Spark dropped Akka some time ago... I think the main issue he will face is a library for simulating the state machines (randomly), storing a huge amount of files (HDFS is probably the way to go if you want it fast) and distributing the work (here you can select different options). Are you trying to have some mathematical guarantees on the state machines, such as Markov chains/steady state?
> On 7. Jul 2017, at 08:46, Esa Heikkinen <esa.heikki...@student.tut.fi> wrote: > > > Would it be better to use Akka as simulator rather than Spark ? > > http://akka.io/ > > Akka > akka.io > Build powerful reactive, concurrent & distributed applications more easily. > Akka is a toolkit and runtime for building highly concurrent, distributed, > and resilient ... > The spark was originally built on it (Akka). > > Esa > > Lähettäjä: Mahesh Sawaiker <mahesh_sawai...@persistent.com> > Lähetetty: 21. kesäkuuta 2017 14:45 > Vastaanottaja: Esa Heikkinen; Jörn Franke > Kopio: user@spark.apache.org > Aihe: RE: Using Spark as a simulator > > 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 > Spark Programming Guide - Spark 2.1.1 Documentation > spark.apache.org > Spark Programming Guide. Overview; Linking with Spark; Initializing Spark. > Using the Shell; Resilient Distributed Datasets (RDDs) Parallelized > Collections > 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> > Lähetetty: 20. kesäkuuta 2017 17:12 > Vastaanottaja: Esa Heikkinen > Kopio: 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> 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.
