Como solucionar e resolver problemas de simultaneidade em bancos de dados distribuídos?

Identifying the root cause of concurrency issues is undeniably the pivotal first step in resolving database conflicts effectively. By meticulously pinpointing the source of contention, whether it's conflicting transactions, locking conflicts, or resource contention, database administrators can devise targeted strategies to address the problem. This process involves thorough analysis of database logs, performance metrics, and transaction histories to uncover patterns and anomalies indicative of concurrency issues. Only with a clear understanding of the underlying issue can appropriate measures be implemented to alleviate concurrency challenges and ensure the smooth operation of the database system.

DBA need to work on the issue by analysing the query and related locks if any. Check the patterns in it. How different isolation levels are sets and how they behaving during executing the query. With this DBA to suggest query optimisation to the developers and gets implemented in the test enviornment.

Solving of concurrency disputes in distributed databases entails the main task of fixing the emerging problems including occurrences of temporary update, incorrect summary, lost update, unrepeatable read, and phantom read issues. Strategies to solve this challenge include applying concurrency control mechanisms such as Pessimistic Concurrency Control (PCC) and Optimistic Concurrency Control (OCC) that work in such a way that they preserve the standards of transaction integrity and ACID properties across the distributed framework. Besides that, isolation levels are used to define the amount of transaction baseline which saves from inconsistencies when transactions are being changed.

Distributed databases offer amazing scalability, but keeping things consistent when multiple users access the same data can be tricky. That's where concurrency control comes in! My experience involves identifying these conflicts – like two users trying to update the same record at once. By analyzing transaction logs and leveraging techniques like optimistic locking, we can ensure data integrity and a smooth user experience.

By carefully analyzing the lock types, durations, and granularity in the context of the application's workload, developers can optimize the locking strategy to maintain data integrity while maximizing concurrency and system throughput.

Use tools available in the RDBMS to analyse the locks and related pattern in the datavase. Like, in MSSQL activity monitor system procs like SP_who2, SP_lock,etc can be used in SSMS or in query editor. These tools gives you in detail information about the locks held including duration and on which table locks are imposed along with duration and type of lock. Detail report to be provided to the developers to optimise the locks.

DBA always talks about a good and bad query. Most of the time queries are developed by the developers but being proactive DBA he/she always work on the query investigation, see the bottleneck, provide suggestions to the developer which in turn helps the database enviornment to work more smoothly and effectively.

Selecting right isolation levels for optimised data reading along with optimised performance is challenging task. As rightly mentioned having low isoloation level increases dirty reads and having high isolation level impact database performance but reduces concurrency. Hence selecting right level of isolation levels in discussion with application developers is the key.

Scaling resources will surely enhance the worload execution capacity and in turn performance of the database and box. But prior that DBA & developer must analyse the concurrency issues and try to eliminate the issue occuring again. Otherwise, despite of scaling up the server, issue migh reoccur as the workload gets increases.

Sharding database into smaller and manageble chuncks i.e. into data shards surely increses the performance as it avoids concurrency issues. As each data shard is kept on separate database servers, reduces data response and workload gets shifted to multple servers.