sparklyr (version 1.5.1)

stream_write_parquet: Write Parquet Stream

Description

Writes a Spark dataframe stream into a parquet stream.

Usage

stream_write_parquet(
  x,
  path,
  mode = c("append", "complete", "update"),
  trigger = stream_trigger_interval(),
  checkpoint = file.path(path, "checkpoints", random_string("")),
  options = list(),
  ...
)

Arguments

x

A Spark DataFrame or dplyr operation

path

The destination path. 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 runnnig every 5 seconds. See stream_trigger_interval and stream_trigger_continuous.

checkpoint

The location where the system will write all the checkpoint information to guarantee end-to-end fault-tolerance.

options

A list of strings with additional options.

...

Optional arguments; currently unused.

See Also

Other Spark stream serialization: stream_read_csv(), stream_read_delta(), stream_read_json(), stream_read_kafka(), stream_read_orc(), stream_read_parquet(), stream_read_socket(), stream_read_text(), stream_write_console(), stream_write_csv(), stream_write_delta(), stream_write_json(), stream_write_kafka(), stream_write_memory(), stream_write_orc(), stream_write_text()

Examples

# NOT RUN {
sc <- spark_connect(master = "local")

sdf_len(sc, 10) %>% spark_write_parquet("parquet-in")

stream <- stream_read_parquet(sc, "parquet-in") %>% stream_write_parquet("parquet-out")

stream_stop(stream)
# }
# NOT RUN {
# }