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https://issues.apache.org/jira/browse/FLINK-6216?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15949926#comment-15949926
]
ASF GitHub Bot commented on FLINK-6216:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3646#discussion_r109041002
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/datastream/DataStreamGroupAggregate.scala
---
@@ -0,0 +1,117 @@
+/*
+ * 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.plan.nodes.datastream
+
+import org.apache.calcite.plan.{RelOptCluster, RelTraitSet}
+import org.apache.calcite.rel.`type`.RelDataType
+import org.apache.calcite.rel.core.AggregateCall
+import org.apache.calcite.rel.{RelNode, RelWriter, SingleRel}
+import org.apache.flink.streaming.api.datastream.DataStream
+import org.apache.flink.table.api.StreamTableEnvironment
+import org.apache.flink.table.calcite.FlinkTypeFactory
+import org.apache.flink.table.runtime.aggregate._
+import org.apache.flink.table.plan.nodes.CommonAggregate
+import org.apache.flink.types.Row
+import org.apache.flink.table.runtime.aggregate.AggregateUtil.CalcitePair
+
+/**
+ *
+ * Flink RelNode for data stream group (without window & early-firing)
aggregate
+ *
+ * @param cluster Cluster of the RelNode, represent for an
environment of related
+ * relational expressions during the optimization
of a query.
+ * @param traitSet Trait set of the RelNode
+ * @param inputNode The input RelNode of aggregation
+ * @param namedAggregates List of calls to aggregate functions and their
output field names
+ * @param rowRelDataType The type of the rows of the RelNode
+ * @param inputType The type of the rows of aggregation input
RelNode
+ * @param groupings The position (in the input Row) of the grouping
keys
+ */
+class DataStreamGroupAggregate(
+ cluster: RelOptCluster,
+ traitSet: RelTraitSet,
+ inputNode: RelNode,
+ namedAggregates: Seq[CalcitePair[AggregateCall, String]],
+ rowRelDataType: RelDataType,
+ inputType: RelDataType,
+ groupings: Array[Int])
+ extends SingleRel(cluster, traitSet, inputNode)
+ with CommonAggregate
+ with DataStreamRel {
+
+ override def deriveRowType() = rowRelDataType
+
+ def getGrouping() = groupings
+
+ override def copy(traitSet: RelTraitSet, inputs:
java.util.List[RelNode]): RelNode = {
+ new DataStreamGroupAggregate(
+ cluster,
+ traitSet,
+ inputs.get(0),
+ namedAggregates,
+ getRowType,
+ inputType,
+ groupings)
+ }
+
+ override def toString: String = {
+ s"Aggregate(${
+ if (!groupings.isEmpty) {
+ s"groupBy: (${groupingToString(inputType, groupings)}), "
+ } else {
+ ""
+ }
+ }select:(${aggregationToString(inputType, groupings, getRowType,
namedAggregates, Nil)}))"
+ }
+
+ override def explainTerms(pw: RelWriter): RelWriter = {
+ super.explainTerms(pw)
+ .itemIf("groupBy", groupingToString(inputType, groupings),
!groupings.isEmpty)
+ .item("select", aggregationToString(inputType, groupings,
getRowType, namedAggregates, Nil))
+ }
+
+ override def translateToPlan(tableEnv: StreamTableEnvironment):
DataStream[Row] = {
+
+ val inputDS =
input.asInstanceOf[DataStreamRel].translateToPlan(tableEnv)
+
+ val rowTypeInfo = FlinkTypeFactory.toInternalRowTypeInfo(getRowType)
+
+ val aggString = aggregationToString(
+ inputType,
+ groupings,
+ getRowType,
+ namedAggregates,
+ Nil)
+
+ val keyedAggOpName = s"groupBy: (${groupingToString(inputType,
groupings)}), " +
+ s"select: ($aggString)"
+
+ val processFunction = AggregateUtil.createGroupAggregateFunction(
+ namedAggregates,
+ inputType,
+ groupings)
+
+ inputDS
+ .keyBy(groupings: _*)
--- End diff --
I think we can easily support the case of non-grouped aggregates as well.
We use `.keyBy(new NullByteKeySelector[Row])` to send all data to a single
instance of the process function.
This is also done in `DataStreamOverAggregate` to implement the
non-partitioned OVER windows.
> DataStream unbounded groupby aggregate with early firing
> --------------------------------------------------------
>
> Key: FLINK-6216
> URL: https://issues.apache.org/jira/browse/FLINK-6216
> Project: Flink
> Issue Type: New Feature
> Components: Table API & SQL
> Reporter: Shaoxuan Wang
> Assignee: Shaoxuan Wang
>
> Groupby aggregate results in a replace table. For infinite groupby aggregate,
> we need a mechanism to define when the data should be emitted (early-fired).
> This task is aimed to implement the initial version of unbounded groupby
> aggregate, where we update and emit aggregate value per each arrived record.
> In the future, we will implement the mechanism and interface to let user
> define the frequency/period of early-firing the unbounded groupby aggregation
> results.
> The limit space of backend state is one of major obstacles for supporting
> unbounded groupby aggregate in practical. Due to this reason, we suggest two
> common (and very useful) use-cases of this unbounded groupby aggregate:
> 1. The range of grouping key is limit. In this case, a new arrival record
> will either insert to state as new record or replace the existing record in
> the backend state. The data in the backend state will not be evicted if the
> resource is properly provisioned by the user, such that we can provision the
> correctness on aggregation results.
> 2. When the grouping key is unlimited, we will not be able ensure the 100%
> correctness of "unbounded groupby aggregate". In this case, we will reply on
> the TTL mechanism of the RocksDB backend state to evicted old data such that
> we can provision the correct results in a certain time range.
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