Category Archives: Development

Saving costs with a new scheduler in Cloud Foundry Diego

Cloud Foundry

In the Mendix Cloud we run thousands of Mendix apps on Cloud Foundry on AWS. Mendix Runtime Engines that currently run in 2, 4, 8 or 16 GB memory containers. Mendix developers have the possibility to start, stop, scale and upload new versions of their app themselves via our Developer Portal.

This results in the fact that we must have diego-cell instances with at least 16 GB memory available at all times so that a Mendix developer can start their 16 GB memory Runtime Engine.

We found out the way Diego schedules LRPs (Long-Running Processes) on diego-cell EC2 instances can be more optimal in our usecase. In Diego there is only one scheduling algorithm. In a nutshell, app instances (LRPs) get deployed to a diego-cell with most resources available. This way app instances get balanced across diego-cell instances equally.

Nima and Jen did a really nice presentation during the last Cloud Foundry Summit in The Hague about how scheduling in Cloud Foundry works.

Let’s say you have a number of AWS EC2 m5.4xlarge (64 GB memory) diego-cell instances. At some point all diego-cell instances are filled up equally with app instances (LRPs) and all diego-cell instances have about 16 GB memory available. At some point this gets to 14~15 GB memory available. Then we have to add additional diego-cell instances to keep supporting the deployment of 16 GB memory Mendix Runtime Engines. But.. when deploying more app instances (LRPs) after scaling up, they get scheduled to the new diego-cell instances, also when they are 2, 4 or 8 GB app instances, until all diego-cell instances have ~16 GB available again.

In practice it looks like this (20 diego-cell instances, 64GB memory):

Graph: Remaining Memory (per diego-cell)

Result: 25% of the memory of our diego-cell instances is unused, wasted.

Now we could scale up to AWS EC2 m5.8xlarge (128 GB memory), so we only waste 12.5%, but at some point we also want to support app instances with 32 GB memory.

We have looked into isolation segments. Having for example an isolation segments per app instance size. Unfortunately that does not work for us. Mendix developers don’t notice this because its abstracted away for them, but they run different app instance sizes in one “Org” and “isolation segments” apply to an “Org”.

The quest to a new scheduling algorithm

I’ve been looking at this inefficient usage of resources for quite a while now. I also investigated how the scheduling algorithm in Diego works before Nima and Jen gave the presentation. During the Cloud Foundry Summit I had a chat with Nima if it would make sense to invest time in adding or changing the scheduler in Diego. Project Eirini was close to a version 1.0 release, where app instances run on Kubernetes. Kubernetes is more flexible with scheduling algorithms. So that could solve our issue as well.

First I thought: “No, let’s wait for Eirini.” But it would probably still take a year before we would migrate to Eirini in production. Having an improved scheduler in Diego would mean a cost saver for us right now.

Goal of the new scheduling algorithm

Mendix apps are memory heavy. In a shared environment, with running many Mendix Runtime Engines on one Cloud Foundry diego-cell instance, we notice that there is more then enough CPU resources available. Mendix developers mainly scale up their app by adding more memory (or adding more instances). So in our case we want to fill up diego-cell instances as much as possible.

How scheduling LRPs in Diego works technically

Like Nima explained in the presentation, the scheduler makes a decision where to deploy an app instance (LRP) based on a score the diego-cell instances provide. The lowest score wins. The score is calculated here:

It basically drills down to:

Score = ((Memory + Disk + Containers) / 3) + StartingContainers + Locality

  • Memory: percentage of memory that is still available
  • Disk: percentage of disk that is still available
  • Containers: percentage of containers it still can host (max 256 per diego-cell)
  • StartingContainers: number of starting containers x weight (usually 0.25)
  • Locality: 1000 when its already hosting an instance of the same app

For example:

((0.5 + 0.5 + 0.39) / 3) + 0.25 + 0 = 0.7133

  • Memory: diego-cell has 50% of its memory available
  • Disk: diego-cell has 50% of its disk available
  • Containers: diego-cell runs 100 containers (100/256)
  • StartingContainers: there is currently 1 container starting
  • Locality: this diego-cell does not run an instance of the same app

The idea: Bin Pack First Fit Weight

Scaling up and down the number of diego-cell instances is based on the index number BOSH assigns to an instance. When you add 1 diego-cell instance and after that remove 1 diego-cell instance the instance that was just created gets removed.

