Category Archives: Qlik Sense

If() Tips

Summary:  I offer some tips for writing better performing and easier to maintain syntax when using the Qlik If() function. 

The Qlik If() function is very powerful and  frequently appears in Qlik Sense and QlikView apps.

Expressions using multiple If() functions can easily get out of hand and become difficult to maintain or debug, as well as poor performers.

In this post I’ll offer some advice on avoiding If() pitfalls and tips to write easier to understand expressions.

The Qlik syntax diagram for the If function is:

if(condition , then [, else])

That’s perfectly clear to most people, but I prefer to think of it more like:

if(condition , true result [, false result])

Tip#1: If() does not short circuit.

Both the true & false branches are calculated even when only one is possibly true.  For example:

If(Only(Currency = 'LC',  Sum(Sales), Sum ([Sales LC])

In this case both Sum() expressions will be calculated even though only one value will be utilized.  In most cases this behavior is not of concern and in many applications will perform very well.   However, a nested If() with many possible branches or a large data set may perform poorly.

For more on the short circuit issue see “How to Choose an Expression“.


Tip#2: Use indentation sparingly.

The true or false result may be an additional, “nested” If(), which is where we start to see some ugly syntax.  Following traditional programming conventions many people automatically indent the nested if like this:

If(Sum(Sales) > 100000, 'Large',
    If(Sum(Sales) > 75000, 'Med', 
      If(Sum(Sales) > 50000, 'Demi',  'Small')

Essentially,  the expression above classifies into one of four values.  I don’t think indentation  adds to the readability and indentation will lead you into “tab hell” when you get many possibilities.  I prefer to write this expression as:

If(Sum(Sales) > 100000, 'Large'
,If(Sum(Sales) > 75000, 'Med' 
,If(Sum(Sales) > 50000, 'Demi'

No indentation, all the closing right parens collected on one line at the end. Makes it very easy in the expression editor to see that you have the right number of parens.

The leading (vs trailing) commas are my personal preference.  This make it easier to comment out logic and in my view, the comma belongs to the If that follows it, not the preceding If.

I think the above syntax makes it very easy to understand that I am choosing  one of four results, and what the rule is for each result.  Syntactically each If() is the else parameter of the preceding If().  I don’t think of the Ifs as “combined”, rather as “sequential”.

Do indent when you are using If() as the then parameter,  as shown in Tip#4 below.


Tip#3: Simplify by testing from high to low. 

The business rule that created this sample expression may have been stated to the Qlik developer like this:

“Classify sales of 0 to 50 000 as “Small”, 50 001 to 75 000 as “Demi”, 75 001 to 100 000 as “Med” and above 100 000 as “Large”.

The developer may faithfully translate the requirement into this expression.

If(Sum(Sales) > 0 and sum(Sales) <= 50000, 'Small'
,If(Sum(Sales) > 50000 and Sum(Sales) <= 75000, 'Demi', 
,If(Sum(Sales) > 75000 and <= 100000, 'Med'

This returns the correct result. Testing from low to high values forces the use of “and” which makes the expression more complex than necessary and potentially slower to execute.  In my experience, testing from high to low, as in the Tip#2 example, yields a cleaner syntax.


Tip#4: Use “and” when you mean and.

Here’s a sample expression requirement:

When Sales > 1000 and Region=’US’, it’s “Mega US”. When Sales > 750 and Region = ‘UK’, it’s “Mega UK”. Otherwise it’s “General”.

I have seen this written as:

If(Sum(Sales) > 1000, 
    If(Region = 'US', 'Mega US'),
If(Sum(Sales) > 750, 
    If(Region = 'UK', 'Mega UK'), 

While the “and” requirement may be satisfied with a then-if  nesting, I find it clearer with the “and” keyword.

If(Sum(Sales) > 1000 and Region = 'US', 'Mega US'
,If(Sum(Sales) > 750 and Region = 'UK', 'Mega UK' 

What if the requirement for  both US & UK were 1000?  You could argue that this is clear case for nesting in that there is a shared  condition and perhaps it would be a good practice to not repeat ourselves on the Sum(Sales).

