How to create Snowflake indexes

July 5, 2024

Snowflake indexes are secondary indexes designed to improve the performance of data retrieval operations on hybrid tables in Snowflake. These secondary indexes enhance query performance by allowing faster access to specific columns within a table. Introduced in select AWS regions, Snowflake indexes can be created using the CREATE INDEX SQL command, although they are not available to trial accounts.

How Does the Creation of Snowflake Indexes Work?

The creation of a Snowflake index is a non-blocking or online operation, meaning that the hybrid table remains available for SELECT and DML (Data Manipulation Language) statements during the index build. This feature ensures minimal disruption to ongoing database operations, providing a seamless experience for users.

Syntax and Parameters

CREATE [ OR REPLACE ] INDEX [ IF NOT EXISTS ] <index_name> ( <col_name> [, <col_name> ] ) ON <table_name>


  • index_name: A unique identifier for the new index within the hybrid table.
  • table_name: The name of the existing hybrid table.
  • col_name: The name of the existing column(s) in the hybrid table.

Key Restrictions and Requirements

Several key restrictions and requirements apply to Snowflake indexes:

  • Unique Index Names: Each index must have a unique name within the hybrid table.
  • Column Restrictions: Certain column types, such as VARIANT, cannot be used in a secondary index.
  • Single Build Operation: Only one active index build operation is allowed per hybrid table at any time.
  • Index Status Tracking: The progress of an index build can be tracked using the SHOW INDEXES command. Possible statuses include ACTIVE, SUSPENDED, BUILD FAILURE, or BUILD IN PROGRESS.

Example of Creating an Index

val INT,
val2 INT

SELECT seq, seq + 100, seq + 200
FROM (SELECT seq8() seq FROM TABLE(GENERATOR(rowcount => 100)) v);

CREATE OR REPLACE INDEX vidx (val) ON mytable;

If the index build fails, it can be dropped with the following command:

DROP INDEX vidx ON mytable;

How Do Snowflake Indexes Improve Performance?

Snowflake indexes significantly enhance query performance by allowing faster access to specific columns within a table. Here are some key ways they improve performance:

  • Non-Blocking Operations: Since the creation of Snowflake indexes is an online operation, it allows ongoing data retrieval and manipulation tasks to continue without interruption. This approach ensures high availability of the database and minimizes downtime, which is crucial for mission-critical applications.
  • Enhanced Query Performance: By indexing specific columns, Snowflake indexes reduce the time required to locate and retrieve data. This enhancement is particularly beneficial for large datasets where full table scans can be time-consuming. Indexes help in quickly narrowing down the search space, thus improving query performance.
  • Index Status Monitoring: The ability to monitor the status of an index build using the SHOW INDEXES command provides transparency and control over the indexing process. This feature allows administrators to track progress and identify any issues that may arise during the build.

What Are the Limitations of Snowflake Indexes?

While Snowflake indexes offer significant benefits, they also come with certain limitations:

  • Column Type Restrictions: One of the primary limitations of Snowflake indexes is that certain column types, such as VARIANT, cannot be used in a secondary index. This restriction can limit the flexibility of indexing in scenarios where such data types are prevalent.
  • Single Index Build Operation: Only one active index build operation is allowed per hybrid table at any given time. This limitation can be a bottleneck in scenarios where multiple indexes need to be created simultaneously, potentially delaying the overall indexing process.
  • DML Transactions and Index Creation: Active DML transactions during index creation might cause the build to abort if they remain idle for more than five minutes. This requirement necessitates careful planning and coordination to avoid disruptions during the index build process.

How Do Snowflake Indexes Compare with Other Database Indexes?

Comparing Snowflake indexes with traditional database indexes reveals several unique features and limitations:

Feature Snowflake Indexes Traditional Database Indexes Non-Blocking Creation Yes Varies by database Unique Index Names Required within the hybrid table Required Column Type Restrictions Certain types like VARIANT not allowed Depends on the database Single Build Operation Only one active build per table Varies by database Index Status Monitoring Available using SHOW INDEXES Varies by database tools DML Transaction Impact Aborts if idle for more than 5 minutes Depends on the database

What Are the Practical Considerations for Using Snowflake Indexes?

When using Snowflake indexes, several practical considerations must be taken into account:

  • Privilege Requirements: Creating an index requires OWNERSHIP privilege on the hybrid table. This requirement ensures that only authorized users can create or modify indexes, maintaining data integrity and security.
  • Impact on DML Transactions: DML transactions that are active during index creation must not remain idle for more than five minutes, or the index build will abort. This consideration necessitates careful coordination of DML activities during index creation to avoid disruptions.
  • Index Creation and Maintenance: Index creation does not block other workloads, but only one index build can occur at a time. Administrators must plan and sequence index creation activities to optimize performance and minimize downtime.
  • Dropping Columns: To drop a column that is part of an index being built, the index must be dropped first. This dependency requires careful planning when modifying table schemas to ensure indexes are appropriately managed.

How Do Snowflake Indexes Fit into the Broader Context of Database Optimization?

Snowflake indexes are one of many tools available for optimizing database performance. They complement other techniques such as:

  • Partitioning: Dividing a table into smaller, more manageable pieces to improve query performance.
  • Materialized Views: Storing the results of a query to speed up subsequent queries.
  • Caching: Temporarily storing frequently accessed data to reduce retrieval times.

Snowflake indexes are particularly beneficial in scenarios where:

  • Large Datasets: Indexes significantly improve query performance by reducing the search space.
  • Frequent Queries: Indexes optimize the performance of frequently executed queries.
  • Complex Queries: Indexes help in efficiently retrieving data for complex queries involving multiple conditions and joins.

Common Challenges and Solutions

  • Index Build Failures: If an index build fails, it can be dropped and recreated. Use the SHOW INDEXES command to monitor the status and identify issues.
  • Column Restrictions: Ensure that the columns being indexed are of supported types. Avoid using VARIANT and other unsupported types in secondary indexes.
  • DML Transaction Coordination: Plan and coordinate DML transactions to avoid idle periods during index creation. This approach helps prevent index build aborts due to idle transactions.

Recap of Snowflake Indexes

Snowflake indexes offer a powerful tool for improving query performance on hybrid tables. By providing non-blocking, online index creation, Snowflake ensures minimal disruption to ongoing database operations. While there are some limitations, such as column type restrictions and the single index build operation, the benefits of enhanced query performance and index status monitoring make Snowflake indexes a valuable addition to the database optimization toolkit. Careful planning and coordination are essential to effectively leverage Snowflake indexes and maximize their impact on database performance.

  • Non-Blocking Operations: Snowflake indexes allow ongoing data retrieval and manipulation tasks to continue without interruption, ensuring high availability and minimal downtime.
  • Enhanced Query Performance: By indexing specific columns, Snowflake indexes reduce the time required to locate and retrieve data, particularly beneficial for large datasets.
  • Practical Considerations: Effective use of Snowflake indexes requires careful planning, coordination of DML transactions, and adherence to privilege requirements and column restrictions.

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