What techniques can you employ to scale database concurrency for large systems?

Listed all strategies are deeply insightful to scale database concurrency for large systems. Moreover, database partitioning, filegroup partitioning & placing files at different LUN and database lock escalation, database maintenance work like index rebuild, statistics update, log management, temp files & temp db management are also important to improve concurrency for large systems.

Vertical scaling involves increasing the capacity of a single server, such as by adding more CPU, memory, or storage resources.

Horizontal scaling, or "scaling out," is a technique where you increase system capacity by adding more machines or nodes to your database infrastructure. This is particularly effective for handling increased concurrency and scaling large systems.

Connection pooling is a fundamental technique for scaling database concurrency, especially in large systems with a high number of simultaneous connections. However, it’s just one part of the puzzle.

Scaling database concurrency for large systems is a complex challenge, especially when dealing with high read and write operations. Implementing effective caching strategies can significantly improve database performance, reduce load, and scale concurrency.

Define what Concurrency is for your application. If you are going to have concurrent report jobs for applications , consider scaling CPU’s and ensuring stats are updated before such jobs. If you are going to have concurrent transactional load and reports , consider having dedicated nodes / services for OLTP and reports ( dedicated RAC nodes ) However these don’t guarantee the results it’s helps segregate the load to have better control on varying concurrent workloads.

You should consider also: Load Balancing: Employ load balancers to distribute database requests evenly across servers. This not only helps in managing the load but also in failover and high availability. Database Partitioning: Partition the database to distribute data across different tables or databases based on certain keys or ranges. This helps in reducing the row-level locking and contention issues, thereby improving performance. Optimizing Queries and Indexes: Optimize SQL queries and use appropriate indexing to speed up query processing. Efficient queries and well-planned indexes reduce locking and blocking issues and improve overall concurrency.