If(Sum(Sales) > 1000, 
    If(Region = 'US', 'Mega US',
    If(Region = 'UK', 'Mega UK'), 'General'), 

Notice  we needed to repeat the ‘General’ result to cover the null case.  So it’s not super clean, but it may be worth it to not repeat the sum(Sales) calculation.  Generally I find the performance difference between “and” and “nested if” to be insignificant and tend to favor whatever is the clearer syntax for the given requirement.

What about Pick(Match())? 

I’ve heard it occasionally claimed that a Pick/Match combination will run faster than a nested If.   The expression might look like this:

      ,Region= 'US' and Sum(Sales) > 1000
      ,Region= 'UK' and Sum(Sales) > 1000
      , -1
,'Mega US', 'Mega UK','General')

In my own testing and reading I’ve never found any performance advantage to Pick/Match.  That said, sometimes the syntax is appealing.

One thing I don’t like about Pick/Match is the distance between the condition list  and the result list. It’s fairly easy to get the lists  mis-aligned as the expression grows.

I  wish Qlik had a Switch type function like:

Switch (
  condition1 : result1
  [,condition2 : result2, ...]  
  [: defaultResult]


Tip#5: Simplify by using Column(n) or Measure Name

If your if() refers to something that has already been calculated in the chart, you can use the Column(n) function to refer to the value of a measure/expression column. For example, in a color expression:

If(Column(2) > 0, Green(), Red())

This can be much neater than repeating the expression text and typically runs faster as well.

If you are on Qlik Sense May 2021 you can use Master Measure names in the expression like:

If([Total Sales] > 0, Green(), Red())

[Total Sales] need not be a measure in this chart.

Both QlikView and Qlik Sense also allow you to reference the Label of a measure/expression column in the chart. In most versions the syntax checker will declare this an error even though it calculates correctly. I tend to avoid the label technique due to this confusion.


Tip#6: Don’t use If() as a chart filter

Use If when you want to dynamically select from two or more alternatives.  If should not be used simply to filter data like this:

Sum(If(Region = 'EU' and CYTDFlag = 1, Sales)

Filtering is best done with Set Analysis. The same expression written with a Set:

Sum({<Region={'EU'}, CYTDFlag={1}>} Sales)

Set Analysis is much faster than If.  If you are new to Set Analysis, you might initially find the syntax more challenging than If.  But SA  is much more powerful than If and well worth mastering.


Tip#7:  Consider the other conditional functions. 

Alt() and Coalesce() can be a more compact and elegant approach to testing for nulls. Instead of:

If(IsNull(SalesRep), Manager, SalesRep)


Coalesce(SalesRep, Manager)
// If you want to consider empty and 
// blank strings as Null:
Coalesce(EmptyIsNull(Trim(SalesRep)), Manager)

When testing against a list of values,  instead of multiple If() or “or”, use the Match() or WildMatch() functions instead.

If (Match(StateCode, 'VA', 'TN', 'FL', 'GA'), 'South',  'Other')


I hope you find these tips useful.  You can use my QSDA Pro tool to quickly filter and examine all the uses of the If() function in a Qlik Sense App, located on-prem or in SaaS.




Deconstructing Visualization Performance

Have you ever had a Qlik Sense Visualization take longer to calculate than you like?  You may have been measuring the response time with QSDA (or the older QVF QS Document Analyzer) ,  Add Sense or a stopwatch.

Your chart is likely made up of several expressions — Measures, color expressions, maybe reference lines.  Which expression(s) are the hogs? Some? All?

In QSDA Pro version 1.4 I’ve introduced a “Viz Deconstruction” feature that calculates each chart expression individually and lets you see where time is spent.  In the QSDA Pro Viz page, click the  button under a Viz:

Clicking the  button will pop up the Viz Deconstruction dialog.  Each expression in the chart will be executed and reported individually.

For this chart I can see that one Measure takes much longer than any other expression.  Now I know where to focus my efforts if I want to improve response time.

Here’s another example. Big chart, lots of data, seven Dimensions.

The Measures require over seven seconds each to generate  7M+ output rows. Maybe less detail would result in better performance and a more usable chart.  I’ll uncheck a few Dimensions and 

Wow! Big improvement in calc time and a more manageable number of rows.

As a final example we’ll see that long calc times are not always due to Measures.

