Setting Up a Datadog-to-AWS Integration

When approaching a new service provider, sometimes it can be confusing on how to get set up to best communicate with them – some processes involve multiple steps, multiple interfaces, confusing terminology, and

Amazon Web Services is an amazing cloud services provider, and in order to allow access informational services inside a customer’s account, a couple of known mechanisms exist to delegate access:

  • Account Keys, where you generate a key and secret and share them. The other party stores these (usually in either clear text or using reversible encryption) and uses them as needed to make API calls
  • Role Delegation, where you create a Role and shared secret to provide to a the external service provider, who then is allowed to use their own internal security credentials to request temporary access to your account’s resources via API calls

In the former model, the keys are exchanged once, and once out of your immediate domain, you have little idea what happens to them.
In the latter, a rule is put into place that requires ongoing authenticated access to request assumption of a known role with a shared secret.

Luckily, in both scenarios, a restrictive IAM Policy is in place that allows only the actions you’ve decided to allow ahead of time.

Setting up the desired access is made simpler by having good documentation on how to do this manually. In this modern era, we likely want to keep our infrastructure as code where possible, as well as have a mechanism to apply the rules and test later if they are still valid.

Here’s a quick example I cooked up using Terraform, a new, popular tool to compose cloud infrastructure as code and execute to create the desired state.
[gist https://gist.github.com/miketheman/72197ec28bd527137e196054b3ab6dec#file-datadog-role-delegation-tf /]

The output should look a lot like this:

[gist https://gist.github.com/miketheman/72197ec28bd527137e196054b3ab6dec#file-output-sh-session /]

The Account ID is actually a full ARN, and you can copy your Account ID from there.
Terraform doesn’t have a mechanism to emit only the Account ID yet – so if you have some ideas, contribute!

Use the Account ID, Role Name and External ID and paste those into the Datadog Integrations dialog, after selecting Role Delegation. This will immediately validate that the permissions are correct, and return an error otherwise.

Don’t forget to click “Install Integration” when you’re done (it’s at the very bottom of the screen).

Now metrics and events will be collected by Datadog from any allowed AWS services, and you can keep this setup instruction in any revision system of your choice.

P.S. I tried to set this up via CloudFormation (Sparkleformation, too!). I ended up writing it “freehand” and took more than 3 times as long to get similar functionality.

You can see the CloudFormation Stack here, and decide which works for you.


Further reading:

Counts are good, States are better

Datadog is great at pulling in large amounts of metrics, and provides a web-based platform to explore, find, and monitor a variety of systems.

One such system integration is PostgresQL (aka ‘Postgres’, ‘PG’) – a popular Open Source object-relational database system, ranking #4 in its class (at the time of this writing), with over 15 years of active development, and an impressive list of featured users.
It’s been on an upwards trend for the past couple of years, fueled in part by Heroku Postgres, and has spun up entire companies supporting running Postgres, as well as Amazon Web Services providing PG as one of their engines in their RDS offering.

It’s awesome at a lot of things that I won’t get into here, but it definitely my go-to choice for relational data.

One of the hardest parts of any system is determining whether the current state of the system is better or worse than before, and tracking down the whys, hows and wheres it got to a worse state.

That’s where Datadog comes in – the Datadog Agent has included PG support since 2011, and over the past 5 years, has progressively improved and updated the mechanisms by which metrics are collected. Read a summary here.

Let’s Focus

Postgres has a large number of metrics associated with it, and there’s much to learn from each.

The one metric that I’m focusing on today is the “connections” metric.

By establishing a periodic collection of the count of connections, we can examine the data points over time and draw lines to show the values.
This is built-in to the current Agent code, named postgresql.connections in Datadog, by selecting the value of the numbackends column from the pg_stat_database table.

01-default-connections

Another two metrics exist, introduced into the code around 2014, that assist with using the counts reported with alerting.
These are postgresql.max_connections and postgresql.percent_usage_connections.

(Note: Changing PG’s max_connections value requires a server restart and in a replication cluster has other implications.)

The latter, percent_usage_connections, is a calculated value, returning ‘current / max’, which you could compute yourself in an alert definition if you wanted to account for other variables.
It is normally sufficient for these purposes.

02-pct_used-connections

A value of postgresql.percent_usage_connections:0.15 tells us that we’re using 15% of our maximum allowable connections. If this hits 1, then we will receive this kind of response from PG:

FATAL: too many connections for role...

And you likely have a Sad Day for a bit after that.

