What DStream means?

A Discretized Stream (DStream), the basic abstraction in Spark Streaming, is a continuous sequence of RDDs (of the same type) representing a continuous stream of data (see spark. RDD for more details on RDDs).

What is StreamingContext?

public class StreamingContext extends Object implements Logging. Main entry point for Spark Streaming functionality. It provides methods used to create DStream s from various input sources. It can be either created by providing a Spark master URL and an appName, or from a org.

What is DStream internally?

DStream represents a continuous stream of data. Internally, DStream is portrait as a sequence of RDDs. Thus, like RDD, we can obtain DStream from input DStream like Kafka, Flume etc. Also, the transformation could be applied on the existing DStream to get a new DStream.

How many RDDs can Cogroup () can work at once?

cogroup() can be used for much more than just implementing joins. We can also use it to implement intersect by key. Additionally, cogroup() can work on three or more RDDs at once.

What does saveAsTextFiles prefix suffix do?

saveAsTextFiles(prefix, [suffix]) Save this DStream’s contents as text files. The file name at each batch interval is generated based on prefix and suffix: “prefix-TIME_IN_MS[. suffix]”.

Which of the following transformations can be applied to a DStream?

Different transformations in DStream in Apache Spark Streaming are: 1-map(func) — Return a new DStream by passing each element of the source DStream through a function func. 2-flatMap(func) — Similar to map, but each input item can be mapped to 0 or more output items.

What is spark foreachRDD?

foreachRDD is an “output operator” in Spark Streaming. It allows you to access the underlying RDDs of the DStream to execute actions that do something practical with the data. For example, using foreachRDD you could write data to a database.

What is spark checkpointing?

Checkpointing is actually a feature of Spark Core (that Spark SQL uses for distributed computations) that allows a driver to be restarted on failure with previously computed state of a distributed computation described as an RDD .

Is caching allowed for DStream pipelines?

Like Spark RDDs, DStreams can be cached in memory. The use cases for caching are similar to those for RDDs-if we expect to access the data in a DStream multiple times (perhaps performing multiple types of analysis or aggregation or outputting to multiple external systems), we will benefit from caching the data.

What is a batch interval?

batch interval – it is time in seconds how long data will be collected before dispatching processing on it. For example if you set batch interval 5 seconds – Spark Streaming will collect data for 5 seconds and then kick out calculation on RDD with that data.