Memory Issues

[kferris10]

I am running into some errors trying to scrape large amounts of PITCHf/x data on my Windows 7 computer. Here are some screenshots to illustrate

• When I start a new R session, I'm only using about 20 MB of memory.

• I run this code to scrape several months of PITCHf/x data

  library(pitchRx)
  library(dplyr)
  library(DBI)
  
  # setwd("~/pitchfx")
  db <- src_sqlite("pitchfx14.sqlite3", create = T)
  
  update_db(db$con, "2014-12-01")
  
  scrape(start = "2014-01-01", 
  end = "2014-04-01", 
  suffix = c("inning/inning_all.xml", 
                 "inning/inning_hit.xml", 
                 "miniscoreboard.xml", 
                 "players.xml"), 
  connect = db$con)

• When this is finished running, the R session is now using almost 1 GB of memory.

• Running gc() appears to have no effect

• The only solution I have found is to restart R completely

P.S. Sorry if those numbers are impossible to see. Let me know if it would help to improve the quality of any of the screenshots.

[colemanconley]

I'm having what appears to be the exact same issue and read through both #27 above and the referenced issue #22. I tried gc() like you suggested in 22, but it doesn't work on my machine just as it doesn't work above. What is the solution? I can restart R, but usually have to restart my machine for everything to run in a reasonable amount of time.

Related: I was trying to scrape data for all games starting on 03/01/2010 through the present by grabbing only one month at a time. R crashed midway through the games on 5/16/2012, so I restart, load my packages and define my connection, then run:

update_db(mysqlconnection, end="2012-05-20")

This starts getting the games from 5/17/2012 through 5/20, which obviously misses the remaining 5/16 games I didn't get to. How can I get the rest of the 5/16 games now without duplicating what I already have for that day?

[kferris10]

@colemanconley I've never had an issue with duplicating games when using update_db. My strategy is to first scrape one year of data. Then I can just run update_db one year at a time. Is that not working for you?

[myellen]

I have these memory issues on windows but not on mac. The only way I've found to free up the memory is to restart the R session. What I do is make a new SNOW cluster with one node to run the scrape method each time, which is the same as having a new r session each time.

some code I use

ll <- seq(as.Date(start_date), as.Date(end_date), "1 year")
 ntasks <- length(ll)-1
 for(i in 1:ntasks) {
    print(ll[i])
    print(ll[i+1])]
      cl<-makeCluster(1, type="SOCK", outfile = "")
      clusterEvalQ(cl, library(pitchRx))
      clusterEvalQ(cl, library(DBI))
      clusterEvalQ(cl, library(RSQLite))
      clusterEvalQ(cl, library(dplyr))

      clusterExport(cl, list = c("ll", "files", "dbpath"), envir=environment())
      clusterCall(cl, function(i) {
        db <- src_sqlite(dbpath, create = TRUE)
        scrape(start = ll[i], end = ll[i+1], suffix = files, connect = db$con)
        dbDisconnect(db$con)
      },i)
      stopCluster(cl)
    ]
  }