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Learn Linux from the basics to advanced topics.
Learn how to use a Raspberry Pi Pico
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Learn Docker, the leading containerization platform. Docker is used to build, ship, and run applications in a consistent and reliable manner, making it a popular choice for DevOps and cloud-native development.
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Learn how to build robots, starting with the basics, then move on to learning Python and MicroPython for microcontrollers, finally learn how to make things with Fusion 360.
Learn Python, the most popular programming language in the world. Python is used in many different areas, including Web Development, Data Science, Machine Learning, Robotics and more.
Learn how to create robots in 3D, using Fusion 360 and FreeCAD. The models can be printed out using a 3d printer and then assembled into a physical robot.
Learn how to create Databases in Python, with SQLite3 and Redis.
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By Kevin McAleer, 3 Minutes
Module 9 focuses on optimizing the performance of SQLite databases by utilizing indexes and employing various techniques to improve query execution speed.
Indexes are data structures that improve the speed of data retrieval operations in a database. They provide a way to quickly locate and access specific data within a table.
By default, SQLite creates a primary key index for each table, ensuring fast access to individual records based on their primary key values.
To improve the performance of queries that involve filtering, sorting, or joining, we can create additional indexes on specific columns.
# Create an index on a column connection.execute("CREATE INDEX idx_books_author ON books(author)")
In this example, we create an index named “idx_books_author” on the “author” column of the “books” table. This index enhances query performance when filtering or sorting based on the “author” column.
SQLite provides the EXPLAIN QUERY PLAN command, which helps analyze the execution plan of a query and identify areas for optimization.
EXPLAIN QUERY PLAN
# Analyze query performance query = "SELECT * FROM books WHERE author = ?" result = connection.execute("EXPLAIN QUERY PLAN " + query, ("F. Scott Fitzgerald",)) data = result.fetchall()
By examining the output of the EXPLAIN QUERY PLAN command, we can identify potential performance bottlenecks and make informed decisions on index creation or query optimization.
There are various techniques to optimize query execution and improve overall database performance.
To improve query performance, it’s important to limit the number of rows returned by a query. Use the WHERE clause to filter rows based on specific conditions and retrieve only the necessary data.
WHERE
If you only need a subset of records, use the LIMIT clause to restrict the number of rows returned by a query. This helps reduce the data transferred and improves query response time.
LIMIT
Joins can impact query performance, especially when dealing with large tables. Be selective when joining tables and use appropriate indexes on join columns.
Retrieve only the columns that are required for a particular query. Avoid selecting unnecessary columns to reduce the amount of data transferred and improve query performance.
Regular monitoring and tuning of your SQLite database can help identify performance issues and optimize query execution.
SQLite provides tools such as the sqlite3_analyzer command-line utility and the sqlite_stat tables to gather statistics and analyze database performance.
sqlite3_analyzer
sqlite_stat
Optimizing queries involves techniques such as using appropriate indexes, rewriting complex queries, and denormalizing data in certain cases.
To measure the performance improvements achieved through optimizations, it’s important to conduct performance testing and benchmarking on representative workloads.
Database performance is an ongoing concern. Regularly monitor performance, analyze query execution plans, and fine-tune indexes and queries to ensure optimal performance over time.
By understanding indexes and employing performance optimization techniques, you can significantly enhance the speed and efficiency of your SQLite databases.
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