| write.df {SparkR} | R Documentation |
The data source is specified by the source and a set of options (...).
If source is not specified, the default data source configured by
spark.sql.sources.default will be used.
write.df(df, path = NULL, ...) saveDF(df, path, source = NULL, mode = "error", ...) write.df(df, path = NULL, ...) ## S4 method for signature 'SparkDataFrame' write.df( df, path = NULL, source = NULL, mode = "error", partitionBy = NULL, ... ) ## S4 method for signature 'SparkDataFrame,character' saveDF(df, path, source = NULL, mode = "error", ...)
df |
a SparkDataFrame. |
path |
a name for the table. |
... |
additional argument(s) passed to the method. |
source |
a name for external data source. |
mode |
one of 'append', 'overwrite', 'error', 'errorifexists', 'ignore' save mode (it is 'error' by default) |
partitionBy |
a name or a list of names of columns to partition the output by on the file system. If specified, the output is laid out on the file system similar to Hive's partitioning scheme. |
Additionally, mode is used to specify the behavior of the save operation when data already exists in the data source. There are four modes:
'append': Contents of this SparkDataFrame are expected to be appended to existing data.
'overwrite': Existing data is expected to be overwritten by the contents of this SparkDataFrame.
'error' or 'errorifexists': An exception is expected to be thrown.
'ignore': The save operation is expected to not save the contents of the SparkDataFrame and to not change the existing data.
write.df since 1.4.0
saveDF since 1.4.0
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.jdbc(),
write.json(),
write.orc(),
write.parquet(),
write.stream(),
write.text()
## Not run:
##D sparkR.session()
##D path <- "path/to/file.json"
##D df <- read.json(path)
##D write.df(df, "myfile", "parquet", "overwrite", partitionBy = c("col1", "col2"))
##D saveDF(df, parquetPath2, "parquet", mode = "append", mergeSchema = TRUE)
## End(Not run)