Gyana Guity

3 results

Introducing Query Shape Insights in MongoDB Atlas

As modern applications scale, databases are often the first to show signs of stress, especially when query patterns shift or inefficiencies arise. MongoDB has invested in building a robust observability suite to help teams monitor and optimize performance. Tools such as the Query Profiler and, more recently, Namespace Insights provide deep visibility into query behavior and collection-level activity. While powerful, these capabilities primarily focus on individual queries or collections, limiting their ability to surface systemic patterns that impact overall application performance. Today, MongoDB is excited to announce Query Shape Insights, a powerful new feature for MongoDB Atlas that offers a high-resolution, holistic view of how queries behave at scale across clusters. Query Shape Insights delivers a paradigm shift in visibility by surfacing aggregated statistics for the most resource-intensive query shapes. This accelerates root cause analysis, streamlines optimization workflows, and improves operational efficiency. Figure 1. Overview page of Query Shape Insights showing the most resource-intensive query shapes. A new granularity for performance analysis Previously, if a modern application experienced a traffic surge, it risked overloading the database with queries, causing rapid performance degradation. In those critical moments, developers and database administrators must quickly identify the queries contributing most acutely to the bottleneck. This necessitated scrutinizing logs or per-query samples. With the launch of Query Shape Insights, the top 100 query shapes are surfaced by grouping structurally similar queries with shared filters, projects, and aggregation stages into defined query shapes. These query shapes are then ranked by total execution time, offering MongoDB Atlas users greater visibility into the most resource-intensive queries. Each query shape is enriched with detailed metrics such as execution time, operation count, number of documents examined and returned, and bytes read. These metrics are rendered as time series data, enabling developers and database administrators to pinpoint when the regressions began, how long they persisted, and what triggered them. Figure 2. Detailed view of a query shape, with a pop-up displaying associated metrics. This new feature integrates seamlessly into the performance workloads teams use to monitor, debug, and optimize applications. Each query shape includes associated client metadata, such as application name, driver version, and host. This empowers teams to identify which services, applications, or teams impact performance. This level of visibility is particularly valuable for microservices-based environments, where inefficiencies might manifest across multiple teams and services. Query Shape Insights adapts based on cluster tier to support varying workload sizes. Teams can analyze the performance data of each query shape over a 7-day window. This enables them to track trends, find changes in application behavior, and identify slow regressions that might otherwise be missed. Integration with MongoDB’s observability suite Query Shape Insights was designed to enable MongoDB Atlas users to move from detection to resolution with unprecedented speed and clarity. Built directly into the MongoDB Atlas experience, this feature is a clear starting point for performance investigations. This is imperative for dynamic environments where application behavior evolves rapidly and bottlenecks must be identified and resolved rapidly. The Query Shape Insights dashboard offers comprehensive, time series–based analysis of query patterns across clusters. It enables teams to detect inefficiencies and understand when and how workloads have changed. Query Shape Insights answers critical diagnostic questions by surfacing the most resource-intensive query shapes. It identifies the workloads that consume the most resources and can help determine whether these workloads are expected or anomalous. Query Shape Insights can also help identify the emergence of new workloads and reveal how workloads have changed over time. To support this level of analysis, Query Shape Insights offers a rich set of capabilities, giving teams the clarity and speed they need to troubleshoot intelligently and maintain high-performing applications: Unified query performance view: Monitor query shapes to rapidly identify and investigate bottlenecks. Detailed query shape statistics: Track key metrics including execution time, document return counts, and execution frequency. Interactive analysis tools: Query shape drill-downs to view detailed metadata and performance trends. Flexible filtering options: Narrow analysis by shard/host, data range, namespace, or operation type. Programmatic access: Leverage MongoDB’s new Admin API endpoint to integrate query shape data with the existing observability stack. After using Query Shape Insights, MongoDB Atlas users can pivot directly to Query Profiler with filters pre-applied to the specific collection and operation type for more information beyond that provided by Query Shape Insights. Once they have traced the issue to its root, users can continue their diagnostics journey by visiting Performance Advisor . This recommends indexes tailored to the query shape, ensuring that cluster optimizations are data-driven and precise. Query Shape Insights is a leap forward in how teams manage, investigate, and respond to performance issues with MongoDB. By introducing a high-level, shape-aware view of query activity, Query Shape Insights enhances traditional reactive troubleshooting with greater clarity. This enables teams to troubleshoot faster and monitor performance effectively. Query Shape Insights is now available for all MongoDB Atlas dedicated clusters (M10 and above) deployments. Clusters must run on MongoDB 8.0 or later to access this feature. Support for Cloud Manager deployments is planned for the future. Check out MongoDB’s documentation for more details on Query Shape Insights. Start using Query Shape Insights today through your MongoDB Atlas portal.

