Configuring Drillbridge with Financial Reporting Web Studio

Drillbridge works perfectly with Financial Reporting Web Studio – the successor to the desktop-based version of Financial Reporting (also commonly called HFR, FR). FR was stuck with a very archaic client (let’s just say it’s from around the Clinton administration), but it has revamped for the future, with a completely web-based interface now. In retrospect, and based on my interactions with the interface, I think this product overall can be thought of as gap coverage for FR users. It’s not necessarily the place you want to do new development, especially given some of the other shifts/developments in the reporting ecosystem lately. My colleague Opal Alapat has posted some really great thoughts on FR and its place in this ever-changing world, which I encourage you to read.

In the meantime, there are countless current installs of FR that organizations need to support and perhaps transition to this newer incarnation of FR. As with before, Drillbridge works seamlessly to give you and your users advanced drill-through capabilities in Smart View, Hyperion Planning/PBCS, FR, and now FR web. I found that the UI had a few quirks to it, but I’ll walk through a simple example and try to point those out along the way.

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Improving MySQL JNDI Connection Reliability

I blogged quite some time ago about using JNDI to configure database connections in Dodeca. As I mentioned then, JNDI can bring some useful improvements to your configuration, management, security, and administration of your environment versus how you might be configuring normal JDBC connections. To be clear, this isn’t because JNDI connections are inherently better from a performance standpoint, it’s just that it might be a cleaner solution in various ways.

My original blog post looked at configuring a pretty typical MySQL connection in JNDI. As I have worked with this in the last few months, I have run into a few issues with the configuration as it related to connection timeout issues. I was occasionally getting some timeout issues like this:

Dodeca error dialog reporting a timed out MySQL JNDI connection

MySQL connection timeout when configured with JNDI

Helpfully enough (or perhaps unhelpfully) the error message itself reports that perhaps the autoReconnect=true setting would be of help. I’ve actually used that setting in the past and it seemed to help things out. But as it turns out, that setting is deprecated and should not be used. There are some alternative techniques that can/should be used to ensure the program gets a valid connection back.

One common technique is to specify a “validation query”. This is often something like SELECT 1 or SELECT 1 FROM DUAL depending on the particular database technology being used. You can use SELECT 1 for MySQL. What this essentially means is that before returning a connection via JNDI to the Java servlet to do things with, the connection pool manager is going to run the validation query to ensure that it is indeed a valid connection (able to connect, doesn’t error out, and so on.

Interestingly enough, MySQL in particular has added an optimization for this use case such that you can give it a sort of fake query (code: /* ping */) and it’s slightly more optimized than the overhead involved with a SELECT 1.

Together with this optimized test query, some additional attributes on the JNDI configuration (testWhileIdle, testOnBorrow, testOnReturn, and removedAbandoned, I’ve updated the overall JNDI configuration and it seems to be much more robust. Here’s the new connection JNDI code from my Tomcat context.xml:

<Resource name="jdbc/dodeca_sample" auth="Container" type="javax.sql.DataSource" username="dodeca" password="password" driverClassName="com.mysql.jdbc.Driver" url="jdbc:mysql://localhost/dodeca_sample?noDatetimeStringSync=true" maxActive="100" maxIdle="30" maxWait="10000" removeAbandoned="true" removeAbandonedTimeout="20" logAbandoned="true" validationQuery="/* ping */" testWhileIdle="true" testOnBorrow="true" testOnReturn="false" />
<ResourceLink name="jdbc/dodeca_sample" global="jdbc/dodeca_sample" type="javax.sql.DataSource"/>

Interesting Time Period Conversion with Drillbridge/PBCS

I recently helped a customer with their Drillbridge installation/configuration for PBCS that had an interesting time period conversion issue I wanted to write about.

Drillbridge helps convert a given POV into a SQL query, webpage link, MDX query, or whatever you want (such as with a custom plugin). Out of the box, Drillbridge contains a number of commonly-used convenience functions for easily converting months to numbers (as well as other functions). You can do this in SQL too but it seems to almost always be a little “cleaner” to let Drillbridge do it for you, especially when it comes to upper-level drill-through.

