Workload-Isolierung für mehr Skalierbarkeit und Verfügbarkeit: Search Nodes jetzt auf Google Cloud

Elliott Gluck
March 28, 2024

Heute haben wir den nächsten Schritt bei der Einführung einer skalierbaren, dedizierten Architektur für Ihre Sucherlebnisse gemacht und Atlas Search Nodes als öffentliche Vorschau für Google Cloud vorgestellt.

Nach unserer ersten Ankündigung von Search Nodes im Juni 2023 haben wir den Zugang zu der am besten skalierbaren dedizierten Architektur schnell beschleunigt, angefangen mit der allgemeinen Verfügbarkeit auf AWS und jetzt auch mit der öffentlichen Vorschau auf Google Cloud. Im Folgenden erläutern wir etwas genauer, was Search Nodes sind und warum sie für jedes Sucherlebnis im großen Maßstab wichtig sind.

Search Nodes bieten eine dedizierte Infrastruktur für Atlas Search- und Vector Search-Workloads, um eine noch bessere Kontrolle über Search-Workloads zu ermöglichen. Isolieren und optimieren Sie Rechenressourcen, um Such- und Datenbankanforderungen unabhängig voneinander zu skalieren, und sorgen Sie so für eine bessere Leistung im großen Maßstab und eine höhere Verfügbarkeit.

Eines der letzten Dinge, mit denen sich Entwickler bei der Entwicklung und Skalierung von Apps beschäftigen möchten, sind Probleme mit der Infrastruktur. Jede Ausfallzeit oder schlechte Benutzererfahrung bedeutet verlorene Benutzer oder Einnahmen, insbesondere wenn es um Ihre Datenbank und die Suchfunktion geht. Dies ist einer der Gründe, warum sich Entwickler für MongoDB entscheiden, denn es ist einfach, ein einheitliches System für Ihre Datenbank- und Suchlösung zu haben.

Mit der Einführung von Atlas Search Nodes haben wir den nächsten Schritt unternommen, um unseren Entwicklern die ultimative Kontrolle zu geben. Sie bleiben flexibel, indem sie Sucharbeitslasten skalieren können, ohne gezwungen zu sein, die Datenbank zu überlasten. Durch die Isolierung Ihrer Such- und Datenbank-Workloads und die gleichzeitige automatische Synchronisierung Ihrer Suchclusterdaten mit den Betriebsdaten machen Atlas Search und Atlas Vector Search ein separates ETL-Tool überflüssig, dessen Einrichtung Zeit und Mühe kostet und einen weiteren Fehlerpunkt für Ihre skalierende App darstellt. Dies sorgt für eine überragende Leistung und höhere Verfügbarkeit und reduziert gleichzeitig die architektonische Komplexität und die Zeitverschwendung bei der Wiederherstellung nach Synchronisationsfehlern. In der Tat konnten wir bei vielen komplexen Abfragen eine Verringerung der Abfragezeit um 40 bis 60 % feststellen, während gleichzeitig die Gefahr von Ressourcenkonflikten oder Ausfallzeiten beseitigt wurde.

Mit einem einfachen Knopfdruck bieten Search Nodes auf Google Cloud unseren bestehenden Atlas Search- und Vector Search-Benutzern die folgenden Vorteile:

Höhere Verfügbarkeit
Erhöhte Skalierbarkeit
Workload-Isolation
Bessere Skalierungsleistung
Verbesserte Abfrageleistung

Wir bieten sowohl rechenintensive suchspezifische Knoten für die relevanzbasierte Textsuche als auch eine speicheroptimierte Option, die für semantische und RAG-Produktionsanwendungen mit Atlas Vector Search optimal ist. Damit gehören Ressourcenkonflikte oder Verfügbarkeitsprobleme der Vergangenheit an.

Graphic of Search Nodes Search and Database structure, where Atlas Nodes is the Database and Search Nodes are the Search function.

Search Nodes sind einfach zu aktivieren und einzurichten. Gehen Sie dazu in die MongoDB-Benutzeroberfläche und führen Sie die folgenden Schritte aus:

Navigieren Sie in der MongoDB-Benutzeroberfläche zu Ihrem Abschnitt „Datenbankbereitstellungen“
Klicken Sie auf die grüne Schaltfläche „+Erstellen“
Ändern Sie auf der Seite „Neuen Cluster erstellen“ die Optionsschaltfläche für Google Cloud für „Multi-Cloud, mehrere Regionen & Workload-Isolierung“, um es zu aktivieren.
Schalten Sie das Optionsfeld für „Search Nodes für Workload-Isolierung“ um, um es zu aktivieren. Wählen Sie die Anzahl der Knoten im Textfeld aus
Aktivieren Sie das Kontrollkästchen „Vereinbarung“
Klicken Sie auf „Cluster erstellen“

Für bestehende Atlas Search-Benutzer klicken Sie in der MongoDB Atlas Search-Benutzeroberfläche auf „Konfiguration bearbeiten“ und aktivieren Sie den Schalter für die Workload-Isolierung. Dann sind die Schritte die gleichen wie oben beschrieben.

