RateStreamSource

RateStreamSource is a streaming source that generates consecutive numbers with timestamp that can be useful for testing and PoCs.

RateStreamSource is created for rate format (that is registered by RateSourceProvider).

val rates = spark
  .readStream
  .format("rate") // <-- use RateStreamSource
  .option("rowsPerSecond", 1)
  .load

Table 1. RateStreamSource’s Options
Name	Default Value	Description
`numPartitions`	(default parallelism)	Number of partitions to use
`rampUpTime`	`0` (seconds)
`rowsPerSecond`	`1`	Number of rows to generate per second (has to be greater than `0`)

RateStreamSource uses a predefined schema that cannot be changed.

val schema = rates.schema
scala> println(schema.treeString)
root
 |-- timestamp: timestamp (nullable = true)
 |-- value: long (nullable = true)

Table 2. RateStreamSource’s Dataset Schema (in the positional order)
Name	Type
`timestamp`	`TimestampType`
`value`	`LongType`

Table 3. RateStreamSource’s Internal Registries and Counters
Name	Description
`clock`
`lastTimeMs`
`maxSeconds`
`startTimeMs`

Tip	Enable `INFO` or `DEBUG` logging levels for `org.apache.spark.sql.execution.streaming.RateStreamSource` to see what happens inside. Add the following line to `conf/log4j.properties`: `log4j.logger.org.apache.spark.sql.execution.streaming.RateStreamSource=DEBUG` Refer to Logging.

Getting Maximum Available Offsets — `getOffset` Method

getOffset: Option[Offset]

Note	`getOffset` is a part of the Source Contract.

Caution

FIXME

Generating DataFrame for Streaming Batch — `getBatch` Method

getBatch(start: Option[Offset], end: Offset): DataFrame

Note	`getBatch` is a part of Source Contract.

Internally, getBatch calculates the seconds to start from and end at (from the input start and end offsets) or assumes 0.

getBatch then calculates the values to generate for the start and end seconds.

You should see the following DEBUG message in the logs:

DEBUG RateStreamSource: startSeconds: [startSeconds], endSeconds: [endSeconds], rangeStart: [rangeStart], rangeEnd: [rangeEnd]

If the start and end ranges are equal, getBatch creates an empty DataFrame (with the schema) and returns.

Otherwise, when the ranges are different, getBatch creates a DataFrame using SparkContext.range operator (for the start and end ranges and numPartitions partitions).

Creating RateStreamSource Instance

RateStreamSource takes the following when created:

SQLContext
Path to the metadata
Rows per second
RampUp time in seconds
Number of partitions
Flag to whether to use ManualClock (true) or SystemClock (false)

RateStreamSource initializes the internal registries and counters.

RateStreamSource