# Go
> 43 posts about Go, summarised, each linking to the original.

## Articles

### [Democratising Fare Storage at Scale Using Event Sourcing](https://yomu.fyi/post/democratising-fare-storage-at-scale-using-event-sourcing.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Sourabh Suman
- Published: Nov 23, 2020

Grab's legacy system stored booking and fare details in a single relational table, creating a bloated booking entity that tracked only the latest fare state and hindered rapid feature iteration. To resolve scalability, stability, and debugging challenges across millions of daily bookings, the team developed Fare Storage using the Event Sourcing pattern. The new architecture persists all fare modification events chronologically in DynamoDB, backed by a cache for eventually consistent reads and message streaming for downstream processing. The platform employs optimistic locking with versioning to manage concurrent updates, enforces idempotency through client-generated transaction UUIDs, and delegates metadata serialization to an SDK to prevent storage API changes.


### [Go Modules- A Guide for monorepos (Part 2)](https://yomu.fyi/post/go-modules-a-guide-for-monorepos-part-2.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Michael Cartmell
- Published: Aug 12, 2020

Managing dependencies in a multi-module monorepo created developer friction at Grab due to unexpected changes from previous vendoring attempts and accidental imports. Because Go modules were not yet enabled directly for builds, the team implemented a continuous integration check that executes go mod vendor and rejects merge requests if any diffs exist in go.mod or the vendor directory. Adopting this CI check required configuring SSH deploy keys for private repositories, adding retry logic for network-related false positives, and standardizing on a single Go version to prevent checksum discrepancies. To streamline ongoing maintenance across hundreds of dependencies, the team developed an automated tool named AutoVend Bot. The bot runs go list -m -u all to detect updates and opens a scheduled batch of merge requests each day for human review.


### [Go Modules- A Guide for monorepos (Part 1)](https://yomu.fyi/post/go-modules-a-guide-for-monorepos-part-1.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Michael Cartmell
- Published: May 29, 2020

Grab transitioned its large Go monorepo dependency management from Glide to Go modules while retaining an existing vendor directory structure. The team generated root go.mod configurations from glide.yaml and used go mod vendor without directly enabling module-mode builds. Incompatible nested sub-vendor paths were excluded by placing empty go.mod files, relying on the rule that modules cannot contain other modules. Post-migration maintenance revealed challenges with dependency inheritance and implicit go.mod updates during builds, which engineers investigated using go mod graph and digraph to trace dependency paths.


### [Plumbing At Scale](https://yomu.fyi/post/plumbing-at-scale.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Karan Kamath
- Published: Jan 6, 2020

Grab's backend services process terabytes of data ingress per hour, generating recurring needs for stream transformations, joins, and time-windowed aggregations across diverse workloads. To support these asynchronous processing patterns across their Go ecosystem, the Coban team developed a managed, NoOps event sourcing and stream processing platform. The architecture packages stateless processing pipelines as Kubernetes deployments on AWS, polling Kafka event logs and using ScyllaDB as a shared metastore for stateful needs like deduplication and windowing. Stream processing pods combine ingestion triggers, a worker pool runtime, and user-provided domain logic plugins with customizable failure handling. This infrastructure scales to handle over 300 billion events weekly while maintaining workload isolation and elastic autoscaling.


### [Marionette - Enabling E2E User-scenario Simulation](https://yomu.fyi/post/marionette-enabling-e2e-user-scenario-simulation.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Anish Jha
- Published: Dec 23, 2019

Conducting end-to-end testing across Grab's transport microservices became difficult due to service availability, environment construction, cross-service authentication, and complex data setups for real-world user accounts. To address these challenges without relying on physical mobile devices or emulators, Grab built Marionette, an internal simulation platform for passenger and driver interactions. The platform provisions required test data, coordinates booking lifecycles, and isolates test executions across distinct user groups using localized cohorts. Engineers can configure driver and passenger behaviors, execute workflows, and run load or integration tests through a dedicated user interface, a Go SDK, and RESTful APIs.


