Ever had trouble understanding how to use Snowflake JOINs or looking to join multiple tables efficiently in Snowflake? Joins are extremely important for unlocking insights from the vast sea of data. But, if done haphazardly, they can slow things down, making it crucial to know which type of join to use and when. Snowflake really comes to the rescue here, fine-tuning joins operations to run smoothly and swiftly, no matter the heap of data you're dealing with.
In this article, we will dive into the basics of Snowflake joins—covering everything from inner, outer, cross, natural and lateral joins—and also dive into some of the more advanced tricks to optimize your join operations.
What are Snowflake JOINs—Understanding the Basics?
Snowflake JOINs are operations that combine rows from two tables, or other table-like sources such as views or table functions, to generate a new combined row that can be utilized in the query. The Snowflake JOINs operation is described within the FROM clause, where the JOIN subclause specifies (either explicitly or implicitly) the manner in which rows from one table relate to the corresponding rows in the other table. While the recommended approach for joining tables in Snowflake is to use the JOIN construct with the ON subclause within the FROM clause, an alternative method involves using the WHERE clause for the joining operation.
How do Snowflake Joins work?
Snowflake JOINs operate by matching rows from the joining tables based on a specified condition, typically where the values in one column equate to the values in another column. There are a variety of Snowflake JOINs, each serves a unique purpose depending on the desired data outcome.
The process of how Snowflake joins works is as follows:
- Snowflake analyzes the query and determines an optimal join order and type to use based on factors like key constraints and table sizes.
- Tables are scanned and the join condition is evaluated, matching rows between tables on the specified columns.
- Columns from the tables are concatenated for each matched row based on the join type.
- Any intermediate temporary tables created are eliminated, and the final joined output table is returned.
Importance of Snowflake JOINs
At its core, the magic of Snowflake JOINs lies in their knack for bringing together data from different tables, making them a must-have in our day-to-day data-driven decision-making toolkit. They open up the doors to tackle complex questions, offering a wider lens to look at the data world around us.
Here are a few key reasons/examples why Snowflake JOINs are important:
- Bringing together data from different tables for deeper analysis—Snowflake JOINs act like a bridge, connecting rows from two or more different tables to give a fuller picture.
- Crafting a single view from scattered data—With Snowflake JOINs, you don't have to juggle between multiple tables; it brings all the needed data under one roof for easier and more convenient analysis.
- Rolling up related data for a summary view—Snowflake JOINs make it a breeze to tally up sales by customer location, giving a neat summary of sales performance.
- Weeding out specific data based on criteria—Snowflake JOINs help in filtering out, say, customers who haven't made recent purchases, making the data more manageable.
- Tackling tricky analytical questions across different areas—Snowflake JOINs come in handy when there's a need to correlate data across different domains, making complex analysis simpler to handle.
Basic Snowflake JOINs Syntax:
Basic syntax of Snowflake JOINs begins with a SELECT statement to specify the columns you want to retrieve, followed by FROM to identify the first table (<table_1>). Next, specify the type of JOIN you wish to perform before the JOIN keyword. After JOIN, identify the second table (<table_2>), then use the ON keyword to specify the condition that relates the two tables.
SELECT ...
FROM <table_1>
{
INNER
| { LEFT | RIGHT | FULL } [ OUTER ]
}
JOIN <table_2>
[ ON <condition> ]
[ ... ]Snowflake JOINs Syntax with USING Clause:
Similar to the basic syntax but instead of using the ON keyword to specify the condition, you use USING(<column_list>) to specify the common column(s) between the two tables.
SELECT *
FROM <table_1>
{
INNER
| { LEFT | RIGHT | FULL } [ OUTER ]
}
JOIN <table_2>
[ USING( <column_list> ) ]
[ ... ]Natural, Cross Snowflake JOINs Syntax:
With a NATURAL join, Snowflake automatically performs the join based on all columns with the same name in both tables. You can specify LEFT, RIGHT, or FULL to dictate the type of natural join. A CROSS join returns the Cartesian product of the two tables, which means every row from the first table is combined with every row from the second table. (We will dive into each of these sections in detail later on).
