Write the streaming SparkDataFrame to a data source.
write.stream.RdThe 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.stream(df, source = NULL, outputMode = NULL, ...)
# S4 method for SparkDataFrame
write.stream(
df,
source = NULL,
outputMode = NULL,
partitionBy = NULL,
trigger.processingTime = NULL,
trigger.once = NULL,
...
)Arguments
- df
a streaming SparkDataFrame.
- source
a name for external data source.
- outputMode
one of 'append', 'complete', 'update'.
- ...
additional external data source specific named options.
- 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.
- trigger.processingTime
a processing time interval as a string, e.g. '5 seconds', '1 minute'. This is a trigger that runs a query periodically based on the processing time. If value is '0 seconds', the query will run as fast as possible, this is the default. Only one trigger can be set.
- trigger.once
a logical, must be set to
TRUE. This is a trigger that processes only one batch of data in a streaming query then terminates the query. Only one trigger can be set.
Details
Additionally, outputMode specifies how data of a streaming SparkDataFrame is written to a
output data source. There are three modes:
append: Only the new rows in the streaming SparkDataFrame will be written out. This output mode can be only be used in queries that do not contain any aggregation.
complete: All the rows in the streaming SparkDataFrame will be written out every time there are some updates. This output mode can only be used in queries that contain aggregations.
update: Only the rows that were updated in the streaming SparkDataFrame will be written out every time there are some updates. If the query doesn't contain aggregations, it will be equivalent to
appendmode.
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.df(),
write.jdbc(),
write.json(),
write.orc(),
write.parquet(),
write.text()
Examples
if (FALSE) {
sparkR.session()
df <- read.stream("socket", host = "localhost", port = 9999)
isStreaming(df)
wordCounts <- count(group_by(df, "value"))
# console
q <- write.stream(wordCounts, "console", outputMode = "complete")
# text stream
q <- write.stream(df, "text", path = "/home/user/out", checkpointLocation = "/home/user/cp",
partitionBy = c("year", "month"), trigger.processingTime = "30 seconds")
# memory stream
q <- write.stream(wordCounts, "memory", queryName = "outs", outputMode = "complete")
head(sql("SELECT * from outs"))
queryName(q)
stopQuery(q)
}