Streaming joins in Apache Spark - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a streaming join in Apache Spark?

A streaming join in Apache Spark combines two continuous data streams based on a common key, allowing real-time data from both streams to be matched and processed together.

beginner

What are the two main types of streaming joins in Spark Structured Streaming?

The two main types are inner join and outer join. Inner join returns matching records from both streams, while outer join includes unmatched records from one or both streams.

intermediate

Why do streaming joins require watermarks in Spark?

Watermarks help Spark manage late data by setting a threshold for how long to wait for late events. This prevents the join state from growing indefinitely and helps Spark clean up old data.

intermediate

What is the difference between a stream-static join and a stream-stream join?

A stream-static join joins a streaming dataset with a static dataset (like a table). A stream-stream join joins two streaming datasets, both continuously updating.

intermediate

How does Spark handle state in streaming joins?

Spark keeps track of data from both streams in memory as state. It uses this state to match records across streams. State is cleaned up based on watermarks to avoid memory overflow.

What does a streaming join in Spark do?

A Combines two continuous data streams based on a key

B Combines two static datasets

C Deletes old data from a stream

D Converts batch data to streaming data

Streaming joins combine two continuous data streams based on a common key to process data in real-time.

Which type of join returns only matching records from both streams?

A Inner join

B Left join

C Right join

D Outer join

Inner join returns only records that have matching keys in both streams.

Why are watermarks important in streaming joins?

A To speed up the join operation

B To convert streams to static data

C To handle late data and clean up state

D To encrypt streaming data

Watermarks help manage late data and allow Spark to clean up old join state to save memory.

What is a stream-static join?

A Joining two streaming datasets

B Joining a streaming dataset with a static dataset

C Joining two static datasets

D Joining two batch datasets

A stream-static join combines a streaming dataset with a static dataset like a table.

How does Spark manage the memory used for streaming joins?

A By converting streams to batch

B By storing all data permanently

C By compressing data streams

D By using watermarks to remove old state

Spark uses watermarks to remove old join state and prevent memory overflow.

Explain how streaming joins work in Apache Spark and why watermarks are necessary.

Think about how two live data streams can be combined and how Spark deals with delays.

Describe the difference between stream-static joins and stream-stream joins in Spark Structured Streaming.

Consider what kinds of data sources are joined in each case.