Setting an alert threshold at 0.85 – or a Change Alert to watch the percent change in the values over the previous time window – should prompt an operator to investigate the cause of the connections increase.
This can happen for a variety of reasons such as configuration errors, SQL queries with too-long timeouts, and a host of other possibilities, but at least we’ll know before that Sad Day hits.

Large Connection Counts

If you’ve launched your application, and nobody uses it, you’ll have very low connection counts, you’ll be fine. #dadjoke

If your application is scaling up, you are probably running more instances of said application, and if it uses the database (which is likely), the increase in connections to the database is typically linear with the count of running applications.

Some PG drivers offer connection pooling to the app layer, so as methods execute, instead of opening a fresh connection to the database (which is an expensive operation), the app maintains some amount of “persistent connections” to the database, and the methods can use one of the existing connections to communicate with PG.

This works for a while, especially if the driver can handle application concurrency, and if the overall count of application servers remains low.

The Postgres Wiki has an article on handling the number of database connections, in which the topic of a connection pooler comes up.
An excerpt:

If you look at any graph of PostgreSQL performance with number of connections on the x axis
and tps on the y access [sic] (with nothing else changing), you will see performance climb as
connections rise until you hit saturation, and then you have a “knee” after which performance
falls off.

The need for connection pooling is well established, and the decision to not have this part of core is spelled out in the article.

So we install a PG connection pooler, like PGBouncer (or pgpool, or something else), configure it to connect to PG, and point our apps at the pooler.

In doing so, we configure the pooler to establish some amount of connections to PG, so that when an application requests a connection, it can receive one speedily.

Interlude: Is Idle a Problem?

Over the past 4 years, I’ve heard the topic raised again and again:

If the max_connections is set in the thousands, and the majority of them are in idle state,
is that bad?

Let’s say that we have 10 poolers, and each establishes 100 connections to PG, for a max of 1000. These poolers serve some large number of application servers, but have the 1000 connections at-the-ready for any application request.

It is entirely possible that most of the time, a significant portion of these established connections are idle.

You can see a given connection’s state in the pg_stat_activity table, with a query like this:

SELECT datname, state, COUNT(state)
FROM pg_stat_activity
GROUP BY datname, state
HAVING COUNT(state) > 0;

A sample output from my local dev database that’s not doing much:

datname  | state  | count
---------+--------+-------
postgres | active |     1
postgres | idle   |     2
(2 rows)

We can see that there is a single active connection to the postgres database (that’s me!) and two idle connections from a recent application interaction.

If it’s idle, is it harming anyone?

A similar question was asked on the PG Mailing List in 2015, to which Tom Lane responds to the topic of idle: (see link for full quote):

Those connections have to be examined when gathering snapshot information, since you don’t know that they’re idle until you look.
So the cost of taking a snapshot is proportional to the total number of connections, even when most are idle.
This sort of situation is known to aggravate contention for the ProcArrayLock, which is a performance bottleneck if you’ve got lots of CPUs.

So we now know why idling connections can impact performance, despite not doing anything, especially with modern DBs that we scale up to multi-CPU instances.

Back to the show!

Post-Pooling Idling

Now that we know that high connection counts are bad, and we are able to cut the total count of connections with pooling strategies, we must ask ourselves – how many connections do we actually need to have established, yet not have a high count of idling connections that impact performance.

We could log in, run the SELECT statement from before, and inspect the output, or we could add this to our Datadog monitoring, and trend it over time.

The Agent docs show how to write an Agnet Check, and you could follow the current postgres.py to write another custom check, or you could use the nifty custom_metrics syntax in the default postgres.yaml to extend the check to perform more checks.

Here’s an example:

custom_metrics:
  - # Postgres Connection state
    descriptors:
      - [datname, database]
      - [state, state]
    metrics:
      COUNT(state): [postgresql.connection_state, GAUGE]
    query: >
      SELECT datname, state, %s FROM pg_stat_activity
      GROUP BY datname, state HAVING COUNT(state) > 0;
    relation: false

Wait, what was that?

Let me explain each key in this, in an order that made sense to me, instead of alphabetically.

  • relation: false informs the check to perform this once per collection, not against each of any specified tables (relations) that are part of this database entry in the configuration.
  • query: This is pretty similar to our manual SELECT, with one key differentiation – the %s informs the query to replace this with the contents of the metrics key.
  • metrics: For each entry in here, the query will be run, substituting the key into the query. The metric name and type are specified in the value.
  • descriptors: Each column returned has a name, and here’s how we convert the returned name to a tag on the metric.

Placing this config section in our postgres.yaml file and restarting the Agent gives us the ability to define a query like this in a graph:

sum:postgresql.connection_state{*} by {state}

03-conn_state-by-state

As can be seen in this graph, the majority of my connections are idling, so I might want to re-examine my configuration settings on application or pooler configuration.

