| write.orc {SparkR} | R Documentation |
Save the contents of a SparkDataFrame as an ORC file, preserving the schema. Files written out with this method can be read back in as a SparkDataFrame using read.orc().
write.orc(x, path, ...)
## S4 method for signature 'SparkDataFrame,character'
write.orc(x, path, mode = "error", ...)
x |
A SparkDataFrame |
path |
The directory where the file is saved |
... |
additional argument(s) passed to the method. You can find the ORC-specific options for writing ORC files in https://spark.apache.org/docs/latest/sql-data-sources-orc.html#data-source-option Data Source Option in the version you use. |
mode |
one of 'append', 'overwrite', 'error', 'errorifexists', 'ignore' save mode (it is 'error' by default) |
write.orc since 2.0.0
Other SparkDataFrame functions:
SparkDataFrame-class,
agg(),
alias(),
arrange(),
as.data.frame(),
attach,SparkDataFrame-method,
broadcast(),
cache(),
checkpoint(),
coalesce(),
collect(),
colnames(),
coltypes(),
createOrReplaceTempView(),
crossJoin(),
cube(),
dapplyCollect(),
dapply(),
describe(),
dim(),
distinct(),
dropDuplicates(),
dropna(),
drop(),
dtypes(),
exceptAll(),
except(),
explain(),
filter(),
first(),
gapplyCollect(),
gapply(),
getNumPartitions(),
group_by(),
head(),
hint(),
histogram(),
insertInto(),
intersectAll(),
intersect(),
isLocal(),
isStreaming(),
join(),
limit(),
localCheckpoint(),
merge(),
mutate(),
ncol(),
nrow(),
persist(),
printSchema(),
randomSplit(),
rbind(),
rename(),
repartitionByRange(),
repartition(),
rollup(),
sample(),
saveAsTable(),
schema(),
selectExpr(),
select(),
showDF(),
show(),
storageLevel(),
str(),
subset(),
summary(),
take(),
toJSON(),
unionAll(),
unionByName(),
union(),
unpersist(),
withColumn(),
withWatermark(),
with(),
write.df(),
write.jdbc(),
write.json(),
write.parquet(),
write.stream(),
write.text()
## Not run:
##D sparkR.session()
##D path <- "path/to/file.json"
##D df <- read.json(path)
##D write.orc(df, "/tmp/sparkr-tmp1/")
## End(Not run)