Flink Forward San Francisco 2018 keynote: Stephan Ewen - "What turns stream processing from a tool into a platform?"
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Stream Processing is a powerful paradigm, especially when backed by a system like Apache Flink. With each release and year, we see Flink being used for more challenging use case and applications. But beyond the individual application (though it may be grand and challenging in itself), stream processing is a much broader building block: the foundational piece of a platform that brings together the different parts of a data architecture. A platform that integrates data analytics, data ingestion, SQL, Machine Learning, data provenance, databases, and other aspects of a data-driven infrastructure in a meaningful way. In this keynote, we look at what goes into building a stream processing platform that is more than the sum of its parts.
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The Stream Processor as the Database - Apache Flink @ Berlin buzzwords
Presentation about Apache Flink at Berlin Buzzwords conference.
"The Stream Processor as the Database"
Flink Forward SF 2017: Srikanth Satya & Tom Kaitchuck - Pravega: Storage Rei...
Pravega is a stream storage system that we designed and built from the ground up for modern day stream processors such as Flink. Its storage layer is tiered and designed to provide low latency for writing and reading, while being able to store an unbounded amount of stream data that eventually becomes cold. We rely on a high-throughput component to store cold stream data, which is critical to enable applications to rely on Pravega alone for storing stream data. Pravega’s API enables applications to manipulate streams with a set of desirable features such as avoiding duplication and writing data transactionally. Both features are important for applications that require exactly-once semantics. This talk goes into the details of Pravega’s architecture and establishes the need for such a storage system.
Flink Forward San Francisco 2018: Andrew Torson - "Extending Flink metrics: R...
This document discusses extending metrics capabilities in Apache Flink for real-time business intelligence (BI) on streaming data pipelines at Walmart Labs. It proposes adding metric calculators and decorators to Flink operators to compute BI metrics as data flows through pipelines. Metrics can be handled in-process for small dimensions or via side outputs for large dimensions, requiring downstream aggregation. The right metrics database and aggregation techniques should be chosen based on dimensionality.
Elastic Streams at Scale @ Flink Forward 2018 Berlin
This document discusses Elastic Streams at scale using Apache Flink and Mesos. It describes how Flink jobs can be deployed on Mesos clusters by having the Flink master process request resources from the Mesos resource manager. The resource manager then allocates Mesos containers for the Flink master and task managers, allowing the Flink processes to be deployed and tasks to run on the Mesos cluster resources. A Mesos dispatcher can be used to start and monitor the Flink master process.
Tuning Flink For Robustness And Performance
Flink's stateful stream processing engine presents a huge variety of optional features and configuration choices to the user. Figuring out the "optimal" choices for any production environment and use-case can therefore often be challenging. In this talk, we will explore and discuss the universe of Flink configuration with respect to robustness and performance.
We will start with a closer look under the hood, at core data structures and algorithms, to build the foundation for understanding the impact of tuning parameters and the costs-benefit-tradeoffs that come with certain features and options. In particular, we will focus on state backend choices (Heap vs RocksDB), tuning checkpointing (incremental checkpoints, ...) and recovery (local recovery), file systems, TTL state, and considerations for the network stack. This also includes a discussion about estimating memory requirements and memory partitioning.
Stefan Richter
Flink Forward 2018
Berlin
Flink Forward Berlin 2017: Fabian Hueske - Using Stream and Batch Processing ...
Apache Flink's DataStream API is very expressive and gives users precise control over time and state. However, many applications do not require this level of expressiveness and can be implemented more concisely and easily with a domain-specific API. SQL is undoubtedly the most widely used language for data processing but usually applied in the domain of batch processing. Apache Flink features two relational APIs for unified stream and batch processing, the Table API, a language-integrated relational query API for Scala and Java, and SQL. A Table API or SQL query computes the same result regardless whether it is evaluated on a static file or on a Kafka topic. While Flink evaluates queries on batch input like a conventional query engine, queries on streaming input are continuously processed and their results constantly updated and refined. In this talk we present Flink’s unified relational APIs, show how streaming SQL queries are processed, and discuss exciting new use-cases.
January 2016 Flink Community Update & Roadmap 2016
This presentation from the 13th Flink Meetup in Berlin contains the regular community update for January and a walkthrough of the most important upcoming features in 2016
Flink Forward San Francisco 2018: Steven Wu - "Scaling Flink in Cloud"
Over 109 million subscribers are enjoying more than 125 million hours of TV shows and movies per day on Netflix. This leads to massive amount of data flowing through our data ingestion pipeline to improve service and user experience. They are powering various data analytic cases like personalization, operational insight, fraud detection. At the heart of this massive data ingestion pipeline is a self-serve stream processing platform that processes 3 trillion events and 12 PB of data every day. We have recently migrated this stream processing platform from Samza to Flink. In this talk, we will share the challenges and issues that we run into when running Flink at scale in cloud. We will dive deep into the troubleshooting techniques and lessons learned.
