When working with large datasets and complex queries, optimizing query performance is crucial. Here are some best practices for making your queries more efficient when using Matik:
Pre-Aggregation
- Pre-Aggregate Tables: Perform aggregations at a higher level (e.g., monthly instead of daily) in your data warehouse (e.g., Snowflake). This reduces the volume of data processed during queries.
Efficient Filtering
- Use WHERE Clauses Wisely: Filter using indexed columns to speed up data retrieval.
- Limit Rows: Use the LIMIT and OFFSET clauses to return only the necessary rows.
- Avoid Functions in WHERE Clauses: Using functions can prevent indexes from being used.
Joins and Subqueries
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Optimize Joins:
- Prefer inner joins over outer joins whenever possible.
- Use indexed columns for joining tables.
- Avoid Correlated Subqueries: These can slow down your query significantly. Use Common Table Expressions (CTEs) instead.
Aggregation and Sorting
- Efficient GROUP BY: Only group by columns that are necessary.
- Use WHERE Before Aggregation: Filter rows before aggregation using the WHERE clause and filter after aggregation using the HAVING clause.
- Avoid DISTINCT Operations: Use them sparingly as they can be resource-intensive.
- Optimize Sorting: Avoid ORDER BY unless required, as it consumes additional resources.
Additional Tips
- Batch Operations: Use IN and BETWEEN operators to shorten and sometimes speed up queries.
- Indexes: Set up indexes on fields that are commonly used in WHERE clauses to improve query speed.
Stepwise Query Optimization Example
Breaking down a complex query into simpler steps can greatly improve performance. You can follow these rules as a guide:
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Select Only the Fields You Need
Start by selecting only the necessary fields from your tables. This reduces the amount of data processed and speeds up the query.
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Filter on Indexed Fields
Use indexed fields in your WHERE clause to make filtering more efficient. Indexes help the database retrieve data faster.
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Do the Same for the Other Table
Repeat the process for any other tables you need to join. Select only the necessary fields and apply filters on indexed columns.
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Join the Tables
Once you have filtered and selected the required fields, join the tables. Ensure you are joining on indexed columns to improve performance.
Run Aggregations and Ordering
Finally, perform any necessary aggregations and ordering. By doing this as the last step, you ensure that you're working with the smallest possible dataset, which improves performance.
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