2. Mailing Lists
  3. in
  4. Unlocking the Power of SQL Queries for Improved Performance

Archives

New Relic AI monitoring—the industry’s first APM for AI—now generally available

A microscope on small businesses: Spotting opportunities to boost productivity

Unlocking the Power of SQL Queries for Improved Performance

Unlocking the Power of SQL Queries for Improved Performance

SQL, or Structured Query Language, is the backbone of modern data management. It enables efficient retrieval, manipulation, and management of data in a Database Management System (DBMS). Each SQL command taps into a complex sequence within a database, building on concepts like the connection pool, query cache, command parser, optimizer, and executor, which we covered in our last issue.
͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­͏     ­
Forwarded this email? Subscribe here for more

Latest articles

If you’re not a subscriber, here’s what you missed this month.

  1. A Brief History of Scaling Netflix

  2. 15 Open-Source Projects That Changed the World

  3. The Top 3 Resume Mistakes Costing You the Job

  4. How to Design a Good API?

  5. Virtualization and Containerization: Which one to pick?

To receive all the full articles and support ByteByteGo, consider subscribing:

SQL, or Structured Query Language, is the backbone of modern data management. It enables efficient retrieval, manipulation, and management of data in a Database Management System (DBMS). Each SQL command taps into a complex sequence within a database, building on concepts like the connection pool, query cache, command parser, optimizer, and executor, which we covered in our last issue.

Crafting effective queries is essential. The right SQL can enhance database performance; the wrong one can lead to increased costs and slower responses. In this issue, we focus on strategies such as using the Explain Plan, adding proper indexes, and optimizing commands like COUNT(*) and ORDER BY. We also dive into troubleshooting slow queries.

While MySQL is our primary example, the techniques and strategies discussed are applicable across various database systems. Join us as we refine SQL queries for better performance and cost efficiency.

Explain Plan

In MySQL, the EXPLAIN command, known as EXPLAIN PLAN in systems like Oracle, is a useful tool for analyzing how queries are executed. By adding EXPLAIN before a SELECT statement, MySQL provides information about how it processes the SQL. This output shows the tables involved, operations performed (such as sort, scan, and join), and the indexes used, among other execution details. This tool is particularly useful for optimizing SQL queries, as it helps developers see the query execution plan and identify potential bottlenecks.

When an EXPLAIN statement is executed in MySQL, the database engine simulates the query execution. This simulation generates a detailed report without running the actual query. This report includes several important columns:

  • id: Identifier for each step in the query execution.

  • select_type: The type of SELECT operation, like SIMPLE (a basic SELECT without unions or subqueries), SUBQUERY, or UNION.

  • table: The table involved in a particular part of the query.

  • type: The join type shows how MySQL joins the tables. Common types include ALL (full table scan), index (index scan), range (index range scan), eq_ref (unique index scan), const/system (constant value optimization).

  • possible_keys: Potential indexes that might be used.

  • key: The key (index) chosen by MySQL.

  • key_len: The length of the chosen key.

  • ref: Columns or constants used with the key to select rows.

  • rows: Estimated number of rows MySQL expects to examine when executing the query.

  • Extra: Additional details, such as the use of temporary tables or filesorts.

Let's explore a practical application of the EXPLAIN command using a database table named orders. Suppose we want to select orders with user_id equal to 100.

SELECT * FROM orders WHERE user_id = 100;

To analyze this query with EXPLAIN, we would use:

EXPLAIN SELECT * FROM orders WHERE user_id = 100;

The output might look like this: 

Continue reading this post for free, courtesy of Alex Xu.

Claim my free post

A subscription gets you:

An extra deep dive on Thursdays
Full archive
Many expense it with team's learning budget
 
Like
Comment
Restack
 

© 2024 ByteByteGo
548 Market Street PMB 72296, San Francisco, CA 94104
Unsubscribe

Get the appStart writing

by "ByteByteGo" <bytebytego@substack.com> - 11:38 - 2 May 2024