Hello, community!

I've stumbled on some unexpected behavior, and decided to check with you if this is normal. Basically, I'm rebuilding indices and the result is not journaling (which leads to missing indices at shadow server).

The $ZV is "Cache for UNIX (Red Hat Enterprise Linux for x86-64) 2015.2.1 (Build 705U) Mon Aug 31 2015 16:53:38 EDT"

I have an example class

Class tmp.A As %Persistent;

Index IP1 On P1;

Property P1 As %String;

for example there is one object which have P1 = 1, so

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.

Suppose you have an application that allows users to write posts and comment on them. (Wait... that sounds familiar...)

For a given user, you want to be able to list all of the published posts with which that user has interacted - that is, either authored or commented on. How do you make this as fast as possible?

Here's what our %Persistent class definitions might look like as a starting point (storage definitions are important, but omitted for brevity):

Image search like Google's is a nice feature that wonder me - as almost anything related to image processing.

A few months ago, InterSystems released a preview for Python Embedded. As Python has a lot of libs for deal with image processing, I decided to start my own attemptive to play with a sort of image search - a much more modest version in deed :-)

InterSystems FAQ rubric

For volatile tables (tables with many INSERTs and DELETEs), storage for bitmap indexes can become inefficient over time.

For example, suppose that there are thousands of data with the following definition, and the operation of bulk deletion with TRUNCATE TABLE after being retained for a certain period of time is repeatedly performed.

Quotes (1NF/2NF/3NF)^ru:

Every row-and-column intersection contains exactly one value from the applicable domain (and nothing else).
The same value can be atomic or non-atomic depending on the purpose of this value. For example, “4286” can be

• atomic, if its denotes “a credit card’s PIN code” (if it’s broken down or reshuffled, it is of no use any longer)
• non-atomic, if it’s just a “sequence of numbers” (the value still makes sense if broken down into several parts or reshuffled)

This article explores the standard methods of increasing the performance of SQL queries involving the following types of fields: string, date, simple list (in the $LB format), "list of <...>" and "array of <...>".