Interestingly enough, a client has an interesting but not incredibly uncommon fiscal calendar where February is actually period 1,  March is 2, and so on. In this calendar, January is actually period 12. But the Drillbridge calendar conversion functions usually return the common month numbers. What to do? Just adjust the expression a little to check for January specifically, otherwise convert the month and subtract one. For example:

SELECT 1 WHERE FROM DUAL WHERE
PERIOD IN ({{"name":"Period","expression":"#Period == 'Jan' ? 12 : #monthAbbreviationToDigit(#Period) - 1","drillToBottom":true,"sampleValue":"Q1","quoteMembers":false,"suppressParentheses":true,"overflow":"","overflowAt":0,"flags":""}})

There are a few variant methods to handle this, but this one is pretty straightforward and clean. This token actually also handles upper level drill (such as from member Q1, Q2, and so on), so the query predicate to use is a SQL IN clause, to accommodate multiple values.

Now when we drill on member January, we get this test query:

SELECT 1 WHERE FROM DUAL WHERE
PERIOD IN (12)

And if we drill on Q1, for example, we get this:

SELECT 1 WHERE FROM DUAL WHERE
PERIOD IN (1, 2, 3)

You might have been expecting to see 12, 1, 2 there but it’s actually right since Q1 contains February, March, and April – so everything is mapping as expected.

I’ve been pretty happy over the years with how the original Drillbridge expression/token concept has been able to accommodate some tricky use cases, although this one is relatively straightforward. It’s also nice to be able to write a bit of a “pure” query that doesn’t have to join against a calendar table just to get the right dates. This is just one of the things that makes Drillbridge, in my opinion, a true turnkey drill-through solution.

Understanding the Outline Extractor Relational Extraction Tables

I was going to do a nice in-depth post to follow up on my discussion of the relational cache outline extraction method/improvements on the Next Generation Outline Extractor, but someone already beat me to the punch. It turns out that the tool’s primary author, Tim Tow, blogged about the technique and the tables for ODTUG a couple of years back.

I’ll just add on one thing that I wanted to highlight, though: the general technique behind most relational extractions from an Essbase outline is to generate a single table, with such common columns as PARENT, CHILD, ALIAS, UDA, STORAGE, CONSOLIDATION, and so on. If you think about this, it tends to imply a number of limitations that might make this technique unfeasible for you:

  1. The table can only hold one extraction at a time
  2. You can only get the member name and a certain alias table alias at a time
  3. The columns in the table are variable based on the attribute dimensions that may be associated with the dimension
  4. More than one UDA: ¯\_(ツ)_/¯

Continue Reading…

Next Generation Outline Extractor version 2.1.3: Relational extraction enhancements

We’ve made some enhancements to the Next Generation Outline Extractor to incorporate user feedback and requests. The main improvement to this newest release, version 2.1.3, is with the way that relational database extractions are handled. More specifically, the storing of relational credentials has been improved so that they are no longer stored in cleartext. This will lead to improved security for organizations using this functionality in their automation. Additionally, the configuration for relational extractions has been simplified a bit. There is now no longer a need to edit the persistence.xml file, rather, everything is stored in the main properties file.

As part of this  post, I want to go over how the new functionality works, including a full “soup to nuts” use case. I think a lot of people use the outline extractor for “one-off” extractions, although a lot of people might be unaware that it can just as easily be used to quite easily automate extractions.

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My Top 10 Favorite Drillbridge Features

Drillbridge is a tool with an ostensibly narrow focus – drill from Essbase/Hyperion data to somewhere else. Typically that “somewhere else” is the relational data that has been summarized to load into the cube. While the concept of drill-through is very simple in principle, Drillbridge has been extensively engineered to make take this simple process and augment it with dozens of features that enhance its usefulness.

That said, in no particular order, I thought it might be fun to point out my ten favorite Drillbridge features. Continue Reading…

JDBC and JNDI connections compared (with a Dodeca example)

Have you ever wondered what the difference between a JDBC and a JNDI connection is? If you’re familiar with at least one of these, it’s likely that you’re familiar with JDBC (but probably not JNDI).

