使用 MongoDB 8.0 的四大理由

Scott Amerman and Cristopher Stauffer
October 2, 2024 | Updated: November 4, 2024
#MongoDB 8.0

我们很高兴地宣布，MongoDB 最新版本 8.0 现已全面上市。MongoDB 是一款广受全球欢迎的文档数据库，数百万名开发者和 50,000 多名客户都在使用这一数据库。MongoDB 8.0 以 MongoDB 行业领先的各项功能为基础，可显著提升性能，降低成本，增强易用性，适用于从本地部署到企业级全球分布式应用程序构建等各种使用场景。

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许多开发者长期以来一直青睐使用 MongoDB 构建应用程序，因此我们确保 8.0 版将开发者可用性保持在极高水平。MongoDB 8.0 的构建还超越了客户最严格的安全性、韧性、可用性和性能要求，是 MongoDB 迄今为止表现最为出色的版本。MongoDB 8.0 为客户现在和将来构建各种应用程序提供了最坚实的基础。

Jim Scharf, Chief Technology Officer, MongoDB

在开发 MongoDB 8.0 时，我们的工程团队专注于四项关键目标：

  • 优化各种应用程序的性能

  • 引入创新的加密技术，从而解锁新的使用案例

  • 降低成本，同时通过快速直观的水平扩展增强可扩展性，进而实现高可用性

  • 确保处理意外应用程序需求的韧性

在团队构建和管理应用程序的过程中，上述目标究竟会给他们带来哪些好处？首先，我们来探讨一下您为何应该使用 MongoDB 8.0。

无论您是 MongoDB 经验丰富的资深用户，还是新手用户，MongoDB 8.0 都可为您开发新应用程序和增强现有应用程序打下坚实基础。8.0 版不仅保留了最受开发者喜爱的一些 MongoDB 功能特性，比如提供直观统一的开发者体验，支持丰富的使用案例，确保操作的易用性，而且还大幅提升了性能表现。

改用 MongoDB 8.0 的主要理由

1. MongoDB 8.0 比前代产品速度快了 30% 以上

随着应用程序生成和使用的数据不断增加，即便效率小幅下滑也会导致基础设施的成本大幅攀升。由于不少客户主要通过应用程序与企业互动，如果应用程序性能不佳或有失稳定，可能会引起客户不满，导致错失机会、收入下降。因此，组织必须确保他们应用程序的表现始终如一。

MongoDB 8.0 可让应用程序快速高效地查询和转换数据，从而大大提升性能表现，将吞吐量最高增加 36%。MongoDB 8.0 进行了架构优化，降低了内存使用量，缩短了查询时间，通过更高效的批处理和优化，将数据复制期间的批量写入速度加快 56%，并发写入速度提高 20%。此外，MongoDB 8.0 中的优化意味着数据库可以处理更大量的时间序列数据，操作速度也能提高 200% 以上，而资源使用量和成本却不增反降。

2. MongoDB 8.0 比前代产品更加安全

数据保护和安全至关重要。随着跨环境传输、存储和处理的数据复杂性和数量不断增加，使用强大的加密技术保护敏感信息比以往任何时候都更加重要。组织必须确保在数据的整个生命周期中对其进行保护，包括确保数据在通过网络传输、静态存储以及查询和处理时的安全性。然而，在查询和处理数据时对其进行加密可能具有挑战性，数据容易被恶意行为者利用，造成暴露或外泄。

MongoDB Queryable Encryption 是 MongoDB 密码学研究小组开发的行业首创创新技术。该技术允许客户在客户端加密敏感数据，将其作为完全随机的加密数据安全地存储在MongoDB 数据库中，并对加密数据运行表达查询以进行处理。

MongoDB 8.0 除了支持等值查询之外，现在还支持范围查询，以扩展安全数据检索，为常用搜索提供更大的灵活性。借助 Queryable Encryption，所需数据将一直保持加密状态，直到到达使用客户控制的解密密钥的获授权最终用户处为止，而且无需用到任何加密专业知识。

3. MongoDB 8.0 让扩展变得更经济、更容易

随着组织不断发展壮大，其对应用程序的需求往往也会随之改变。例如，如果组织最初设计应用程序是为了供数千名用户使用，那么要想其扩展到支持数百万名用户可能就会比较困难。这是因为在生产应用程序中实施架构更改可能涉及大量的工作，而且成本高昂且耗费时间。

有了 MongoDB 8.0，实现水平扩展变得更快速、更简便，而且成本更低。通过水平扩展，应用程序可以突破传统数据库资源的限制，将数据拆分到多台服务器（称为分片）上，而无需为单台服务器预配越来越多的计算资源。MongoDB 8.0 中新增的分片功能可以将数据在分片中的分发速度提高最多 50 倍，并将初始成本降低 50%。

