Home
Performance recommendations
This documentation contains general performance tips and recommendations to improve performance.
General performance overview
This documentation describes what takes place during product execution and presents performance tips and recommendations regardless of the platform on which a specific HCL Link product is running.
Map performance and tuning best practices
Many functions available to users for developing HCL Link maps are very powerful and can perform difficult and complicated tasks. Some functions may sacrifice performance for powerful capabilities, and it is important to understand the implications of these.
Data search performance best practices
There are many ways to develop HCL Link maps that search through data. These different options provide varying balances between functionality and performance.

Performance recommendations
This documentation contains general performance tips and recommendations to improve performance.
- General performance overview
  This documentation describes what takes place during product execution and presents performance tips and recommendations regardless of the platform on which a specific HCL Link product is running.
  - Performance measurement factors
    When analyzing how well an application has performed, there are potentially a large number of measurements that can be used to gauge workload efficiency in terms of the specific resources the application used.
  - Command Server operation overview
    The first step in trying to achieve performance improvements running the Command Server in your environment is to understand Command Server operation.
  - Map performance and tuning best practices
    Many functions available to users for developing HCL Link maps are very powerful and can perform difficult and complicated tasks. Some functions may sacrifice performance for powerful capabilities, and it is important to understand the implications of these.
    - Data search performance best practices
      There are many ways to develop HCL Link maps that search through data. These different options provide varying balances between functionality and performance.
      - Data search functions
        As you develop your HCL Link maps to search through data, you should consider the input format and the requirements for the output. Based upon those considerations, you can make your decision on which search function to use to for optimum performance results.
      - Search functions reference
        This documentation contains quantitative comparisons of the data search functions. It presents results of comparing the LOOKUP() against the SEARCHUP() or SEARCHDOWN() functions.
    - Routing
    - Map Profiler (dtxprof)
    - Page Setting Assistant for Maps (dtxpage)
- Glossary
  Performance related terms and definitions.

Data search performance best practices

There are many ways to develop HCL Link maps that search through data. These different options provide varying balances between functionality and performance.

The functions available to the map developer to do data searches are:

EXTRACT()
LOOKUP()
SEARCHUP() or SEARCHDOWN()
CHOOSE() - see POSITIONAL INDEXING in the table below

The following table contains the functions available to the map developer to do data searches.


Function	Use	Advantage	Disadvantage
`EXTRACT()`	When expected results are multiple objects.	One of the most powerful ways of searching through a data file.	Not the most efficient way to locate objects if the expected result set is only a single object.
LOOKUP()	Where expected results are a single object. Scans a data file object-by-object, looking for an object that matches the specified criteria.	More efficient than `Extract()`.	The average amount of time required for search depends proportionally on the number of objects in the data file.
SEARCHUP() or SEARCHDOWN()	When the set of items to be searched is sorted.	Increased performance. Takes advantage of the sorted order of the data file by traversing the data file as a binary tree. Search time is proportional to `log`₂(N), where N is the number of data items.	Cannot be used for unsorted data files. The smaller the input data file, the less performance results benefit.
POSITIONAL INDEXING	When used with `CHOOSE()` and `INDEX()`, locates objects within a data file by position.	Executes repeated `CHOOSE()` requests and maintains a cache of position information for the indexed data file. The cache continues to be used as long as repeated calls to `CHOOSE()` reference the same type, and indexing is fast.	When repeated calls to `CHOOSE()` don't reference the same type, the cache isn't used, and indexing is not as fast.