If your organization has lots of AWS Lambda functions deployed, it’s also got lots of AWS CloudWatch log groups. Keeping track of all the logs across projects and teams can be unwieldy. But with a little effort, you can forward the log entries from every group and stream to one centralizing Lambda function. And from there you can do whatever you want with them–including sending them all to a log aggregator like SumoLogic.
That’s just what we in the Post Enrollment Tech (PET) team at 2U, Inc. have done. Our team has a large handful of functions running on AWS Lambda, and other teams are starting to follow suit. Each such Lambda function has a CloudWatch log group associated with it; each such log group contains a number of timestamped log streams. We wanted to solve the problem of forwarding our CloudWatch logging events to SumoLogic, and we wanted to solve it not only for our team’s projects but for all projects organization-wide.
We called the project that tackled this problem the Log Reflector. Our primary goal for the project was to automate the forwarding of all Lambda logging events to SumoLogic. A secondary (though important) goal was to ensure that any newly-created CloudWatch log group automatically got included in that forwarding process. We achieved both goals, and learned a lot along the way.
The main thing we learned at the outset was that a given CloudWatch log group can subscribe to a given Lambda function, causing that function to be invoked every time a message gets added to any stream in that log group. On invocation the function receives the contents of the log message, plus some metadata–the whole logging event, basically. The function, in turn, can do whatever it wants with the data sent to it, including passing it along to SumoLogic.
log-reflector Lambda function was born. Its purpose is for log groups to subscribe to it, and to listen to the log streams of those log groups and forward the log messages to SumoLogic.
In the service of our secondary goal–automatically subscribing new log groups to the
log-reflector–we created the
log-subscriber function, which listens for new log groups being created and subscribes them to the log reflector. There’s some icing on the cake (for example, a deploy-time script that plays catch-up by subscribing all existing log groups to the reflector); but between them,
log-subscriber make up the heart and soul of the project.
In this article we’ll walk through a good cross-section of what the PET team has done on the Log Reflector project. We’ll start by looking at the handler of
log-reflector. I don’t want to dwell on that handler too much, partly because it was very quick and easy (you’ll see why) and partly because you may or may not be sending your logs to SumoLogic like we are. But you’ll see what it consists of. After that we’ll take a look at how to subscribe a log group to a Lambda function. We’ll use a CLI approach, which isn’t exactly automatic but does give you a good feel for what’s actually going on.
Then we’ll turn to
log-subscriber and look at how that function is configured, and at some of the code. I won’t go over everything, but I’ll show you enough and give you enough pointers that you’ll be able to pick up the threads if you wish to.
Two notes before we get going:
First, you’ll notice that the processes described here are rather heavy on permissions-related tasks. That comes with the territory (AWS): every entity needs permission to do virtually anything with, or to, any other entity. Getting permissions right has been a large part of the project, and worth trying to get a handle on from the start.
Second, keep in mind that sending logging events to SumoLogic is just what we do. It doesn’t have to be what you do with your centralized logging events. The main thing here is to get all your CloudWatch logs talking to a single listener–the log reflector–and to automate the process of hooking new log groups up to that listener. Your log reflector function can connect to SumoLogic or it can do something else. That part is up to you.
Now let’s dig in.
The handler for
If you’re not planning to stream your logs to SumoLogic, you can skim or skip this section. Just remember, conceptually, that the
log-reflector function is listening for logging events from the log groups that are subscribed to it. Each such event gets passed along to
log-reflector in a well-defined JSON data structure. Whatever else your handler does, it needs to know how to pull this structure apart and do whatever it needs to do–SumoLogic or otherwise–with what it finds.
The handler for
log-reflector in our project comes essentially verbatim from SumoLogic’s
sumologic-aws-lambda Github repository–specifically,
cloudwatchlogs_lambda.js. Put this code in a Lambda function, and you’ve got something that knows how to parse log messages that get forwarded to it, and how to forward them to SumoLogic in turn.
The handler uses the node4.3 Lambda runtime. Out of the box it encourages you to hard-code your SumoLogic endpoint in the code. We prefer to inject the endpoint into the handler via the
SUMO_ENDPOINT environment variable. However you do it, the handler code correctly parses the incoming logging event from whatever log stream is talking to it, and it knows how to send it to Sumo.
