Introducing Kafka Streams: Large-scale Stream Processing with Kafka, Neha Narkhede
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The concept of stream processing has been around for a while and most software systems continuously transform streams of inputs into streams of outputs. Yet the idea of directly modeling stream processing in infrastructure systems is just coming into its own after a few decades on the periphery. At its core, stream processing is simple: read data in, process it, and maybe emit some data out. So why are there so many stream processing frameworks that all define their own terminology? And are the components of each even comparable? Why do I need to know about spouts or DStreams just to process a simple sequence of records? Depending on your application’s requirements, you may not need a framework. This talk will be delivered by one of the creators of the popular stream data systems Apache Kafka and will abstract away the details of individual frameworks while describing the key features they provide. These core features include scalability and parallelism through data partitioning, fault tolerance and event processing order guarantees, support for stateful stream processing, and handy stream processing primitives such as windowing. Based on our experience building and scaling Kafka to handle streams that captured hundreds of billions of records per day — this presentation will help you understand how to map practical data problems to stream processing and how to write applications that process streams of data at scale.
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Introducing Kafka Streams, the new stream processing library of Apache Kafka,...
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In this talk, we will take a deeper dive into the design and implementation of a Scala/Java implementation of our client library that allows developers to produce or consume events without worrying about the underlying infrastructure and their location while enjoying the benefits of data compatibility through schemas. We’ll also look at an HTTP based client proxy that exposes the same API but for languages without our native support. Finally, we’ll walk through Web and CLI tools we built to make working with the platform easier.
The content of this talk will be primarily aimed at software developers looking for ideas on how to build Kafka client tools that allow producer/consumer interactions protected by schema-based event definitions while hiding details of the underlying infrastructure.
Deploying Kafka at Dropbox, Mark Smith, Sean Fellows
At Dropbox we are currently handling approximately 10,000,000 messages per second at peak across our handful of Kafka clusters. The largest of which has hit throughputs of 7,000,000 per second (~30 Gbps) on only 20 nodes. We’ll walk you through the steps we took to get where we are, the design that works for us — and those that didn’t. We’ll talk about the tooling we had to build and what we want to see exist.
We’ll dive deeper into configuration and provide a blueprint you can follow. We’ll talk about the trials and tribulations of using Kafka — including ways we’ve set our clusters on fire, ways we’ve lost data, ways we’ve turned our hairs gray, and ways we’ve heroically saved the day for our users. Finally, we’ll spend time on some of the work we’re doing to handle consumer coordination across our many different systems and to integrate Kafka into a well established corporate infrastructure. (I.e., making Kafka “”play nice”” with everybody.)
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In this talk, you’ll learn how we’re taking Comcast’s Technology and Product group’s massive, heterogeneous set of data collection systems and centralizing on a single platform built around Kafka. These data collection systems are used for everything from business analytics, to near-real time operations, to executive reporting.
We’ll go over what it takes to wrangle streaming data across an enterprise, including the need for, and our approaches to:
Schema management, both at schema creation time and when schema evolution is required
Data ingest and cleansing
Multi-datacenter collection and failover
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We are at the dawn of an age where listening to data drives the difference between unicorns and dinosaurs. Learn how HomeAway leverages Kafka as the “dial tone for data” enabling enterprises to connect disparate data sources “supercharging” them for a variety of use cases: SLA monitoring, A/B Testing, Visitor Segmentation, Fraud Detection, real-time ETL and more. The secret? Come find out.
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With 60+ products and over 24% of the US GDP flowing through it, system integration is a tough problem for Intuit. Seasonality, scale, and massive peaks in products like TurboTax, QuickBooks, and Mint.com add extra layers of difficulty when building shared data services around transaction and user graphs, clickstream processing, a/b testing, and personalization. To reduce complexity and latency, we’ve implemented Kafka as the backbone across these data services. This allows us to asynchronously trigger relevant processing, elegantly scaling up and down as needed around peaks, all without the need for point-to-point integrations.
In this talk, we share what we’ve learned about Kafka at Intuit and describe our data services architecture. We found that Kafka is invaluable in achieving a scalable, clean architecture, allowing engineering teams to focus less on integration and more on product development.
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The initial deployment of Espresso relies on MySQL’s built-in mechanism for Master-Slave replication. Storage hosts running MySQL masters service HTTP requests to store and retrieve documents, while hosts running slave replicas remain mostly idle. Since replication is at the MySQL instance level, masters and slaves must contain the exact same partitions – precluding flexible and dynamic partition placement and migration within the cluster.
