Why streaming enables real-time analytics in Apache Spark - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read streaming data from a socket source.

Apache Spark

streamingDF = spark.readStream.format([1]).option("host", "localhost").option("port", 9999).load()

Drag options to blanks, or click blank then click option'

A"json"

B"csv"

C"socket"

D"parquet"

Attempts:

3 left

💡 Hint

Common Mistakes

Using file formats like csv or json instead of socket for streaming source.

Forgetting to specify the host and port options.

The socket format allows Spark to read streaming data from a network socket, enabling real-time data ingestion.

2fill in blank

medium

Complete the code to write streaming data to the console sink.

Apache Spark

query = streamingDF.writeStream.format([1]).outputMode("append").start()

Drag options to blanks, or click blank then click option'

A"parquet"

B"console"

C"memory"

D"csv"

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing file formats like parquet or csv which are not console sinks.

Using memory sink which stores data but does not print it.

The console sink prints streaming data to the console in real-time, useful for debugging and monitoring.

3fill in blank

hard

Fix the error in the code to correctly define the streaming aggregation.

Apache Spark

aggDF = streamingDF.groupBy("category").agg([1]("value"))

Drag options to blanks, or click blank then click option'

Asum

BcountDistinct

Ccollect_list

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using collect_list which collects values but does not aggregate numerically.

Using countDistinct which counts unique items, not sums.

The sum function correctly aggregates the values by category for streaming analytics.

4fill in blank

hard

Fill both blanks to filter streaming data for values greater than 100 and select only the 'category' and 'value' columns.

Apache Spark

filteredDF = streamingDF.filter(streamingDF.value [1] 100).select([2], "value")

Drag options to blanks, or click blank then click option'

A>

B"category"

C<

D"timestamp"

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > in the filter condition.

Selecting the wrong column like 'timestamp' instead of 'category'.

The filter uses > to keep values greater than 100, and select chooses the 'category' and 'value' columns for analysis.

5fill in blank

hard

Fill all three blanks to create a streaming query that writes aggregated data to memory with complete output mode and a query name 'aggQuery'.

Apache Spark

query = aggDF.writeStream.format([1]).outputMode([2]).queryName([3]).start()

Drag options to blanks, or click blank then click option'

A"memory"

B"complete"

C"aggQuery"

D"append"

Attempts:

3 left

💡 Hint

Common Mistakes

Using append mode which only outputs new rows, not full aggregation.

Using console or file formats instead of memory sink.

Not naming the query or using incorrect query name syntax.

Writing to memory sink with complete output mode stores the full aggregation result, and naming the query 'aggQuery' helps manage it.