SELECT ...
FROM <table_1>
{
| NATURAL [ { LEFT | RIGHT | FULL } [ OUTER ] ]
| CROSS
}
JOIN <table_2>
[ ... ]In all the syntax options above, <table_1> and <table_2> represent the tables or table-like data sources you are joining. This syntax guides the structure of JOIN operations within Snowflake, assisting in querying and analyzing data across multiple tables effectively.
Save up to 30% on your Snowflake spend in a few minutes!
What are the Types of Snowflake JOINs?
Now that we have understood what Snowflake JOINs and their syntax are, it is time to dive in-depth into the types of Snowflake JOINs, along with detailed syntax and examples.
There are different types of Snowflake JOINs supported by Snowflake; they are:
- Inner Join
- Outer Join
- Left Outer Join
- Right Outer Join
- Full Outer Join
- Cross Join
- Natural Join
- Lateral Join
Each Snowflake joins type serves a different purpose and returns a different set of records based on the join condition. Let's explore each join type in more detail.
1) Inner Snowflake Join
Inner Snowflake Join is the most common type of join. It returns only those records that match the join condition in both Table A and Table B. Inner join combines rows from multiple tables if the join condition is satisfied.
The basic syntax for an inner join is:
SELECT columns
FROM Table_A A
INNER JOIN Table_B B
ON A.join_column = B.join_column;For example:
First, let's create two tables named "student" and "grades", and insert some dummy data into them (you can see that the grades are not in order). Keep in mind that we will be using these tables and data throughout this entire article.
CREATE OR REPLACE TABLE Students (StudentID INT, StudentName VARCHAR);
CREATE OR REPLACE TABLE Grades (StudentID INT, Course VARCHAR, Grade CHAR);
INSERT INTO Students VALUES
(1, 'Johnny'), (2, 'Jimmy'), (3, 'Chaos'), (4, 'Genius'),
(5, 'Edward'), (6, 'Eddy'), (7, 'George'), (8, 'Hannah'),
(9, 'Josh'), (10, 'Jack');
INSERT INTO Grades VALUES
(1, 'Math', 'A'), (2, 'Science', 'B'), (3, 'Math', 'C'),
(4, 'Math', 'A'), (12, 'Socials', 'B'), (17, 'Math', 'B'),
(7, 'Math', 'A'), (8, 'Math', 'B'), (16, 'Math', 'C'),
(11, 'Math', 'D'), (19, 'Science', 'D'), (12, 'Socials','D');Now, let's see the inner join in action.
-- Inner Snowflake Joins
SELECT S.StudentName, G.Course, G.Grade
FROM Students S
INNER JOIN Grades G
ON S.StudentID = G.StudentID;
As you can see, we performed an Inner Join to merge the two tables based on the StudentID column. The SELECT statement fetches the StudentName, Course, and Grade columns from the joined data, associating each student with their respective courses and grades. (We used aliases S and G for readability and brevity, representing the Students and Grades tables, respectively.)
Note: If you use the JOIN keyword in a query without specifying INNER or OUTER, the join performed will be an inner join by default. For example, a statement like "SELECT * FROM table1 JOIN table2 ON..." will execute an inner join between table1 and table2 even though INNER was not explicitly stated.
2) Outer Snowflake Join
Outer join include left, right, and full outer join. These return rows that do not satisfy the join predicate, unlike inner join.
a) Left Outer Snowflake Join
Left Outer Snowflake Join returns all records from the left table (Table A) regardless of whether they match the join condition or not. It also returns any matching records from the right table (Table B).
The syntax is:
SELECT columns
FROM Table_A A
LEFT OUTER JOIN Table_B B
ON A.join_column = B.join_column;For example:
Now, let's see the left outer Snowflake join in action.
