Discover the differences between gRPC and REST, comparing speed, efficiency, and use cases to find the best fit for your application. 28 October 2024 09 Min. Read gRPC vs. REST: Which is Faster, Efficient, and Better? WhatsApp LinkedIn X (Twitter) Copy link Get Started with HyperTest Microservices teams frequently face challenges in choosing the best communication method for their services, mainly between gRPC and REST. Understanding the pros and cons of both options is crucial to ensuring smooth data exchange and quick responses. The discussion should emphasize the main differences between gRPC and REST. It will help you understand what is faster and better for your software projects. Before we move ahead, if you want to learn about an approach that takes away the pain of manually preparing test data and managing different environments, here's the solution you've been looking for: What is gRPC? gRPC stands for gRPC Remote Procedure Calls. It’s an open-source framework developed by Google to help you build high-performance distributed systems. It simplifies communication between your client and server applications. It uses HTTP/2, which gives you advantages like better data handling, streaming options, and improved flow control. This means your projects can run more smoothly and efficiently. Some of the key features of gRPC that you should know: gRPC is made to create distributed systems that run efficiently. This brings benefits like better data handling and streaming capabilities. It uses Protocol Buffers to serialize data effectively, making it easier to send. gRPC works with a variety of programming languages, so you can use it in different projects. You can choose between single request-response or continuous data streams for your communication. Clients and servers can exchange messages independently, allowing for real-time interaction. It has important features like load balancing, authentication, and encryption to enhance security and performance. What is REST? REST is abbreviated as Representational State Transfer. It is a way to design networked applications. It uses standard HTTP methods to help clients and servers communicate. Here are the main points: Each request from a client includes all the information the server needs to respond, which helps with scalability. This separates the client and server, allowing them to develop independently. REST works with resources identified by URLs and interacts with them using methods like GET. It provides a consistent way to interact with resources, making API design simpler. Resources can be shown in various formats, like JSON or XML. Feature gRPC REST Protocol Uses HTTP/2 Uses HTTP/1.1 Data Format Protocol Buffers (binary) JSON or XML (text-based) Performance Faster due to multiplexing and binary format Slower due to text parsing and larger payloads Streaming Supports bidirectional streaming Typically, stateless and request/response only Error Handling Uses status codes and messages defined in Protocol Buffers Standard HTTP status codes Tooling Fewer specialized tools, but growing Mature ecosystem with many tools available Language Support Strong support for multiple languages Supported in virtually all programming languages Caching Limited due to binary format Leverages HTTP caching mechanisms Use Cases Ideal for microservices, real-time applications Suitable for web applications and public APIs gRPC vs. REST- Performance Comparison When you are deciding between gRPC and REST, performance is a critical factor. This section explores speed, efficiency, and latency to help guide your project choices. Speed gRPC REST Speed often plays a big role in how well an API performs. gRPC, which uses HTTP/2, has significant advantages here. It allows multiple requests and responses to be sent simultaneously over a single connection. This reduces the time it takes to set up new connections, leading to faster response times for you. In contrast, REST usually operates on HTTP/1.1 and can face slowdowns because each interaction often requires a new connection. This adds latency. While you can improve REST with methods like caching and connection pooling, gRPC typically provides quicker data exchange due to its use of Protocol Buffers, which are more compact than the JSON used in REST. Efficiency gRPC REST Efficiency in data transmission is another area where gRPC shines. Its use of Protocol Buffers means smaller payload sizes, which reduces the amount of data you need to send over the network. This compactness speeds up communication and lowers bandwidth usage, making it especially useful in mobile or low-bandwidth situations. While REST is versatile and widely used, it often sends larger JSON payloads. This can lead to increased latency and higher resource consumption, especially when dealing with complex data or large datasets. As a developer, you should consider how efficient your chosen protocol will be based on the type of applications you are building and the expected data sizes. Latency gRPC REST Latency can significantly impact your user experience and overall system performance. In