Vess + ODI to extract Essbase metadata

Well, apparently it’s Friday Fun Day over here in Seattle. I was going to head up to the mountains for the weekend but plans changed. So how about a little frankendriver Vess update with some ODI goodness thrown in?

Vess has some really interesting data integration possibilities that I’ve mentioned before, one of which is being able to drop it into Oracle Data Integrator and use it as you would any other JDBC driver. I hadn’t really tested this out though, until yesterday. It turns out that it works surprisingly well.

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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…

Oracle Open World 2016 Recap

As I mentioned a week or so ago, I made a last minute appearance at Oracle Open World this year. It was my first time attending and presenting at OOW. I actually didn’t catch too much of the conference as I only flew in on Wednesday and flew out on Thursday. Nevertheless, I had a bit of a whirlwind experience, but a very good one. While I hadn’t planned on it (I’m more of a Kscope guy), I am now looking forward to attending Open World next year.

As for the presentation I was part of, I think it went pretty well. Many thanks to Gabby Rubin of Oracle for coming up with the idea for the presentation and facilitating it. The presentation was on “Essbase Tools and Toys” and was meant to highlight, at a high level, some of the interesting things that folks such as myself are doing that involve the Essbase APIs or otherwise work with Essbase. The presentation discussed items created by me, Tim Tow, and Harry Gates. Additionally, Kumar Ramaiyer (also from Oracle) talked a bit about what’s coming with Essbase Cloud Service (EssCS).

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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.


Database stats and outline viewing with Vess

I had a little bit of time this weekend and dusted off Vess for some updates. I was able to greatly enhance the functionality and perhaps more important, widen the number of use cases that it could be used in.

I already wrote about a use case previously: using SQL to create a substitution variable. Vess supports fully symmetric substitution variable updates. That is to say, you can create, read, update, and delete substitution variables on your Essbase server just by changing the data in the proper table. So just to elaborate on my thoughts on the previous post, I think there are some nice use cases here. Of course, there’s nothing wrong with MaxL, but if you have an instance where you are dumping substitution variables via MaxL and scraping the ASCII art off of them in order to get to the real variable values, Vess offers a cleaner approach.

As of now, Vess also provides a window in to a whole slew of properties on various Essbase objects: server, application, and database. Here’s a view of some database properties (as shown in a generic JDBC GUI with Vess configured as a driver):

Using Vess to view Essbase database properties for Sample.Basic

There’s a use case here too. I know of more than a few people that are dumping database stats using MaxL (such as to monitor a database/server stats over time).  So instead of running a script, dumping to a file, parsing that out (or whatever), you could slam the data directly into SQL (using ODI or just some tool that can execute SQL statements).

Next up, Vess models tables for the various dimensions in a cube. This is not unlike how ODI would create a model of a cube: one table per dimension. Here’s a view of some information from Sample/Basic:

Using Vess to view outline information

Outline functionality in Vess is not symmetric: you can read but updates are disabled for now. The use case here is that you could pull outline information without needing to use ODI (or something else) and possibly if you are using the Essbase Outline Extractor in your automation. Since Vess is already a fully functional SQL database, you could pretty easily do some interesting things like just grabbing all of the level-0 members in an outline (“SELECT * FROM SAMPLE.BASIC_DIM_MARKET WHERE LEVEL = 0”).

For now, Vess supports data loads to a cube (look mom, no load rules!). I wrote about this before too. Data reads are turned off for now (as in, you can INSERT but SELECT always returns nothing). It’s a bit of an interesting issue when you want to map this into a relational model.

Lastly, I started writing up the ODI technology for Vess. I think this is an absolute win for Vess because it would mean that you could build a nice clean technology/Knowledge Module that doesn’t have to use a bunch of Jython to glue things together: you can treat everything as a vanilla JDBC database model. No idea when this could be ready but you can rest assured that I’ll blog about it.

Vess Updates Substitution Variables

A colleague of mine is running into an issue with substitution variables and was looking for a solution that he could use to sync values up. He thought maybe Vess would be a good fit. Vess, as I have blogged about before, is a “virtual” Essbase JDBC driver. Vess maps Essbase concepts and crams them into a typical database model. For example, Vess exposes tables that model substitution variables.

In the case of server-wide substitution variables, there is a “VARS” table that has two columns: NAME and VALUE. For each application, there is another table that contains four columns: NAME, VALUE, APPLICATION, DATABASE.

As a quick aside, this might seem a little odd to have separate tables. After all, this table is notionally about the same as the Variables screen in EAS. Well, you have to kind of flip your thinking a little. Don’t think of variables as being only either server or a cube: think about in terms of what variables are applicable to a cube. In other words, if you ask Essbase what variables are applicable to the whole server, then this would be the global variables only. If you ask Essbase what variables are applicable to a cube, then it’s the cube, app, AND the server specific variables. This is one of the reasons there are multiple tables to model the variables.

Getting back on track, given that we have these tables and we can treat them just like normal SQL tables, we can do some interesting things. Let’s say we want to create or update a variable specific to an app that exists in the global scope. We can do this in one line:

SELECT NAME, VALUE, 'Sample', 'Basic'<br />

What’s going on here? In Vess, a schema named VESS_SCHEMA is presented for server-wide things (server wide variables are in the table VARS in this schema, as shown above). The server VARS table only has columns NAME and VALUE.

Each application on the Essbase sever is modeled as its own schema. In this case, our favorite app – Sample – gets a schema named SAMPLE. This schema also contains a VARS table (containing columns NAME, VALUE, APPLICATION, and DATABASE).

Given these tables we have, it’s a simple matter of selecting the server variable with the name we want (in this case, a variable named ‘Foo’), and insert it into the variables for the Sample app.

