10 commons optimizations mistakes

Here are ten common optimization mistakes that developers may make in Microsoft Dynamics 365 Business Central:

• Overuse of SIFT and NCCI
  • While SIFT (SumIndexFieldTable) and NCCI (Non-clustered Columnstore Indexes) can improve query performance, over-indexing or indexing unnecessary fields can lead to increased storage requirements and performance overhead.
• Inefficient Data Retrieval
  • Fetching more data than necessary from the database, such as retrieving all fields when only a subset is needed, can degrade performance and increase network traffic.
• Lack of Table Filtering
  • Failing to apply appropriate filters when querying tables can result in retrieving and processing excessive data, impacting performance. It’s essential to filter data at the source whenever possible.
• Excessive Recalculation
  • Recalculating data unnecessarily, such as re-aggregating values or performing redundant calculations, can consume resources and slow down system performance.
• Suboptimal Queries
  • Writing inefficient SQL queries, such as not using appropriate indexing, joins, or where clauses, can result in slow query performance and decreased system responsiveness.
• Unnecessary Logging
  • Excessive logging of non-critical information can impact system performance, especially in high-transaction environments. It’s essential to balance logging requirements with system performance considerations.
• Heavy Client-Side Processing
  • Performing computationally intensive operations on the client-side rather than leveraging server-side processing can lead to slower user interactions and increased network traffic.
• Inefficient Code Execution
  • Writing code with inefficient algorithms or data structures can result in slower execution times and decreased system performance.
• Ignoring Caching Opportunities
  • Failing to utilize caching mechanisms for frequently accessed data or computations can result in unnecessary overhead and slower response times.
• Not Monitoring and Tuning Performance
  • Neglecting to monitor system performance regularly and make necessary adjustments, such as optimizing queries, indexing, or resource allocation, can lead to degraded performance over time.

10 Short practical examples for all 10 points

Here are short practical examples for each of the ten optimization mistakes listed above:

• Overuse of SIFT and NCCI:
  1. Mistake: Creating SIFT or NCCI indexes for fields that are rarely queried or updated, leading to unnecessary overhead.
  2. Example: Creating a SIFT index on a field that is only used for occasional reporting purposes and doesn’t significantly improve query performance.
• Inefficient Data Retrieval:
  1. Mistake: Fetching all fields from a table when only a few fields are needed for a specific operation.
  2. Example: Retrieving all customer information when only the customer’s name and contact information are needed for a sales order.
• Lack of Table Filtering:
  1. Mistake: Not applying filters when querying large tables, resulting in unnecessary processing of irrelevant data.
  2. Example: Querying the entire sales order table instead of applying filters to retrieve orders for a specific customer or time period.
• Excessive Recalculation:
  1. Mistake: Recalculating aggregates or totals repeatedly instead of caching or updating them incrementally.
  2. Example: Re-aggregating sales totals for each report generation instead of updating the totals incrementally as new sales are recorded.
• Suboptimal Queries:
  1. Mistake: Writing SQL queries without appropriate indexing, joins, or where clauses, resulting in inefficient data retrieval.
  2. Example: Selecting data from a large table without using a proper index or filtering criteria, leading to slow query performance.
• Unnecessary Logging:
  1. Mistake: Logging excessive details, such as every database operation or user interaction, without considering the impact on system performance.
  2. Example: Logging every read or write operation performed by the system, resulting in increased disk I/O and slower overall performance.
• Heavy Client-Side Processing:
  1. Mistake: Performing complex calculations or data processing tasks on the client-side instead of leveraging server-side processing.
  2. Example: Running complex business logic or data manipulation scripts in JavaScript on the client-side instead of executing them on the server.
• Inefficient Code Execution:
  1. Mistake: Writing code with inefficient algorithms or data structures, leading to slow execution times.
  2. Example: Using a linear search algorithm to find records in a large dataset instead of utilizing a more efficient indexing or sorting technique.
• Ignoring Caching Opportunities:
  1. Mistake: Not utilizing caching mechanisms for frequently accessed data or computations, resulting in unnecessary processing overhead.
  2. Example: Not caching lookup tables or frequently accessed reference data, causing the system to retrieve the data from the database repeatedly.
• Not Monitoring and Tuning Performance:
  1. Mistake: Neglecting to monitor system performance regularly and make necessary adjustments to optimize queries, indexing, or resource allocation.
  2. Example: Failing to monitor database query execution times or server resource utilization, leading to undetected performance bottlenecks and degraded system performance over time.

10 Long practical examples for all 10 points

Here are long practical examples for each of the ten optimization mistakes listed above:

• Overuse of SIFT and NCCI:
  1. Mistake: Suppose you create a SIFT index on a field that is rarely queried or updated, such as a custom field tracking secondary contact information for customers. This index consumes additional storage space and maintenance overhead without providing significant performance benefits for the occasional queries that use this field.
• Inefficient Data Retrieval:
  1. Mistake: Consider a scenario where a report requires only the total sales amount for each customer, but the query retrieves all fields from the sales order table. This inefficient data retrieval unnecessarily retrieves large amounts of data from the database, increasing network traffic and processing overhead.
• Lack of Table Filtering:
  1. Mistake: Imagine a situation where a report retrieves all sales orders from the past year without applying filters for specific customers or regions. This results in the database processing a large volume of unnecessary data, slowing down the report generation process and consuming additional system resources.
• Excessive Recalculation:
  1. Mistake: Suppose a system recalculates sales totals from scratch for each report generation, even though the underlying sales data remains largely unchanged. This redundant recalculation wastes computational resources and increases report generation times unnecessarily.
• Suboptimal Queries:
  1. Mistake: Consider writing a SQL query to retrieve customer information without specifying appropriate filtering criteria or using indexed fields. This results in the database performing a full table scan, searching through all records sequentially, instead of using efficient index-based retrieval methods.
• Unnecessary Logging:
  1. Mistake: Imagine logging every database operation performed by the system, including routine read and write operations, without filtering or prioritizing critical information. This results in log files growing rapidly and consuming excessive disk space, potentially impacting system performance.
• Heavy Client-Side Processing:
  1. Mistake: Suppose a web application running on Microsoft Dynamics 365 Business Central performs complex data processing tasks, such as data validation or business rule enforcement, entirely on the client-side using JavaScript. This approach increases client-side processing overhead and can lead to slower user interactions, especially on devices with limited computational resources.
• Inefficient Code Execution:
  1. Mistake: Consider writing a custom algorithm to search for records in a large dataset that uses an inefficient linear search approach instead of leveraging database indexes or optimized search algorithms. This results in longer execution times and increased CPU usage, impacting overall system performance.
• Ignoring Caching Opportunities:
  1. Mistake: Suppose a frequently accessed lookup table containing product categories is not cached by the application, resulting in the system repeatedly querying the database for the same data. Implementing caching mechanisms for such lookup tables can significantly reduce database load and improve application responsiveness.
• Not Monitoring and Tuning Performance:
  1. Mistake: Neglecting to monitor database query execution times, server resource utilization, and overall system performance metrics regularly. Without proactive monitoring and tuning, performance issues may go unnoticed, leading to degraded system performance and user experience over time.

By avoiding these optimization mistakes and following best practices for performance tuning, developers can ensure that Microsoft Dynamics 365 Business Central operates efficiently and delivers optimal performance for end-users.