An Introduction to Riemann

If only I had the theorems! Then I should find the proofs easily enough - Bernard Riemann

For the last year I’ve been using nights and weekends to look to a variety of monitoring and logging tools. For reasons. I’ve spent a lot of hours playing with Nagios again (some years ago I wrote a book about it) as well as looking at tools like Sensu and Heka. One of the tools I am reviewing and am quite excited about is Riemann.

Riemann is a monitoring tool that aggregates events from hosts and applications and can feed them into a stream processing language to be manipulated, summarized or actioned. The idea behind Riemann is to make monitoring and measuring events an easy default. Riemann also provides alerting and notifications, the ability to send events onto other services and storage and a variety of other integrations. Overall, Riemann is fast and highly configurable. Most importantly however it is an event-centric push model.

So why does this matter? Most monitoring systems I’ve been examining are pull or polling-based systems like Nagios where your monitoring system queries the components being monitored. A classic (perhaps even traditional) check might be an ICMP-based ping of a server. This type of polling is focused on measuring uptime and availability. There’s nothing fundamentally wrong with wanting to know that assets are available and running. Except if that’s the only question you ask. Then it reinforces the view of IT as a cost center.1 Everything in the IT organization tends to be focused around minimizing downtime rather than maximizing value.

Push based models in comparison are generally about measurement. You still get availability measurement but as a side effect of measuring components and services. The push model also introduces some changes in the way monitoring is architected. Monitoring is no longer a monolithic central function and we don’t need to vertically scale that monolith as hosts are added. Instead pushes are decentralized and the focus is on measuring your applications, your business and your user experience. This changes the focus inside your IT organization towards measuring value, throughput and performance. All levers that are about profit rather than cost.2

So with this in mind, let’s take a look at installing Riemann, configuring it and doing some basic service and event monitoring.

Introducing Riemann 🔗︎

Riemann is open source and licensed with the Eclipse Public license. It is primarily authored by Kyle Kingsbury aka Aphyr.3 Riemann is written in Clojure and runs on top of a JVM.

Installing Riemann 🔗︎

We’re going to install Riemann onto an Ubuntu 14.04 host. We’re going to use the Riemann project’s DEB packages. Also available are RPM packages and tarballs. I am going to do a manual install so you can see the steps involved but you could also install Riemann via Docker, Puppet, Vagrant, or Chef.

First, we’ll need Java and Ruby installed. The Java to run Riemann itself and Ruby for some supporting libraries, a client and the Riemann dashboard. For Java we’re going to use the default OpenJDK available on Ubuntu. For Ruby we’re going to install the ruby-dev package which will drag in Ruby and all the required dependencies we need. We also need the build-essential package to allow us to compile some of the Ruby dependencies.

$ sudo apt-get -y install default-jre ruby-dev build-essential

Then let’s check Java is installed correctly.

$ java -version
java version "1.7.0_65"
OpenJDK Runtime Environment (IcedTea 2.5.3) (7u71-2.5.3-0ubuntu0.14.04.1)
OpenJDK 64-Bit Server VM (build 24.65-b04, mixed mode)

Now let’s grab the DEB package of the current release.

$ wget

And then install it via the dpkg command.

$ sudo dpkg -i riemann_0.2.8_all.deb

The Riemann DEB package installs the riemann binary and supporting files, service management and a default configuration file.

Lastly, let’s install some supporting tools, the Riemann client and dashboard.

$ sudo gem install --no-ri --no-rdoc riemann-client riemann-tools riemann-dash

Running Riemann 🔗︎

We can run Riemann interactively via the command line or as a daemon. If we’re running it as a daemon we can use the Ubuntu service management commands:

$ sudo service riemann start
$ sudo service riemann stop
. . .

Let’s start though with running it interactively using the riemann binary. To do this we need to specify a configuration file. Conveniently the installation process has added one at /etc/riemann/riemann.config.

$ sudo riemann /etc/riemann/riemann.config
loading bin
INFO [2014-12-21 18:13:21,841] main - riemann.bin - PID 18754
INFO [2014-12-21 18:13:22,056] clojure-agent-send-off-pool-2 - riemann.transport.websockets - Websockets server 5556 online
INFO [2014-12-21 18:13:22,091] clojure-agent-send-off-pool-4 - riemann.transport.tcp - TCP server 5555 online
INFO [2014-12-21 18:13:22,099] clojure-agent-send-off-pool-3 - riemann.transport.udp - UDP server 5555 16384 online
INFO [2014-12-21 18:13:22,102] main - riemann.core - Hyperspace core online

We can see that Riemann has been started and a couple of services have been started: a Websockets server on port 5556 and TCP and UDP servers on port 5555. By default Riemann binds to localhost only.

The default configuration on Ubuntu logs to /var/log/riemann/riemann.log and you can also follow the daemon’s activity there.

Configuring Riemann 🔗︎

Riemann is configured using a Clojure configuration file, by default on Ubuntu it is available at /etc/riemann/riemann.config. Let’s take a quick look at the default file.

; -*- mode: clojure; -*-
; vim: filetype=clojure

(logging/init {:file "/var/log/riemann/riemann.log"})

; Listen on the local interface over TCP (5555), UDP (5555), and websockets
; (5556)
(let [host ""]
(tcp-server {:host host})
(udp-server {:host host})
(ws-server  {:host host}))

; Expire old events from the index every 5 seconds.
(periodically-expire 5)

(let [index (index)]
; Inbound events will be passed to these streams:
  (default :ttl 60
    ; Index all events immediately.

    ; Log expired events.
      (fn [event] (info "expired" event))))))

We can see the file is broken into a few stanzas. The first stanza sets up Riemann’s logging to a file: /var/log/riemann/riemann.log. The second stanza controls Riemann’s interfaces: binding TCP, UDP and Websockets interfaces to localhost by default. Let’s make a quick change here to bind these interfaces to all available networks.

(let [host ""]
(tcp-server {:host host})
(udp-server {:host host})
(ws-server  {:host host}))

We’ve updated the host value from to This means if one of your interfaces is on the Internet then your Riemann server is now on the Internet. If you’re worried about security you can also configure Riemann with TLS.

The remaining sections configure indexing and streams. Streams are a big part of why Riemann is very cool. Streams are functions you can pass events to for aggregation, modification, or escalation. Streams can also have child-streams that they can pass events to, allowing filtering or partitioning of the event stream. Using streams is amazingly powerful and you can find sample configurations and a wide variety of howtos on the Riemann site.

Let’s make a small change to our streams stanza to output events to STDOUT and our log file. Add the following at the bottom of the file after all of the other stanzas.

;print events to the log

  #(info %))

The prn prints all events to STDOUT and the #(info %) sends events to the log file. Now restart Riemann to enable our new configuration.

Sending data to Riemann 🔗︎

Riemann has a variety of ways you can send data to it including a set of tools and a variety of client native language bindings. You can find a full list of the clients here and we’ll see how to use a client below. The collection of tools are written in Ruby and available via the riemann-tools gem we installed above. Each tool ships as a separate binary and you can see a list of the available tools here. They include basic health checks, web services like Apache and Nginx, Cloud services likes AWS and a variety of others. The code is clear and you could easily extend or adapt these to provide a variety of other monitoring capabilities.

The easiest of these tools to test is riemann-health. It sends CPU, Memory and load statistics to Riemann. Open up a new session and launch it now.

$ riemann-health

You can either run it locally on the same host you’re running Riemann on or you can point it at a Riemann server using the --host flag.

$ riemann-health --host

Remember the default Riemann is only bound to localhost but we updated our configuration to bind to all interfaces.

Now let’s look at our incoming data. Let’s start with looking at the Riemann log file.

$ tail -f /var/log/riemann/riemann.log
INFO [2014-12-23 17:23:47,050] pool-1-thread-16 - riemann.config - #riemann.codec.Event{:host, :service disk /, :state ok, :description 11% used, :metric 0.11, :tags nil, :time 1419373427, :ttl 10.0}
INFO [2014-12-23 17:23:47,055] pool-1-thread-18 - riemann.config - #riemann.codec.Event{:host, :service load, :state ok, :description 1-minute load average/core is 0.11, :metric 0.11, :tags nil, :time 1419373427, :ttl 10.0}
. . .

Here we can see a couple of events, one for disk space and another for load. Each Riemann event is a struct. Each event can contain one of a number of optional fields including: host, service, state, a time and description, a metric value or a TTL. They can also contain custom fields.

Let’s examine one of the disk events riemann-health has sent:

:host, :service disk /, :state ok, :description 11% used, :metric 0.11, :tags nil, :time 1419373427, :ttl 10.0

We can see the event has a host, service, and state. If we peek over at the code that produced the event we can how it is generated and sent. As event APIs go it’s very lightweight but still hugely extensible.

Let’s try another tool, riemann-varnish, which reports Varnish metrics. On one of my hosts with Varnish installed I run.

$ riemann-varnish --host

And on the Riemann host I see in /var/log/riemann/riemann.log.

