The app game is now available in the SAS Global Forum conference app. See the Click Game icon for our version of a photo scavenger hunt for #SASGF 2018. Play starting now until Wednesday, April 11 at 10:00 a.m. MT to earn points to win prizes.    The top 20 point earners will be entered into a drawing for the following: YETI Hopper Flip 8 Portable Cooler ENO Hammock Echo Spot Moodo Scent Machine Let the games begin! Can't wait to see the photos.  ... View more

Been a long-time SAS user and wanted to try out CAS. I came across the concept of a Personal CAS Server which is like a Sandbox environment. So seems perfect for my process right now. But there is surprisingly no information to be found on how to set it up. So how do I set up a Personal CAS Server - does anyone know ? ... View more

Some people need time to warm-up to new things. As a member of the Global Enablement and Learning team, I see this every day. While "late adopters" are generally quite willing to learn the latest twist or tweak on something they already know well, they balk at the truly novel and complete paradigm shifts. Such it is with CAS. Many SAS users are taking a "long" approach.   One group that might be reluctant to adopt CAS is surprisingly SAS' most ardent supporters. These users have invested lots of time into their SAS knowledge. With so much invested, they can be reluctant to work in another construct where they aren't so confident or skilled.   So, for you SAS true believers, let's look at how to get the best performance in CAS and compare it to SAS (standard disclaimers about these tests being done on non-optimal, virtual hardware...) and, hopefully, once you know how to get vastly better performance from CAS than single engine (but still awesome!) base SAS, you'll be more comfortable giving it a try. 1. Group BY Aggregation -- Low Cardinality When choosing data processing techniques in base SAS, you usually only have DATA Step or PROCs. You have these in CAS as well, but, when looking for optimum performance, the best place to start is with the CAS Actions, and aggregation is no exception.   Thanks to Nicolas Robert, we already know which CAS action aggregates the fastest (at least in his scenario), simple.summary. So, let's compare its performance on some big data against base SAS on the same. Test Parameters Test Parameter Value Input Rows 160 million Distinct BY Groups (Cardinality) 8 CAS Code -- Simple.Summary proc cas ; simple.summary result=r status=s / inputs={"revenue","expenses"}, subSet={"SUM"}, table={ name="mega_corp" caslib="visual" groupBy={"facilityType","productline"} }, casout={name="summaryMC", replace=True, replication=0} ; quit ; Base SAS Code -- PROC MEANS proc means data=mega_corp noprint; var revenue expenses; class facilityType productline; output out=summaryMC sum(revenue)=sumRevenue sum(expenses)=sumExpenses; run; Results Engine Method Real Time CAS Simple.Summary 7.39 SAS PROC MEANS 2:32.44 2. Group BY Aggregation -- High Cardinality There has been some talk that CAS does not perform well with high cardinality operations. Let's take a look by increasing the number of BY-Groups. We'll use the same code as above but replace the GroupBy and CLASS variables with productID, date, and unit. This gives us approximately 88,000 distinct groups. Test Parameters Test Parameter Value Input Rows 160 million Distinct BY Groups (Cardinality) 88,000 Results Engine Method Real Time CAS Simple.Summary 18.22 SAS PROC MEANS 2:31.65 3. De-Duplication As with aggregation, picking the right technique is key and, thankfully again, Nicolas Robert has already shown us which method to use for de-duplication, the simple.GroupBy CAS action.   So, let's compare simple.GroupBy with PROC SORT. Test Parameters Test Parameter Value Input Rows 160 million Unique Keys 88,000 CAS Code -- Simple.GroupBy proc cas; simple.groupBy result=r status=rc / inputs={"productID", "date", "unit"} table={caslib="casuser",name="mega_corp"} casOut={name="dedupMC",replace=true,replication=0} ; run ; quit ; Base SAS Code -- PROC SORT proc sort data=mega_corp nodupkey out=dedupMC; by productID date unit; run; Results Engine Method Real Time CAS Simple.GroupBy 12.57 SAS PROC SORT 3:09.29 Discussion So, there you have it. CAS is fast. It is plowing through some decent sized data here on a few (5), relatively small (4-way) virtual servers in seconds.   If you want performance like this however, you need to know which techniques to use. Luckily some of the hard work has already been done for you. In particular check out these posts: CAS answers to 4 common data manipulation tasks – Part 1 – APPEND CAS answers to 4 common data manipulation tasks – Part 2 – SORT CAS answers to 4 common data manipulation tasks – Part 3 – DE-DUPLICATE CAS answers to 4 common data manipulation tasks – Part 4 – AGGREGATE You'll also need to know more about CAS Actions. In particular, you'll need to know how to enhance them so they do exactly what you want. This post should help with that: CAS Action Computed Columns ... View more

I want to create a master detail subdetail report within SAS BASE (proc report). You woud have a master row with colums. The master has detail rows with different columns and detail row also subdetail rows with comments. I don't  want all the columns master,detail, subdetail on one row and master, detail, subdetail should be on the same page.   The result will be converted to pdf.   The output would look something like the below example: Procesname                  Start date               end date                          Detail info ----------------------------------------------------------------------------------------- Walking                           01-01-2024          10-04-2024                     I walk very slow     Subproces                 start date               end data                          Start place           end place     -----------------------------------------------------------------------------------------------------     Part 1                           01-1-2024              10-01-2024                    New York             Amsterdam                   Comments                   --------------------------------------------------------------------------------------------                   I started very well                  The walk was nice    Subproces                 start date               end data                          Start place           end place     -----------------------------------------------------------------------------------------------------     Part 2                           10-1-2024              24-01-2024                    Amsterdam        Berlin                   Comments                   --------------------------------------------------------------------------------------------                   Was perfect   swimming                        10-06-2024          10-07-2024                   I love swimming      detail          subdetail ... View more