Incremental processing patterns in Apache Spark - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is incremental processing in data science?

Incremental processing means handling only new or changed data since the last time you processed, instead of reprocessing all data every time. It saves time and resources.

intermediate

Name one common pattern for incremental processing in Apache Spark.

One common pattern is using watermarking with Structured Streaming to process only new data that arrives after a certain event time, avoiding reprocessing old data.

beginner

How does the 'append' output mode work in Spark Structured Streaming?

In 'append' mode, Spark only outputs new rows added since the last trigger. It never updates or deletes old rows, making it suitable for incremental data that only grows.

intermediate

What role does a checkpoint play in incremental processing with Spark?

A checkpoint saves the progress of streaming jobs so Spark knows which data it already processed. This helps recover from failures and avoid reprocessing the same data.

beginner

Explain the difference between batch and incremental processing.

Batch processing handles all data at once, often reprocessing everything. Incremental processing handles only new or changed data since the last run, making it faster and more efficient.

What does incremental processing focus on?

A Processing only new or changed data

B Processing all data every time

C Ignoring new data

D Deleting old data before processing

Incremental processing focuses on handling only new or changed data since the last processing.

Which Spark Structured Streaming output mode only adds new rows without updates?

A Complete

B Update

C Overwrite

D Append

The 'append' mode outputs only new rows added since the last trigger.

Why is checkpointing important in incremental processing with Spark?

A To speed up batch jobs

B To save progress and avoid reprocessing

C To delete old data

D To increase memory usage

Checkpointing saves progress so Spark knows what data was processed, helping avoid reprocessing.

What is watermarking used for in Spark streaming?

A To speed up batch processing

B To delete old checkpoints

C To track event time and handle late data

D To store data permanently

Watermarking tracks event time to handle late-arriving data and avoid reprocessing.

Which processing type reprocesses all data every time?

A Batch processing

B Incremental processing

C Streaming processing

D Real-time processing

Batch processing handles all data at once, often reprocessing everything.

Describe how incremental processing improves efficiency in Apache Spark.

Think about how Spark avoids reprocessing old data.

Explain the difference between 'append' and 'complete' output modes in Spark Structured Streaming.

Consider how each mode handles data updates.

Practice

1.

What is the main idea behind incremental processing in Apache Spark?

easy

A. Delete old data before processing new data

B. Process all data repeatedly for accuracy

C. Process only new or changed data instead of all data every time

D. Use only batch processing without updates

Solution

1. Step 1: Understand incremental processing concept

   Incremental processing means working only on data that is new or has changed, not the entire dataset.

2. Step 2: Compare options to concept

   Process only new or changed data instead of all data every time matches this idea, while others describe full or batch processing.

3. Final Answer:

   Process only new or changed data instead of all data every time -> Option C

4. Quick Check:

   Incremental processing = new or changed data only [OK]

Hint: Incremental means update only new or changed data [OK]

Common Mistakes:

• Thinking incremental means reprocessing all data
• Confusing incremental with deleting old data
• Assuming incremental ignores changes

2.

Which Spark code snippet correctly filters new data for incremental processing?

df_new = df_all.filter(____)

easy

A. df_all.timestamp > last_processed_time

B. df_all.timestamp < last_processed_time

C. df_all.id == last_processed_id

D. df_all.date == '2020-01-01'

Solution

1. Step 1: Identify filtering condition for new data

   To get new data, filter rows with timestamp greater than last processed time.

2. Step 2: Match condition to options

   df_all.timestamp > last_processed_time uses > last_processed_time, which correctly selects new data.

3. Final Answer:

   df_all.timestamp > last_processed_time -> Option A

4. Quick Check:

   Filter new data by timestamp > last processed [OK]

Hint: Filter with timestamp greater than last processed time [OK]

Common Mistakes:

• Using less than instead of greater than
• Filtering by exact id instead of timestamp
• Filtering by unrelated fixed date

3.

Given this Spark code for incremental processing, what will df_incremental.count() output?

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
data = [(1, '2024-01-01'), (2, '2024-01-02'), (3, '2024-01-03')]
df_all = spark.createDataFrame(data, ['id', 'date'])
last_processed_date = '2024-01-01'
df_incremental = df_all.filter(df_all.date > last_processed_date)
print(df_incremental.count())

medium

A. 2

B. 3

C. 1

D. 0

Solution

1. Step 1: Understand filtering condition

   Filter keeps rows where date > '2024-01-01'. Data has dates '2024-01-01', '2024-01-02', '2024-01-03'.

2. Step 2: Count rows matching condition

   Rows with dates '2024-01-02' and '2024-01-03' match, so count is 2.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Dates after '2024-01-01' = 2 rows [OK]

Hint: Count rows with date greater than last processed date [OK]

Common Mistakes:

• Including the date equal to last_processed_date
• Counting all rows instead of filtered
• Confusing greater than with less than

4.

Find the error in this Spark code for incremental processing:

df_incremental = df_all.filter(df_all.timestamp < last_processed_time)

Assuming last_processed_time is the timestamp of the last processed batch.

medium

A. The variable last_processed_time is undefined

B. The filter should use <= to include last processed data

C. The filter should use == to match last processed time

D. The filter should use > instead of < to get new data

Solution

1. Step 1: Understand filtering logic for incremental data

   Incremental processing requires filtering data newer than last processed time, so timestamp > last_processed_time.

2. Step 2: Identify incorrect operator

   The code uses < which selects older data, not new data.

3. Final Answer:

   The filter should use > instead of < to get new data -> Option D

4. Quick Check:

   New data filter uses > last processed time [OK]

Hint: New data means timestamp greater than last processed [OK]

Common Mistakes:

• Using less than instead of greater than
• Confusing equality with incremental filter
• Assuming variable is undefined without checking

5.

You have a Delta Lake table storing daily sales data. You want to update it incrementally with new data from a streaming source. Which approach is best?

hard

A. Delete the Delta table and recreate it daily

B. Use merge operation to upsert new and updated records into the Delta table

C. Append new data without checking for duplicates or updates

D. Overwrite the entire Delta table with new streaming data each time

Solution

1. Step 1: Understand Delta Lake incremental update capabilities

   Delta Lake supports merge to upsert (insert or update) records efficiently.

2. Step 2: Evaluate options for incremental update

   Use merge operation to upsert new and updated records into the Delta table uses merge, which handles new and changed data correctly. Others cause data loss or duplicates.

3. Final Answer:

   Use merge operation to upsert new and updated records into the Delta table -> Option B

4. Quick Check:

   Delta Lake incremental update = merge upsert [OK]

Hint: Use merge for incremental upsert in Delta Lake [OK]

Common Mistakes:

• Overwriting entire table loses old data
• Appending without deduplication causes duplicates
• Deleting table loses all history