Incremental processing patterns in Apache Spark - Step-by-Step Execution

Concept Flow - Incremental processing patterns

Start: New Data Arrives

↓

Read New Data Batch

↓

Process Only New Data

↓

Update Results/State

↓

Store Updated Output

↓

Wait for Next Batch

↩Back to Start

Incremental processing reads and processes only new data batches, updates results, and stores them, repeating this cycle continuously.

Execution Sample

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
stream = spark.readStream.format('csv').load('data/new_files/')
query = stream.writeStream.format('console').start()
query.awaitTermination()

This code reads new CSV files incrementally from a folder and prints new data batches to the console as they arrive.

Execution Table

Step	Action	Data Read	Processing	Output Produced
1	Start streaming job	No data yet	No processing	No output
2	New file arrives: batch1.csv	Reads batch1.csv	Processes batch1.csv	Outputs batch1 results
3	New file arrives: batch2.csv	Reads batch2.csv only	Processes batch2.csv only	Outputs batch2 results
4	No new files	No new data	No processing	No output
5	New file arrives: batch3.csv	Reads batch3.csv only	Processes batch3.csv only	Outputs batch3 results
6	Stream stopped	Stop reading	Stop processing	Stop output

💡 Streaming stops when user terminates or no more data arrives.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5	Final
Data Batch	None	batch1.csv	batch2.csv	None	batch3.csv	None
Processed Data	None	Processed batch1	Processed batch2	None	Processed batch3	None
Output	None	Output batch1	Output batch2	No output	Output batch3	Stopped

Key Moments - 3 Insights

Why does the system process only new files and not all files every time?

Because incremental processing reads only new data batches (see execution_table rows 2, 3, 5), it avoids reprocessing old data to save time and resources.

What happens when no new data arrives?

No processing or output happens (see execution_table row 4), the system waits for new data to arrive before continuing.

How does the system know when to stop processing?

The stream stops when the user terminates the job or no more data arrives (see execution_table row 6).

Visual Quiz - 3 Questions

Look at the execution table, what data batch is processed at Step 3?

A batch1.csv

B batch2.csv

C batch3.csv

D No data

💡 Hint

Check the 'Data Read' column at Step 3 in the execution_table.

At which step does the system produce no output because no new data arrived?

A Step 4

B Step 2

C Step 5

D Step 6

💡 Hint

Look for 'No output' in the 'Output Produced' column in the execution_table.

If a new file arrives after Step 5, what will happen next?

A The system will reprocess all old files

B The system will stop processing

C The system will process only the new file

D The system will ignore the new file

💡 Hint

Refer to the pattern in execution_table rows 2, 3, and 5 where only new files are processed.

Concept Snapshot

Incremental processing reads only new data batches as they arrive.
It processes these batches and updates outputs continuously.
This saves time by avoiding reprocessing old data.
Common in streaming data and file arrival scenarios.
Use Spark Structured Streaming to implement this pattern.

Full Transcript

Incremental processing patterns in Apache Spark involve reading and processing only new data batches as they arrive, rather than reprocessing all data repeatedly. This approach improves efficiency by focusing on new data only. The process starts by waiting for new data, then reading the new batch, processing it, updating results, and storing outputs. If no new data arrives, the system waits without processing. The streaming job continues until stopped by the user or no more data arrives. This pattern is common in streaming and file-based data ingestion scenarios. The example code shows how Spark Structured Streaming reads new CSV files incrementally and outputs results to the console.

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