Robert Metzger

Join this fast-paced session through major themes in the Flink community: What are the most important FLIPs around Flink SQL, core engine improvements or LLMs? Flink is a big project, it is difficult to keep track of the main developments. In this talk, we will give a high level overview over the most important developments in the community, providing "tl;dr" summaries on important topics so that you can decide what to dive deeper on. Attendees will get a good overview over the recent and upcoming developments in Flink, and how they relate each other. We won’t be able to go very deep into each FLIP, but attendees will get an overview of what is going on in Flink and which FLIPs are worth exploring further.

Have questions about stream processing, stateful architecture, or the future of Apache Flink®? Join an interactive panel of core Flink Committers for an Ask Me Anything (AMA) session.

A common question when adopting Apache Flink is about sizing the workload: How many CPUs, how much memory will Flink require for a particular use-case? What throughput and latency can you expect given your hardware? We’ll kick off this talk discussing why these questions are extremely difficult to answer for a generic stream processing framework like Flink. But we won’t stop there. The best approach to answer sizing questions is to benchmark your Flink workload. We will present how we’ve set up a Flink SQL-based benchmarking environment and some benchmarking results for attendees to correlate our results with their workloads to approximate their resource requirements. Naturally when benchmarking, the topic of performance tuning comes up: Are you optimally using the allocated resources? How to identify performance bottlenecks? What are the most common performance issues, and how to resolve them? In our case, a few configuration drastically improved the performance. How many CPU cores are needed for that in Flink? Attend the talk to find out, less than you would expect. This talk is for both Flink beginners wanting to get an idea about Flink’s performance and operational behavior,…

Join this session to ask long-term Flink community members Robert, Timo and David whatever you want about Flink and the stream processing space. - How does Flink SQL allow you to query historic data? - What's the recommended way of deploying Flink to start learning it? - How do I best contribute a fix I found in production? We are happy to provide our perspective on those and more questions in the session.

In this session, we will briefly introduce how the apache software foundation, and Flink in particular, works as an open source community. The Flink community very open and happy about new contributions, so we will share how our community works, the community over code principle, consensus models and our tools. Then, we'll open it up to the audience to ask their questions.

A common question when adopting Apache Flink is about sizing the workload: How many CPUs, how much memory will Flink require for a particular use-case? What throughput and latency can you expect given your hardware? We’ll kick off this talk discussing why these questions are extremely difficult to answer for a generic stream processing framework like Flink. But we won’t stop there. The best approach to answer sizing questions is to benchmark your Flink workload. We will present how we’ve set up a Flink SQL-based benchmarking environment and some benchmarking results for attendees to correlate our results with their workloads to approximate their resource requirements. Naturally when benchmarking, the topic of performance tuning comes up: Are you optimally using the allocated resources? How to identify performance bottlenecks? What are the most common performance issues, and how to resolve them? In our case, a few configuration drastically improved the performance. How many CPU cores are needed for that in Flink? Attend the talk to find out, less than you would expect. This talk is for both Flink beginners wanting to get an idea about Flink’s performance and operational behavior,…

A common question when adopting Apache Flink is about sizing the workload: How many CPUs, how much memory will Flink require for a particular use-case? What throughput and latency can you expect given your hardware? We’ll kick off this talk discussing why these questions are extremely difficult to answer for a generic stream processing framework like Flink. But we won’t stop there. The best approach to answer sizing questions is to benchmark your Flink workload. We will present how we’ve set up a Flink SQL-based benchmarking environment and some benchmarking results for attendees to correlate our results with their workloads to approximate their resource requirements. Naturally when benchmarking, the topic of performance tuning comes up: Are you optimally using the allocated resources? How to identify performance bottlenecks? What are the most common performance issues, and how to resolve them? In our case, a few configuration changes improved the throughput from 230mb/s to over 3200mb/s. How many CPU cores are needed for that in Flink? Attend the talk to find out, it's less than you would expect. This talk is for both Flink beginners wanting to get an idea about Flink’s…

We will explore options to run Apache Flink with a very low resource footprint, allowing users to run full streaming SQL queries or custom streaming applications on JVMs with less than 500mb

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.

Flink is moving quickly, and so does the community driving it. There are many efforts such as the integration of the Chinese-speaking community, the development of a community packages website or improvements to the contribution process.The sheer number of efforts make it difficult to keep track of all of them — that’s why this talk will provide an overview of what happened in the past year, an...

This talk will introduce you to the various ways of contributing to Apache Flink. We will start by an introduction into the Apache Software Foundation, its history and present-day status, and where Apache Flink fits within the Foundation.

The Apache Flink community has pushed (and continues to push) the boundary for Stream Processing over the last years, following the understanding that Stream Processing is unifying paradigm to build data processing applications, beyond real-time analytics.The latest major effort in the Flink community is nothing less then re-architecting the API and runtime stack, with the goal to naturally sup...