Who done it?

Let’s take this one step further, and ask ourselves – now that we know the state of each connection, how might we determine which of our many applications connecting to PG is idling, and target our efforts?

As luck would have it, back in PG 8.5, a change was added to allow for clients to set an application_name value during the connection, and this value would be available in our pg_stat_activity table, as well as in logs.

This typically involves setting a configuration value at connection startup. In Django, this might be done with:

DATABASES = {
  'default': {
    'ENGINE': 'django.db.backends.postgresql',
    ...
    'OPTIONS': {
      'application_name': 'myapp',
    }
    ...

No matter what client library you’re using, most have the facility to pass extra arguments along, some in the form of a database connection URI, so this might look like:

postgresql:[email protected]/otherdb?connect_timeout=10&application_name=myapp

Again, this all depends on your client library.

I can see clearly now

So now that we have the configuration in place, and have restarted all of our apps, a modification to our earlier Agent configuration code for postgres.yaml would look like:

custom_metrics:
  - # Postgres Connection state
    descriptors:
      - [datname, database]
      - [application_name, application_name]
      - [state, state]
    metrics:
      COUNT(state): [postgresql.connection_state, GAUGE]
    query: >
      SELECT datname, application_name, state, %s FROM pg_stat_activity
      GROUP BY datname, application_name, state HAVING COUNT(state) > 0;
    relation: false

With this extra dimension in place, we can craft queries like this:

sum:postgresql.connection_state{state:idle} by {application_name}

04-conn_state-idle-by-app_name

So now I can see that my worker-medium application has the most idling connections, so there’s some tuning to be done here – either I open too many connections for the application, or it’s not doing much.

I can confirm this with refining the query structure to narrow in on a single application_name:

sum:postgresql.connection_state{application_name:worker-medium} by {state}

05-conn_state-app_name-by-state

So now that I’ve applied methodology of surfacing connection states, and increased visibility into what’s going on, before making any changes to resolve.

Go forth, measure, and learn how your systems evolve!

Reduce logging volume

Quick self-reminder on reducing logging volume when monitoring an http endpoint with the Datadog Agent HTTP Check.

For nginx, add something like:

    location / {
        if ($http_user_agent ~* "Datadog Agent/.*") {
            access_log off;
        }
        ....

to your site’s location statement.

This should cut down on your logging volume, at the expense of not having a log statement for every time the check runs (once every 20 seconds).

Tracking application performance on Heroku with Datadog

I thought about using a clickbait title – “You’ll never believe how this guy captures metrics!” – but decided that 99% of these are not worth the time invested in coming up with the catch title.

So instead, I’ll simply talk about what I wanted to, and you be the judge of my title.

Application Performance Monitoring, or APM, is a crazily complex landscape, with an enormous amount of tooling, terminology, and providers looking to get some piece of the action.
There are many vendors, and all have their advantages, as well as disadvantages.

The vendor that I am pretty happy with (and I now work there) is Datadog.

One solution that has caught on quite well for surgical application monitoring is the use of the statsd protocol to send metrics from inside your application to a listener which can then store these metrics for querying later on. This is achieved by placing strategic “emitter” callouts in your code so that they can report metrics during runtime.

Flickr, then Etsy have started these projects, and they have been refined, ported to most languages, and are seeing adoption in companies where a focus on measuring is an important goal.
A blog post on Datadog’s implementation and extension of Statsd was written last year and goes into deeper detail.

One common question has always been “How do I collect metrics from an application running on Heroku with Datadog?”.

And I think we finally have one answer.

The Heroku Dyno container is pretty simple – you wanna run a process? Describe it in a Procfile.
You wanna scale? You tell Heroku to launch more Dynos with the process name, as specified in the Procfile.

However, the actual Dyno is a fairly limited environment by design – the root filesystem is read-only, the only writable area is in the application’s root directory, and disappears when terminated. There’s no sysvinit, upstart or systemd for people to bicker about. Use a Procfile, which is also really simple.

So a challenge to overcome became: “how to install a Datadog Agent package that runs a dogstatsd listener as a second process, inside an environment that is pretty locked down?”

First, we have to install the package. Heroku has a concept of “[buildpacks]”(https://devcenter.heroku.com/articles/buildpacks) that can be used to run compilation steps before adding your application code and launching it. The use of multiple buildpacks is also available, to chain steps together to achieve the desired outcome.

