sparklyr (version 1.7.1)

stream_write_kafka: Write Kafka Stream

Description

Writes a Spark dataframe stream into an kafka stream.

Usage

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

Arguments

x

A Spark DataFrame or dplyr operation

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.

partition_by

Partitions the output by the given list of columns.

...

Optional arguments; currently unused.

Details

Please note that Kafka requires installing the appropriate package by setting the packages parameter to "kafka" in spark_connect()

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_memory(), stream_write_orc(), stream_write_parquet(), stream_write_text()

Examples

# NOT RUN {
library(sparklyr)
sc <- spark_connect(master = "local", version = "2.3", packages = "kafka")

read_options <- list(kafka.bootstrap.servers = "localhost:9092", subscribe = "topic1")
write_options <- list(kafka.bootstrap.servers = "localhost:9092", topic = "topic2")

stream <- stream_read_kafka(sc, options = read_options) %>%
  stream_write_kafka(options = write_options)

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