Manage and Store Data Where You Want with MongoDB

MongoDB
November 22, 2022
#Atlas

Increasingly, data is stored in a public cloud as companies realize the agility and cost benefits of running on cloud infrastructure. At any given time, however, organizations must know where their data is located, replicated, and stored — as well as how it is collected and processed to constantly ensure personal data privacy.

Creating a proper structure for storing your data just where you want it can be complex, especially with the shift towards geographically dispersed data and the need to comply with local and regional privacy and data security requirements. Organizations without a strong handle on where their data is stored potentially risk millions of dollars in regulatory fines for mishandling data, loss of brand credibility, and distrust from customers.

Geographically dispersed data and various compliance regulations also impact how organizations design their applications, and many see these challenges as an opportunity to transform how they engage with data. For example, organizations get the benefits of a multi-cloud strategy and avoid vendor lock-in, knowing that they can still run on-premises or on a different cloud provider. However, a flexible data model is needed to keep data within the confines of the country or region where the data originates.

MongoDB runs where you want your data to be — on-premises, in the cloud, or as an on-demand, fully managed global cloud database. In this article, we’ll look at ways MongoDB can help you keep your data exactly where you need it.

Major considerations for managing data

When managing data, organizations must answer questions in several key areas, including:

Process: How is your company going to scale security practices and automate compliance for the most prevalent data security and privacy regulatory frameworks?
Penalties: Are your business leaders fully aware of the costs associated with not adhering to regulations when storing and managing your data?
Scalability: Do you have an application that you anticipate will grow in the future and can scale automatically as demand requires?
Infrastructure: Is legacy infrastructure keeping you from being able to easily comply with data regulations?
Flexibility: Is your data architecture agile enough to meet regulations quickly as they grow in breadth and complexity?
Cost: Are you wasting time and money with manual processes when adhering to regulations and risking hefty fines related to noncompliance?

How companies use MongoDB to store data where they want and need it

When storing and managing data in different regions and countries, organizations must also understand the rules and regulations that apply. MongoDB is uniquely positioned to support organizations to meet their data goals with intuitive security features and privacy controls, as well as the ability to geographically deploy data clusters and backups in one or several regions.

Zones in sharded clusters

MongoDB uses sharding to support deployments with very large data sets and high-throughput operations. In sharded clusters, you can create zones of sharded data based on the shard key, which helps improve the locality of data.

Network isolation and access

Each MongoDB Atlas project is provisioned into its own virtual private cloud (VPC), thereby isolating your data and underlying systems from other MongoDB Atlas users. This approach allows businesses to meet data requirements while staying highly available within each region. Each shard of data will have multiple nodes that automatically and transparently fail over for zero downtime, all within the same region.

Multi-cloud clusters

MongoDB Atlas is the only globally distributed, multi-cloud database. It lets you deploy a single cluster across AWS, Microsoft Azure, and Google Cloud without the operational complexity of managing data replication and migration across clouds. With the ability to define a geographic location for each document, your teams can also keep relevant data close to end users for regulatory compliance.

IP whitelists

IP whitelists allow you to specify a specific range of IP addresses against which access will be granted, delivering granular control over data.

Queryable encryption

Queryable encryption enables encryption of sensitive data from the client side, stored as fully randomized, encrypted data on the database server side. This feature delivers the utmost in security without sacrificing performance and is available on both MongoDB Atlas and Enterprise Advanced.

MongoDB Atlas global clusters

Atlas global clusters allow organizations with distributed applications to geographically partition a fully managed deployment in a few clicks and control the distribution and placement of their data with sophisticated policies that can be easily generated and changed. Thus, your organization can not only achieve compliance with local data protection regulations more easily but also reduce overhead.

Client-Side Field Level Encryption

MongoDB’s Client-Side Field Level Encryption (FLE) dramatically reduces the risk of unauthorized access or disclosure of sensitive data. Fields are encrypted before they leave your application, protecting them everywhere — in motion over the network, in database memory, at rest in storage and backups, and in system logs.

