Output modes (append, complete, update) in Apache Spark - Mini Project: Build & Apply

Understanding Output Modes in Apache Spark Structured Streaming

📖 Scenario: You work at a company that processes live sales data from multiple stores. You want to analyze this data in real-time using Apache Spark Structured Streaming. Different output modes control how the results are saved or displayed as new data arrives.

🎯 Goal: Learn how to use the three output modes append, complete, and update in Apache Spark Structured Streaming to control how streaming query results are output.

📋 What You'll Learn

Create a streaming DataFrame from a static DataFrame simulating sales data

Define a trigger interval for streaming

Use output modes: append, complete, and update

Print the streaming query output to the console

💡 Why This Matters

🌍 Real World

Companies use streaming data to monitor sales, website clicks, or sensor data in real-time to make quick decisions.

💼 Career

Understanding output modes in Spark Structured Streaming is essential for data engineers and data scientists working with real-time data pipelines.

1

Create a static DataFrame simulating sales data

Create a Spark DataFrame called sales_data with these exact rows: ("Store1", 100), ("Store2", 150), ("Store1", 200). The columns should be store and amount.

Apache Spark

# Create the static DataFrame called sales_data with the given rows and columns
# Your code here

Hint

Use spark.createDataFrame with a list of tuples and specify the column names as a list.

2

Create a streaming DataFrame from the static DataFrame

Create a streaming DataFrame called streaming_sales by using sales_data.writeStream.format("memory").queryName("sales_table").start() to simulate streaming data.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("OutputModesDemo").getOrCreate()
sales_data = spark.createDataFrame([
    ("Store1", 100),
    ("Store2", 150),
    ("Store1", 200)
], ["store", "amount"])

# Create streaming_sales by writing sales_data as a streaming memory table
# Your code here

Hint

Use writeStream.format("memory") with queryName and start() to create a streaming DataFrame.

3

Apply output modes to a streaming aggregation

Create a streaming aggregation DataFrame called agg_sales by grouping sales_data by store and summing amount. Then write three streaming queries named query_append, query_complete, and query_update using output modes append, complete, and update respectively. Use format("console") and start() for each query.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("OutputModesDemo").getOrCreate()
sales_data = spark.createDataFrame([
    ("Store1", 100),
    ("Store2", 150),
    ("Store1", 200)
], ["store", "amount"])

streaming_sales = sales_data.writeStream.format("memory").queryName("sales_table").start()

# Create agg_sales by grouping sales_data by store and summing amount
# Then create three streaming queries with output modes append, complete, and update
# Your code here

Hint

Use groupBy("store").sum("amount") to aggregate. Then use writeStream.outputMode(...).format("console").start() for each output mode.

4

Print the streaming query output

Print the status of the three streaming queries query_append, query_complete, and query_update by using print(query_append.status), print(query_complete.status), and print(query_update.status).

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("OutputModesDemo").getOrCreate()
sales_data = spark.createDataFrame([
    ("Store1", 100),
    ("Store2", 150),
    ("Store1", 200)
], ["store", "amount"])

streaming_sales = sales_data.writeStream.format("memory").queryName("sales_table").start()

agg_sales = sales_data.groupBy("store").sum("amount")

query_append = agg_sales.writeStream.outputMode("append").format("console").start()
query_complete = agg_sales.writeStream.outputMode("complete").format("console").start()
query_update = agg_sales.writeStream.outputMode("update").format("console").start()

# Print the status of query_append, query_complete, and query_update
# Your code here

Hint

Use print(query_append.status) and similarly for the other queries to see their current status.

Practice

1. Which output mode in Apache Spark streaming adds only new rows to the sink without updating existing rows?

easy

A. Update

B. Complete

C. Append

D. Overwrite

Solution

1. Step 1: Understand output modes

   Append mode adds only new rows to the output sink without changing existing data.

2. Step 2: Compare with other modes

   Complete mode outputs the entire result table, and Update mode outputs only changed rows.