Sehen Sie sich unsere Dokumente direkt an, um mehr zu erfahren!

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Building AI With MongoDB: How DevRev is Redefining CRM for Product-Led Growth

OneCRM from DevRev is purpose-built for Software-as-a-Service (SaaS) companies. It brings together previously separate customer relationship management (CRM) suites for product management, support, and software development. Built on a foundation of customizable large language models (LLMs), data engineering, analytics, and MongoDB Atlas , it connects end users, sellers, support, product owners, and developers. OneCRM converges multiple discrete business apps and teams onto a common platform. As the company states on its website “Our mission is to connect makers (Dev) to customers (Rev) . When every employee adopts a “product-thinking” mindset, customer-centricity transcends from a department to become a culture.” DevRev was founded in October 2020 and raised over $85 million in seed funding from investors such as Khosla Ventures and Mayfield. At the time, this made it the largest seed in the history of Silicon Valley. The company is led by its co-founder and CEO, Dheeraj Pandey, who was previously the co-founder and CEO of Nutanix, and by Manoj Agarwal, DevRev's co-founder and former SVP of Engineering at Nutanix. DevRev is headquartered in Palo Alto and has offices in seven global locations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. CRM + AI: Digging into the stack DevRev’s Support and Product CRM serve over 4,500 customers: Support CRM brings support staff, product managers, and developers onto an AI-native platform to automate Level 1 (L1), assist L2, and elevate L3 to become true collaborators. Product CRM brings product planning, software work management, and product 360 together so product teams can assimilate the voice of the customer in real-time. Figure 1: DevRev’s real-time dashboards empower product teams to detect at-risk customers, monitor product health, track development velocity, and more. AI is central to both the Support and Product CRMs. The company’s engineers build and run their own neural networks, fine-tuned with application data managed by MongoDB Atlas. This data is also encoded by open-source embedding models where it is used alongside OpenAI models for customer support chatbots and question-answering tasks orchestrated by autonomous agents. MongoDB partner LangChain is used to call the models, while also providing a layer of abstraction that frees DevRev engineers to effortlessly switch between different generative AI models as needed. Data flows across DevRev’s distributed microservices estate and into its AI models are powered by MongoDB change streams . Downstream services are notified in real-time of any data changes using a fully reactive, event-driven architecture. MongoDB Atlas: AI-powered CRM on an agile and trusted data platform MongoDB is the primary database backing OneCRM, managing users, customer and product data, tickets, and more. DevRev selected MongoDB Atlas from the very outset of the company. The flexibility of its data model, freedom to run anywhere, reliability and compliance, and operational efficiency of the Atlas managed service all impact how quickly DevRev can build and ship high-quality features to its customers. The flexibility of the document data model enables DevRev’s engineers to handle the massive variety of data structures their microservices need to work with. Documents are large, and each can have many custom fields. To efficiently store, index, and query this data, developers use MongoDB’s Attribute pattern and have the flexibility to add, modify, and remove fields at any time. The freedom to run MongoDB anywhere helps the engineering team develop, test, and release faster. Developers can experiment locally, then move to integration testing, and then production — all running in different environments — without changing a single line of code. This is core to DevRev’s velocity in handling over 4,000 pull requests per month: Developers can experiment and test with MongoDB on local instances — for example adding indexes or evaluating new query operators, enabling them to catch issues earlier in the development cycle. Once unit tests are complete, developers can move to temporary instances in Docker containers for end-to-end integration testing. When ready, teams can deploy to production in MongoDB Atlas. The multi-cloud architecture of Atlas provides flexibility and choice that proprietary offerings from the hyperscalers can’t match. While DevRev today runs on AWS, in the early days of the company, they evaluated multiple cloud vendors. Knowing that MongoDB Atlas could run anywhere gave them the confidence to make a choice on the platform, knowing they would not be locked into that choice in the future. With MongoDB Atlas, our development velocity is 3-4x higher than if we used alternative databases. We can get our innovations to market faster, providing our customers with even more modern and useful CRM solutions. Anshu Avinash, Founding Engineer, DevRev The HashiCorp Terraform MongoDB Atlas Provider automates infrastructure deployments by making it easy to provision, manage, and control Atlas configurations as code. “The automation provided by Atlas and Terraform means we’ve avoided having to hire a dedicated infrastructure engineer for our database layer,” says Anshu. “This is a savings we can redirect into adding developers to work on customer-facing features.” Figure 2: The reactive, event-driven microservices architecture underpinning DevRev’s AI-powered CRM platform Anshu goes on to say, “We have a microservices architecture where each microservice manages its own database and collections. By using MongoDB Atlas, we have little to no management overhead. We never even look at minor version upgrades, which Atlas does for us in the background with zero downtime. Even the major version upgrades do not require any downtime, which is pretty unique for database systems.” Discussing scalability, Anshu says, “As the business has grown, we have been able to scale Atlas, again without downtime. We can move between instance and cluster sizes as our workloads expand, and with auto-storage scaling, we don’t need to worry about disks getting full.” DevRev manages critical customer data, and so relies on MongoDB Atlas’ native encryption and backup for data protection and regulatory compliance. The ability to provide multi-region databases in Atlas means global customers get further control over data residency, latency, and high availability requirements. Anshu goes on to say, “We also have the flexibility to use MongoDB’s native sharding to scale-out the workloads of our largest customers with complete tenant isolation.” DevRev is redefining the CRM market through AI, with MongoDB Atlas playing a critical role as the company’s data foundation. You can learn more about how innovators across the world are using MongoDB by reviewing our Building AI case studies . If your team is building AI apps, sign up for the AI Innovators Program . Successful companies get access to free Atlas credits and technical enablement, as well as connections into the broader AI ecosystem. Head over to our quick-start guide to get started with Atlas Vector Search today.