real-world scenarios, gRPC often shows lower latency compared to REST. For example, if you are working on applications that require real-time data streaming, like video conferencing or online gaming, you will find that gRPC’s efficient communication model makes a difference. Its ability to handle bidirectional streaming allows for immediate data exchange, improving responsiveness. On the other hand, REST may introduce delays in situations that need frequent updates or fast data exchange, such as stock price updates or live notifications. The need to establish new connections and larger payloads can slow things down in these cases, affecting your application’s performance. When to Use gRPC vs REST? Deciding whether to use gRPC or REST ultimately depends on your application's specific requirements, performance needs, and the nature of the services being utilized. By comprehending the advantages of each choice, you can make a better decision that fits your structure and objectives. Think about what is most effective for you and your team to ensure seamless progress in your project. ➡️When to Use gRPC? gRPC is great for: Microservices Architectures: It helps different services communicate efficiently. Real-Time Streaming: It works well for applications like chat and online gaming that need fast, two-way data flow. High-Performance Applications: It's suitable for low-latency needs, like video conferencing and trading platforms. Mobile Applications: It reduces bandwidth usage with smaller data packets. Complex Data Types: It handles complex data structures effectively using Protocol Buffers. ➡️When to Use REST REST is effective for: Public APIs: Accessible and easy to use for third-party developers. Web Applications: Fits well with CRUD operations in traditional web environments. Caching Needs: Leverages HTTP caching to enhance performance. Document-Based Interactions: Clear resource-oriented structure for handling documents. Simplicity and Familiarity: Easier for teams experienced with REST, benefiting from extensive documentation. Testing Challenges in API Development Developing APIs presents challenges that can impact the quality and dependability of your services. Here are some typical challenges you may encounter when testing REST APIs and gRPC. Common Testing Issues with REST APIs Inconsistent Responses: You may find that different API endpoints return data in various formats or structures, making it tough to test effectively. Authentication and Authorization: Verifying user credentials and ensuring proper access control can complicate your testing scenarios. Rate Limiting: Many APIs implement rate limiting, which can restrict your ability to conduct thorough tests without hitting those limits. Error Handling: It can be challenging to test how APIs handle errors, especially when different endpoints behave differently. Versioning Issues: Managing multiple API versions can lead to confusion and make testing for backward compatibility more difficult. Unique Testing Challenges with gRPC Binary Protocol: gRPC uses Protocol Buffers for serialization, which makes it harder for you to inspect and debug compared to text-based formats like JSON. Streaming: The support for streaming adds complexity to testing both client and server interactions, especially for bidirectional streams. Compatibility: You need to ensure that gRPC services work well with various programming languages, which can complicate your testing strategies. Latency: Testing for performance and latency in gRPC calls requires a different approach, as the overhead and optimizations differ from REST APIs. HyperTest simplifies gRPC API testing with a no-code approach, allowing your team to focus on functionality instead of writing test code. It automatically generates test cases from your network traffic, saving time and minimizing errors. The user-friendly interface offers clear visualizations of request and response flows, making debugging easier. With comprehensive reporting, you can quickly identify issues and track performance metrics. By reducing the complexities of gRPC testing, HyperTest helps your team conduct efficient tests, boosting your confidence in your APIs' reliability and performance. Why Testing is a Constant Problem? Testing remains a constant challenge because APIs evolve rapidly, requirements change, and frequent updates are necessary. The increasing complexity of distributed systems and microservices architecture adds to these difficulties. As you implement new features, ensuring comprehensive test coverage becomes critical, making ongoing testing a priority for your team. The Need for Specialized Testing Tools You have understood the challenges in testing API with gRPC and REST. To overcome this, you need a specialized tool like HyperTest, which is designed to handle the unique requirements of both REST and gRPC. It provides capabilities such as automated testing, monitoring performance, and seamless integration with CI/CD pipelines. Comparison of Testing Approaches Testing gRPC APIs When testing gRPC APIs, here are some methodologies and best practices