..................................................................................... 14 4.12 Monitoring Latency....................................................................................... ............ 23 caolei – 监控Apache Flink应用程序(入门) – 4 原文地址：https://www.ververica.com/blog/monitoring-apache-flink-applications-101 这篇博文介绍了Apache Flink内置的监控和度量系统，通过该系统，开发人员可以有效地监控他们的Flink作 业。通常，对于一个刚刚开始使用Apache org/projects/flink/flink-docs-release-1.7/monitoring/metrics.html#registering-metrics 2 https://ci.apache.org/projects/flink/flink-docs-release-1.7/monitoring/metrics.html#reporter Flink指标体系 – 5 1 Flink指标体系

Vasiliki Kalavri | Boston University 2020 Grading Scheme (2) Final Project (50%): • A real-time monitoring and anomaly detection framework • To be implemented individually Deliverables • One (1) written Boston University 2020 Final Project You will use Apache Flink and Kafka to build a real-time monitoring and anomaly detection framework for datacenters. Your framework will: • Detect “suspicious” Online recommendations Vasiliki Kalavri | Boston University 2020 Sensor measurements analysis • Monitoring applications • Complex filtering and alarm activation • Aggregation of multiple sensors and joins

the jth update (k, c[j]), it must hold that c[j] ≥ 0. This can model insertion-only streams: • monitoring the total packets exchanged between two IP addresses • the collection of IP addresses accessing continuously inserted and deleted from the stream. It can model fully dynamic situations: • Monitoring active IP network connections is a Turnstile stream, as connections can be initiated or terminated

changed. • Logging to multiple systems • a Google Compute Engine instance can write logs to the monitoring system, to a database for later querying, and so on. • Data streaming from various processes

load imbalance • Resource management • utilization, isolation • Automation • continuous monitoring • bottleneck detection • stability, accuracy 11 Challenges of reconfiguration ??? Vasiliki

QuickStart: DataStream API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.2 User Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 execute() [8]: 1.2 User Guide 1.2.1 RealTime Feature 1.2.1.1 Coming Soon. 1.2.2 PyFlink + Flink ML 1.2.2.1 Coming Soon. 1.3

QuickStart: DataStream API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.2 User Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 execute() [8]: 1.2 User Guide 1.2.1 RealTime Feature 1.2.1.1 Coming Soon. 1.2.2 PyFlink + Flink ML 1.2.2.1 Coming Soon. 1.3

Asynchronous barriers 76 / 79 Summary 77 / 79 References ▶ M. Zaharia et al., “Spark: The Definitive Guide”, O’Reilly Media, 2018 - Chapters 20-23. ▶ M. Zaharia et al., “Discretized Streams: An Efficient