In Java, you can do something like:
sc.setConf('spark.sql.parquet.binaryAsString','true')
What would the equivalent be in R? I've looked at the methods available to the sc object, and can't find any obvious way of doing this
Thanks
You can set environment variables during SparkContext initialization. sparkR.init has a number of optional arguments including:
sparkEnvir - a list of environment variables to set on worker nodes.
sparkExecutorEnv - a list of environment variables to be used when launching executors
In your case something like this should do the trick:
sparkEnvir <- list('spark.sql.parquet.binaryAsString'='true')
sc <- sparkR.init(master, app_name, sparkEnvir=sparkEnvir)
I found the solution to the problem.
We can do the following:
sql(sqlContext,'SET spark.sql.parquet.binaryAsString=true')
This fixes everything.
Related
I have a bunch of functions and I'm trying to keep my workspace clean by defining them in an environment and attaching the environment. Some of the functions are S3 generics, and they don't seem to play well with this approach.
A minimum example of what I'm experiencing requires 4 files:
testfun.R
ttt.xxx <- function(object) print("x")
ttt <- function(object) UseMethod("ttt")
ttt2 <- function() {
yyy <- structure(1, class="xxx")
ttt(yyy)
}
In testfun.R I define an S3 generic ttt and a method ttt.xxx, I also define a function ttt2 calling the generic.
testenv.R
test_env <- new.env(parent=globalenv())
source("testfun.R", local=test_env)
attach(test_env)
In testenv.R I source testfun.R to an environment, which I attach.
test1.R
source("testfun.R")
ttt2()
xxx <- structure(1, class="xxx")
ttt(xxx)
test1.R sources testfun.R to the global environment. Both ttt2 and a direct function call work.
test2.R
source("testenv.R")
ttt2()
xxx <- structure(1, class="xxx")
ttt(xxx)
test2.R uses the "attach" approach. ttt2 still works (and prints "x" to the console), but the direct function call fails:
Error in UseMethod("ttt") :
no applicable method for 'ttt' applied to an object of class "xxx"
however, calling ttt and ttt.xxx without arguments show that they are known, ls(pos=2) shows they are on the search path, and sloop::s3_dispatch(ttt(xxx)) tells me it should work.
This questions is related to Confusion about UseMethod search mechanism and the link therein https://blog.thatbuthow.com/how-r-searches-and-finds-stuff/, but I cannot get my head around what is going on: why is it not working and how can I get this to work.
I've tried both R Studio and R in the shell.
UPDATE:
Based on the answers below I changed my testenv.R to:
test_env <- new.env(parent=globalenv())
source("testfun.R", local=test_env)
attach(test_env)
if (is.null(.__S3MethodsTable__.))
.__S3MethodsTable__. <- new.env(parent = baseenv())
for (func in grep(".", ls(envir = test_env), fixed = TRUE, value = TRUE))
.__S3MethodsTable__.[[func]] <- test_env[[func]]
rm(test_env, func)
... and this works (I am only using "." as an S3 dispatching separator).
It’s a little-known fact that you must use .S3method() to define methods for S3 generics inside custom environments (outside of packages).1 The reason almost nobody knows this is because it is not necessary in the global environment; but it is necessary everywhere else since R version 3.6.
There’s virtually no documentation of this change, just a technical blog post by Kurt Hornik about some of the background. Note that the blog post says the change was made in R 3.5.0; however, the actual effect you are observing — that S3 methods are no longer searched in attached environments — only started happening with R 3.6.0; before that, it was somehow not active yet.
… except just using .S3method will not fix your code, since your calling environment is the global environment. I do not understand the precise reason why this doesn’t work, and I suspect it’s due to a subtle bug in R’s S3 method lookup. In fact, using getS3method('ttt', 'xxx') does work, even though that should have the same behaviour as actual S3 method lookup.
I have found that the only way to make this work is to add the following to testenv.R:
if (is.null(.__S3MethodsTable__.)) {
.__S3MethodsTable__. <- new.env(parent = baseenv())
}
.__S3MethodsTable__.$ttt.xxx <- ttt.xxx
… in other words: supply .GlobalEnv manually with an S3 methods lookup table. Unfortunately this relies on an undocumented S3 implementation detail that might theoretically change in the future.
Alternatively, it “just works” if you use ‘box’ modules instead of source. That is, you can replace the entirety of your testenv.R by the following:
box::use(./testfun[...])
This code treats testfun.R as a local module and loads it, attaching all exported names (via the attach declaration [...]).
1 (and inside packages you need to use the equivalent S3method namespace declaration, though if you’re using ‘roxygen2’ then that’s taken care of for you)
First of all, my advice would be: don't try to reinvent R packages. They solve all the problems you say you are trying to solve, and others as well.
Secondly, I'll try to explain what went wrong in test2.R. It calls ttt on an xxx object, and ttt.xxx is on the search list, but is not found.
