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Optimize Delta Lake tables with V-Order

The Lakehouse and Delta Lake table format are central to Microsoft Fabric. Keeping Delta tables optimized is key to performance and cost efficiency for analytics workloads.

This article helps you decide when to use V-Order and shows the main configuration and maintenance patterns for Delta tables.

Use this article to:

  • Understand what V-Order changes and when it helps.
  • Understand how Z-Order and V-Order complement each other.
  • Choose the right control level: session, table property, or write operation.
  • Apply Delta table maintenance patterns in the right Spark runtime context.

For cross-workload guidance on when to apply V-Order based on consumption scenarios, see Cross-workload table maintenance and optimization.

What is V-Order?

V-Order is a write-time optimization for Parquet files that can improve downstream query performance across Fabric engines.

At a glance:

  • Where it helps most: Read-heavy patterns such as dashboarding, interactive analytics, and repeated scans.
  • How it helps: Reorganizes Parquet layout (for example, row-group distribution, encoding, and compression) to improve read efficiency.
  • Typical tradeoff: Writes might take longer (often around 15% on average), while reads can improve significantly depending on workload.
  • Engine compatibility: Files remain open-source Parquet compliant, and Delta features such as Z-Order remain compatible.
  • Scope: V-Order is file-level. Delta operations such as compaction, vacuum, and time travel can be used with it.

Control V-Order writes

V-Order is used to optimize Parquet file layout for faster query performance, especially in read-heavy scenarios. In Microsoft Fabric, V-Order is disabled by default for all newly created workspaces to optimize performance for write-heavy data engineering workloads.

V-Order behavior in Apache Spark is controlled through the following configurations:

Configuration Default Value Description
spark.sql.parquet.vorder.default false Controls session-level V-Order writing. Set to false by default in new Fabric workspaces.
TBLPROPERTIES("delta.parquet.vorder.enabled") Unset Controls default V-Order behavior at the table level.
DataFrame writer option: parquet.vorder.enabled Unset Used to control V-Order at the write operation level.

Use the following commands to enable or override V-Order writes as needed for your scenario.

V-Order is disabled by default in new Fabric workspaces (spark.sql.parquet.vorder.default=false) to improve write performance for ingestion and transformation pipelines.

For read-heavy workloads such as interactive queries or dashboarding, enable V-Order by setting spark.sql.parquet.vorder.default to true. You can also switch to readHeavyforSpark or ReadHeavy resource profiles, which automatically enable V-Order for read-focused performance.

In Fabric runtime 1.3 and later, the spark.sql.parquet.vorder.enable setting is removed. Because V-Order can be applied automatically during Delta optimization with OPTIMIZE, you don't need this older setting. If you're migrating from earlier runtime versions, remove this setting from your code.

Check V-Order configuration in Apache Spark session

Use these commands to confirm the current session value before you change it.

%%sql 
SET spark.sql.parquet.vorder.default

Disable V-Order writing in Apache Spark session

Use these commands when your workload is write-heavy and you want faster ingestion or transformation writes.

%%sql 
SET spark.sql.parquet.vorder.default=FALSE

Enable V-Order writing in Apache Spark session

When you enable V-Order at the session level, all Parquet writes in that session use V-Order, including non-Delta Parquet tables and Delta tables even if parquet.vorder.enabled is explicitly set to false.

%%sql 
SET spark.sql.parquet.vorder.default=TRUE

Control V-Order using Delta table properties

This section uses Spark SQL only because table properties are defined through SQL DDL and ALTER TABLE statements.

Use table properties when you want a table-level default that applies across sessions.

Enable V-Order table property during table creation:

%%sql 
CREATE TABLE person (id INT, name STRING, age INT) USING parquet TBLPROPERTIES("delta.parquet.vorder.enabled" = "true");

When the table property is set to true, INSERT, UPDATE, and MERGE apply V-Order at write time. Session-level and write-level settings still take precedence, so writes can still use V-Order even when TBLPROPERTIES is set to false.

Enable or disable V-Order by altering the table property:

%%sql 
ALTER TABLE person SET TBLPROPERTIES("delta.parquet.vorder.enabled" = "true");

ALTER TABLE person SET TBLPROPERTIES("delta.parquet.vorder.enabled" = "false");

ALTER TABLE person UNSET TBLPROPERTIES("delta.parquet.vorder.enabled");

After you enable or disable V-Order using table properties, only future writes to the table are affected. Parquet files keep the ordering used when it was created. To change the current physical structure to apply or remove V-Order, read Table compaction.

Controlling V-Order directly on write operations

This section uses PySpark to demonstrate the DataFrame writer API. The same pattern is available in Scala DataFrame APIs with equivalent options.

Use write-level options when you need per-operation control instead of session-wide or table-wide defaults.

All Apache Spark write commands inherit the session setting when not explicitly overridden. The following examples write using V-Order by inheriting the session configuration.

df_source.write\
  .format("delta")\
  .mode("append")\
  .saveAsTable("myschema.mytable")

DeltaTable.createOrReplace(spark)\
  .addColumn("id","INT")\
  .addColumn("firstName","STRING")\
  .addColumn("middleName","STRING")\
  .addColumn("lastName","STRING",comment="surname")\
  .addColumn("birthDate","TIMESTAMP")\
  .location("Files/people")\
  .execute()

df_source.write\
  .format("delta")\
  .mode("overwrite")\
  .option("replaceWhere","start_date >= '2025-01-01' AND end_date <= '2025-01-31'")\
  .saveAsTable("myschema.mytable")

V-Order only applies to files affected by the predicate.

In a session where spark.sql.parquet.vorder.default is unset or set to false, the following commands write using V-Order:

df_source.write\
  .format("delta")\
  .mode("overwrite")\
  .option("replaceWhere","start_date >= '2025-01-01' AND end_date <= '2025-01-31'")\
  .option("parquet.vorder.enabled","true")\
  .saveAsTable("myschema.mytable")

DeltaTable.createOrReplace(spark)\
  .addColumn("id","INT")\
  .addColumn("firstName","STRING")\
  .addColumn("middleName","STRING")\
  .addColumn("lastName","STRING",comment="surname")\
  .addColumn("birthDate","TIMESTAMP")\
  .option("parquet.vorder.enabled","true")\
  .location("Files/people")\
  .execute()

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