The answer is NO. Indexes are not utilized or supported by Snowflake. By keeping extensive metadata in each of its micro divisions, it avoids performing unnecessary scanning, which improves speed. Snowflake stores data in micropartitions, or, to put it another way, divides large data sets into smaller files, formats them into columns, and applies compression. All the details about each micropartition, including which partition contains which data, are contained in the Snowflake metadata manager at the service layer. This is considerably better than indexes in comparison to traditional databases since each partition will include information about itself in the header, such as the maximum value, minimum value, cardinality, etc. One of the reasons Snowflake scales so well for random queries is due to this. To determine which sections of which tables/records to actually load in order to perform a query, Snowflake instead calculates statistics about the columns and records in the files that you load. Additionally, it makes use of a columnar store file format to reduce I/O by only reading the portions of the table that contain the fields (columns) you actually utilize in the query.
Large tables (gigabyte, terabyte, or bigger) are divided into smaller “micro partitions” by Snowflake. It compiles statistics on the value ranges that each column contains for each micropartition. Only micropartitions with values in the range required by your query are then loaded. Let’s use a column of time stamps as an example. Based on the statistics kept for dates in the micropartition files, if your query requests data between June 1 and July 1, partitions that don’t contain any data in this period won’t be loaded or processed. However, when you perform analytics queries on huge datasets, you almost usually work with significant subsets of each table in your joins and aggregates. Indexes are frequently used for online transaction processing since they speed up processes when you interact with one or a few records. Such huge queries are automatically accelerated by the storage mechanism’s automatic statistics, without the need for you to define an index or adjust any kind of parameters.
Due to the fact that these systems are MPP, neither Snowflake nor any high-performance big data / OLAP solutions will allow [unique] indexes (Massively Parallel Processing). MPP systems load data while simultaneously inserting thousands of rows into the same table. Snowflake is a columnar database with automatic micro-partitioning. [Unique] Indexes are a concept from much smaller / OLTP systems. Be aware that Microsoft refers to their columnar storage option as a column store index in SQL Server.