### [How We Implemented Domain-Driven Development in Golang](https://yomu.fyi/post/how-we-implemented-domain-driven-development-in-golang.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Kapil Chaurasia
- Published: Nov 21, 2019

Building GrabPlatform's partner integration self-service portal initially resulted in an unstructured codebase where individual files exceeded 500 lines and lacked proper segregation. Modifying existing functions carried high risks of breaking functionality across imported source collections. To resolve this, the team restructured the Go application using Domain-Driven Design principles in coordination with product domain experts. They mapped business rules into bounded contexts, identified entities and aggregate roots, introduced repository interfaces, and utilized domain events for cross-context communication. The refactoring distributed core functionality evenly, simplified onboarding, and aligned technical terminology with business concepts.


### [Preventing Pipeline Calls from Crashing Redis Clusters](https://yomu.fyi/post/preventing-pipeline-calls-from-crashing-redis-clusters.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Michael Cartmell
- Published: May 5, 2019

A single Redis slave node failure caused Grab's Apollo booking service to suffer an over 95 percent call failure rate for one minute despite running a three-shard cluster with two replicas per partition. Investigation revealed that the service configured Go-Redis to route all read queries exclusively to slave nodes to offload master CPU usage. When the slave node dropped offline, batched HMGET pipeline calls failed completely because the client wrapper treated a single command failure as a failure of the entire pipeline. Furthermore, the Go-Redis client cached cluster topology and only lazily refreshed state every sixty seconds, continuing to direct traffic to the dead replica until the timer expired. Grab addressed this risk by recommending dedicated pipeline clients configured with latency-based routing to allow reads to fall back to responsive master nodes.


### [Loki, a Dynamic Mock Server for HTTP/TCP Testing](https://yomu.fyi/post/loki-a-dynamic-mock-server-for-http-tcp-testing.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Thuy Nguyen
- Published: Apr 10, 2019

Grab built Loki, a dynamic mock server written in Golang that simulates backend services on local developer machines and CI pipelines. Mobile app testing previously suffered from heavy dependencies on complex, brittle staging environments and interconnected services communicating over HTTP, HTTPS, and TCP. Loki handles both HTTP and TCP traffic on distinct ports while exposing a unified RESTful API to manage test expectations. It provides runtime flexibility through sandboxed JavaScript execution, configurable request sequence ordering, and an in-memory cron scheduler for TCP push messages. Adopting Loki decoupled mobile releases from staging stability, improving delivery cycles and enabling automated UI testing with Espresso and XCUITest.


### [Context Deadlines and How to Set Them](https://yomu.fyi/post/context-deadlines-and-how-to-set-them.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Michael Cartmell
- Published: Mar 11, 2019

Microservice architectures operating under heavy network traffic require robust timeout handling to prevent slow or failing dependencies from causing cascading failures across services. Naive, static timeout configurations across a call chain often cause downstream components to waste compute effort on requests that upstream callers have already abandoned. To establish predictable timeout thresholds, engineers can align limits with service-level latency percentiles, pairing P99 limits with median latency retry allowances. Go's context package improves upon static network timeouts by propagating request-scoped deadlines and cancellation signals across service boundaries. This distributed context ensures downstream servers recognize remaining time budgets and terminate unneeded processing immediately when parent deadlines expire or callers manually cancel requests.


### [Structured Logging: The Best Friend You’ll Want When Things Go Wrong](https://yomu.fyi/post/structured-logging-the-best-friend-you-ll-want-when-things-go-wrong.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Aditya Praharaj
- Published: Mar 5, 2019

Grab redesigned its backend logging approach to address mounting vendor costs, query language limitations, and debugging difficulties across a growing microservices ecosystem. Most services previously emitted syslog-style key-value logs almost entirely at the INFO level, which made volume reduction difficult and lacked causal ordering and automated correlation. The engineering team migrated to a self-managed Elastic stack backend and built a structured logging library in Go from the ground up. This framework introduces dynamic log-level adjustment at runtime, automatic trace-based log correlation via Grab-Kit, and the Common Grab Log Schema to enforce consistent JSON formatting without Elasticsearch indexing conflicts.


