| 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", ...) ## 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) |
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, except,
explain, filter,
first, gapplyCollect,
gapply, getNumPartitions,
group_by, head,
hint, histogram,
insertInto, intersect,
isLocal, isStreaming,
join, limit,
localCheckpoint, merge,
mutate, ncol,
nrow, persist,
printSchema, randomSplit,
rbind, registerTempTable,
rename, repartition,
rollup, sample,
saveAsTable, schema,
selectExpr, select,
showDF, show,
storageLevel, str,
subset, summary,
take, toJSON,
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")
##D saveDF(df, parquetPath2, "parquet", mode = "append", mergeSchema = TRUE)
## End(Not run)