Streaming joins in Apache Spark

Introduction

Streaming joins let you combine data from two live streams as new data arrives. This helps you analyze related events happening in real time.

Matching user clicks with ad impressions as they happen on a website.

Joining sensor data streams from two machines to detect correlated faults.

Combining live stock trades with real-time news feeds to spot market trends.

Merging live GPS data from vehicles with traffic updates to optimize routes.

Syntax

Apache Spark

stream1.join(stream2, join_expression, join_type)

stream1 and stream2 are streaming DataFrames.

join_expression defines how to match rows (like keys).

Examples

Inner join streams on matching keys, keeping only matching rows.

Apache Spark

stream1.join(stream2, stream1.key == stream2.key, 'inner')

Left outer join keeps all rows from stream1 and matches from stream2 if available.

Apache Spark

stream1.join(stream2, stream1.id == stream2.id, 'left_outer')

Right outer join keeps all rows from stream2 and matches from stream1 if available.

Apache Spark

stream1.join(stream2, expr("stream1.key == stream2.key"), 'right_outer')

Sample Program

This code creates two streaming sources that generate numbers and timestamps. It renames columns to create keys, then joins the streams where keys match. The result prints to the console for 5 seconds.

Apache Spark

from pyspark.sql import SparkSession
from pyspark.sql.functions import expr

spark = SparkSession.builder.appName('StreamingJoinsExample').getOrCreate()

# Simulate streaming data with rate source
stream1 = spark.readStream.format('rate').option('rowsPerSecond', 5).load()
stream2 = spark.readStream.format('rate').option('rowsPerSecond', 5).load()

# Rename columns to simulate keys
stream1 = stream1.selectExpr('value as key1', 'timestamp as ts1')
stream2 = stream2.selectExpr('value as key2', 'timestamp as ts2')

# Join streams on keys where key1 == key2
joined_stream = stream1.join(stream2, expr('key1 == key2'), 'inner')

query = joined_stream.writeStream.format('console').outputMode('append').start()

query.awaitTermination(5000)  # Run for 5 seconds then stop
query.stop()

Important Notes

Streaming joins require careful handling of late or missing data to avoid memory issues.

Use watermarking to limit how long Spark waits for matching data in streams.

Not all join types are supported in streaming; inner and some outer joins are common.

Summary

Streaming joins combine two live data streams based on matching keys.

They help analyze related events happening at the same time.

Use Spark's join syntax with streaming DataFrames and manage late data with watermarks.

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