us access to deeper levels of the API, where we can directly operate on RDDs, transform them to Datasets to make use of that abstraction or store the data for later processing. Between these API layers lie many hooks that we can manipulate to enrich our Spark Streaming jobs. In this presentation we will demonstrate how to tap into the Spark Streaming scheduler to run arbitrary data workloads, we will show practical uses of the forgotten ‘ConstantInputDStream’ and will explain how to combine Spark Streaming with probabilistic data structures to optimize the use of memory in order to improve the resource usage of long-running streaming jobs. Attendees of this session will come out with a richer toolbox of techniques to widen the use of Spark Streaming and improve the robustness of new or existing jobs.
Knoldus organized a Meetup on 1 April 2015. In this Meetup, we introduced Spark with Scala. Apache Spark is a fast and general engine for large-scale data processing. Spark is used at a wide range of organizations to process large datasets.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
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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!
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Your Digital Assistant.
Making complex approach simple. Straightforward process saves time. No more waiting to connect with people that matter to you. Safety first is not a cliché - Securely protect information in cloud storage to prevent any third party from accessing data.
Would you rather make your visitors feel burdened by making them wait? Or choose VizMan for a stress-free experience? VizMan is an automated visitor management system that works for any industries not limited to factories, societies, government institutes, and warehouses. A new age contactless way of logging information of visitors, employees, packages, and vehicles. VizMan is a digital logbook so it deters unnecessary use of paper or space since there is no requirement of bundles of registers that is left to collect dust in a corner of a room. Visitor’s essential details, helps in scheduling meetings for visitors and employees, and assists in supervising the attendance of the employees. With VizMan, visitors don’t need to wait for hours in long queues. VizMan handles visitors with the value they deserve because we know time is important to you.
Feasible Features
One Subscription, Four Modules – Admin, Employee, Receptionist, and Gatekeeper ensures confidentiality and prevents data from being manipulated
User Friendly – can be easily used on Android, iOS, and Web Interface
Multiple Accessibility – Log in through any device from any place at any time
One app for all industries – a Visitor Management System that works for any organisation.
Stress-free Sign-up
Visitor is registered and checked-in by the Receptionist
Host gets a notification, where they opt to Approve the meeting
Host notifies the Receptionist of the end of the meeting
Visitor is checked-out by the Receptionist
Host enters notes and remarks of the meeting
Customizable Components
Scheduling Meetings – Host can invite visitors for meetings and also approve, reject and reschedule meetings
Single/Bulk invites – Invitations can be sent individually to a visitor or collectively to many visitors
VIP Visitors – Additional security of data for VIP visitors to avoid misuse of information
Courier Management – Keeps a check on deliveries like commodities being delivered in and out of establishments
Alerts & Notifications – Get notified on SMS, email, and application
Parking Management – Manage availability of parking space
Individual log-in – Every user has their own log-in id
Visitor/Meeting Analytics – Evaluate notes and remarks of the meeting stored in the system
Visitor Management System is a secure and user friendly database manager that records, filters, tracks the visitors to your organization.
"Secure Your Premises with VizMan (VMS) – Get It Now"
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
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Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
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Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
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Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
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Multiple Your Crypto Portfolio with the Innovative Features of Advanced Crypt...Hivelance Technology
Cryptocurrency trading bots are computer programs designed to automate buying, selling, and managing cryptocurrency transactions. These bots utilize advanced algorithms and machine learning techniques to analyze market data, identify trading opportunities, and execute trades on behalf of their users. By automating the decision-making process, crypto trading bots can react to market changes faster than human traders
Hivelance, a leading provider of cryptocurrency trading bot development services, stands out as the premier choice for crypto traders and developers. Hivelance boasts a team of seasoned cryptocurrency experts and software engineers who deeply understand the crypto market and the latest trends in automated trading, Hivelance leverages the latest technologies and tools in the industry, including advanced AI and machine learning algorithms, to create highly efficient and adaptable crypto trading bots
Advanced Flow Concepts Every Developer Should KnowPeter Caitens
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In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
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This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
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How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
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5. Use Case
Tens of Millions of transactions per minute (and growing …)
~ 15TB daily (24/7 99.99999 resiliency)
Data Aggregation: (#Video Success Rate)
Real time Aggregation and DB update
Raw data persistency as recovery backup
Retrospective aggregation updates (recalculate)
Analytic Data :
Persist incoming events (Raw data persistency)
Real time analytics and ML algorithm (inside)
6.
