Output modes (append, complete, update) in Apache Spark - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the append output mode do in Apache Spark Structured Streaming?

The append mode adds only new rows to the output sink. It does not update or remove existing rows. This is useful when you want to keep adding new data without changing old data.

beginner

Explain the complete output mode in Apache Spark Structured Streaming.

The complete mode writes the entire updated result table to the output sink every time there is new data. It replaces the previous output with the full current state.

intermediate

What is the purpose of the update output mode in Apache Spark Structured Streaming?

The update mode outputs only the rows that have changed since the last trigger. It updates existing rows and adds new rows but does not output the entire table.

beginner

Which output mode should you use if you want to keep a running total and overwrite the entire output each time?

You should use the complete mode because it writes the full updated result every time, replacing the previous output.

intermediate

Can the append mode be used with aggregations that update previous results?

No, append mode only adds new rows and does not support updating previous aggregation results. For aggregations, update or complete modes are needed.

Which output mode in Apache Spark Structured Streaming outputs only new rows without modifying existing data?

A overwrite

B complete

C append

D update

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

In which output mode does Spark write the entire updated result table to the sink every time?

A complete

B append

C update

D incremental

The complete mode writes the full updated result table each time there is new data.

Which output mode outputs only rows that have changed since the last trigger?

A append

B update

C complete

D replace

The update mode outputs only changed rows, updating existing ones and adding new ones.

If you want to keep a running aggregation and update the output incrementally, which mode should you choose?

A update

B complete

C append

D delete

Update mode is suitable for incremental updates of aggregations.

Can append mode be used with aggregations that update previous results?

A Only if output is to memory sink

B Yes, always

C Only with windowed aggregations

D No, append mode does not support updating aggregations

Append mode cannot update previous aggregation results; update or complete modes are needed.

Describe the differences between append, complete, and update output modes in Apache Spark Structured Streaming.

Think about how each mode handles new and existing data.

When would you choose the complete output mode over append or update modes?

Consider scenarios where partial updates are not enough.

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