I read the heroku-buildpack-apt and found a bunch of good ideas, and came up with a Datadog-Agent-specific installer buildpack that drops off the package, as well as the needed environment for the runtime.

Now how do I run the listener process alongside my application?

Enter foreman. Foreman, not to be confused with “theforeman“, has long been a great way for application developers writing Heroku-targeted applications to run them locally in a similar manner that they will be run on the remote platform.

Foreman reads the Profile, and runs the processes based on the directives contained inside.

This feature is the one that we leverage to run multiple processes on a single Dyno.

By using foreman inside the Dyno, we are able to tell foreman to run more than one process type at a time, with another Procfile that specifies the startup process for the actual application as well as the dogstatsd listener.

When deploying any code revision, Heroku will read the base Procfile, and run a foreman process inside the Dyno, which will in turn, start up the app & dogstasd.

And while foreman is a Ruby gem, your project may be in Python (use honcho), Go (use forego or goreman) and I’m sure there are others out there. I haven’t found or tested all of them, tell me if they work out for you.

I did, however, take the time to write up a README with the procedure to follow to use this, as well as commit-by-commit example application.

Here’s the buildpack code: http://miketheman.github.io/heroku-buildpack-datadog/

Here’s the example application: https://github.com/miketheman/buildpack-example-ruby

Here’s an image of the stats collected by the example application in Datadog, with increasing web load:
Heroku App Load

Here’s a random dog:

Hope this helps you find deeper insight into how you monitor your applications!

Update (2014-12-15)

A quick addition on this topic.

A couple of days after this was published, I had a short Twitter exchange with Bo Jeanes, after which he submitted a Pull Request to the buildpack, (as well as an update to the example app).
This simplifies the end-user’s deployment of the Agent package, in that the user no longer has to spend any time on doing Procfile-in-Procfile solutions, as well as remove the need from foreman and the like from inside the container, rather the dogstatsd process will be started via the profile.d mechanism which is run on Dyno startup.

This makes the solution even more elegant, so thanks a ton, Bo!

Ask your systems: “What’s going on?”

This is a sysadmin/devops-style post.
Disclaimers are that I work with these tools and people, and like what they do.

In some amount of our professional lives, we are tasked with bringing order to chaos, keep systems running and have the businesses we work for continue functioning.

In our modern days of large-scale computing, web technology growth explosions, multiple datacenter deployments, cloud providers and other virtualization technologies, the manpower needed to handle the vast amount of technologies, services and systems seems to have a pretty high overhead cost associated with it. “You’ve got X amount of servers? Let’s hire Y amount of sysadmins!”

A lot of tech startups start out with some of the developers performing a lot of the systems tasks, and since this isn’t always their core expertise, decisions are made, scripts are written, and “it works”.  When the team/systems grow large enough to need their own handler, in walks a system admin-style person, and may keel over, due to the state of affairs.

Yes, there are many tech companies where this is not the case, and I commend them of keeping their systems lean, mean and clean.

A lot of companies have figured out that in order to make the X:Y ratio work well, automation is required.  Here’s an article that covers some numbers from earlier this year.  I find that the statement of a ratio of 50 servers to 1 sysadmin pretty low on my view of how things can be, especially given the tools that we have available to us.

One of the popular systems configuration tools I’ve been using heavily is Chef, from Opscode. They provide a hosted solution, as well as an open-source version of their software, for anyone to use.  Getting up and running with some basics is really fast, and there’s a ton of information available, as well as a really responsive community (from mailing lists, bug tracker site and IRC channel).  Once you’re working with Chef, you may wonder how you ever got anything done before you had it.  It’s really treating a large part of your infrastructure as code – something readable, executable, and repeatable.

But this isn’t about getting started with Chef. It’s about “what’s next”.

In any decent starting-out tech company, the amount of servers used will typically range from 2-3 all the way to 200 – or even more.  If you’ve gone all the way to 200 without something like Chef or Puppet, I commend your efforts, and feel somewhat sorry for you.  Once you’re automating your systems creation, deployment and change, then you typically want some feedback on what’s going on. Did what I asked this system to do succeed, or did it fail.

Enter Datadog.

Datadog attempts to bring many sources of information together, to help whomever it is that is supposed to be looking at the systems to make more sense of the situation, from collecting metrics from systems, events from services and other sources, to allowing a timeline and newsfeed that is very human-friendly.

Having all the data at your disposal makes it easier to find patterns and correlations between events, systems and behaviors – helping to minimize the “what just happened?” question.

