endorsedOngoing

Staff Augmentation for gRPC Migration and Microservices

Services Covered:

Staff Augmentation

Industry

Information Technology

Duration

113 months

Budget

1M - 10M

Start Date

1 March 2017

Team Size

6-10 Employees

Engagement Model

Onshore

Project Capability Score (PCS)

75/100

Good

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Client

Confidential

New York City, United States of America

Project Summary

Rails Reactor provided ongoing staff-augmentation support to an engineering firm, contributing to a multi-year gRPC migration, decomposition of a monolith into microservices, and the development and deployment of machine-learning models. The vendor worked cross-team to remediate protocol library bugs, improve performance, and deliver reliable, well-tested code. The client praised the team's collaboration, proactivity, and the measurable improvements to platform stability and features.

Key Challenges

Break up a monolith into microservices and support new features on those services
Execute a complex gRPC migration across services written in multiple languages
Build and deploy machine-learning models and features
Maintain ongoing support to keep systems operational while delivering new features

Project Deliverables

gRPC migration across multiple services
Decomposition of a monolith into microservices
Machine-learning models and related feature implementations
Bug discovery and remediation for PHP gRPC library
High-quality, performant and well-tested code contributions

Project Solution

Rails Reactor supplied an engineering team to support a multi-year gRPC migration and microservices decomposition across services written in multiple languages. Their engineers collaborated heavily with on-site teams, implemented controlled deployment techniques, performed monitoring and analysis, discovered and fixed bugs in the PHP gRPC library, and developed machine-learning models and related features. The work included detailed cross-team coordination, manual protocol workarounds when libraries lacked needed features, and delivering high-quality, performant code.

Project Outcome

Contributed new machine-learning based features for end users
Converted hundreds of thousands, if not millions, of lines of code into more reliable, robust, and better-tested code
Improved platform stability and performance through bug fixes and cross-team technical solutions

Platforms

Desktop
Web

Tech Stack

PHP
Scala

Client Endorsement

Overall Review Rating

4.63

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“Collaborative and proactive, Rails Reactor integrated successfully into many teams, with a smooth transition. They have contributed immensely to the projects they're staffed on, for instance converting millions of lines of code into a more reliable, robust and package that improves performance.”
Anonymous
Head of Engineering
Date: 03/31/2020