Strangulating Bare-Metal Infrastructure to Containers

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Strangulating Bare-Metal Infrastructure to Containers

How we used incremental strangulation to drastically reduce time to create environments and optimize the underlying hardware.

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Change is inevitable. Change for the better is a full-time job — Adlai Stevenson I

We run a successful digital platform for one of our clients. It manages huge amounts of data aggregation and analysis in the Out of Home advertising domain.

The platform had been running successfully for a while. Our original implementation was focused on time to market. As it expanded across geographies and impact, we decided to shift our infrastructure to containers for reasons outlined later in this post. Our day to day operations and release cadence needed to remain unaffected during this migration. To ensure those goals, we chose an approach of incremental strangulation to make the shift.

The Strangler pattern is an established pattern that has been used in the software industry at various levels of abstraction. It has been documented by Microsoft and talked about by Martin Fowler. The basic premise is to build an incremental replacement for an existing system or sub-system. The approach often involves creating a Strangler Facade that abstracts both existing and new implementations consistently. 

As features are re-implemented with improvements behind the facade, the traffic or calls are incrementally routed via new implementation. This approach is taken until all the traffic/calls go only via new implementation and old implementation can be deprecated. We applied the same approach to gradually rebuild the infrastructure in a fundamentally different way for a platform where our production disruption was under a few minutes.

This writeup will explore some of the scaffolding we did to enable the transition and the approach leading to a quick switch over with confidence. We will also talk about tech stack from an infrastructure point of view and the shift that we brought in. We believe the approach is generic enough to be applied across a wide array of deployments.

The As-Is


We rely on Amazon Web Service to do the heavy lifting for infrastructure. At the same time, we try to stay away from cloud-provider lock-in by using components that are open source or can be hosted independently if needed. Our infrastructure consisted of services in double digits, at least 3 different data stores, messaging queues, an elaborate centralized logging setup (Elastic-search, Logstash and Kibana) as well as monitoring cluster with (Grafana and Prometheus). The provisioning and deployments were automated with Ansible. A combination of queues and load balancers provided us with the capability to scale services. Databases were configured with replica sets with automated failovers.

The service deployment topology across servers was pre-determined and configured manually in Ansible config. Auto-scaling was not built into the design because our traffic and user-base are pretty stable and we have reasonable forewarning for a capacity change. All machines were bare-metal machines and multiple services co-existed on each machine.

All servers were organized across various VPCs and subnets for security fencing and were accessible only via bastion instance.

Release Cadence

Delivering code to production early and frequently is core to the way we work. All the code added within a sprint is released to production at the end. Some features can span across sprints. The feature toggle service allows features to be enabled/disable in various environments. We are a fairly large team divided into small cohesive streams. To manage release cadence across all streams, we trigger an auto-release to our UAT environment at a fixed schedule at the end of the sprint. The point in time snapshot of the git master is released. We do a subsequent automated deploy to production that is triggered manually.

CI and Release Pipelines

Code and release pipelines are managed in Gitlab. Each service has GitLab pipelines to test, build, package and deploy. Before the infrastructure migration, the deployment folder was co-located with source code to tag/version deployment and code together. The deploy pipelines in GitLab triggered Ansible deployment that deployed binary to various environments.

Figure 1 - The as-is release process with Ansible + Bare metal machines

The Gaps

While we had a very stable infrastructure and matured deployment process, we had aspirations which required some changes to the existing infrastructure. This section will outline some of the gaps and aspirations.

Cost of Adding a New Service

Adding a new service meant that we needed to replicate and setup deployment scripts for the service. We also needed to plan deployment topology. This planning required taking into account the existing machine loads, resource requirements as well as the resource needs of the new service. When required new hardware was provisioned. Even with that, we couldn’t dynamically optimize infrastructure use. All of this required precious time to be spent planning the deployment structure and changes to the configuration.

Lack of Service Isolation

Multiple services ran on each box without any isolation or sandboxing. A bug in service could fill up the disk with logs and have a cascading effect on other services. We addressed these issues with automated checks both at package time and runtime however our services were always susceptible to noisy neighbour issue without service sandboxing.

Multi-AZ Deployments

High availability setup required meticulous planning. Even with that, we had a multi-node deployment for each component but not a safeguard against an availability zone failure. Planning for an availability zone required leveraging Amazon Web Service’s constructs which would have locked us in deeper into the AWS infrastructure. We wanted to address this without a significant lock-in.