Till Rohrmann - Dynamic Scaling - How Apache Flink adapts to changing workloads
http://flink-forward.org/kb_sessions/dynamic-scaling-how-apache-flink-adapts-to-changing-workloads/
Modern stream processing engines not only have to process millions of events per second at sub-second latency but also have to cope with constantly changing workloads. Due to the dynamic nature of stream applications where the number of incoming events can strongly vary with time, systems cannot reliably predetermine the amount of required resources. In order to meet guaranteed SLAs as well as utilizing system resources as efficiently as possible, frameworks like Apache Flink have to adapt their resource consumption dynamically. In this talk, we will take a look under the hood and explain how Flink scales stateful application in and out. Starting with the concept of key groups and partionable state, we will cover ways to detect bottlenecks in streaming jobs and discuss efficient strategies how to scale out operators with minimal down-time.
What's hot (20)
Modern Stream Processing With Apache Flink @ GOTO Berlin 2017
Modern Stream Processing With Apache Flink @ GOTO Berlin 2017
Flink Forward Berlin 2017: Jörg Schad, Till Rohrmann - Apache Flink meets Apa...
Flink Forward Berlin 2017: Jörg Schad, Till Rohrmann - Apache Flink meets Apa...
Virtual Flink Forward 2020: Production-Ready Flink and Hive Integration - wha...
Virtual Flink Forward 2020: Production-Ready Flink and Hive Integration - wha...
Flink Forward Berlin 2017: Stephan Ewen - The State of Flink and how to adopt...
Flink Forward Berlin 2017: Stephan Ewen - The State of Flink and how to adopt...
Flink Forward Berlin 2017: Zohar Mizrahi - Python Streaming API
Flink Forward Berlin 2017: Zohar Mizrahi - Python Streaming API
Unify Enterprise Data Processing System Platform Level Integration of Flink a...
Unify Enterprise Data Processing System Platform Level Integration of Flink a...
Stephan Ewen - Experiences running Flink at Very Large Scale
Stephan Ewen - Experiences running Flink at Very Large Scale
Flink Forward Berlin 2017: Mihail Vieru - A Materialization Engine for Data I...
Flink Forward Berlin 2017: Mihail Vieru - A Materialization Engine for Data I...
The Stream Processor as the Database - Apache Flink @ Berlin buzzwords
The Stream Processor as the Database - Apache Flink @ Berlin buzzwords
Flink Forward SF 2017: Srikanth Satya & Tom Kaitchuck - Pravega: Storage Rei...
Flink Forward SF 2017: Srikanth Satya & Tom Kaitchuck - Pravega: Storage Rei...
Flink Forward San Francisco 2018: Andrew Torson - "Extending Flink metrics: R...
Flink Forward San Francisco 2018: Andrew Torson - "Extending Flink metrics: R...
Elastic Streams at Scale @ Flink Forward 2018 Berlin
Elastic Streams at Scale @ Flink Forward 2018 Berlin
Flink Forward Berlin 2017: Fabian Hueske - Using Stream and Batch Processing ...
Flink Forward Berlin 2017: Fabian Hueske - Using Stream and Batch Processing ...
January 2016 Flink Community Update & Roadmap 2016
January 2016 Flink Community Update & Roadmap 2016
Flink Forward San Francisco 2018: Steven Wu - "Scaling Flink in Cloud"
Flink Forward San Francisco 2018: Steven Wu - "Scaling Flink in Cloud"
Till Rohrmann - Dynamic Scaling - How Apache Flink adapts to changing workloads
Till Rohrmann - Dynamic Scaling - How Apache Flink adapts to changing workloads
Similar to Flink Forward San Francisco 2018 keynote: Stephan Ewen - "What turns stream processing from a tool into a platform?"
Apache Flink and what it is used for
Aljoscha Krettek is the PMC chair of Apache Flink and Apache Beam, and co-founder of data Artisans. Apache Flink is an open-source platform for distributed stream and batch data processing. It allows for stateful computations over data streams in real-time and historically. Flink supports batch and stream processing using APIs like DataSet and DataStream. Data Artisans originated Flink and provides an application platform powered by Flink and Kubernetes for building stateful stream processing applications.
Introducing Events and Stream Processing into Nationwide Building Society (Ro...