What if we could make a diego-cell more attractive to deploy to based on the index number it has. This way diego-cell instances with a lower index number could be filled up first. As long as it has enough resources available. This could be called Bin Pack First Fit.

The index number can be displayed using the “bosh instances” command:

$ bosh -d cf instances -i --column=instance --column=index
Instance                                                    Index
diego-cell/0342c42b-756e-4951-8280-495261e38f53            	0	
diego-cell/16be34ce-bd34-4837-8431-51f6bc4a0fa8            	1	
diego-cell/e3bec1d3-0899-4502-9f43-4049f53721b1            	2	
diego-cell/2581addf-4f08-421e-ab9d-c52772f50315            	3	

Like with “StartingContainers“, we could add some weight to the total score based on the index number a diego-cell instance has. This way it is also still possible to completely disable the Bin Pack First Fit weight component in the algorithm by setting the weight to 0 and keep the existing algorithm Diego has currently.

It will work like this:

Score = ((Memory + Disk + Containers) / 3) + StartingContainers + Locality + Index

  • Memory: percentage of memory that is still available
  • Disk: percentage of disk that is still available
  • Containers: percentage of containers it still can host (max 256 per diego-cell)
  • StartingContainers: number of starting containers x weight (usually 0.25)
  • Locality: 1000 when its already hosting an instance of the same app
  • Index: BOSH index number x weight

Let’s take the previous example, assume all diego-cell instances are filled up equally and add an index weight of 0.25:

  • diego-cell 0: ((0.5 + 0.5 + 0.39) / 3) + 0.25 + 0 + (0*0.25) = 0.7133
  • diego-cell 1: ((0.5 + 0.5 + 0.39) / 3) + 0.25 + 0 + (1*0.25) = 0.9633
  • diego-cell 2: ((0.5 + 0.5 + 0.39) / 3) + 0.25 + 0 + (2*0.25) = 1.2133
  • diego-cell 3: ((0.5 + 0.5 + 0.39) / 3) + 0.25 + 0 + (3*0.25) = 1.4633

In this case the next app instance will be deployed to diego-cell 0. Exactly what we want. The weight, 0.25 currently, can be increased to make diego-cell instances with a lower BOSH index number even more attractive.

A Proof of Concept

As a Proof of Concept the above has been developed in Diego:

To test the updated scheduling algorithm, while this is not part of the official diego-release (yet), we create a custom diego-release and use that in our Cloud Foundry setup.

NOTE: this diego-release is based on diego-release v2.34 (cf-deployment v9.5)

git clone --recurse-submodules --branch bin-pack-first-fit
cd diego-release
bosh --sha2 cr --timestamp-version --tarball=diego-release-bin-pack-first-fit-v2.34.0-5-g0b5569154.tgz --force

Upload diego-release-bin-pack-first-fit-v2.34.0-5-g0b5569154.tgz somewhere online and create an ops file to deploy this diego-release version instead of the default one:

- type: replace
  path: /releases/name=diego
    name: diego
    url: https://<your-domain.tld>/diego-release-bin-pack-first-fit-v2.34.0-5-g0b5569154.tgz
    sha1: <sha1 of diego-release-bin-pack-first-fit-v2.34.0-5-g0b5569154.tgz>
    version: <version from the `bosh --sha2 cr` command>
- type: replace
  path: /instance_groups/name=scheduler/jobs/name=auctioneer/properties/diego/auctioneer/bin_pack_first_fit_weight?
  value: 0.25

The result: Weighted Bin Pack First Fit

The result is actually pretty amazing ūüôā (15 diego-cell instances, 128GB memory):

Graph: Remaining Memory (per diego-cell)

