한국 대표 트래블 테크 플랫폼 야놀자, 멀티 클라우드 혁신 위한 최적의 데이터베이스를 찾다

MongoDB
October 14, 2024

활기찬 여행 산업의 중심에 선 야놀자는 디지털 시대로의 도약을 통해 여행의 미래를 혁신하고 있습니다. 지난 2007년 국내 온라인 호텔 예약 서비스로 시작한 야놀자는 현재 전 세계 206개국에서 숙박, F&B, 레저, 광고 솔루션 등 다양한 서비스를 제공하며 글로벌 여행 테크 기업으로 성장했습니다.

야놀자가 오늘날의 스마트한 여행객들에게 최고의 편의성과 혁신적인 서비스를 제공할 수 있는 비결은 바로 데이터베이스 현대화입니다. 최근 야놀자 클라우드기술전략실의 김지환 실장이 MongoDB.local Seoul 행사에 참석해 MongoDB Atlas를 도입한 경험을 공유했습니다.

야놀자는 서비스 확장을 위해 클라우드를 적극 활용하고 있습니다. 일찌감치 퍼블릭 클라우드 환경에 적합한 마이크로서비스 아키텍처를 구축했고, 내부 DB 엔지니어링 조직을 통해 관계형 데이터베이스를 운영 및 관리하고 있습니다.

한편 NoSQL 환경에서는 개발자들이 각자 맡은 서비스에 대한 데이터베이스를 직접 관리하다보니 운영 정책을 표준화하는 데 어려움이 있었고, 전문 엔지니어를 통한 보다 체계적인 NoSQL 통합 운영 관리의 필요성이 절실했습니다. 또한 여러 퍼블릭 클라우드를 사용하는 만큼 무엇보다 클라우드에 친화적인 데이터베이스도 필요했습니다.

김지환 실장은 MongoDB Atlas를 선택한 이유로 “데이터 모델, 확장성, 가용성, 트랜잭션, 인덱스, 어그리게이션(Aggregation), 운영관리, 비용 효과 등 여러 요소를 종합적으로 비교 분석한 결과, MongoDB의 도큐먼트 모델이 복잡한 쿼리와 스키마에 대한 가장 뛰어난 성능을 보여주었고 야놀자의 환경에 가장 적합하다고 판단했다”며 “특히 MongoDB Atlas는 ACID(원자성, 일관성, 고립성, 지속성) 호환 트랙잭션을 실행할 수 있는 우수한 성능과 확장성을 제공할 뿐만 아니라 시계열 데이터, 데이터 파이프 라인, 암호화를 지원하고 타사 대비 강력한 분석 및 모니터링 툴을 보유하고 있다”고 말했습니다.

현재 야놀자는 기존의 NoSQL 데이터베이스를 MongoDB Atlas로 마이그레이션하고 목표로 했던 통합 운영 관리 체계를 구축하고 있으며, 이를 통해 운영 효율성을 높이고 세밀한 모니터링으로 이슈 대응 역량을 강화해 안정적인 대고객 서비스를 제공하고 있습니다.

앞으로 야놀자는 MongoDB와의 협력을 멀티 클라우드 환경으로 확대하며 글로벌 데이터 기업으로 도약할 계획입니다.

김지환 실장은 “현재 AWS, Azure, Google Cloud 등 다양한 클라우드 서비스를 이용해 각 서비스의 성격에 맞게 비즈니스를 발전시키고 있다. 이런 멀티 클라우드 환경에 적합한 유연한 인프라를 구축하는 데 MongoDB의 멀티 클라우드 개발자 데이터 플랫폼이 매우 중요한 역할을 할 것”이라고 밝혔습니다.

