Watermarking for late data in Apache Spark - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is watermarking in Apache Spark Structured Streaming?

Watermarking is a technique to handle late data by specifying a threshold time. It tells Spark to wait for late data up to a certain delay and then drop data older than that to manage state and avoid infinite waiting.

beginner

Why do we need watermarking when processing streaming data?

Because streaming data can arrive late or out of order, watermarking helps Spark decide when to stop waiting for late data and clean up old state, ensuring efficient and timely processing.

intermediate

How do you set a watermark in Spark Structured Streaming code?

You use the method `.withWatermark(eventTimeColumn, delayThreshold)` on a streaming DataFrame, where `eventTimeColumn` is the timestamp column and `delayThreshold` is the max allowed lateness, like '10 minutes'.

beginner

What happens to data that arrives later than the watermark threshold?

Data arriving later than the watermark threshold is considered too late and is dropped from processing to avoid incorrect results and to free resources.

intermediate

Explain the relationship between watermarking and state cleanup in streaming aggregations.

Watermarking defines how long Spark keeps state for aggregations. Once data is older than the watermark, Spark can safely remove its state, preventing memory buildup and improving performance.

What does watermarking in Spark Structured Streaming help manage?

A Data visualization

B Data encryption

C Batch job scheduling

D Late arriving data

Watermarking helps manage late arriving data by setting a threshold for how late data can arrive before being dropped.

Which method is used to set a watermark in Spark Structured Streaming?

A withWatermark()

B setWatermark()

C watermark()

D defineWatermark()

The correct method to set watermark is withWatermark(eventTimeColumn, delayThreshold).

If the watermark delay is set to '5 minutes', what happens to data arriving 6 minutes late?

A It is dropped as too late

B It is processed normally

C It triggers an error

D It is stored for later processing

Data arriving later than the watermark delay is dropped to avoid stale or incorrect results.

Watermarking helps Spark to:

A Keep state forever

B Ignore all late data

C Clean up old state after a delay

D Run batch jobs faster

Watermarking allows Spark to clean up old state after the watermark delay, managing memory efficiently.

Which column is typically used for watermarking in streaming data?

A User ID column

B Event timestamp column

C Partition column

D Random number column

Watermarking uses the event timestamp column to track lateness of data.

Describe how watermarking works in Apache Spark Structured Streaming and why it is important.

Think about how Spark decides when to stop waiting for late data.

Explain the impact of watermarking on streaming aggregation state and resource management.

Consider how watermarking helps Spark manage memory and correctness.