7. Based on high-level Kafka consumer
The receiver stores Kafka messages in executors/workers
Write-Ahead Logs to recover data on failures – Recommended
ZK offsets are updated by Spark
Data duplication (WAL/Kafka)
Receiver Approach - ”KafkaUtils.createStream”
11. Architecture
Pros:
Worked just fine with single MySQL server
Simplicity – legacy code stays the same
Real-time DB updates
Partial Aggregation was done in Spark, DB was updated via
“Insert On Duplicate Key Update”
Cons:
MySQL limitations (MySQL sharding is an issue, Cassandra is optimal)
S3 raw data (in standard formats) is not trivial when using Spark
15. Architecture
Pros:
Parquet is ideal for Spark analytics
Backup data requires less disk space
Cons:
DB is not updated in real time (streaming), we could use combination with
MySQL for current hour...
What has been changed:
C* uses counters for “sum/update” which is a “bad” practice
(no “insert on duplicate key” using MySQL)
Parquet conversion is a heavy job and it seems that streaming hourly
conversions (using batch in case of failure) is a better approach
16. Direct Approach – ”KafkaUtils.createDirectStream”
Based on Kafka simple consumer
Queries Kafka for the latest offsets in each topic+partition, define offset range for batch
No need to create multiple input Kafka streams and consolidate them
Spark creates an RDD partition for each Kafka
partition so data is consumed in parallel
ZK offsets are not updated by Spark, offsets are
tracked by Spark within its checkpoints (might not
recover)
No data duplication (no WAL)
17. S3 / HDFS
Save metadata – needed for recovery from driver failures
RDD for statefull transformations (RDDs of previous batches)
Checkpoint...
18. Transfer data from driver to workers
Broadcast -
keep a read-only variable cached on each machine rather than shipping a copy of it with tasks
Accumulator - used to implement counters/sum, workers can only add to accumulator, driver can read its
value (you can extends AccumulatorParam[Vector])
Static (Scala Object)
Context (rdd) – get data after recovery
20. def start(sparkConfig: SparkConfiguration, decoder: String) {
val ssc = StreamingContext.getOrCreate(sparkCheckpointDirectory(sparkConfig),()=>functionToCreateContext(decoder,sparkConfig))
sys.ShutdownHookThread {
ssc.stop(stopSparkContext = true, stopGracefully = true)
}
ssc.start()
ssc.awaitTermination()
}
In house code
21. def functionToCreateContext(decoder: String,sparkConfig: SparkConfiguration ):StreamingContext = {
val sparkConf = new SparkConf().setMaster(sparkClusterHost).setAppName(sparkConfig.jobName)
sparkConf.set(S3_KEY, sparkConfig.awsKey)
sparkConf.set(S3_CREDS, sparkConfig.awsSecret)
sparkConf.set(PARQUET_OUTPUT_DIRECTORY, sparkConfig.parquetOutputDirectory)
val sparkContext = SparkContext.getOrCreate(sparkConf)
// Hadoop S3 writer optimization
sparkContext.hadoopConfiguration.set("spark.sql.parquet.output.committer.class",
"org.apache.spark.sql.parquet.DirectParquetOutputCommitter")
// Same as Avro, Parquet also supports schema evolution. This work happens in driver and takes
// relativly long time
sparkContext.hadoopConfiguration.set("parquet.enable.summary-metadata", "false")
sparkContext.hadoopConfiguration.setInt("parquet.metadata.read.parallelism", 100)
val ssc = new StreamingContext(sparkContext, Seconds(sparkConfig.batchTime))
ssc.checkpoint(sparkCheckpointDirectory(sparkConfig))
In house code (continue)
22. // evaluate stream value happened only if checkpoint folder is not exist