Facing Open Banking regulation, rapidly increasing transaction volumes and increasing customer expectations, Nationwide took the decision to take load off their back-end systems through real-time streaming of data changes into Kafka. Hear about how Nationwide started their journey with Kafka, from their initial use case of creating a real-time data cache using Change Data Capture, Kafka and Microservices to how Kafka allowed them to build a stream processing backbone used to reengineer the entire banking experience including online banking, payment processing and mortgage applications. See a working demo of the system and what happens to the system when the underlying infrastructure breaks. Technologies covered include: Change Data Capture, Kafka (Avro, partitioning and replication) and using KSQL and Kafka Streams Framework to join topics and process data.
SnapLogic- iPaaS (Elastic Integration Cloud and Data Integration)
Especially this document provide very useful and meaningful concepts about SnapLogic. Also this document will be more useful for beginner/intermediate level SnapLogic learners.
Keeping Analytics Data Fresh in a Streaming Architecture | John Neal, Qlik
Qlik is an industry leader across its solution stack, both on the Data Integration side of things with Qlik Replicate (real-time CDC) and Qlik Compose (data warehouse and data lake automation), and on the Analytics side with Qlik Sense. These two “sides” of Qlik are coming together more frequently these days as the need for “always fresh” data increases across organizations.
When real-time streaming applications are the topic du jour, those companies are looking to Apache Kafka to provide the architectural backbone those applications require. Those same companies turn to Qlik Replicate to put the data from their enterprise database systems into motion at scale, whether that data resides in “legacy” mainframe databases; traditional relational databases such as Oracle, MySQL, or SQL Server; or applications such as SAP and SalesForce.
In this session we will look in depth at how Qlik Replicate can be used to continuously stream changes from a source database into Apache Kafka. From there, we will explore how a purpose-built consumer can be used to provide the bridge between Apache Kafka and an analytics application such as Qlik Sense.
Databus - LinkedIn's Change Data Capture Pipeline
Introduction to Databus - Linkedin's Change Data Capture Pipeline
https://github.com/linkedin/databus
as presented at Eventbrite - May 07 2013
Introducing Events and Stream Processing into Nationwide Building Society
Watch this talk here: https://www.confluent.io/online-talks/introducing-events-and-stream-processing-nationwide-building-society
Open Banking regulations compel the UK’s largest banks, and building societies to enable their customers to share personal information with other regulated companies securely. As a result companies such as Nationwide Building Society are re-architecting their processes and infrastructure around customer needs to reduce the risk of losing relevance and the ability to innovate.
In this online talk, you will learn why, when facing Open Banking regulation and rapidly increasing transaction volumes, Nationwide decided to take load off their back-end systems through real-time streaming of data changes into Apache Kafka®. You will hear how Nationwide started their journey with Apache Kafka®, beginning with the initial use case of creating a real-time data cache using Change Data Capture, Confluent Platform and Microservices. Rob Jackson, Head of Application Architecture, will also cover how Confluent enabled Nationwide to build the stream processing backbone that is being used to re-engineer the entire banking experience including online banking, payment processing and mortgage applications.
View now to:
-Explore the technologies used by Nationwide to meet the challenges of Open Banking
-Understand how Nationwide is using KSQL and Kafka Streams Framework to join topics and process data.
-Learn how Confluent Platform can enable enterprises such as Nationwide to embrace the event streaming paradigm
-See a working demo of the Nationwide system and what happens when the underlying infrastructure breaks.
Leveraging Mainframe Data for Modern Analytics
The document provides an overview of leveraging mainframe data for modern analytics using Attunity Replicate and Confluent streaming platform powered by Apache Kafka. It discusses the history of mainframes and data migration, how Attunity enables real-time data migration from mainframes, the Confluent streaming platform for building applications using data streams, and how Attunity and Confluent can be combined to modernize analytics using mainframe data streams. Use cases discussed include query offloading and cross-system customer data integration.
Databricks Platform.pptx
The document provides an overview of the Databricks platform, which offers a unified environment for data engineering, analytics, and AI. It describes how Databricks addresses the complexity of managing data across siloed systems by providing a single "data lakehouse" platform where all data and analytics workloads can be run. Key features highlighted include Delta Lake for ACID transactions on data lakes, auto loader for streaming data ingestion, notebooks for interactive coding, and governance tools to securely share and catalog data and models.
Revolutionary Storage for Modern Databases, Applications and Infrastrcture
Sanjay Sabnis presented on next generation storage solutions for modern big data applications. He discussed how NVMe storage provides significantly higher performance than SATA, with speeds over 6x faster for reads and over 40x faster for writes. Pavilion Data offers an all-NVMe rack scale storage array that provides 120GB/s of throughput with DAS-level latency. This solution can meet the performance and scalability demands of big data workloads like MongoDB, Splunk, and containerized applications.
The Power of Distributed Snapshots in Apache Flink
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2FcTsIA.
Stephan Ewen talks about how Apache Flink handles stateful stream processing and how to manage distributed stream processing and data driven applications efficiently with Flink's checkpoints and savepoints. Filmed at qconsf.com.