The problem is how the search for ttt.xxx happens. The search doesn't look for ttt.xxx in the search list, it looks for it in the environment from which ttt was called, then in an object called .__S3MethodsTable__.. I think there are two reasons for this:
First, it's a lot faster. It only needs to look in one or two places, and the table can be updated whenever a package is attached or detached, a relatively rare operation.
Second, it's more reliable. Each package has its own methods table, because two packages can use the same name for generics that have nothing to do with each other, or can use the same class names that are unrelated. So package code needs to be able to count on finding its own definitions first.
Since your call to ttt() happens at the top level, that's where R looks first for ttt.xxx(), but it's not there. Then it looks in the global .__S3MethodsTable__. (which is actually in the base environment), and it's not there either. So it fails.
There is a workaround that will make your code work. If you run
.__S3MethodsTable__. <- list2env(list(ttt.xxx = ttt.xxx))
as the last line of testenv.R, then you'll create a methods table in the global environment. (Normally there isn't one there, because that's user space, and R doesn't like putting things there unless the user asks for it.)
R will find that methods table, and will find the ttt.xxx method that it defines. I wouldn't be surprised if this breaks some other aspect of S3 dispatch, so I don't recommend doing it, but give it a try if you insist on reinventing the package system.
I'm trying to use R's "pool" package to execute a set of queries against a set of databases.
I have a list of queries, queryList (I confirmed that each element is a character vector, e.g. "SELECT...FROM...").
library(pool)
library(DBI)
# queryList defined earlier
myPool <- dbPool (...)
Results <- lapply(queryList, pool::dbGetQuery, myPool) # fails here!
The error I get says this: "unable to find an inherited method for function 'dbGetQuery' for signature '"character", "Pool"'.
One SO thread says this is related to S4 incompatibility. pool::dbGetQuery is an S4 method.
Is there a workaround ?
The use of an anonymous function (e.g. function(x)..., as suggested by #neilfws) worked. However, I'm not sure why, since I didn't need to use anonymous functions when I was dealing directly with dbiConnection objects. So this works
lapply(queryList, DBI::dbGetQuery, conn) # conn is dbiConnection
but this doesn't work
lapply(queryList, pool::dbGetQuery, pool) # pool is a pool of dbiConnections
Maybe I'm misreading the official documentation?
I noticed in checking a package that I obtain notes "no visible binding for global variable" when I use functions like subset that use verbatim names of list elements as arguments.
For example with a data frame:
foo <- data.frame(a=c(TRUE,FALSE,TRUE),b=1:3)
I can do silly things like:
subset(foo,a)
transform(foo,a=b)
Which work as expected. The R code check in R CMD however doesn't understand that these refer to elements and complains about there not being any visible bindings of global variables.
While this works ok, I don't really like having notes in my package and prefer for it to pass the check with no errors, warnings and notes at all. I also don't really want to rework my code too much. Is there a way to write these codes so that it is clear the arguments do not refer to global variables?
To get it past R CMD check you can either :
Use get("b") (but that is onerous)
Place a=b=NULL somewhere higher up in your function (that's what I do)
There was a thread on r-devel a while ago where somebody from r-core basically said (from memory) "NOTES are ok, you know. The assumption is that the author checked it and is ok with the NOTE.". But, I agree with you. I do prefer to have CRAN checks return a clean "OK" on all platforms. That way the user is left in no doubt that it passes checks ok.
EDIT :
Here is the r-devel thread I was remembering (from April 2010). So that appears to suggest that there are some situations where there is no known way to avoid the NOTE, but that's ok.
This is one of the potential "unanticipated consequences" of using subset non-interactively. As it says in the Warning section of ?subset:
This is a convenience function intended for use interactively. For
programming it is better to use the standard subsetting functions like
‘[’, and in particular the non-standard evaluation of argument
‘subset’ can have unanticipated consequences.
From R version 2.15.1 onwards there is a way around this:
if(getRversion() >= "2.15.1") utils::globalVariables(c("a", "othervar"))
As per the warning section of ?subset it is better to use subset interactively, and [ for programming.
I would replace a command like
subset(foo,a)
with
foo[foo$a]
or if foo is a dataframe:
foo[foo$a, ]
you might also like to use with if foo is a dataframe and the expression to be evaluated is complex:
with(foo, foo[a, ])
I had this issue and traced it to my ggplot2 section.