This graph shows a 48 hour period, where the deployment pattern of Mendix app instances is equal to the previous graph. It is definitely noticeable that the added “Bin Pack First Fit Weight” has impact. App instances (LRPs) are not spread equally anymore. In this case we could remove 2 or 3 diego-cell instances, while keeping at least 2 to 3 diego-cell instances with 16 GB memory available. ūüėÄ

And the cost saver? An AWS On-Demand EC2 m5.4xlarge instance costs around $18.432 per day in AWS region us-east-1. Let’s say you run 100 diego-cell instances in total and you could now remove 20 to 25, while keeping 16 GB memory available on at least a couple of diego-cell instances. That is a saving of $368.60~$460.80 per day, $134,553.60~$168,192.00 per year of On-Demand EC2 costs. ūüėé (With Reserved or Spot Instances this is of course less)

We’re hiring

Want to join our team and work on cool stuff like this?
Apply for a job at Mendix:

Collectd Graph Panel v1

v1 is here. CGP is finished ūüėÜ

Joking aside. It has been requested multiple times. So let’s get it over with. The last version was more then 3.5 years ago. This will be the last tagged version of CGP. Every commit in the master branch after this release can be considered as a new release. ūüėČ

Use git and “git pull” to keep up-to-date or download the latest version here.

Notable Changes since v0.4.1:

  • mobile support (responsive design)
  • automatic support for all plugins (markup/styling in json)
  • hybrid graph type (canvas graph on detail page, png on the others)
  • svg graph support
  • support for newer PHP versions
  • deprecate support for collectd 4

Special thanks for this version go to Peter Wu for improving security, Manuel Luis for maintaining jsrrdgraph and Vincent Brillault for his amount of contributions.

Git: git clone

Linux bcache SSD caching statistics using collectd

In October 2012 I started using bcache as an SSD caching solution for my Debian Linux server. I’ve been very happy about it so far. Back then I used a manually compiled 3.2 Linux kernel based on the bcache-3.2 git branch provided by Kent (which has been removed). This patch needed to be applied to make bcache work with grsecurity. I also created a Debian package of the bcache-tools userspace tools to be able to create the bcache setup.

At the start of this year I moved to a 3.12 kernel, also manually compiled. It’s quiet a relief that bcache is included in mainline since the 3.10 kernel. ūüôā

This is my setup:

  1. 500GB backing device – 20GB caching device (qcow2 images)
  2. 1.3TB backing device – 36GB caching device (file storage)

The past year I’ve definitely noticed the performance difference using bcache. But I was still curious about when and how bcache was using the attached SSD. Is it using the write-back cache a lot? How many times can bcache read it’s data from the SSD cache instead of accessing the HDD?

I created a python script to collect all kinds of bcache statistics (parts of the code in this script are copied from bcache-status). This script outputs the statistics to STDOUT in a collectd exec plugin compatible way. The collectd exec plugin can be configured in collectd.conf this way:

&lt;Plugin exec&gt;
Exec "user:group" "/path/to/collectd-bcache"

To visualize the collected data I created a bcache plugin for CGP. This is the result:

bcache-cache-hit-ratio bcache-access bcache-usage bcache-bypassed

Write-back to HDD throttled

At some time I noticed that in my case flushing data from the write-back cache to the HDD was somehow rate-limited to ~3 MB/s. You can nicely see this in these graphs:

bcache-dirty-data bcache-throughput

These threads on the mailinglist of bcache mention the same thing:

Kent explained that this is managed by the PD controller in bcache. The PD controller has been rewritten in the 3.13 Linux kernel, so I’m very interested if this behavior changed. I didn’t upgrade my kernel to 3.13 yet because I’m a very cautious about it. Still a lot of development is going on at the bcache project. But I’m looking forward to upgrading to 3.13, 3.14 or probably 3.15.

CGP bcache plugin:…/bcache.json

Mendix shipped in a Docker container

Imagine… Imagine if you could setup a new Mendix hosting environment in seconds, everywhere. A lightweight, secure and isolated environment where you just have to talk to a RESTful API to deploy your MDA (Mendix Deployment Archive) and start your App.