March 27, 2024

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Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana

For all the hype surrounding generative AI, cynics tend to view the few real-world implementations as little more than “fancy chatbots.” But for Abhinav Aggarwal, CEO of Hanabi Technologies, the idea of a generative AI-powered bot that is more than just an assistant was intriguing. “I’d been using ChatGPT since it launched,” said Aggarwal. “That got me thinking: How could we make a chatbot that was like a team member?” And with that concept, Hana was born. The problem with bots “Most generative AI chatbots do not act like people; they wait for a command and give a response,” said Aggarwal. “We wanted to create a human-like chatbot that would proactively help people based on what they wanted—automating reminders, for example, or fetching time zones from your calendar to correctly schedule meetings.” Hanabi’s flagship product, Hana, is an AI assistant designed to enhance team collaboration within Google Chat, working in concert with Google Workspace and its suite of products. “Our target customers are smaller companies of between 10 and 50 people. At this size you’re not going to build your own agent from scratch,” he said. Hana integrates with Google APIs to deliver a human-like assistant that chimes in with helpful interventions, such as automatically setting reminders and making sure meetings are booked in the right time zone for each participant. “Hana is designed to bring AI to smaller companies and help them collaborate in a space where they are already working—Google Workspace,” Aggarwal explained. The MongoDB Atlas solution For Hana to act like a member of the team, Hanabi needed to process massive amounts of data to support advanced features like retrieval-augmented generation (RAG) for better information retrieval across Google Docs and many other sources. And with a rapidly growing user base of over 600 organizations and 17,000+ installs, Hanabi also required a secure, scalable, and high-performing data storage solution. MongoDB Atlas provided a flexible document model, built-in vector database, and scalable cloud-based infrastructure, freeing Hanabi engineers to build new features for Hana rather than focusing on rote tasks like data extract, transform, and load processes or manual scaling and provisioning. Now, MongoDB Atlas handles a variety of responsibilities: Scalability and security: MongoDB Atlas’s auto-scaling and automatic backup features have enabled Hanabi to seamlessly grow its user base without the need for manual database management. RAG: MongoDB Atlas plays a critical role in Hana’s RAG functionality. The platform enables Hanabi to split Google Docs into small sections, create embeddings, and store these sections in Atlas’s vector database. Development Processes: According to Aggarwal, MongoDB’s flexibility in managing changing schemas has been essential to the company’s fast-paced development cycle. Data Visualization: Using MongoDB Atlas Charts has enabled Hanabi to create comprehensive dashboards for real-time data visualization. This has helped the team track usage, set reminders, and optimize performance without needing to build a manual dashboard. Impact and results With MongoDB Atlas, Hanabi can successfully scale Hana to meet the demands of its rapidly expanding user base. The integration is also enabling Hana to offer powerful features like automatic interactions with customers, advanced information retrieval from Google Docs, and manually added memory snippets, making it an essential tool for teams around the world. Next steps Hanabi plans to continue integrating more tools into Hana while expanding its reach to personal Gmail users. The company is also rolling out a new automatic-interaction feature, further enhancing Hana’s ability to proactively assist users without direct commands. MongoDB Atlas remains a key component of Hanabi’s stack, alongside Google Kubernetes Engine, NestJS, and LangChain, enabling Hanabi to focus on innovating to improve the customer experience. Tech Stack MongoDB Atlas Google Kubernetes Engine NestJS LangChain Are you building AI apps? Join the MongoDB AI Innovators Program today! Successful participants gain access to free MongoDB Atlas credits, technical enablement, and invaluable connections within the broader AI ecosystem. If your company is interested in being featured, we’d love to hear from you. Connect with us at ai_adopters@mongodb.com.

November 21, 2024