4. MongoDB 8.0 赋予您更多的控制权，助力您的应用程序顺利运行

应用程序的最终用户会希望即便在高需求期和使用高峰期，也能获得始终如一的使用体验。然而，如果没有高持久性的操作型数据库，应用程序在高需求时段会出现行为滞后（甚至停机）的情况，组织可能因此面临客户体验不佳的风险。

MongoDB 8.0 为优化数据库性能的团队赋予更大的控制权，以便应对难以预测的使用高峰期和持续高需求期。MongoDB 8.0 新增了各种功能，包括设置运行查询的默认最大时间限制，拒绝重复出现有问题的查询类型，以及设置查询设置以在数据库重启等事件中持久化。这些功能有助于确保应用程序始终保持一致的行为和较高的性能，不受需求激增或突发事件的影响。

准备好试用 MongoDB 8.0 了吗？

如果您要构建新的应用程序，MongoDB 8.0 最简单的上手方法就是访问 mongodb.com/try。您可以在此网站上免费注册 Atlas 帐户，下载 Community Edition，了解更多有关通过 Enterprise Advanced 订阅实现 MongoDB 自管理的信息。

如果您运行的是旧版 MongoDB，我们有关 MongoDB Atlas 和自管理部署的升级教程可以为您提供帮助。此外，MongoDB 的专业服务团队可随时提供文档和专家帮助。

如果您的现有应用程序暂未使用 MongoDB 数据库，请先了解 MongoDB Relational Migrator 工具。Relational Migrator 可以帮助您将现有关系模式映射到 MongoDB 模式，执行数据迁移，并将现有的关系查询、触发器和存储过程转换为适用于 MongoDB 的形式。

MongoDB 工程和产品团队认真听取了开发者的反馈，在构建 MongoDB 8.0 时将开发者可用性以及安全性、持久性、可用性和性能都放在了首位。我们期待您的试用，相信您将享受 MongoDB 8.0 带来的性能提升和其他各种好处！

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MongoDB 8.0: Raising the Bar