### [How We Simplified Our Data Ingestion & Transformation Process](https://yomu.fyi/post/how-we-simplified-our-data-ingestion-transformation-process.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Yichao Wang
- Published: Mar 3, 2019

Grab evolved its real-time data ingestion pipeline after an initial architecture built on Spark Streaming and Python encountered operational complexity, node failures, and data loss from S3 eventual consistency. Because the streaming workload primarily handled event partitioning and ORC file generation, the team consolidated these tasks directly into an existing Golang processing service. They implemented sharded concurrent maps for high-throughput partitioning and optimized heap allocations to resolve memory bottlenecks. This refactor removed intermediate Avro conversions and intermediate storage hops. The simplified Go pipeline eliminated data loss and reduced processing lag from up to 13 minutes down to approximately 1 minute.


### [Querying Big Data in Real-time with Presto & Grab's TalariaDB](https://yomu.fyi/post/querying-big-data-in-real-time-with-presto-grab-s-talariadb.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Roman Atachiants
- Published: Jan 2, 2019

Grab developed TalariaDB to support real-time SQL querying over high-velocity event streams while maintaining predictable sub-second latencies and low infrastructure costs. The distributed time-series store retains only the most recent hour of data and integrates directly with Presto via its PrestoThriftService interface. Internally, TalariaDB uses the Go-based Badger key-value store to maintain an in-memory key index of metric names and timestamps while mapping columnar event payloads directly to disk. Ingestion occurs by processing pre-partitioned event batches written to Amazon S3 via SQS notifications. By combining a zero-copy decoder with parallel split evaluation across gossiping cluster nodes, the architecture scales horizontally while serving millions of events per second.


### [Designing Resilient Systems: Circuit Breakers or Retries? (Part 1)](https://yomu.fyi/post/designing-resilient-systems-circuit-breakers-or-retries-part-1.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Corey Scott
- Published: Dec 21, 2018

Distributed architectures frequently encounter upstream failures triggered by networking issues, system overloads, resource starvation, and invalid deployments. Implementing software circuit breakers interposes a monitoring mechanism between components to halt requests when failure thresholds are met, giving struggling upstream dependencies time to recover. Circuit breakers save CPU, memory, and network resources by failing fast or routing execution through defined fallbacks such as cached data, alternate services, or approximation algorithms. Grab utilizes Hystrix-Go to manage upstream interactions and configure key thresholds for concurrency, timeouts, and error ratios. This approach protects downstream consumers from cascading latency while insulating upstream resources from excess traffic.


### [Reliable and Scalable Feature Toggles and A/B Testing SDK at Grab](https://yomu.fyi/post/reliable-and-scalable-feature-toggles-and-a-b-testing-sdk-at-grab.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Roman Atachiants
- Published: Nov 2, 2018

Grab previously managed experiments using custom service-level code and a toggling library that queried a shared Redis instance, creating latency risks and a single point of failure across backend microservices. To achieve reliable, sub-microsecond feature evaluations, the team designed a Go SDK that resolves rollouts and A/B tests entirely in memory without runtime network I/O. Backend services periodically poll JSON-defined configuration schemas stored in Amazon S3 through a Universal Configuration Manager. The SDK evaluates contextual attributes called facets locally and pushes decision telemetry asynchronously to an S3 and Presto data lake. This architecture allows engineering and product teams to gate deployments and run server-side experiments safely without service disruption.


### [Mockers - Overcoming Testing Challenges at Grab](https://yomu.fyi/post/mockers-overcoming-testing-challenges-at-grab.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Mayank Gupta
- Published: Sep 18, 2018

Grab operates over 250 microservices communicating over HTTP and gRPC, making shared staging environments costly, ambiguous in ownership, and fragile due to inconsistent data and uncoordinated deployments. To address these testing bottlenecks, Grab created Mockers, a Go SDK and CLI tool backed by a central monorepo of mock servers for local-box and CI testing. Mockers automatically generates HTTP and gRPC mock servers from Swagger specifications and protobuf files, returning configured network responses without internal business logic. By incorporating Grab's in-house chaos SDK middleware, Mockers also enables repeatable resiliency and contract testing locally without relying on code-level mocks. While Grab still mandates integration testing on distributed staging environments with live data, Mockers enables developers to detect complex defects and contract mismatches earlier.


