When discussing serverless computing (Functions as a Service) with developers, a common concern that arises is the complexity of testing and debugging your functions. Fortunately, the MongoDB Stitch UI makes this simple.
It's a bit old school, but if you want to display debug info from your functions, then it's as simple as adding
console.log() commands to your code. If testing the function through the Stitch UI, the output appears in the Results panel. When executed normally, the output appears in the Stitch logs.
To test a Stitch function from the UI, select a user for the function to run as (that way the function can access whatever data the user is entitled to). In the Console panel, call
exports(<parameters>), including any parameters that the function expects – these could be simple values or complex documents.
The results of the function call (the returned data + any
console.log() output) appear in the Results panel.
If you want to check on what's happening in your production apps, check out the Logs panel in the Stitch UI.
Creating your first Stitch app? Start with one of the Stitch tutorials.
Want to learn more about MongoDB Stitch? Read the white paper.
Millions of Users and a Developer-Led Culture: How Blinkist Powers its Berlin Startup on MongoDB Atlas
Not unlike other startups, Blinkist grew its roots in a college dorm. Only, its creators didn’t know it at the time. It took years before the founders decided to build a business on their college study tricks. Blinkist condenses nonfiction books into pithy, but accessible 15-minute summaries which you can read or listen to via its app. “It all started with four friends,” says Sebastian Schleicher, Director of Engineering at Blinkist. “After leaving university, they found jobs and built lifestyles that kept them fully occupied—but they were pretty frustrated because their packed schedules left them no time for reading and learning new things.” Rather than resign themselves to a life without learning, they racked their brains as to how they could find a way to satisfy their craving for knowledge. They decided to revive their old study habits from university where they would write up key ideas from material that they’d read and then share it with each other. It didn’t take long for them to realise that they could build a business on this model of creating valuable easily accessible content to inspire people to keep learning. In 2012, Blinkist was born. Six years later, the Berlin-based outfit has nearly 100 employees, but instead of writers and editors, they have Tea Masters and Content Ninjas. Blinkist has no formal hierarchical management structure, having replaced bosses with BOS, the Blinkist Operating System . The app has over five million users and, at its foundation, it has MongoDB Atlas , the fully managed service for MongoDB, running on AWS. But it didn’t always. “In four years, we had a million users and 2,500 books,” says Schleicher. “We’d introduced audiobooks and seen them become the most important delivery channel. We tripled our revenue, doubled our team, moved into a larger, open-plan office, and even got a dog. Things were good.” Running into trouble with 3rd party MongoDB as a Service Then came an unwelcome plot twist. Blinkist had built its service on Compose, a third-party database as a service, based on MongoDB. MongoDB had been an obvious choice as the document model provided Blinkist with the flexibility needed to iterate quickly, but the team was too lean to spend time on infrastructure management In 2016, Compose unexpectedly decided to change the architecture of its database, creating major obstacles for Blinkist as they would become locked-in to an old version of MongoDB. “They left us alone,” says Schleicher. “They said, ‘Here’s a tool, migrate your data.’ I asked if they’d help. No dice. I offered them money. Not interested, no support. After being a customer for all those years? I said goodbye.” After years of issues, it became clear last year that Blinkist would need to leave Compose, which meant choosing a new database provider. “We looked at migrating to MySQL, we were that desperate. That would have meant freezing development and concentrating on the move ourselves. On a live service. It was bleak.” Discovering MongoDB Atlas By this time, MongoDB’s managed cloud Atlas service was well established and seemed to be the logical solution. “We downloaded MongoDB’s free mongomirror service to make the transition,” says Schleicher, “but we hit a brick wall. Compose had locked us into a very old version of the database and who knows what else, and we couldn’t work it out.” At that point, Schleicher made a call to MongoDB. MongoDB didn’t say, ‘Do it yourself.’ Instead, they sent their own data ninja—or, in more conventional, business-card wording, a principal consulting engineer. “It was the easiest thing in the world,” Schleicher remembers. “In one day, he implemented four feature requests, got the migration done and our databases were in live sync. Such a great experience.” Now that Blinkist is on Atlas, Schleicher feels like they have a very solid base for the future. “Performance is terrific. Our mobile app developers accidentally coded in a distributed denial of service attack on our own systems. Every day at midnight, in each time zone, our mobile apps all simultaneously sync. This pushes the requests load up from a normal peak of 7,500 requests a minute to 40,000 continuous. That would have slaughtered the old system, with real business impacts — killing sign-ups and user interactions. This time, nobody noticed anything was wrong." Right now it feels like we have a big tech advantage. With MongoDB Atlas and AWS, we’re on the shoulders of people who can scale the world. I know for the foreseeable future I have partners I can really rely on. Sebastian Schleicher, Director of Engineering, Blinkist Schleicher adds: “We’re building our future through microarchitecture with all the frills. Developers know they don’t have to worry about what’s going on behind the API in MongoDB. It just works. We’re free to look at data analytics and AI—whatever techniques and tools we believe will help us grow—and not spend all our time maintaining a monolithic slab of code.” With Blinkist’s global ambitions, scaling isn’t just a technical challenge; it tests company culture—no matter how modern—to the limits. MongoDB’s own customer-focused culture, it turns out, is proving as compatible as MongoDB’s data platform. “Talking to MongoDB isn’t like being exposed to relentless sales pressure. It’s cooperative, it’s reassuring. There are lots of good technical people on tap. It’s holistic, no silos, whatever it takes to help us.” This partnership is helping make Blinkist a great place to be a developer. “A new colleague we hired last year told me we’ve created an island of happiness for engineers. Once you have an understanding of the business needs and vision, you get to drive your own projects. We believe in super transparency. Everyone is empowered.” “Oh, and did I mention we have a dog?” Atlas is the easiest and fastest way to get started with MongoDB. Deploy a free cluster in minutes.
How DataSwitch And MongoDB Atlas Can Help Modernize Your Legacy Workloads
Data modernization is here to stay, and DataSwitch and MongoDB are leading the way forward. Research strongly indicates that the future of the Database Management System (DBMS) market is in the cloud, and the ideal way to shift from an outdated, legacy DBMS to a modern, cloud-friendly data warehouse is through data modernization. There are a few key factors driving this shift. Increasingly, companies need to store and manage unstructured data in a cloud-enabled system, as opposed to a legacy DBMS which is only designed for structured data. Moreover, the amount of data generated by a business is increasing at a rate of 55% to 65% every year and the majority of it is unstructured. A modernized database that can improve data quality and availability provides tremendous benefits in performance, scalability, and cost optimization. It also provides a foundation for improving business value through informed decision-making. Additionally, cloud-enabled databases support greater agility so you can upgrade current applications and build new ones faster to meet customer demand. Gartner predicts that by 2022, 75% of all databases will be on the cloud – either by direct deployment or through data migration and modernization. But research shows that over 40% of migration projects fail. This is due to challenges such as: Inadequate knowledge of legacy applications and their data design Complexity of code and design from different legacy applications Lack of automation tools for transforming from legacy data processing to cloud-friendly data and processes It is essential to harness a strategic approach and choose the right partner for your data modernization journey. We’re here to help you do just that. Why MongoDB? MongoDB is built for modern application developers and for the cloud era. As a general purpose, document-based, distributed database, it facilitates high productivity and can handle huge volumes of data. The document database stores data in JSON-like documents and is built on a scale-out architecture that is optimal for any kind of developer who builds scalable applications through agile methodologies. Ultimately, MongoDB fosters business agility, scalability and innovation. Key MongoDB advantages include: Rich JSON Documents Powerful query language Multi-cloud data distribution Security of sensitive data Quick storage and retrieval of data Capacity for huge volumes of data and traffic Design supports greater developer productivity Extremely reliable for mission-critical workloads Architected for optimal performance and efficiency Key advantages of MongoDB Atlas , MongoDB’s hosted database as a service, include: Multi-cloud data distribution Secure for sensitive data Designed for developer productivity Reliable for mission critical workloads Built for optimal performance Managed for operational efficiency To be clear, JSON documents are the most productive way to work with data as they support nested objects and arrays as values. They also support schemas that are flexible and dynamic. MongoDB’s powerful query language enables sorting and filtering of any field, regardless of how nested it is in a document. Moreover, it provides support for aggregations as well as modern use cases including graph search, geo-based search and text search. Queries are in JSON and are easy to compose. MongoDB provides support for joins in queries. MongoDB supports two types of relationships with the