July 2, 2025

Introducing New Navigation for MongoDB Atlas and Cloud Manager

MongoDB is excited to announce a major update to MongoDB Atlas and MongoDB Cloud Manager : a redesigned user experience that improves the workflow and navigation to access services and tools. This redesign ensures users can seamlessly navigate the Atlas and Cloud Manager platforms, intuitively accessing their most-used services and completing tasks more efficiently. Figure 1. Previous project-level homepage and primary side navigation in MongoDB Atlas. The Atlas platform has expanded exponentially since the last navigation redesign in 2020, with MongoDB introducing a plethora of new features and functionality, including Atlas Search and Vector Search , Atlas Charts , and Atlas Stream Processing . The latest navigation redesign has been architected from the outset to encompass these capabilities, addressing users' diverse needs—from monitoring deployments and managing billing to enhancing data visualization and enabling advanced search functionality—while delivering a streamlined, workflow-driven platform for users. Figure 2. Previous resource context (e.g., organization, project, cluster) for workflow tracking in MongoDB Atlas. Figure 3. Previous top navigation architecture in MongoDB Atlas. Starting two and a half years ago, MongoDB’s Design Strategy team began the redesign process by collecting customer feedback and engaging in dialogue. The team’s overall goal with the Atlas and Cloud Manager redesign was to create a holistic, seamlessly integrated platform that streamlined the developer experience. Figure 4. Redesigned homepage and primary side navigation at the project level in MongoDB Atlas. The redesigned navigation improves developers’ experience in the following ways: Workflow-focused architecture: The new architecture is clean and intuitive. It preserves developers’ “flow state” by guiding them through drill-down workflows. The new navigation prioritizes platform services, highlighting them based on the user’s workflow. This makes it easier for developers to focus on the most relevant tools for their current tasks, enabling them to work more efficiently and innovate faster. Consistent, familiar experience: The new navigation design provides a consistent experience across Atlas and Cloud Manager platforms. This makes it easier for developers to switch between the two interfaces. This consistent, intuitive interface enhances wayfinding and boosts overall productivity. What’s changing in MongoDB Atlas? The redesigned Atlas navigation introduces the following key updates: 1. Clearer resource context The updated top navigation bar, the resource navigator, ensures developers always know which resource (e.g., project, organization, cluster) they are working on. Switching between resources is now simpler, with improved context clarity as users navigate deeper into Atlas. For example, imagine switching between search indexes across different collections. Now, it can be done in a single click. The new workflow negates any need to backtrack to a project’s overview. Figure 5. Redesigned secondary side navigation at the project level in MongoDB Atlas, with an extended resource navigator in the top navigation bar. 2. Centralized utilities hub Essential utilities like Alerts, Billing, Help, and Identity Management (IAM) are consolidated in one location at the top-right corner. This ensures rapid access and saves time. Users can also access the product menu to find MongoDB University , Documentation , Community Forums , and Support . Figure 6. Redesigned utilities hub with an expanded product menu in MongoDB Atlas. 3. Simplified left navigation The side navigation is now organized into four categories: Database , Data , Services , and Security . These categories act as distinct containers, grouping Atlas’s capabilities to reflect the tasks a developer needs to perform within Atlas. This new structure makes navigating Atlas easier, helping developers find the right tools faster. Below is a breakdown of where features will be housed to make access to your essential tools even more straightforward: Database: Contains all core database capabilities. Includes cluster management and monitoring tools for browsing and querying, backups, and Online Archive . Data: Contains tools for working with data. Includes tools like visualization (Atlas Charts) to create and embed data visualizations, Atlas Search and Vector Search for powerful search capabilities and Data Federation for cross-source queries. Services: Contains features for event-driven data processing and automation. Includes capabilities such as Stream Processing for real-time data analysis, Triggers for automating database actions, and Migration for migrating existing deployments to Atlas. Security: Contains controls for data access and protection. Includes capabilities like project settings, Identity & Access Management (IAM), auditing, and advanced security. On the organizational level, the new architecture for the side navigation will be organized into two categories: identity and access , and billing . Figure 7. Redesigned primary side navigation at the organization level in MongoDB Atlas. What’s changing in Cloud Manager? Although Cloud Manager will function similarly to Atlas, we did make several changes to refine the Cloud Manager experience: 1. Left navigation On the organizational level, the new architecture for the side navigation will be organized into three categories: Identity and access: Add, delete, and manage users, teams, and API Keys within a specific project. Billing: View, track, and manage your charges while using Cloud Manager. Management: Set up Kubernetes , and manage additional administrative functions. Figure 8. Redesigned primary side navigation at the organization level in Cloud Manager. On a project level, the side navigation will be organized into two categories: Database: Manage Processes, Servers, Agents, Security, and Continuous Backup for your deployments. Admin: Monitor Pings, MongoDB Process Arguments, Deleted Hosts, Profiler Request History, and Raw Automation Config. Figure 9. Redesigned primary side navigation at the project level in Cloud Manager. 2. Resource navigator Cloud Manager will have the same resource navigator tool in the top navigation bar as Atlas. This provides clear visibility of the resource users are working on in Cloud Manager, whether it’s a project or an organization. 3. Centralized utilities hub Cloud Manager will also feature the updated utility hub mentioned in the changes coming to Atlas. This hub allows users to access the same essential utilities and product menu to discover other MongoDB offerings in one place. Rollout timeline To ensure a smooth transition, MongoDB will be rolling out the new navigation experience in phases. The Atlas update is currently going live, and the Cloud Manager update will begin the week of May 12, 2025 . Note that the Atlas experience will take 6 to 8 weeks to be available to all Atlas organizations. All organizations will experience the new navigation by June 2025 . Atlas and Cloud Manager users can submit feedback to share their thoughts on the new navigation experience. Explore MongoDB’s updated documentation for more details on the latest changes to the navigation. Try the new navigation today through your MongoDB Atlas or Cloud Manager portal.

