In this article, I’d like to introduces a solution to collect events from various sources and send them into HTTP Trigger function in Azure Functions using fluent-plugin-azurefunctions. Triggers in Azure Functions are event responses used to trigger your custom code. HTTP Trigger functions allow you to respond to HTTP events sent from fluentd and cook them into whatever you want!
[note] Azure Functions is a (“serverless”) solution for easily running small pieces of code, or “functions,” in Azure. Fluentd is an open source data collector, which lets you unify the data collection and consumption for a better use and understanding of data. fluent-plugin-azurefunctions is a fluentd output plugin that enables to collect events into Azure Functions.
Pre-requisites
- A basic understanding of fluentd – if you’re not familiar with fluentd, fluentd quickstart guide is good starting point
- Azure subscription – you need to have Azure subscription that grants you access to Microsoft Azure services, and under which you can create Azure Functions account. If you don’t have yet click here to create it
Setup: Azure Functions (HTTP Trigger Function)
Create a function (HTTP Trigger). First, you need to have an function app that hosts the execution of your functions in Azure if you don’t already have. Once you have an function app, you can create a function. Here are instructions:
A quick-start HTTP trigger function sample is included under examples/function-csharp in Github repository. You simply need to save the code (run.csx) and configuration files (function.json, project.json) in the same Azure function folder. Explaining a little bit about each of files, the function.json file defines the function bindings and other configuration settings. The runtime uses this file to determine the events to monitor and how to pass data into and return data from function execution. The project.json defines packages that the application depends. The run.csx is a core application file where you write your code to process Your jobs. Here is a sample run.csx:
Setup: Fluentd
First of all, install Fluentd. The following shows how to install Fluentd using Ruby gem packger but if you are not using Ruby Gem for the installation, please refer to this installation guide where you can find many other ways to install Fluentd on many platforms.
# install fluentd
sudo gem install fluentd --no-ri --no-rdoc
# create fluent.conf
fluentd --setup <directory-path-to-fluent-conf>
Also, install fluent-plugin-azurefunctions for fluentd aggregator to send collected event data into Azure Functions.
sudo gem install fluent-plugin-azurefunctions
Next, configure fluent.conf, a fluentd configuration file as follows. Please refer to this for fluent-plugin-azurefunctions configuration. The following is a sample configuration where the plugin writes only records that are specified by key_names in incoming event stream out to Azure Functions:
# This is used by event forwarding and the fluent-cat command
<source>
@type forward
@id forward_input
</source>
# Send Data to Azure Functions
<match azurefunctions.**>
@type azurefunctions
endpoint AZURE_FUNCTION_ENDPOINT # ex. https://<accountname>.azurewebsites.net/api/<functionname>
function_key AZURE_FUNCTION_KEY # ex. aRVQ7Lj0vzDhY0JBYF8gpxYyEBxLwhO51JSC7X5dZFbTvROs7uNg==
key_names key1,key2,key3
add_time_field true
time_field_name mytime
time_format %s
localtime true
add_tag_field true
tag_field_name mytag
</match>
[note] If key_names not specified above, all incoming records are posted to Azure Functions (See also this).
Finally, run fluentd with the fluent.conf that you configure above.
fluentd -c ./fluent.conf -vv &
TEST
Let’s check if test events will be sent to Azure Functions that triggers the HTTP function (let’s use the sample function included in Github repo this time). First, generate test events using fluent-cat like this:
echo ' { "key1":"value1", "key2":"value2", "key3":"value3"}' | fluent-cat azurefunctions.msg
As both add_time_field and add_tag_field are enabled, time and tag fields are added to the record that are selected by key_names before posting to Azure Functions, thus actual HTTP Post request body would be like this:
{
"payload": '{"key1":"value1", "key2":"value2", "key3":"value3", "mytime":"1480195100", "mytag":"azurefunctions.msg"}'
}
If events are sent to the function successfully, a HTTP trigger function handles the events and the following logs can be seen in Azure Functions log stream:
2016-11-26T21:18:55.200 Function started (Id=5392e7ae-3b8e-4f65-9fc1-6ae529cdfe3a)
2016-11-26T21:18:55.200 C# HTTP trigger function to process fluentd output request.
2016-11-26T21:18:55.200 key1=value1
2016-11-26T21:18:55.200 key2=value2
2016-11-26T21:18:55.200 key3=value3
2016-11-26T21:18:55.200 mytime=1480195100
2016-11-26T21:18:55.200 mytag=azurefunctions.msg
2016-11-26T21:18:55.200 Function completed (Success, Id=5392e7ae-3b8e-4f65-9fc1-6ae529cdfe3a)
Advanced Senarios
1. Near Real-time processing
Function Apps can output messages to different means or data stores. For example, fluentd collects events generated from IoT devices and send them to Azure Function, and the the HTTP trigger function transforms the events and processes the data to store in a persistent storage or to pass them to different means. Here are some of options available at the time of writing:
2. Background jobs processing
If the jobs are expected to be large long running ones, it’s recommended that you refactor them into smaller function sets that work together and return fast responses. For example, you can pass the HTTP trigger payload into a queue to be processed by a queue trigger function. Or if the payload is too big to pass into the queue, you can store them onto Azure Blob storage at first, then pass only limited amount of the data into a queue just to trigger background workers to process the actual work. These approaches allow you to do the actual work asynchronously and return an immediate response.
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