ability to reference and embed. It has all the power of a relational database and much, much more. Companies of all sizes can use MongoDB as it successfully operates on a large and mature platform ecosystem. Developers enjoy a great user experience with the ability to provision MongoDB Atlas clusters and commence coding instantly. A global community of developers and consultants makes it easy to get the help you need, if and when you need it. In addition, MongoDB supports all major languages and provides enterprise-grade support. Why DataSwitch as a partner for MongoDB? Automated schema re-design, data migration & code conversion DataSwitch is a trusted partner for cost-effective, accelerated solutions for digital data transformation, migration and modernization through a modern database platform. Our no-code and low-code solutions along with cloud data expertise and unique, automated schema generation accelerates time to market. We provide end-to-end data, schema and process migration with automated replatforming and refactoring, thereby delivering: 50% faster time to market 60% reduction in total cost of delivery Assured quality with built-in best practices, guidelines and accuracy Data modernization: How “DataSwitch Migrate” helps you migrate from RDBMS to MongoDB DataSwitch Migrate (“DS Migrate”) is a no-code and low-code toolkit that leverages advanced automation to provide intuitive, predictive and self-serviceable schema redesign from a traditional RDBMS model to MongoDB’s Document Model with built-in best practices. Based on data volume, performance, and criticality, DS Migrate automatically recommends the appropriate ETTL (Extract, Transfer, Transform & Load) data migration process. DataSwitch delivers data engineering solutions and transformations in half the timeframe of the existing typical data modernization solutions. Consider these key areas: Schema redesign – construct a new framework for data management. DS Migrate provides automated data migration and transformation based on your redesigned schema, as well as no-touch code conversion from legacy data scripts to MongoDB Atlas APIs. Users can simply drag and drop the schema for redesign and the platform converts it to a document-based JSON structure by applying MongoDB modeling best practices. The platform then automatically migrates data to the new, re-designed JSON structure. It also converts the legacy database script for MongoDB. This automated, user-friendly data migration is faster than anything you’ve ever seen. Here’s a look at how the schema designer works. Refactoring – change the data structure to match the new schema. DS Migrate handles this through auto code generation for migrating the data. This is far beyond a mere lift and shift. DataSwitch takes care of refactoring and replatforming (moving from the legacy platform to MongoDB) automatically. It is a game-changing unique capability to perform all these tasks within a single platform. Security – mask and tokenize data while moving the data from on-premise to the cloud. As the data is moving to a potentially public cloud, you must keep it secure. DataSwitch’s tool has the capability to configure and apply security measures automatically while migrating the data. Data Quality – ensure that data is clean, complete, trustworthy, consistent. DataSwitch allows you to configure your own quality rules and automatically apply them during data migration. In summary: first, the DataSwitch tool automatically extracts the data from an existing database, like Oracle. It then exports the data and stores it locally before zipping and transferring it to the cloud. Next, DataSwitch transforms the data by altering the data structure to match the re-designed schema, and applying data security measures during the transform step. Lastly, DS Migrate loads the data and processes it into MongoDB in its entirety. Process Conversion Process conversion, where scripts and process logic are migrated from legacy DBMS to a modern DBMS, is made easier thanks to a high degree of automation. Minimal coding and manual intervention are required and the journey is accelerated. It involves: DML – Data Manipulation Language CRUD – typical application functionality (Create, Read, Update & Delete) Converting to the equivalent of MongoDB Atlas API Degree of automation DataSwitch provides during Migration Schema Migration Activities DS Automation Capabilities Application Data Usage Analysis 70% 3NF to NoSQL Schema Recommendation 60% Schema Re-Design Self Services 50% Predictive Data Mapping 60% Process Migration Activities DS Automation Capabilities CRUD based SQL conversion (Oracle, MySQL, SQLServer, Teradata, DB2) to MongoDB API 70% Data Migration Activities DS Automation Capabilities Migration Script Creation 90% Historical Data Migration 90% 2 Catch Load 90% DataSwitch Legacy Modernization as a Service (LMaas): Our consulting expertise combined with the DS Migrate tool allows us to harness the power of the cloud for data transformation of RDBMS legacy data systems to MongoDB. Our solution delivers legacy transformation in half the time frame through pay-per-usage. Key strengths include: ● Data Architecture Consulting ● Data Modernization Assessment and Migration Strategy ● Specialized Modernization Services DS Migrate Architecture Diagram Contact us to learn more.