Since the 2nd quarter of this year a great piece of software became very popular to help to achieve this goal: Docker. Docker provides a high-level API on top of Linux Containers (LXC), which provides a lightweight virtualization solution that runs processes in isolation.

Mendix on Docker


Run a Mendix App in a Docker container in seconds:

root@host:~# docker run -d mendix/mendix
root@host:~# curl -XPOST -F model=@project.mda
File uploaded.
root@host:~# curl -XPOST
Runtime downloaded and Model unpacked.
root@host:~# curl -XPOST -d "DatabaseHost=" -d "DatabaseUserName=docker" -d "DatabasePassword=docker" -d "DatabaseName=docker"
Config set.
root@host:~# curl -XPOST
App started. (Database updated)


There has been a lot of buzz around Docker since its start in March 2013. Being able to create an isolated environment once, package it up, and run it everywhere makes it very exciting. Docker provides easy-to-use features like Filesystem isolation, Resource isolation, Network isolation, Copy-on-write, Logging, Change management and more.

For more details about Docker, please read “The whole story”. We’d like to go on with the fun stuff.

Mendix on Docker

Once a month a so-called FedEx Day (Research Day, ShipIt day, Hackatron) is organized at Mendix. On that day, Mendix developers have the freedom to work on whatever they want. We’ve been playing with Docker a couple of Research Day’s ago. Just see how it works, that kind of stuff. But this time we really wanted to create something we’d potentially use in production. A proof of concept how to run Mendix on Docker.

The plan:

  1. Create a Docker Container containing all software to run Mendix
  2. Create a RESTful API to upload, start and stop a Mendix App within that container

What about the database, you may be wondering? We’ll just use a Docker container that provides us a PostgreSQL service! You can also build your own PostgreSQL container or use an existing PostgreSQL server in your network.

Start off with an image:


This is what we are building. A Docker container containing:

  • All required software to run a Mendix App, like the Java Runtime Environment and the m2ee library
  • A RESTful API (m2ee-api) to upload, start and stop an App (listening on port 5000)
  • A webserver (nginx), to serve static content and proxy App paths to the Mendix runtime (listening on port 7000)
  • When an App is deployed the Mendix runtime will be listening on port 8000 locally

Building the base container

Before we can start to install the software, we need a base image. A minimal install of an operating system like Debian GNU/Linux, Ubuntu, Red Hat, CentOS, Fedora, etc. You could download a base container from the Docker Index. But because this is so basic and we’d like to create a Mendix container we can trust 100% (a 3rd party base image could contain back-doors), we created one ourselves.

A Debian GNU/Linux Wheezy image:

debootstrap wheezy wheezy
tar -C wheezy -c . | docker import - mendix/wheezy

That’s all! Let’s show the image we’ve just created:

root@host:~# docker images
mendix/wheezy    latest    1bee0c7b9ece   6 seconds ago     218.6 MB

Building the Mendix container

On top of the base image we just created, we can start to install all required software to run Mendix. Creating a Docker container can be done using a Dockerfile. It contains all instructions to provision the container and information like what network ports to expose and what executable to run (by default) when you start using the container.

There is an extensive manual available about how to run Mendix on GNU/Linux. We’ve used this to create our Dockerfile. This Dockerfile also installs files like /home/mendix/.m2ee/m2ee.yaml, /home/mendix/nginx.conf and /etc/apt/sources.list. They must be in your current working directory when running the docker build command. All files have been published to GitHub.

To create the Mendix container run:

docker build -t mendix/mendix .

That’s it! We’ve created our own Docker container! Let’s show it:

mendix/mendix    latest    c39ee75463d6   10 seconds ago    589.6 MB
mendix/wheezy    latest    1bee0c7b9ece   3 minutes ago     218.6 MB

Our container has been published to the Docker Index: mendix/mendix


When you look at the Dockerfile, it shows you it’ll start the m2ee-api on startup. This API will listen on port 5000 and currently supports a limited set of actions:

GET  /about/        # about m2ee-api
GET  /status/       # app status
GET  /config/       # show configuration
POST /config/       # set configuration
POST /upload/       # upload a new MDA
POST /unpack/       # unpack the uploaded MDA
POST /start/        # start the app
POST /stop/         # stop the running app
POST /terminate/    # terminate the running app
POST /kill/         # kill the running app
POST /emptydb/      # empty the database


Now that we’ve created the container and published it to the Docker Index we can start using it. And not only we can start using it. Everyone can!

