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Q. System Design — Database Sharding ✅ Explanation: Sharding means splitting a database into multiple smaller databases (shards), each holding a portion of the data. Example: - Users A–M → users_shard_1 - Users N–Z → users_shard_2 Benefit: - Improves performance and scalability. Trade-off: - More complex queries and maintenance. Use when your data grows beyond one server’s limits.

What is Database Replication? There’s one Primary Database, this is where all the writing happens (adding or updating data). Then there are several Replica Databases (or readers). These get copies of the data from the primary and handle reading (like when users fetch information). By splitting responsibilities this way: 1. Performance improves since many read requests can happen at once on different replicas. 2 . Reliability increases - if one database goes down, others still have the data. This approach helps modern systems stay fast and scalable.

More indexes = better performance, right? Wrong. This is the most common mistake I see in database design. I recently audited a database with 47 indexes on a single table. Query performance? Terrible. Here's the indexing paradox: → Too few indexes = slow SELECT queries → Too many indexes = slow INSERT/UPDATE/DELETE operations → The goal isn't maximum indexes—it's optimal indexes My approach: Analyze query patterns (not just individual queries) Create indexes that serve multiple queries Monitor index usage statistics Remove unused indexes ruthlessly One client reduced their database size by 40% and improved overall performance by removing redundant indexes. Less is often more in database optimization. #SQLServer #DatabaseDesign #PerformanceTuning

Quick question : You need to add a new, required phone_number column to your users table, which has 500 million rows. You write a simple ALTER TABLE script. You run it during a "maintenance window." It locks the entire users table for 8 hours while it adds the new column to every row. For 8 hours, no one can sign up or log in. Pain right ? You cannot perform "stop the world" operations on a live, large scale database How will you do a “Painless Database Migration” ?

New Post: Enabling Database Log in D365FO From Anitha Santosh Database logging provides a way to track specific types of changes to the tables and fields in the system. Changes that can be tracked include insert, update, delete, and rename key operations. When you configure logging for a table or field, a record of every change to that table or field is stored in the […] https://lnkd.in/eRZ-yGJg

We’ve had our fair share of over-indexing horror stories. I remember one where we opened a client database during our standard health check. Found hundreds of indexes on single tables. Every INSERT took several seconds. Every DELETE took several seconds. Why? Each row change had to update 100+ indexes. Database size was massive - each index replicates key and included columns, so space usage multiplied fast. Storage costs through the roof. Maintenance windows ran for hours every night rebuilding unnecessary indexes. What caused this? They enabled auto-tuning on their development database. Let SQL Server create indexes automatically. Then published that schema to production. Auto-tuning optimizes for the workload it sees. Development workload is nothing like production workload. Those indexes were optimized for the wrong thing. We removed duplicates and unused indexes. Kept only what production queries actually needed. INSERT and DELETE performance improved immediately. Database size dropped. Maintenance windows shortened. More indexes does not mean better performance. Wrong indexes mean worse performance. Index your production database based on production queries. Not what worked in development. Got any? I'd love to hear them. -- SQL Server experts trusted by Coca-Cola, Siemens, Sony, NCR and hundreds more. Want to reduce SQL Server infrastructure costs by 50-75% and improve performance? Speak to a SQL Server expert here: https://lnkd.in/eb9ZbiCn

Learn how to structure Excel for database-like operations and recognize when to migrate to a proper database system.  #statology https://lnkd.in/g8c6gcPp

🗄️ Database Performance Tuning: Queries to Speed of Light Slow queries can destroy your application performance. Learn how to optimize like a pro! ⚡ Database Performance Hierarchy: 1️⃣ Query Optimization

The Context Variable Vault: Thread-Safe State Without Globals Timothy stared at his laptop screen, frustration mounting. The library's new async web server was working—mostly—but the logs were a disaster. "Margaret, look at this," he said, spinning his screen toward her. The senior librarian walked over from the reference desk. \[INFO\] Processing request abc123: Starting checkout \[INFO\] Processing request xyz789: Starting checkout \[INFO\] Processing request abc123: User verification \[INFO\] Processing request xyz789: Database query \[INFO\] Processing request abc123: Database query \[INFO\] Processing request xyz789: User verification "The request IDs are all mixed up," Timothy said. "Request abc123 shows database query, but that log line is actually from xyz789. I can't trace what's happening to individual requests." Margaret nodded knowingly. "Show me your logging code." Timothy pulled up his code: import asyncio import logging # Global variable to track current request current\_request\_id = None async def handle\_request\(request\_id\): global cu https://lnkd.in/gQvf4fei

Insights on Controlling Db2 Memory consumption(DB2 LUW Memory Model) Db2 memory consumption varies depending on workload and configuration. In addition to this factor,self-tuning of the database_memory becomes a factor if it is enabled. Self-tuning of the database_memory is enabled when database_memory is set to AUTOMATIC and the self-tuning memory manager (STMM) is active. If the instance is running on a Db2 database product without memory usage restrictions and the instance_memory parameter is set to AUTOMATIC, an instance memory limit is not enforced. Behaviour when the INSTANCE_MEMORY is set to automatic ------------------------------------------------------------- The database manager allocates system memory as needed. If the self-tuning of database_memory is enabled, STMM updates the configuration to achieve optimal performance while it monitors available system memory. The monitoring of available memory ensures that system memory is not over-committed. Behaviour when the INSTANCE_MEMORY is set to fixed value -------------------------------------------------------------- If the instance is running on a Db2 database product with memory usage restrictions or instance_memory is set to a specific value, an instance memory limit is enforced. The database manager allocates system memory up to this limit. The application can receive memory allocation errors when this limit is reached.