Struggling with database performance and storage limitations?

Optimizing queries is one of the best ways to improve overall database performance: Analyzing query plans - by identifying slow queries (ones that take longer to run) by improving the query itself, minimizing the use of “*” as it retrieves all the data and some of it may not be needed. Optimizing indexes - ensures that data retrieval is faster because they use a lookup table Partitioning & archiving data - split larger tables into smaller ones based on how closely they relate, this helps access data mich faster than the traditional method - building storage plans outside the database to save old archives and backups helps maintain space management for the database

I'll go with option #1... Query tuning & optimization. By far, long running, bad build queries (that pull unnecessary data) affect DB performance the most. I once worked for a company that had data extract/push queries running over 1 hour. They executed such queries 4-5 times a day through automated jobs, so just imagine... I coordinate an effort to analyze, optimize and build better queries depending on data requirements, reducing executions from over 1 hour to less than 25 mins. It is good to remember that queries have dependency on the Tables/Views from where data is being pulled from. Therefore, keeping proper indexing management is also a key point to improving performance of queries & Database itself.

I optimize queries by working with the development team and demonstrating tools like Oracle SQL Tuning Advisor and Microsoft SQL Server Query Store. This collaborative approach enhances performance and leverages technology effectively.

Optimize Queries ?? Efficient queries are key to maintaining good database performance. Here’s how to optimize them: Use Indexes Wisely ?? Indexes speed up data retrieval by creating quick lookup tables. ?? Analyze the execution plan to ensure your queries are leveraging the right indexes. Avoid SELECT * Statements ?? Avoid fetching all columns from a table. ?? Specify only the columns you need to minimize data processing. Refactor Complex Queries ??? Break down long-running or complex queries into simpler components. ?? Reducing query complexity can improve execution speed.

Scale Resources ?? When faced with performance issues, scaling resources can help alleviate the strain on your database. Consider these approaches: Vertical Scaling ?? Increase the capacity of your existing server by upgrading CPU, RAM, or storage. ?? Quick and effective, but may be limited by physical or financial constraints. Horizontal Scaling ? Add more servers to distribute the load. ?? Ideal for distributed systems, but requires careful planning to maintain consistency and availability. Balance Load Efficiently ?? Ensure even distribution of data and queries across servers. ?? Regularly monitor and adjust as needed to maintain optimal performance.

1. Use Indexes Wisely Identify columns frequently used in WHERE, JOIN, and ORDER BY clauses and create indexes on these columns. Regularly review and remove unnecessary indexes to improve write performance. 2. Avoid Using Wildcards at the Beginning of LIKE Queries 3. Minimize the Use of Subqueries Rewrite as Joins: Whenever possible, rewrite subqueries as joins, as joins are generally more efficient. 4. Use EXPLAIN to Analyze Query Performance 5. Optimize JOIN Operations INNER JOINs: Use INNER JOINs instead of OUTER JOINs whenever possible, as they typically perform faster. Only select the columns you need in your joins to reduce the amount of data processed. 6. Optimize WHERE and GROUP BY Clauses

scale resources effectively by upgrading hardware, scaling vertically with more powerful servers, or horizontally by adding nodes. Employ indexing, archiving, and query optimization to ensure efficient operation.

Move old or rarely accessed data to an archive to free up space in your primary database. Use data compression techniques to reduce the storage footprint of your database. Regularly clean up unused or redundant data to free up space.

?? Archive Data: Move infrequently accessed data to separate storage to improve performance. ?? Reduce Dataset Size: Minimize the size of your working dataset by archiving historical data. ??? Separate Storage System: Store archived data in a different system to keep it accessible but not actively queried. ?? Manage Storage Limitations: Free up space in your primary database to accommodate new data and maintain efficiency.

As part of my normal practice to overcome storage limitations, I automate regular maintenance and defragmentation by scheduling these tasks. This helps manage storage efficiently and maintain consistent database performance.