Stephan Ewen is a PMC member and one of the original creators of Apache Flink, and co-founder and CTO of data Artisans.
Reintroducing the Stream Processor: A universal tool for continuous data anal...
The talk motivates the use of data stream processing technology for different aspects of continuous data computation, beyond "real-time" analysis, to incorporate historical data computation, reliable application logic and interactive analysis.
Spark and Couchbase: Augmenting the Operational Database with Spark
The document discusses integrating Couchbase NoSQL with Apache Spark for augmenting operational databases with analytics. It outlines architectural alignment between Couchbase and Spark, including automatic data sharding and locality, data streaming replication from Couchbase to Spark, predicate pushdown to Couchbase global indexes from Spark, and flexible schemas. Integration points discussed include using the Couchbase data locality hints in Spark, limitations on predicate pushdown for Couchbase views and N1QL, and using the Couchbase change data capture protocol for low-latency data streaming into Spark Streaming.
PNUTS: Yahoo!’s Hosted Data Serving Platform
PNUTS is a massively parallel and geographically distributed database system for Yahoo!’s web applications. It provides data storage organized as hashed or ordered tables, low latency for large numbers of concurrent requests including updates and queries, and novel per-record consistency guarantees. It is a hosted, centrally managed, and geographically distributed service, and utilizes automated load-balancing and failover to reduce operational complexity. The first version of the system is currently serving in production. This presentation describes the motivation for PNUTS and the design and implementation of its table storage and replication layers, and then presents experimental results.
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...
Many had dubbed 2020 as the decade of data. This is indeed an era of data zeitgeist.
From code-centric software development 1.0, we are entering software development 2.0, a data-centric and data-driven approach, where data plays a central theme in our everyday lives.
As the volume and variety of data garnered from myriad data sources continue to grow at an astronomical scale and as cloud computing offers cheap computing and data storage resources at scale, the data platforms have to match in their abilities to process, analyze, and visualize at scale and speed and with ease — this involves data paradigm shifts in processing and storing and in providing programming frameworks to developers to access and work with these data platforms.
In this talk, we will survey some emerging technologies that address the challenges of data at scale, how these tools help data scientists and machine learning developers with their data tasks, why they scale, and how they facilitate the future data scientists to start quickly.
In particular, we will examine in detail two open-source tools MLflow (for machine learning life cycle development) and Delta Lake (for reliable storage for structured and unstructured data).
Other emerging tools such as Koalas help data scientists to do exploratory data analysis at scale in a language and framework they are familiar with as well as emerging data + AI trends in 2021.
You will understand the challenges of machine learning model development at scale, why you need reliable and scalable storage, and what other open source tools are at your disposal to do data science and machine learning at scale.
Streaming data for real time analysis
See how to use Amazon Web Services, such as Amazon Kinesis, Amazon Redshift, to do real time big data streaming and analytics.
A Data Streaming Architecture with Apache Flink (berlin Buzzwords 2016)
This document discusses Apache Flink, an open source stream processing framework. It describes how Flink enables streaming Extract, Transform, Load (ETL) workflows with low latency and high throughput. The document outlines how streaming ETL can continuously move and transform data as it arrives, rather than in periodic batch jobs. It concludes with an announcement for an upcoming Flink hackathon and questions.
QCon London - Stream Processing with Apache Flink
Robert Metzger presented on Apache Flink, an open source stream processing framework. He discussed how streaming data enables real-time analysis with low latency compared to traditional batch processing. Flink provides unique building blocks like windows, state handling, and fault tolerance to process streaming data reliably at high throughput. Benchmark results showed Flink achieving throughputs over 15 million messages/second, outperforming Storm by 35x.
Continus sql with sql stream builder
This document discusses Continuous SQL with SQL Stream Builder. It provides an agenda for a meetup covering an introduction, overview of Apache Flink and SQL Stream Builder, demos and Q&A. SQL Stream Builder allows anyone familiar with SQL to create powerful stream processors for real-time analytics by leveraging Apache Flink's scalable and high performance processing. It provides an interactive interface and deep integration features beyond just UI capabilities.
GOTO Night Amsterdam - Stream processing with Apache Flink
This document discusses Apache Flink, an open source stream processing framework. It provides an overview of Flink and how it enables low-latency stream processing compared to traditional batch processing systems. Key aspects covered include windowing, state handling, fault tolerance, and performance benchmarks showing Flink can achieve high throughput. The document demonstrates how Flink addresses challenges like out-of-order events, state management, and exactly-once processing through features like event-time processing, managed state, and distributed snapshots.