Of course, if we wanted to for some reason, we could alter the name using normal SQL (string truncating, substrings, etc), or whatever. We can also delete variables, such as this:


As I’ve said before, Vess continues to be an “interesting” proof of concept. As time permits I am filling out more and more functionality. At present, Vess models things like substitution variables, metadata you might see in MaxL or EAS (cube statistics, user sessions, etc), can load data to cubes, and can do certain outline related operations.

Vess is not available as a public download at this time but I have handed a few copies out to get feedback. I think Vess is just about good enough to be used in automation and other things. If you’re interested in using this in a production situation (automation or otherwise), please contact me to discuss support options.

More fun with Vess, the Essbase JDBC driver

All I can say about Vess is that it’s still… interesting. This is definitely one of the more complex things I’ve ever worked on, as well as one of the more elegant. I did a lot of plumbing on how the driver works which is now enabling so interesting and rapid progress.

Here are some interesting things that you can do as of right now:

  1. Query list of applications and metadata
  2. Query list of cubes and metadata
  3. Query list of sessions (same as Sessions in EAS)
  4. Load data without a load rule by using a SQL INSERT statement
  5. List users
  6. List substitution variables

Some of the really fun things are actually the most subtle. Wherever possible Vess tries to supply metadata for a modeled table that matches up with Essbase semantics or naming limits. For example, substitution variables are in a table where you have the variable name, value, application, and database. Each of these columns are normal String columns but they have character limits that match the maximum Essbase length (320, 256, 30, and 30, respectively, taking into account Unicode).

Similarly for member names on the data load table. Also wherever possible, a primary key or nullability constraint is set on a table where it also makes sense. For example, substitution variables are assumed to be non-null, as well as member names for an Essbase fact table, cube names, and more.

I have been using a free generic JDBC tool called Squirrel SQL to help facilitate testing. Squirrel supports any database for which you can supply a generic JDBC driver. Here are a few screenshots from testing out Vess when connected to Sample/Basic (list of cubes on server):

View list of cubes and metadata with Vess

List of active sessions on server:

Viewing list of active sessions on Essbase server using Vess

Some of the tables also support DELETE semantics, for example, deleting a row from the session table is equivalent to killing the session, as with substitution variables.

Lastly, all tables that are modeled in Vess are part of the SQL metadata store, so that tools that go in to ask for metadata (such as Squirrel) get it back. This is interesting/notable because theoretically you could already use Vess with ODI to perform data loads and some other things. I haven’t tried this yet, but theoretically I could add the drivers, reverse the metadata, and use one of the tables in an interface. So it’d be fairly trivial to update a substitution variable directly from SQL, perform a data load, and you could even kick off a calc script simply by invoking a procedure.

In short, things are exciting in the Vess world. While many of you want to load data to Essbase cubes without a load rule, just as many are interested in pulling database metadata (things you normally dump with MaxL and scrape out), and this tool might just fill that need in a clean way. More to come later, I have some presentations to get ready for Kscope15 (although sadly none on this little technological flight of fancy…)

Your daily Vess (Virtual Essbase JDBC driver) update

The other week I mentioned that I’d been playing around with the idea of a virtual JDBC driver for Essbase. Much to my amazement, quite a few people (relatively speaking…) expressed an interest in this project and even graciously offered to help test it.

To reiterate from last week, I still think that Vess is “interesting”. It has also been one of the more complicated things I have tried to create. Implementing such a thing is really kind of an exercise in pounding a round peg into a square whole (or pounding a cube into a cylinder…), requiring advanced knowledge of Java, the JDBC driver model, Essbase, the Essbase Java API, network programming, and more. I’m leveraging code from a half dozen projects I have written, some public and some not.

That said if you wondered why Oracle wasn’t nice enough to make a true JDBC driver for Essbase, it’s because you have to be a little crazy to do it. And apparently I’m just that right kind of masochistic crazy.

Anyway, the driver works – amazingly. It’s not production ready and won’t be for some time, but you can view your substitution variables from a table and even load data to a cube by doing a normal SQL INSERT.

In fact, you can even read a CSV file on the fly and insert it to a cube with no load rule, like this:

INSERT INTO VESS_SCHEMA.SAMPLE_BASIC_DATA ("Year", "Measures", "Product", "Market", "Scenario", "DATA")
SELECT * FROM CSVREAD('/Users/jasonwjones/test/sample_basic_data.txt');

Crazy, eh? So all you load rule haters rejoice.

You can even use SQL transformations to adjust the data and load that into a cube from a remote JDBC table:


What next?

A lot of the hard stuff is in place so the name of the game now would be to iterate and start to introduce a few more table representations of Essbase concepts, such as cube stats, server sessions, and some other interesting things. I’m still contemplating how you could layer in the outline editing model in a sensible way.

Getting back to this being “interesting” – I think it’s really interesting in a couple of ways. One is the idea of getting to use any off-the-shelf JDBC tool to be able to do things with an Essbase server. The other thing that is interesting is ODI (which I guess is interesting by way of the first reason, since databases/JDBC “fit” into ODI better than OLAP). The way Essbase works in ODI right now is that some custom Java libraries are used and glued together with some Jython scripts and Knowledge Module steps. In theory you could instead drop in the Vess driver as a generic SQL data source and do all of this with vanilla RKMs, LKMs, and IKMs. How cool would that be? To the extent that supporting Hyperion inside of ODI has been “problematic” for Oracle, you could perhaps sidestep it this way.

Anyway, I have a few folks that have valiantly offered to help test this thing out. If you have a spare cube, Java 1.8 (or greater), can download the free Squirrel SQL tool, and want to play with this, please hit me up, I would love some feedback.