Java Database Connectivity (JDBC) is an application programming interface (API) for the programming language Java, which defines how a client may access a database.
There is not just one class in this package: rcc.gstream.cls but also rcc.gstreamT.cls
While rcc.gstream works with direct access to the stream globals, the *T version uses a Process Private Global (PPG) as Temporary storage. using SELECT * FROM RCC.gstreamT WHERE RCC.useT('^jpgS')=1 and similar.
In general Global Streams are data objects embedded in Classes / Tables. Using and viewing them with SQL is normally a part of the access to the containing tables.
SO WHAT?
During debugging or searching for strange or unexpected behavior there could be the need to get closer to the stored stream. No big problem with direct access to Globals with SMP or Terminal. But with SQL you are lost. So my tool provides dynamic access to Global Streams wherever you may need this Special thanks to @Oliver Wilms for the inspiration for this tool.
I just wrote up a quick sample to help a colleague load data into IRIS from R using RJDBC, and figured it's worth sharing here for future reference.
Ultimately it was pretty simple, aside from IRIS not liking "." in column names; the workaround is to just rename the columns. Someone better at R than me could probably provide some generic approach.
As we all know, Caché is a great database that accomplishes lots of tasks within itself. However, what do you do when you need to access an external database? One way is to use the Caché SQL Gateway via JDBC. In this article, my goal is to answer the following questions to help you familiarize yourself with the technology and debug some common problems.
The last days I've work with the great new feature: LOAD DATA With this post I would like to share my first experiences with you. The following points do not contain any order or other evaluation. These are only things that I noticed when using the LOAD DATA command. It should also be noted that these points are based on the IRIS Version 2021.2.0.617 which is a preview release. So it may be that my observations do not apply to newer IRIS versions.
However, few people know what technology the agency chose for storing and processing the data collected by Gaia. Two years before the launch, in 2011, the developers were considering a number of candidates (see “Astrostatistics and Data Mining” by Luis Manuel Sarro, Laurent Eyer, William O’Mullane, Joris De Ridder, pp. 111-112):
IRIS and Ensemble are designed to act as an ESB/EAI. This mean they are build to process lots of small messages.
But some times, in real life we have to use them as ETL. The down side is not that they can't do so, but it can take a long time to process millions of row at once.
To improve performance, I have created a new SQLOutboundAdaptor who only works with JDBC.
BatchSqlOutboundAdapter
Extend EnsLib.SQL.OutboundAdapter to add batch batch and fetch support on JDBC connection.
One of our apps uses a class query to support a ZEN Report and works just fine in that report, producing the expected results every time. We’ve since migrated to InterSystems Reports and noticed that, for a report using the same class query, 100s of extra rows with the same column values appear at its bottom.
DataGrip is a multi-engine database environment targeting the specific needs of professional SQL developers, DataGrip makes working with databases an enjoyable and productive experience.
To work with InterSystems IRIS from DataGrip you'll need to add InterSystems JDBC driver first (once per DataGrip) and after that add all your InterSystems IRIS connections.
This is another 5-minute simple note on invoking the IRIS JDBC driver via Python 3 within i.e. a Jupyter Notebook, to read from and write data into an IRIS database instance via SQL syntax, for demo purpose.
Many organisations implement centralised log management systems to separate and centralise the log data in order to e.g. automate threat detection (and response) and to comply with regulatory requirements. The primary systems of interest are the various user facing applications, but increasingly also other kinds of systems including integration platforms.
The Caché System Management Portal includes a robust web-based SQL query tool, but for some applications it’s more convenient to use a dedicated SQL client installed on a user’s PC.
SQuirreL SQL is a well known open source SQL client built in Java, which uses JDBC to connect to a DBMS. As such, we can configure SQuirreL to connect to Caché using the Caché JDBC driver.
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.
Apache Spark has rapidly become one of the most exciting technologies for big data analytics and machine learning. Spark is a general data processing engine created for use in clustered computing environments. Its heart is the Resilient Distributed Dataset (RDD) which represents a distributed, fault tolerant, collection of data that can be operated on in parallel across the nodes of a cluster. Spark is implemented using a combination of Java and Scala and so comes as a library that can run on any JVM.
I've asked a lot of questions leading up to this, so I wanted to share some of my progress.
The blue line represents the number of messages processed. The background color represents the average response time. You can see ticks for each hour (and bigger ticks for each day). Hovering over any point in the graph will show you the numbers for that period in time.
This is super useful for "at a glance" performance monitoring as well as establishing patterns in our utilization.
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