20 Spark Structured Streaming Interview Questions and Answers

Structured Streaming is a new streaming API, introduced in Apache Spark 2.0, that makes it easy to build end-to-end streaming applications. This API enables developers to build scalable and fault-tolerant stream processing applications with ease.

If you’re applying for a job that involves Spark Structured Streaming, it’s important to be prepared for the interview. In this article, we’ll go over some of the most common Spark Structured Streaming interview questions. With these questions and answers in mind, you’ll be one step closer to acing your interview and getting the job.

Spark Structured Streaming Interview Questions and Answers

Here are 20 commonly asked Spark Structured Streaming interview questions and answers to prepare you for your interview:

1. What is Structured Streaming in Spark?

Structured Streaming is a new streaming API in Spark that can be used to process data in a streaming fashion. It is based on the structured data processing API in Spark, and it allows for the processing of data in a more efficient and scalable way.

2. How is it different from traditional streaming?

Structured streaming is a new streaming API, introduced in Spark 2.0, that can be used to build end-to-end streaming applications. It is different from traditional streaming in a few key ways:

1. Structured streaming uses a micro-batch processing model, as opposed to the continuous processing model used by traditional streaming. This means that each batch of data is processed as a unit, and the results of each batch are made available as soon as the batch is processed.

2. Structured streaming supports event time processing, which allows for late data to be handled appropriately.

3. Structured streaming offers a higher level of abstraction than traditional streaming, making it easier to develop streaming applications.

3. What types of sources can be used to ingest data into a structured stream?

There are many types of sources that can be used to ingest data into a structured stream. Some common sources include Kafka, Flume, and Kinesis.

4. Can you explain how the various components work together in Spark Structured Streaming?

The various components in Spark Structured Streaming work together in order to provide a high-level API that can be used to process streaming data. The API is built on top of the Spark SQL engine, which is used to process the data. The API also uses the Spark Streaming engine to process the data in a streaming fashion.

5. What are some common use cases for Spark Structured Streaming?

Some common use cases for Spark Structured Streaming include streaming ETL pipelines, streaming machine learning, and streaming data analytics.

6. Can you give me some examples of real-world companies that use Spark Structured Streaming?

Some companies that use Spark Structured Streaming include Netflix, Uber, and Airbnb.

7. What do you understand by watermarks? Why are they important?

A watermark is a timestamp that is used to track the progress of data in a stream. Watermarks are important because they allow you to control how late data can be before it is considered too late to be processed.

8. What is the difference between an event time and processing time?

Event time is the timestamp associated with the data itself, while processing time is the timestamp associated with when the data is processed. In other words, event time is the “true” timestamp of when something happened, while processing time is the timestamp of when that event was recorded or processed.

9. What are the key differences between batch processing and streaming processing?

The key difference between batch processing and streaming processing is that batch processing can handle data that is stored in a static location, while streaming processing is designed to handle data that is constantly changing and moving. With batch processing, you can process a large amount of data all at once, but you have to wait for all of the data to be collected before you can begin. With streaming processing, you can begin processing the data as soon as it arrives, which makes it much more efficient for handling real-time data.

10. What’s the role played by state stores in Spark Structured Streaming?

State stores are used in Spark Structured Streaming to keep track of the data that has been processed in a stream. This is necessary in order to ensure that the stream can be correctly processed in a fault-tolerant manner, as well as to provide the ability to perform stateful operations on the data.

11. What is a Kafka topic?

A Kafka topic is a named stream of data that is stored in a Kafka cluster. Topics are divided into a number of partitions, each of which is an ordered, immutable sequence of messages that is replicated across a set of servers.

12. What is the difference between Output Modes Append and Complete?

The difference between Append and Complete output modes is that Append only outputs new rows to the sink as they are received, while Complete outputs the entire updated result set every time there is a trigger.

13. Is there any way to aggregate messages within a window before writing them to a sink?

Yes, you can use the groupBy and aggregate functions to do this.

14. Is it possible to disable automatic checkpointing in Spark Structured Streaming? If yes, then how?

Yes, it is possible to disable automatic checkpointing in Spark Structured Streaming. You can do this by setting the “checkpointLocation” option to “none”.

15. What happens if your system crashes while running a streaming query in Apache Spark?

If your system crashes while running a streaming query in Apache Spark, the query will be automatically restarted from the last saved checkpoint. This ensures that your streaming query does not lose any data.

16. Can you tell me about some limitations of Spark Structured Streaming?

Some potential limitations of Spark Structured Streaming include:
-It can be difficult to debug streaming applications since there is no clear starting or ending point
-There is no guarantee of exactly-once processing, which means that some data may be processed multiple times or not at all
-It can be challenging to maintain state across multiple streaming micro-batches
-It can be difficult to reprocess data from a previous micro-batch if there are errors

17. What is the difference between mapGroupsWithState and flatMapGroupsWithState?

The main difference between mapGroupsWithState and flatMapGroupsWithState is that mapGroupsWithState will only allow you to return a value that is of the same type as the input, while flatMapGroupsWithState will allow you to return a value that is of a different type. This can be useful if you want to perform some transformation on the input data before returning it.

18. When should you use groupByKey instead of reduceByKey?

You should use groupByKey when you need to maintain data locality (data is processed in the same partition it is received in) and when you need the ordering of the data to be maintained. You should use reduceByKey when you need to perform an aggregation operation (such as a sum or average) on the data.

19. What is the significance of the repartition() operator in Spark Structured Streaming?

The repartition() operator is used to change the number of partitions in a Spark Structured Streaming dataset. This can be useful if you want to ensure that each partition contains a specific type of data, for example.

20. What is the significance of the reduceByKeyAndWindow() operator in Spark Structured Streaming?

The reduceByKeyAndWindow() operator is used to perform a reduction operation on a DStream of key-value pairs. The operator will apply the given function to each key-value pair in the DStream and return a new DStream containing the results. The function used must be associative and commutative in order for the operator to work correctly.