reswqa commented on code in PR #25100: URL: https://github.com/apache/flink/pull/25100#discussion_r1680957274
########## docs/content/docs/dev/datastream/operators/full_window_partition.md: ########## @@ -0,0 +1,112 @@ +--- +title: "Full Window Partition" +weight: 5 +type: docs +aliases: + - /dev/stream/operators/full_window_partition.html +--- +<!-- +Licensed to the Apache Software Foundation (ASF) under one +or more contributor license agreements. See the NOTICE file +distributed with this work for additional information +regarding copyright ownership. The ASF licenses this file +to you under the Apache License, Version 2.0 (the +"License"); you may not use this file except in compliance +with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, +software distributed under the License is distributed on an +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +KIND, either express or implied. See the License for the +specific language governing permissions and limitations +under the License. +--> + +# Full Window Partition Processing on DataStream + +This page explains the use of full window partition processing API on DataStream. +Flink enables both keyed and non-keyed DataStream to directly transform into +`PartitionWindowedStream` now. +The PartitionWindowedStream represents the collection all records of each subtask separately +into a full window. +The `PartitionWindowedStream` support four APIs: `mapPartition`, `sortPartition`, `aggregate` +and `reduce`. + +Note: Details about the design and implementation of the full window partition processing can be +found in the proposal and design document +[FLIP-380: Support Full Partition Processing On Non-keyed DataStream](https://cwiki.apache.org/confluence/display/FLINK/FLIP-380%3A+Support+Full+Partition+Processing+On+Non-keyed+DataStream). + +## MapPartition + +`MapPartition` represents collecting all records of each subtask separately into a full window +and process them using the given `MapPartitionFunction` within each subtask. The +`MapPartitionFunction` is called at the end of inputs. + +An example of calculating the sum of the elements in each subtask is as follows: + +```java +DataStream<Integer> dataStream = //... +PartitionWindowedStream<Integer> partitionWindowedDataStream = dataStream.fullWindowPartition(); + +DataStream<Integer> resultStream = partitionWindowedDataStream.mapPartition( + new MapPartitionFunction<Integer, Integer>() { + @Override + public void mapPartition( + Iterable<Integer> values, Collector<Integer> out) { + int result = 0; + for (Integer value : values) { + result += value; + } + out.collect(result); + } + } +); +resultStream.executeAndCollect(); Review Comment: ```suggestion ``` Not necessary to show this `execute` line. ########## docs/content/docs/dev/datastream/operators/full_window_partition.md: ########## @@ -0,0 +1,112 @@ +--- +title: "Full Window Partition" +weight: 5 +type: docs +aliases: + - /dev/stream/operators/full_window_partition.html +--- +<!-- +Licensed to the Apache Software Foundation (ASF) under one +or more contributor license agreements. See the NOTICE file +distributed with this work for additional information +regarding copyright ownership. The ASF licenses this file +to you under the Apache License, Version 2.0 (the +"License"); you may not use this file except in compliance +with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, +software distributed under the License is distributed on an +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +KIND, either express or implied. See the License for the +specific language governing permissions and limitations +under the License. +--> + +# Full Window Partition Processing on DataStream + +This page explains the use of full window partition processing API on DataStream. +Flink enables both keyed and non-keyed DataStream to directly transform into +`PartitionWindowedStream` now. +The PartitionWindowedStream represents the collection all records of each subtask separately +into a full window. +The `PartitionWindowedStream` support four APIs: `mapPartition`, `sortPartition`, `aggregate` +and `reduce`. + +Note: Details about the design and implementation of the full window partition processing can be +found in the proposal and design document +[FLIP-380: Support Full Partition Processing On Non-keyed DataStream](https://cwiki.apache.org/confluence/display/FLINK/FLIP-380%3A+Support+Full+Partition+Processing+On+Non-keyed+DataStream). + +## MapPartition + +`MapPartition` represents collecting all records of each subtask separately into a full window +and process them using the given `MapPartitionFunction` within each subtask. The +`MapPartitionFunction` is called at the end of inputs. + +An example of calculating the sum of the elements in each subtask is as follows: + +```java +DataStream<Integer> dataStream = //... +PartitionWindowedStream<Integer> partitionWindowedDataStream = dataStream.fullWindowPartition(); + +DataStream<Integer> resultStream = partitionWindowedDataStream.mapPartition( + new MapPartitionFunction<Integer, Integer>() { + @Override + public void mapPartition( + Iterable<Integer> values, Collector<Integer> out) { + int result = 0; + for (Integer value : values) { + result += value; + } + out.collect(result); + } + } +); +resultStream.executeAndCollect(); +``` + +## SortPartition +`SortPartition` represents collecting all records of each subtask separately into a full window +and sorts them by the given record comparator in each subtask at the end of inputs. + +An example of sorting the records by the first element in each subtask is as follows: + +```java +DataStream<Tuple2<Integer, Integer>> dataStream = //... +PartitionWindowedStream<Tuple2<Integer, Integer>> partitionWindowedDataStream = dataStream.fullWindowPartition(); + +DataStream<Integer> resultStream = partitionWindowedDataStream.sortPartition(0, Order.ASCENDING); +resultStream.executeAndCollect(); +``` + +## Aggregate +`Aggregate` represents collecting all records of each subtask separately into a full window and +applies the given `AggregateFunction` to the records of the window. The `AggregateFunction` +is called for each element, aggregating values incrementally within the window. + +An example of aggregate the records in each subtask is as follows: + +```java +DataStream<Tuple2<Integer, Integer>> dataStream = //... +PartitionWindowedStream<Tuple2<Integer, Integer>> partitionWindowedDataStream = dataStream.fullWindowPartition(); + +DataStream<Integer> resultStream = partitionWindowedDataStream.aggregate(new AggregateFunction<>{...}); +resultStream.executeAndCollect(); +``` + +## Reduce +`Reduce` represents applies a reduce transformation on the records of the window. Review Comment: ```suggestion `Reduce` represents applies a reduce transformation on all the records in the partition. ``` ########## docs/content/docs/dev/datastream/operators/full_window_partition.md: ########## @@ -0,0 +1,112 @@ +--- +title: "Full Window Partition" +weight: 5 +type: docs +aliases: + - /dev/stream/operators/full_window_partition.html +--- +<!-- +Licensed to the Apache Software Foundation (ASF) under one +or more contributor license agreements. See the NOTICE file +distributed with this work for additional information +regarding copyright ownership. The ASF licenses this file +to you under the Apache License, Version 2.0 (the +"License"); you may not use this file except in compliance +with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, +software distributed under the License is distributed on an +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +KIND, either express or implied. See the License for the +specific language governing permissions and limitations +under the License. +--> + +# Full Window Partition Processing on DataStream + +This page explains the use of full window partition processing API on DataStream. +Flink enables both keyed and non-keyed DataStream to directly transform into +`PartitionWindowedStream` now. +The PartitionWindowedStream represents the collection all records of each subtask separately Review Comment: ```suggestion The `PartitionWindowedStream` represents the collection all records of each subtask separately ``` ########## docs/content/docs/dev/datastream/operators/full_window_partition.md: ########## @@ -0,0 +1,112 @@ +--- +title: "Full Window Partition" +weight: 5 +type: docs +aliases: + - /dev/stream/operators/full_window_partition.html +--- +<!-- +Licensed to the Apache Software Foundation (ASF) under one +or more contributor license agreements. See the NOTICE file +distributed with this work for additional information +regarding copyright ownership. The ASF licenses this file +to you under the Apache License, Version 2.0 (the +"License"); you may not use this file except in compliance +with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, +software distributed under the License is distributed on an +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +KIND, either express or implied. See the License for the +specific language governing permissions and limitations +under the License. +--> + +# Full Window Partition Processing on DataStream + +This page explains the use of full window partition processing API on DataStream. +Flink enables both keyed and non-keyed DataStream to directly transform into +`PartitionWindowedStream` now. +The PartitionWindowedStream represents the collection all records of each subtask separately +into a full window. +The `PartitionWindowedStream` support four APIs: `mapPartition`, `sortPartition`, `aggregate` +and `reduce`. + +Note: Details about the design and implementation of the full window partition processing can be +found in the proposal and design document +[FLIP-380: Support Full Partition Processing On Non-keyed DataStream](https://cwiki.apache.org/confluence/display/FLINK/FLIP-380%3A+Support+Full+Partition+Processing+On+Non-keyed+DataStream). + +## MapPartition + +`MapPartition` represents collecting all records of each subtask separately into a full window +and process them using the given `MapPartitionFunction` within each subtask. The +`MapPartitionFunction` is called at the end of inputs. + +An example of calculating the sum of the elements in each subtask is as follows: + +```java +DataStream<Integer> dataStream = //... +PartitionWindowedStream<Integer> partitionWindowedDataStream = dataStream.fullWindowPartition(); + +DataStream<Integer> resultStream = partitionWindowedDataStream.mapPartition( + new MapPartitionFunction<Integer, Integer>() { + @Override + public void mapPartition( + Iterable<Integer> values, Collector<Integer> out) { + int result = 0; + for (Integer value : values) { + result += value; + } + out.collect(result); + } + } +); +resultStream.executeAndCollect(); +``` + +## SortPartition +`SortPartition` represents collecting all records of each subtask separately into a full window +and sorts them by the given record comparator in each subtask at the end of inputs. + +An example of sorting the records by the first element in each subtask is as follows: Review Comment: ```suggestion An example of sorting the records by the first element of tuple in each subtask is as follows: ``` -- This is an automated message from the Apache Git Service. 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