Output modes (append, complete, update) in Apache Spark - Time & Space Complexity

Time Complexity: Output modes (append, complete, update)

O(n)

Understanding Time Complexity

When working with streaming data in Apache Spark, it's important to know how the output mode affects processing time.

We want to understand how the time to write results grows as the data size increases for different output modes.

Scenario Under Consideration

Analyze the time complexity of this streaming output code snippet.

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

streamingDF.writeStream
  .outputMode("complete")
  .format("console")
  .start()

streamingDF.writeStream
  .outputMode("update")
  .format("console")
  .start()

This code writes streaming data to the console using three output modes: append, complete, and update.

Identify Repeating Operations

Look at what happens each time new data arrives in the stream.

• Primary operation: Writing output rows to the sink (console here).
• How many times: For each batch of data, the output mode decides how many rows are written.

How Execution Grows With Input

As the total data processed grows, the amount of output work changes by mode.

Input Size (n rows)	Append Mode	Complete Mode	Update Mode
10	Writes ~10 new rows	Writes all 10 rows	Writes changed rows only
100	Writes ~100 new rows	Writes all 100 rows	Writes changed rows only
1000	Writes ~1000 new rows	Writes all 1000 rows	Writes changed rows only

Pattern observation: Append mode writes only new rows, so work grows with new data size. Complete mode writes all rows every time, so work grows with total data size. Update mode writes only changed rows, which can vary but often less than total data.

Final Time Complexity

Time Complexity: O(n)

This means the time to write output grows linearly with the number of rows processed or updated, depending on the mode.

Common Mistake

[X] Wrong: "All output modes take the same time regardless of data size."

[OK] Correct: Different modes write different amounts of data each time, so their time grows differently with input size.

Interview Connect

Understanding how output modes affect processing time helps you explain trade-offs in streaming applications clearly and confidently.

Self-Check

"What if we changed the output sink from console to a database? How might that affect the time complexity for each output mode?"

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