-- Left Outer Snowflake Joins
SELECT S.StudentName, G.Course, G.Grade
FROM Students S
LEFT OUTER JOIN Grades G
ON S.StudentID = G.StudentID;
As you can see in this example, a Left Outer Join is being performed between the Students and Grades tables on the StudentID column. The SELECT statement is targeting the StudentName column from the Students table and the Course and Grade columns from the Grades table. The result will include all records from the Students table, and wherever there's a matching StudentID in the Grades table, the corresponding Course and Grade will be displayed. In cases where there isn't a match, NULL values will appear for the Course and Grade columns.
b) Right Outer Snowflake Join
Right Outer Snowflake Join is the inverse of a left outer join. It returns all records from the right table (Table B) regardless of whether they satisfy the join condition or not. It also returns any matching records from the left table (Table A).
The syntax is:
SELECT columns
FROM Table_A A
RIGHT OUTER JOIN Table_B B
ON A.join_column = B.join_column;For example:
Now, let's see the right outer Snowflake join in action.
-- Right Outer Snowflake Joins
SELECT S.StudentName, G.Course, G.Grade
FROM Students S
RIGHT OUTER JOIN Grades G
ON S.StudentID = G.StudentID;
As you can see, this query will return all records from the Grades table, and the matched records from the Students table. If there is no match, NULL values will be displayed for the StudentName column from the Students table.
c) Full Outer Snowflake Join
Full Outer Snowflake Joins combines the effects of left and right outer joins. It returns all records from both tables, matching or not. Where no match exists, NULL values are placed.
The syntax is:
SELECT columns
FROM Table_A A
FULL OUTER JOIN Table_B B
ON A.join_column = B.join_column;For example:
Now, let's see the full outer Snowflake join in action.
-- Full Outer Snowflake Joins
SELECT S.StudentName, G.Course, G.Grade
FROM Students S
FULL OUTER JOIN Grades G
ON S.StudentID = G.StudentID;
As you can see, this query will return all records from both the Students and Grades tables. When there's a matching StudentID in both tables, the corresponding StudentName, Course, and Grade will be displayed together. If there's no match, NULL values will be displayed for the missing columns.
3) Cross Snowflake Join
Cross Snowflake Join produces a cartesian product of rows from Table A and Table B. Every row from A is matched to every row from B, resulting in a large output set.
The syntax is:
SELECT columns
FROM Table_A A
CROSS JOIN Table_B B; For example:
Now, let's see the cross Snowflake join in action.
-- Cross Snowflake Joins
SELECT S.StudentName, G.Course, G.Grade
FROM Students S
CROSS JOIN Grades G;
…Result is too long to display
As you can see, in this query, every row from the Students table is combined with every row from the Grades table, resulting in a Cartesian product of the two tables. Each student will be paired with every course and grade from the Grades table, creating a new row for each combination.
Cross join can be useful when you need to generate a set of all possible combinations between tables. But it can also produce very large result sets, so it should be used carefully.
4) Natural Snowflake Join
Natural Snowflake Join automatically joins tables based on columns with the same name and data types, without needing to specify the join criteria.
The syntax is:
SELECT columns
FROM Table_A A
NATURAL JOIN Table_B B;For example:
Now, let's see the natural Snowflake joins in action.
As you can see, in this query, a Natural Join is performed between the Students and Grades tables based on the common column StudentID. The resulting table will contain all columns from both tables, with rows merged on matching StudentID values.
5) Lateral Snowflake Join
Snowflake Lateral Join is a unique type of join where a subquery in a FROM clause can refer to columns of a preceding table expression. Unlike other joins, a Lateral Join operates more like a correlated subquery, iterating through each row of the left-hand table and executing the subquery on the right-hand side using values from the current row. This allows for complex querying and data transformation tasks that might not be easily achievable with standard join types. The resulting output includes rows generated from the inline view, effectively merging data based on the specified conditions.
