Learn how to tackle slow database queries with effective problem-solving skills in database engineering.

Query Structure: Select only the necessary columns, SELECT name, age FROM users; instead of SELECT * FROM users Subqueries: Replace a subquery with a join if possible, SELECT * FROM orders WHERE customer_id IN (SELECT id FROM customers WHERE status = 'active') can be optimized by using a join Joins: Ensure efficient joins, avoid joining unnecessary tables Selectivity: Use specific WHERE clauses, SELECT * FROM products WHERE price < 100; to filter out expensive products early Functions: Avoid functions in WHERE, SELECT * FROM events WHERE MONTH(event_date) = 1; is not great Data Types: SELECT * FROM users WHERE age = 25; ensure ‘age’ is an integer, not a string

Analyze the execution plan. Eliminate full table scan. The use of indexes (if they exist) is essential for large tables. Evaluate partitioning your tables. Use best practices for your database. Always consult the documentation. Each database has its rules and techniques for retrieving data. Eliminating unused columns is no guarantee of improvement unless we are talking about a columnar database.

What do you do if your database query is taking too long to execute?

When you're faced with a database query that's dragging its virtual feet, it can be a frustrating experience. Slow queries can bottleneck your application, leading to a poor user experience and potentially impacting your business. But there's no need to despair; with a methodical approach, you can diagnose and optimize your database performance. The following steps will guide you through troubleshooting and fixing a slow database query, ensuring your data retrieval is as swift as it should be.

1 Examine Query

First, closely examine the query itself. Is it well-formed and optimized? Sometimes, the issue lies with the query structure. You might be using SELECT * unnecessarily, fetching more data than needed. Ensure that you're only selecting the columns you require. Also, look for suboptimal JOIN operations, and consider whether indexes are being used effectively. If not, restructuring the query or adding appropriate indexes can lead to significant performance improvements.

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Rajat Bhatheja

Senior Software Engineer at Microsoft | Ex-Paytm | Ex-UHG | IIT Delhi
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Query Structure: Select only the necessary columns, SELECT name, age FROM users; instead of SELECT * FROM users Subqueries: Replace a subquery with a join if possible, SELECT * FROM orders WHERE customer_id IN (SELECT id FROM customers WHERE status = 'active') can be optimized by using a join Joins: Ensure efficient joins, avoid joining unnecessary tables Selectivity: Use specific WHERE clauses, SELECT * FROM products WHERE price < 100; to filter out expensive products early Functions: Avoid functions in WHERE, SELECT * FROM events WHERE MONTH(event_date) = 1; is not great Data Types: SELECT * FROM users WHERE age = 25; ensure ‘age’ is an integer, not a string

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Emerson Alvim

Especialista em Análise de Dados | Localiza
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Analyze the execution plan. Eliminate full table scan. The use of indexes (if they exist) is essential for large tables. Evaluate partitioning your tables. Use best practices for your database. Always consult the documentation. Each database has its rules and techniques for retrieving data. Eliminating unused columns is no guarantee of improvement unless we are talking about a columnar database.

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Tarun Jindal

Digital Transformation | ERP | Analytics
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First of all find the execution plan of the query and then find which joins are expensive , whether there are any full table joins etc. If there are full table scans then we should create the indexes on that particular table incase there are no indexes or use appropriate index. We may have to gather stats on the table if its stats are stale. One common mistake I usually encounter is although the developers have created composite indexes but composite index columns are not properly sequenced so databases do skip scan index which is costly operation. We may need to correct such indexes, and use appropriate hints if required.

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Kunal Kumar

17K+ Followers | Module Lead | Full Stack Developer
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In one of my projects, retrieving booking history was slow because of numerous joins. I decided to analyze all the joins and where conditions. After understanding the requirements thoroughly, I rewrote the query to minimise the number of joins.

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2 Check Indexes

Indexes are critical for improving database search speed. If your query is slow, check if the fields used in WHERE clauses and JOIN conditions have indexes. Without proper indexing, the database has to perform full table scans, which are time-consuming on large datasets. Adding indexes to these fields can drastically reduce search time. However, be mindful that over-indexing can also slow down write operations and consume more storage.

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3 Analyze Load

Understanding the overall load on your database server can provide insights into query performance issues. If the server is experiencing high traffic or running multiple complex queries simultaneously, this can cause delays. Tools like the slow query log can help identify long-running queries. You may need to consider optimizing the server's workload by improving query efficiency, increasing hardware resources, or load balancing.

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4 Optimize Schema

Database schema optimization is often overlooked but can significantly affect performance. Ensure your data types are appropriate for the content they store—using a BIGINT when an INT will suffice can waste space and processing time. Additionally, normalize your database where it makes sense to reduce data redundancy, but also be aware of the trade-offs, as some denormalization can be beneficial for read-heavy operations.

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5 Review Configuration

Database configuration settings can have a profound impact on performance. Review your database's configuration to ensure it's tuned for your workload. Memory allocation, cache sizes, and query execution settings should be aligned with your hardware capabilities and performance expectations. Sometimes, adjusting a few configuration parameters can resolve long-running query issues without the need for deeper structural changes.

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6 Monitor Continuously

Finally, implement continuous monitoring to catch slow queries before they become a problem. Use monitoring tools to track query performance over time, set up alerts for unusual patterns, and regularly review query execution plans. By proactively monitoring your database, you can make incremental changes that prevent performance degradation and keep your queries running smoothly.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Lillian Teixeira

Sr Data Engeneer at AlgarTech MSP
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Verifique as manutenções de seu banco de dados, como manutenções de índices e análise de estatísticas. Mantenha seu banco de dados sempre com baixa fragmentação de índices, pois isso dificulta o otimizador a encontrar o melhor plano de execução.

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What do you do if your database query is taking too long to execute?

1

2

3

4

5

6

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1 Examine Query

2 Check Indexes

3 Analyze Load

4 Optimize Schema

5 Review Configuration

6 Monitor Continuously

7 Here’s what else to consider

Database Engineering

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