pyflink-docs Release release-1.15 PyFlink Nov 23, 2022 CONTENTS 1 How to build docs locally 3 1.1 Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . pyflink-docs, Release release-1.15 PyFlink is a Python API for Apache Flink that allows you to build scalable batch and streaming workloads, such as real-time data processing pipelines, large-scale exploratory HOW TO BUILD DOCS LOCALLY 1. Install dependency requirements python3 -m pip install -r dev/requirements.txt 2. Conda install pandoc conda install pandoc 3. Build the docs python3 setup.py build_sphinx

pyflink-docs Release release-1.16 PyFlink Nov 23, 2022 CONTENTS 1 How to build docs locally 3 1.1 Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . pyflink-docs, Release release-1.16 PyFlink is a Python API for Apache Flink that allows you to build scalable batch and streaming workloads, such as real-time data processing pipelines, large-scale exploratory HOW TO BUILD DOCS LOCALLY 1. Install dependency requirements python3 -m pip install -r dev/requirements.txt 2. Conda install pandoc conda install pandoc 3. Build the docs python3 setup.py build_sphinx

updated in the result table since the last trigger will be changed in the external storage. • This mode works for output sinks that can be updated in place, such as a MySQL table. 59 / 79 Output Modes updated in the result table since the last trigger will be changed in the external storage. • This mode works for output sinks that can be updated in place, such as a MySQL table. 59 / 79 Output Modes updated in the result table since the last trigger will be changed in the external storage. • This mode works for output sinks that can be updated in place, such as a MySQL table. 59 / 79 Structured Streaming

ResourceManager, e.g. in a YARN setup • A manual TaskManager re-start or a backup is required in standalone mode • The restart strategy determines how often the JobManager tries to restart the application and metadata about application execution, such as pointers to completed checkpoints. • A high-availability mode migrates the responsibility and metadata for a job to another JobManager in case the original JobManager

| | | Frank | 5 | 12:06 | | ------------------------- | ---------------------------- Stream Mode: 12:01> SELECT Name, SUM(Score), MAX(Time) FROM USER_SCORES GROUP BY Name; Flink 在阿里的服务情况 集群规模

and processing guarantees of streaming systems • be proficient in using Apache Flink and Kafka to build end-to-end, scalable, and reliable streaming applications • have a solid understanding of how stream Vasiliki Kalavri | 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: •

same initial state and given the same sequence of input tuples • convergent-capable: it can re-build internal state in a way that it eventually converges to a non-failure execution output • repeatable: acknowledge reception of input tuples notify upstream of oldest logged tuples necessary to re-build current state Vasiliki Kalavri | Boston University 2020 Upstream backup Recovery time • The

faults caused by high congestion. • In the presence of bursty traffic, CFC causes backpressure to build up fast and propagate along congested VCs to their sources which can be throttled. • Essentially

??? Vasiliki Kalavri | Boston University 2020 22 • Multi-tenancy • in streaming systems that build one dataflow graph for several queries • when applications analyze data streams from a small set

from the StreamExecutionEnvironment val cpConfig: CheckpointConfig = env.getCheckpointConfig // set mode to at-least-once cpConfig.setCheckpointingMode(CheckpointingMode.AT_LEAST_ONCE); // make sure we