you can follow: Use Protocol Buffers: This helps you maintain clear API contracts. Mock Services: Isolate your tests by using mock services to simulate interactions. Focus on Performance Metrics: Pay special attention to performance, especially when it comes to streaming. Implement Automated Testing Frameworks: This can save you time and reduce errors. Testing REST APIs For REST APIs, consider these methodologies and best practices: Validate Each Endpoint: Ensure each endpoint returns the expected responses. Test Authentication and Authorization: Make sure to rigorously test user access controls. Ensure Proper Error Handling: Check how your APIs handle different error scenarios. Manage Multiple API Versions: Keep track of and test different versions of your API. How HyperTest Streamlines Testing for Both? HyperTest simplifies testing for both gRPC and REST APIs by providing a unified platform. It features advanced collaboration tools, allowing teams to share test cases and results easily. It also makes creating mock services and validating responses straightforward, helping you ensure comprehensive coverage and efficiency in your testing processes. Conclusion If you are looking for speed and efficiency, gRPC tends to outperform REST, especially when your applications rely on real-time data streaming or microservices. It’s built for high-performance scenarios, giving you the edge where fast communication is essential. On the other hand, REST remains a versatile and familiar choice for simpler, document-based APIs. Testing both can be challenging, but tools like HyperTest simplify the process for you. It automates the complexities of gRPC testing, allowing you to focus more on development and less on manual testing, making your work smoother and more efficient. Related to Integration Testing Frequently Asked Questions 1. Is gRPC faster than REST? Yes, gRPC is faster than REST due to its binary data format and HTTP/2 support, which enables multiplexing and streaming. 2. Which is more efficient, gRPC or REST? gRPC is more efficient for server-to-server communication, while REST is simpler and more compatible with browsers and external clients. 3. What are the key differences between gRPC and REST? gRPC uses Protocol Buffers and HTTP/2, while REST relies on JSON and HTTP/1.1, impacting speed, efficiency, and compatibility. For your next read Dive deeper with these related posts! 07 Min. Read All you need to know about Apache Kafka: A Comprehensive Guide Learn More 08 Min. Read Using Blue Green Deployment to Always be Release Ready Learn More 09 Min. Read What are stacked diffs and how do they work? Learn More

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Stateful vs. stateless architecture: Understand key differences, pros, and cons to make informed decisions for scalable, efficient systems. 7 March 2025 09 Min. Read Stateful vs Stateless Architecture: Guide for Leaders WhatsApp LinkedIn X (Twitter) Copy link Choose the right architecture @DevOpsGuru: "Hot take: stateless services are ALWAYS the right choice. Your architecture should be cattle, not pets." @SystemsArchitect: "Spoken like someone who's never built a high-throughput trading system. Try telling that to my 2ms latency requirements." @CloudNative23: "Both of you are right in different contexts. The question isn't which is 'better' - it's about making intentional tradeoffs." After 15+ years architecting systems that range from global payment platforms to real-time analytics engines, I've learned one truth: dogmatic architecture decisions are rarely the right ones. The stateful vs. stateless debate has unfortunately become one of those religious wars in our industry, but the reality is far more nuanced. The Fundamentals: What we're really talking about? Let's level-set on what these terms actually mean in practice. In the trenches, here's what this actually means for your team: Stateless Services Any instance can handle any request Instances are replaceable without data loss Horizontal scaling is straightforward Stateful Services Specific instances own specific data Instance failure requires data recovery Scaling requires data rebalancing Real Talk: Where I've seen each shine? ➡️ When Stateless Architecture Was the Clear Winner Back in 2018, I was leading engineering at a SaaS company hitting explosive growth. Our monolithic application was crumbling under load, with database connections maxed out and response times climbing. We identified our authentication flow as a perfect candidate for extraction into a stateless service. Here's what happened: Before : 3-second p95 response time, maximum 5,000 concurrent users After : 200ms p95 response time, handles 50,000+ concurrent users The key was offloading session state to Redis and making the service itself completely stateless. Any instance could validate any token, allowing us to scale horizontally with simple auto-scaling rules. ➡️ When Stateful Architecture Saved the Day Contrast that with a real-time bidding platform I architected for an adtech company. We had milliseconds to process bid requests, and network