Lack of Artifact Promotion

Our release process was centered around branches, not artifacts. Every auto-release created a branch called RELEASE that was promoted across environments. Artifacts were rebuilt on the branch. This isn’t ideal as a change in an external dependency within the same version can cause a failure in a rare scenario. Artifact versioning and promotion are more ideal in our opinion there is higher confidence attached to a tested binary.

Need for a Low-Cost Spin-Up of Environment

As we expanded into more geographical regions rapidly, spinning up full-fledged environments quickly became crucial. In addition to that without infrastructure optimization, the cost continued to mount up, leaving a lot of room for optimization. If we could re-use the underlying hardware across environments, we could reduce operational costs.

Provisioning Cost at Deployment Time

Any significant changes to the underlying machine were made during deployment time. This effectively meant that we paid the cost of provisioning during deployments. This led to longer deployment downtime in some cases.

Considering Containers and Kubernetes

It was possible to address most of the existing gaps in the infrastructure with additional changes. For instance, Route53 would have allowed us to set up services for high availability across AZs, extending Ansible would have enabled multi-AZ support and changing build pipelines and scripts could have brought in artifact promotion.

However, containers, specifically Kubernetes solved a lot of those issues either out of the box or with small effort. Using KOps also allowed us to remained cloud-agnostic for a large part. We decided that moving to containers will provide the much-needed service isolation as well as other benefits including lower cost of operation with higher availability.

Since containers differ significantly in how they are packaged and deployed. We needed an approach that had a minimum or zero impact to the day to day operations and ongoing production releases. This required some thinking and planning. Rest of the post covers an overview of our thinking, approach and the results.

The Infrastructure Strangulation

A big change like this warrants experimentation and confidence that it will meet all our needs with reasonable trade-offs. So we decided to adopt the process incrementally. The strangulation approach was a great fit for an incremental rollout. It helped in assessing all the aspects early on. It also gave us enough time to get everyone on the team up to speed. Having a good operating knowledge of deployment and infrastructure concerns across the team is crucial for us. The whole team collectively owns the production, deployments, and infrastructure setup. We rotate on responsibilities and production support.

Our plan was a multi-step process. Each step was designed to give us more confidence and incremental improvement without disrupting the existing deployment and release process. We also prioritized the most uncertain areas first to ensure that we address the biggest issues at the start itself.

We chose Helm as the Kubernetes package manager to help us with the deployments and image management. The images were stored and scanned in AWS ECR.

The First Service

We picked the most complicated service as the first candidate for migration. A change was required to augment the packaging step. In addition to the existing binary file, we added a step to generate a docker image as well. Once the service was packaged and ready to be deployed, we provisioned the underlying Kubernetes infrastructure to deploy our containers. We could deploy only one service at this point but that was ok to prove the correctness of the approach. We updated GitLab pipelines to enable dual deploy. Upon code check-in, the binary would get deployed to existing test environments as well as to new Kubernetes setup.

Some of the things we gained out of these steps were the confidence of reliably converting our services into Docker images and the fact that dual deploy could work automatically without any disruption to existing work.

Migrating Logging and Monitoring

The second step was to prove that our logging and monitoring stack could continue to work with containers. To address this, we provisioned new servers for both logging and monitoring. We also evaluated Loki to see if we could converge tooling for logging and monitoring. However, due to various gaps in Loki given our need, we stayed with ElasticSearch stack. We did replace logstash and filebeat with Fluentd. This helped us address some of the issues that we had seen with filebeat our old infrastructure. Monitoring had new dashboards for the Kubernetes setup as we now cared about both pods as well in addition to host machine health.

At the end of the step, we had a functioning logging and monitoring stack which could show data for a single Kubernetes service container as well across logical service/component. It made us confident about the observability of our infrastructure. We kept new and old logging & monitoring infrastructure separate to keep the migration overhead out of the picture. Our approach was to keep both of them alive in parallel until the end of the data retention period.

Addressing Stateful Components

One of the key ingredients for strangulation was to make any changes to stateful components post initial migration. This way, both the new and old infrastructure can point to the same data stores and reflect/update data state uniformly.

So as part of this step, we configured newly deployed service to point to existing data stores and ensure that all read/writes worked seamlessly and reflected on both infrastructures.

Deployment Repository and Pipeline Replication

With one service and support system ready, we extracted out a generic way to build images with docker files and deployment to new infrastructure. These steps could be used to add dual-deployment to all services. We also changed our deployment approach. In a new setup, the deployment code lived in a separate repository where each environment and region was represented by a branch example uk-qa,uk-prod or in-qa etc. 

