On Tuesday, May 22, 2018 at 1PM EDT, we'll be hosting a webcast introducing the recently released InformixHQ. Check out this blog to learn more about the upcoming webinar.
Modernized monitoring, alerting, and administration for Informix. Check out our latest blog on InformixHQ!
It’s 6:02am, a cool winter morning in Friedrichshafen, Germany. It’s dark night outside. My friend Richard Luft, our partner at www.bytec.eu, told me he would pick me up at 6:00am at the hotel. I looked out through the window and while I couldn’t see the Lake Constanze, I could see his car right there, of course, and I knew I was very late now. I rushed up and by 6:04 I sat in, apologizing for my tardiness. “No problem” he said, “I used this time to buy a ticket online for a show that I saw advertised in one of the major TV channels, by the way, did you know that the biggest online ticket sales in Germany runs on Informix?”, I quickly replied “and did you know that that TV channel is part of the biggest TV group and it also runs in Informix?”. Smiles.
Compression of TimeSeries data was first introduced in 12.10.xC3. This feature provides compression algorithms on sensor data to maximize the storage efficiency. It was limited to numeric types (smallint, integer, bigint, real, and float). While this worked well for applications that only captured numeric sensor data, there are many use cases where other data is captured that is not numeric. This latest enhancement to TS compression supports these applications capturing non-numeric data from the sensors.
For years, Informix has been powering the world’s most critical business applications. Thousands of clients globally trust the reliability of Informix, with its legendary almost zero downtime and low administration required. Many clients have been leveraging its unique features like NoSQL, Pattern Matching, Smart Triggers, Time Series, and in-database analytics capabilities. More recently with the rise of IoT, Informix’s small footprint and “set it and forget it” capability have made it attractive to full-stack IoT solution developers as they embed Informix in millions of devices and sensors.
One of the key features that makes Informix the ideal choice as an IoT database is its built-in support for MQTT protocol. It allows users to publish data to an Informix database directly via MQTT. This article will show the users how to do it and we will be using the informix sample code that is available in the GitHub repository - https://github.com/informix/samples-MQTT-python.
In this post we look at methods to define data marts in the Informix Warehouse Accelerator (IWA). IWA is a software appliance for the Informix database server, using in-memory, columnar data storage for business intelligence applications doing OLAP. For such workloads IWA typically can achieve a query acceleration of 10 - 100 times faster than running the workload within the Informix database server.
This feature was introduced in the Informix 12.10.xC10 fixpack release. It allows you to assign a character string to your CSDK or JDBC client session and identify that character string on the database server. This is useful for environments where same userid runs multiple instances of the same application, and there is a need to distinguish one session from the other.
Hopefully you have an established routine of local backups or cloud backups, and you should be sleeping a little better (when you get a chance to sleep). And like fire-drills or other emergency procedures, you are probably practicing a restore of your backups on another server. You have probably discovered, depending on the size of your data and speed of the system, this restore may take hours. Moving from ontape to onbar helped by enabling parallel backup and restore of your dbspaces, but while backups can happen with the lights on, during a restore, the server won’t be accessible until the restore is complete. You may not be able to afford for your applications to be down that long.
The headline here is that BLOBspace BLOBs can now be compressed. We made additional enhancements to partition BLOB compression and the auto-compress feature in general. Details follow.