Query Data in Snowflake

Snowflake supports standard SQL, including a subset of ANSI SQL:1999 and the SQL:2003 analytic extensions. Snowflake also supports common variations for a number of commands where those variations do not conflict with each other.


You can use the search optimization service to improve query performance. For details, see Search Optimization Service.

Working with Joins

A join combines rows from two tables to create a new combined row that can be used in the query.

Learn join concepts, types of joins, and how to work with joins.

Analyzing time-series data

Analyze time-series data, using SQL functionality designed for this purpose, such as the ASOF JOIN feature, date and time helper functions, aggregate functions for downsampling, and functions that support sliding window frames.

Using ASOF JOIN, learn how to join tables on timestamp columns when their values closely follow each other, precede each other, or match exactly.

Eliminate Redundant Joins

A join on a key column can refer to tables that are not needed for the join. Such a join is referred to as a redundant join.

Learn about redundant joins, and how to eliminate them to improve query performance.

Working with Subqueries

A subquery is a query within another query.

Learn about subqueries and how to use them.

Querying Hierarchical Data

Relational databases often store hierarchical data by using different tables.

Learn about querying hierarchical data using joins, Common Table Expressions(CTEs) and CONNECT BY.

Working with CTEs (Common Table Expressions)

A CTE (common table expression) is a named subquery defined in a WITH clause, the result of which is effectively a table.

Learn how to write and work with CTE expressions.

Querying Semi-structured Data

Semi-structured data represents arbitrary hierarchical data structures, which can be used to load and operate on data in semi-structured formats (e.g. JSON, Avro, ORC, Parquet, or XML).

Learn how to use special operators and functions to query complex hierarchical data stored in a VARIANT.

Using Window Functions

Window functions operate on windows, which are groups of rows that are related in some way.

Learn about windows, window functions, and how to use window functions to examine data.

Identifying Sequences of Rows That Match a Pattern

In some cases, you might need to identify sequences of table rows that match a pattern.

Learn about pattern matching, and how to use MATCH_RECOGNIZE to work with table rows matching patterns.

Using Sequences

Sequences are used to generate unique numbers across sessions and statements, including concurrent statements.

Learn what are sequences, and how to use them.

Using Persisted Query Results

When a query is executed, the result is persisted for a period of time.

Learn how query results are persisted, how long persisted results are available, and how to use persisted query results to improve performance.

Computing the Number of Distinct Values

Various methods exist to determine the count of distinct elements within a column.

Learn methods to identify and report distinct elements in data.

Estimating Similarity of Two or More Sets

Snowflake provides mechanisms to compare data sets for similarity.

Learn how Snowflake determines similarity and how to compare multiple data sets for similarity.

Estimating Frequent Values

Snowflake can examine data to determine how frequent values are within the data.

Learn how frequency is determined and how to query data to determine data frequency using the through the APPROX_TOP_K family of functions.

Estimating Percentile Values

Snowflake can estimate percentages of values using an improved version of the t-Digest algorithm.

Learn how to estimate percentages using the APPROX_PERCENTILE family of functions

Analyzing Queries Using Query Profile

Query Profiles provide execution details for a query.

Learn how examine queries, using query profiles, to understand and improve performance.

Using the Query Hash to Identify Patterns and Trends in Queries

To identify patterns and trends in queries, you can use the hash of the query text, which is included in the query_hash and query_parameterized_hash columns in selected Account Usage view and in the output of selected Information Schema table functions.

Learn how to use the query hash in these columns to identify repeated queries and detect patterns and trends in queries.

Canceling Statements

Executing statements are typically cancelled using the interface used to start the query.

Learn how to use system functions to cancel a specific query or all currently executing queries.