SparkR (version 2.4.6)

saveAsTable: Save the contents of the SparkDataFrame to a data source as a table

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

saveAsTable(df, tableName, source = NULL, mode = "error", ...)
# S4 method for SparkDataFrame,character
saveAsTable(df, tableName, source = NULL, mode = "error", ...)

Arguments

df

a SparkDataFrame.

tableName

a name for the table.

source

a name for external data source.

mode

one of 'append', 'overwrite', 'error', 'errorifexists', 'ignore' save mode (it is 'error' by default)

...

additional option(s) passed to the method.

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(), schema(), selectExpr(), select(), showDF(), show(), storageLevel(), str(), subset(), summary(), take(), toJSON(), unionByName(), union(), unpersist(), withColumn(), withWatermark(), with(), write.df(), 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)
saveAsTable(df, "myfile")
# }