I recently received an automated reminder that I was approaching a work anniversary, which took me somewhat by surprise. It’s hard to believe that it’s already been a year (to the day) that I joined MongoDB ! So I thought I’d take a moment to reflect on my MongoDB journey so far, share some exciting product updates, and signal where we’re headed next. Our customers I joined MongoDB because it built a product developers love. The innovation of MongoDB’s document model empowered developers to simply build. No longer encumbered by having to formalize and denormalize their data schema before their application was even designed, MongoDB enabled developers to interact with data in an intuitive JSON format, and made it easy to evolve data structures as the life of their application evolved. One of my first steps upon joining the company was to learn more about our customers. I was excited to learn that in addition to delighting developers, MongoDB had launched capabilities that enabled it to win mission-critical workloads from enterprise class customers—including 70% of the Fortune 100 and highly regulated global financial institutions, health care providers, and government agencies. I found it remarkable that customers could replicate data across AWS, Google Cloud, and Microsoft Azure in MongoDB Atlas (our fully-managed cloud database service) with just a few mouse clicks, and that some customers replicate data between the cloud and on premises using MongoDB Enterprise Advanced. This optionality struck me as powerful in the era of rapid advancements in AI, as it enables customers to easily bring their data to the best cloud provider for AI. Soon after I joined MongoDB, the team was firming up the development roadmap for the next version of MongoDB, and they asked for my input on the plan. The team was debating whether to focus on features developers would love, or governance capabilities required by large enterprises. I knew that ideally we would please all of our customers, so we had to try to make this an “and” and not an “or.” While I was new to MongoDB, from my 17+ years at AWS I learned that all customers demand security, durability, availability, and performance (in that order) from any modern technology offering. If a product or service doesn’t have those four elements, customers won’t buy whatever you’re selling. So as a team, we agreed that our next release of MongoDB—MongoDB 8.0—had to raise the bar for all of our customers, delivering great security, durability, availability, and performance. The plan We had less than a year before our target launch, so we knew we had to get moving, fast. My team and I brought MongoDB’s product and engineering organizations together to align on the plan for our next release. We set goals around delivering significant improvements in security, durability, and availability. And we set a line in the sand—that we weren’t going to release MongoDB 8.0 unless it was the best-performing version of MongoDB yet. Measuring the performance of a feature-rich database like MongoDB can be tricky, as customers run a wide range of workloads. So we decided to run a suite of benchmarks to simulate customer workloads. We also developed Andon cord -inspired automation that would automatically roll back any code contributions that regressed our performance metrics. Finally, a set of senior engineering leaders met regularly to review our progress and immediately escalated any blockers that could jeopardize our launch, so that we could quickly fix things. From my experience, I knew that great teams really respond when they’re given clear goals, and when they’re empowered to innovate, so I was excited to see what they would come up with. I’m proud to say that our product and engineering teams rose to the challenge. Announcing MongoDB 8.0 Today, I’m thrilled to announce the general availability of MongoDB 8.0 —the most secure, durable, available, and performant version of MongoDB yet! The team came up with architectural optimizations in MongoDB 8.0 that have significantly reduced memory usage and query times, and have made batch processing more efficient than previous versions. Specifically, MongoDB 8.0 features: 36% better read throughput 56% faster bulk writes 20% faster concurrent writes during data replication 200% faster on complex aggregations of times series data In making these improvements, we're seeing benchmarks for typical web applications perform 32% better overall. Here’s a breakdown of how MongoDB 8.0 performs against some of our benchmarks: Improved performance benefits all users of applications built atop MongoDB, and for MongoDB customers, it can mean reduced costs (due to an improved price/performance ratio). In addition to significant performance gains, MongoDB 8.0 delivers a wide range of improvements, including (but not limited to): Improving availability by delivering sharding enhancements to distribute data across shards up to 50 times faster and at up to 50% lower starting cost, with reduced need for additional configuration or setup. Improving support for a wide range of search and AI applications at higher scale and lower cost, via the delivery of quantized vectors—compressed representations of full-fidelity vectors—that require up to 96% less memory and are faster to retrieve while preserving accuracy. Enabling customers to encrypt data at rest, in transit, and in use by expanding MongoDB’s Queryable Encryption to also support range queries. Queryable Encryption is a groundbreaking, industry-first innovation developed by the MongoDB Cryptography Research Group that allows customers to encrypt sensitive application data, store it securely as fully randomized encrypted data in the MongoDB database, and run expressive queries on the encrypted data —with no cryptography expertise required. You might wonder why we’re so confident that customers are going to love MongoDB 8.0. Well, we’ve been acting as our own customer, and have moved our own applications over to 8.0. This approach is generally called “ dogfooding ,” but we think that “eating our own pizza” sounds more appetizing. Our internal build system—which our software developers use daily—is built atop MongoDB, and when we upgraded to MongoDB 8.0 we saw query latencies drop by approximately 75%! This was a double win, as it improved the performance of our own tooling, and it set our performance chat room abuzz with excitement in anticipation of delighting external customers. While results may vary based on your particular workload, the point is that we just couldn’t wait to share MongoDB 8.0’s performance gains with customers. Indeed, customers are also already seeing great results on MongoDB 8.0. For example, Felix Horvat, Chief Technology Officer at OCELL , a climate technology company in Germany, said: “With MongoDB 8.0, we have seen an incredible boost in performance, with some of our queries running twice as fast as before . This improvement not only enhances our data processing capabilities but also aligns perfectly with our commitment to resource efficiency. By optimizing our backend operations, we can be more effective in our climate initiatives while conserving resources—a true reflection of our dedication to sustainable solutions.” I encourage you to check out MongoDB 8.0 yourself. It’s available today via MongoDB Atlas, as part of MongoDB Enterprise Advanced for on-premises and hybrid deployments, and as a free download from mongodb.com/try with MongoDB Community Edition. In addition, customers upgrading from previous versions of MongoDB to 8.0 can find helpful upgrade guides on mongodb.com. What’s next? We’re excited for you to try MongoDB 8.0 and to share your feedback, as customer feedback helps us guide our roadmap for future releases. Going forward, please watch this space. Over the next few weeks, we’ll be publishing a series of engineering blog posts that dig into MongoDB’s investments in the technology behind MongoDB 8.0. We’re also planning posts about horizontal scaling in MongoDB 8.0, and one that will look closely at queryable encryption (QE), but let me know what you’d like to hear more about. It’s been an exciting year at MongoDB—I can’t wait to see what the next one has in store! –Jim

October 2, 2024
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AI-Powered Call Centers: A New Era of Customer Service