(Past), Present, and Future of Apache Flink
This document discusses the past, present, and future of Apache Flink. It describes how Flink enables stateful computations over data streams, as well as batch and stream processing. New features in Flink 1.5 include improved integration with resource managers, localized failure recovery, and a 50% rewritten network stack. Upcoming features in Flink 1.6 involve autoscaling, hot-standby replication for high availability, and zero-downtime upgrades. The document concludes that Flink is already the best open-source stream processing system and more exciting features are planned for the future.
Similar to Flink Forward San Francisco 2018 keynote: Stephan Ewen - "What turns stream processing from a tool into a platform?" (20)
Introducing Events and Stream Processing into Nationwide Building Society (Ro...
Introducing Events and Stream Processing into Nationwide Building Society (Ro...
SnapLogic- iPaaS (Elastic Integration Cloud and Data Integration)
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Keeping Analytics Data Fresh in a Streaming Architecture | John Neal, Qlik
Keeping Analytics Data Fresh in a Streaming Architecture | John Neal, Qlik
Introducing Events and Stream Processing into Nationwide Building Society
Introducing Events and Stream Processing into Nationwide Building Society
Revolutionary Storage for Modern Databases, Applications and Infrastrcture
Revolutionary Storage for Modern Databases, Applications and Infrastrcture
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The Power of Distributed Snapshots in Apache Flink
Reintroducing the Stream Processor: A universal tool for continuous data anal...
Reintroducing the Stream Processor: A universal tool for continuous data anal...
Spark and Couchbase: Augmenting the Operational Database with Spark
Spark and Couchbase: Augmenting the Operational Database with Spark
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The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...
A Data Streaming Architecture with Apache Flink (berlin Buzzwords 2016)
A Data Streaming Architecture with Apache Flink (berlin Buzzwords 2016)
GOTO Night Amsterdam - Stream processing with Apache Flink
GOTO Night Amsterdam - Stream processing with Apache Flink
More from Flink Forward
Building a fully managed stream processing platform on Flink at scale for Lin...
Apache Flink is a distributed stream processing framework that allows users to process and analyze data in real-time. At LinkedIn, we developed a fully managed stream processing platform on Flink running on K8s to power hundreds of stream processing pipelines in production. This platform is the backbone for other infra systems like Search, Espresso (internal document store) and feature management etc. We provide a rich authoring and testing environment which allows users to create, test, and deploy their streaming jobs in a self-serve fashion within minutes. Users can focus on their business logic, leaving the Flink platform to take care of management aspects such as split deployment, resource provisioning, auto-scaling, job monitoring, alerting, failure recovery and much more. In this talk, we will introduce the overall platform architecture, highlight the unique value propositions that it brings to stream processing at LinkedIn and share the experiences and lessons we have learned.
Evening out the uneven: dealing with skew in Flink
Flink Forward San Francisco 2022.
When running Flink jobs, skew is a common problem that results in wasted resources and limited scalability. In the past years, we have helped our customers and users solve various skew-related issues in their Flink jobs or clusters. In this talk, we will present the different types of skew that users often run into: data skew, key skew, event time skew, state skew, and scheduling skew, and discuss solutions for each of them. We hope this will serve as a guideline to help you reduce skew in your Flink environment.
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Jun Qin & Karl Friedrich
“Alexa, be quiet!”: End-to-end near-real time model building and evaluation i...
Flink Forward San Francisco 2022.
To improve Amazon Alexa experiences and support machine learning inference at scale, we built an automated end-to-end solution for incremental model building or fine-tuning machine learning models through continuous learning, continual learning, and/or semi-supervised active learning. Customer privacy is our top concern at Alexa, and as we build solutions, we face unique challenges when operating at scale such as supporting multiple applications with tens of thousands of transactions per second with several dependencies including near-real time inference endpoints at low latencies. Apache Flink helps us transform and discover metrics in near-real time in our solution. In this talk, we will cover the challenges that we faced, how we scale the infrastructure to meet the needs of ML teams across Alexa, and go into how we enable specific use cases that use Apache Flink on Amazon Kinesis Data Analytics to improve Alexa experiences to delight our customers while preserving their privacy.
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Flink Forward San Francisco 2022.
Probably everyone who has written stateful Apache Flink applications has used one of the fault-tolerant keyed state primitives ValueState, ListState, and MapState. With RocksDB, however, retrieving and updating items comes at an increased cost that you should be aware of. Sometimes, these may not be avoidable with the current API, e.g., for efficient event-time stream-sorting or streaming joins where you need to iterate one or two buffered streams in the right order. With FLIP-220, we are introducing a new state primitive: BinarySortedMultiMapState. This new form of state offers you to (a) efficiently store lists of values for a user-provided key, and (b) iterate keyed state in a well-defined sort order. Both features can be backed efficiently by RocksDB with a 2x performance improvement over the current workarounds. This talk will go into the details of the new API and its implementation, present how to use it in your application, and talk about the process of getting it into Flink.
