Time travel and versioning in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a Delta table with time travel to a specific version.

Apache Spark

df = spark.read.format("delta").option("versionAsOf", [1]).load("/data/delta/events")

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

A5

B"latest"

C"2023-01-01"

Dtrue

Attempts:

3 left

💡 Hint

Common Mistakes

Using a date string instead of a version number.

Using a boolean value instead of a version number.

The versionAsOf option lets you read the table as it was at a specific version number. Here, 5 is the version number.

2fill in blank

medium

Complete the code to read a Delta table as it was at a specific timestamp.

Apache Spark

df = spark.read.format("delta").option("timestampAsOf", [1]).load("/data/delta/events")

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

A5

Btrue

C"version"

D"2023-06-01T12:00:00.000Z"

Attempts:

3 left

💡 Hint

Common Mistakes

Using a version number instead of a timestamp string.

Using a boolean or other data type.

The timestampAsOf option lets you read the table as it was at a specific timestamp. The timestamp must be a string in ISO format.

3fill in blank

hard

Fix the error in the code to write data to a Delta table with versioning enabled.

Apache Spark

df.write.format("delta").mode([1]).save("/data/delta/events")

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

A"append"

B"update"

C"overwrite"

D"create"

Attempts:

3 left

💡 Hint

Common Mistakes

Using append mode which adds data but may not replace existing data.

Using invalid modes like create or update.

To update a Delta table and create a new version, use mode("overwrite"). This replaces the data and creates a new version.

4fill in blank

hard

Fill both blanks to create a Delta table and enable versioning.

Apache Spark

spark.sql("CREATE TABLE events USING delta LOCATION '[1]' [2]")

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

A"/data/delta/events"

BTBLPROPERTIES ('delta.enableChangeDataFeed' = 'true')

COPTIONS ('versionAsOf' = '5')

DPARTITIONED BY (date)

Attempts:

3 left

💡 Hint

Common Mistakes

Using OPTIONS instead of TBLPROPERTIES.

Forgetting to specify the table location.

The LOCATION specifies the path to the Delta table. The TBLPROPERTIES option enables features like Change Data Feed for versioning.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps version numbers to their timestamps for versions greater than 3.

Apache Spark

version_times = {v: m['timestamp'] for v, m [1] history.items() if v [2] 3 and m.[3]('timestamp')}

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

Afor

B>

Cget

Din

Attempts:

3 left

💡 Hint

Common Mistakes

Using for instead of in in the comprehension.

Using ['timestamp'] directly which may cause errors if key missing.

This dictionary comprehension loops over history.items() (using in), filters versions greater than 3 (using >), and safely accesses the 'timestamp' key using get.

Practice

1. What is the main purpose of time travel in Apache Spark's Delta Lake?

easy

A. To access previous versions of data easily

B. To speed up data processing

C. To create new tables automatically

D. To delete old data permanently

Solution

1. Step 1: Understand time travel concept

   Time travel allows users to look at past versions of data stored in Delta Lake.

2. Step 2: Identify main use

   This feature helps in recovering old data, auditing changes, and comparing versions.

3. Final Answer:

   To access previous versions of data easily -> Option A

4. Quick Check:

   Time travel = Access past data versions [OK]

Hint: Time travel means looking back at old data versions [OK]

Common Mistakes:

• Thinking time travel speeds up processing
• Confusing time travel with data deletion
• Assuming it creates new tables automatically

2. Which option is the correct way to load a Delta table at a specific version using Spark?

easy

A. spark.read.format('delta').option('version', 5).load('/data/table')

B. spark.read.format('delta').option('versionAsOf', 5).load('/data/table')

C. spark.read.format('delta').option('timestampAsOf', '5').load('/data/table')

D. spark.read.format('delta').option('versionAt', 5).load('/data/table')

Solution

1. Step 1: Recall correct option name for version

   The correct option to specify version is 'versionAsOf'.

2. Step 2: Check syntax correctness

   spark.read.format('delta').option('versionAsOf', 5).load('/data/table') uses 'versionAsOf' with a number, which is correct syntax.

