How we optimized CircleCI for pace & reduce our construct occasions by…

Sam Rubin

Tuning a steady integration server presents an fascinating problem — infrastructure engineers must stability construct pace, value, and queue occasions on a system that many builders shouldn't have intensive expertise managing at scale. The outcomes, when finished proper, could be a main profit to your organization as illustrated by the current journey we took to enhance our CI setup.

As Coinbase has grown, retaining our builders pleased with our inside instruments has been a excessive precedence. For many of Coinbase’s historical past we've used CircleCI server, which has been a performant and low-maintenance software. As the corporate and our codebase have grown, nevertheless, the calls for on our CI server have elevated as properly. Previous to the optimizations described right here, builds for the monorail utility that runs had elevated considerably in size (doubling or tripling the earlier common construct occasions) and builders generally complained about prolonged or non-finishing builds.

Our CI builds have been now not assembly our expectations, and it was with the earlier points in thoughts that we determined to embark on a marketing campaign to get our setup again into form.

It’s value sharing right here that Coinbase particularly makes use of the on-premise server model of CircleCI moderately than their cloud providing — internet hosting our personal infrastructure is necessary to us for safety causes, and these ideas particularly apply to self-managed CI clusters.

We discovered the primary key to optimizing any CI system to be observability, as and not using a technique to measure the consequences of your tweaks and modifications it’s unattainable to really know whether or not or not you truly made an enchancment. In our case, server-hosted CircleCI makes use of a nomad cluster for builds, and on the time didn't present any methodology of monitoring your cluster or the nodes inside. We needed to construct programs of our personal, and we determined a very good method could be utilizing the framework of the four golden signals, Latency, Site visitors, Errors, and Saturation.


Latency is the entire period of time it takes to service a request. In a CI system, this may be thought-about to be the entire period of time a construct takes to run from begin to end. Latency is healthier measured on a per-repo and even per-build foundation as construct size can differ massively based mostly on the mission.

To measure this, we constructed a small utility that queried CircleCI’s API often for construct lengths, after which shipped over that info to Datadog to permit us to construct graphs and visualizations of common construct occasions. This allowed us to chart the outcomes of our enchancment experiments empirically and robotically moderately than counting on anecdotal or manually curated outcomes as we had finished beforehand.

CircleCI API outcomes

Site visitors

Site visitors is the quantity of demand being positioned in your system at anybody time. In a CI system, this may be represented by the entire variety of concurrently working builds.

We have been capable of measure this through the use of the identical system we constructed to measure latency metrics. This got here in helpful when figuring out the higher and decrease bounds for using our construct assets because it allowed us to see precisely what number of jobs have been working at anybody time.


Errors are the entire quantity of requests or calls that fail. In a CI system this may be represented by the entire variety of builds that fail resulting from infrastructural causes. It’s necessary right here to make a distinction between builds that fail accurately, resulting from assessments, linting, code errors, and so on. moderately than builds that fail resulting from platform points.

One problem we encountered was that often AWS would give us “dangerous” cases when spinning up new builders that may run a lot slower than a traditional “good” occasion. Including error detection into our builder startup scripts allowed us to terminate these and spin up new nodes earlier than they may decelerate our working builds.


Saturation is how “full” your service is, or how a lot of your system assets are getting used. In a CI system, that is pretty simple — how a lot I/O, CPU, and reminiscence are the builders below load utilizing.

To measure saturation for our setup we have been capable of faucet into cluster metrics by putting in a Datadog Agent on every of our builders, which allowed us to get a view into system stats throughout the cluster.

Datadog job statistics

As soon as your monitoring setup is in place it turns into simpler to dig into the basis explanation for construct slowdowns. One of many difficulties in diagnosing CI issues with out cluster-wide monitoring is that it may be exhausting to determine which builders are experiencing load at anybody time or how that load impacts your builds. Latency monitoring can mean you can work out which builds are taking the longest, and saturation monitoring can mean you can determine the nodes working these builds for nearer investigation.

For us, the brand new latency measuring we added allowed us to shortly affirm what we had beforehand guessed: not each construct was equal. Some builds ran on the fast speeds we had beforehand been experiencing however different builds would drag on for much longer than we anticipated.

In our case this discovery was the massive breakthrough — as soon as we might shortly determine builds with elevated latency and discover the saturated nodes the issue shortly revealed itself: useful resource rivalry between beginning builds! Because of the giant variety of assessments for our bigger builds we use CircleCI’s parallelization characteristic to separate up our assessments and run them throughout the fleet in separate docker containers. Every take a look at container additionally requires one other set of assist containers (Redis, MongoDB, and so on.) in an effort to replicate the manufacturing atmosphere. Beginning all the vital containers for every construct is a resource-intensive operation, requiring vital quantities of I/O and CPU. Since Nomad makes use of bin-packing for job distributions our builders would typically launch as much as 5 totally different units of those containers directly, inflicting huge slow-downs earlier than assessments might even begin working.

Organising a growth atmosphere is vital to debugging CI issues as soon as discovered because it permits you to push your system to its limits whereas guaranteeing that none of your testing impacts productiveness in manufacturing. Coinbase maintains a growth cluster for CircleCI that we use to check out new variations earlier than pushing them out to manufacturing, however in an effort to examine our choices we turned the cluster right into a smaller reproduction of our manufacturing occasion, permitting us to successfully load take a look at CircleCI builders. Maintaining your growth cluster as shut as attainable to manufacturing may also help guarantee any options you discover are reflective of what can truly assist in an actual atmosphere.

As soon as we had recognized why our builds have been encountering points, and we’d arrange an atmosphere to run experiments in, we might begin growing an answer. We repeatedly ran the identical giant builds that have been inflicting the issues on our manufacturing cluster on totally different sizes and types of EC2 cases in an effort to work out which was essentially the most time and cost-effective choices to make use of.

EC2 occasion sort comparability

Whereas we beforehand had been utilizing smaller numbers of huge cases to run our builds it seems the optimum setup for our cluster was truly a really giant variety of smaller cases (m5.larges in our case) — sufficiently small that CircleCI would solely ship one parallelized construct container to every occasion, stopping the construct trampling points that have been the reason for the gradual downs. A pleasant aspect impact of figuring out the right occasion varieties was that it truly allowed us to scale back our server value footprint considerably as we have been capable of dimension our cluster extra carefully to its utilization.

Making use of your modifications to a manufacturing atmosphere is the ultimate step. Figuring out whether or not the consequences of the tuning labored could be finished the identical approach the issues have been recognized — with the 4 golden alerts.

After we had recognized what labored finest on our growth cluster we shortly applied the brand new builder sizing in manufacturing. The outcomes? A 75% lower in construct time for our largest builds, vital value financial savings because of the right-sizing of our cluster, and most necessary of all: blissful builders!

Builds, earlier than and after

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