#Key Value

1

A key-value data model, or key-value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, a data structure more commonly known today as a dictionary or hash. Dictionaries contain a collection of objects, or records, which in turn have many different fields within them, each containing data InterSystems Data platform supports key-value concept of data management.

Learn more.

Introduction

Businesses often use in-memory databases or key-value stores (caching layers) when applications require extremely high performance. However, in-memory databases incur a high total cost of ownership and have hard scalability limits, incurring reliability problems and restart delays when memory limits are exceeded. In-memory key-value stores share these limitations and introduce architectural complexity and network latency as well.

This article explains why InterSystems IRIS™ data platform is a superior alternative to in-memory databases and key-value stores for highperformance SQL and NoSQL applications.

Taking Performance and Efficiency to the Next Level

InterSystems IRIS is the only persistent database that can match or beat the performance of in-memory databases and caching layers for concurrent data ingestion and analytics processing. It can process incoming transactions, persist the data to disk, and index it for analytics in under one microsecond on commercially available hardware without introducing network latency.

The superior ingest performance of InterSystems IRIS results in part from its multi-dimensional data engine, which allows efficient and compact storage in a rich data structure. Using an efficient, multi-dimensional data model with sparse storage techniques instead of two-dimensional tables, random data access and updates are accomplished with very high performance, fewer resources and less disk capacity. It also provides in-memory, in-process APIs in addition to traditional TCP/IP access APIs to optimize ingest performance.

6 0
2 59

Overview

This web interface is designed to facilitate the management of Data Lookup Tables via a user-friendly web page. It is particularly useful when your lookup table values are large, dynamic, and frequently changing. By granting end-users controlled access to this web interface (read, write, and delete permissions limited to this page), they can efficiently manage lookup table data according to their needs.

The data managed through this interface can be seamlessly utilized in HealthConnect rules or data transformations, eliminating the need for constant manual monitoring and management of the lookup tables and thereby saving significant time.

Note:
If the standard Data Lookup Table does not meet your mapping requirements, you can create a custom table and adapt this web interface along with its supporting class with minimal modifications. Sample class code is available upon request.

Open Exchange app
2 0
1 32

Dynamic Entities (objects and arrays) in IRIS are incredibly useful in situations where you are having to transform JSON data into an Object Model for storage to the database, such as in REST API endpoints hosted within IRIS. This is because these dynamic objects and arrays can easily serve as a point of conversion from one data structure to the other.

Dynamic Objects

Dynamic Objects are very similar to the standard ObjectScript object model you get when you create a new instance of a class object, but with some key differences:

4 1
2 111

PHP, from the beginning of its time, is renowned (and criticized) for supporting integration with a lot of libraries, as well as with almost all the DB existing on the market. However, for some mysterious reasons, it did not support hierarchical databases on the globals.

Globals are structures for storing hierarchical information. They are somewhat similar to key-value database with the only difference being that the key can be multi-level:

Open Exchange app
2 1
3 1K

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:

8 3
1 1.5K

I have a lookup table and record batch Table I would like to do a count on the records stored in that batch by counting the number of records in that batch that have a certain key on the responseKey column. This column keys are stored in the look up table for comparison. So I would like to do a join sql pivot that will use my keys stored in the lookup as columns and count as values

so far I have managed to do this but this is not efficient I would like to fire that sql once not on every count

0 1
0 1.6K

We're developing Ensemble PoC and one day our frontend developer (who doesn't have Ensemble production running) said that Populate just doesn't cut it and he needs to see the real data. He needed only one object, but the problem was - it's a big object. Still, I checked ids of everything related and wrote this command (parts omitted, but you get the idea):

2 1
0 775