SparkR (version 3.1.2)

write.df: Save the contents of SparkDataFrame to a data source.

Description

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.

Usage

write.df(df, path = NULL, ...)
saveDF(df, path, source = NULL, mode = "error", ...)
write.df(df, path = NULL, ...)
# S4 method for SparkDataFrame
write.df(
  df,
  path = NULL,
  source = NULL,
  mode = "error",
  partitionBy = NULL,
  ...
)
# S4 method for SparkDataFrame,character
saveDF(df, path, source = NULL, mode = "error", ...)

Arguments

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.

Details

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.

See Also

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()

Examples

# NOT RUN {
sparkR.session()
path <- "path/to/file.json"
df <- read.json(path)
write.df(df, "myfile", "parquet", "overwrite", partitionBy = c("col1", "col2"))
saveDF(df, parquetPath2, "parquet", mode = "append", mergeSchema = TRUE)
# }