PyFlink 1.15 DocumentationStreamExecutionEnvironment.get_execution_environment() t_env = StreamTableEnvironment.create(env) ds = env.from_collection([(1, 'Hi'), (2, 'Hello')], type_info=Types.ROW_NAMED( ["id", "data"], [Types.BYTE(), Types.STRING()])) DataStream from a Python List Object [2]: from pyflink.common.typeinfo import Types ds = env.from_collection([(1, 'aaa|bb'), (2, 'bb|a'), (3, 'aaa|a')]) # if you don't specify the `type_info`, the default PickledByteArrayTypeInfo Create a DataStream with an explicit type_info. [3]: ds = env.from_collection( collection=[(1, 'aaa|bb'), (2, 'bb|a'), (3, 'aaa|a')], type_info=Types.ROW([Types.INT(), Types.STRING()]))0 码力 | 36 页 | 266.77 KB | 1 年前3
PyFlink 1.16 DocumentationStreamExecutionEnvironment.get_execution_environment() t_env = StreamTableEnvironment.create(env) ds = env.from_collection([(1, 'Hi'), (2, 'Hello')], type_info=Types.ROW_NAMED( ["id", "data"], [Types.BYTE(), Types.STRING()])) DataStream from a Python List Object [2]: from pyflink.common.typeinfo import Types ds = env.from_collection([(1, 'aaa|bb'), (2, 'bb|a'), (3, 'aaa|a')]) # if you don't specify the `type_info`, the default PickledByteArrayTypeInfo Create a DataStream with an explicit type_info. [3]: ds = env.from_collection( collection=[(1, 'aaa|bb'), (2, 'bb|a'), (3, 'aaa|a')], type_info=Types.ROW([Types.INT(), Types.STRING()]))0 码力 | 36 页 | 266.80 KB | 1 年前3
监控Apache Flink应用程序(入门)Generation garbage collection. Status.JVM.GarbageCollector.G1 Old Generation.Time job-/ taskmana ger The total time spent performing G1 Old Generation garbage collection. caolei – 监控Apache TaskManager memory consumption and garbage collection times. caolei – 监控Apache Flink应用程序(入门) 进度和吞吐量监控 – 20 Figure 7: JobManager memory consumption and garbage collection times. 4.13.1.3 Possible Alerts (event-time skew) than usual. A sudden increase in the CPU load might also be attributed to high garbage collection pressure, which should be visible in the JVM memory metrics as well. If one or a few TaskManagers0 码力 | 23 页 | 148.62 KB | 1 年前3
Introduction to Apache Flink and Apache Kafka - CS 591 K1: Data Stream Processing and Analytics Spring 2020University 2020 Configuration options conf/flink-conf.yaml contains the configuration options as a collection of key-value pairs with format key:value Common options you might need to adjust: jobmanager0 码力 | 26 页 | 3.33 MB | 1 年前3
Stream processing fundamentals - CS 591 K1: Data Stream Processing and Analytics Spring 2020insertion-only streams: • monitoring the total packets exchanged between two IP addresses • the collection of IP addresses accessing a web server 12 With some practical value for use-cases with append-only0 码力 | 45 页 | 1.22 MB | 1 年前3
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