Segmenting data by location with sharded clusters

As your application gets more popular, you may reach a point where your servers will reach their maximum load. Before you reach that point, you must plan for scaling your database to adjust resources to meet demand. Scaling can occur temporarily, with a sudden burst of traffic, or permanently with a constant increase in the popularity of your services.

Increased usage of your application brings three main challenges to your database server:

The CPU and/or memory becomes overloaded, and the database server either cannot respond to all the request throughput, or do so in a reasonable amount of time.
Your database server runs out of storage and thus cannot store all the data.
Your network interface is overloaded, so it cannot support all the network traffic received.

When your system resource limits are reached, you will want to consider scaling your database. Horizontal scaling refers to bringing on additional nodes to share the load. This process is difficult with relational databases because of the difficulty in spreading out related data across nodes. With non-relational databases, this is made simpler because collections are self-contained and not coupled relationally. This approach allows them to be distributed across nodes more simply, as queries do not have to “join” them together across nodes.

Horizontal scaling with MongoDB Atlas is achieved through sharding. With sharded clusters, you can create zones of sharded data based on the shard key. You can associate each zone with one or more shards in the cluster. A shard can be associated with any number of zones.

In a balanced cluster, MongoDB migrates chunks covered by a zone only to those shards associated with the zone:

If one of the data centers goes down, the data is still available for reads unlike a single data center distribution.
If the data center with a minority of the members goes down, the replica set can still serve write operations as well as read operations.
However, if the data center with the majority of the members goes down, the replica set becomes read-only.

Figure 1 illustrates a sharded cluster that uses geographic zones to manage and satisfy data segmentation requirements.

Other benefits of MongoDB Atlas

MongoDB Atlas also provides organizations with an intuitive UI or administration API to efficiently perform tasks that would otherwise be very difficult. Upgrading your servers or setting up sharding without having to shut down your servers can be a challenge, but MongoDB Atlas removes this layer of difficulty through the features described here. With MongoDB, scaling your databases can be done with a couple of clicks.

Meeting your data goals with MongoDB

Organizations are uniquely positioned to store and manage data where they want it with MongoDB’s range of features discussed above. With the shift towards geographically dispersed data, organizations must make sure they are aware of – and fully understand – the local and regional rules and requirements that apply for storing and managing data.

To learn more about how MongoDB can help you meet your data goals, check out the following resources:

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Optimizing Your MongoDB Deployment with Performance Advisor

