What is Concurrency control?
Concurrency control is a critical aspect of CRM systems, ensuring that multiple users can access and manipulate customer data simultaneously without causing conflicts or data inconsistencies. This document explores the strategies and techniques employed in CRM systems to manage concurrent data access effectively.
Concurrency control is indeed the mechanism that determines the outcome of operations like updates or deletes when multiple users are working on the same record concurrently. It ensures that data integrity is maintained in situations where multiple users may attempt to modify the same piece of data simultaneously. Concurrency control strategies and techniques are used to prevent conflicts and determine how concurrent operations are managed to avoid data inconsistencies and corruption.
What is the importance of Concurrency Control in CRM?
CRM systems serve as central repositories of customer data. Multiple users and processes frequently access and modify this data concurrently. Without proper concurrency control, the following issues can arise:
1. Data Inconsistency: Simultaneous updates can lead to conflicting data states, making it challenging to maintain accurate customer records.
2. Data Corruption: Concurrent writes can corrupt data, rendering it unusable.
3. Lost Updates: Without control mechanisms, one user's changes might overwrite another user's updates, resulting in lost data.
Types of Concurrency Control:
1. Pessimistic Concurrency Control: This approach involves locking data to prevent simultaneous access by multiple users. When one user locks a data record, others must wait until it's released. This ensures data consistency but can reduce overall system concurrency.In pessimistic concurrency control, records are locked to prevent other users from accessing them simultaneously.
2. Optimistic Concurrency Control: Optimistic control assumes that conflicts are infrequent. It allows multiple users to work on the same data concurrently. Conflicts are detected and resolved when users attempt to save their changes. This approach maximizes system concurrency. In the optimistic concurrency control you don’t allow update to the record if it is updated since it was retrieved.
In a complex multi-threaded and multi-user environment such as Power Apps, various operations and data modifications frequently occur concurrently. A challenge arises when multiple attempts are made to update or delete the same data element simultaneously. This scenario poses a risk of potential data loss. Fortunately, the optimistic concurrency feature equips your applications with the capability to identify whether a server-side alteration has taken place in a table record between the moment your application initially fetched the record and when it attempts to execute an update or deletion of that specific record.
This optimistic concurrency mechanism is applicable across all default tables configured for offline synchronization and extends its support to custom tables as well. You can determine if a table supports optimistic concurrency by retrieving the table's metadata using code or by viewing the metadata using the Metadata Browser, and check if the column IsOptimisticConcurrencyEnabled is set to true. By default, this property is configured as "true" for custom tables.
Challenges in Concurrency Control:
1. Deadlocks: Deadlocks occur when two or more users are waiting for each other to release locks. Effective deadlock detection and resolution strategies are essential to prevent system gridlock.
2. Resource Contention: Concurrency control mechanisms can lead to contention over system resources like memory or CPU, potentially affecting system performance. Tuning and optimization are necessary.
3. Performance Impact: Balancing data consistency with system performance is a critical challenge. Excessive locking can lead to reduced concurrency and slower response times.