This code provided the error:
ggplot2::ggplot(data = spec.df, ggplot2::aes(E.avg, fraction)) +
ggplot2::geom_line() +
ggplot2::ggtitle(paste0(title))
Adding the data name to the parameters eliminated the not:
ggplot2::ggplot(data = spec.df, ggplot2::aes(spec.df$E.avg, spec.df$fraction)) +
ggplot2::geom_line() +
ggplot2::ggtitle(paste0(title))
I apologize that this question will be hard to make fully reproducible because it involves a running spark context (referenced to as sc below), but I am trying to set a hadoopConfiguration in sparklyr, specifically for accessing swift/objectStore objects from RStudio sparklyr as a Spark object, but in general for a scala call to hadoopConfiguration. Something like (scala code):
sc.hadoopConfiguration.set(f"fs.swift.service.$name.auth.url","https://identity.open.softlayer.com"/v3/auth/tokens")
where sc is a running spark context. In SparkR I can run (R code)
hConf = SparkR:::callJMethod(sc, "hadoopConfiguration")
SparkR:::callJMethod(hConf, "set", paste("fs.swift.service.keystone.auth.url"), paste("https://identity.open.softlayer.com/v3/auth/tokens",sep=""))
in sparklyr I have tried every incantation of this that I think of, but my best guess is (again R code)
sc %>% invoke("set", paste("fs.swift.service.keystone,auth.url"), paste("https://identity.open.softlayer.com/v3/auth/tokens",sep=""))
but this results in the non-verbose error (and irregular spelling) of
Error in enc2utf8(value) : argumemt is not a character vector
of course I tried to encode the inputs in every way that I can think of (naturally enc2utf8(value) being the first, but many others including lists and as.character(as.list(...)) which appears to be a favorite for sparklyr coders). Any suggestions would be greatly appreciated. I have combed the source code for sparklyr and cannot find any mentions of hadoopConfiguration in the sparklyr github, so I am afraid that I missing something very basic in the core configuration. I have also tried to pass these configs in the config.yml in the spark_connect() core call, but while this is working in setting the "fs.swift.service.keystone.auth.url" as a sc$config$s.swift.service.keystone.auth.url setting, it is apparently failing to set these as a core hadoopConfiguration.
By the way, I am using Spark1.6, scala 2.10, R 3.2.1, and sparklyr_0.4.19.
I figured this out
set_swift_config <- function(sc){
#get spark_context
ctx <- spark_context(sc)
#set the java spark context
jsc <- invoke_static(
sc,
"org.apache.spark.api.java.JavaSparkContext",
"fromSparkContext",
ctx
)
#set the swift configs:
hconf <- jsc %>% invoke("hadoopConfiguration")
hconf %>% invoke("set","fs.swift.service.keystone.auth.url",
"https://identity.open.softlayer.com/v3/auth/tokens" )
}
which can be run with set_swift_config(sc).
I noticed in checking a package that I obtain notes "no visible binding for global variable" when I use functions like subset that use verbatim names of list elements as arguments.
For example with a data frame:
foo <- data.frame(a=c(TRUE,FALSE,TRUE),b=1:3)
I can do silly things like:
subset(foo,a)
transform(foo,a=b)
Which work as expected. The R code check in R CMD however doesn't understand that these refer to elements and complains about there not being any visible bindings of global variables.
While this works ok, I don't really like having notes in my package and prefer for it to pass the check with no errors, warnings and notes at all. I also don't really want to rework my code too much. Is there a way to write these codes so that it is clear the arguments do not refer to global variables?
To get it past R CMD check you can either :
Use get("b") (but that is onerous)
Place a=b=NULL somewhere higher up in your function (that's what I do)
There was a thread on r-devel a while ago where somebody from r-core basically said (from memory) "NOTES are ok, you know. The assumption is that the author checked it and is ok with the NOTE.". But, I agree with you. I do prefer to have CRAN checks return a clean "OK" on all platforms. That way the user is left in no doubt that it passes checks ok.
EDIT :
Here is the r-devel thread I was remembering (from April 2010). So that appears to suggest that there are some situations where there is no known way to avoid the NOTE, but that's ok.
This is one of the potential "unanticipated consequences" of using subset non-interactively. As it says in the Warning section of ?subset:
This is a convenience function intended for use interactively. For
programming it is better to use the standard subsetting functions like
‘[’, and in particular the non-standard evaluation of argument
‘subset’ can have unanticipated consequences.
From R version 2.15.1 onwards there is a way around this:
if(getRversion() >= "2.15.1") utils::globalVariables(c("a", "othervar"))
As per the warning section of ?subset it is better to use subset interactively, and [ for programming.
I would replace a command like
subset(foo,a)
with
foo[foo$a]
or if foo is a dataframe:
foo[foo$a, ]
you might also like to use with if foo is a dataframe and the expression to be evaluated is complex:
with(foo, foo[a, ])
I had this issue and traced it to my ggplot2 section.
This code provided the error:
ggplot2::ggplot(data = spec.df, ggplot2::aes(E.avg, fraction)) +
ggplot2::geom_line() +
ggplot2::ggtitle(paste0(title))
Adding the data name to the parameters eliminated the not:
ggplot2::ggplot(data = spec.df, ggplot2::aes(spec.df$E.avg, spec.df$fraction)) +
ggplot2::geom_line() +
ggplot2::ggtitle(paste0(title))