stream = t_env.to_append_stream( t_env.from_path('my_source'), Types.ROW([Types.SQL_TIMESTAMP(), Types.STRING(), Types.STRING()])) watermarked_stream = stream.assign_timestamps_and_watermarks( WatermarkStrategy.for_monotonous_timestamps() .with_timestamp_assigner(MyTimestampAssigner())...
dump(clf, f) 流式数据预测代码 stream_predict.py import pickle import pandas as pd from pyflink.datastream import StreamExecutionEnvironment from pyflink.table import EnvironmentSettings, StreamTableEnvironment, DataTypes from pyflink.table.udf import udf from pyflink.common.typeinfo import Types env ...
SELECT * FROM FilteredOrders """).to_append_stream().print()env.execute("PyFlink Example")if__name__=="__main__":main() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33....
stream = t_env.to_append_stream( t_env.from_path('my_source'), Types.ROW([Types.SQL_TIMESTAMP(), Types.STRING(), Types.STRING()])) watermarked_stream = stream.assign_timestamps_and_watermarks( WatermarkStrategy.for_monotonous_timestamps() .with_timestamp_assigner(MyTimestampAssigner())...
ds = t_env.to_append_stream( t_env.from_path('my_source'), Types.ROW([Types.INT(), Types.STRING()])) 说明: 由于当前 PyFlink DataStream API 中 built-in 支持的 connector 种类还比较少,推荐通过这种方式来创建 PyFlink DataStream API 作业中使用的数据源表,这样的话,所有 PyFlink Table API...
data = t_env.to_append_stream(result, Types.ROW([Types.FLOAT(), Types.FLOAT()])) data.print() env.execute('stream predict job') 四、完整代码 模型保存代码 model.py importpickleimportpandasaspdfromsklearn.treeimportDecisionTreeClassifier ...
( 'connector' = 'datagen', 'number-of-rows' = '10' ) """) ds = t_env.to_append_stream( t_env.from_path('my_source'), Types.ROW([Types.INT(), Types.STRING()])) def split(s): splits = s[1].split("|") for sp in splits: yield s[0], sp ds = ds.map(lambda i:...
6, 7, 8, 9, 0)); 然后进行分组 Map<Boolean, List<Integer>> collect = integerList.stream()...
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However, one common use case is to run idempotent queries * (e.g., REPLACE or INSERT OVERWRITE) to upsert into the database and * achieve exactly-once semantic. */ public class ClickHouseTableSink implements AppendStreamTableSink<Row> { private static final Integer BATCH_SIZE_DEFAULT = 5000...