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https://issues.apache.org/jira/browse/FLINK-5654?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15935239#comment-15935239
]
ASF GitHub Bot commented on FLINK-5654:
---------------------------------------
Github user rtudoran commented on a diff in the pull request:
https://github.com/apache/flink/pull/3550#discussion_r107264081
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/DataStreamProcTimeAggregateGlobalWindowFunction.scala
---
@@ -0,0 +1,106 @@
+/*
+ * 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.
+ */
+
+package org.apache.flink.table.runtime.aggregate
+
+import
org.apache.flink.streaming.api.functions.windowing.RichAllWindowFunction
+import org.apache.flink.types.Row
+import org.apache.flink.util.Collector
+import org.apache.flink.streaming.api.windowing.windows.Window
+import org.apache.flink.configuration.Configuration
+import org.apache.flink.table.functions.Accumulator
+
+import java.lang.Iterable
+import org.apache.flink.table.functions.AggregateFunction
+
+/**
+ * Computes the final aggregate value from incrementally computed
aggreagtes.
+ *
+ * @param aggregates The aggregates to be computed
+ * @param aggFields the fields on which to apply the aggregate.
+ * @param forwardedFieldCount The fields to be carried from current row.
+ */
+class DataStreamProcTimeAggregateGlobalWindowFunction[W <: Window](
--- End diff --
@sunjincheng121 @fhueske
Thanks for the input and suggestion. As of now i followed the model from
the Unounded Partiton/noPartition. I have finished acutally the implementation.
However, when i run the tests i see that it crashes for the non-partition
example
then i checked better and it seems that it also crashes for the:
UnboundedNonPartitionedProcessingOverProcessFunction.scala
..i do not understand how come
testUnboundNonPartitionedProcessingWindowWithRange(org.apache.flink.table.api.scala.stream.sql.SqlITCase)
Time elapsed: 1.022 sec <<< ERROR!
org.apache.flink.runtime.client.JobExecutionException: Job execution failed.
at
org.apache.flink.runtime.jobmanager.JobManager$$anonfun$handleMessage$1$$anonfun$applyOrElse$7.apply$mcV$sp(JobManager.scala:915)
at
org.apache.flink.runtime.jobmanager.JobManager$$anonfun$handleMessage$1$$anonfun$applyOrElse$7.apply(JobManager.scala:858)
at
org.apache.flink.runtime.jobmanager.JobManager$$anonfun$handleMessage$1$$anonfun$applyOrElse$7.apply(JobManager.scala:858)
at
scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
at
scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
at akka.dispatch.TaskInvocation.run(AbstractDispatcher.scala:40)
at
akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:397)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
at
scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
at
scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
at
scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
Caused by: java.lang.IllegalStateException: Serializer not yet initialized.
at
org.apache.flink.api.common.state.StateDescriptor.getSerializer(StateDescriptor.java:169)
at
org.apache.flink.api.common.state.ListStateDescriptor.getElementSerializer(ListStateDescriptor.java:93)
at
org.apache.flink.runtime.state.DefaultOperatorStateBackend.getOperatorState(DefaultOperatorStateBackend.java:110)
at
org.apache.flink.runtime.state.DefaultOperatorStateBackend.getOperatorState(DefaultOperatorStateBackend.java:91)
at
org.apache.flink.table.runtime.aggregate.UnboundedNonPartitionedProcessingOverProcessFunction.initializeState(UnboundedNonPartitionedProcessingOverProcessFunction.scala:102)
at
org.apache.flink.streaming.api.functions.util.StreamingFunctionUtils.tryRestoreFunction(StreamingFunctionUtils.java:178)
at
org.apache.flink.streaming.api.functions.util.StreamingFunctionUtils.restoreFunctionState(StreamingFunctionUtils.java:160)
at
org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator.initializeState(AbstractUdfStreamOperator.java:106)
at
org.apache.flink.streaming.api.operators.AbstractStreamOperator.initializeState(AbstractStreamOperator.java:242)
at
org.apache.flink.streaming.runtime.tasks.StreamTask.initializeOperators(StreamTask.java:681)
at
org.apache.flink.streaming.runtime.tasks.StreamTask.initializeState(StreamTask.java:669)
at
org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:251)
at org.apache.flink.runtime.taskmanager.Task.run(Task.java:670)
at java.lang.Thread.run(Thread.java:745)
> Add processing time OVER RANGE BETWEEN x PRECEDING aggregation to SQL
> ---------------------------------------------------------------------
>
> Key: FLINK-5654
> URL: https://issues.apache.org/jira/browse/FLINK-5654
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: Fabian Hueske
> Assignee: radu
>
> The goal of this issue is to add support for OVER RANGE aggregations on
> processing time streams to the SQL interface.
> Queries similar to the following should be supported:
> {code}
> SELECT
> a,
> SUM(b) OVER (PARTITION BY c ORDER BY procTime() RANGE BETWEEN INTERVAL '1'
> HOUR PRECEDING AND CURRENT ROW) AS sumB,
> MIN(b) OVER (PARTITION BY c ORDER BY procTime() RANGE BETWEEN INTERVAL '1'
> HOUR PRECEDING AND CURRENT ROW) AS minB
> FROM myStream
> {code}
> The following restrictions should initially apply:
> - All OVER clauses in the same SELECT clause must be exactly the same.
> - The PARTITION BY clause is optional (no partitioning results in single
> threaded execution).
> - The ORDER BY clause may only have procTime() as parameter. procTime() is a
> parameterless scalar function that just indicates processing time mode.
> - UNBOUNDED PRECEDING is not supported (see FLINK-5657)
> - FOLLOWING is not supported.
> The restrictions will be resolved in follow up issues. If we find that some
> of the restrictions are trivial to address, we can add the functionality in
> this issue as well.
> This issue includes:
> - Design of the DataStream operator to compute OVER ROW aggregates
> - Translation from Calcite's RelNode representation (LogicalProject with
> RexOver expression).
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