That Neat Little Symbol From Half-Life, Part 2
Now that I am feeling less like dying horribly from a terrible plague, its time to continue to talk about processing ELB logs into an ELK stack via AWS Lambda.
To summarise the plan, the new log processor system needs 3 main parts:
- The Lambda function code, which is written in Javascript, and would need to parse an ELB log file one line at a time, break it up, convert it into a JSON structure and then push it to a Logstash TCP endpoint to be written into Elasticsearch
- The configuration/creation of the Lambda function, which would need to be done via CloudFormation as part of our normal environment setup (i.e. spin up an environment, it comes with the Lambda function that will process any ELB logs encountered)
- The deployment of the code to the Lambda function via Octopus (because that’s how we roll)
I’m basically going to do a short blog post on each of those pieces of work, and maybe one at the end to tie it all together.
With that in mind, lets talk Javascript.
Unforeseen Consequences
When you’re writing Lambda functions, Javascript (via Node.js) is probably your best bet. Sure you can run Java or Python (and maybe one day C# using .NET Core), but Javascript is almost certainly going to be the easiest. Its what we chose we when put together the faster S3 clone prototype, and while the fundamentally asynchronous nature of Node.js took some getting used to, it worked well enough.
When it comes to Javascript, I don’t personally write a lot of it. If I’m writing a server side component, I’m probably going to pick C# as my language of choice (with all its fancy features like “a compiler” and “type safety”) and I don’t find myself writing things like websites or small Javascript applications very often, if at all. My team definitely writes websites though, and we use React.js to do it, so its not like Javascript is an entirely foreign concept.
For the purposes of reading in and parsing an ELB log file via a Lambda function, we didn’t need a particularly complex piece of Javascript. Something that reads the specified file from S3 after the Lambda function triggers, something to process the contents of that file line by line, something to parse and format those lines in a way that a Logstash input wll accept, and something to push that JSON payload to the Logstash listener over raw TCP.
Without further ado, I give you the completed script:
'use strict';
let aws = require('aws-sdk');
let s3 = new aws.S3({ apiVersion: '2006-03-01' });
let readline = require('readline');
let net = require('net');
const _type = 'logs';
const _sourceModuleName = 'ELB';
const _logHost = '#{LogHost}'
const _logPort = #{LogPort}
const _environment = '#{Octopus.Environment.Name}'
const _component = '#{Component}'
const _application = '#{Application}'
function postToLogstash(entry){
console.log("INFO: Posting event to logstash...");
var socket = net.createConnection(_logPort, _logHost);
var message = JSON.stringify(entry) + "\n";
socket.write(message);
socket.end();
console.log("INFO: Posting to logstash...done");
}
exports.handler = (event, context, callback) => {
console.log('INFO: Retrieving log from S3 bucket...');
const bucket = event.Records[0].s3.bucket.name;
const key = decodeURIComponent(event.Records[0].s3.object.key.replace(/\+/g, ' '));
const params = {
Bucket: bucket,
Key: key
};
const reader = readline.createInterface({
input: s3.getObject(params).createReadStream()
});
const expectedColumns = 12;
reader.on('line', function(line) {
console.log("INFO: Parsing S3 line entry...");
const columns = line.split(/ (?=(?:(?:[^"]*"){2})*[^"]*$)/);
if(columns.length >= expectedColumns){
var entry = {
EventReceivedTime: columns[0],
LoadBalancerName: columns[1],
PublicIpAndPort: columns[2],
InternalIpAndPort: columns[3],
TimeToForwardRequest: parseFloat(columns[4]) * 1000,
TimeTaken: parseFloat(columns[5]) * 1000,
TimeToForwardResponse: parseFloat(columns[6]) * 1000,
Status: columns[7],
BackendStatus: columns[8],
BytesUploadedFromClient: parseInt(columns[9]),
BytesDownloadedByClient: parseInt(columns[10]),
FullRequest: columns[11],
Component: _component,
SourceModuleName: _sourceModuleName,
Environment: _environment,
Application: _application,
Type: _type
};
postToLogstash(entry);
} else {
console.log("ERROR: Invalid record length was expecting " + expectedColumns.length + " but found " + columns.length);
console.log('ERROR: -------');
console.log(line);
console.log('ERROR: -------');
}
});
};Nothing to fancy.
In the interest of full disclosure, I did not write the script above. It was written by a few guys from my team initially as a proof of concept, then improved/hardened as a more controlled piece of work.
Office Complex
You might notice some strange variable names at the top of the script (i.e. #{Octopus.Environment.Name}).
We use Octopus deploy for all of our deployments, and this includes the deployment of Lambda functions (which we package via Nuget and then deploy via the AWS Powershell Cmdlets/CLI inside Octopus). The #{NAME} notation is a way for Octopus to substitute variable values into files during deployment. This substitution is very useful, and can be scoped via a variety of things (like Environment, Machine, Role, etc), so by the time the script actually gets into AWS those variables are filled in with actual values.
Other than our use of Octopus variables, other things to note in this piece of Javascript are:
- At no point does the function specify which credentials are being used to access the S3 bucket containing the ELB log files. This is because the Lambda function has been configured with an IAM role allowing it to access the required resources. The AWS Javascript library has built in support for running inside supported AWS contexts (like EC2 instances and Lambda functions), such that it can use the role applied to the context to get appropriate temporary credentials. This is a much better approach than using specific credentials in the script, which can result in unintended consequences if you aren’t eternally vigilant.
- You need to make sure that you’re Lambda function is configured with an appropriate security group that allows it to use the expected outbound channel (i.e. make sure it can get to the Logstash host you’re trying to connect to). This was somewhat of an issue for us as our ELK stack is hosted inside another AWS account (our OPs account), so we had to make sure that all of the appropriate VPC peering was configured before it would work correctly. It can be a bit of a pain to setup the smallest possible surface area, but don’t be tempted to just configure the Lambda function to be able to do everything and go anywhere. Smaller surface area = more security and the policy of least privilege is one you should always follow.
- Its important to ensure that if you’re doing TCP communication in a Lambda function, that you make sure to close your socket when you’re done with it, or the Lambda function might not exit. It might, but it also might not, and it can really throw you for a loop if you don’t know why.
To Be Continued
That’s it for this week. The Javascript I’ve included above is pretty generic (apart from the specific set of fields that we like to have in our log events) and will successfully process an ELB log file from S3 to a Logstash instance listening on a port of your choosing (probably 6379) when used in a Lambda function. Feel free to reuse it for your own purposes.
Next week I’ll continue this series of posts with information about how we use CloudFormation to setup our Lambda function as part of one of our environment definitions.
CloudFormation and Lambda aren’t the best of friends yet, so there is some interesting stuff that you have to be aware of.