Pull the container and start it.

root@host:~# docker pull mendix/mendix
Pulling repository mendix/mendix
c39ee75463d6: Download complete
eaea3e9499e8: Download complete
855acec628ec: Download complete
root@host:~# docker run -d mendix/mendix
CONTAINER ID        IMAGE                      COMMAND                CREATED             STATUS              PORTS                NAMES
bd7964940dfc        mendix/mendix:latest       /bin/su mendix -c /u   19 seconds ago      Up 18 seconds       5000/tcp, 7000/tcp   tender_hawkings
root@host:~# docker inspect bd7964940dfc | grep IPAddress | awk '{ print $2 }' | tr -d ',"'

In this container the RESTful API started and is now listening on port 5000. We can for example ask for its status or show its configuration.

root@host:~# curl -XGET
The application process is not running.
root@host:~# curl -XGET
"DatabaseHost": "",
"DTAPMode": "P",
"MicroflowConstants": {},
"BasePath": "/home/mendix",
"DatabaseUserName": "mendix",
"DatabasePassword": "mendix",
"DatabaseName": "mendix",
"DatabaseType": "PostgreSQL"

To run an App in this container, we first need a database server. Pull a PostgreSQL container from the Docker Index and start it.

root@host:~# docker pull zaiste/postgresql
Pulling repository zaiste/postgresql
0e66fd3d6a6f: Download complete
27cf78414709: Download complete
046559147c70: Download complete
root@host:~# docker run -d zaiste/postgresql
root@host:~# docker ps
CONTAINER ID        IMAGE                      COMMAND                CREATED             STATUS              PORTS                NAMES
9ba56a7c4bb1        zaiste/postgresql:latest   /bin/su postgres -c    22 seconds ago      Up 21 seconds       5432/tcp             jolly_darwin
bd7964940dfc        mendix/mendix:latest       /bin/su mendix -c /u   30 seconds ago      Up 29 seconds       5000/tcp, 7000/tcp   tender_hawkings
root@host:~# docker inspect 9ba56a7c4bb1 | grep IPAddress | awk '{ print $2 }' | tr -d ',"'

Now configure Mendix to use this database server.

root@host:~# curl -XPOST -d "DatabaseHost=" -d "DatabaseUserName=docker" -d "DatabasePassword=docker" -d "DatabaseName=docker"
Config set.
root@host:~# curl -XGET
"DatabaseHost": "",
"DTAPMode": "P",
"MicroflowConstants": {},
"BasePath": "/home/mendix",
"DatabaseUserName": "docker",
"DatabasePassword": "docker",
"DatabaseName": "docker",
"DatabaseType": "PostgreSQL"

Upload, unpack and start an MDA:

root@host:~# curl -XPOST -F model=@project.mda
File uploaded.
root@host:~# curl -XPOST
Runtime downloaded and Model unpacked.
root@host:~# # set config after unpack (unpack will overwrite your config)
root@host:~# curl -XPOST -d "DatabaseHost=" -d "DatabaseUserName=docker" -d "DatabasePassword=docker" -d "DatabaseName=docker"
Config set.
root@host:~# curl -XPOST
App started. (Database updated)

Check if the application is running:

root@host:~# curl -XGET
-- a lot of html --
root@host:~# curl -XGET
-- a lot of html --

Great success! We’ve deployed our Mendix App in a completely new environment in seconds.


Docker is a very powerful tool to deploy lightweight, secure and isolated environments. The addition of a RESTful API makes it very easy to deploy and start Apps.