The Measures take only a few milliseconds to calculate.  But the color expression takes more then 1/2 second.  Can we do this coloring more efficiently?

You may already be deconstructing charts by making clones, commenting code and remeasuring.   Of course, you have to deal with the effects of caching and cleaning up after yourself.  QSDA Pro provides a much faster and more structured approach.

QSDA Pro is free to try with applications containing 100 or fewer objects.  If you want remove the limits, get support or acknowledge the value you get from QSDA, purchase a Monthly or Annual subscription.

I hope you find the Viz Deconstruction feature useful. I have!


Note: There was a bug in the 1.4.0 version that kept the Deconstruction feature from calculating when using a DESKTOP Connection. This has been fixed in version 1.4.2.



QSDA Pro as a Quality Tool

I my last two posts I introduced QSDA Pro and the flag workflow.  In this post I’m going to show how you can use QSDA Pro to quickly uncover quality issues in your Qlik Sense App.

QSDA groups flags by category, and the “Quality” category is where I usually begin my app review.  Quality flags are used to indicate where something appears to be seriously broken such as:

  • An expression syntax error.
  • A reference to a Master Measure that no longer exists in the library.
  • A missing extension.
  • A bookmarked field that is no longer exists in the data model.

These are the kinds of things you want to discover before your users do!

It’s relatively easy to create new errors in an existing Qlik app.  Qlik does not warn or block you from deleting a resource like a Field or Measure that is in use.

A properly done field rename may update names in expressions.  But it will not update variables or bookmarks.  And I’ve seen plenty of field renames go wrong and break expressions as well.

It can be difficult to detect all app errors by visually checking your app.  The broken piece may be a color expression or a calculation condition that isn’t readily visible.

So yes, easy to break, sometimes hard to detect and heros find  problems before their users do.  Enter QSDA Pro.

In QSDA menu,  Help -> Flag List will display a list of all potential flags in the installed version. Here’s the current list for Quality (The “{}” bits are values that will get filled in when the flag is created.)

Some of these are fairly straightforward, some a bit more subtle.

Why would you have a missing Master Dimension or Measure?  A fairly common scenario is not understanding that Master Items are referenced by internal Id, not by Name.  So you create something called “Sales” and use it in several charts.  Later you are doing some exploration and create something called “Sales2” which you like better. So you think if you delete “Sales” and rename “Sales2” to “Sales” everything will be using the new “Sales”.  Wrong.

Because the master item is now gone, I can’t tell you what used to be in this master item.  You will have to rely on an app backup. Or…if you have a previous QSDA Analysis for this app you can look up the master item definition there!

All Expressions and Dimensions are validated using the Qlik syntax checker. A typical error might be a bad fieldname (perhaps because the field was removed from the model).

Flag Details will give us a detailed error message, location of this expression  (Master Library) and importantly the Use Count. This use count is zero so we know deleting the measure is a viable option.

QSDA uses the Qlik syntax checker so the test is only as good as what I  get from Qlik.  There are some limitations, for example when column labels are used in an expression.  You’ll note that the Qlik expression editor flags this as invalid so QSDA will pass this message on.

The “Parse Error” flag is raised when Qlik Syntax says the expression is ok but QSDA finds the expression does not conform to expected syntax rules. This could be a problem with the QSDA parser  (a work in progress) or it could be Qlik tolerating something unusual.  For example, do you think this is a valid expression?

sum({<Sales={">10 between < 90"}>}Sales)

The Qlik syntax checker will declare it valid. And it will return an official looking result — which may or may not be correct.  But what is that “between” keyword? (if you’re curious why “>10 के बीच < 90” also seems to work, see

Nervous about refactoring or changing your apps?  Can you relate to “Change Paralysis”  or Fear Driven Development (FDD)?  We all need a tool like QSDA Pro to proactively plan changes as well as audit after the fact for unintended errors.

Download QSDA Pro now and analyze some of your own apps.  QSDA Pro is free to use during the beta period.

You can analyze published and unpublished apps, so go ahead and take a look at some of those production apps.   Let me know in the comments if you find something interesting!






