*Yes, my code is shown below(I also post my code in another mail)*
/**
* input
*/
val logs = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", BROKER_SERVER)
.option("subscribe", TOPIC)
.option("startingOffset", "latest")
.load()
/**
* process
*/
val logValues = logs.selectExpr("CAST(value AS STRING)").as[(String)]
val events = logValues
.map(parseFunction)
.select(
$"_1".alias("date").cast("timestamp"),
$"_2".alias("uuid").cast("string")
)
val results = events
.withWatermark("date", "1 day")
.dropDuplicates("uuid", "date")
.groupBy($"date")
.count()
/**
* output
*/
val query = results
.writeStream
.outputMode("update")
.format("console")
.option("truncate", "false")
.trigger(Trigger.ProcessingTime("1 seconds"))
.start()
query.awaitTermination()
*and I use play json to parse input logs from kafka ,the parse function is
like*
def parseFunction(str: String): (Long, String) = {
val json = Json.parse(str)
val timestamp = (json \ "time").get.toString().toLong
val date = (timestamp / (60 * 60 * 24) * 24 -8) * 60 * 60
val uuid = (json \ "uuid").get.toString()
(date, uuid)
}
and the java heap space is like (I've increase the executor memory to 15g):
[image: image.png]
Michael Armbrust <[email protected]>于2017年9月13日周三 上午2:23写道：
> Can you show the full query you are running?
>
> On Tue, Sep 12, 2017 at 10:11 AM, 张万新 <[email protected]> wrote:
>
>> Hi,
>>
>> I'm using structured streaming to count unique visits of our website. I
>> use spark on yarn mode with 4 executor instances and from 2 cores * 5g
>> memory to 4 cores * 10g memory for each executor, but there are frequent
>> full gc, and once the count raises to about more than 4.5 millions the
>> application will be blocked and finally crash in OOM. It's kind of
>> unreasonable. So is there any suggestion to optimize the memory consumption
>> of SS? Thanks.
>>
>
>
