Incremental processing patterns in Apache Spark - Mini Project: Build & Apply

Incremental Processing Patterns with Apache Spark

📖 Scenario: You work as a data engineer for an online store. Every day, new sales data arrives. You want to process only the new sales records each day to update your total sales summary efficiently.

🎯 Goal: Build a simple Apache Spark program that reads daily sales data, keeps track of the last processed date, and processes only new sales records incrementally.

📋 What You'll Learn

Create a Spark DataFrame with sample sales data including dates and amounts

Create a variable to store the last processed date

Filter the sales data to include only records after the last processed date

Show the filtered new sales records as output

💡 Why This Matters

🌍 Real World

Incremental processing is used in real-world data pipelines to efficiently update reports and analytics without reprocessing all data every time.

💼 Career

Data engineers and data scientists use incremental processing patterns to handle large datasets efficiently and keep data systems up to date.

1

Create initial sales data

Create a Spark DataFrame called sales_df with these exact rows: {'date': '2024-04-01', 'amount': 100}, {'date': '2024-04-02', 'amount': 150}, {'date': '2024-04-03', 'amount': 200}. Use the schema with date as string and amount as integer.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('IncrementalProcessing').getOrCreate()
# Create the sales_df DataFrame with the exact data
# Your code here

Hint

Use spark.createDataFrame with a list of tuples and a schema to create sales_df.

2

Set last processed date

Create a variable called last_processed_date and set it to the string '2024-04-01'.

Apache Spark

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, StringType, IntegerType

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

schema = StructType([
    StructField('date', StringType(), True),
    StructField('amount', IntegerType(), True)
])
data = [
    ('2024-04-01', 100),
    ('2024-04-02', 150),
    ('2024-04-03', 200)
]
sales_df = spark.createDataFrame(data, schema)

# Create last_processed_date variable
# Your code here

Hint

Just assign the string '2024-04-01' to last_processed_date.

3

Filter new sales records

Create a new DataFrame called new_sales_df by filtering sales_df to include only rows where the date is greater than last_processed_date. Use the filter method with the condition sales_df.date > last_processed_date.

Apache Spark

new_sales_df = sales_df.filter(sales_df.date > last_processed_date)
# Show the new_sales_df DataFrame

Hint

Use sales_df.filter(sales_df.date > last_processed_date) to get only new sales.

4

Show new sales records

Use print to display the contents of new_sales_df by calling new_sales_df.show().

Apache Spark

Hint

Call new_sales_df.show() to print the filtered new sales records.

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