QSDA Pro Flags

In my first post introducing QSDA  Pro, I told how Document Analyzer was a tool I developed for use in my own consulting work and shared with the community of Qlik developers.  The Qlik Sense QVF version got a big boost in utility when Axis Group joined the project and contributed their many many years of Qlik experience.

In this post I will  introduce the  Flag workflow of QSDA.

QSDA communicates it’s advice through “Flags”. Flags are issues that warrant your attention.

The Summary page gives an overview of Flags for this analysis. Clicking any of the Flag boxes or Flags in the Nav Ribbon will take you the Flags page.

The Flags page provides a collapsible list of all flags and buttons for more detail and flag status.


Flag text identifies the error and in most cases provides some detail, such as a fieldname or number of bytes that could be saved.

The dimension failed validation. The error is: Bad field name(s) "[PostalCode]" .

Click  to  show the Flag Details panel.

The details panel will provide more context for the flag to help you evaluate your response.  Depending on the flag, there may be additional hyperlinks.  Click the “?” icon to link to the Help article for this flag type.

To support a workflow of “checking off” the flags,  buttons and flag status indicators are available in the both the Flag Details and Flag List views.

 Mark this flag instance status  as “Resolved”.

 Mark this flag instance status  as “Hidden”.

Resolved and Hidden flags are not shown in the list unless the filter is activated on the filter bar. Flags with no status are “Unresolved” and are always shown.

Buttons will automatically toggle to allow you undo a status. You can set/unset statuses on individual flags or at the flag type level.  Hover over a button to discover its function.

From the Flag List you can view all flags.  In the resource pages, you will also see the same flags but attached to the resource.  For example, on the Dimension page,  any associated flag will be shown with the specific Dimension.

These workflow features allow you to punch through the list of issues in your app, hiding those you don’t care about and checking off issues as you resolve them.

Learn more about QSDA Pro and download the latest version.






Introducing QSDA Pro

QSDA Pro is the next generation of my Document Analyzer tool for Qlik Sense.  I published the first Document Analyzer for QlikView in 2009 followed by the Qlik Sense version in 2017.  I think it’s fair to say both are the most widely used Qlik developer add-on tool.

Qlik Sense Document Analyzer (QSDA) has been extremely useful. But it’s had some limitations, especially in the server environment. I’ve wanted to address those shortcomings,  and now I bring you my response :

QSDA Pro is a completely rewritten standalone app that provides  features of the current QVF based QSDA and more.  QSDA Pro can analyze and provide advice on apps in QS on Windows, Qlik Cloud, and QS Desktop.

QSDA can help you produce cleaner, smaller and faster apps by identifying:

  • Unused resources, such as fields, that can be removed.
  • Errors in your app such as missing fields, syntax errors or missing key values.
  • Bad practices and recommendations on how to remediate.
  • Highlight performance hotspots.

QSDA Pro can do all of the above and more for public sheets and private sheets.  “Is anyone using this field in a personal chart or bookmark?” is no longer an unknowable thing.

QSDA is also your goto companion for those ad hoc tasks like modifying use of a field. Maybe a renaming or dividing by 1000.

Where am I using the field “Company Name”?  Easy.  Filter on the field name and I can instantly see the field is not referenced by Variables, but it is used in 22 Charts, 23 Dimensions and 5 Expressions. Oh and 1 Bookmark.

What Charts where?  Click Viz on the ribbon to get a list.  I can explore the chart Dimensions and Expressions there or click a link to open the associated sheet in the Qlik Sense Hub.

QSDA is smart! It can determine that all four of these measures calculate the same thing, even with differences of variables, casing and whitespace.  And it recommends you should settle on one Measure while very helpfully showing how many times each variation is used to help you in your decision.

In the coming weeks I’ll be posting more about the capabilities of QSDA Pro.  I’ll also be responding to feedback on the QSDA Pro Community site.

QSDA Pro is currently in public beta testing and is free to use during the testing period, which is expected to last for several months.

You can learn more about QSDA Pro and download your own copy.

Licensing terms for the supported production version will be announced later. There will be a free tier which may have  limits on volume and  features.




Can I Update a Mapping Table?

Summary: Can you concatenate rows to a Mapping Table? The answer is “Yes” and I show you how.