### [Building Grab’s Experimentation Platform](https://yomu.fyi/post/building-grab-s-experimentation-platform.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Abeesh Thomas
- Published: Jul 13, 2018

Grab built its internal Experimentation Platform (ExP) to replace a manual, expensive testing process that required bespoke meetings, custom logging pipelines, and service modifications for each experiment. ExP provides a unified infrastructure featuring a centralized management UI, automated real-time data streaming to S3, and SDKs for Android, iOS, and Go. The platform leverages JSON-based experiment definitions delivered through dynamic configuration management, enabling client-side evaluation without costly network calls. It addresses marketplace network effects and inter-experiment interference through mechanisms such as geo-temporal segmentation and domain-layer models. The platform has scaled to run approximately 25 concurrent experiments while computing roughly 2,500 metrics and 50,000 experiment-metric combinations daily.


### [Introducing Grab-Kit: Distributed Service Design at Grab](https://yomu.fyi/post/introducing-grab-kit-distributed-service-design-at-grab.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Karen Kue
- Published: Jun 8, 2018

As Grab migrated from a monolith to microservices, maintaining consistency, coordination, and code quality across rapidly expanding teams became a major engineering challenge. To address this, the Developer Experience team built Grab-Kit, a Go framework that automates service scaffolding, code generation, and distributed system design patterns. The framework uses Protocol Buffer definition files as a single source of truth to generate data transfer objects, communication bindings, and standardized middleware for logging and profiling. Grab-Kit also features declarative metrics definitions that synchronize with the DataDog API to build and update service dashboards automatically. Adopting the framework reduced development time for creating new services by up to 70% in teams such as GrabFood while improving overall system stability.


### [How We Scaled Our Cache and Got a Good Night's Sleep](https://yomu.fyi/post/how-we-scaled-our-cache-and-got-a-good-night-s-sleep.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Gao Chao
- Published: Jun 19, 2017

Growing business load on the Common Data Service (CDS) created potential bottlenecks for its single-threaded Redis cache on ElastiCache, necessitating horizontal scaling for greater capacity and throughput. After ruling out master-slave replication and intermediate Twemproxy setups due to memory constraints and proxy I/O bottlenecks, the team implemented client-side sharding. Using an internal Go package for consistent hashing, CDS instances hash cache keys locally to determine the target shard. The implementation encapsulates hashing inside a thin \`ShardedCache\` wrapper sharing the original cache interface while supporting Ketama and custom hash functions. Deploying via double-writing cron jobs during off-peak hours reduced database read pressure and improved P99 latency.


### [DNS Resolution in Go and Cgo](https://yomu.fyi/post/dns-resolution-in-go-and-cgo.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Ryan Law
- Published: May 24, 2017

Go applications experiencing load balancing issues across AWS Elastic Load Balancer (ELB) nodes trace uneven traffic distribution to IP address sorting defined in RFC 6724. Comparing Go's native DNS resolver with Cgo and glibc's getaddrinfo shows that both initially sort destination addresses using Rule 9 longest matching prefix rules. Disabling IPv6 on the network interface causes C and Cgo resolvers to return IP addresses in randomized order, while the native Go resolver continues deterministic sorting. Examination of net/addrselect.go reveals that Go's native resolver implements only a subset of the RFC rules and omits dynamic source address selection. Achieving permanent parity requires modifying the Go source code directly.


### [Troubleshooting Unusual AWS ELB 5XX Error](https://yomu.fyi/post/troubleshooting-unusual-aws-elb-5xx-error.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Dharmarth Shah
- Published: May 10, 2017

Grab experienced intermittent HTTP 5XX alerts when its Gothena service sent driver location updates to the Astrolabe service through an AWS Elastic Load Balancer (ELB). CloudWatch metrics revealed that requests were failing to reach healthy backend instances because of an uneven load distribution favoring a single ELB node in one Availability Zone. The team verified that Route 53 was properly using Alias records and ruled out OS-level DNS caching since Linux does not cache DNS queries by default. Connection inspection with netstat across multiple Go services confirmed a heavily skewed distribution of connections toward specific ELB IP addresses. Comparative tests with cURL, tcpdump, Go, Python, and Ruby in an isolated environment demonstrated that Go reused connections across requests while other runtimes opened new connections per request.


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