April 8, 2025

Exploring Chart Types in MongoDB Atlas Charts

As you begin your chart building journey, you’ll find there are many ways for you to visualize your data in Atlas Charts . Specific data visualization needs vary by team, and we have a growing collection of chart types with various and specific purposes to help you discover insights and communicate effectively. Charts are an essential story-telling piece when working with large amounts of data. Another great way to think of it is that visualizations help condense vast data into a coherent format that makes information more consumable to a wide range of data consumers. When analyzing your data, it's important to recognize that different chart types serve distinct purposes. That is why it's important to choose the right chart type for each potential insight, so that when you put it all together, you have a diverse and all encompassing dashboard. How to effectively use Charts Charts was designed with a simple user interface that makes it quick for you to build charts and visualize your data. However, to properly utilize Charts, this guide on chart types can give you extra help on making charts more quickly and efficiently. Our chart types are split into the following series: Column and Bar Charts Line and Area Charts Combo Charts Grid Charts Circular Charts Text Charts, and Geospatial Charts Determining the best chart type can be an overwhelming task when there are so many to pick from, but knowing the specific strengths of each chart type can help you select the right chart for your use case. Most common chart types in Atlas Charts 1. Data tables What is a data table? Data tables are used to organize data in a tabular view, ultimately allowing viewers to quickly read the results of detailed data. What is an example use case for a data table? A data table can be used for healthcare system applications, where users can store patient information and records, medical history and treatment plans, and enable healthcare professionals to access patient data more easily and effectively. 2. Number charts What is a number chart? Number charts display a single aggregated value from a data field, often representing a grand total or overall state of data. What is an example use case for number charts? A number chart can be used for social media analytics, where engagement metrics, subscriber count, and post performance is summarized for users to track account growth. 3. Grouped column and bar charts What is a grouped column and bar chart? Grouped column and bar charts are used to show detailed data distribution across categories instead of a singular category. What is an example use case for grouped column and bar charts? To analyze financial performance, a grouped column and bar chart would be useful for viewing revenue, expenses, and profits of multiple business units over a period of time. 4. Donut charts What is a donut chart? Donut charts display the proportional distribution of a dataset, often used to showcase the general trends of data instead of exact data values. What is an example use case for donut charts? To track website traffic or customer churn rates, a donut chart is useful to visualize the proportion of website visitors coming from various sources and the percentage of those visitors who have churned or stayed with the company over a period of time. These are a few of the most commonly used chart types in Charts. Now let’s walk through some less common chart types to enrich your data visualization toolkit. Chart types you might have not used in Charts before 1. Line and area charts What is a line and area chart? Line and area charts display a series of data points connected by straight line segments. For area charts specifically, the space beneath the segments are filled with color.. Both of these chart types are used to track trends over time, such as sales and stock prices, or website traffic. What is an example use case for line and area charts? A line and area chart can be used for e-commerce applications, to show sales performance, revenue growth, and profitability trends over specific time intervals. 2. Stacked column charts What is a stacked column chart? Stacked column charts are used to show the composition and comparison of multiple variables over a period of time. They visually look like a series of columns stacked on top of one another, and most useful for analyzing changes across several categories. What is an example use case for stacked column charts? A stacked column chart can be used for product comparison, where the features, prices, and user ratings of various products or services are compared to one another side by side. 3. Geospatial charts What is a geospatial chart? Geospatial charts are map-based charts that are created from geospatial data and other forms of data to define specific geographical locations in the form of latitude and longitude coordinates or text fields with country and state names. Atlas Charts allows users to visualize geospatial data in three different chart formats: choropleth, scatter, and heatmap. What is an example use case for geospatial charts? A geospatial chart can be used for environmental monitoring, where soil and air quality data, pollution levels, deforestation rates, and other environmental factors are analyzed to locate areas for conservation. 4. Heatmaps What is a heatmap? Heatmaps are used to show relationships between two variables, showcased in a tabular format as a range of colors. Darker, more intense shades represent larger aggregated values while lighter shades represent smaller aggregated values across the dataset. What is an example use case for heatmap charts? A heatmap chart can be used for user behavior analytics, where user interactions, clicks, and total engagement across different web pages are tracked and monitored to improve customer experience. Now you have an idea of the many chart types, common and uncommon, that are available to you in Atlas Charts. Now it’s time to give it a try! Use your own data, or some of MongoDB’s sample datasets, to practice what you’ve learned and implement your next charting option! Log in to Atlas Charts today to create your visualizations! New to Atlas Charts? Get started today by logging into or signing up for MongoDB Atlas .

August 2, 2023