Streaming joins in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform an inner join on streaming DataFrames.

Apache Spark

joined_stream = stream_df1.join(stream_df2, on=[1], how='inner')

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

A'value'

B'timestamp'

C'id'

D'name'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column not present in both DataFrames causes errors.

Using a non-unique column can cause unexpected duplicates.

The join key is usually a common column like 'id' to match records between streams.

2fill in blank

medium

Complete the code to specify a watermark on the streaming DataFrame.

Apache Spark

stream_df = stream_df.withWatermark('[1]', '10 minutes')

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

AeventTime

BprocessingTime

Ctimestamp

Ddate

Attempts:

3 left

💡 Hint

Common Mistakes

Applying watermark on processing time instead of event time.

Using a column that is not a timestamp type.

Watermarking is applied on event time columns to handle late data.

3fill in blank

hard

Fix the error in the join condition for streaming DataFrames.

Apache Spark

joined_stream = stream_df1.join(stream_df2, on=stream_df1.[1] == stream_df2.id)

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

Atimestamp

Bvalue

Cname

Did

Attempts:

3 left

💡 Hint

Common Mistakes

Using different columns that do not match for join condition.

Using a column that does not exist in stream_df1.

The join condition must compare the same key column from both DataFrames.

4fill in blank

hard

Fill both blanks to create a streaming join with watermark and time range condition.

Apache Spark

joined_stream = stream_df1.withWatermark('[1]', '5 minutes')\n    .join(stream_df2.withWatermark('[2]', '5 minutes'), on='id')

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

AeventTime

BprocessingTime

Ctimestamp

Ddate

Attempts:

3 left

💡 Hint

Common Mistakes

Using different columns for watermark in each stream.

Using processing time instead of event time.

Watermarking both streams on the event time column is required for stream-stream joins.

5fill in blank

hard

Fill all three blanks to filter joined streaming data by time range and select columns.

Apache Spark

result = joined_stream.filter((joined_stream.[1] >= joined_stream.[2] - expr('interval 1 hour')) &\n                              (joined_stream.[3] <= joined_stream.[2] + expr('interval 1 hour')))\n               .select('id', 'value', 'timestamp')

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

Atimestamp

BeventTime

CprocessingTime

Dtime

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up timestamp and eventTime columns.

Using processingTime which is not suitable for event-time filtering.

Filtering uses the timestamp column compared to eventTime with a 1 hour window.

Practice

1. What is the main purpose of a streaming join in Apache Spark?

easy

A. To combine two live data streams based on matching keys

B. To save streaming data permanently to disk

C. To convert batch data into streaming data

D. To filter out duplicate records in a stream

Solution

1. Step 1: Understand streaming join concept

   Streaming joins are used to combine two live streams by matching keys in real time.

2. Step 2: Differentiate from other operations

   Saving data, converting batch to stream, or filtering duplicates are different tasks, not streaming joins.

3. Final Answer:

   To combine two live data streams based on matching keys -> Option A

4. Quick Check:

   Streaming join = combine streams by keys [OK]

Hint: Streaming joins combine live streams on keys [OK]

Common Mistakes:

• Confusing streaming join with saving data
• Thinking streaming join converts batch to stream
• Mixing up filtering duplicates with joining

2. Which of the following is the correct syntax to perform a streaming join between two DataFrames streamA and streamB on column id in Spark?

easy

A. streamA.concat(streamB, 'id')

B. streamA.merge(streamB, on='id')

C. streamA.join(streamB, streamA.id == streamB.id)

D. streamA.union(streamB).filter('id')

Solution

1. Step 1: Recall Spark join syntax

   Spark uses .join() with a join condition like streamA.id == streamB.id.

2. Step 2: Check other options

   .merge(), .concat(), and .union() are not valid for streaming joins in Spark.

3. Final Answer:

   streamA.join(streamB, streamA.id == streamB.id) -> Option C

4. Quick Check:

   Streaming join syntax = .join(condition) [OK]

Hint: Use .join() with condition for streaming joins [OK]

Common Mistakes:

• Using .merge() instead of .join()
• Trying to concatenate streams for join
• Using union which just stacks data

3. Given the following Spark Structured Streaming code, what will be the output schema of the joined stream?

streamA = spark.readStream.format('socket').option('host', 'localhost').option('port', 9999).load()
streamB = spark.readStream.format('socket').option('host', 'localhost').option('port', 9998).load()
joinedStream = streamA.join(streamB, streamA.value == streamB.value)
joinedStream.printSchema()

medium

A. StructType with only one 'value' column

B. StructType with two 'value' columns from streamA and streamB

C. Error due to ambiguous column names

D. Empty schema because streams are empty

Solution

1. Step 1: Understand join output schema

   Joining two streams with same column names results in both columns included with source prefixes.

2. Step 2: Check for ambiguity handling

   Spark automatically prefixes columns to avoid ambiguity, so no error occurs.

3. Final Answer:

   StructType with two 'value' columns from streamA and streamB -> Option B

4. Quick Check:

   Join output = both columns with prefixes [OK]

Hint: Joined streams keep both columns with prefixes [OK]

Common Mistakes:

• Assuming only one column remains after join
• Expecting error due to same column names
• Thinking schema is empty before data arrives

4. Consider this code snippet for joining two streaming DataFrames:

joinedStream = streamA.join(streamB, 'id')

When running this code, you get a runtime error about missing watermark. What is the likely cause?

medium

A. The join type is missing

B. The join condition 'id' is invalid syntax

C. The streams have different schemas

D. No watermark defined on one or both streams causing state to grow indefinitely

Solution

1. Step 1: Understand watermark role in streaming joins

   Watermarks limit how long Spark keeps state for late data in streaming joins.

2. Step 2: Identify cause of runtime error

   Without watermark, Spark cannot clean old state, causing errors about state size.

3. Final Answer:

   No watermark defined on one or both streams causing state to grow indefinitely -> Option D

4. Quick Check:

   Missing watermark = runtime error in streaming join [OK]

Hint: Always set watermark to avoid state errors [OK]

Common Mistakes:

• Thinking join condition syntax is wrong
• Assuming schema mismatch causes this error
• Believing join type must always be specified

5. You have two streaming DataFrames: orders with columns orderId, customerId, orderTime and customers with columns customerId, customerName, updateTime. You want to join them on customerId but only match orders with customer info updated within 1 hour before the order. Which approach correctly implements this streaming join?

hard

A. Use event-time watermark on both streams and join with condition: orders.customerId = customers.customerId AND customers.updateTime BETWEEN orders.orderTime - interval 1 hour AND orders.orderTime

B. Join on customerId only without watermark or time condition

C. Use batch join after collecting streaming data

D. Join on customerId and filter results after join for time difference

Solution

1. Step 1: Use watermarks to handle late data

   Set watermarks on orderTime and updateTime to manage state and late events.

2. Step 2: Apply join condition with time range

   Join on customerId and add time condition to match updates within 1 hour before order.

3. Step 3: Avoid filtering after join or batch join

   Filtering after join wastes resources; batch join loses streaming benefits.

4. Final Answer:

   Use event-time watermark on both streams and join with condition: orders.customerId = customers.customerId AND customers.updateTime BETWEEN orders.orderTime - interval 1 hour AND orders.orderTime -> Option A

5. Quick Check:

   Watermark + time condition in join = correct streaming join [OK]

Hint: Use watermark and time range in join condition [OK]

Common Mistakes:

• Skipping watermark causing state issues
• Joining without time condition losing accuracy
• Filtering after join instead of in join condition
• Using batch join losing streaming benefits