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.
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.
We are sometimes asked for examples of how to do 3rd party SQL database selects/updates/inserts/stored procedures from an Ensemble SQL Business Operation.
In the attached file there are 4 sample Ensemble Operations that demonstrate four different types of transactions with an external SQL DB:
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
(Fausto Cercignani)
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”).
If you want to import data from a mySQL export file (exported with mysqldump), you will find here a little script that could help.
Only the INSERT commands in the sql file are executed into Caché. Indices are not computed for better performance. %NOINDEX, %NOCHECK and %NOLOCK are generated on each INSERT line.
Currently, the file can not contain a "),(" pattern inside the values part of the INSERT command. If this is the case, the line is skipped. This feature may be implemented in the extractValuesList method.
Presenter: Anton Umnikov Task: Run SQL queries quickly on huge amounts of data Approach: Use a shared-nothing architecture to leverage a cluster of small, cost-effective servers
Explains how a shared nothing architecture allows you to leverage a cluster of small, costeffective servers to serve query results that would have taken much longer on a single, big and expensive box. This brings affordable horizontal scalability.
Content related to this session, including slides, video and additional learning content can be found here.
Presenter: Andreas Dieckow Task: Apply SQL security to multiple servers in a distributed system Approach: Provide code samples for using new API calls to apply SQL security statements to multiple instances of our products
Description: The requirement that started at all. See examples on how to use this new feature and integrate it into your application by discuss code examples.
Problem: SQL Security is local to the instance and most of time driven by customer application code. That it is only local to the instance and is not automatically going to other instances requires a solution.
Solution: With application code use new API calls to issue SQL security statements that is applied to multiple instances.
Content related to this session, including slides, video and additional learning content can be found here.
Presenter: Anton Umnikov Task: Identify your slowest SQL queries and tune them for better performance Approach: Use InterSystems’ query profiling and analysis tools. Discuss how system configuration can affect performance
This session will show you how you identify the weakest link in your application SQL and introduce you to the fine art of tuning those queries. To do this we will take a look at InterSystems query profiling and analysis tools, as well as how system configuration can impact SQL performance.
Problem: Obscurity on how our SQL engine works
Content related to this session, including slides, video and additional learning content can be found here.
Presenter: Kyle Baxter Task: Upgrade to a new version of Caché without concerns about slowing existing SQL queries Approach: Use the new Frozen Plans feature in Caché
Content related to this session, including slides, video and additional learning content can be found here.
If I have defined a class query in one of my classes and I want to use that query from a method of another class, what are the pros and cons of using the %SQL.Statement interface versus the %Library.ResultSet interface?
I am pleased to announce the next 2016.2 field test kit, 2016.2.0.595.0.
It may look like a slow week, with less than fifty changes having been checked in, but this kit includes the following fixes to problems found by you, the ones running the kits in the field:
Class Queries in InterSystems IRIS (and Cache, Ensemble, HealthShare) is a useful tool that separates SQL queries from Object Script code. Basically, it works like this: suppose that you want to use the same SQL query with different arguments in several different places.In this case you can avoid code duplication by declaring the query body as a class query and then calling this query by name. This approach is also convenient for custom queries, in which the task of obtaining the next row is defined by a developer. Sounds interesting? Then read on!
What do you do if you want to have the ID field have a meaningful name for your application?
Sometimes it comes to pass that when you're making a new table that you want to have the unique row identifier (a.k.a. IDKEY) to be a field that has a name that is meaningful for your data. Moreover, sometimes you want to set this value directly. Caché fully supports this functionality and it works Suppose you have a class Test.Kyle. The data will be stored like so:
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.
This is a sample Ensemble/Health Connect production which demonstrates how to receive an HL7 order (ORM) inbound from a file, extract fields (in this case, basic demographic information), and insert those into a table in an external SQL database via ODBC.
Customers who switch to Caché from relational databases report that their average performance is up to 20 time faster, running on the same hardware, with no changes to the application. What is it about Caché that lets applications run so fast?
With the maturation and wide acceptance of Java, object-oriented programming has moved to the foreground of the application development landscape. Because of their rich data models and support for productivity-enhancing concepts such as encapsulation, inheritance, and polymorphism, object technologies like Java, C++, and COM, are favored by today's application developers.
Impedance mismatch is a term commonly used to describe the problem of an object-oriented (OO) application housing its data in legacy relational databases (RDBMS). C++ programmers have dealt with it for years, and it is now a familiar problem to Java and other OO programmers.
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