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TextSocketSource is a streaming source that reads text lines from a socket at the host and port.

TextSocketSource uses lines internal in-memory buffer to keep all of the lines that were read from a socket forever.


This source is not for production use due to design contraints, e.g. infinite in-memory collection of lines read and no fault recovery.

It is designed only for tutorials and debugging.

Creating Instance

When TextSocketSource is created (see TextSocketSourceProvider), it gets 4 parameters passed in:

  1. host
  2. port
  3. includeTimestamp flag

CAUTION: It appears that the source did not get "renewed" to use[SparkSession] instead.

It opens a socket at given host and port parameters and reads a buffering character-input stream using the default charset and the default-sized input buffer (of 8192 bytes) line by line.

CAUTION: FIXME Review Java's Charset.defaultCharset()

It starts a readThread daemon thread (called TextSocketSource(host, port)) to read lines from the socket. The lines are added to the internal <> buffer.

lines Internal Buffer

lines: ArrayBuffer[(String, Timestamp)]

lines is the internal buffer of all the lines TextSocketSource read from the socket.

=== [[getOffset]] Maximum Available Offset (getOffset method)

TextSocketSource's offset can either be none or LongOffset of the number of lines in the internal <> buffer.

getOffset is a part of the Source abstraction.

=== [[schema]] Schema (schema method)

TextSocketSource supports two[schemas]:

  1. A single value field of String type.
  2. value field of StringType type and timestamp field of[TimestampType] type of format yyyy-MM-dd HH:mm:ss.


Refer to sourceSchema for TextSocketSourceProvider.

Stopping TextSocketSource

When stopped, TextSocketSource closes the socket connection.


import org.apache.spark.sql.SparkSession
val spark: SparkSession = SparkSession.builder.getOrCreate()

// Connect to localhost:9999
// You can use "nc -lk 9999" for demos
val textSocket = spark.
  option("host", "localhost").
  option("port", 9999).

import org.apache.spark.sql.Dataset
val lines: Dataset[String] =[String].map(_.toUpperCase)

val query = lines.writeStream.format("console").start

// Start typing the lines in nc session
// They will appear UPPERCASE in the terminal

Batch: 0
|    value|

scala> query.explain
== Physical Plan ==
*SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#21]
+- *MapElements <function1>, obj#20: java.lang.String
   +- *DeserializeToObject value#43.toString, obj#19: java.lang.String
      +- LocalTableScan [value#43]

scala> query.stop