Packages like RMySQL and sqldf allow one to interface with local or remote database servers. I\'m creating a portable project which involves import
depending on what you want to extract from the table, here is how you can play around with the data
numLines <- R.utils::countLines("sportsdb_sample_mysql_20080303.sql")
# [1] 81266
linesInDB <- readLines("sportsdb_sample_mysql_20080303.sql",n=60)
Then you can do some regex to get tables names (after CREATE TABLE), column names (between first brackets) and VALUES (lines after CREATE TABLE and between second brackets)
Reference: Reverse engineering a mysqldump output with MySQL Workbench gives "statement starting from pointed line contains non UTF8 characters" error
EDIT: in response to OP's answer, if i interpret the python script correct, it is also reading it line by line, filter for INSERT INTO lines, parse as csv, then write to file. This is very similar to my original suggestion. My version below in R. If the file size is too large, it would be better to read in the file in chunks using some other R package
options(stringsAsFactors=F)
library(utils)
library(stringi)
library(plyr)
mysqldumpfile <- "sportsdb_sample_mysql_20080303.sql"
allLines <- readLines(mysqldumpfile)
insertLines <- allLines[which(stri_detect_fixed(allLines, "INSERT INTO"))]
allwords <- data.frame(stri_extract_all_words(insertLines, " "))
d_ply(allwords, .(X3), function(x) {
#x <- split(allwords, allwords$X3)[["baseball_offensive_stats"]]
print(x[1,3])
#find where the header/data columns start and end
valuesCol <- which(x[1,]=="VALUES")
lastCols <- which(apply(x, 2, function(y) all(is.na(y))))
datLastCol <- head(c(lastCols, ncol(x)+1), 1) - 1
#format and prepare for write to file
df <- data.frame(x[,(valuesCol+1):datLastCol])
df <- setNames(df, x[1,4:(valuesCol-1)])
#type convert before writing to file otherwise its all strings
df[] <- apply(df, 2, type.convert)
#write to file
write.csv(df, paste0(x[1,3],".csv"), row.names=F)
})