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Nico Kruber
Introducing the Apache Flink Kubernetes Operator
Flink Forward San Francisco 2022.
The Apache Flink Kubernetes Operator provides a consistent approach to manage Flink applications automatically, without any human interaction, by extending the Kubernetes API. Given the increasing adoption of Kubernetes based Flink deployments the community has been working on a Kubernetes native solution as part of Flink that can benefit from the rich experience of community members and ultimately make Flink easier to adopt. In this talk we give a technical introduction to the Flink Kubernetes Operator and demonstrate the core features and use-cases through in-depth examples."
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Thomas Weise
Autoscaling Flink with Reactive Mode
Flink Forward San Francisco 2022.
Resource Elasticity is a frequently requested feature in Apache Flink: Users want to be able to easily adjust their clusters to changing workloads for resource efficiency and cost saving reasons. In Flink 1.13, the initial implementation of Reactive Mode was introduced, later releases added more improvements to make the feature production ready. In this talk, we’ll explain scenarios to deploy Reactive Mode to various environments to achieve autoscaling and resource elasticity. We’ll discuss the constraints to consider when planning to use this feature, and also potential improvements from the Flink roadmap. For those interested in the internals of Flink, we’ll also briefly explain how the feature is implemented, and if time permits, conclude with a short demo.
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Robert Metzger
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Flink Forward San Francisco 2022.
Flink consumers read from Kafka as a scalable, high throughput, and low latency data source. However, there are challenges in scaling out data streams where migration and multiple Kafka clusters are required. Thus, we introduced a new Kafka source to read sharded data across multiple Kafka clusters in a way that conforms well with elastic, dynamic, and reliable infrastructure. In this presentation, we will present the source design and how the solution increases application availability while reducing maintenance toil. Furthermore, we will describe how we extended the existing KafkaSource to provide mechanisms to read logical streams located on multiple clusters, to dynamically adapt to infrastructure changes, and to perform transparent cluster migrations and failover.
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Mason Chen
One sink to rule them all: Introducing the new Async Sink
Flink Forward San Francisco 2022.
Next time you want to integrate with a new destination for a demo, concept or production application, the Async Sink framework will bootstrap development, allowing you to move quickly without compromise. In Flink 1.15 we introduced the Async Sink base (FLIP-171), with the goal to encapsulate common logic and allow developers to focus on the key integration code. The new framework handles things like request batching, buffering records, applying backpressure, retry strategies, and at least once semantics. It allows you to focus on your business logic, rather than spending time integrating with your downstream consumers. During the session we will dive deep into the internals to uncover how it works, why it was designed this way, and how to use it. We will code up a new sink from scratch and demonstrate how to quickly push data to a destination. At the end of this talk you will be ready to start implementing your own Flink sink using the new Async Sink framework.
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Steffen Hausmann & Danny Cranmer
Tuning Apache Kafka Connectors for Flink.pptx
Flink Forward San Francisco 2022.
In normal situations, the default Kafka consumer and producer configuration options work well. But we all know life is not all roses and rainbows and in this session we’ll explore a few knobs that can save the day in atypical scenarios. First, we'll take a detailed look at the parameters available when reading from Kafka. We’ll inspect the params helping us to spot quickly an application lock or crash, the ones that can significantly improve the performance and the ones to touch with gloves since they could cause more harm than benefit. Moreover we’ll explore the partitioning options and discuss when diverging from the default strategy is needed. Next, we’ll discuss the Kafka Sink. After browsing the available options we'll then dive deep into understanding how to approach use cases like sinking enormous records, managing spikes, and handling small but frequent updates.. If you want to understand how to make your application survive when the sky is dark, this session is for you!
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Olena Babenko
Flink powered stream processing platform at Pinterest
Flink Forward San Francisco 2022.
Pinterest is a visual discovery engine that serves over 433MM users. Stream processing allows us to unlock value from realtime data for pinners. At Pinterest, we adopt Flink as the unified streaming processing engine. In this talk, we will share our journey in building a stream processing platform with Flink and how we onboarding critical use cases to the platform. Pinterest has supported 90+near realtime streaming applications. We will cover the problem statement, how we evaluate potential solutions and our decision to build the framework.
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Rainie Li & Kanchi Masalia
Apache Flink in the Cloud-Native Era
Flink Forward San Francisco 2022.
This talk will take you on the long journey of Apache Flink into the cloud-native era. It started all the way from where Hadoop and YARN were the standard way of deploying and operating data applications.
We're going to deep dive into the cloud-native set of principles and how they map to the Apache Flink internals and recent improvements. We'll cover fast checkpointing, fault tolerance, resource elasticity, minimal infrastructure dependencies, industry-standard tooling, ease of deployment and declarative APIs.