JDBC connections come up often in the Oracle world (for good reason). It’s a standard model/framework for designing drivers that interact with relational databases. As it pertains to us in the Hyperion, Dodeca (and even Drillbridge!) world is that we often define connections in terms of specifying JDBC parameters. This typically means a driver class name (like com.mysql.jdbc.Driver for a MySQL driver), a JDBC URL (a URL specifying a server and optionally a database/schema and other parameters), and credentials (username/password). So if you’ve poked around in your infrastructure much at all, there’s a good chance that you’ve come across a JDBC connection.

You may have even come across something called JNDI and even vaguely known it was sort of an alternate way to configure a connection but never really had to bother with it. I’ll spare you the acronym details, but think of JNDI as a way of organizing database connections (and other objects actually, but we don’t need to worry about that at the moment) such that instead of our app/system having to know the server name and credentials, it just asks “Hello, can I have the resource that was defined for me with name XYZ?”

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Running MDX queries through a JDBC driver (for fun?)

So there I am, sitting in front of the Alaska Airlines gate at Boston Logan airport, waiting for my flight home to Seattle. It’s not a particularly glamorous terminal – the divorce from Delta hasn’t been too kind to Alaska at BOS; Delta seems to have kept the house and kids while Alaska microwaves Lean Cuisine on a futon in its bachelor pad…

As I’m pondering why there are white rocking chairs in the terminal, my phone rings with a familiar name: Mr. Brian Marshall. We catch up and exchange pleasantries before pivoting over to more important matters (all things EPM of course!).

Brian: “So… Vess.”

Jason: “Oh boy…”

So we get to talking about accessing Essbase data through a Java database driver, á la Vess. And we get to talking about running MDX queries and dumping the output – á la Camshaft.

And as the talk goes on I end up saying something stupid like this: “You know what might work? Jjust pass an MDX query through the driver over to Essbase and map the output to a fake table… It’d be like an unholy combination of Vess and Camshaft. You could probably knock it out in a day or two.”

And at that moment I knew I wouldn’t be able to resist opening my laptop for the five plus hour flight home. Continue Reading…

Vess + Dodeca for Substitution Variable Management

I’m gonna go a little crazy today and combine two worlds, just for fun: the Vess “virtual” Essbase JDBC driver, and of course, Dodeca. I’ve written about Vess before, and even talked about it for a bit during Kscope16 earlier this year during a presentation with Tim Tow and Harry Gates on various interesting things we’re doing with Java and the Essbase Java API.

As a quick crash course on Vess, it’s a highly experimental Java JDBC driver that models an Essbase server’s applcations/cubes/properties into variable relational tables (I’ve written about Vess a few times before). At the moment this includes cube outline data, cube data, substitution variables, miscellaneous properties, and more. For example, when you connect Vess to, say, Sample/Basic, one of the tables you’ll get is SAMPLE.BASIC_VARS and it’ll contain four columns: the application, cube, variable name, and variable value. You might think you wouldn’t need to know the application and cube for this table but due to a nuance with Essbase variables (you can have the same variable name at both the cube, application, and server level) it’s actually needed.

In any case, not only you can read values using any SQL you want from these columns, but you can perform operations on the table that in turn affect the Essbase server. So you can do an UPDATE or DELETE and it’ll change the variable’s value, or delete a variable.

With that in mind, I thought to myself, you know what might be interesting – What if we added a Vess driver to Dodeca (since Dodeca supports third-party database drivers) and wire up a simple view that can edit the variables? So that’s exactly what I did and I thought it’d be fun to share.

Adding Vess to Dodeca

The first thing to do is add the Vess library and a couple of other Java libraries that it leans on to the Dodeca servlet. Typically you’d want to add these to your Dodeca WAR file when you build it with the “Click Once Prep Utility”, but since this is just for testing purposes, I can just add the JAR files to the already deployed servlet. I wouldn’t want to do it this way in production because when I went to deploy a new WAR file, I’d lose my Vess drivers. Here’s the drivers added to the /dodeca servlet:

Vess Java JAR files added to Dodeca (dodeca) servlet

Vess Java JAR files added to Dodeca (dodeca) servlet

For good measure I restarted the servlet container (in this case, restarting Tomcat 7 using sudo service tomcat7 restart on this little Ubuntu VM). Then we can login to Dodeca and create a new SQL connection:

A Vess connection is created inside of Dodeca

A Vess connection is created inside of Dodeca

There’s not a lot to see here other than to “show off” that Vess is indeed just a normal JDBC driver as far as other software is concerned – in this case, Dodeca. As you can see, Vess introduces a JDBC URL format. Vess can connect in embedded mode (in this case, indicated in the scheme of the URL. The rest is fairly standard: the address of the server (Vess assumes the default port of 1423 if none is specified), and in this case, a particular app/cube to connect to. Other than the URL, the driver class is specified. As with Oracle/SQL Server/MySQL, the class is just the Java class implementing the Driver Java interface. These typically are thing like com.mysql.Driver or something similar, and Vess is no different in this regard. Lastly for purposes of the Dodeca connection, a username and password are specified. This should be the credentials for an Essbase user, since internally Vess will use them to connect.

With the SQL connection mapped in, I can create the SQL Passthrough DataSet that will contain my SELECT queries, and optionally, parameterized INSERT/UPDATE/DELETE statements if I want to have support for those (which I will).

Configuring the SQL Passthrough DataSet for Vess variables

Configuring the SQL Passthrough DataSet for Vess variables

You can see that unlike some of the other SQL Passthrough DataSet examples I have shown lately, this one has two queries. It’s worth noting, briefly, that a SQLPTDS isn’t an object that just contains one query or otherwise concerns itself with one dataset. It can contain an arbitrary number of [usually related] queries. In this case I have two: one for server wide substitution variables, and one for variables just applicable to Sample/Basic (these actually overlap a bit as I’ll show in a bit).

The definition for the “server variables” query is very straightforward and only contains a SelectSQL configuration:

On the Dodeca query editor, looking at the first query for pulling out global variables from the Essbase server

On the Dodeca query editor, looking at the first query for pulling out global variables from the Essbase server

As noted earlier, Vess creates a table in the schema VESS_SCHEMA called VARS that contains the names and values of server-wide substitution variables. Over on the Sample/Basic variables configuration, there’s a little more to it:

The second query is modeled on a specific table for the Sample/Basic database

The second query is modeled on a specific table for the Sample/Basic database

Here there are queries that model the DELETE, INSERT, UPDATE, and of course SELECT operations. Not pictured (it’s collapsed on the config screen) is that I defined the primary key for this table as the combination of APPLICATION, CUBE, and NAME columns (while the final column, VALUE, is not part of the primary key).

To get a flavor for what the various queries look like, here’s the UpdateSQL configuration:

Dodeca UpdateSQL query for updating a variable's value

Dodeca UpdateSQL query for updating a variable’s value

You can see that the particular variable is identified by three column values (the primary key values), and that the value gets updated for this operation. There are four tokens in play, which will come from the row being edited in the view. There’s no primary key value being generated on the server-side (some of my previous examples had an integer that was generated server-side), so there’s no need for a post-insert select statement.

With all of the SQL Passthrough DataSet configuration out of the way (but a little more to come on the view configuration), I can now build a simple view template for showing the data:

Creating a template to display the variables from the SQL Passthrough DataSet

Creating a template to display the variables from the SQL Passthrough DataSet

If you’ve followed some of my other examples, this should seem pretty basic by now. There are two data ranges on this sheet but I’m just showing one in the preceding screenshot. The dataset has four columns, and so there are four columns on the range. That’s actually all there is to the view template itself. The rest of the configuration is on the view to set its data range and wire it up to the SQL Passthrough DataSet:

The configuration of the View that will display/edit the variables

The configuration of the View that will display/edit the variables

Noting too special here. You can see that I turned off RowAndColumnHeadersVisible to clean up the final appearance of the view a bit, and I have my one DataSet range defined. Over in the DataSet range definition:

The DataSet Range definition for the view

The DataSet Range definition for the view

There are two DataTable ranges defined (again, one for server variables and one for the Sample/Basic variables). Now opening up the configuration for the SampleBasicVarsData range (I’ll skip showing the details on the server variables range since it’s pretty simple):

DataTable Range Editor for the Sample/Basic data set

DataTable Range Editor for the Sample/Basic data set

I’ve turned on the abilities to add, delete, and modify rows (INSERT, DELETE, UPDATE). This is a really nice bit of granularity to have in Dodeca since in this case there’s a very legitimate use-case where I’d perhaps want a user to only be able to change a variable’s value but not otherwise delete it or add a new variable. Other than that bit of configuration, I’ve specified the corresponding range name on the sheet/template, and turned on InsertCells and NoColumnHeaders which is fairly standard for me with data sets like this.