Recently my Masters Summit colleague Oleg Troyanksy asked me “What are the limits on updating an existing Mapping Table in Qlik script?”.  My immediate answer was “You can’t update a Mapping Table after it’s been created”.  Then Oleg showed me a script that surprised me — adding to a mapping table in a loop,  implicit concatenation.  The loop works, but what if we need to load from multiple sources?

When the contents of a mapping table come from multiple Load statements, I have always advised doing standard loads to a temp table and then a final Mapping Load from the temp table.

Turns out you can concatenate to a mapping table.  Maybe that temp table is unnecessary.  I don’t find any doc on the topic. Here’s what I’ve found from experimentation.

We may wish to do something like this:

 Mapping Load * Inline [
 from, to
 1, A
 2, B
Concatenate (MapX)
Mapping Load * Inline [
 from, to
 3, C

That script will fail with  “Table ‘Mapx’ not found” error. You cannot directly reference a mapping table this way.

Interestingly, if we leave off the “Concatenate (MapX)”, it will concatenate and result in the desired mapping.  Implicit concatenation will kick-in and the second load will add rows to the mapping table.  I’ve included some ApplyMap() code in this example so you can copy/paste and test for yourself.

 Mapping Load * Inline [
 from, to
 1, A
 2, B
Mapping Load * Inline [
 from, to
 3, C

 Load RecNo() as Recid,
 ApplyMap('MapX', RecNo()) as Mapped
 AutoGenerate 5;

The resulting output looks like this, proving that 3/C has been added to the map.

Unlike implicit concatenation for standard tables, the fieldnames  need not be the same. This script will concatenate. Note the different fieldnames in the second load.

 Mapping Load * Inline [
 from, to
 1, A
 2, B
Mapping Load * Inline [
 X, Y
 3, C

What if there is an intervening standard load?  Will concatenation occur?

 Mapping Load * Inline [
 from, to
 1, A
 2, B
 LOAD 1 as X AutoGenerate 1; 

Mapping Load * Inline [
 X, Y
 3, C

The answer is no, concatenation will not happen. The second Mapping Load will create a new invisible mapping table.

So if I  can’t name the mapping table in a Concatenate prefix, is there some other way to explicitly concatenate?  Turns out there is. This will work.

 Mapping Load * Inline [
 from, to
 1, A
 2, B

 LOAD 1 as X AutoGenerate 1;

 Mapping Load * Inline [
 X, Y
 3, C

When naming the mapping table again with the same label. explicit concatenation will occur!  This is unlike a standard load where repeating a label results in  a new table with “-1” suffix  (when fieldnames don’t match).

In summary, you can add rows to a mapping table. Repeating the table label again will ensure you are adding,  whether there is an intervening standard load or not.

Now you may be wondering …can I do an implied Join? I think not.

On Jan 20, 2021 I’ll be sharing more scripting tips and techniques in my “Advanced Scripting” on-line course as part of the “Masters Summit at Home” series.   Join me there for more on topics like creating reusable script and maintaining optimized loads.



When working on Qlik Sense performance issues I frequently find I want to measure the performance of specific expressions. I might want to know how variations of an expression may perform against each other.  In a slow chart with many measures I want calculation time individually for each measure to focus my efforts.  Or perhaps I’m just satisfying a general curiosity or trying to settle a bet.

You can measure the performance of expression variations by modifying the chart and measuring the overall chart response time with something like Chrome Add Sense or QS Document Analyzer.  That approach can get kind of clunky especially when you are focused on a subset of measures in the chart.

I prefer a more structured approach to testing expressions. The tool I reach for is CubeTester.

CubeTester is an open source Nodejs command line tool for testing the performance of Qlik HyperCubes (Dimensions and Measures).  The test specification is written in a json file as either a HyperCubeDef or the “simplified” Dimension/Measure syntax.

Here’s a sample test written in simplified syntax that tests three variations of a cube (chart) containing one Dimension and three Measures.

I’ll run  CubeTester specifying the file that holds this test:

node index.js test tests/columns.json

And receive this output:

There is no significant difference in performance between the variations. Importantly, I can also see that all three return identical  total values as well.

CubeTester supports two commands:

  • test : Run tests.
  • extract: Extract app charts into a test file.

