Thank you for reply.
I checked WEB UI and found that the total number of tasks is 10.
So, I changed the number of cores from 1 to 10, then it works well.
But I haven't figure out what is happening.
My assumption is that each Job consists of 10 tasks in default and each
task occupies 1 core.
So, in my case, assigning only 1 core cause the issue.
In other words, Continuous mode needs at least 10 cores.
Is it right?
Regards;
Yuta
On 2018/05/16 15:24, Shixiong(Ryan) Zhu wrote:
One possible case is you don't have enough resources to launch all tasks
for your continuous processing query. Could you check the Spark UI and
see if all tasks are running rather than waiting for resources?
Best Regards,
Shixiong Zhu
Databricks Inc.
shixi...@databricks.com <mailto:shixi...@databricks.com>
databricks.com <http://databricks.com/>
http://databricks.com <http://databricks.com/>
<https://databricks.com/sparkaisummit/north-america>
On Tue, May 15, 2018 at 5:38 PM, Yuta Morisawa
<yu-moris...@kddi-research.jp <mailto:yu-moris...@kddi-research.jp>> wrote:
Hi all
Now I am using Structured Streaming in Continuous Processing mode
and I faced a odd problem.
My code is so simple that it is similar to the sample code on the
documentation.
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#continuous-processing
<https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#continuous-processing>
When I send the same text data ten times, for example 10 lines text,
in Batch mode the result has 100 lines.
But in Continuous Processing mode the result has only 10 lines.
It appears duplicated lines are removed.
The difference of these two codes is only with or without trigger
method.
Why these two code behave differently ?
--
Regard,
Yuta
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