Tag Archives: ExecuteSQL

FM/SQL Portal Filter + Dynamic Sort

Editor’s note: Today I’m pleased to present a guest article and accompanying demo file written by Joel Englander on using ExecuteSQL + a global multi-line key as an alternative to FileMaker’s built-in portal filtering mechanism, with dynamic portal sorting included as an added bonus.

Demo file: fm-sqlportal-filter-and-dynsort.zip (75 Mb compressed, 230 Mb uncompressed)

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Fast Summaries Revisited

If you do complex data analysis, then from time to time you probably need to group, summarize, and parse data into variables and/or fields. There are various ways to accomplish this, including the Fast Summary technique, which regular readers of this blog may be familiar with, as it has made a number of appearances here over the years.

Well today we’re going to look at a couple alternatives to Fast Summaries, with the help of some demo files, which you can use to do your own benchmarking.


Or, if you’d rather not do your own benchmarking, scroll down to see results from my tests. Bottom line: each of these three techniques can be fastest, depending on circumstances.

(Also, if you’ve been putting off exploring Perform Script On Server, a.k.a. PSOS, host the demo files on FileMaker Server 13 or later. The reporting routine optionally uses PSOS, so you can dive in painlessly and see what you’ve been missing.) Continue reading

FM 14: Separation Model Data Mining

Note: Interface file #1 requires FM 14 or later; interface file #2 works with FM 12 or later.

What do you get when you combine the Separation Model + FM 14 placeholder text + ExecuteSQL + a million-record table + a variable array + Get(CalculationRepetitionNumber) + the Mod and Ceiling functions + a couple custom functions, with blazingly fast (local, LAN and WAN) performance thrown in for good measure?

This article w/ accompanying demo (fm-14-separated-data-mining.zip), that’s what.

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The Challenge

Provide a data-mining interface to query a million-record table (cc_transactions) containing 20 years’ worth of credit card transactions. The client wants to be able to pick a date via a calendar widget, and see transaction info for that date, summarized by card type, transaction type and region.

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This is a separated solution, with cc_transactions living in a file called z_data.fmp12, and a specific requirement for this project is to not make any schema changes to the data file. The data-mining will take place in a separate interface file… or in this case, two interface files, since we’re going to look at two methods.  Continue reading

Outer Joins in FileMaker 12, part 3

I’ve said before, and no doubt will say again, that one of my favorite things about this blog is how much I learn from your feedback and the demo files you send me.

Recently I received a file from Otmar Kramnis of the Hochschule Luzern demonstrating the fastest SQL-based method I have yet seen to solve the challenge we looked at in part 1 and in part 2, and with a few minor modifications, this is the demo we’re going to focus on today:  Outer Join Demo 7

As you may recall, the aim is to show a week’s worth of daily sales totals for all employees whether they had any sales or not.

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Or, to restate the problem in more generic terms: we need to show all values from table A, whether or not there are any matching values in table B. This is known as an “outer join”, or more precisely a “left outer join”, since we want to see all values in the “left” table (Employees), whether or not they have corresponding matches in the “right” table (Sales).

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PDF version of FM 12 ExecuteSQL Reference

Beverly Voth has produced a PDF version of The Missing SQL Reference with some additional material not available in her original posting from October 19th.

And the SQL4_fmdev2.fmp12 demo file has been updated with new queries.

Thank you Beverly for this major contribution to the FileMaker community.

The Missing FM 12 ExecuteSQL Reference

There seem to be many questions about the usage of SQL (Structured Query Language) with the ExecuteSQL function in FileMaker 12. This tutorial attempts to explain some of the SQL terms, if you are new to writing SQL statements. Since there are already many examples of how to write the ExecuteSQL queries, links to these will be listed at the end of this article. If you don’t need to learn the terms, jump right to the Helpful Example Databases section, below. There you will find links to solutions that help you create and test your queries.

This is not a complete SQL guide, as other databases may use other syntax. This is not a complete FileMaker and SQL guide, as FileMaker may be an ODBC source and the SQL queries made against it may vary from the terms used by ExecuteSQL(). This is not a complete FileMaker and ESS guide using SQL calls (if using Import or Execute SQL script steps or ExecuteSQL() function or ESS). It may not have all the nuances needed for other data sources. This is the ExecuteSQL() function reference for which you’ve been waiting. The FileMaker 12 ODBC and JDBC Guide is helpful, but it has uses outside (and beyond) the ExecuteSQL() function. Any discrepancies between the reference and the function will be noted here, if possible.

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Outer Joins in FileMaker 12, part 2

Last week in part 1 we looked at four “outer join” reporting approaches. Two of them involved ExecuteSQL, and I ended that section with the plea: Of course it’s possible that you, dear reader, know some FileMaker SQL voodoo to speed things up, and would be willing to share? Well Dr. Osamu Noda of Japan was kind enough to not only respond, but has provided a pair of demos (Outer Join Demo 5 and Outer Join Demo 6) which are significantly faster and which I am sharing with his permission.

Both of the demos are based on my original files from last week, and as you may recall, the aim was to show a week’s worth of sales for all employees whether they had any sales or not.

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Outer Joins in FileMaker 12, part 1

Recently I had an on-screen reporting challenge, and decided to try several different approaches to see which would be fastest. The challenge: Starting with two tables, one containing 20 Employees, and one containing 2,000 Sales records for the current year…

…display daily sales totals per employee in a seven-day grid, like so:

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Aggregates (Summary Fields) in Filtered Portals

The question on one or more of the FileMaker forums was asked and answered. Q: How do you get the Count() of the related records in a filtered portal? A: (paraphrased) duplicate the filtered portal and make it one row in height. Place the related summary field, “Count of…”, in the single row filtered portal. Voila! your count is now filter-specific. (The instructions for creating the filter for a portal and summary field are below.)

This recent topic led me to consider what other aggregate fields could be used with the filtered portal. And what about that Go To Related Record script step? Does it only show the related FILTERED records or all related records? The demo (bvoth_aggregates_in_portals.fmp12) and article have been created to answer these questions.

I started using filtered portals in cross-tab reports shortly after they were introduced. These are generally ways to show something very specific in each portal with sorts and filters and usually one row only. I had not explored the use of aggregates in these cross-tab reports until now.

Kudos to those before me that may have discovered these answers and tricks, too.

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Dwindling Value Lists, part 1

A few months ago I mentioned “dwindling value lists” in passing, and said I would do a proper article on them at some point in the future. Well, then FM 12 was released and I went on an ExecuteSQL binge, so I’m just now getting around to honoring my promise.

Dwindling Value Lists (DVLs) are value lists that shrink up, by removing individual list items as they are selected. They can come in very handy when you need to schedule resources and want to prevent double booking. This example comes from one of today’s demo files, Dwindling Value Lists, and shows how a DVL might be used to schedule employees for a work shift.

Essentially, a DVL is a filtered value list that updates in real time (or something very close to real time), and this is done by building a multiline key of selected values, and then filtering the VL to only show remaining eligible values. Continue reading