Check out this documentation to dive deeper and understand the ins and outs of Lateral Snowflake Joins
The syntax for Lateral join is:
SELECT columns
FROM Table_A A,
LATERAL (subquery) B;For example:
Now, let's see the lateral Snowflake join in action.
-- Simple Snowflake Lateral Joins Example
SELECT S.StudentName, G.Course, G.Grade
FROM Students S, LATERAL (
SELECT *
FROM Grades
WHERE StudentID = S.StudentID
) G;
As you can see in this example, the query begins with a SELECT statement targeting StudentName, Course, and Grade columns. In the FROM clause, a LATERAL join is performed between the Students table and a subquery. This subquery selects all records from the Grades table where the StudentID matches the current StudentID from the Students table. For each row in the Students table, the subquery executes, fetching matching records from the Grades table. The results are then combined, pairing each student with their respective courses and grades, and displayed as specified in the outer SELECT statement.
A lateral join is useful when you need to associate additional data with each returned row. The subquery can reference columns from the outer table in its WHERE clause.
Advanced Snowflake JOIN Concepts
There are a few crucial techniques and underlying concepts that are essential for you to understand when working with Snowflake JOINs. Understanding these aspects can significantly enhance your proficiency with Snowflake joins, enabling optimized performance and scalability, especially when handling large datasets.
1) Chaining Multiple Snowflake Joins
Snowflake allows chaining multiple JOIN clauses together in a single query to join data across more than two tables. For example:
SELECT *
FROM table1
JOIN table2 ON table1.id = table2.id
JOIN table3 ON table2.id = table3.idThis query allows safely joining data from multiple tables in one query while optimizing the join order and type.
2) Tuning and Optimizing Snowflake Join Speed
Snowflake uses various optimizations for fast join performance including:
- Automatic tuning of join order, type, and strategy based on query plan analysis
- Columnar storage to read only required columns instead of full tables
- Clustering related tables on join key columns
- Caching of join query results for reuse without recomputation
- Parallel distributed execution of joins across multiple compute clusters
These optimizations provide excellent join speed and scalability across large datasets.
We'll delve into the performance implications of Snowflake JOIN in a later section
3) Snowflake's Default Join Type
If the JOIN keyword is used in a query without specifying INNER or OUTER, it will default to an inner join. Outer join types like LEFT/RIGHT/FULL must be explicitly stated.
4) Removing Duplicates from Join Output
Joins can produce duplicate rows in the result set. The SELECT DISTINCT clause can be used to eliminate duplicate rows and unique results. Also, Snowflake can eliminate redundant JOINs if there's a guarantee that every row in one table has a corresponding row in the other table.
5) Snowflake Join Over Subqueries
Subqueries can come in handy, but they can also slow down your queries. It's a good idea to avoid using them when you can, or consider rewriting them as joins instead. Joins are executed at the database level providing better performance.
Check out this article to learn more about why it's best to use Snowflake JOIN over subqueries.
Snowflake Join Performance Considerations
To optimize join performance in Snowflake, follow these best practices:
1) Joining Large Tables First
When joining multiple tables, join the largest table first. Snowflake's query optimizer will build an optimal plan based on the Snowflake joins order. Joining the biggest table first provides the most data upfront for subsequent joins. This allows the detection of ineligible rows early and improves overall query execution.
2) Leveraging Clustering Keys
Define clustering keys on columns frequently used for Snowflake joins. Clustering organizes the table by the key, co-locating related data. This minimizes data movement for merge joins. Queries join by scanning the clustering key rather than scanning the full table. Ensure tables are clustered on the foreign keys used in Snowflake joins.
3) Partitioning Tables
Partition large fact tables on columns commonly joined on. This allows partition pruning where the query only scans partitions that satisfy the join condition.Let's say for example, joining sales and customer data partitioned by date allows skipping non-matching data partitions. Define range or list partitions aligned to join keys.
4) Sorting Tables
Sorting tables on the join key enables fast merge joins without shuffling data. Make sure tables are created with the sort key matching the join column. Sort keys can also be defined after table creation. Sorting both tables on the join column provides optimal performance.