These branches carried the variables for the region + environment. In addition to that, we provisioned a Hashicorp Vault to manage secrets and introduced structure to retrieve them by region + environment combination. We introduced namespaces to accommodate multiple environments over the same underlying hardware.

Crowd-Sourced Migration of Services

Once we had basic building blocks ready, the next big step was to convert all our remaining services to have a dual deployment step for new infrastructure. This was an opportunity to familiarize the team with new infrastructure. So we organized a session where people paired up to migrate one service per pair. This introduced everyone to docker files, new deployment pipelines and infrastructure setup.

Because the process was jointly driven by the whole team, we migrated all the services to have dual deployment path in a couple of days. At the end of the process, we had all services ready to be deployed across two environments concurrently.

Test Environment Migration

At this point, we did a shift and updated the Nameservers with updated DNS for our QA and UAT environments. The existing domain started pointing to Kubernetes setup. Once the setup was stable, we decommissioned the old infrastructure. We also removed old GitLab pipelines. Forcing only Kubernetes setup for all test environments forced us to address the issues promptly.

In a couple of days, we were running all our test environments across Kubernetes. Each team member stepped up to address the fault lines that surfaced. Running this only on test environments for a couple of sprints gave us enough feedback and confidence in our ability to understand and handle issues.

Establishing Dual Deployment Cadence

While we were running Kubernetes on the test environment, the production was still on old infrastructure and dual deployments were working as expected. We continued to release to production in the old style.

We would generate images that could be deployed to production but they were not deployed and merely archived.

new deployment path
Figure 2 - Using dual deployment to toggle release path to new infrastructure

As the test environment ran on Kubernetes and got stabilized, we used the time to establish dual deployment cadence across all non-prod environments.

Troubleshooting and Strengthening

Before migrating to the production we spent time addressing and assessing a few things.

  • We updated the liveness and readiness probes for various services with the right values to ensure that long-running DB migrations don’t cause container shutdown/respawn. We eventually pulled out migrations into separate containers which could run as a job in Kubernetes rather than as a service.
  • We spent time establishing the right container sizing. This was driven by data from our old monitoring dashboards and the resource peaks from the past gave us a good idea of the ceiling in terms of the baseline of resources needed. We planned enough headroom considering the roll out updates for services.
  • We setup ECR scanning to ensure that we get notified about any vulnerabilities in our images in time so that we can address them promptly.
  • We ran security scans to ensure that the new infrastructure is not vulnerable to attacks that we might have overlooked.
  • We addressed a few performance and application issues. Particularly for batch processes, which were split across servers running the same component. This wasn’t possible in Kubernetes setup, as each instance of a service container feeds off the same central config. So we generated multiple images that were responsible for part of batch jobs and they were identified and deployed as separate containers.

Upgrading Production Passively

Finally, with all the testing we were confident about rolling out Kubernetes setup to the production environment. We provisioned all the underlying infrastructure across multiple availability zones and deployed services to them. The infrastructure ran in parallel and connected to all the production data stores but it did not have a public domain configured to access it. Days before going live the TTL for our DNS records was reduced to a few minutes. Next 72 hours gave us enough time to refresh this across all DNS servers.

Meanwhile, we tested and ensured that things worked as expected using an alternate hostname. Once everything was ready, we were ready for DNS switchover without any user disruption or impact.

DNS Record Update

The go-live switch-over involved updating the nameservers’ DNS record to point to the API gateway fronting Kubernetes infrastructure. An alternate domain name continued to point to the old infrastructure to preserve access. It remained on standby for two weeks to provide a fallback option. However, with all the testing and setup, the switch over went smooth. Eventually, the old infrastructure was decommissioned and old GitLab pipelines deleted.


 Advanced issue found

DNS record update
Figure 3 - DNS record update to toggle from legacy infrastructure to containerized setup
We kept old logs and monitoring data stores until the end of the retention period to be able to query them in case of a need. Post-go-live the new monitoring and logging stack continued to provide needed support capabilities and visibility.

Observations and Results

Post-migration, time to create environments has reduced drastically and we can reuse the underlying hardware more optimally. Our production runs all services in HA mode without an increase in the cost. We are set up across multiple availability zones. Our data stores are replicated across AZs as well although they are managed outside the Kubernetes setup.

Kubernetes had a long learning curve and it required a few significant architectural changes, however, because we planned for an incremental rollout with coexistence in mind, we could take our time to change, test and build confidence across the team. While it may be a bit early to conclude, the transition has been seamless and benefits are evident.

agile architecture, ansible, cloud, infrastructure automation, kubernetes, microservices and containers, strangulation

Published at DZone with permission of Priyank Gupta . See the original article here.

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