After this talk you'll get a broader understanding of the operational requirements for a modern streaming application and where the current limits are.
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David Moravek
Where is my bottleneck? Performance troubleshooting in Flink
Flinkn Forward San Francisco 2022.
In this talk, we will cover various topics around performance issues that can arise when running a Flink job and how to troubleshoot them. We’ll start with the basics, like understanding what the job is doing and what backpressure is. Next, we will see how to identify bottlenecks and which tools or metrics can be helpful in the process. Finally, we will also discuss potential performance issues during the checkpointing or recovery process, as well as and some tips and Flink features that can speed up checkpointing and recovery times.
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Piotr Nowojski
Using the New Apache Flink Kubernetes Operator in a Production Deployment
Flink Forward San Francisco 2022.
Running natively on Kubernetes, using the new Apache Flink Kubernetes Operator is a great way to deploy and manage Flink application and session deployments. In this presentation, we provide: - A brief overview of Kubernetes operators and their benefits. - Introduce the five levels of the operator maturity model. - Introduce the newly released Apache Flink Kubernetes Operator and FlinkDeployment CRs - Dockerfile modifications you can make to swap out UBI images and Java of the underlying Flink Operator container - Enhancements we're making in: - Versioning/Upgradeability/Stability - Security - Demo of the Apache Flink Operator in-action, with a technical preview of an upcoming product using the Flink Kubernetes Operator. - Lessons learned - Q&A
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James Busche & Ted Chang
The Current State of Table API in 2022
Flink Forward San Francisco 2022.
The Table API is one of the most actively developed components of Flink in recent time. Inspired by databases and SQL, it encapsulates concepts many developers are familiar with. It can be used with both bounded and unbounded streams in a unified way. But from afar it can be difficult to keep track of what this API is capable of and how it relates to Flink's other APIs. In this talk, we will explore the current state of Table API. We will show how it can be used as a batch processor, a changelog processor, or a streaming ETL tool with many built-in functions and operators for deduplicating, joining, and aggregating data. By comparing it to the DataStream API we will highlight differences and elaborate on when to use which API. We will demonstrate hybrid pipelines in which both APIs interact with one another and contribute their unique strengths. Finally, we will take a look at some of the most recent additions as a first step to stateful upgrades.
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David Andreson
Flink SQL on Pulsar made easy
Flink Forward San Francisco 2022.
Based on the new Flink-Pulsar connector, we implemented Flink's TableAPI and Catalog to help users to interact with the Pulsar cluster via Flink SQL easily. We would like to go through the design and implementation of the SQL connector in the following aspects:
1. Two different modes of use Pulsar as a metadata store
2. Data format transformation and management
3. SQL semantics support within Pulsar context
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Sijie Guo & Neng Lu
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Flink Forward San Francisco 2022.
At Bloomberg, we deal with high volumes of real-time market data. Our clients expect to be notified of any anomalies in this market data, which may indicate volatile movements in the markets, notable trades, forthcoming events, or system failures. The parameters for these alerts are always evolving and our clients can update them dynamically. In this talk, we'll cover how we utilized the open source Apache Flink and Siddhi SQL projects to build a distributed, scalable, low-latency and dynamic rule-based, real-time alerting system to solve our clients' needs. We'll also cover the lessons we learned along our journey.
by
Ajay Vyasapeetam & Madhuri Jain
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Flink Forward San Francisco 2022.
At Stripe we have created a complete end to end exactly-once processing pipeline to process financial data at scale, by combining the exactly-once power from Flink, Kafka, and Pinot together. The pipeline provides exactly-once guarantee, end-to-end latency within a minute, deduplication against hundreds of billions of keys, and sub-second query latency against the whole dataset with trillion level rows. In this session we will discuss the technical challenges of designing, optimizing, and operating the whole pipeline, including Flink, Kafka, and Pinot. We will also share our lessons learned and the benefits gained from exactly-once processing.
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Xiang Zhang & Pratyush Sharma & Xiaoman Dong
Processing Semantically-Ordered Streams in Financial Services
Flink Forward San Francisco 2022.
What if my data is already in order? Stream Processing has given us an elegant and powerful solution for running analytic queries and logic over high volumes of continuously arriving data. However, in both Apache Flink and Apache Beam, the notion of time-ordering is baked in at a very low level, making it difficult to express computations that are interested in a semantic-, rather than time-ordering of the data. In financial services, what often matters the most about the data moving between systems is not when the data was created, but in what order, to the extent that many institutions engineer a global sequencing over all data entering and produced by their systems to achieve complete determinism. How, then, can financial institutions and others best employ Stream Processing on streams of data that are already ordered? I will cover various techniques that can make this work, as well as seek input from the community on how Flink might be improved to better support these use-cases.
by
Patrick Lucas
Tame the small files problem and optimize data layout for streaming ingestion...