5) Using Staging Tables
For complex joins with ETL, load data into staging tables first. Apply any transformations needed before joining. Staging tables allow operational changes without locking target tables needed for joins. Optimize and prepare data in stages prior to the final join.
6) Limiting Returned Columns
Avoid using SELECT * in joins where possible. SELECT * returns all columns from all tables, resulting in large data transfers. Only return the specific columns needed. Snowflake's columnar storage means reading fewer columns reduces query processing.
If you follow these tips, you'll make sure that your joins run as swiftly as possible on large datasets in Snowflake. Pay extra attention to proper clustering, partitioning, and sorting of tables based on the common join keys.
Want to take Chaos Genius for a spin?
It takes less than 5 minutes.
Conclusion
And that's a wrap! As you can see, Snowflake JOINs are extremely powerful for unlocking deeper insights from vast datasets by relating data across tables. But improperly optimized joins can become a bottleneck.
In this article, we covered:
- What are Snowflake joins, and how do they work?
- Importance and use cases of Snowflake JOINs
- Snowflake join syntax and types - INNER, OUTER, CROSS, NATURAL and LATERAL
- Advanced Snowflake join techniques
- Performance Implication of using Snowflake JOINs
If you master the spectrum of Snowflake joins techniques showcased in this article, you will be well-equipped to architect high performing Snowflake data pipelines. Combining datasets seamlessly through swift, efficient joins will unlock deeper insights from your data.
FAQs
What are Snowflake JOINs?
Snowflake JOINs are operations that combine data from two or more tables based on common columns or conditions.
How can tables be joined in Snowflake?
There are two ways to join tables in Snowflake: using the JOIN with the ON sub-clause of the FROM clause (recommended), or using the WHERE clause.
What types of JOINs are supported in Snowflake?
Snowflake supports Inner Join, Left Outer Join, Right Outer Join, Full Outer Join, Cross Join, Natural Join, and Lateral Join.
What is the significance of the ON condition in Snowflake JOINs?
The ON condition specifies how rows in one table relate to rows in the other table.
How does Snowflake handle JOIN performance?
While Snowflake is generally fast for most queries, it may exhibit poor performance with certain types of JOINs like range JOINs and non-equi JOINs.
How can Snowflake eliminate redundant JOINs?
Snowflake can eliminate redundant JOINs if there's a guarantee that every row in one table has a corresponding row in the other table.
Can you chain multiple JOINs in Snowflake?
Yes, multiple JOINs can be chained in Snowflake to combine data from three or more tables.
What is the default JOIN type in Snowflake?
The default JOIN type would typically be the INNER JOIN unless specified otherwise.
Can JOINs be used instead of subqueries in Snowflake?
Yes, JOINs can often be used as an alternative to subqueries for combining data from different tables.
How does a LEFT OUTER JOIN work in Snowflake?
LEFT OUTER JOIN returns all the records from the left table, and the matching records from the right table, with NULL values for non-matching rows.
When would you use a RIGHT OUTER JOIN in Snowflake?
RIGHT OUTER JOIN is used when you want to return all the records from the right table and the matching records from the left table.
What does a FULL OUTER JOIN do in Snowflake?
FULL OUTER JOIN combines the results of both LEFT and RIGHT OUTER JOINs, returning all records from both tables with NULL values for non-matching rows.
When is a CROSS JOIN useful in Snowflake?
CROSS JOIN is useful when you want to create a Cartesian product of the rows from the joined tables.
What distinguishes a NATURAL JOIN in Snowflake?
A NATURAL JOIN automatically joins tables based on columns with the same name in both tables.
How does a LATERAL JOIN work in Snowflake?
LATERAL JOIN enables a subquery in the FROM clause to refer to columns of the preceding table.
Can you join tables from different databases in Snowflake?
Yes, in Snowflake, you can join tables from different databases by fully qualifying the table names with the database and schema names.
