There’s a lot of excitement in the EPM world these days when it comes to REST APIs – and rightfully so. As a developer heavily invested in the EPM space I am excited about some of the possibilities these new APIs offer – and what they will offer in the future. But all of this great new REST API stuff can be quite daunting – how does it work, why should you care, where does it fit in with your overall architecture, and so on. And with ODTUG‘s Kscope18 just around the corner I thought it might be useful to write a primer – or a crash course of sorts – for the EPM professional on what all this REST API business is about. Also be sure to check out one of my presentations at Kscope this year as I will be discussing the OAC Essbase REST API, how to use it, what it does, and more. Continue Reading…
I was talking to a colleague the other day that wants to do some scripting with PBCS using Groovy. Of course, since PBCS has a REST API, we can do scripting with pretty much any modern language. There are even some excellent examples of scripting with PBCS using Groovy out there.
However, since Groovy runs on the JVM (Java Virtual Machine), we can actually leverage any existing Java library that we want to – including the already existing PBJ library that provides a super clean domain specific language for working with PBCS via its REST API. To make things nice and simple, PBJ can even be packaged as an “uber jar” – a self-contained JAR that contains all of its dependency JARs. This can make things a little simpler to manage, especially in cases where PBJ is used in places like ODI.
For this example I’m going to take the PBJ library uber jar, add it to a new Groovy project (in the IntelliJ IDE), then write some code to connect, fetch the list of applications, then iterate over those and print out the list of jobs in each application.
Continuing on with the idea of getting insight into the Essbase feature set over time, as viewed through the lens of its Essbase Java API evolution, you can quite clearly see that the open/URL-style drill-through (as opposed to classic LRO-based drill-through) showed up in version 220.127.116.11, which in fact is pretty much the only thing that seemed to get added to this particular release, Java API-wise, along with some ancillary drill-through methods/functionality in some related classes.
More near to my heart: this is the exact functionality that paved the way for Drillbridge! Although it wasn’t available as a feature on day 1, subsequent versions of Drillbridge gained the ability to automatically deploy drill-through definitions to a given cube, and it uses exactly these API methods to accomplish it.
Just another quick post today about possibly speeding up data loads to an ASO database when loading from SQL. I got on a quick call with a former colleague that was looking to gain a little more performance on their load process to a massive ASO database, and the first thing that jumped out at me was that I recall you can do parallel loads with some native MaxL syntax.
Here’s a quick example of the syntax:
import database $APPLICATION.$DATABASE data connect as $SQL_USER identified by $SQL_PW using multiple rules_file $RULE1, $RULE2, $RULE3, $RULE4, $RULE5 to load_buffer_block starting with buffer_id 100 on error write to "errors.txt";
Basically, you provide multiple rules files (configured for your SQL datasource of course). The rules files are likely to be the same as each other but I suppose it’s possible you might want to partition the data in some logical way to try and speed things up even more.
For example, let’s say that in the code above, we are loading five years of data from a relational database. We might then make it so that each rule is set for this particular year by doing the following things:
- Set the year in the data header
- Remove that column from the list of
- Put a filter/predicate in the
WHEREclause on the query
- Bonus points for using substitution variables in both the header definition and the where clause
Performance in this particular use case went up substantially. It’s my understanding that data loads that were taking an hour are now cut down to 17 minutes. Your mileage may vary, of course.
Let’s Not Forget About Hybrid BSO
That said, I think this can be an effective strategy for trying to squeeze performance out of some ASO cubes that need a smaller load window and you don’t want to go changing a lot of the internals in play. If you’re doing new development, then I strongly, strongly recommend using hybrid BSO (or rather, BSO and making sure the cube is configured properly so as to get the hybrid BSO performance benefits). I have been seeing hybrid BSO cubes absolutely killing it in performance, what with their ability to leverage ASO technology for aggregates, and massive calculation improvements owing to the smaller block sizes and indexes you get from having so many dynamic calc members in dimensions. Plus, you of course get all of the classic/rich/awesome BSO functionality out of the box, like dynamic time series, expense tagging, time balance, and more. These were never very strong areas for ASO and often required a lot of non-optimal workarounds to make users happy.
A fair bit of my job is dealing with and building solutions around the Essbase Java API. For many years, the Java API has been the premier way to programmatically work with Essbase (compared to say, the C and VB APIs, which have fallen out of favor). As part of this development work, it’s often important to see when (in terms of version) a certain class, method, interface, or other object has been added, modified, removed, or deprecated.
As a bit of a side project, I have been working with a library for comparing Java JARs to each other (japicmp). By processing and interpreting the results of just about every single Essbase Java JAR from 7.0.1, through the 9.x series, multiple 11.x’s, and finally to version 12.2.x, I have come up with something of a master table that shows all of these changes. You can view the initial results of the Essbase JAPI JAR evolution analysis. I’ll probably refresh this and enhance the output as new library versions become available or as I determine that additional insights become useful.
Drillbridge Plus has recently gained a new feature at the request of a customer. This one is kind of interesting and required a bit of deep thinking in terms of the best way to architect it. Here’s the deal: Smart View will let you drill-through on a data value where your grid is using attribute dimensions, but it won’t pass the attribute associations as part of the request. And as it turns out, there are instances where it’d be useful to have that attribute member so you can use it to dial in the SQL query that Drillbridge creates and executes.
What to do? Ask Drillbridge to go fetch those attribute member values for you anyway! In this post I’m going to walk through a use-case showing off the new feature, how to set it up, and I’m also going to show off some recent debugging enhancements that are really useful and have been around for awhile.
Let’s start. First, consider a normal Drillbridge report definition with a simple query:
The most recent version of Dodeca brought several exciting enhancements for MDX-related functionality. One of these is a new selector list based on a reusable MDX script object. Although MDX queries are probably most often associated with queries that return numerical data from a cube, they also have incredibly useful metadata capabilities that can be employed for various purposes. In Dodeca, it’s common to use a report script or member query specification to return members from an outline. For example, you might want to provide your users with a selector such that they can choose a particular product (or products) from your Product dimension in order to customize a report that they will build dynamically.
I see MDX scripts as being a natural, clean, and flexible way to populate these selectors, and moving forward I will recommend them whenever possible over the more arcane report scripts that have been around for years.
That all said, what I want to show today is the following: I’m going to edit an existing Dodeca view so as to replace one of its existing selector lists with a new list based on an MDX query.
MDX has been around for many years, but it seems to be enjoying something of a renaissance right now. I think there are various reasons for this. Dodeca has supported MDX in various ways for quite some time, and even dramatically enhances MDX support in its latest 7.3 release, including an MDX editor with advanced syntax highlighting and autocomplete (!), support for member lists generated from MDX queries, and more. I really prefer MDX over report scripts especially when it comes to generating member lists. The equivalent MDX queries always seem a little cleaner and succinct.
To that end, I thought I would start collecting various MDX examples that process dimensions/members in certain ways put them up on a page. There are examples for the Sample/Basic database that show fetching members from a dimension at various levels, with a UDA, sorted forwards/backwards, removing duplicates, and more. It’s nothing earth shattering (considering the super complex things that MDX can achieve) but in the future I foresee MDX being used even more for things like this.
Remember the last time you thought, “You know, Excel is just a little too modern, I wish I could do multi-dimensional analysis using my keyboard, in a terminal, the way the Pilgrims did it.”
Yet, here we are.
I was going to originally
throw this over the fence release this as a bit of an April Fool’s joke, but I didn’t have quite enough time. I actually showed this off to the fine folks at my Collaborate session last month, and believe it or not, some of the people there thought it had some interesting use-cases. Continue Reading…
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…