Customer satisfaction is critical for insurance companies. Studies have shown that companies with superior customer experiences consistently outperform their peers. In fact, McKinsey found that life and property/casualty insurers with superior customer experiences saw a significant 20% and 65% increase in Total Shareholder Return , respectively, over five years. A satisfied customer is a loyal customer. They are 80% more likely to renew their policies, directly contributing to sustainable growth. However, one major challenge faced by many insurance companies is the inefficiency of their call centers. Agents often struggle to quickly locate and deliver accurate information to customers, leading to frustration and dissatisfaction. This article explores how Dataworkz and MongoDB can transform call center operations. By converting call recordings into searchable vectors (numerical representations of data points in a multi-dimensional space), businesses can quickly access relevant information and improve customer service. We'll dig into how the integration of Amazon Transcribe, Cohere, and MongoDB Atlas Vector Search—as well as Dataworkz's RAG-as-a-service platform— is achieving this transformation. From call recordings to vectors: A data-driven approach Customer service interactions are goldmines of valuable insights. By analyzing call recordings, we can identify successful resolution strategies and uncover frequently asked questions. In turn, by making this information—which is often buried in audio files— accessible to agents, they can give customers faster and more accurate assistance. However, the vast volume and unstructured nature of these audio files make it challenging to extract actionable information efficiently. To address this challenge, we propose a pipeline that leverages AI and analytics to transform raw audio recordings into vectors as shown in Figure 1: Storage of raw audio files: Past call recordings are stored in their original audio format Processing of the audio files with AI and analytics services (such as Amazon Transcribe Call Analytics ): speech-to-text conversion, summarization of content, and vectorization Storage of vectors and metadata: The generated vectors and associated metadata (e.g., call timestamps, agent information) are stored in an operational data store Figure 1: Customer service call insight extraction and vectorization flow Once the data is stored in vector format within the operational data store, it becomes accessible for real-time applications. This data can be consumed directly through vector search or integrated into a retrieval-augmented generation (RAG) architecture, a technique that combines the capabilities of large language models (LLMs) with external knowledge sources to generate more accurate and informative outputs. Introducing Dataworkz: Simplifying RAG implementation Building RAG pipelines can be cumbersome and time-consuming for developers who must learn yet another stack of technologies. Especially in this initial phase, where companies want to experiment and move fast, it is essential to leverage tools that allow us to abstract complexity and don’t require deep knowledge of each component in order to experiment with and realize the benefits of RAG quickly. Dataworkz offers a powerful and composable RAG-as-a-service platform that streamlines the process of building RAG applications for enterprises. To operationalize RAG effectively, organizations need to master five key capabilities: ETL for LLMs: Dataworkz connects with diverse data sources and formats, transforming the data to make it ready for consumption by generative AI applications. Indexing: The platform breaks down data into smaller chunks and creates embeddings that capture semantics, storing them in a vector database. Retrieval: Dataworkz ensures the retrieval of accurate information in response to user queries, a critical part of the RAG process. Synthesis: The retrieved information is then used to build the context for a foundational model, generating responses grounded in reality. Monitoring: With many moving parts in the RAG system, Dataworkz provides robust monitoring capabilities essential for production use cases. Dataworkz's intuitive point-and-click interface (as seen in Video 1) simplifies RAG implementation, allowing enterprises to quickly operationalize AI applications. The platform offers flexibility and choice in data connectors, embedding models, vector stores, and language models. Additionally, tools like A/B testing ensure the quality and reliability of generated responses. This combination of ease of use, optionality, and quality assurance is a key tenet of Dataworkz's "RAG as a Service" offering. Diving deeper: System architecture and functionalities Now that we’ve looked at the components of the pre-processing pipeline, let’s explore the proposed real-time system architecture in detail. It comprises the following modules and functions (see Figure 2): Amazon Transcribe , which receives the audio coming from the customer’s phone and converts it into text. Cohere ’s embedding model, served through Amazon Bedrock , vectorizes the text coming from Transcribe. MongoDB Atlas Vector Search receives the query vector and returns a document that contains the most semantically similar FAQ in the database. Figure 2: System architecture and modules Here are a couple of FAQs we used for the demo: Q: “Can you explain the different types of coverage available for my home insurance?” A: “Home insurance typically includes coverage for the structure of your home, your personal belongings, liability protection, and additional living expenses in case you need to temporarily relocate. I can provide more detailed information on each type if you'd like.” Q: “What is the process for adding a new driver to my auto insurance policy?" A: “To add a new driver to your auto insurance policy, I'll need some details about the driver, such as their name, date of birth, and driver's license number. We can add them to your policy over the phone, or you can do it through our online portal.” Note that the question is reported just for reference, and it’s not used for retrieval. The actual question is provided by the user through the voice interface and then matched in real-time with the answers in the database using Vector Search. This information is finally presented to the customer service operator in text form (see Fig. 3). The proposed architecture is simple but very powerful, easy to implement, and effective. Moreover, it can serve as a foundation for more advanced use cases that require complex interactions, such as agentic workflows , and iterative and multi-step processes that combine LLMs and hybrid search to complete sophisticated tasks. Figure 3: App interface, displaying what has been asked by the customer (left) and how the information is presented to the customer service operator (right) This solution not only impacts human operator workflows but can also underpin chatbots and voicebots, enabling them to provide more relevant and contextual customer responses. Building a better future for customer service By seamlessly integrating analytical and operational data streams, insurance companies can significantly enhance both operational efficiency and customer satisfaction. Our system empowers businesses to optimize staffing, accelerate inquiry resolution, and deliver superior customer service through data-driven, real-time insights. To embark on your own customer service transformation, explore our GitHub repository and take advantage of the Dataworkz free tier .

November 27, 2024