3. Final Answer:

   spark.read.format('delta').option('versionAsOf', 5).load('/data/table') -> Option B

4. Quick Check:

   Use 'versionAsOf' to load specific version [OK]

Hint: Use 'versionAsOf' option to load specific version [OK]

Common Mistakes:

• Using 'version' instead of 'versionAsOf'
• Passing version as string in 'timestampAsOf'
• Using wrong option names like 'versionAt'

3. Given the code below, what will be the output count?

df = spark.read.format('delta').option('versionAsOf', 2).load('/data/events')
print(df.count())

Assuming version 2 of the table has 150 rows.

medium

A. Error due to wrong option

B. 0

C. Latest row count

D. 150

Solution

1. Step 1: Understand versionAsOf usage

   The option 'versionAsOf' loads the table exactly at version 2.

2. Step 2: Check row count at version 2

   Since version 2 has 150 rows, the count will be 150.

3. Final Answer:

   150 -> Option D

4. Quick Check:

   Count at version 2 = 150 [OK]

Hint: Count matches rows in specified version [OK]

Common Mistakes:

• Assuming count is zero for old versions
• Confusing latest count with versioned count
• Expecting error from correct syntax

4. Identify the error in the following code snippet for loading a Delta table at a timestamp:

df = spark.read.format('delta').option('timestampAsOf', 20230101).load('/data/sales')

medium

A. Load path is incorrect

B. timestampAsOf option does not exist

C. timestampAsOf value should be a string with date-time format

D. Missing .option('versionAsOf', )

Solution

1. Step 1: Check timestampAsOf option usage

   The 'timestampAsOf' option expects a string representing date and time, e.g., '2023-01-01T00:00:00'.

2. Step 2: Identify error in value type

   Here, 20230101 is an integer, not a string with proper format, causing error.

3. Final Answer:

   timestampAsOf value should be a string with date-time format -> Option C

4. Quick Check:

   timestampAsOf needs string date-time [OK]

Hint: Use string date-time format for timestampAsOf [OK]

Common Mistakes:

• Passing integer instead of string for timestamp
• Confusing versionAsOf with timestampAsOf
• Assuming option does not exist

5. You want to compare the number of rows between version 3 and version 5 of a Delta table located at '/data/logs'. Which code snippet correctly loads both versions for comparison?

hard

A. df_v3 = spark.read.format('delta').option('versionAsOf', 3).load('/data/logs')
df_v5 = spark.read.format('delta').option('versionAsOf', 5).load('/data/logs')

B. df_v3 = spark.read.format('delta').option('timestampAsOf', 3).load('/data/logs')
df_v5 = spark.read.format('delta').option('timestampAsOf', 5).load('/data/logs')

C. df_v3 = spark.read.format('delta').option('version', 3).load('/data/logs')
df_v5 = spark.read.format('delta').option('version', 5).load('/data/logs')

D. df_v3 = spark.read.format('delta').option('versionAsOf', '3').load('/data/logs')
df_v5 = spark.read.format('delta').option('versionAsOf', '5').load('/data/logs')

Solution

1. Step 1: Identify correct option for version number

   The option 'versionAsOf' expects an integer version number, not a string.

2. Step 2: Check code correctness for both versions

   df_v3 = spark.read.format('delta').option('versionAsOf', 3).load('/data/logs') df_v5 = spark.read.format('delta').option('versionAsOf', 5).load('/data/logs') uses 'versionAsOf' with integers 3 and 5, correctly loading both versions.

3. Final Answer:

   df_v3 = spark.read.format('delta').option('versionAsOf', 3).load('/data/logs') df_v5 = spark.read.format('delta').option('versionAsOf', 5).load('/data/logs') -> Option A

4. Quick Check:

   Use 'versionAsOf' with integers for version loading [OK]

Hint: Use versionAsOf with integers to load specific versions [OK]

Common Mistakes:

• Using timestampAsOf with integers
• Passing version numbers as strings
• Using non-existent 'version' option