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https://issues.apache.org/jira/browse/FLINK-6200?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15949432#comment-15949432
]
ASF GitHub Bot commented on FLINK-6200:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3649#discussion_r108983942
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/UnboundedEventTimeOverProcessFunction.scala
---
@@ -204,21 +183,145 @@ class UnboundedEventTimeOverProcessFunction(
* If timestamps arrive in order (as in case of using the RocksDB state
backend) this is just
* an append with O(1).
*/
- private def insertToSortedList(recordTimeStamp: Long) = {
+ private def insertToSortedList(recordTimestamp: Long) = {
val listIterator = sortedTimestamps.listIterator(sortedTimestamps.size)
var continue = true
while (listIterator.hasPrevious && continue) {
val timestamp = listIterator.previous
- if (recordTimeStamp >= timestamp) {
+ if (recordTimestamp >= timestamp) {
listIterator.next
- listIterator.add(recordTimeStamp)
+ listIterator.add(recordTimestamp)
continue = false
}
}
if (continue) {
- sortedTimestamps.addFirst(recordTimeStamp)
+ sortedTimestamps.addFirst(recordTimestamp)
}
}
+ /**
+ * Process the same timestamp datas, the mechanism is different between
+ * rows and range window.
+ */
+ def processElementsWithSameTimestamp(
+ curRowList: JList[Row],
+ lastAccumulator: Row,
+ out: Collector[Row]): Unit
+
+}
+
+/**
+ * A ProcessFunction to support unbounded ROWS window.
+ * With the ROWS option you define on a physical level how many rows are
included in your window frame
--- End diff --
This line violates the 100 character limit of the Scala code style.
Please run a local build before opening a PR to capture such problems (`mvn
clean install` inside of the `./flink-libraries/flink-table` folder is usually
sufficient and takes ~5 mins).
> Add event time OVER RANGE BETWEEN UNBOUNDED PRECEDING aggregation to SQL
> ------------------------------------------------------------------------
>
> Key: FLINK-6200
> URL: https://issues.apache.org/jira/browse/FLINK-6200
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: sunjincheng
> Assignee: hongyuhong
>
> The goal of this issue is to add support for OVER RANGE aggregations on event
> time streams to the SQL interface.
> Queries similar to the following should be supported:
> SELECT
> a,
> SUM(b) OVER (PARTITION BY c ORDER BY rowTime() RANGE BETWEEN UNBOUNDED
> PRECEDING AND CURRENT ROW) AS sumB,
> MIN(b) OVER (PARTITION BY c ORDER BY rowTime() RANGE BETWEEN UNBOUNDED
> PRECEDING AND CURRENT ROW) AS minB
> FROM myStream
> 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 rowTime() as parameter. rowTime() is a
> parameterless scalar function that just indicates processing time mode.
> bounded PRECEDING is not supported (see FLINK-5655)
> 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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