We are happy to announce additional enhancements to MongoDB’s Performance Advisor, now available in MongoDB Atlas , MongoDB Cloud Manager , and MongoDB Ops Manager . MongoDB’s Performance Advisor automatically analyzes logs for slow-running queries and provides index suggestions to improve query performance. In this latest update, we’ve made some key updates, including: A new ranking algorithm and additional performance statistics (e.g., average documents scanned, average documents returned, and average object size) make it easier to understand the relative importance of each index recommendation. Support for additional query types including regexes, negation operators (e.g., $ne, $nin, $not), $count, $distinct, and $match to ensure we cover with optimized index suggestions. Index recommendations are now more deterministic so they are less impacted by time and provide more consistent query performance benefits. Before diving further into MongoDB’s Performance Advisor, let’s look at tools MongoDB provides out of the box to simplify database monitoring. Background Deploying your MongoDB cluster and getting your database running is a critical first step, but another important aspect of managing your database is ensuring that your database is performant and running efficiently. To make this easier for you, MongoDB offers several out-of-the-box monitoring tools , such as the Query Profiler, Performance Advisor, Real-Time Performance Panel, and Metrics Charts, to name a few. Suppose you notice that your database queries are running slower. The first place you might go is to the metrics charts to look at the “Opcounters” metrics to see whether you have more operations running. You might also look at the “Operation Execution Time” to see if your queries are taking longer to run. The “Query Targeting” metric shows the ratio of the number of documents scanned over the number of documents returned. This datapoint is a great measure of the overall efficiency of a query — the higher the ratio, the less efficient the query. These and other metrics can help you identify performance issues with your overall cluster, which you can then use as context to dive a level deeper and perform more targeted diagnostics of individual slow-running queries . MongoDB’s Performance Advisor takes this functionality a step further by automatically scanning your slowest queries and recommending indexes where appropriate to improve query performance. Getting started with Performance Advisor The Performance Advisor is a unique tool that automatically monitors MongoDB logs for slow-running queries and suggests indexes to improve query performance. Performance Advisor also helps improve both your read and write performance by intelligently recommending indexes to create and/or drop (Figure 1). These suggestions are ranked by the determined impact on your cluster. Performance Advisor is available on M10 and above clusters in MongoDB Atlas as well as in Cloud Manager and Ops Manager. Figure 1:  Performance Advisor can recommend indexes to create or drop. Performance Advisor will suggest which indexes to create, what queries will be affected by the index, and the expected improvements to query performance. All of these user interactions are available in the user interface directly within Performance Advisor, and indexes can be easily created with just a few clicks. Figure 2 shows additional Performance Advisor statistics about the performance improvements this index would provide. The performance statistics that are highlighted for each index recommendation include: Execution Count: The number of queries per hour that would be covered by the recommended index Avg Execution Time: The average execution time of queries that would be covered by the recommended index Avg Query Targeting: The inefficiency of queries that would be covered by the recommended index, measured by the number of documents or index keys scanned in order to return one document In Memory Sort: The number of in-memory sorts performed per hour for queries that would be covered by the recommended index Avg Docs Scanned: The average number of documents that were scanned by slow queries with this query shape Avg Docs Returned: The average number of documents that were returned by slow queries with this query shape Avg Object Size: The average object size of all objects in the impacted collection If you have multiple index recommendations, they are ranked by their relative impact to query performance so that the most beneficial index suggestion is displayed at the top. This means that the most impactful index is displayed at the top and would be the most beneficial to query performance. Figure 2:  Detailed performance statistics. Creating optimal indexes ensures that queries are not scanning more documents than they return. However, creating too many indexes can slow down write performance, as each write operation needs to check each index before writing. Performance Advisor provides suggestions on which indexes to drop based on whether they are unused or redundant (Figure 3). Users also have the option to “hide” indexes as a way to evaluate the impact of dropping an index without actually dropping the index. Figure 3: Performance Advisor shows which indexes are unused or redundant. The Performance Advisor in MongoDB provides a simple and cost-efficient way to ensure you’re getting the best performance out of your MongoDB database. If you’d like to see the Performance Advisor in action, the easiest way to get started is to sign up for MongoDB Atlas , our cloud database service. Performance Advisor is available on MongoDB Atlas on M10 cluster tiers and higher. Learn more from the following resources: Monitor and Improve Slow Queries Monitor Your Database Deployments

November 22, 2022

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Retrieval Augmented Generation for Claim Processing: Combining MongoDB Atlas Vector Search and Large Language Models