INFO [2014-12-24 02:01:41,660] pool-1-thread-19 - riemann.config - #riemann.codec.Event{:host, :service varnish client_conn, :state ok, :description Client connections accepted, :metric 13795.0, :tags nil, :time 1419404501, :ttl 10.0}
INFO [2014-12-24 02:01:41,706] pool-1-thread-21 - riemann.config - #riemann.codec.Event{:host, :service varnish client_drop, :state ok, :description Connection dropped, no sess/wrk, :metric 0.0, :tags nil, :time 1419404501, :ttl 10.0}
INFO [2014-12-24 02:01:41,751] pool-1-thread-22 - riemann.config - #riemann.codec.Event{:host, :service varnish client_req, :state ok, :description Client requests received, :metric 15452.0, :tags nil, :time 1419404501, :ttl 10.0}

And to drill down to a specific event.

:host, :service varnish client_conn, :state ok, :description Client connections accepted, :metric 13795.0, :tags nil, :time 1419404501, :ttl 10.0

Here we can see the Varnish client connections accepted metric. If we look at the riemann-varnish code we can see a shell-out to varnishstat that captures our metrics and sends them to Riemann. Pretty easy to replicate for a variety of services.

If you think the shell-out and parse is a little clumsy then we can also write our own tool or use the Riemann client directly. Let’s embed Riemann into a Sinatra application.

require 'rubygems'
require 'sinatra'
require 'riemann/client'
require 'socket'

configure do
  set :bind, ''

get '/' do
  send_event(metric = rand)
  '<h1>This does something awesome</h1>'

def send_event(metric)
  c = host: 'localhost', port: 5555, timeout: 5
  c << {
    host: Socket.gethostname,
    service: 'something awesome',
    metric: metric,
    description: "What an awesome number: #{metric}",
    time: - 10

Our Sinatra app is very basic. It responds on / with the HTML: <h1>This does something awesome</h1>. As part of that connection it also sends an event to Riemann using the Riemann client we installed earlier.

To do this we’ve required the riemann/client and inside the send_event method we’ve connected to the Riemann host on localhost. This method then accepts a metric, which is a random number created by the rand method, from the get block and sends that metric with an event.

If we run this app (you might need to gem install sinatra to install Sinatra first).

$ ruby riemann_sinatra.rb

And then look at our Riemann logs we’ll see an event much like this:

:host, :service something awesome, :state nil, :description What an awesome number: 0.9984397664300542, :metric 0.9984397664300542, :tags nil, :time 1419449388, :ttl nil

Displaying Riemann events 🔗︎

Obviously reading events from the log output isn’t overly practical or useful. To allow you to work with your events Riemann comes with a dashboard. It’s a Sinatra application and we already installed it via the riemann-dash gem.

Let’s start it now.

$ riemann-dash

You can then view it on port 4567 on the localhost. You can also change the dashboard’s configuration by creating a config.rb file in the directory from which you’ve launch the dashboard. This provides control over where and how the dashboard binds and some other configuration options.

Riemann Dashboard

The dashboard is a little janky in places but can produce some excellent dashboards. The dashboard is made up of view panels that are configurable. You can select or add a view using the boxes and plus symbol in the top left of the dashboard.

We just want to see the events coming into our dashboard though. So let’s edit our current view to show those events. First, Ctrl-Click (or Meta-Click on OSX) on the big Riemann title in the centre top of the dashboard to select this view. This will highlight it gray (The Escape key de-selects the view). Now type “e” to edit the view.

Edit Riemann Dashboard

Change the view from Title to Grid and then put true into the query box.

Edit Riemann Dashboard

This will change this view into a grid, which shows a table of events, and select all events, the true in the query box. This is the simplest query you can create but you can do much more. To get started you can find some sample queries here).

Now you should see some of the events you’re generating displayed in a per-host grid.

Edit Riemann Dashboard

If you’re not taken with the Riemann dashboard there is a Grid layout alternative or for graphing you could direct all your metrics to Graphite which has a very fully-featured dashboard.

Summary 🔗︎

We’ve barely scratched the surface of Riemann’s capabilities with this introduction. From here we could configure a variety of streams, matching events by service or host, and convert our events into summaries, metrics and collections.4 We can take alerting actions (email, PagerDuty) based on everything from failed services (replace Nagios anyone?), to metric thresholds, or even Holt-Winters anomaly detection. We can also send data onto longer-term storage or into other tools like Graphite. The Riemann HOWTO has a number of examples and ideas to help you build your Riemann environment further. I really recommend taking a look at Riemann if you’re interested in where modern monitoring is headed.

  1. It also tends to reward conservatism and fear of change. ↩︎

  2. This is a highly simplistic analysis of the potential for change in IT monitoring behaviour. Your mileage may vary. ↩︎

  3. Kingsbury also published an excellent series on the CAP properties of a variety of distributed systems. ↩︎

  4. Of couse there’s even a Puppet Riemann report processor. ↩︎

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