Optimizing joins in Snowflake: Leveraging constraints for performance gains

Snowflake’s driver plays a crucial role in optimizing query performance, particularly when dealing with complex joins. Many users experience slow query execution times without realizing that proper constraint definitions can significantly improve performance. When dealing with customers trying to get data to Snowflake, this increased execution time leads to increased costs in Snowflake, which can artificially balloon warehouse costs.

This article explores how the Snowflake driver operates and provides practical techniques for optimizing joins using constraints.

How the Snowflake driver operates

The Snowflake driver is responsible for executing SQL queries efficiently by leveraging Snowflake’s cloud-native architecture. It optimizes query execution through several mechanisms:

1. Query Compilation – The driver translates SQL statements into an execution plan
2. Pruning and Micro-Partitioning – It minimizes the data scanned by leveraging metadata
3. Query Caching – Frequently executed queries benefit from result set caching
4. Join Optimization – The optimizer determines the best strategy based on available constraints and statistics.

Among these mechanisms, join optimization is critical, particularly when working with large datasets.

Join optimization techniques

Join operations can become performance Obottlenecks if not handled correctly. Snowflake uses various join algorithms:

1. Broadcast joins – When one table is small, it is broadcast to all nodes processing the query.
2. Merge joins – Used when both tables are sorted on the join keys.
3. Hash joins – Suitable when the tables are large and not sorted.
4. Nested loop joins – Applied when no indexes or optimizations are available.

The optimizer automatically selects the best join type, but users can provide hints or structure data appropriately to improve performance.

Using constraints for optimization

One of the most effective ways to improve join performance is by enforcing constraints on tables. Snowflake does not enforce constraints like traditional relational databases, but defining them helps the optimizer make better decisions.

Primary and Foreign Key Constraints

Even though Snowflake does not enforce foreign keys, defining them provides optimization hints. To demonstrate their impact, let’s consider a large-scale scenario with an orders table and a customers table containing millions of records.

Step 1: Creating Large-Scale Tables Without Constraints

CREATE OR REPLACE TABLE customers (
    customer_id INT,
    customer_name STRING
);

CREATE OR REPLACE TABLE orders (
    order_id INT,
    customer_id INT,
    order_total DECIMAL(10,2)
);

Step 2: Generating Large-Scale Data

INSERT INTO customers
SELECT SEQ4(), 'Customer_' || SEQ4()
FROM TABLE(GENERATOR(ROWCOUNT => 1000000));

INSERT INTO orders
SELECT SEQ4(), UNIFORM(1, 1000000, RANDOM()), UNIFORM(10, 500, RANDOM())
FROM TABLE(GENERATOR(ROWCOUNT => 10000000));

Step 3: Running a Join Query Without Constraints

SELECT o.order_id, o.order_total, c.customer_name
FROM orders o
JOIN customers c ON o.customer_id = c.customer_id;

Since no constraints are specified, the optimizer does not assume referential integrity and may perform unnecessary checks, leading to slower query execution.

Step 4: Adding Primary and Foreign Key Constraints

ALTER TABLE customers ADD CONSTRAINT pk_customer PRIMARY KEY (customer_id);
ALTER TABLE orders ADD CONSTRAINT fk_customer FOREIGN KEY (customer_id) REFERENCES customers(customer_id);

Step 5: Running the Join Query Again

SELECT o.order_id, o.order_total, c.customer_name
FROM orders o
JOIN customers c ON o.customer_id = c.customer_id;

By adding these constraints, the optimizer can safely assume referential integrity, potentially reducing join processing time.

Step 6: Measuring Performance Impact on Large Data

To compare query execution times, use Snowflake’s QUERY_HISTORY view:

SELECT query_id, total_elapsed_time
FROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY())
WHERE query_text LIKE '%JOIN customers c ON o.customer_id = c.customer_id%'
ORDER BY start_time DESC;

Running this before and after adding constraints provides insight into the performance improvement. Typically, queries leveraging constraints execute significantly faster at scale, particularly when tables contain millions of records.

Unique Constraints and Performance Impact

Defining uniqueness constraints helps Snowflake optimize certain operations, particularly lookups and join optimizations.

Step 1: Running a Query Without Unique Constraints

SELECT DISTINCT customer_id
FROM orders;

Since Snowflake does not assume customer_id is unique, it scans all rows to find distinct values, which can be slow on large datasets.

Step 2: Adding a Unique Constraint

ALTER TABLE customers ADD CONSTRAINT unique_customer_id UNIQUE (customer_id);

Step 3: Running the Query Again

SELECT DISTINCT customer_id
FROM orders;

With the unique constraint in place, Snowflake optimizes the query execution by leveraging metadata, reducing the need for full scans.

Step 4: Measuring Speed Improvement

SELECT query_id, total_elapsed_time
FROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY())
WHERE query_text LIKE '%SELECT DISTINCT customer_id FROM orders%'
ORDER BY start_time DESC;

This allows users to compare execution times before and after enforcing the unique constraint. Typically, queries leveraging uniqueness constraints execute faster due to improved indexing and reduced redundant scans.

Clustering Keys

Clustering keys improve data locality and optimize join operations.

ALTER TABLE orders CLUSTER BY (customer_id);

By clustering on the join key, related data is stored together, reducing the need for expensive scans.

Optimizing Specific Join Scenarios

Joining Large Fact Tables with Dimension Tables

When joining large fact tables with smaller dimension tables, using a broadcast join can be beneficial.

SELECT f.order_id, d.customer_name
FROM fact_orders f
JOIN DIM_customers d
ON f.customer_id = d.customer_id
USING BROADCAST;

The optimizer can take advantage of the smaller dimension table and distribute it across all compute nodes.

Using Materialized Views for Join Performance

Materialized views can precompute joins and store results for faster retrieval.

CREATE MATERIALIZED VIEW mv_customer_orders AS
SELECT f.order_id, d.customer_name
FROM fact_orders f
JOIN DIM_customers d
ON f.customer_id = d.customer_id;

Subsequent queries against this view avoid expensive recomputation.

Leveraging Result Set Caching

If joins involve frequently accessed data, Snowflake’s result caching can significantly boost performance. Ensure the same queries are reused to benefit from caching.

SELECT * FROM mv_customer_orders;

Since Snowflake caches query results for 24 hours, repeated executions are near-instantaneous.

Driving Optimization in Snowflake

Understanding how the Snowflake driver operates and applying optimization techniques can significantly improve query performance. By leveraging constraints, clustering, and optimized join strategies, users can minimize query execution times and improve overall efficiency. With these techniques, Snowflake users can ensure they are getting the most out of their cloud data platform.

When evaluating total cost of your data movement platform, it’s important to take into consideration Snowflake costs and how you can optimize it to lessen total cost of ownership of their data stack. But optimizing queries is just one part of the bigger pictures. Matia can help data teams works smarter by ensuring data consistency, reducing operational friction, and providing better visibility into how data is used.