The Chef model for managing systems is a centralized server (either the open source in your environment or the hosted service in Opscode), which tells a server what it is meant to “be”.  Not what it is meant to “do now”, but the final state it should be in.  They call this model “idempotent” – meaning that no matter how many time you execute the same code on the same server, the behavior should end up the same every time.  But it doesn’t follow up very much on the results of the actions.

An analogy could be that every morning, before your kid leaves the house, your [wife|mother|husband|guardian|pet dragon] tells them “You should wear a coat today.” and then goes on their merry way, not checking whether they wore a coat or not. The next morning, there will get the same comment, and so on and so forth.

So how do we figure out what happened? Did the kid wear a hat or not? I suppose I could check by asking the kid and get the answer, but what if there are 200 of us? Do I have time to ask every kid whether or not they ended up wearing a hat? I’m going to be spending a lot of time dealing with this simple problem, I can tell you now.

Chef has built-in functionality to report on what Chef did – after it has received its instructions from the centralized server. It’s called the “Exception and Report Handlers” – and this is how I tie these two technologes together.

I adapted some code started by Adam Jacob @Opscode, and extended it further into a complete RubyGem with modifications for content, functionality and some rigorous testing.

Once the gem was ready, now I have to distribute it to my servers, and then have it execute every time Chef runs on that server. So, based on the chef_handler cookbook, I added a new recipe to the datadog cookbook – dd-handler.

What this does is adds the necessary components to a Chef execution, and when placed at the beginning of a “run”, will capture all the events and report back on the important ones to the Datadog newsfeed.  It will also push some metrics, like how long the Chef execution too, how many resources were updated, etc.

The process for getting this done was really quite simple, once you boil down all the reading, how’s and why’s – especially if you use git to version control your chef-repo.  The `knife cookbook site install` command is a great method for keeping your git repo “safe” for future releases, thus preserving your changes to the cookbook, allowing for merging of new code automatically. Read more here.

THE MOST IMPORTANT STUFF:

Here’s pretty much the process I used (under chef/knife version 0.10.x):

$ cd chef-repo
$ knife cookbook site install datadog
$ vi cookbooks/datadog/attributes/default.rb

At this point, I head over to Datadog, hit the “Setup” page, and grap my organization’s API Key, as well as create a new Application Key named “chef-handler” and copy the Hash that is created.

I place these two values into the `attributes/default.rb` file, save and close.

$ knife cookbook upload datadog

This places the cookbook on my Chef server, and is now ready to be referenced by a node or role. I use roles, as it’s much more manageable across multiple nodes.

I update the `common-node` role we have to include “recipe[datadog::dd-handler]” as one of the first receipes to execute in the run list.

The common-node role applies to all of our systems, and since they all run chef, I want them all to report on their progress.

And then let it run.

END MOST IMPORTANT STUFF

Since our chef-client runs on a 30 minute interval, and not all execute at the same time, this makes for some interesting graphs at the more recent time slices – not all the data comes in at the same time.  That’s something to get used to.

Here’s an image of a system’s dashboard with only the Chef metrics:

Single Instance dashboard
It displays a 24-hour period, and shows that this particular instance had a low variance in its execution time, as well as not much is being updated during this time (a good thing, since it is consistent).

On a test machine I tossed together, I created a failure, and here’s how it gets reported back to the newsfeed:

 

Testing a failure
As you can see, the stacktrace attempt to provide me with the information I need to diagnose and repair the issue. Once I fix it, and apache can start, this event was logged in the “Low Priority” section of the feed (since succeses are expected, and failures are aberrant behavior):

Test passes

All this is well and wonderful, but what about a bunch of systems? Well, I grabbed a couple snaps off the production environment for you!

These are aggregates I created with the graphing language (had never really read it before today!)

Production aggregate metrics

By being able to see the execution patterns, and a bump closer to the left side of the “Resource Updated” graph – I then investigated, and someone had deployed a new rsyslog package – so there was a temporary increase in deploying the resources, and now there are slightly more resources to manage overall.

The purple bump seen in the “Execution Time” graph led me to investigate, and found a timeout in that system’s call to an “apt-get update” request – probably the remote repo was unavailable for a minute. Having the data available to make that correlation made this task of investigating this problem really fast, easy, and simple – more importantly since it has been succeeding ever since, no cause for alarm.

So now I have these two technologies – Chef to tell the kids (the servers) to wear coats, and Datadog to tell the parents (me) if the kids wore the coats or not, and why.

Really, just wear a coat. It’s cold out there.

———–

Tested on:

  • CentOS 5.7 (x64), Ruby 1.9.2 v180, Chef 0.10.4
  • Ubuntu 10.04 (x64), Ruby 1.8.7 v352, Chef 0.9.18
Used: