Fluentd is a unified logging layer that collects all sorts of logs and processes them into a unified format. The sources of the data can be access logs, weblogs, system logs, database logs, infrastructure logs, or just about any type of logs that you can think of. This data can then be used for things such as archiving information, creating alerts for certain situations, or being further processed by some analysis tool.
Consistency is key when it comes to fluentd. It needs to run continuously and gather as much data as possible without fail. This means that it can't, for any reason, stop working. This is the architecture on which fluentd was built.
Now, you might think that this sounds a little bit like Logstash. That's because it is. If you are using fluentd, then you don't have to also use LogStash. However, the rest of the ELK stack is fully usable alongside fluentd.
To start, you need to deploy fluentd to your cluster. Once that's done, Fluentd will start collecting logs from each and every application. This includes applications that you have created yourself, as well as any third-party applications that you may be using. All of these applications may choose to output logs in different ways, and this is where Fluentd steps in again.
Fluentd will process all this data and create and group everything into a single file format. You could then choose to add additional data that you may deem useful, such as what pod the log came from, what namespace it was in, and so on. This means that later if you choose to group logs by pod name or namespace, you can do this easily.
Now that the data has all been collected and transformed, it's time to store it. This is where Elasticsearch comes back into the picture. Of course, there is no limitation as to what type of data store is used. For instance, you could just as well use MongoDB or RabbitMQ. But hopefully seeing Elasticsearch being used can give you an idea as to where fluentd fits in with the rest of the architecture.
While there is no limitation saying that you can't use MongoDB instead of Elasticsearch, there is also no limitation saying that you can't use both at the same time. Thanks to the routing flexibility provided by fluentd, you can have some set of logs stored in a database, while the rest of the logs get stored in a separate data store.
Fluentd runs in your cluster as a DaemonSet. If you need a refresher on what DaemonSets are, head over to the DaemonSet101 section of this course. In short, a DaemonSet is a component that runs on each node, which is ideal for fluentd since it is supposed to be present everywhere. You configure fluentd using a configuration file, which can be complex to configure but allows for a great deal of flexibility and configuration using fluentd plugins. If you want to see what plugins exist for fluentd, take a look at the list of all available plugins. You'll notice that it covers everything from Kafka to Windows metrics in terms of what data it can process.
The first part of setting up fluentd is to introduce an input plugin. This corresponds to the source of your data. For instance, if you were using s3 as your input source, you would use the s3 plugin. The full list of available input and output plugins can be found here.
Next comes the parser plugins. A parser is something that reads and understands data, and a parser plugin does more or less the same thing in this context. For example, you may need to parse some JSON input, for which you would use a plugin such as the lazy JSON parser. A full list of available parser plugins is listed on the same page.
After that, you use the filter plugins. Remember that earlier, I mentioned how we could transform the data and add different information to it, such as the pod name and namespace which will help you filter the logs later? Well, this is what filter plugins are for. For instance, if you want to attach the geographical information related to a log, you could use the geoip plugin. A list of all available filter plugins is available here.
Finally comes the output plugins. The output plugins share the same list of plugins as input plugins since data sources and data storage tends to go both ways.
Additionally, there are formatter plugins, which number significantly lower compared to all other plugin types, and will likely be left unused in most use cases. Formatter plugins create custom output formats in case the format given by an output plugin doesn't match your requirements. You can extend the output provided and turn it into whatever you like.
So, that's the full process, but we still haven't seen what an actual configuration file looks like. Let's remedy that.
The fluentd configuration file is already present when you deploy fluentd. An environment variable also gets conveniently set (FLUENT_CONF) to access the config file. Depending on how you installed fluentd, the location and name of the file may change so the env variable is the best option when locating the config file.
Now, let's talk about the syntax of this conf file. The syntax looks similar to XML, although there are several notable differences. The "tags", officially called directives, that are available for use are:
As the name implies this refers to the input source of fluentd and corresponds to the input plugins we discussed. @type should be used to specify the plugin being used.
<source>
@type forward
port 24224
</source>
This would be the output destination fluentd should use and corresponds to the output plugins we discussed. But why is it called "match"? Well, the primary purpose of this directive is to match tags so that they can be processed.
# Match events tagged with "myapp.access" and
# store them to /var/log/fluent/access.%Y-%m-%d
<match myapp.access>
@type file
path /var/log/fluent/access
</match>
This handles the processing within fluentd and corresponds to the filter plugins we discussed.
<filter myapp.access>
@type record_transformer
<record>
host_param "#{Socket.gethostname}"
</record>
</filter>
This sits in the middle of the other two plugins and allows filter plugins to be chained to each other. So you will have an input, which leads to a filter (or filters), which eventually leads to an output.
This is used if you want to set a directive that affects the entire system.
<system>
# equal to -qq option
log_level error
# equal to --without-source option
without_source
# ...
</system>
Various parameters can be used under the system directive, and you can get a list of them here.
Handling tags can be a complex process, and the label syntax is present to simplify this to some extent. To add a label, you simply use @label @SYSTEM.
<source>
@type tail
@label @SYSTEM
</source>
If you already have a comprehensive configuration file that has to get duplicated for a different configuration file, there is no reason to create the same config file twice. Instead, you can use the include syntax to simply include one config file in another.
@include a.conf
@include config.d/*.conf
Alright, so we know all about Fluentd now, and we have taken a look at the different tags we can use in the fluentd configuration files, let's get it set up on a cluster. For that, you need to have a cluster up and running, and Minikube is the perfect solution. Its easy to set up and use, and is an excellent tool for local development. You can also use clusters hosted elsewhere, as well as the cloud.
Note that Fluentd v0.12 does not support Windows since it's very rare that your cluster would have to deal with a Windows machine. However, if you need to collect logs from a Windows server, Fluent has provided a guide on how you can do that. If you are working with a Redhat-based environment, refer to the guide for redhat, and if you are using a Debian-based server, refer to the guide for Debian. We will be installing fluentd using a Ruby gem, and this is a standard way you can use to install fluentd on any platform that supports gems.
The first thing you need to do is to set up a Network time protocol Daemon on your operating system so that Fluentd can have an accurate timestamp. Since the time an event occurs is crucial to the system, this is a rather necessary step.
The next thing that needs consideration is the file descriptor limit. The file descriptor limit determines how many open files you can have at a moment, and since fluent would generally deal with thousands of files at once, your operating system needs to be able to cater to this need:
root soft nofile 65536
root hard nofile 65536
* soft nofile 65536
* hard nofile 65536You likely don't need to do this last preparation step, but if you are running a high-load environment, make sure you optimize your network kernel parameters.
Since we are using a ruby gem, you would have to install a ruby interpreter. Make sure your version is 1.9.3 or higher. You also need the ruby-dev package installed. Both can be installed using sudo apt install ruby-full on ubuntu and sudo yum install ruby in RHEL.
Next, we get to installing the Fluentd gem and running it:
gem install fluentd -v "~> 0.12.0" --no-ri --no-rdoc
fluentd --setup ./fluent
fluentd -c ./fluent/fluent.conf -vv &
echo '{"json":"message"}' | fluent-cat debug.testIf you now get a message output similar to debug.test: {"json":"message"}, then you have successfully set up Fluentd on your development environment. Now it's time to create some Fluentd configuration files. We have already looked at the syntax that makes up these files, and now we will look at how to use this syntax from end to end.
Since we installed Fluentd with a gem, use:
sudo fluentd --setup /etc/fluent
sudo vi /etc/fluent/fluent.confIf you installed Fluentd with RPM/DEB/DMG, then use
sudo vi /etc/td-agent/td-agent.confSince there are a large number of Java applications out there in the world, and since most logging applications are also written in Java, let us see how we can collect logs into Fluentd from these Java applications. For this, we will be using the fluent-logger-java library that is recommended by Fluent. We will also be using the forward input plugin which listens to TCP sockets to receive event data. Let's start by defining the source section of the configuration file:
<source>
@type forward
port 24224
</source>
<match fluentd.test.**>
@type stdout
</match>
Then restart your td-agent for the changes to take effect. Now, let's start using the fluent-logger-java by adding the dependency information to the pom.xml:
<dependency>
<groupId>org.fluentd</groupId>
<artifactId>fluent-logger</artifactId>
<version>${logger.version}</version>
</dependency>This will get the library. Now, you need a Java class that imports this library, initializes an instance of the logger, and writes something to it:
import java.util.HashMap;
import java.util.Map;
import org.fluentd.logger.FluentLogger;
public class Main {
private static FluentLogger LOG = FluentLogger.getLogger("fluentd.test");
public void doApplicationLogic() {
// ...
Map<String, Object> data = new HashMap<String, Object>();
data.put("from", "userA");
data.put("to", "userB");
LOG.log("follow", data);
// ...
}
}The code itself should be self-explanatory, and this consists of the data we will be feeding into Fluentd. You can go ahead and execute this script:
java -jar test.jarThis would send the logs you placed above to Fluentd. Of course, this is a very small sample of what Fluentd can do with a Java application as an input source, but this solution scales up for other large-scale applications as well. Now, you can open up the td-agent.log file located in /var/log/td-agent/ and check the logs that you sent which should be written to it now.
Writing logs to a log file in your local server is nice, but let's consider the abilities Fluentd has to write logs to a cloud storage platform, such as S3. For this, we will be using the Amazon output plugin. You will also need a free tier AWS account as well as Apache. To install the plugin using gems, use:
gem install fluent-plugin-s3
Once again, you would have to dip into the fluentd config (/etc/td-agent/td-agent.conf or /etc/fluentd/fluentd.conf). It will track logs from Apache (/var/log/apache2/access_log) and will tag messages with s3.apache.access to be identified by Fluentd. We will be using the Tail input plugin which can be used to monitor text files.
<source>
@type tail
format apache2
path /var/log/apache2/access_log
pos_file /var/log/td-agent/apache2.access_log.pos
tag s3.apache.access
</source>
Note that if you are using a custom log format, the plugin might have some difficulty reading it, so the standard combined log format, or refer to the in_tail page for more information. Now that we have set an input source, it's time to set the output source. This will be your S3 bucket, and you can define it in the configuration file like so:
<match s3.*.*>
@type s3
aws_key_id YOUR_AWS_KEY_ID
aws_sec_key YOUR_AWS_SECRET/KEY
s3_bucket YOUR_S3_BUCKET_NAME
path logs/
buffer_path /var/log/td-agent/s3
time_slice_format %Y%m%d%H
time_slice_wait 10m
utc
buffer_chunk_limit 256m
</match>
Make sure you replace the parts of the configuration file with your AWS details. This match section has its type defined as s3, and the match tag itself has a regular expression in it, which looks for matching tags. A regex like this will be able to match something like s3.apache.access which is produced by the input. Now, if you were to pint the Apache server, you should be able to test the configuration. A good tool to help you test this is the Apache Bench tool which is included in the apache2-utils package. Ping it with:
ab -n 100 -c 10 http://localhost/
This should generate a log that gets written into your S3 bucket. You should be able to see it by logging in to your AWS account and checking the bucket specified in the configuration file.
NodeJS and MongoDB make up two parts of the MERN/MEAN stacks, and NodeJS (along with Express) acts as the server in this stack while MongoDB acts as the database. In the same way, you can use NodeJS as the input source and MongoDB as the output source with Fluentd. Note that you need a basic knowledge of Node, Mongo, and npm to follow through with this guide. We will see how we can do this. First of all, let's consider Node. As always, we start by modifying the configuration file, and we will be using our usual forward input plugin:
<source>
@type forward
port 24224
</source>
<match fluentd.test.**>
@type stdout
</match>
We will also have to create a simple NodeJS application to get this to work, and we will be using the code below. However, if you already have an application that uses the MERN/MEAN stack on your machine, feel free to use it.
{
"name": "node-example",
"version": "0.0.1",
"dependencies": {
"express": "^4.15.3",
"fluent-logger": "^2.4.0"
}
}This will be the package.json file which has fluent-logger as a dependency. Install everything with npm install.
var express = require('express');
var logger = require('fluent-logger');
var app = express();
// The 2nd argument can be omitted. Here is a default value for options.
logger.configure('fluentd.test', {
host: 'localhost',
port: 24224,
timeout: 3.0,
reconnectInterval: 600000 // 10 minutes
});
app.get('/', function(request, response) {
logger.emit('follow', {from: 'userA', to: 'userB'});
response.send('Hello World!');
});
var port = process.env.PORT || 3000;
app.listen(port, function() {
console.log("Listening on " + port);
});Now, we need to get the respective plugin for Node, which is the fluent-logger-node. This is an npm package that plugs right into your existing NodeJS project:
npm install fluent-logger
Now we have a basic application that sets up a set of simple routes using Express. You can start it with node index.js or npm start depending on how much you have configured the application. When you visit the application on the browser (normally on http://localhost:3000), the application should start producing logs that get written to your td-agent.log.
As an alternative to this output log, let us consider using MongoDB. We will be using the official MongoDB output plugin. Note that you need to have MongoDB and the MongoDB output plugin installed within the same node of your Kubernetes cluster. You can install the plugin with:
gem install fluent-plugin-mongo
There is one point to note about how Fluentd works with MongoDB, and it is that the logs aren't continuously written to MongoDB as a stream. This would be costly in terms of performance, so the data is buffered first, after which the data gets written to MongoDB periodically. To support this data buffering, the configuration must be set in the Fluentd configuration file:
<source>
@type tail
format apache2
path /var/log/apache2/access_log
pos_file /var/log/td-agent/apache2.access_log.pos
tag mongo.apache.access
</source>
This gets the tail of the log and tags it with mongo.apache.access. This will then be matched when writing the output to MongoDB. For this, add the specific configuration to the Fluentd configuration file.
<match mongo.**>
# plugin type
@type mongo
# mongodb db + collection
database apache
collection access
# mongodb host + port
host localhost
port 27017
# interval
flush_interval 10s
# make sure to include the time key
include_time_key true
</match>
If you are running MongoDB on a different host/port, then make sure to change the above values to match. Remember that this won't write a continuous stream of data, and will buffer the data and write periodically. You can define the period by changing the flush_interval attribute. The default is set to 10 seconds.
To test this system out, ping the Apache server as you did last time using ab.
ab -n 100 -c 10 http://localhost/
This should generate logs that would get buffered, and eventually written once the flush interval is reached. You can then look at the logs by logging into MongoDB (using MongoDB compass or the console):
$ mongo
> use apache
> db["access"].findOne();
{ "_id" : ObjectId("4ed1ed3a340765ce73000001"), "host" : "127.0.0.1", "user" : "-", "method" : "GET", "path" : "/", "code" : "200", "size" : "44", "time" : ISODate("2011-11-27T07:56:27Z") }
{ "_id" : ObjectId("4ed1ed3a340765ce73000002"), "host" : "127.0.0.1", "user" : "-", "method" : "GET", "path" : "/", "code" : "200", "size" : "44", "time" : ISODate("2011-11-27T07:56:34Z") }
{ "_id" : ObjectId("4ed1ed3a340765ce73000003"), "host" : "127.0.0.1", "user" : "-", "method" : "GET", "path" : "/", "code" : "200", "size" : "44", "time" : ISODate("2011-11-27T07:56:34Z") }
And that's it! We have successfully used Node + Mongo with FluentD.
Now that we have looked at an end to end usage of fluentd, let's look at how you can get fluentd running on a Kubernetes cluster while aggregating logs from within that same cluster.