There are many storage technologies available today from various vendors. The storage technology and configuration best for your application depends on the application access patterns and workloads.
The attached document discusses the various design considerations and recommendations for various technologies. This guide is to help you during discussions with your storage vendor to determine the appropriate storage technologies and products that will work best to meet the performance goals for your applications.
This article created as side effect of preparations to the longer set of articles about simple, but still handy MapReduce implementation in Caché. I was looking for relatively easy way to pass arguments to (potentially) multiple targets via remote calling facilities. And after several attempts I have realized that we do have very powerful mechanism in the Caché ObjectScript which might be of particular help here – dynamic dispatch for methods and properties.
While Studio and Atelier are useful development interfaces, there are occasionally situations where a quick edit needs to be made to code and only terminal access is available. A useful set of tools to do this are the zload, zprint, zinsert, zremove, and zsave commands. These are abbreviated to zl, zp, zi, zr, and zs respectively. While each of these commands has its own page in documentation, this article will synthesize that information with examples to provide instruction for their combined use.
NewBie's Corner Session 3 More Read and Write commands & Multiple commands
Welcome to NewBie's Corner, a weekly or biweekly post covering basic Caché Material.
Click on the Caché Cube in your system tray and select Terminal to try out the commands.
Write command with carriage return and line feed
When the exclamation point "!" is inserted after a Write command, a carriage return and line feed combination is produced. Note in this example, that a comma separates the exclamation point from the variable "X".
Today my customer ask me a question about how to write the MDX with a summary row however this row with different aggregate functions for each column.
We know in DeepSee analyzer has pivot table option "Summary", user can select sum, avg ... aggregate functions to get the summary row/column. However we can not specify different aggregate function for each measure column.
Here show the example to use All level and IIF function achieve that. see the example (Holefood cube in Sample namespace) below
This article provides a reference architecture as a sample for providing robust performing and highly available applications based on InterSystems Technologies that are applicable to Caché, Ensemble, HealthShare, TrakCare, and associated embedded technologies such as DeepSee, iKnow, Zen and Zen Mojo.
Azure has two different deployment models for creating and working with resources: Azure Classic and Azure Resource Manager. The information detailed in this article is based on the Azure Resource Manager model (ARM).
InterSystems has corrected a defect with Mirroring that can impact data integrity
The defect is present in all released versions Caché, Ensemble, and HealthShare beginning with 2015.2.0. It is present for all platforms and affects both failover and asynchronous mirroring configurations.
First post! In order to somewhat redeem myself for an unnecessary call to support, I've decided to post some classes that I've written to monitor certain metrics inside our Ensemble Live instance (yeah, Kyle, you WERE laughing at me, but it's okay). What the classes do is to run queries and code to get database sizes, status of the mirror, counts of rows in tables such as EnsLib.HL7.Message and Ens.MessageHeader. The data is collected and written to tables and then an email is sent out daily upon completion. I've found this quite useful in keeping an eye on what's going on. It's help
Earlier in this series, we've presented four different demo applications for iKnow, illustrating how its unique bottom-up approach allows users to explore the concepts and context of their unstructured data and then leverage these insights to implement real-world use cases. We started small and simple with core exploration through the Knowledge Portal, then organized our records according to content with the Set Analysis Demo, organized our domain knowledge using the Dictionary Builder Demo and finally build complex rules to extract nontrivial patterns from text with the Rules Builder Demo.
This time, we'll dive into a different area of the iKnow feature set: iFind. Where iKnow's core APIs are all about exploration and leveraging those results programmatically in applications and analytics, iFind is focused specifically on search scenarios in a pure SQL context. We'll be presenting a simple search portal implemented in Zen that showcases iFind's main features.
I am pleased to announce the next 2016.2 field test kit, 2016.2.0.677.0.
I haven’t sent an update to this thread in a while and it should come as no surprise that there has been quite a lot of development going on since I wrote about build 632. In fact, there have been almost 300 changes covering most areas of the product.
The accumulated list of fixes to problems found in the field since build 632 includes the following changes:
Recently I have been posting some updates to our JSON capabilities and I am very glad that so many of you provided feedback. Today I would like to focus on another facet: Producing JSON with a SQL query.
The Update Checklist for v2015.1 recommends setting the TZ environment variable on Linux platforms and points to the manpage for tzset. This is recommended to improve the performance of Cache’s time-related functions. You can find out more about this here:
The field test of Caché 2016.2 has been available for quite some time and I would like to focus on one of the substantial features that is new in this version: the document data model. This model is a natural addition to the multiple ways we support for handling data including Objects, Tables and Multidimensional arrays. It makes the platform more flexible and suitable for even more use cases.
Order is a necessity for everyone, but not everyone understands it in the same way
Disclaimer: This article uses Russian language and Cyrillic alphabet as examples, but is relevant for anyone who uses Caché in a non-English locale. Please note that this article refers mostly to NLS collations, which are different than SQL collations. SQL collations (such as SQLUPPER, SQLSTRING, EXACT which means no collation, TRUNCATE, etc.) are actual functions that are explicitly applied to some values, and whose results are sometimes explicitly stored in the global subscripts. When stored in subscripts, these values would naturally follow the NLS collation in effect (“SQL and NLS Collations”).