Output modes (append, complete, update) in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the output mode to append in a streaming query.

Apache Spark

query = df.writeStream.outputMode('[1]').start()

Drag options to blanks, or click blank then click option'

Acomplete

Bappend

Cupdate

Doverwrite

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'complete' mode when only new rows should be added.

Using 'update' mode which updates existing rows instead of appending.

The append mode adds only new rows to the output sink.

2fill in blank

medium

Complete the code to set the output mode to complete in a streaming query.

Apache Spark

query = df.writeStream.outputMode('[1]').start()

Drag options to blanks, or click blank then click option'

Acomplete

Bupdate

Coverwrite

Dappend

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'append' mode which only adds new rows.

Using 'update' mode which only outputs changed rows.

The complete mode writes the entire result table to the sink every time there is an update.

3fill in blank

hard

Fix the error in setting the output mode to update in a streaming query.

Apache Spark

query = df.writeStream.outputMode('[1]').start()

Drag options to blanks, or click blank then click option'

Aappend

Breplace

Ccomplete

Dupdate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'replace' which is not a valid output mode.

Using 'append' or 'complete' when update is needed.

The update mode outputs only the rows that have changed since the last trigger.

4fill in blank

hard

Fill both blanks to create a streaming query that writes to console with update mode and no truncation.

Apache Spark

query = df.writeStream.format('[1]').outputMode('[2]').option('truncate', false).start()

Drag options to blanks, or click blank then click option'

Aconsole

Bappend

Cupdate

Dmemory

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'append' mode with console when update is required.

Using 'memory' format which stores data in memory, not console.

The console format outputs to the console, and update mode outputs only changed rows.

5fill in blank

hard

Fill all three blanks to write a streaming DataFrame to memory with complete mode and query name 'myQuery'.

Apache Spark

query = df.writeStream.format('[1]').outputMode('[2]').queryName('[3]').start()

Drag options to blanks, or click blank then click option'

Amemory

Bcomplete

CmyQuery

Dappend

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'append' mode with memory format which is not supported.

Forgetting to name the query with queryName.

Writing to memory allows querying the result table, complete mode outputs full results, and myQuery names the query.

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