Espresso is migrating to a new deployment topology where each Storage Node may host a combination of master and slave partitions; thus distributing the application requests equally across all available hardware resources. This topology requires per-partition replication between master and slave nodes. Kafka will be used as the transport for replication between partitions.
For use as the replication stream for the source-of-truth data store for LinkedIn’s most valuable data, Kafka must be as reliable as MySQL replication. The session will cover Kafka configuration options to ensure highly reliable, in-order message delivery. Additionally, the application logic maintains state both within the Kafka event stream and externally to detect message re-delivery, out of order delivery, and messages inserted out-of-band. These application protocols to guarantee high fidelity will be discussed.
More Datacenters, More Problems
Presented at Kafka Summit 2016
Operating out of multiple datacenters is a large part of most disaster recovery plans, but it brings extra complications to our data pipelines. Instead of having a straight path from front to back, it now has forks and dead ends and odd little use cases that don’t match up with a perfect view of the world. This talk will focus on how to best utilize Apache Kafka in this world, including basic architectures for multi-datacenter and multi-tier clusters. We will also touch on how to assure messages make it from producer to consumer, and how to monitor the entire ecosystem.
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In the financial industry, losing data is unacceptable. Financial firms are adopting Kafka for their critical applications. Kafka provides the low latency, high throughput, high availability, and scale that these applications require. But can it also provide complete reliability? As a system architect, when asked “Can you guarantee that we will always get every transaction,” you want to be able to say “Yes” with total confidence.
In this session, we will go over everything that happens to a message – from producer to consumer, and pinpoint all the places where data can be lost – if you are not careful. You will learn how developers and operation teams can work together to build a bulletproof data pipeline with Kafka. And if you need proof that you built a reliable system – we’ll show you how you can build the system to prove this too.
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Why are there so many stream processing frameworks that each define their own terminology? Are the components of each comparable? Why do you need to know about spouts or DStreams just to process a simple sequence of records? Depending on your application’s requirements, you may not need a full framework at all.
Processing and understanding your data to create business value is the ultimate goal of a stream data platform. In this talk we will survey the stream processing landscape, the dimensions along which to evaluate stream processing technologies, and how they integrate with Apache Kafka. Particularly, we will learn how Kafka Streams, the built-in stream processing engine of Apache Kafka, compares to other stream processing systems that require a separate processing infrastructure.
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Siphon is a highly available and reliable distributed pub/sub system built using Apache Kafka. It is used to publish, discover and subscribe to near real-time data streams for operational and product intelligence. Siphon is used as a “Databus” by a variety of producers and subscribers in Microsoft, and is compliant with security and privacy requirements. It has a built-in Auditing and Quality control. This session will provide an overview of the use of Kafka at Microsoft, and then deep dive into Siphon. We will describe an important business scenario and talk about the technical details of the system in the context of that scenario. We will also cover the design and implementation of the service, the scale, and real world production experiences from operating the service in the Microsoft cloud environment.
Advanced Streaming Analytics with Apache Flink and Apache Kafka, Stephan Ewen
Flink and Kafka are popular components to build an open source stream processing infrastructure. We present how Flink integrates with Kafka to provide a platform with a unique feature set that matches the challenging requirements of advanced stream processing applications. In particular, we will dive into the following points:
Flink’s support for event-time processing, how it handles out-of-order streams, and how it can perform analytics on historical and real-time streams served from Kafka’s persistent log using the same code. We present Flink’s windowing mechanism that supports time-, count- and session- based windows, and intermixing event and processing time semantics in one program.
How Flink’s checkpointing mechanism integrates with Kafka for fault-tolerance, for consistent stateful applications with exactly-once semantics.
We will discuss “”Savepoints””, which allows users to save the state of the streaming program at any point in time. Together with a durable event log like Kafka, savepoints allow users to pause/resume streaming programs, go back to prior states, or switch to different versions of the program, while preserving exactly-once semantics.
We explain the techniques behind the combination of low-latency and high throughput streaming, and how latency/throughput trade-off can configured.
We will give an outlook on current developments for streaming analytics, such as streaming SQL and complex event processing.
Securing Kafka
With Apache Kafka 0.9, the community has introduced a number of features to make data streams secure. In this talk, we’ll explain the motivation for making these changes, discuss the design of Kafka security, and explain how to secure a Kafka cluster. We will cover common pitfalls in securing Kafka, and talk about ongoing security work.
Data integration with Apache Kafka
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
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