val streams = sparkConfig.kafkaConfig.streams map { c =>
val topic = c.topic.split(",").toSet
KafkaUtils.createDirectStream[String, String, StringDecoder, JsonDecoder](ssc, c.kafkaParams, topic)
}
streams.foreach { dsStream => {
dsStream.foreachRDD { rdd =>
val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
for (o <- offsetRanges) {
logInfo(s"Offset on the driver: ${offsetRanges.mkString}")
}
val sqlContext = SQLContext.getOrCreate(rdd.sparkContext)
sqlContext.setConf("spark.sql.parquet.compression.codec", "snappy")
// Data recovery after crash
val s3Accesskey = rdd.context.getConf.get(S3_KEY)
val s3SecretKey = rdd.context.getConf.get(S3_CREDS)
val outputDirectory = rdd.context.getConf.get(PARQUET_OUTPUT_DIRECTORY)
In house code (continue)
23. val data = sqlContext.read.json(rdd.map(_._2))
val carpetData = data.count()
if (carpetData > 0) {
// coalesce(1) – Data transfer optimization during shuffle
data.coalesce(1).write.mode(SaveMode.Append).partitionBy "day", "hour").parquet(“s3a//...")
// In case of S3Exception will not continue to update ZK.
zk.updateNode(o.topic, o.partition.toString, kafkaConsumerGroup, o.untilOffset.toString.getBytes)
}
}
}
}
ssc
}
In house code (continue)
24. SaveMode (Append/Overwrite) used to handle exist data (add new file / overwrite)
Spark Streaming does not update ZK (http://curator.apache.org/)
Spark Streaming saves offset in its checkpoint folder. Once it crashes it will continue from the last
offset
You can avoid using checkpoint for offsets and manage it manually
Config...
25. val sparkConf = new SparkConf().setMaster("local[4]").setAppName("demo")
val sparkContext = SparkContext.getOrCreate(sparkConf)
val sqlContext = SQLContext.getOrCreate(sparkContext)
val data = sqlContext.read.json(path)
data.coalesce(1).write.mode(SaveMode.Overwrite).partitionBy("table", "day") parquet (outputFolder)
Batch Code
26. Built in support for backpressure Since Spark 1.5 (default is disabled)
Reciever – spark.streaming.receiver.maxRate
Direct – spark.streaming.kafka.maxRatePerPartition
Back Pressure
30. Architecture 3.0
Stream
Events
Events
Raw Data
Events
Consumer
Consumer
Raw Data
Aggregation
Aggregation
Raw Data
Analytic data uses spark stream to transfer Kafka raw data to Parquet.
Regular Kafka consumer saves raw data backup in S3 (for streaming failure, spark
batch will convert them to parquet)
Aggregation data uses statefull Spark Streaming (mapWithState) to update C*
In case streaming failure spark batch will update data from Parquet to C*
34. val ssc = new StreamingContext(sparkConfig.sparkConf, Seconds(batchTime))
val kafkaStreams = (1 to sparkConfig.workers) map {
i => new
FixedKafkaInputDStream[String, AggregationEvent, StringDecoder,
SerializedDecoder[AggregationEvent]](ssc,
kafkaConfiguration.kafkaMapParams,
topicMap,
StorageLevel.MEMORY_ONLY_SER).map(_._2) // for write ahead log
}
val unifiedStream = ssc.union(kafkaStreams) // manage all streams as one
val mapped = unifiedStream flatMap {
event => Aggregations.getEventAggregationsKeysAndValues(Option(event))
// convert event to aggregation object which contains
//key (“advertiserId”, “countryId”) and values (“click”, “impression”)
}
val reduced = mapped.reduceByKey {
_ + _ // per aggregation type we created “+” method that
//describe how to do the aggregation
}
K1 =
advertiserId = 5
countryId = 8
V1 =
clicks = 2
impression = 17
k1(e), v1(e)
k1(e), v2(e)
k2(e), v3(e)
k1(e), v1+v2
k2(e), v3(e)
In house Code –