There are a number of options that can be specified on the command line or in the test definition. See the readme for more information on available options.

in addition to testing variations or trying out a theory, here are some other cases where I’ve used CubeTester.

  • When working with a mashup where my HyperCube exists only in code, there is no chart to test.
  • In a slow rendering chart I can test individual measures, combinations of measures and non-data expressions (like color expressions) to find the culprit.

Using CubeTester I can quickly try out ideas and document my progress as I work through an issue. I’ve made some interesting discoveries!

Some notes:

  • Testing against a server uses certificates for authentication.  (Pull request welcome if you want more auth options).
  • Make sure you specify “wss” when using a server endpoint eg
  • You’ll need to test with enough data to get calculation times of sufficient magnitude.  Two results of 5 milliseconds vs 7 milliseconds are not precise enough to draw conclusions from.
  • Calculation time is affected by the capacity of the target machine and what else is running.  I recommend to repeat tests until you see a stable pattern.  Use the –repeat option and take the lowest result from each repeat.

CubeTester is free to use. Have fun!




Data Browser Tricks

Summary: I demonstrate my latest “Data Browser” sheet for use in Qlik data modeling. Download here.

Today I want to share the latest version of my “Data Browser” sheet.

What’s a “Data Browser”? It’s my name for something you may already have. The ubiquitous “System Sheet” or sometimes “Profiler” where you may have listboxes or charts utilizing the system fields “$Field”, “$Table”, “$Rows”.  These fields are part of the shadow data model that hold useful metadata about the data loaded in your app.

Some very useful example profile sheets have been shared in the community over the years.

You can download the latest QlikView and Qlik Sense versions of my Data Browser here.

For QV, I just copy the objects into my new app in one go. For QS,  I either copy the charts one-by-one or if I’m lucky they are already in my template (who’s going to build the sheet copy extension?) .

I’ll do this walk through with the QV version, the QS version is similar.

When I make selections in $Field I can see values and frequency counts in a listbox. After selecting for example the “Sales” field, the values and frequency counts are shown in a listbox. I also have a  histogram and some descriptive statistics about the field values.

I can select field values and drill into data using  this sheet or my application sheets. For example, I might want to select the outlier high Sales value, then select Product in $Field to find out what Product is associated with this Sale, clear the Sales field to see what other Sales look like for this product and so on.

There are several properties such as Null Count (Information Density), field alpha/num content ($tags) that I can get from the built in Table Model Viewer.  The Viewer requires me to examine one field at a time. In the chart below I get an overview of those properties of interest.  Because it’s a chart, I can use sorting and selecting to focus.

Something I don’t get from the Table Viewer is the difference between numeric count and text count for a field.  I like to surface this problem (highlighted in yellow) early in my modeling.

One of my favorite features is the “Value Association” chart below. It’s likely a favorite because it took me a long time to work out the expression!

In  Table Viewer we use “Subset Ratio” to understand where we have connected and un-connected data in our model.  Subset Ratio is limited to reporting the relationship between key fields. It can’t tell me how data field  values in this table associate to data field values in other tables.  Subset ratio, like other stats in the Viewer, does not respect selections. For example, if I select a specific Customer, how many SalesReps are linked to the Customer?

The chart below (highlights added) covers all these use cases and can also surface problems in the model.

I’ll start by selecting a field central to my model, in this case “OrderID”.   I then sort by the “Pct” column.  This column represents the percentage of field values associated to the selected field,

In the green highlight, I’ve called out a Table “Sales” that has zero association with Orders. Something to look into.

In the orange callout, I can see that only 19% of my Employees are associated with Orders.  That might be a candidate for trimming the Employees table when loading.

In the red callout, I see something puzzling. Only 47% of my OrderDates are linked to Orders. That may be ok, I would need to review the data.  What looks not ok is that WeekDay Pct is also 47%. I would have expected something like 5/7 or 6/7. No fraction of weekdays would equal “47%”.  And there are 1092 values for WeekDay…something is off.

I’ll select WeekDay from the chart and examine the values in the listbox. Aha, the Weekday values were created incorrectly.  They were created with a Date() function instead of the correct WeekDay() function.

I used to hide the sheet before production but now I generally leave it in as I and others find it useful in production as well.