One of the limitations after finishing this is that the App isn’t reachable from the outside world. The port redirection feature from Docker can be used for that. To run more Mendix containers on one host there must be some kind of orchestrator on the Docker host that administrates the containers and keeps track of what is running where.

The RESTful API provides a limited set of features in comparison with m2ee-tools. When you start your App using m2ee-tools and your database already contains data, the CLI will ask you kindly what to do. Currently the m2ee-api will just try to upgrade the database scheme if needed and start the App without a notice.

Collectd Graph Panel v0.4

After 2,5 years and about 100 commits I’ve tagged version 0.4 of Collectd Graph Panel.

This version includes a new interface with a sidebar for plugin selection.

Javascript library jsrrdgraph has been integrated. Graphs will be rendered in the browser using javascript and HTML5 canvas by setting the “graph_type” configuration option to “canvas”. This saves a lot of CPU power on the server. Jsrrdgraph has some nice features. When rendered, you can move through time by dragging the graph from left to right and zoom in and out by scrolling on the graph.


The Collectd compatibility setting has been changed to Collectd 5. If you’re still using Collectd 4, please set the “version” configuration setting to “4”, otherwise the graphs of a couple plugins won’t be showed right (like the interface, df, users plugins).

In this version of CGP, total values are added to the legend of I/O graphs and generated colors will be created using a rainbow palette instead of 9 predefined colors. Please read the changelog or git log for more information about the changes.

New plugins:

Special thanks for this version go to Manuel Luis, who developed jsrrdgraph,¬†xian310 for the new interface, Manuel CISS√Č, Rohit Bhute, Matthias Viehweger, Erik Grinaker, Peter Chiochetti, Karol Nowacki, Aur√©lien Rougemont, Benjamin Dupuis, yur, Philipp Hellmich, Jonathan Huot, Neptune Ning and Nikoli for their contributions.

I’ve been using GitHub for a while now. You can download or checkout this version of CGP from the GitHub URL’s below. When you have improvements or fixes for CGP, don’t hesitate to send in a Pull Request on GitHub!

v0.4.1 update

I just removed the dependency on mod_rewrite when using jsrrdgraph to draw the graphs. This may solve javascript error:¬†Invalid RRD: “Wrong magic id.” undefined.

Git: git clone

Bonnie++ to Google Chart

Bonnie++ is the perfect tool to benchmark disk I/O performance. When you want to run heavy disk I/O consuming applications on a server, you need to know what the disk I/O limits are to have an indication what you can run on it. Bonnie gives you information about read-, write-, rewrite Block I/O performance and the performance of file creation and deletion.

Below is a sample piece of Bonnie++ output. It is hard to read, and when you’ve done a couple of runs on different hosts, it’s hard to compare these results.

Version  1.96       ------Sequential Output------ --Sequential Input- --Random-
Concurrency   1     -Per Chr- --Block-- -Rewrite- -Per Chr- --Block-- --Seeks--
Machine        Size K/sec %CP K/sec %CP K/sec %CP K/sec %CP K/sec %CP  /sec %CP
host3           16G  1028   0 94051   0 40554   0  1668   0 121874   0 380.3   0
Latency             16101us    7694ms    7790ms   13804us     124ms   76495us
Version  1.96       ------Sequential Create------ --------Random Create--------
host3               -Create-- --Read--- -Delete-- -Create-- --Read--- -Delete--
              files  /sec %CP  /sec %CP  /sec %CP  /sec %CP  /sec %CP  /sec %CP
                256 50115   0 332943   0  2338   0 51591   0 +++++ +++  1189   0
Latency               312ms    1088us   10380ms     420ms      15us   15792ms

Inspired by bonnie-to-chart I’ve developed a tool called bonnie2gchart. With the CSV data Bonnie provides after a run, you can visualize that information in a graph with bonnie2gchart. bonnie2gchart uses the Google Chart API to create the graphs. It’s compatible with Bonnie++ 1.03 and 1.96. And it is also able to show the results of both versions in one graph.