Flink Forward San Francisco 2022.
In modern data platform architectures, stream processing engines such as Apache Flink are used to ingest continuous streams of data into data lakes such as Apache Iceberg. Streaming ingestion to iceberg tables can suffer by two problems (1) small files problem that can hurt read performance (2) poor data clustering that can make file pruning less effective. To address those two problems, we propose adding a shuffling stage to the Flink Iceberg streaming writer. The shuffling stage can intelligently group data via bin packing or range partition. This can reduce the number of concurrent files that every task writes. It can also improve data clustering. In this talk, we will explain the motivations in details and dive into the design of the shuffling stage. We will also share the evaluation results that demonstrate the effectiveness of smart shuffling.
by
Gang Ye & Steven Wu
Batch Processing at Scale with Flink & Iceberg
Flink Forward San Francisco 2022.
Goldman Sachs's Data Lake platform serves as the firm's centralized data platform, ingesting 140K (and growing!) batches per day of Datasets of varying shape and size. Powered by Flink and using metadata configured by platform users, ingestion applications are generated dynamically at runtime to extract, transform, and load data into centralized storage where it is then exported to warehousing solutions such as Sybase IQ, Snowflake, and Amazon Redshift. Data Latency is one of many key considerations as producers and consumers have their own commitments to satisfy. Consumers range from people/systems issuing queries, to applications using engines like Spark, Hive, and Presto to transform data into refined Datasets. Apache Iceberg allows our applications to not only benefit from consistency guarantees important when running on eventually consistent storage like S3, but also allows us the opportunity to improve our batch processing patterns with its scalability-focused features.
by
Andreas Hailu
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Overview
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Flink Forward San Francisco 2018 keynote: Stephan Ewen - "What turns stream processing from a tool into a platform?"
- 1. - STEPHAN EWEN, CO-FOUNDER & CTO STREAM PROCESSING FROM APPLICATIONS TO PLATFORMS
- 2. 2 A platform makes building new applications simple by taking care of the common and repeatable parts.
- 3. 3 Internal streaming data platforms built with Apache Flink
- 4. 4 Observation 1 Stream Processing is about building applications
- 5. Batch / Data Lake Architecture 5 a.k.a. collect now, figure out later
- 6. Streaming / Data-driven Applications 6 build applications directly on data streams
- 7. 7 Observation 2 Stream Processing changes the database-centric architecture
- 8. Recall last Flink Forward… 8 Classic tiered architecture Streaming architecture database layer compute layer application working state + historic state compute + stream storage and snapshot storage (backup) application state
- 9. Changing the Two Tier Architecture 9 reads/writes across tier boundary asynchronous writes of large blobs all modifications are local Classic tiered architecture Streaming architecture
- 13. Kubernetes & Stateful Applications 13 Kubernetes Database
- 14. What about stateful containers? 14 Kubernetes • Example: Scaling down a replicated database • 3 replicas, 4 node scale down need to move or reorganize data before container shutdown
- 15. Stateful Questions consistent stateful upgrades • application evolution and bug fixes migration of application state • cluster migration, A/B testing re-processing and reinstatement • fix corrupt results, bootstrap new applications state evolution (schema evolution) 15 A B
- 16. 16 Container-based Resource Orchestration Stateful Stream Processing & Snapshots Kubernetes Apache Flink Container-based platform for stateful data-driven applications dA Platform Code, Resource, Config, and Snapshot Management Application Manager
- 18. Versioned Applications, not Jobs/Jars 18 Stream Processing Application Version 3 Version 2 Version 1 Code and Application Snapshot upgrade upgrade New Application Version 3a Version 2a fork / duplicate
- 19. Architecture Apache Flink Stateful stream processing Kubernetes Container platform Logging Metrics dA Application Manager Application lifecycle management
- 20. 20 What could the future of a Streaming Data Platform look like?
- 21. The Usual Suspects Role-based access control Metadata management Cross Datacenter Failover / Disaster Recovery 21
- 22. Support for Batch Processing 22 Everything is a stream. Finite applications as a special case.
- 23. Periodic Bursty Stream Processing 23 time Bursty Event Stream (events only at end-of-day ) Checkpoint / Savepoint Store
- 24. Support a Broad Developer Audience 24 Streaming Data Platform …
- 25. Use Case Vertical Libraries 25 Streaming Data Platform SQL CEP … Machine Learning
- 26. Apache Flink Stateful stream processing Kubernetes Container platform Logging Metrics dA Application Manager Application lifecycle management dA Platform is a turnkey solution for stateful stream processing with Apache Flink.