SQL optimization (SPSS Modeler)

Nodes supporting SQL pushback

Last updated: Feb 12, 2025

The tables in this section show nodes representing data-mining operations that support SQL pushback. If a node doesn't appear in these tables, it doesn't support SQL pushback.

Table 1. Record Operations nodes
Nodes supporting SQL generation	Notes
Select	Supports generation only if SQL generation for the select expression itself is supported. If any fields have nulls, SQL generation does not give the same results for discard as are given in native SPSS Modeler.
Sample	Simple sampling supports SQL generation to varying degrees depending on the database.
Aggregate	SQL generation support for aggregation depends on the data storage type.
RFM Aggregate	Supports generation except if saving the date of the second or third most recent transactions, or if only including recent transactions. However, including recent transactions does work if the `datetime_date(YEAR,MONTH,DAY)` function is pushed back.
Sort
Merge	No SQL generated for merge by order. Merge by key with full or partial outer join is only supported if the database/driver supports it. Non-matching input fields can be renamed by means of a Filter node, or the Filter settings of an import node. Supports SQL generation for merge by condition. For all types of merge, `SQL_SP_EXISTS` is not supported if inputs originate in different databases.
Append	Supports generation if inputs are unsorted. SQL optimization is only possible when your inputs have the same number of columns.
Distinct	A Distinct node with the (default) mode Create a composite record for each group selected doesn't support SQL optimization.

Table 2. SQL generation support in the Sample node for simple sampling
Mode	Sample	Max size	Seed	Db2 for z/OS	Db2 for OS/400	Db2 for Win/UNIX	Oracle	SQL Server	Teradata
Include	First	n/a		Y	Y	Y	Y	Y	Y
	1-in-n	off		Y	Y	Y	Y		Y
		max		Y	Y	Y	Y		Y
	Random %	off	off	Y		Y	Y		Y
			on	Y		Y	Y
		max	off	Y		Y	Y		Y
			on	Y		Y	Y
Discard	First	off					Y
		max					Y
	1-in-n	off		Y	Y	Y	Y		Y
		max		Y	Y	Y	Y		Y
	Random %	off	off	Y		Y	Y		Y
			on	Y		Y	Y
		max	off	Y		Y	Y		Y
			on	Y		Y	Y

Table 3. SQL generation support in the Aggregate node
Storage	Sum	Mean	Min	Max	SDev	Median	Count	Variance	Percentile
Integer	Y	Y	Y	Y	Y	Y*	Y	Y	Y*
Real	Y	Y	Y	Y	Y	Y*	Y	Y	Y*
Date			Y	Y		Y*	Y		Y*
Time			Y	Y		Y*	Y		Y*
Timestamp			Y	Y		Y*	Y		Y*
String			Y	Y		Y*	Y		Y*

* Median and Percentile are supported on Oracle.

Table 4. Field Operations nodes
Nodes supporting SQL generation	Notes
Type	Supports SQL generation if the Type node is instantiated and no `ABORT` or `WARN` type checking is specified.
Filter
Derive	Supports SQL generation if SQL generated for the derive expression is supported (see expressions later on this page).
Ensemble	Supports SQL generation for Continuous targets. For other targets, supports generation only if the Highest confidence wins ensemble method is used.
Filler	Supports SQL generation if the SQL generated for the derive expression is supported.
Anonymize	Supports SQL generation for Continuous targets, and partial SQL generation for Nominal and Flag targets.
Reclassify
Binning	Supports SQL generation if the Tiles (equal count) binning method is used and the Read from Bin Values tab if available option is selected. Due to differences in the way that bin boundaries are calculated (this is caused by the nature of the distribution of data in bin fields), you might see differences in the binning output when comparing normal flow execution results and SQL pushback results. To avoid this, use the Record count tiling method, and either Add to next or Keep in current tiles to obtain the closest match between the two methods of flow execution.
RFM Analysis	Supports SQL generation if the Read from Bin Values tab if available option is selected, but downstream nodes will not support it.
Partition	Supports SQL generation to assign records to partitions.
Set To Flag
Restructure

Table 5. Graphs nodes
Nodes supporting SQL generation	Notes
Distribution
Web
Evaluation

For some models, SQL for the model nugget can be generated, pushing back the model scoring stage to the database. The main use of this feature is not to improve performance, but to allow flows containing these nuggets to have their full SQL pushed back. See Generating SQL from model nuggets for more information.

Table 6. Model nuggets
Model nuggets supporting SQL generation	Notes
C&R Tree	Supports SQL generation for the single tree option, but not for the boosting, bagging, or large dataset options.
QUEST
CHAID
C5.0
Decision List
Linear	Supports SQL generation for the standard model option, but not for the boosting, bagging, or large dataset options.
Neural Net	Supports SQL generation for the standard model option (Multilayer Perceptron only), but not for the boosting, bagging, or large dataset options.
PCA/Factor
Logistic	Supports SQL generation for Multinomial procedure but not Binomial. For Multinomial, generation isn't supported when confidences are selected, unless the target type is Flag.
Generated Rulesets
Auto Classifier	If a User Defined Function (UDF) scoring adapter is enabled, these nuggets support SQL pushback. Also, if either SQL generation for Continuous targets, or the Highest confidence wins ensemble method are used, these nuggets support further pushback downstream.
Auto Numeric

Table 7. Outputs nodes
Nodes supporting SQL generation	Notes
Table	Supports generation if SQL generation is supported for highlight expression.
Matrix	Supports generation except if All numerics is selected for the Fields option.
Analysis	Supports generation, depending on the options selected.
Transform
Statistics	Supports generation if the Correlate option isn't used.
Report
Set Globals