メルカリグループの技術スタック
メルカリグループのエンジニア組織の技術スタックをご紹介します。サービスやプロダクトに相応しい最適な技術を選定し、チームが自律的に意思決定できるような体制をつくっています。
*最終更新日：2023年2月14日
| Category | Technology Stack |
|---|---|
| Programming Languages/ Library etc. | Web Frontend HTML, CSS, JavaScript, TypeScript, React, Gatsby, Next.js, GraphQL, Apollo Client, Redux, Cypress, Rendertron, Lit, Playwright, Vue.js, Nuxt, Jest |
| Android Kotlin, Gradle Kotlin DSL, Jetpack Compose, Hilt, RxJava, Kotlin Coroutines, Protocol Buffers, JUnit, Espresso, Java |
|
| iOS Swift, SwiftUI, UIKit, Swift Concurrency, Combine, Protocol Buffers, Bazel, Xcode |
|
| Backend Go, PHP, gRPC, Java, Scala, GraphQL, Python, TypeScript, Node.js, NestJS |
|
| DataPlatform Python, Java, Scala |
|
| Infrastructure | Google Cloud Platform, Amazon Web Services |
| Middleware | NGINX, Istio, Cloud Pub/Sub, Memorystore for Redis, Hashicorp Vault, Apache Spark, Apache Flink, Cloud Functions, AWS Lambda, Kafka, Debezium, Polyaxon, Neo4j, Unleash |
| Database | Cloud Spanner, MySQL, Cloud SQL(MySQL, PostgreSQL), Datastore, BigTable, Firestore [Storage] Google Cloud Storage, Amazon S3 |
| Monitoring | Datadog, Mackerel, PagerDuty, Kibana, Cloud Monitoring, Sentry, Crashlytics |
| Data analytics | BigQuery, Looker, Superset, Data Studio, Cloud Logging, Splunk Cloud |
| Environment setup (環境構築) | Docker, Terraform, Spinnaker, Cloud Build, Ansible, Bazel, CUE |
| Container Orchestration | Kubernetes, Cloud Run |
| CI | CircleCI, GitHub Actions, Cloud Build |
| Machine learning Library | Kubeflow, scikit-learn, TensorFlow, PyTorch, LightGBM, Optuna, PyTorch Lightning, ONNX, Vertex AI, Feature Store(FEAST), Neo4j, networkx, Python |
| Search Engine | Elasticsearch, Apache Solr, Elastic Search Cloud |
| Workflow Engine | Apache Airflow, DigDag, Argo Workflows, Dataflow, Cloud Workflows |
| Code Management | GitHub, Gerrit |
| Test automation tools | JavaScript, Go, gRPC, GitHub, CircleCI, Cypress, Postman |