Have some free text fields in your application that you wish you could search efficiently? Tried using some methods before but found out that they just cannot match the performance needs of your customers? Do I have one weird trick that will solve all your problems? Don’t you already know!? All I do is bring great solutions to your performance pitfalls!

As usual, if you want the TL;DR (too long; didn’t read) version, skip to the end. Just know you are hurting my feelings.

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The object and relational data models of the Caché database support three types of indexes, which are standard, bitmap, and bitslice. In addition to these three native types, developers can declare their own custom types of indexes and use them in any classes since version 2013.1. For example, iFind text indexes use that mechanism.

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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):

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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.

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In the previous parts (1, 2) we talked about globals as trees. In this article, we will look at them as sparse arrays.

A sparse array - is a type of array where most values assume an identical value.

In practice, you will often see sparse arrays so huge that there is no point in occupying memory with identical elements. Therefore, it makes sense to organize sparse arrays in such a way that memory is not wasted on storing duplicate values.

In some programming languages, sparse arrays are part of the language - for example, in J, MATLAB. In other languages, there are special libraries that let you use them. For C++, those would be Eigen and the like.

Globals are good candidates for implementing sparse arrays for the following reasons:

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Article
· Jul 7, 2017 19m read
Indexing of non-atomic attributes

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 <...>".

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An interesting pattern around unique indices came up recently (in internal discussion re: isc.rest) and I'd like to highlight it for the community.

As a motivating use case: suppose you have a class representing a tree, where each node also has a name, and we want nodes to be unique by name and parent node. We want each root node to have a unique name too. A natural implementation would be:

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Overview

Encryption of sensitive data becomes more and more important for applications. For example patient names, SSN, address-data or credit card-numbers etc..

Cache supports different flavors of encryption. Block-level database encryption and data-element encryption. The block-level database encryption protects an entire database. The decryption/encryption is done when a block is written/read to or from the database and has very little impact on the performance.

With data-element encryption only certain data-fields are encrypted. Fields that contain sensitive data like patient data or credit-card numbers. Data-element encryption is also useful if a re-encryption is required periodically. With data-element encryption it is the responsibility of the application to encrypt/decrypt the data.

Both encryption methods leverage the managed key encryption infrastructure of Caché.

The following article describes a sample use-case where data-element encryption is used to encrypt person data.

But what if you have hundreds of thousands of records with an encrypted datafield and you have the need to search that field? Decryption of the field-values prior to the search is not an option. What about indices?

This article describes a possible solution and develops step-by-step a small example how you can use SQL and indices to search encrypted fields.

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Our team is reworking an application to use REST services that use the same database as our current ZEN application. One of the new REST endpoints uses a query that ran very slowly when first implemented. After some analysis, we found that an index on one of the fields in the table greatly improved performance (a query that took 35 seconds was now taking a fraction of a second).

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Article
· Feb 2, 2021 12m read
A custom SQL index with Python features

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 :-)

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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.

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Hi guys!


Unique, PrimaryKey and IDKey?
In what contexts does it apply?

IDKey sets the registry key access to the store.
PrimaryKey, Unique, and IDKey define the uniqueness in the records, but what is correct?

I use everyone? What is the context of each?

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Question
· Nov 23, 2017
Indexing null value

Dear community!

I have problem with index NULL value. Unique index doesn't work for this case. If I use insert and one of parameter is "NULL". Message of constraint doesn't appear and row is inserted into table successfully. How Can I use index with NULL?

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I have a class which defines a property as array of %String. Is it possible to index values of this property and use this property in SQL?

I have tried 'Index idx On prop(ELEMENTS)' and then a select from the generated collection table, but this is still orders of magnitude slower than queries to the containing class.

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Question
· Mar 5, 2019
PrimaryKey vs Idkey

Just wondering an Insight in the difference between these two indexes

IdKey / PrimaryKey
=================

Property Identifier As %Integer

Index Index1 on Identifier [Idkey]

Index Index2 on Identifier [PrimaryKey]

What's the difference?

1. If I don't have Index1 and only have Index2, then cache does still make its own id.
So how and why do I ever use the PrimaryKey. In Joins ??

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Hi,

I am new to Cache' MV but have extensive experience with other Pick flavors especially Unidata.

I need to determine the impact of adding several indexes to a large file with over 51,000,000 records.

On other systems, I could use FILE.STAT, ANALYZE.FILE and shell to the OS to determine how large the index file was.

None of those seem to be available in Cache' MV. Shelling to the OS just tells me the size of CACHE.DAT.

What is the best way to determine what the disk impact would be if I added an index (CREATE-INDEX) to a file?

TIA,

Steve

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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

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Hi,

I have a class with around 400k lines and 60 columns. Class storage is Cache SQL storage (Mapped from a global).

I want to create multiple indices on certain fields.

I am familiar with two approaches:

1. Create a new map (Index type) on a pointer global.

2. Create a bitmap index

Which approach is more recommended to be used in the case I described? If there are any other approaches, I will be happy to hear.

Thanks :)

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Question
· Dec 26, 2019
Performant index on date field

Is there a way to get a good performing index on a date field? I have tried various date property indexes and the query plan is always in a pretty high range. Below are query plan result values I have observed:

StartDate > '2019-12-01' --cost = 699168
StartDate = '2019-12-21' --cost 70666
StartDate between '2019-12-21' and '2019-21-28' --cost = 492058

The query plans above were for type %TimeStamp.

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Question
· Jun 1, 2016
Activating an index

I have a production system that has a large dataset of about 2 million rows. I need to create an index on a property but don't want it available to queries until the index is fully populated. Is there a way I can create the indexed, fire off the build, then "activate" the index so queries can use it.

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Hi, folks!

Suppose you have a Caché class with %String property which contains relatively large text (from 10 to 2000 symbols).

The class:

Class Test.Duplicates Extends %Persistent 

{

Property Text As %String (MAXLEN = 2000);

}

And you have thousands of entries.

What are the best options to find entries which are duplicates on this property?

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I'm using Cache SQL and want the ability to choose a specific index.

I've boiled the problem down to one table and simplified the query down to

SELECT *
FROM Registration.PatResp
WHERE SchedApptNum=8450022

SchedApptNum is indexed, but instead of using that column, "Show Plan" indicates that it's looping through the entire Registration.PatResp table on Id (the primary key for the table).

I've done a tune-table with no change.

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Hello Fellow Cache Developers:

Has anyone ever created an index on values of a list property? If so, would you be willing to share an example?

Also, feel free to offer input and suggestions regarding use of indexes on List values.

Here is my database scenario:

Parent Class:

PropertyA - %String

PropertyB - %Integer

Child Class:

PropertyC - %Integer

PropertyD - list of %Integer

Data illustration:

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