Overview on approaches to make TIS faster by proper node and operation use

Approach	Description	Pro's	Con's
Reduce size of data set in SQL	Build corresponding SQL Statements in order to use database mechanisms to select & aggregate	Reduces data	In Version 5.1. one has to go into the operator in [TIS]Editor and type
Have several data sets and switch	Have a data set for each part of the job	Saves massively	In Version 5.1. one has to go into the node in [TIS]Editor and select
Pre-process data	Separate the processing in several stages.	Saves massively	Can become cumbersome when updating data
Pre-process data with Solution Runner	(WORK IN PROGRESS)	Saves massively	Can become cumbersome when updating data
Make TIS faster by proper node & operation use	Consider design recommendations – See: Make TIS faster by proper node & operation use	Often easy to do	Can become cumbersome with complex data

Tactical issues in making TIS faster

Eliminate columns as early as possible	Reduces data to be stored and retrieved
Filter as early as possible	Reduces data to be stored and retrieved
Keep number of nodes small	For each data node, its result table is stored. By having many operations in the same data node, less data has to be stored and retrieved.
Keep texts that are used very often short	Reduces data to be stored and retrieved
Merge order	Within theMerge data operator use those columns first that restrict the result set most strongly.

Filter	SLOW: Two nodes A: Result of Merge 1000.000 rows B: refers to A - filter has a result set of 1.000 rows	FAST: One Node Result of Merge 1000.000 rows Result of filter has 1.000 rows
	RESULT: Filtering directly in the node saves storage of 1mio records.
Merge	ASSUMPTION: DATE-Column with no duplicates and ID-Column with a constant value
	SLOW: Selection in DataMerge DATE, ID	FAST: Selection in DataMerge DATE, ID
	If ID's are combined first then the Join leads to an enormous number of rows which are reduced later on. This can be avoided by having DATE as the first column to be used.

Idea	Prepare small test nodes to facilitate design. Only when everything seems to work fine, switch to the complete data-set
Examples how to build test data sets	Prepare you SQL statement in a way so that it is easy to restrict the number of rows given back Prepare additional data nodes, where you filter large data sets down to one identifier or a short period of time (with many identifiers) – depending on what you develop.