PyFlink 1.15 Documentation
management: User-defined function registration, dropping, listing, etc. • Executing SQL queries • Job configuration • Python dependency management • Job submission For more details of how to create a TableEnvironment table import EnvironmentSettings, TableEnvironment env_settings = EnvironmentSettings.in_batch_mode() table_env = TableEnvironment.create(env_settings) table_env [1]:[2]: # Create a streaming TableEnvironment env_settings = EnvironmentSettings.in_streaming_mode() table_env = TableEnvironment.create(env_settings) table_env [2]: 0 码力 | 36 页 | 266.77 KB | 1 年前3PyFlink 1.16 Documentation
management: User-defined function registration, dropping, listing, etc. • Executing SQL queries • Job configuration • Python dependency management • Job submission For more details of how to create a TableEnvironment table import EnvironmentSettings, TableEnvironment env_settings = EnvironmentSettings.in_batch_mode() table_env = TableEnvironment.create(env_settings) table_env [1]:[2]: # Create a streaming TableEnvironment env_settings = EnvironmentSettings.in_streaming_mode() table_env = TableEnvironment.create(env_settings) table_env [2]: 0 码力 | 36 页 | 266.80 KB | 1 年前3Streaming in Apache Flink
and shrinks • queryable: Flink state can be queried via a REST API Rich Functions • open(Configuration c) • close() • getRuntimeContext() DataStream> input = … DataStream > { private ValueState averageState; @Override public void open (Configuration conf) { ValueStateDescriptor descriptor = new ValueStateDescriptor<>("moving String, String> { private ValueState blocked; @Override public void open(Configuration config) { blocked = getRuntimeContext().getState(new ValueStateDescriptor<>("blocked", Boolean 0 码力 | 45 页 | 3.00 MB | 1 年前3Elasticity and state migration: Part I - CS 591 K1: Data Stream Processing and Analytics Spring 2020
Boston University 2020 Control: When and how much to adapt? Mechanism: How to apply the re-configuration? 3 • Detect environment changes: external workload and system performance • Identify bottleneck Flink wordcount Every reconfiguration takes ~30s during which the system is unavailable Re-configuration requires state migration with correctness guarantees. ??? Vasiliki Kalavri | Boston University only complete state is migrated Helpers buffer data that cannot yet be safely routed and configuration commands that cannot yet be applied Live state migration ??? Vasiliki Kalavri | Boston University0 码力 | 93 页 | 2.42 MB | 1 年前3Exactly-once fault-tolerance in Apache Flink - CS 591 K1: Data Stream Processing and Analytics Spring 2020
system execution that yields a system configuration Validity (safety): Termination (liveness): Obtain a valid system configuration A full system configuration is eventually captured A snapshot algorithm system execution that yields a system configuration Validity (safety): Termination (liveness): Obtain a valid system configuration A full system configuration is eventually captured A snapshot algorithm oAk+MBjAE7zAqyOcZ+fNeZ+3lpxiZh9+wfn4BnBTjW4= Epoch-Completeness: Obtain an epoch-complete system configuration 36 ??? Vasiliki Kalavri | Boston University 2020 p10 码力 | 81 页 | 13.18 MB | 1 年前 3Introduction to Apache Flink and Apache Kafka - CS 591 K1: Data Stream Processing and Analytics Spring 2020
factorValues)) } }) 17 Vasiliki Kalavri | Boston University 2020 Configuration options conf/flink-conf.yaml contains the configuration options as a collection of key-value pairs with format key:value0 码力 | 26 页 | 3.33 MB | 1 年前3State management - CS 591 K1: Data Stream Processing and Analytics Spring 2020
handle object private var lastTempState: ValueState[Double] = _ override def open(parameters: Configuration): Unit = { // create state descriptor val lastTempDescriptor = new ValueStateDescriptor[Double]("lastTemp" rideState; private ValueStatefareState; @Override public void open(Configuration config) { // initialize the state descriptors here rideState = getRuntimeContext() 0 码力 | 24 页 | 914.13 KB | 1 年前3Stream processing fundamentals - CS 591 K1: Data Stream Processing and Analytics Spring 2020
the development of streaming algorithms With limited practical value in distributed, real-world settings Vasiliki Kalavri | Boston University 2020 Cash-Register Model: In this model, multiple updates0 码力 | 45 页 | 1.22 MB | 1 年前3Fault-tolerance demo & reconfiguration - CS 591 K1: Data Stream Processing and Analytics Spring 2020
Boston University 2020 Control: When and how much to adapt? Mechanism: How to apply the re-configuration? 12 • Detect environment changes: external workload and system performance • Identify bottleneck0 码力 | 41 页 | 4.09 MB | 1 年前3共 9 条- 1