2019 Microservices Trends Reflection

Last January we wrote a piece called, “5 Microservices Trends to Watch in 2019,” which identified 1) test automation, 2) Continuous Deployment/Verification (CD/CV), 3) incident response, 4) Cloud Service Expense Management (CSEM), and 5) Kubernetes extending to Machine Learning (ML). Were we right? Over the past year, each of these technologies exhibited accelerated adoption and popularity. Below we underscore some highlights of 2019.

1. Emerging test automation

• Last year we wrote a piece called “Introducing Redpoint’s Design and Front-end Engineering Landscape,” highlighting testing tools that targeted front-end engineers including Cypress, Chromatic, and Percy.
• More broadly our research also found that teams are looking for solutions that accelerate testing by intelligently ordering tests based on past test runs and changes to the source code. This not only accelerates developer productivity but saves on infrastructure costs.
• Last year fuzzing, a security testing technique that involves inputting random data to the test subject in an attempt to make it crash, started to gain visibility. It is clear fuzzing is picking up as the industry’s first fuzzing event, FuzzCon, will be hosted in a few weeks. A slew of vendors will be there including Fuzzing IO, Google, VDA Labs, Whitescope, FuzzBuzz, Microsoft, Synopsys, and ForAllSecure.

2. Continuous Deployment/Verification for improved productivity

• Our research finds that while businesses aspire to standardize on one commercial CD system, there are still a slew of homegrown systems still running in production. We often hear businesses adopt CD systems department by department so a single enterprise may be running multiple systems.
• In 2017 Weavework first shared their idea of GitOps, “a way of implementing Continuous Deployment for cloud native applications.” GitOps is a developer-centric approach in which git always “contains declarative descriptions of the infrastructure currently desired in the production environment and an automated process to make the production environment match the described state in the repository. If you want to deploy a new application or update an existing one, you only need to update the repository — the automated process handles everything else.” Last year Kelsey Hightower acknowledged the importance of GitOps, “GitOps is the best thing since configuration as code. Git changed how we collaborate, but declarative configuration is the key to dealing with infrastructure at scale, and sets the stage for the next generation of management tools.”
• In line with GitOps, GitHub announced Actions in late 2018. With GitHub Actions, devs can build, test, and deploy their code right from GitHub. There are a slew of GitHub Actions now including using LaunchDarkly feature flags to control GitHub workflows .
• Codefresh released a pipeline debugger for its CI/CD system an indication that DevOps teams are asking for more tooling around CI/CD.
• CD provider Harness announced its $50M Series B.

3. Incident response to the rescue

• We continue to be excited about opportunities in the incident response space and share some of our research in a post called, “Introducing Redpoint’s SRE Landscape.”
• We’ve cataloged ~10 modern incident response platforms that target DevOps/SRE teams. Each platform leads with a different core value-prop from a service catalog to ChatOps to runbook automation.
• Of all the 2019 trend categories, we believe incident response experienced the most change over the last year having identified at least five new vendors in the space.
• Of the incumbents, we hear operators using PagerDuty’s solution as it ties closely to its alerting product.

4. Cloud Service Expense Management (CSEM) saves $$$

• Parkmycloud estimates $14.1B of cloud compute spending was wasted in 2019.
• Flexera’s State of the Cloud Survey also identified managing cloud costs as a top priority for the third year in a row.
• In 2019 the Redpoint team leaned forward with an investment in a cost optimization solution called Opsani. Opsani’s optimization solution fine-tunes runtime environments continuously. Leveraging ML models, Opsani evaluates the efficient frontier between the performance and the cost of running services in the cloud. Read about our investment here.

5. Kubernetes extending to Machine Learning (ML)

• Kubeflow, a ML toolkit for Kubernetes, reached over 8.3K GitHub stars (see below).
• Lyft open sourced Flyte, a Kubernetes-native extensible orchestration engine that can manage core ML pipelines.
• Cloudera launched a ML service, a version of its Data Science Workbench that can run on public cloud Kubernetes services.
• Even AWS announced Amazon SageMaker Operators for Kubernetes that makes it easier for individuals using Kubernetes to train, tune, and deploy ML models in Amazon Sagemaker.

During 2019 testing, CD/CV, incident response, Cloud Service Expense Management, and using Kubernetes for ML continued to increase in popularity. In 2020 we expect these emerging technologies to gain more momentum.