Once you have your
log-reflector function in place, doing what you want it to do (or perhaps for now just logging, to its own CloudWatch log, the fact that it has received an event), you’ll want to know how exactly to subscribe a log group to a Lambda function. Let’s look at that next.
Subscribing a log group to a Lambda function
Say you’ve got a Lambda function called
update-student. Every time someone hits that function, you want the CloudWatch logging event to make its way to
In other words, you want to subscribe
update-student‘s log group–which is probably called something like
You can wire up the subscription of a log group to
log-reflector in a number of ways:
- in the Lambda console UI (hint: create a “trigger”)
- in the CloudWatch logs console UI (hint: choose a log group and then “Stream to AWS Lambda” from the “Actions” dropdown)
- via the AWS CLI (the
We’ll look at the third of these options (but feel encouraged to look at the first two also). But first, you’ll need to set the right permissions on the
log-reflector function. You need to tell the function that CloudWatch log groups are allowed to invoke it.
Make sure you’ve got your AWS credentials and defaults in place, and then issue this command (adjusting the argument values appropriately, of course):
aws lambda add-permission \
--function-name 'log-reflector' \
--statement-id 'something_unique' \
--action 'lambda:InvokeFunction' \
--principal 'logs.us-east-1.amazonaws.com' \
--source-arn 'arn:aws:logs:us-east-1:123123123123:log-group:/aws/lambda*:*' \
You’ll get back some JSON with details of what you just did. Now
log-reflector can be invoked by your log groups. So it’s time to subscribe the
/aws/lambda/update-student log group to the log reflector.
To create the actual subscription, you specific the log group you want listened to and the function you want to do the listening:
aws logs put-subscription-filter \
--log-group-name '/aws/lambda/update-student' \
--destination-arn 'arn:aws:lambda:us-east-1:123456789012:function:log-reflector' \
--filter-name 'update-student-filter' \
(Filter patterns offer you the opportunity to get fancy with what’s passed through. We’re not going to delve into them here.)
If all goes well there will be no screen output. But if you wait a minute or two and go to (or reload) your CloudWatch logs screen, you should see that
/aws/lambda/update-student now has a subscription listed. And you can examine your handiwork with another CLI call:
aws logs describe-subscription-filters \
which will give you back something like this:
(Note that we get an array of filters. At present, no log group can have more than one subscription, so it will always be an array of one element or an empty array.)
You’ve created the subscription; now try it out! Using your favorite client (cURL, postman, etc.), hit the endpoint for your
update-student function. Be patient… and in not too long you’ll see your request logged in a stream in the function’s log group. You should also see that your log reflector function got invoked; check
log-reflector‘s log group for a recent entry.
So now you have a function in place that will receive logging events from subscribed log groups, and you’ve got a technique for subscribing log groups to that function. But you don’t want to go on doing this manually. So let’s look at how to automate the creating of subscriptions. Our goal will be to assign yet another Lambda function the task of listening for the creation of new log groups, and subscribing each such log group to the log reflector.
Automating subscriptions with
This time, we’ll look at the permissions-ish chores first, and then turn to the handler code.
The IAM role that’s in effect when
log-subscriber is executed needs permission to create subscription filters on CloudWatch log groups. You can create or edit a policy containing this permission in the console UI (in IAM go to “Roles” and choose the relevant role), or you can do it through the CLI. We’ll do the latter here.
The process involves two steps: (1) creating the policy, and (2) attaching it to a role. Here’s how to create it:
aws iam create-policy \
--policy-name 'let-me-do-something' \
(You may not need the describe and delete operations yet, but adding them in makes it easier to add functionality to your code later.)
This command will come back at you with, among other things, the ARN of the new policy. You’ll need it for the next command: attaching the policy to the role. You’ll also need the role name, which you can get from the IAM console. Cut and paste as needed to issue the following command:
aws iam attach-role-policy \
--policy-arn 'arn:aws:iam::123123123123:policy/let-me-do-something' \
Now that the lambda’s role is allowed to create subscription filters, it’s time to flesh out the
log-subscriber function itself. We’re not going to go into all the possible detail of the process here, but the general idea is this: the handler for
log-subscriber will be invoked whenever a new log group is created, and will go through the permission and subscription-creation steps we went through above–but automatically.
