If Delta Lake Uses Immutable Files, How Do UPDATE, DELETE, and MERGE Work?

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Listen and Watch here One of the most common questions data engineers ask is: if Delta Lake stores data in immutable Parquet files, how can it support operations like UPDATE , DELETE , and MERGE ? The answer lies in Delta Lake’s transaction log and its clever file rewrite mechanism. 🔍 Immutable Files in Delta Lake Delta Lake stores data in Parquet files, which are immutable by design. This immutability ensures consistency and prevents accidental corruption. But immutability doesn’t mean data can’t change — it means changes are handled by creating new versions of files rather than editing them in place. ⚡ How UPDATE Works When you run an UPDATE statement, Delta Lake: Identifies the files containing rows that match the update condition. Reads those files and applies the update logic. Writes out new Parquet files with the updated rows. Marks the old files as removed in the transaction log. UPDATE people SET age = age + 1 WHERE country = 'India'; Result: ...
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Optimizing SQL queries

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 🚀 Optimizing SQL queries is crucial for improving database performance and ensuring efficient use of resources.



👉 Few SQL query optimization techniques are as below:

✅ Index Optimization

➡️ Ensure indexes are created on columns that are frequently used in 'WHERE' clauses, 'JOIN' conditions and as part of 'ORDER BY' clauses.
➡️Use composite indexes for columns that are frequently queried together.
➡️Regularly analyze and rebuild fragmented indexes.

✅ Query Refactoring

➡️ Break complex queries into simpler subqueries or use common table expressions (CTEs).
➡️ Avoid unnecessary columns in the 'SELECT' clause to reduce the data processed.

✅ Join Optimization

➡️ Use the appropriate type of join (INNER JOIN, LEFT JOIN, etc.) based on the requirements.
➡️ Ensure join columns are indexed to speed up the join operation.
➡️ Consider the join order, starting with the smallest table.

✅ Use of Proper Data Types

➡️ Choose the most efficient data type for your columns to reduce storage and improve performance.
➡️ Avoid using 'SELECT *', specify only the columns you need.

✅ Query Execution Plan Analysis

➡️ Use tools like 'EXPLAIN or 'EXPLAIN PLAN' to analyze how the database executes a query.
➡️ Look for full table scans, inefficient joins, or unnecessary sorting operations.

✅ Temporary Tables and Materialized Views

➡️ Use temporary tables to store intermediate results that are reused multiple times in complex queries.
➡️ Use materialized views to store precomputed results of expensive queries.

✅ Efficient Use of Subqueries and CTEs

➡️ Replace correlated subqueries with joins when possible to avoid repeated execution.
➡️ Use CTEs to improve readability and reusability, and sometimes performance, of complex queries.

✅ Optimization of Aggregate Functions

➡️ Use indexed columns in 'GROUP BY' clauses to speed up aggregation.
➡️ Consider using window functions for complex aggregations instead of traditional 'GROUP BY'.

✅ Avoiding Functions in Predicates

➡️ Avoid using functions on columns in the 'WHERE' clause as it can prevent the use of indexes.
➡️ Rewrite conditions to allow the use of indexes.

✅ Parameter Sniffing and Query Caching

➡️ Be aware of parameter sniffing issues where SQL Server caches execution plans based on initial parameter values.
➡️ Use query hints or option recompile to address specific performance issues.
➡️ Take advantage of query caching mechanisms where appropriate to reuse execution plans.

🛠 By applying these advanced techniques, you can significantly enhance the performance of your SQL queries and ensure that your database runs efficiently.

#dataengineering

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