Example chart

Git: git://

OTRS the RT way

I have used RT (Request Tracker) for quite a while. Starting with RT 2.0.15 + MySQL. Later upgraded that version manually (with a couple of nasty PHP scripts) to RT 3.6.x + PostgreSQL. It worked and stayed consistent. I’ve never been very happy about RT. It now contains about 80K tickets and it is slow. I have been reading a lot of forum/mailinglist threads about migrating from OTRS to RT and RT to OTRS because RT or OTRS should be faster. Unfortunately I’ve not found a real conclusion.

While starting a new project I gave OTRS a try. Just because I think it doesn’t really matter if you use RT or OTRS. I liked the way some things worked in RT, so I created some patches to make OTRS work a little bit like RT.

Empty body check

First of all the body part in OTRS is required for some reason. This is annoying. For example when a ticket is resolved and a customer replies to thank you, you just want to close the ticket. Or when you just want to change the owner of a ticket. This patch disables the emtpy body check:


RT-like Dashboard

OTRS: RT-like DashboardIn RT you are responsible for your own tickets. The RT 3.6 dashboard shows you the most recent open tickets you own. And it shows the most recent unowned new tickets. OTRS shows all open tickets, instead of only the ones you own. OTRS also shows all new tickets including the ones that are already locked.

This patch adds a new module called DashboardTicketRT. It’s a customized DashboardTicketGeneric module to make your dashboard work like RT. After you have applied the patch, you can use this module with the following steps:

  1. Login as admin
  2. Go to Sysconfig
  3. Go to Frontend::Agent::Dashboard (Group: Ticket)
  4. In TicketNew change:
    • Attributes: StateType=new; to StateType=new;OwnerIDs=1;
    • Module: Kernel::Output::HTML::DashboardTicketGeneric to Kernel::Output::HTML::DashboardTicketRT
  5. In TicketOpen change:
    • Attributes: StateType=open; to StateType=open;StateType=new;OwnerIDs=UserID;
    • Module: Kernel::Output::HTML::DashboardTicketGeneric to Kernel::Output::HTML::DashboardTicketRT
    • Title: Open Tickets / Need to be answered to My Tickets / Need to be answered
  6. Restart your server


Instant Close / Spam

OTRS: InstaClose linkTo work a little bit more efficient this patch adds an InstaClose and InstaSpam link to each ticket. With a single click you can close a ticket or move a ticket to the Junk queue. The patch adds a new module called AgentTicketInsta. You must add this code to Kernel/ to enable it:

$Self->{'Frontend::Module'}->{'AgentTicketInsta'} = {
 Group       => [ 'admin', 'users' ],
 Description => 'Instant Ticket Actions',
 Title       => 'Insta',


Collectd Graph Panel v0.3

It has been a while and many people were already using one of the development versions of CGP. Time to release a new version of CGP: v0.3.

Special thanks for this version go to Manuel CISS√Č, Jakob Haufe, Edmondo Tommasina, Michael Stapelberg, Julien Rottenberg and Tom Gallacher for sending me patches to improve CGP. Also thanks to everyone that replied after the previous release.

In this version there are a couple of minor improvements and some new settings to configure. But the most important: Support has been added for 13 Collectd plugins. CGP now supports 26 Collectd plugins. Also a couple of plugins have been updated. Please read the changelog or git log for more information about the changes.

New plugins:

Download the .tgz package, the patch file to upgrade from v0.2 or checkout the latest version from the git repository.


Collectd Graph Panel v0.2

Version 0.2 of Collectd Graph Panel (CGP) has been released. This version has some interesting new features and changes since version 0.1.

A new interface is introduced, based on Daniel Von Fange’s styling. A little bit of web 2.0 ajax is used to expand and collapse plugin information on the host page. The width and heigth of a graph is configurable, also the bigger detailed one. UnixSock flush support is added to be able to let collectd write cached data to the rrd file, before an up-to-date graph of the data is generated. CPU support for Linux 2.4 kernel and Swap I/O support is added. And some changes under the hood.

Download the .tgz package, the patch file to upgrade from v0.1 or checkout the latest version from my public git repository.

CGP Overview Page CGP Server Page CGP Detailed Page