Snowflake Query Optimization

1. Snowflake Query Optimization

Welcome back! Let's delve into query optimization in Snowflake.

2. Why Optimize Queries in Snowflake?

Why should we optimize our queries? Optimization improves two things: speed and cost! Optimized queries yield faster results, saving time, especially in projects requiring quick data retrieval. As for cost, shorter query times in Snowflake save money. Snowflake's virtual warehouses use resources like CPU and memory. The longer a query runs, the more resources are used, converting into credits and ultimately costing money. So, optimization isn't just about speed; it's also about cost-effectiveness.

3. Common query problems

Let's tackle common issues that can slow down queries. One big culprit is 'exploding joins,' which happens when we miss specifying join conditions. This can create a 'Cartesian product,' where every record from one table matches with every record from another, leading to a significantly bigger result.

4. Common query problems

Using proper `ON` conditions leads to accurate and faster results by returning only the required number of rows.

5. Common query problems

Another issue is using `UNION` when `UNION ALL` could be faster. `UNION` removes duplicates, but it takes extra time to do that. If we're sure our data doesn't have duplicates, use `UNION ALL` for quicker results. Also, using filters and limits can yield quicker and more cost-effective results when dealing with large datasets. Let's explore further how to optimize our queries better through basic rewriting.

6. How to optimize queries?

We'll use sample Snowflake data to demonstrate how minor query adjustments can significantly improve performance on larger datasets. One such adjustment is using the `LIMIT` clause. As shown, using `LIMIT 10` limits results to 10 rows and reduces query time.

7. How to optimize queries?

Another way to optimize our queries is avoiding `SELECT *` when we only need specific columns. This makes queries faster and more resource-efficient. For example, specify those columns if we only need the order date and order status columns from the sample orders data. We can see noticeable impact on query duration.

8. How to optimize queries?

Use a `WHERE` clause early to speed up queries. This is especially useful before joining. When we filter first, the join operation processes fewer rows, thereby making it faster and more resource-efficient.

9. Without early filtering

For instance, considering our pizza dataset, if we want to analyze orders from a specific date, this query filters after the JOINS.

10. With early filtering

Instead, it would be more efficient to first filter in a CTE, like here,then perform the joins later. This is especially beneficial in databases with large datasets.

11. Query history

After mastering basic query optimization, the next step is identifying what needs optimizing. Snowflake offers various ways to do this, including built-in views, methods, and a Web UI Query Profiling tool. Here, we'll use the query_history view of the account_usage schema to examine query history. The query_history view provides various metrics like start time, end time, and execution time, among others. For example, here we use the `ILIKE` keyword to filter for queries on the order_details table. `ILIKE` is a case-insensitive way to match strings.

12. Query history

We can identify longer duration queries by filtering `execution_time` column, which contains values in milliseconds. This helps us pinpoint which queries to focus on for further optimization.

13. Let's practice!

Well done on completing this important video on query optimization! Now, let's reinforce your learning with some exercises.

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