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Introducing: Multi-Kubernetes Cluster Deployment Support

Resilience and scalability are critical for today's production applications. MongoDB and Kubernetes are both well known for their ability to support those needs to the highest level. To better enable developers using MongoDB and Kubernetes, we’ve introduced a series of updates and capabilities that makes it easier to manage MongoDB across multiple Kubernetes clusters. In addition to the previously released support for running MongoDB replica sets and Ops Manager across multiple Kubernetes clusters, we're excited to announce the public preview release of support for Sharded Clusters spanning multiple Kubernetes clusters (GA to follow in November 2024). Support for deployment across multiple Kubernetes clusters is facilitated through the Enterprise Kubernetes Operator. As a recap for anyone unaware, the Enterprise Operator automates the deployment, scaling, and management of MongoDB clusters in Kubernetes. It simplifies database operations by handling tasks such as backups, upgrades, and failover, ensuring consistent performance and reliability in the Kubernetes environment. Multi-Kubernetes cluster deployment support enhances availability, resilience, and scalability for critical MongoDB workloads, empowering developers to efficiently manage these workloads within Kubernetes. This approach unlocks the highest level of availability and resilience by allowing shards to be located closer to users and applications, increasing geographical flexibility and reducing latency for globally distributed applications. Deploying replica sets across multiple Kubernetes clusters MongoDB replica sets are engineered to ensure high availability, data redundancy, and automated failover in database deployments. A replica set consists of multiple MongoDB instances—one primary and several secondary nodes—all maintaining the same dataset. The primary node handles all write operations, while the secondary nodes replicate the data and are available to take over as primary if the original primary node fails. This architecture is critical for maintaining continuous data availability, especially in production environments where downtime can be costly. Support for deploying MongoDB replica sets across multiple Kubernetes clusters helps ensure this level of availability for MongoDB-based applications running in Kubernetes. Deploying MongoDB replica sets across multiple Kubernetes clusters enables you to distribute your data, not only across nodes in the Kubernetes cluster, but across different clusters and geographic locations, ensuring that the rest of your deployments remain operational (even if one or more Kubernetes clusters or locations fail) and facilitating faster disaster recovery. To learn more about how to deploy replica sets across multiple Kubernetes clusters using the Enterprise Kubernetes Operator, visit our documentation . Sharding MongoDB across multiple Kubernetes clusters While replica sets duplicate data for resilience (and higher read rates), MongoDB sharded clusters divide the data up between shards, each of which is effectively a replica set, providing resilience for each portion of the data. This helps your database handle large datasets and high-throughput operations since each shard has a primary member handling write operations to that portion of the data; this allows MongoDB to scale up the write throughput horizontally, rather than requiring vertical scaling of every member of a replica set. In a Kubernetes environment, these shards can now be deployed across multiple Kubernetes clusters, giving higher resilience in the event of a loss of a Kubernetes cluster or an entire geographic location. This also offers the ability to locate shards in the same region as the applications or users accessing that portion of the data, reducing latency and improving user experience. Sharding is particularly useful for applications with large datasets and those requiring high availability and resilience as they grow. Support for sharding MongoDB across multiple Kubernetes clusters is currently in public preview and will be generally available in November. Deploying Ops Manager across multiple Kubernetes clusters Ops Manager is the self-hosted management platform that supports automation, monitoring, and backup of MongoDB on your own infrastructure. Ops Manager's most critical function is backup, and deploying it across multiple Kubernetes clusters greatly improves resilience and disaster recovery for your MongoDB deployments in Kubernetes. With Ops Manager distributed across several Kubernetes clusters, you can ensure that backups of deployments remain robust and available, even if one Kubernetes cluster or site fails. Furthermore, it allows Ops Manager to efficiently manage and monitor MongoDB deployments that are themselves distributed across multiple clusters, improving resilience and simplifying scaling and disaster recovery. To learn more about how to deploy Ops Manager across multiple Kubernetes clusters using the Enterprise Kubernetes Operator, visit our documentation . To leverage multi-Kubernetes-cluster support, you can get started with the Enterprise Kubernetes Operator .

October 10, 2024

Next →

MongoDB Database Observability: Integrating with Monitoring Tools

This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.

November 14, 2024