Your production codebase is suffering from slow queries. How can you boost its performance?

Now days most of database engine has monitoring service and provides recommendations. Resource( cpu, memory, IO) intensive queries will be reported there. Here are some steps to improve query response time 1) Use query execution cost plan to analyze it 2) Create index if require if still query response time is not acceptable as per business need then perform more steps 3) check resource usage and increase hardware resources(CPU, Memory, Storage). It would be easy if its cloud managed 4) Use caching if possible 5) de-normalize the transaction tables data for red operation to avoid costly JOINS 6) use database replication for read queries 7) if still need to optimize then use partition/sharding Hope this would helpful

First, we need to identify the bottlenecks to determine whether the slowness of database queries is due to high CPU usage or inefficient data handling. Based on this analysis, we can then optimize the system accordingly. Here are some optimization techniques: 1. **Indexing**: Create indexes on frequently queried columns. 2. **Caching**: Implement caching to store frequently accessed data in memory. 3. **Breaking Down Heavy Queries**: Divide complex queries into smaller, more manageable parts. 4. **Using Joins**: Use joins instead of multiple subqueries to fetch related data efficiently. 5. **Checking Cron Jobs**: Ensure scheduled tasks are not overloading the database and optimize or reschedule them if necessary.

Initially , we need to pinpoint the causes of slow database queries to see if the issue is due to high CPU usage or inefficient data handling. Once we understand the problem, we can optimize the system accordingly. Here are some techniques: 1. Creating Indexes: Add indexes to columns that are frequently searched. Implementing Caching: Cache frequently accessed data in memory to speed up access. 2. Decomposing Heavy Queries: Split complex queries into smaller, more manageable parts. 3. Efficient Data Retrieval: Use joins instead of multiple subqueries to retrieve related data more efficiently. 4.Optimizing Scheduled Tasks: Ensure that scheduled tasks aren't overloading the database and optimize or reschedule them if needed.

To resolve slow database queries, follow these steps: 1. Identify and Analyze Queries: Use profiling tools and database logs (e.g., MySQL’s slow query log) 2. Implement Indexing: Add appropriate indexes to columns used in WHERE clauses, JOIN conditions, and ORDER BY statements. 3. Implement Caching: Use caching for frequently accessed queries. 4. Optimize Queries: Optimize queries by using efficient joins and rewriting complex queries. 5. Use Asynchronous and Batch Processing: Offload heavy read operations to asynchronous processes and use batch functions to handle large data operations more efficiently.

In case of the composite indexes the index should aim to start with the column which removes most of the data and then next and so on and so forth use the integers(auto inc) columns as primary key and that would be the foreign key in other tables so that insertion is faster instead of some string base primary key. Check whether your system is write intensive or read intensive some query optimization techniques direct specifically to write/read intensive databases and that optimizations can become handy in some cases.

Evitar retornar mais dados que o necessário, utilizando 'limit' quando possível nas consultar e utilizar apenas com as colunas necessárias.

- Writing a SQL query that return the required data is a simple task ... but writing a query that returns the same data in 1/10 of the time isn't a beginner task at all. - You may write an unoptimized query just for the sake of ensuring that the logic is ok .. but for production it's far from finished, refactoring should be done, remove unnecessary subqueries, extra joins and so on ... - We can summarize "The optimum query is : simple and fast"

Make sure following 1. Frequently used queries, if contains where clause, the respective column should have index. 2. Try to have a composite index on primary key column based on columns which are frequently used for where clause

Using the caching while retrieving from database made query significantly faster from 35 min to 500 milliseconds Here’s how Identifying Issue: MongoDB queries for counting documents were causing significant slowdowns. Solution: Implemented caching to store the count data that was slowing down the queries. Cache Storage: Stored the count data at the point of retrieval, ensuring immediate access for users. Background Updates: Set up background processes to update the cached count data, ensuring it remains current. User Experience: Users receive up-to-date count data promptly, improving performance and reducing load on the database. Result: Significantly faster query responses by leveraging cached data for count operations.

This can be most power solution if Cache-Invalidation is handled carefully. 1. CDC or similar solutions can be leveraged to keep cache sync. 2. Uber has a beautiful blogpost on their implementation of db engine which is capable of handling uber’s scale of queries by adding a smart caching layer.

Before Denormalisation try to use ‘analyse’ to understand what is actually happening in your sql query. This should give more than good enough information to take necessary actions to improve performance.

Before Denormalisation try to use ‘analyse’ to understand what is actually happening in your sql query. This should give more than good enough information to take necessary actions to improve performance.

Batch operations are advantageous when performing bulk inserts or copies of n rows simultaneously, as they utilize only one connection and execute the operation in a single batch. However, it's crucial to close the connection once the transaction concludes. On the downside, batch operations can halt if even one row encounters an issue, leading to the entire execution failing.

In modern database management, leveraging stored procedures can significantly enhance both performance and security. By encapsulating complex or frequently executed queries within stored procedures, organizations can streamline database operations and improve efficiency. Moreover, stored procedures can enhance security by limiting direct access to underlying tables, ensuring that sensitive data remains protected from unauthorized access. This approach not only boosts data integrity but also facilitates better management of database resources, making it a valuable practice for any organization looking to optimize its database operations.

If you're dealing with transactions, using an RDBMS is typically the way to go. However, if real-time processing isn't critical or you're managing large volumes of data, NoSQL options may be suitable. These involve defining a flat structure and operating on extensive datasets, albeit with near real-time rather than real-time capabilities, depending on the specific use case.