At Rancher Labs we generate a lot of logs in our internal environments. As we conduct more and more testing on these environments we have found the need to centrally aggregate the logs from each environment. We decided to use Rancher to build and run a scalable ELK stack to manage all of these logs.
For those that are unfamiliar with the ELK stack, it is made up of Elasticsearch, Logstash and Kibana. Logstash provides a pipeline for shipping logs from various sources and input types, combining, massaging and moving them into Elasticsearch, or several other stores. It is a really powerful tool in the logging arsenal.
Elasticsearch is a document database that is really good at search. It can take our processed output from Logstash, analyze and provides an interface to query all of our logging data. Together with Kibana, a powerful visualization tool that consumes Elasticsearch data, you have amazing ability to gain insights from your logging.
Previously, we have been using Elastic’s Found product and have been very impressed. One of the interesting things we realized while using Found for Elasticsearch is that the ELK stack really is made up of discrete parts. Each part of the stack has its own needs and considerations. Found provided us Elasticsearch and Kibana. There was no Logstash end point provided, though it was sufficiently documented how to use Found with Logstash. So, we have always had to run our own Logstash pipeline.
Our Logstash implementation includes three tiers, one each for collection, queueing and processing.
Collection- responsible for providing remote endpoints for logging inputs. Like Syslog, Gelf, Logstash. Once it receives these logs it places them quickly onto a Redis Queue.
Queuing tier – provided by Redis, a very fast in memory database. It acts as a buffer between the collection and processing tier.
Processing tier – removes messages from the queue, and applies filter plugins to the logs that manipulate the data to a desired format. This tier does the heavy lifting and is often a bottleneck in a log pipeline. Once it processes the data it forwards it along to the final destination, which is Elasticsearch.
Each Logstash container has a configuration sidekick that provides configuration through a shared volume.
By breaking the stack into these tiers, you can scale and adapt each part without major impact to the other parts of the stack. As a user, you can also scale and adjust each tier to suit your needs. A good read on how to scale Logstash can be found on Elastic’s web page here: Deploying and Scaling Logstash.
To build the Logstash stack we stared as we usually do. In general, we try to reuse as much as possible from the community. Looking at the DockerHub registry, we found there is already an official Logstash image maintained by Docker. The real magic is in configuration of Logstash at each of the tiers. To achieve maximum flexibility with configuration, we built a confd container that consumes KV, or Key Value, data for its configuration values.
The logstash configurations are the most volatile, and unique to an organization as they provide the interfaces for the collection, indexing, and shipping of the logs. Each organization is going to have different processing needs, formatting, tagging etc. To achieve maximum flexibility we leveraged the confd tool and Rancher sidekick containers. The sidekick creates an atomic scheduling unit within Rancher. In this case, our configuration container exposes the configuration files to our Logstash container through volume sharing. In doing this, there is no modification needed to the default Docker Logstash image. How is that for reuse!
Elasticsearch is built out in three tiers as well. When reading the production deployment recommendations, it discusses having nodes that are dedicated masters, data nodes and client nodes. We followed the same deployment paradigm with this application as the logstash implementation. We deploy each role as a service. Each service is composed of an official image and paired with a Confd sidekick container to provide configuration.
It ends up looking like this:
Each tier in the Elasticsearch stack has a confd container providing configurations through a shared volume. These containers are scheduled together inside of Rancher.
In the current configuration, we use the master service to provide node discovery. When using the Rancher private network, we disable multicast and enable unicast. Since every node in the cluster points to the master they can talk to one another. The Rancher network also allows the nodes to talk to one another. As a part of our stack, we also use the Kopf tool to quickly visualize our clusters health and perform other maintenance tasks. Once you bring up the stack you will see that you can use Kopf to see that all the nodes came up in the cluster.
Finally, in order to view all of these logs and make sense of the data, we bring up Kibana to complete our ELK stack. We have chosen to go with Kibana 4 in this stack. Kibana 4 is launched with an Nginx container to provide basic auth behind a Rancher load balancer. The Kibana 4 instance is the Official image which is hosted on DockerHub. The Kibana 4 image talks to the Elasticsearch client nodes.
So now we have a full ELK stack for taking logs and shipping them to Elasticsearch for visualization in Kibana. The next step is getting the logs from the hosts running your application.
Bringing up the Stack on Rancher
So now you have the backstory on how we came up with our ELK stack configuration. Here are instructions to run the ELK stack on Rancher. This assumes that you already have a Rancher environment running with at least one compute node. We will also be using the Rancher compose CLI tool. Rancher-compose can be found on GitHub here rancher/rancher-compose. You will need API keys from your Rancher deployment. In the instructions below, we will bring up each component of the ELK stack, as its own stack in Rancher. A stack in Rancher is a collection of services that make up an application, and are defined by a Docker Compose file. In this example, we will build the stacks in the same environment and use cross stack linking to connect services. Cross stack linking allows services in different stacks to discover each other through a DNS name.
rancher-compose -p es up (Other services assume es as the elasticsearch stack name)
This will bring up four services.
Once Kopf is up, click on the container in the Rancher UI, and get the IP of the node it is running on.
Open a new tab in your browser and go to the IP. You should see one datanode on the page.
Now lets bring up our Logstash tier.
rancher-compose -p logstash up
This will bring up the following services
At this point, you can point your applications at logtstash://host:5000.
(Optional) Install logspout on your nodes
rancher-compose -p logspout up
This will bring up a logspout container on every node in your Rancher environment. Logs will start moving through the pipeline into Elasticsearch.
Finally, lets bring up Kibana 4
rancher-compose -p kibana up
This will bring up the following services
Click the container in the kibana-vip service in the Rancher UI. Visit the host ip in a separate browser tab. You will be directed to the Kibana 4 landing page to select your index.
Now that you have a fully functioning ELK stack on Rancher, you can start sending your logs through the Logstash collector. By default the collector is listening for Logstash inputs on UDP port 5000. If you are running applications outside of Rancher, you can simply point them to your Logstash endpoint. If your application runs on Rancher you can use the optional Logspout-logstash service above. If your services run outside of Rancher, you can configure your Logstash to use Gelf, and use the Docker log driver. Alternatively, you could setup a Syslog listener, or any number of supported Logstash input plugins.
Running the ELK stack on Rancher in this way provides a lot of flexibility to build and scale to meet any organization’s needs. It also creates a simple way to introduce Rancher into your environment piece by piece. As an operations team, you could quickly spin up pipelines from existing applications to existing Elasticsearch clusters.
Using Rancher you can deploy applications following container best practices by using sidekick containers to customize standard containers. By scheduling these containers as a single unit, you can separate your application out into separate concerns.
On Wednesday, September 16th, we hosted an online meetup focused on container logging, where I demonstrated how to build and deploy your own ELK stack. If you’d like to view a recording of this you can view it here.
If you’d like to learn more about using Rancher, please join us for an upcoming online meetup, or join our beta program or request a discussion with one of our engineers.