Ch 7 Delta Lake Schema Handling (Short Quiz)(True or False)

38 Questions

The schema in Delta Lake is stored in XML format inside the transaction log.

False

The metadata field in Delta Lake columns can contain information about the version of the operating system used.

False

Schema validation in Delta Lake allows writes to a table that does not match the table's schema.

False

The ALTER TABLE statement can be used to add comments to columns in Delta Lake.

True

The DESCRIBE EXTENDED command shows the table properties, including the minimum reader and writer versions, but not the protocol versions.

False

The REPLACE COLUMNS operation sets all column values to their default values if the new schema has different data types or a different order of columns than the old schema.

False

Delta Lake columns are mapped to guide-based column names with new IDs starting with 1.

False

The ALTER TABLE SET TBLPROPERTIES statement can be used to update the schema of a table in Delta Lake.

False

Delta Lake column mapping is a feature in stable support mode.

False

The REPLACE COLUMNS operation can be used to add new columns to a Delta table without rewriting the existing data.

False

Delta Lake provides a warning before executing the REPLACE COLUMNS operation to ensure the user has backed up their data.

False

The REPLACE COLUMNS operation can be used to update the schema of a table in Delta Lake without losing any data.

False

When a column with the same name but a different data type exists in the Delta table, Delta Lake rejects the write operation, if schema evolution is enabled.

False

If a NuHType column is added to the Delta table, all existing rows are updated to a default value for that column.

False

Adding a new column to the Delta table with a different data type will update the existing rows with the new data type.

False

When a column is added to the Delta table, the existing data is rewritten to accommodate the new column.

False

In Delta Lake, the schema evolution rule for adding columns is to drop the existing columns and recreate the table with the new schema.

False

The metadata entry in the transaction log contains information about the created time of the schema.

True

The 'partitioncolumns' field in the metadata entry is used to specify the partitioning scheme of the table.

True

Removing a column from the DataFrame being written to a Delta table will result in the column being dropped from the table.

False

The `delta.columnMapping.id` is the physical name in the Parquet file.

False

Renaming a column in Delta Lake requires rewriting the entire column's existing data.

False

The `ALTER TABLE RENAME COLUMN` command changes the physical name in the Parquet file.

False

Delta Lake column mapping is required to enable the `ALTER TABLE RENAME COLUMN` command.

True

The `ALTER TABLE REPLACE COLUMNS` command sets all column values to their default values if the new schema has different data types or a different order of columns than the old schema.

False

The `ALTER TABLE REPLACE COLUMNS` command can be a destructive operation, as it replaces the entire schema of the Delta table and writes the data in the new schema.

True

The commitinfo operation with CREATE OR REPLACE TABLE AS SELECT is used to add a new column to a Delta table.

False

A remove action can be used to remove a specific column from a Delta table.

False

The add action can be used to add a new column to a Delta table without rewriting the existing data.

False

Using PySpark, we can change the data type of a column in a Delta table without rewriting the entire table.

False

The same approach used to change the data type of a column can be used to drop columns or change column names.

True

The REPLACE COLUMNS operation can be used to update the schema of a table in Delta Lake without losing any data.

False

Delta Lake stores a Delta table's schema in the metaData action.

True

Schema validation in Delta Lake is atomic in nature for operations on Delta tables.

True

Delta Lake supports dynamic schema evolution to add, remove, or modify columns in existing Delta tables.

True

Delta Lake does not enforce schema validation.

False

Schema evolution is activated by default in Delta Lake.

False

When a column with the same name but a different data type exists in the Delta table, Delta Lake allows the write operation.

False

Study Notes

Schema Handling in Delta Lake

Delta Lake stores table schema in JSON format inside the transaction log as a struct with fields representing columns, each with a name, type, and nullable indicator.
Columns also contain a metadata field, a JSON string that can hold various information, such as the username of the person who executed the transaction, timestamp, Delta Lake version, schema partition columns, and application-specific metadata.

Schema Validation and Write Operations

Schema validation rejects writes to a table that do not match the table's schema.
Delta Lake columns are mapped to guid-based column names with new IDs (starting with 4).

Altering Table Schema

Altering table schema can be done using ALTER TABLE and ALTER COLUMN statements.
ALTER COLUMN can be used to change the order of columns in a table, add comments to columns, or combine column ordering and add comments within a single statement.

Protocol Versions

Use the DESCRIBE EXTENDED command to check a table's reader and writer protocol versions.
To update protocol versions and delta.columnmapping.mode, use the ALTER TABLE SET TBLPROPERTIES statement.

REPLACE COLUMNS Operation

The REPLACE COLUMNS operation sets all column values to null if the new schema has different data types or a different order of columns than the old schema.
This ensures the new schema is applied consistently to all records in the table.

Delta Lake Column Mapping

Delta Lake column mapping is currently in experimental support mode.
This feature supports many common scenarios and more information can be found on the Delta Lake documentation website.

REPLACE Columns Operation

The REPLACE COLUMNS operation is a destructive operation that replaces the entire schema of the Delta table and rewrites the data in the new schema.
This operation should be used with caution.
It is recommended to back up data before applying the REPLACE COLUMNS operation.

Schema Evolution in Delta Lake

If a column with the same name but a different data type exists in the Delta table, Delta Lake attempts to convert the data to the new data type.
If the conversion fails, an error is thrown.
If a NuHType column is added to the Delta table, all existing rows are set to null for that column.

Adding a Column

When adding a column to the Delta table, a new column is added with the same name and data type.
All existing rows will have a null value for the new column.
Schema evolution is activated by adding a new metadata entry with the updated schema.