Okay, the SQL Passthrough DataSet is setup, the template is setup, and the view configuration is setup. Let’s build this and see what happens:

Built Substitution Variable view

Built Substitution Variable view

It looks just like I thought it would! I can see my two server-wise substitution variables, and over on the table for Sample/Basic, I can see all of the variables that I have there. You’ll note that the server-wise variables seem to “repeat” in the Sample/Basic table. You simply have to think of the variables for a single database in terms of what variables are applicable to that database, and server-wide variables are applicable. Of course, if there’s a more specific (specified to the database) variable, it’ll trump the server-wide variable.

If you’re particularly astute with your screenshot reading skills you may notice that in preceding shot the cursor is on a cell in the Sample/Basic variables table, and therefore the row editing buttons on the toolbar are active (insert, delete, save). So I can change a value on a variable, hit the save data button, and Dodeca will perform the proper query from the SQLPTDS. Let’s do that and see what happens:

View after updating a variable value

View after updating a variable value

Well, it’s certainly less dramatic with screenshots, you’ll have to take my word for it that the PrevYear variable did indeed update on the server from FY11 to FY10. Under the hood, Dodeca fired off the properly filled in UpdateSQL statement, which of course was handed to the Vess driver, and in turn, Vess translated the call and called the appropriate variable updating logic on the Essbase Java API (magic!).

Summary / Vess Availability & Download

I hope you enjoyed this somewhat unique (or totally unique, I suppose) combination of a couple of different technologies. Vess is a bit of a unique take on things in the Essbase world, whereas Dodeca provides the peas to Essbase’s carrots. And yet, combining the two results in something wildly “interesting”.

I’m not saying that organizations should manage substitution variables this way (and again, the substitution variable aspect of Vess is just one of its facets, but it’s a nice simple one to play with), but this certainly makes it quite possible.

I know of many organizations that specifically or rather, begrudgingly, give EAS to a handful of finance power users that need to be able to tweak variables. Sometimes instead of EAS you’ll see one-off MaxL scripts where the update procedure is to tweak the script or a text file and run it. All too often this also involves plain-text credentials, hassling with installing the MaxL runtime on a ‘regular’ desktop machine, and more. So in this particular case, while the Vess driver actually does a lot more than just substitution variables, it can be leveraged for an innovative solution that is “cleaner” than many alternatives.

As an alternative (and still using Dodeca), we could have actually shelled out to launch a MaxL script and pass along the variable value, achieving much the same effect. This could work but obviously would be much more configuration. And to the extent possible I don’t like to create solutions that are ostensibly web-based that need to “drop down to the file system”, since it usually (in my experience), introduces a somewhat fragile or ‘sensitive’ element to the system that seems to act up or break relatively often.

Vess is still “highly experimental”, which I guess is a nice way of saying “a lot of things can go wrong, there’s no warranty, but it works… mostly. Asterisk.” Anyway, Vess isn’t available as a public download, but if you’d like to play with it, please feel free to contact me and I can provide the file and some basic instructions.

 

Dodeca Dynamic Grouping with Relational Data

I am very pleased today to write about an incredibly awesome Dodeca capability: dynamically built groups based on relational data. This capability is interesting and useful for a variety of reasons. Using Dodeca’s spreadsheet/data/magic build paradigm, we can organize plain relational data into beautifully formatted, insightful, and dynamic views. Just to forecast where I’m headed with this, what we’re going to do is transform this plain relational data:

Some raw forecast related data

Some raw forecast related data

Into this dynamic, grouped, and formatted view:

Dodeca dynamic grouping opened up in Excel

Dodeca dynamic grouping opened up in Excel

And further, we’re going to do it without writing a single line of code (save for the simple SQL Select statement). This post will assume that you’re up and running already with Dodeca’s SQL Passthrough DataSets, which I have written about before, so head over for a refresher if you need it. Also, I’ll be recycling a simple SQL table with forecast data by employee that I also used in an earlier Dodeca relational database input article, so you can read that if you want to know more about the data in play and how it relates to Sample/Basic. Continue Reading…