3. Final Answer:

   Append -> Option C

4. Quick Check:

   Append = adds new rows only [OK]

Hint: Append means add new rows only, no updates [OK]

Common Mistakes:

• Confusing append with complete mode
• Thinking update mode adds only new rows
• Assuming overwrite is an output mode

2. Which of the following is the correct syntax to set the output mode to 'complete' in a Spark Structured Streaming query?

easy

A. query.writeStream.outputMode("complete")

B. query.writeStream.mode("complete")

C. query.writeStream.setOutputMode("complete")

D. query.writeStream.output_mode("complete")

Solution

1. Step 1: Recall Spark streaming syntax

   The correct method to set output mode is outputMode("mode_name") on writeStream.

2. Step 2: Check options

   query.writeStream.outputMode("complete") uses outputMode("complete"), which is correct syntax. Others use incorrect method names or casing.

3. Final Answer:

   query.writeStream.outputMode("complete") -> Option A

4. Quick Check:

   outputMode("complete") is correct method [OK]

Hint: Use outputMode() method exactly as shown [OK]

Common Mistakes:

• Using mode() instead of outputMode()
• Using underscores instead of camelCase
• Trying setOutputMode() which doesn't exist

3. Given the following code snippet, what will be the output mode behavior?

val query = df.writeStream
  .format("console")
  .outputMode("update")
  .start()

What does the 'update' mode do here?

medium

A. Overwrites the entire sink with new data

B. Outputs the entire result table every time

C. Outputs only new rows added since last trigger

D. Outputs only rows that have changed since last trigger

Solution

1. Step 1: Understand 'update' mode meaning

   Update mode outputs only rows that have changed since the last trigger, not the full table.

2. Step 2: Compare with other modes

   Append outputs only new rows, complete outputs full table, overwrite is not a streaming mode.

3. Final Answer:

   Outputs only rows that have changed since last trigger -> Option D

4. Quick Check:

   Update = changed rows only [OK]

Hint: Update mode shows changed rows only, not full or just new [OK]

Common Mistakes:

• Confusing update with append mode
• Thinking update outputs full table
• Assuming overwrite is a streaming output mode

4. You wrote this Spark streaming code but get an error:

df.writeStream
  .format("console")
  .outputMode("append")
  .start()

The query fails because the DataFrame has aggregations. What is the error and how to fix it?

medium

A. Append mode not allowed with aggregations; use complete mode instead

B. Output mode 'append' is misspelled; fix spelling

C. Format 'console' does not support append mode; use 'memory' format

D. Start method missing awaitTermination(); add it

Solution

1. Step 1: Identify cause of error

   Append mode cannot be used with aggregations because new rows alone can't be identified.

2. Step 2: Fix by changing output mode

   Use complete mode to output full aggregation results instead of append.

3. Final Answer:

   Append mode not allowed with aggregations; use complete mode instead -> Option A

4. Quick Check:

   Aggregations require complete mode, not append [OK]

Hint: Aggregations need complete mode, not append [OK]

Common Mistakes:

• Trying to fix by changing format instead of mode
• Ignoring aggregation restrictions
• Assuming spelling or method call errors

5. You have a streaming query with a stateful aggregation that updates counts per key. You want to output only the changed counts to a database sink that supports updates. Which output mode should you choose and why?

hard

A. Append mode, because it adds only new counts

B. Update mode, because it outputs only changed counts for efficient updates

C. Complete mode, because it outputs all counts every time

D. Overwrite mode, because it replaces all data in the sink

Solution

1. Step 1: Understand the use case

   Stateful aggregation updates counts per key, so some counts change over time.

2. Step 2: Choose output mode for efficient updates

   Update mode outputs only changed rows, ideal for sinks supporting updates to avoid full rewrites.

3. Step 3: Exclude other modes

   Append mode can't handle updates, complete mode outputs full data (less efficient), overwrite replaces all data (not streaming mode).

4. Final Answer:

   Update mode, because it outputs only changed counts for efficient updates -> Option B

5. Quick Check:

   Update mode = changed rows only, best for stateful updates [OK]

Hint: Use update mode for stateful aggregation with update-capable sinks [OK]

Common Mistakes:

• Choosing append mode for updates
• Using complete mode causing full data writes
• Confusing overwrite with streaming output mode