Following up on our previous blog, AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases , we’ll pick up the conversation right where we left it. We discussed extensively how Atlas Vector Search can benefit the claim process in insurance and briefly covered Retrieval Augmented Generation (RAG) and Large Language Models (LLMs). MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More One of the biggest challenges for claim adjusters is pulling and aggregating information from disparate systems and diverse data formats. PDFs of policy guidelines might be stored in a content-sharing platform, customer information locked in a legacy CRM, and claim-related pictures and voice reports in yet another tool. All of this data is not just fragmented across siloed sources and hard to find but also in formats that have been historically nearly impossible to index with traditional methods. Over the years, insurance companies have accumulated terabytes of unstructured data in their data stores but have failed to capitalize on the possibility of accessing and leveraging it to uncover business insights, deliver better customer experiences, and streamline operations. Some of our customers even admit they’re not fully aware of all the data in their archives. There’s a tremendous opportunity to leverage this unstructured data to benefit the insurer and its customers. Our image search post covered part of the solution to these challenges, opening the door to working more easily with unstructured data. RAG takes it a step further, integrating Atlas Vector Search and LLMs, thus allowing insurers to go beyond the limitations of baseline foundational models, making them context-aware by feeding them proprietary data. Figure 1 shows how the interaction works in practice: through a chat prompt, we can ask questions to the system, and the LLM returns answers to the user and shows what references it used to retrieve the information contained in the response. Great! We’ve got a nice UI, but how can we build an RAG application? Let’s open the hood and see what’s in it! Figure 1: UI of the claim adjuster RAG-powered chatbot Architecture and flow Before we start building our application, we need to ensure that our data is easily accessible and in one secure place. Operational Data Layers (ODLs) are the recommended pattern for wrangling data to create single views. This post walks the reader through the process of modernizing insurance data models with Relational Migrator, helping insurers migrate off legacy systems to create ODLs. Once the data is organized in our MongoDB collections and ready to be consumed, we can start architecting our solution. Building upon the schema developed in the image search post , we augment our documents by adding a few fields that will allow adjusters to ask more complex questions about the data and solve harder business challenges, such as resolving a claim in a fraction of the time with increased accuracy. Figure 2 shows the resulting document with two highlighted fields, “claimDescription” and its vector representation, “claimDescriptionEmbedding” . We can now create a Vector Search index on this array, a key step to facilitate retrieving the information fed to the LLM. Figure 2: document schema of the claim collection, the highlighted fields are used to retrieve the data that will be passed as context to the LLM Having prepared our data, building the RAG interaction is straightforward; refer to this GitHub repository for the implementation details. Here, we’ll just discuss the high-level architecture and the data flow, as shown in Figure 3 below: The user enters the prompt, a question in natural language. The prompt is vectorized and sent to Atlas Vector Search; similar documents are retrieved. The prompt and the retrieved documents are passed to the LLM as context. The LLM produces an answer to the user (in natural language), considering the context and the prompt. Figure 3: RAG architecture and interaction flow It is important to note how the semantics of the question are preserved throughout the different steps. The reference to “adverse weather” related accidents in the prompt is captured and passed to Atlas Vector Search, which surfaces claim documents whose claim description relates to similar concepts (e.g., rain) without needing to mention them explicitly. Finally, the LLM consumes the relevant documents to produce a context-aware question referencing rain, hail, and fire, as we’d expect based on the user's initial question. So what? To sum it all up, what’s the benefit of combining Atlas Vector Search and LLMs in a Claim Processing RAG application? Speed and accuracy: Having the data centrally organized and ready to be consumed by LLMs, adjusters can find all the necessary information in a fraction of the time. Flexibility: LLMs can answer a wide spectrum of questions, meaning applications require less upfront system design. There is no need to build custom APIs for each piece of information you’re trying to retrieve; just ask the LLM to do it for you. Natural interaction: Applications can be interrogated in plain English without programming skills or system training. Data accessibility: Insurers can finally leverage and explore unstructured data that was previously hard to access. Not just claim processing The same data model and architecture can serve additional personas and use cases within the organization: Customer Service: Operators can quickly pull customer data and answer complex questions without navigating different systems. For example, “Summarize this customer's past interactions,” “What coverages does this customer have?” or “What coverages can I recommend to this customer?” Customer self-service: Simplify your members’ experience by enabling them to ask questions themselves. For example, “My apartment is flooded. Am I covered?” or “How long do windshield repairs take on average?” Underwriting: Underwriters can quickly aggregate and summarize information, providing quotes in a fraction of the time. For example, “Summarize this customer claim history.” “I Am renewing a customer policy. What are the customer's current coverages? Pull everything related to the policy entity/customer. I need to get baseline info. Find relevant underwriting guidelines.” If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: RAG for claim processing GitHub repository From Relational Databases to AI: An Insurance Data Modernization Journey Modernize your insurance data models with MongoDB and Relational Migrator

April 18, 2024