Besides the Table Viewer, another Qlik modeling tool I really like is Catwalk. If you haven’t used Catwalk I encourage you to check it out.  I won’t go in to explaining Catwalk as it has an excellent tutorial built in.

Do you have any favorite profiling or modeling tools to use with Qlik?



Creating Temporary Script Associations

Summary: I review using Join, Lookup() and ApplyMap() as script techniques  to calculate using fields from multiple tables. I ultimately recommend ApplyMap().

Qlik charts  can calculate values on the fly, using fields from multiple tables in the model.  The associative model takes care of navigating (joining) the correct fields together.  Our expression syntax doesn’t  identify what table a field exists in — the associative logic takes care of this detail for us.

We may want to calculate a measure, for example, “Net Amount”, applying a business rule requiring fields from several tables:

Our expression to calculate “Net Amount” might look like this:

Sum(Amount) - 
  Sum(Quantity * UnitCost), 
  Sum(Amount * Discount), 
  Sum(Amount * SalesTaxRate), 
  Sum(Amount * ExciseTaxRate)

There may be cases (such as performance) where we want to pre-calculate “Net Amount” as a new field in the script.  In script, we don’t have the magic associative logic to assemble the fields.  When a script expression is used to create a new field, all fields must be available  in a single load statement.  This is straightforward when all the required fields are in the same table.  But what do we do when the fields come from multiple tables?

Here are three approaches to solving the problem of calculating a new field using multiple tables in script.

  • Join
  • Lookup() function
  • ApplyMap() function

I’ll demonstrate deriving the same “Net Amount” calculation in the script.


The Join option will require us to execute multiple joins to assemble the fields onto each Orders row and then finally do the calculation.  The script might look like this:

Left Join (Orders)
Resident Products
Left Join (Orders)
Resident Customers
Left Join (Orders)
Resident States

 Amount - RangeSum(
   Quantity * UnitCost,
   Amount * Discount,
   Amount * SalesTaxRate,
   Amount * ExciseTaxRate
 ) as NetAmount
Resident Orders
// Drop the extra fields from Orders.
Drop Fields State, UnitCost, Discount, SalesTaxRate,ExciseTaxRate
From Orders

It’s a fairly good option.  It can be a lot of code depending on how many fields and tables we need to traverse. We need to be aware of “how many hops” between tables and may require intermediate joins (State field) to get to the final field (SalesTaxRate & ExciseTaxRate).

When using Join we need to ensure we have no duplicate keys that would mistakenly generate additional rows.


Lookup() seems the most natural to me. It’s the least amount of code and it even sounds right: “look-up”.  It’s a one-to-one operation so there is no danger of generating extra rows.

It’s my least used option due to performance as we shall see.

Lookup takes four parameters  – a field to return, the field to test for a match, a match value to search for and the table to search.  Using Lookup() our script will look like this:

 Amount - RangeSum(
   Quantity * Lookup('UnitCost', 'ProductId', ProductId, 'Products'),
   Amount * Lookup('Discount', 'CustomerId', CustomerId, 'Customers'),
   Amount * Lookup('SalesTaxRate', 'State', Lookup('State', 'CustomerId', CustomerId, 'Customers'), 'States'),
   Amount * Lookup('ExciseTaxRate', 'State', Lookup('State', 'CustomerId', CustomerId, 'Customers'), 'States')
 ) as NetAmount
Resident Orders

Note that for SalesTaxRate and ExciseTaxRate, the third parameter — the match value — is another Lookup() to retrieve the State. This is how we handle  multiple hops, by nesting Lookup().

It’s a nice clean statement that follows a simple pattern.  It performs adequately with small volumes of data.

Lookup does have a significant performance trap in that it uses a scan  to find a matching value.  How long to find a value is therefore dependent on where in the field the value is matched.  If it’s the first value it’s very quick, the 1000th value much longer, the 2000th value exactly twice as long as the 1000th. It’s a bit crazy making that it executes in O(n) time, for which I prefer the notation U(gh).