Missing Data Column in Source DataFrame

If a column exists in the Delta table but not in the DataFrame being written, the column is not changed and retains its existing values.
The new records will have a null value for the missing columns in the source DataFrame.

Renaming a Column

The ALTER TABLE...RENAME COLUMN command can be used to rename a column without rewriting any of the column's existing data.
Column mapping needs to be in place for this to be enabled.
The rename is reflected in the schemastring, but the physicalName remains the same in the Parquet file.

Replacing Table Columns

The ALTER TABLE REPLACE COLUMNS command can be used to replace all the columns of an existing Delta table with a new set of columns.
Delta Lake column mapping needs to be enabled for this operation.
The commitinfo, metaData, and remove and add actions are involved in the REPLACE COLUMNS operation.

Dynamic Schema Updates

Delta Lake allows for dynamic schema updates using the mergeSchema option.
Schema evolution can be used to add, remove, or modify columns in existing Delta tables.

Explicit Schema Updates

Schema updates can be explicitly updated using SQL or DataFrame syntax.
Supported operations include adding, removing, or renaming columns or data types, and adding comments or changing the column order.
Transaction log entries are used to illustrate the schema evolution process.

Quiz about Delta Lake schema handling, including storage in JSON format and metadata fields.

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Ch 7 Delta Lake Schema Handling (Short Quiz)(True or False)

38 Questions

The schema in Delta Lake is stored in XML format inside the transaction log.

The metadata field in Delta Lake columns can contain information about the version of the operating system used.

Schema validation in Delta Lake allows writes to a table that does not match the table's schema.

The ALTER TABLE statement can be used to add comments to columns in Delta Lake.

The DESCRIBE EXTENDED command shows the table properties, including the minimum reader and writer versions, but not the protocol versions.

The REPLACE COLUMNS operation sets all column values to their default values if the new schema has different data types or a different order of columns than the old schema.

Delta Lake columns are mapped to guide-based column names with new IDs starting with 1.

The ALTER TABLE SET TBLPROPERTIES statement can be used to update the schema of a table in Delta Lake.

Delta Lake column mapping is a feature in stable support mode.

The REPLACE COLUMNS operation can be used to add new columns to a Delta table without rewriting the existing data.

Delta Lake provides a warning before executing the REPLACE COLUMNS operation to ensure the user has backed up their data.

The REPLACE COLUMNS operation can be used to update the schema of a table in Delta Lake without losing any data.

When a column with the same name but a different data type exists in the Delta table, Delta Lake rejects the write operation, if schema evolution is enabled.

If a NuHType column is added to the Delta table, all existing rows are updated to a default value for that column.

Adding a new column to the Delta table with a different data type will update the existing rows with the new data type.

When a column is added to the Delta table, the existing data is rewritten to accommodate the new column.

In Delta Lake, the schema evolution rule for adding columns is to drop the existing columns and recreate the table with the new schema.

The metadata entry in the transaction log contains information about the created time of the schema.

The 'partitioncolumns' field in the metadata entry is used to specify the partitioning scheme of the table.

Removing a column from the DataFrame being written to a Delta table will result in the column being dropped from the table.

The delta.columnMapping.id is the physical name in the Parquet file.

Renaming a column in Delta Lake requires rewriting the entire column's existing data.

The ALTER TABLE RENAME COLUMN command changes the physical name in the Parquet file.

Delta Lake column mapping is required to enable the ALTER TABLE RENAME COLUMN command.

The ALTER TABLE REPLACE COLUMNS command sets all column values to their default values if the new schema has different data types or a different order of columns than the old schema.

The ALTER TABLE REPLACE COLUMNS command can be a destructive operation, as it replaces the entire schema of the Delta table and writes the data in the new schema.

The commitinfo operation with CREATE OR REPLACE TABLE AS SELECT is used to add a new column to a Delta table.

A remove action can be used to remove a specific column from a Delta table.

The add action can be used to add a new column to a Delta table without rewriting the existing data.

Using PySpark, we can change the data type of a column in a Delta table without rewriting the entire table.

The same approach used to change the data type of a column can be used to drop columns or change column names.

The REPLACE COLUMNS operation can be used to update the schema of a table in Delta Lake without losing any data.

Delta Lake stores a Delta table's schema in the metaData action.

Schema validation in Delta Lake is atomic in nature for operations on Delta tables.

Delta Lake supports dynamic schema evolution to add, remove, or modify columns in existing Delta tables.

Delta Lake does not enforce schema validation.

Schema evolution is activated by default in Delta Lake.

When a column with the same name but a different data type exists in the Delta table, Delta Lake allows the write operation.

Study Notes

Schema Handling in Delta Lake

Schema Validation and Write Operations

Altering Table Schema

Protocol Versions

REPLACE COLUMNS Operation

Delta Lake Column Mapping

REPLACE Columns Operation

Schema Evolution in Delta Lake

Adding a Column

Missing Data Column in Source DataFrame

Renaming a Column

Replacing Table Columns

Dynamic Schema Updates

Explicit Schema Updates

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The `delta.columnMapping.id` is the physical name in the Parquet file.

The `ALTER TABLE RENAME COLUMN` command changes the physical name in the Parquet file.

Delta Lake column mapping is required to enable the `ALTER TABLE RENAME COLUMN` command.

The `ALTER TABLE REPLACE COLUMNS` command sets all column values to their default values if the new schema has different data types or a different order of columns than the old schema.

The `ALTER TABLE REPLACE COLUMNS` command can be a destructive operation, as it replaces the entire schema of the Delta table and writes the data in the new schema.