5 Steps to Replacing Elasticsearch and Solr with Atlas Search

Mat Keep
March 28, 2022 | Updated: April 5, 2022
#Atlas Search

What do a global auto manufacturer, multinational media and entertainment company, and a challenger bank have in common? They have all made the switch from Elasticsearch to MongoDB Atlas Search to simplify their technology stack and ship application search faster.

But what problems were they solving and how did they migrate? We have a new 5-step guide that takes you through why they switched, and how they did it.

The need for application search

Type almost anything into a search bar on sites like Google, Amazon, and Netflix and you are instantly presented with relevant results. Whether you make a typo or enter a partial search term, the search engine figures out what you are looking for. Results are returned conveniently sorted by relevance and are easy to navigate with features like highlighting, filters, and counts.

Everyone now expects these same fast and intuitive search experiences in every application they use, whether at home or at work. However, creating these experiences is hard with the burden falling onto developers and ops teams who have to build and run the underlying systems.

The pain of building application search

MongoDB has always focused on accelerating and simplifying how developers build with data for any class of application. From our very earliest MongoDB releases, we saw developers needing to expose the application data stored in their database to search and information discovery.

For simple use cases – where it was enough to just match text in a field – developers were able to use the basic text search operators and index built into the MongoDB database. However these lacked the much more sophisticated speed and relevance tuning features offered by dedicated search engines, typically built on top of Apache Lucene.

As a result many developers ended up bolting on an external search engine such as Elasticsearch or Apache Solr to their database. Elasticsearch and Solr were (and remain) popular and proven. However as Figure 1 shows, they introduced a huge amount of complexity to the application stack, reducing developer velocity while driving up risk, complexity, and cost.

*Figure 1: The pain of bolting on a search engine to your database*

Working with the MongoDB community, our product designers and engineers ideated on ways to make building application search easier for developers – without compromising on the key features they needed. The result is MongoDB Atlas Search.

What is Atlas Search and why switch to it?

Atlas Search embeds a fully-managed Apache Lucene search index directly alongside the database and automatically synchronizes data between them.

By integrating the database, search engine, and sync pipeline into a single, fully-managed platform you get to compress three systems into one and simplify your technology stack. Engineering teams and application owners have reported improved development velocity of 30% to 50% after adopting Atlas Search. This is because they get to:

Eliminate the synchronization tax. Data is automatically and dynamically synced from the Atlas database to the Atlas Search indexes. They avoid having to stand up and manage their own sync mechanism, write custom transformation logic, or remap search indexes as their database schema evolves. They escape the 10% of engineering cycles typically lost to manually recovering sync failures, investing that time to innovate for their users instead. (1)
Ship new features faster. They work with a single, unified API across both database and search operations, simplifying query development. No more context switching between multiple query languages, and with a single driver, build dependencies are streamlined so they release faster. They can test queries and preview results with interactive tools to fine-tune performance and scoring before deploying them directly into application code.
Remove operational heavy-lifting. The fully-managed Atlas platform automates provisioning, replication, patching, upgrades, scaling, security, and disaster recovery while providing deep performance visibility into both database and search. By working with a single system, they avoid an exponential increase in the number of system components they need to design, test, secure, monitor, and maintain.

*Figure 2: Dramatic architectual simplification with integrated database, sync, and search in MongoDB Atlas*

5 steps to make the switch to Atlas Search

The benefits Atlas Search provides has led engineering teams across all industry sectors and geographies to make the switch from bolt-on search engines. Through the experiences gained by working with these teams, we have put together a repeatable 5-step methodology to replacing Elasticsearch and Solr.

The guide steps you through how to:

Qualify target workloads for Atlas Search.
Migrate your indexes to Atlas Search.
Migrate your queries to Atlas Search.
Validate and relevance-tune your Atlas Search queries and indexes.
Size and deploy your Atlas Search infrastructure.

*Figure 3: 5-step methodology to replacing Elasticsearch and Solr with Atlas Search*

The guide wraps up with examples of customers that have made the switch and provides guidance on how to get started with Atlas Search.

What's next?

You can get started today by downloading the 5-step guide to replacing Elasticsearch and Solr with Atlas Search.

The 5-step guide is designed to help you plan and execute your migration project. MongoDB's Professional Services team is also available to you as a trusted delivery partner. We can help you through any of the steps in the methodology or throughout your entire journey to Atlas Search.

If you want to dig deeper into Atlas Search, spin it up at no-cost on the Atlas Free Tier. You can follow along with reference materials and tutorials in the Atlas Search documentation using our sample data sets, or load your own data for experimentation within your own sandbox.

Welcome to a world where application search is, at last, simplified!

1. Based on interviews with engineering teams that have replaced bolt on search engines and the associated sync mechanism.

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Dashboard Embedding Comes to Charts

Atlas Charts is the native data visualization that lets you create, share, and embed charts from data in MongoDB Atlas. Today, we are happy to announce the release of Dashboard Embedding . What is an embedded dashboard? Embedding provides the ability to bring charts and dashboards into any webpage or application where your users spend time. It’s a great way to share dynamic, live visualizations with a wide audience. We see customers using embedded charts to share real-time information internally within their companies, as well as externally with their customers. Until now, embedding multiple charts where you need them has been time consuming and somewhat complex. With dashboard embedding , rather than individually embedding one chart at a time, you can embed a full dashboard in one go! Embedding charts and dashboards is easy to use and provides the flexibility you need when it comes to visual customizations and data security. This ensures you are able to freely share information and have it look the way you want, while always keeping data secure. How does Dashboard Embedding work in Atlas Charts? Enabling Dashboard Embedding is as simple as picking a dashboard, going into the Embed menu, and enabling either authenticated or unauthenticated embedding as shown below. The Dashboard Embedding Menu Similar to embedding an individual chart, you can publicly embed a URL in an iFrame of your application or website. Unauthenticated embedding allows you to embed your dashboards, and have visualizations immediately ready for public consumption. Alternatively, you can use our embedding SDK to implement authenticated embedding. Authenticated embedding provides more control and security, requiring users to have appropriate permission to access your dashboard. We support Google, Realm and Custom JSON Web Token authentication options . Charts offers a number of configuration options important to ensuring your embedded dashboards look and feel how you want. These options include support for dark mode, background color selection, chart sizing, and refresh rate cadence. Dashboards can even be set to resize responsively based on your screen size, ensuring a consistent user experience. Play around with dashboard embedding and some of these features in our code sandbox . An embedded dashboard in dark mode Get started today! Dashboard Embedding was one of our most frequently requested features in Atlas Charts and we are excited to see how you take advantage of it in your applications! Head over to any of your dashboards in Charts to enable dashboard embedding or take a look at Github to get started with the embedding SDK. New to Atlas Charts? Get started today by logging into or signing up for MongoDB Atlas , deploying or selecting a cluster, and activating Charts for free.

March 24, 2022

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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