You're facing slow query performance in a high-traffic database. How can you optimize its efficiency?
When high traffic slows down your database queries, efficiency is key. Here are quick fixes to speed things up:
How have you improved database query performance? Share your strategies.
You're facing slow query performance in a high-traffic database. How can you optimize its efficiency?
When high traffic slows down your database queries, efficiency is key. Here are quick fixes to speed things up:
How have you improved database query performance? Share your strategies.
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- Run EXPLAIN (or similar commands) to analyze query execution plans. This helps identify bottlenecks, such as full table scans. - Use pagination or limits to return only the necessary rows instead of fetching all records. - Add indexes on columns frequently used in WHERE clauses, JOIN conditions, and ORDER BY clauses. - Depending on the use case, consider normalizing data to reduce redundancy or denormalizing to speed up read operations. - Use built-in database caching mechanisms or external caching systems (like Redis or Memcached) to cache frequent query results. - Implement read replicas to offload read queries from the primary database, allowing it to handle more write operations.
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1.Rewrite inefficient queries, fetch only necessary columns, and avoid expensive operations like SELECT *. 2. Create and maintain indexes tailored to query patterns, including composite or covering indexes where applicable. 3.Use table partitioning for large datasets and sharding for horizontal scaling to distribute the load. 4.Utilize materialized views, clustering keys (e.g., in Snowflake), and query caching features of your database. 5.Scale vertically (add CPU, memory) or horizontally (replication, read/write separation) to meet demand. 6.Normalize for consistency and denormalize for read-heavy workload. 7.Use CTE. Don't use the same subquery multiple times. It will affect performance.
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Below are the key steps can take: Indexing Optimise the queries Optimise the schema design Use stored procedure Scale the database
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To have fewer queries with the same throughput, implement Stored Procedures and triggers by moving the code into the database and achieve the same tasks with fewer network calls to the database.
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Index Optimization Without indexies, queries have to "flip through every page" to find what they need. ✅ What to do: Review frequently queried columns and create appropriate indexes. Remove unused or duplicate indexes to avoid overhead. Use composite indexes for multi-column filters. Query Refactoring Complex queries often bog down performance. Simplification is your best friend. ✅ What to do: Avoid SELECT *; query only the columns you need. Eliminate unnecessary subqueries and replace them with joins. Use query execution plans to identify bottlenecks. ⚡ Caching Strategies ✅ What to do: Implement application-level caching (e.g., Redis, Memcached). Use database query caching for repetitive queries.
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Goutham G
Software Developer at BNY | Angular | React | NextJs | .Net | Power BI | Tableau | Alteryx
Involves a combination of query tuning, database optimization, and infrastructure considerations. -Identify bottlenecks like full table scans or expensive joins. -Ensure appropriate indexes are in place (Index optimization is the major part to increse performance) -Use the smallest dataset possible in joins by filtering data early -Fetch only the necessary columns instead of using SELECT * -Rebuild indexes and update statistics regularly. -Archive or delete unwanted data to reduce table size.
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1. If using Spring Data JPA - Use QueryHints @QueryHints({ @QueryHint(name= "org.hibernate.readOnly","true"), @QueryHint(name= "org.hibernate.cacheable","true"), @QueryHint(name= "jakarta.persistence.cache.retrieveMode","USE") }) 2. Indexing: Ensure that your database tables have appropriate indexes on columns used in WHERE, JOIN, and ORDER BY clauses. 3. Enable Caching: Use Spring’s caching abstraction to cache query results, especially for expensive operations or frequently requested data. 4. Use @Transactional only when needed and keep your transaction scope as small as possible. 5. Read-Write Splitting - Consider scaling horizontally by adding more read replicas or even using a NoSQL database for specific use cases.
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Use Read-only Replicas of Database, Use Indexing GraphQL: We can for frequently used queries, fetch only what needs Caching: Use tools like Redis to cache frequent queries. Logging: Monitor performance with logging to identify and address bottlenecks.
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There are probably a few things you could do: -Optimise Queries -Indexing -Scale the database -Check any changes in the database. This isn't in order but doing these should help.
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