If you’re using Python you’ll probably want to use the boto3 library, which wraps the AWS API. You’ll need to research that further on your own, but here’s an example showing what our earlier
aws add-permission command would look like translated into boto3:
lamb = boto3.client('lambda')
When the subscriber function gets invoked, it will be sent information about the log group whose creation triggered the invocation. In order to create the subscription, you’ll want to dig out the name of the log group and then do a boto3-style
def handler(event, context):
log_group_name = event['detail']['requestParameters']['logGroupName']
log_client = boto3.client('logs')
(This is of course a “happy path” example. You’ll get an exception if the event your subscriber function receives doesn’t conform to the data structure it’s looking for–but you can always add error-handling of whatever kind you need.)
But how do you get a function like
log-subscriber to be invoked upon log-group creation? To start with, by defining a rule. You can do so from the command line:
aws events put-rule \
--name 'New_log_group_subscriber' \
--region us-east-1 \
"AWS API Call via CloudTrail"
So now there’s a rule in force that says that the creation of a log group should invoke a function. But we haven’t told it which function! To do that we have to create a target for the rule–actually an array of targets, but we only need one. We’ll put the JSON describing the target in a shell variable and then use it inside a JSON array:
aws events put-targets \
--rule "New_log_group_subscriber" \
--region us-east-1 \
(Note that the rule parameter has to match the name we gave the rule in the call to
Let’s try out
log-subscriber. Easy: we’ll create a brand-new log group, and then check whether or not it has been subscribed to
aws logs create-log-group \
Now count to 200…. Not really, but you have to be patient: the creation of the new subscription may not be instantaneous. But in not too long you should, upon reloading, see that the new log group has appeared on your CloudWatch console, and has a subscription to your log reflector function. You can also use the technique we used to check on the success of the subscription creation we did earlier:
aws logs describe-subscription-filters \
You should see something like this:
(Note that the filter name was generated automatically.)
This is the familiar array-of-one description of the subscriptions (really, the single subscription) of the log group to the
So now we’re in a good place. Every time a new log group is created, that log group will be subscribed to the log reflector. And the log reflector can do whatever it needs to do with the stream of logging events coming at it from all the log groups that it’s listening to.
Conclusion(s) and thoughts
Development on the Log Reflector project has gone very smoothly, though there’s been a bit of a learning curve. AWS roles and permissions are famously complex, and it’s not always easy to get the hang of what’s going on. For example: you don’t grant a log group permission to execute a function; you grant the function permission to be executed by the log group. That kind of thing.
And there are a few glitches that haven’t sorted themselves out. One that I’m still puzzled by is the fact that if you use the CloudWatch console to tell a log group to stream to a Lambda function, the subscription of the log group to the function shows up in the Lambda console when you look at the function’s triggers. But if you do it programmatically, it doesn’t; there appear to be no triggers. Yet everything else works identically in the two cases, and I have not yet tracked down the reason for the anomaly.
AWS regions can be a pitfall. I have a slightly dinkier version of the Log Reflector on my personal Github and AWS accounts, as a learning tool. One morning, all my test scripts (delete and create subscriptions, add permissions, etc.) stopped working. I’m not entirely sure but I’m reasonably certain that it was because I’d had the
AWS_DEFAULT_REGION environment variable set in the terminal that I’d used previously, but not in the one I was using that day. Once I set that variable, things largely fell into place and started working again. Old hat to seasoned AWS developers, no doubt, but something to keep an eye on.
My final thought is this: working this intensely on an AWS project has taught me, above all, that there is more to AWS than one is likely to be able to imagine. It’s gargantuan. And it’s correspondingly complex–but the complexity, from what I’ve seen, is packaged about as well as it could be. The documentation is very good, though I tend to add the word
stackoverflow to my Google searches because otherwise all you get, typically, is the official docs and not the down-in-the-trenches problems and solutions.
So enjoy AWS, and happy reflecting!
Thanks to Nate Snyder for pairing on this project and for feedback on a draft of this post.