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From David Kaufman <>
Subject Need Advice: Spark-Streaming Setup
Date Mon, 01 Aug 2016 14:47:09 GMT

I'm currently working on Spark, HBase-Setup which processes log files (~10
GB/day). These log files are persisted hourly on n > 10 application servers
and copied to a 4 node hdfs.

Our current spark-job aggregates single visits (based on a session-uuid)
across all application-servers on a daily basis. Visits are filtered (only
about 1% of data remains) and stored in an HBase for further processing.

Currently there is no use of the Spark-Streaming API, i.e. a cronjob runs
every day and fires the visit calculation.

1) Ist it really necessary to store the log files in the HDFS or can spark
somehow read the files from a local file system and distribute the data to
the other nodes? Rationale: The data is (probably) only read once during
the visit calculation which defies the purpose of a dfs.

2) If the raw log files have to be in the HDFS, I have to remove the files
from the HDFS after processing them, so COPY -> PROCESS -> REMOVE. Is this
the way to go?

3) Before I can process a visit for an hour. I have to wait until all log
files of all application servers have been copied to the HDFS. It doesn't
seem like StreamingContext.fileStream can wait for more sophisticated
patterns, e.g. ("context*/logs-2016-08-01-15"). Do you guys have a
recommendation to solve this problem? One possible solution: After the
files have been copied, create an additional file that indicates spark that
all files are available?

If you have any questions, please don't hesitate to ask.


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