Write files to the stream
Usage
stream_write_csv(
x,
path,
mode = c("append", "complete", "update"),
trigger = stream_trigger_interval(),
checkpoint = file.path(path, "checkpoint"),
header = TRUE,
delimiter = ",",
quote = "\"",
escape = "\\",
charset = "UTF-8",
null_value = NULL,
options = list(),
partition_by = NULL,
...
)
stream_write_text(
x,
path,
mode = c("append", "complete", "update"),
trigger = stream_trigger_interval(),
checkpoint = file.path(path, "checkpoints", random_string("")),
options = list(),
partition_by = NULL,
...
)
stream_write_json(
x,
path,
mode = c("append", "complete", "update"),
trigger = stream_trigger_interval(),
checkpoint = file.path(path, "checkpoints", random_string("")),
options = list(),
partition_by = NULL,
...
)
stream_write_parquet(
x,
path,
mode = c("append", "complete", "update"),
trigger = stream_trigger_interval(),
checkpoint = file.path(path, "checkpoints", random_string("")),
options = list(),
partition_by = NULL,
...
)
stream_write_orc(
x,
path,
mode = c("append", "complete", "update"),
trigger = stream_trigger_interval(),
checkpoint = file.path(path, "checkpoints", random_string("")),
options = list(),
partition_by = NULL,
...
)
stream_write_kafka(
x,
mode = c("append", "complete", "update"),
trigger = stream_trigger_interval(),
checkpoint = file.path("checkpoints", random_string("")),
options = list(),
partition_by = NULL,
...
)
stream_write_console(
x,
mode = c("append", "complete", "update"),
options = list(),
trigger = stream_trigger_interval(),
partition_by = NULL,
...
)
stream_write_delta(
x,
path,
mode = c("append", "complete", "update"),
checkpoint = file.path("checkpoints", random_string("")),
options = list(),
partition_by = NULL,
...
)Arguments
- x
A Spark DataFrame or dplyr operation
- path
The path to the file. Needs to be accessible from the cluster. Supports the "hdfs://", "s3a://" and "file://" protocols.
- mode
Specifies how data is written to a streaming sink. Valid values are
"append","complete"or"update".- trigger
The trigger for the stream query, defaults to micro-batches running every 5 seconds. See
stream_trigger_intervalandstream_trigger_continuous.- checkpoint
The location where the system will write all the checkpoint information to guarantee end-to-end fault-tolerance.
- header
Should the first row of data be used as a header? Defaults to
TRUE.- delimiter
The character used to delimit each column, defaults to
,.- quote
The character used as a quote. Defaults to '"'.
- escape
The character used to escape other characters, defaults to
\.- charset
The character set, defaults to
"UTF-8".- null_value
The character to use for default values, defaults to
NULL.- options
A list of strings with additional options.
- partition_by
Partitions the output by the given list of columns.
- ...
Optional arguments; currently unused.
See also
Other Spark stream serialization:
stream_write_memory(),
stream_write_table()
Examples
if (FALSE) { # \dontrun{
sc <- spark_connect(master = "local")
dir.create("csv-in")
write.csv(iris, "csv-in/data.csv", row.names = FALSE)
csv_path <- file.path("file://", getwd(), "csv-in")
stream <- stream_read_csv(sc, csv_path) %>% stream_write_csv("csv-out")
stream_stop(stream)
} # }