Spark can help you to create one large file if needed, but hdfs itself will provide abstraction over such things, so it's a trivial problem if anything. If you have spark installed, then you can use spark-shell to try a few samples, and build from there.If you can collect all the files in a folder then spark can read all files from there. The programming guide below has enough information to get started.
https://spark.apache.org/docs/latest/programming-guide.html All of Spark's file-based input methods, including textFile, support running on directories, compressed files, and wildcards as well. For example, you can use textFile("/my/directory"), textFile("/my/directory/*.txt"), and textFile("/my/directory/*.gz"). After reading the file you can map it using map function, which will split the individual line and possibly create a scala object. This way you will get a RDD of scala objects, which you can then process functional/set operators. You would want to read about PairRDDs. From: Esa Heikkinen [mailto:esa.heikki...@student.tut.fi] Sent: Wednesday, June 21, 2017 1:12 PM To: Jörn Franke Cc: user@spark.apache.org Subject: VS: Using Spark as a simulator Hi Thanks for the answer. I think my simulator includes a lot of parallel state machines and each of them generates log file (with timestamps). Finally all events (rows) of all log files should combine as time order to (one) very huge log file. Practically the combined huge log file can also be split into smaller ones. What transformation or action functions can i use in Spark for that purpose ? Or are there exist some code sample (Python or Scala) about that ? Regards Esa Heikkinen ________________________________ Lähettäjä: Jörn Franke <jornfra...@gmail.com<mailto:jornfra...@gmail.com>> Lähetetty: 20. kesäkuuta 2017 17:12 Vastaanottaja: Esa Heikkinen Kopio: user@spark.apache.org<mailto:user@spark.apache.org> Aihe: Re: Using Spark as a simulator It is fine, but you have to design it that generated rows are written in large blocks for optimal performance. The most tricky part with data generation is the conceptual part, such as probabilistic distribution etc You have to check as well that you use a good random generator, for some cases the Java internal might be not that well. On 20. Jun 2017, at 16:04, Esa Heikkinen <esa.heikki...@student.tut.fi<mailto:esa.heikki...@student.tut.fi>> wrote: Hi Spark is a data analyzer, but would it be possible to use Spark as a data generator or simulator ? My simulation can be very huge and i think a parallelized simulation using by Spark (cloud) could work. Is that good or bad idea ? Regards Esa Heikkinen DISCLAIMER ========== This e-mail may contain privileged and confidential information which is the property of Persistent Systems Ltd. It is intended only for the use of the individual or entity to which it is addressed. If you are not the intended recipient, you are not authorized to read, retain, copy, print, distribute or use this message. If you have received this communication in error, please notify the sender and delete all copies of this message. Persistent Systems Ltd. does not accept any liability for virus infected mails.
