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https://issues.apache.org/jira/browse/SPARK-43001?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-43001:
---------------------------------
Priority: Major (was: Critical)
> Spark last window dont flush in append mode
> -------------------------------------------
>
> Key: SPARK-43001
> URL: https://issues.apache.org/jira/browse/SPARK-43001
> Project: Spark
> Issue Type: Bug
> Components: PySpark, Structured Streaming
> Affects Versions: 3.3.2
> Reporter: padavan
> Priority: Major
> Original Estimate: 24h
> Remaining Estimate: 24h
>
> The problem is very simple, when you use *TUMBLING* *window* with {*}append
> mode{*}, then the window is closed +only when the next message arrives+
> ({_}+watermark logic{_}).
> In the current implementation, if you *stop* *incoming* streaming data, the
> *last* window will *NEVER close* and we LOSE the last window data.
>
> Business situation:
> Worked correctly and new messages stop incoming and next message come in 5
> hours later and the client will get the message after 5 hours instead of the
> 10 seconds delay of window.
> !https://user-images.githubusercontent.com/61819835/226478055-dc4a123c-4397-4eb0-b6ed-1e185b6fab76.png|width=707,height=294!
> The current implementation needs to be improved. Include in spark internal
> mechanisms to close windows automatically.
>
> *What we propose:*
> Add third parameter
> {{{}DataFrame.{}}}{{{}withWatermark{}}}({_}eventTime{_}, {_}delayThreshold,
> *maxDelayClose*{_}). And then trigger will execute
> {code:java}
> if(now - window.upper_bound > maxDelayClose){
> window.close().flush();
> }
> {code}
> I assume it can be done in a day. It wasn't expected for us that our
> customers couldn't get the notifications. (the company is in the medical
> field).
>
> simple code for problem:
> {code:java}
> kafka_stream_df = spark \
> .readStream \
> .format("kafka") \
> .option("kafka.bootstrap.servers", KAFKA_BROKER) \
> .option("subscribe", KAFKA_TOPIC) \
> .option("includeHeaders", "true") \
> .load()
> sel = (kafka_stream_df.selectExpr("CAST(key AS STRING)", "CAST(value AS
> STRING)")
> .select(from_json(col("value").cast("string"),
> json_schema).alias("data"))
> .select("data.*")
> .withWatermark("dt", "1 seconds")
> .groupBy(window("dt", "10 seconds"))
> .agg(sum("price"))
> )
>
> console = sel \
> .writeStream \
> .trigger(processingTime='10 seconds') \
> .format("console") \
> .outputMode("append")\
> .start()
> {code}
>
>
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