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Chloe He commented on SPARK-32821: ---------------------------------- Hi All - I'm trying to do something similar here. I tried creating a watermark on the stream with the `withWatermark()` function, then creating a temp view over it. Then I call the `.sql()` function to execute my SQL query. However, this is throwing ``` AnalysisException: Append output mode not supported when there are streaming aggregations on streaming DataFrames/DataSets without watermark ``` It seems that the watermark that I created on the stream was not recognized by the downstream processing. If instead of creating a temp view and calling the `.sql()` function I execute the query using Structured Streaming API instead, the query does work. Does that mean there is currently no support for using raw SQL in conjunction with watermark? > cannot group by with window in sql statement for structured streaming with > watermark > ------------------------------------------------------------------------------------ > > Key: SPARK-32821 > URL: https://issues.apache.org/jira/browse/SPARK-32821 > Project: Spark > Issue Type: Improvement > Components: Structured Streaming > Affects Versions: 3.1.0 > Reporter: Johnny Bai > Priority: Major > Attachments: the watermark grammer.md > > > current only support dsl style as below: > {code} > import spark.implicits._ > val words = ... // streaming DataFrame of schema { timestamp: Timestamp, > word: String } > // Group the data by window and word and compute the count of each group > val windowedCounts = words.groupBy(window($"timestamp", "10 minutes", "5 > minutes"),$"word").count() > {code} > > but not support group by with window in sql style as below: > {code} > select ts_field,count(\*) as cnt over window(ts_field, '1 minute', '1 > minute') with watermark 1 minute from tableX group by ts_field > {code} > -- This message was sent by Atlassian Jira (v8.20.10#820010) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org