Sources, Sinks, and Operators: A Performance Deep Dive
At Splunk we have built a Flink streaming infrastructure and scaled it to petabytes of data per day and millions of events per second. Along the way, we've learned a lot about writing performant operations in the DataStream API and putting together high-throughput pipelines. This talk will cover our real-life experiences in scaling for this throughput and the best practices we've learned along the way. We'll talk about aggregation, built-in functions, user-defined functions, the async I/O API, as well as discoveries around GC, Java object management, state backends, and serialization. 0:00 Introduction 1.07 Environmental & Performance Testing 3:57 The basic challenges 10:17 Sources 21:22 Sinks 25:10 Putting it all together

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Watermarks: Time and Progress in Apache Beam and Beyond

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KEYNOTE: Apache Flink in the Cloud-native Era

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Introduction to Stateful Stream Processing with Apache Flink • Robert Metzger • GOTO 2019

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Flink Deep Dive - Concepts and Real Examples

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Flink's Table & DataStream API: A Perfect Symbiosis

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CDC Stream Processing with Apache Flink
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16. HPC Cluster Essentials: Tools, Techniques, and Best Practices [HPC in Julia]

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Unlocking the Power of Apache Flink: An Introduction in 4 Acts

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Apache Flink 101 | Building and Running Streaming Applications

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Webinar “Developing an Energy Balancing Platform MVP”

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Streaming Event-Time Partitioning With Apache Flink and Apache Iceberg - Julia Bennett

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Elasticsearch Deep Dive w/ a Ex-Meta Senior Manager for System Design Interviews

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Rundown of Flink's Checkpoints

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Flink vs Kafka Streams/ksqlDB: Comparing Stream Processing Tools

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Change Data Streaming Patterns With Debezium & Apache Flink | Decodable

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Webinar: Deep Dive on Apache Flink State - Seth Wiesman

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Robust Stream Processing with Apache Flink

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Getting into Low-Latency Gears with Apache Flink

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Is Flink the answer to the ETL problem? (with Robert Metzger)

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