saveAsTable {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.
saveAsTable(df, tableName, source = NULL, mode = "error", ...) ## S4 method for signature 'SparkDataFrame,character' saveAsTable(df, tableName, source = NULL, mode = "error", ...)
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. |
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.
saveAsTable 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
, 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
## Not run:
##D sparkR.session()
##D path <- "path/to/file.json"
##D df <- read.json(path)
##D saveAsTable(df, "myfile")
## End(Not run)