I like to think of the ApplyMap() approach as an optimized form of Lookup().  We first build mapping tables for each field we want to reference and then use ApplyMap() instead of Lookup() in the final statement. Our script will look like this:

Load ProductId, UnitCost
Resident Products
Load CustomerId, Discount
Resident Customers
Load CustomerId, State
Resident Customers
Load State, SalesTaxRate
Resident States
Load State, ExciseTaxRate
Resident States
 Amount - RangeSum(
   Quantity * ApplyMap('Map_ProductId_UnitCost', ProductId, 0),
   Amount * ApplyMap('Map_CustomerId_Discount', CustomerId, 0),
   Amount * ApplyMap('Map_State_SalesTaxRate', ApplyMap('Map_CustomerId_State', CustomerId, 0)),
   Amount * ApplyMap('Map_State_ExciseTaxRate', ApplyMap('Map_CustomerId_State', CustomerId, 0))
 ) as NetAmount
Resident Orders

The mapping setup can be a lot of code depending on how many fields are involved. But it’s well structured and clean.

In the final statement, we are “looking up” the value using ApplyMap() and it performs very quickly.  ApplyMap uses a hashed lookup so it does not matter where in the list the value lies, all values perform equally.

We can re-structure and simplify the mapping setup and subsequent use with a subroutine like this:

Sub MapField(keyField, valueField, table)
// Create mapping table and set vValueField var // equal to ApplyMap() string.
 Mapping Load [$(keyField)], [$(valueField)]
 Resident $(table);
 Set [v$(valueField)] = ApplyMap('Map_$(keyField)_$(valueField)', [$(keyField)]);
End Sub

Call MapField('ProductId', 'UnitCost', 'Products')
Call MapField('CustomerId', 'Discount', 'Customers')
Call MapField('CustomerId', 'State', 'Customers')
Call MapField('State', 'SalesTaxRate', 'States')
Call MapField('State', 'ExciseTaxRate', 'States')

 Amount - RangeSum(
 Quantity * $(vUnitCost),
 Amount * $(vDiscount),
 Amount * $(vSalesTaxRate),
 Amount * $(vExciseTaxRate)
 ) as NetAmount
 $(vState) as State
Resident Orders

Note the use of the preceding load to handle the nested lookup of State.   You could also modify the Sub to handle some level of nesting as well.

I typically use the mapping approach as I find it always gives accurate results (with Join you must be careful of duplicate keys) and generally performs the best, and importantly, consistently.

Whether you are new to Qlik or an old hand I hope you found something useful in reading this far.





Num() — Script vs Chart

Summary: I review a subtle difference between using Num() in the script vs Num() in charts. I make mistakes so you don’t have to 🙂 

2020 marks my 13th year blogging about Qlik.  I’m still learning, and still making mistakes!  I’ll review a problem I created for myself recently, with the hope that you won’t have to repeat the exercise.

Here is a simplified example of the problem, enough to demonstrate the issue:  This is the starting data table.

We have some number of “Metric” and  “Value” along with fields that describe how the data should be formatted for display.  The “Format” field contains a Qlik format specification.  Format may be used in a Num() function as shown in the “Num(Value, Format)” measure in this table. Output is as expected,  so far so good.

In my  project, it became required to format the Value in the script. I moved the Num() measure to the script like this:

Num(Value, Format) as ValueFormatted

I expected ValueFormatted to yield the same result as the chart measure. Adding “ValueFormatted” to the chart yields this:

The results are not the same.  ValueFormatted for “Days to ship” is incorrect. The other values are correct.

Let’s introduce another variation. This time I’ll sort the input when I create ValueFormatted.

Left Join(Data) 
    Num(Value, Format) as ValueFormatted 
Resident Data 
Order by Metric Desc ;

Now “Customers per location” is incorrect and the other values are correct! What gives?

The Num() function returns a Dual() value . Duals have both a string (display) and a numeric value.  When populating a data model field, Qlik will use a single string representation for a given numeric value for that field.  The string selected will be the first encountered for that numeric value.

“Customers per location” and “Days to ship” shared the numeric value 4.  In the data model, one or the other string representation — “4” or “4 days” — will be used, depending on which one is created first.

To get the correct results in this scenario — that is, unique strings dependent on Format — add the Text() function to extract the string at runtime.

Text(Num(Value, Format)) as ValueFormatted

Resolution came of course after I took the advice of my colleague Barry  to “take a walk”.

I hope my story might save you some trouble. Happy scripting!