hops to external databases were killing our latency. We reimagined the system with stateful services that kept hot data in memory, with careful sharding and replication: The business impact was immediate - the improved latency meant we could participate in more bid opportunities and win more auctions. Metric Original Stateless Design Stateful Redesign Improvement Average Latency 28ms 4ms 85.7% 99th Percentile Latency 120ms 12ms 90% Throughput (requests/sec) 15,000 85,000 466.7% Infrastructure Cost $42,000/month $28,000/month 33.3% Bid Win Rate 17.2% 23.8% 38.4% The Hybrid Truth: What nobody tells you? Here's what 15 years of architectural battle scars have taught me: the most successful systems combine elements of both approaches. "It's not about being stateful OR stateless - it's about being stateful WHERE IT MATTERS." Let's look at a common pattern I've implemented multiple times: In this pattern, the majority of the system is stateless, but we strategically introduce stateful components where they deliver the most value - typically in areas requiring: Ultra-low latency access to data Complex aggregations across many data points Specialized processing that benefits from locality The Testing Paradox: Where Both Approaches Fail ➡️ Stateless Testing Pain Points Dependency Explosion : Each service requires mocked dependencies Choreography Complexity : Testing event sequences across services Environment Consistency : Ensuring identical test conditions across CI/CD pipelines Data Setup Overhead : Seeding external databases/caches before each test Example: E-Commerce Order Processing Order Service → Inventory Service → Payment Service → Shipping Service → Notification Service Problem: A simple order flow requires 5 separate services to be coordinated, with 4 integration points that must be mocked or deployed in test environments. ➡️ Stateful Testing Pain Points State Initialization : Setting up precise application state for each test case Non-determinism : Race conditions and timing issues in state transitions Snapshot Verification : Validating the correctness of internal state Test Isolation : Preventing test state from bleeding across test cases Example: Real-time Analytics Dashboard User Session (with cached aggregations) → In-memory Analytics Store → Time-series Processing Engine Problem: Tests require precise seeding of in-memory state with complex data structures that must be identically replicated across test runs. Let me walk you through a real-world scenario I encountered last year with a fintech client. They built a payment processing pipeline handling over $2B in annual transactions: Their testing challenges were immense: Setup Complexity : 20+ minutes to set up test databases, message queues, and external service mocks Flaky Tests : ~30% of CI pipeline failures were due to test environment inconsistencies Long Feedback Cycles : Developers waited 35 minutes (average) for test results Environment Drift : Production bugs that "couldn't happen in test" When a critical bug appeared in the payment authorization flow, it took them 3 days to reliably reproduce it in their test environment. Decision Framework: Questions I Ask My Teams When making architectural decisions with my teams, I guide them through these key questions: What is the business impact of latency in this component? Each additional 100ms of latency reduces conversions by ~7% in consumer applications For internal tools, user productivity usually drops when responses exceed 1 second What is our scaling pattern? Predictable, steady growth favors optimized stateful designs Spiky, unpredictable traffic favors elastic stateless designs What is our team's operational maturity? Stateful systems generally require more sophisticated operational practices What happens if we lose state? Can we reconstruct it? How long would that take? What's the business impact during recovery? How will we test this effectively? What testing challenges are we prepared to address? How much development velocity are we willing to sacrifice for testing? Introducing HyperTest : The Game Changer HyperTest works like a "flight recorder" for your application, fundamentally changing how we approach testing complex distributed systems: How HyperTest Transforms Testing For the payment processing example above: Capturing the Complex Flow Records API requests with complete payloads Logs database queries and their results Captures external service calls to payment gateways Records ORM operations and transaction data Tracks async message publishing Effortless Replay Testing Select specific traces from production or staging Replay exact requests with identical timing Automatically mock all external dependencies Run with real data but without external connections Real-World Impact Setup time : Reduced from 20+ minutes to seconds Test reliability : Flaky tests reduced by 87% Feedback cycle : Developer testing cut from 35 minutes to 2 minutes Bug reproduction : Critical issues reproduced in minutes, not days Get a demo now and experience how seamless it becomes to test your stateful apps Key Takeaways for Engineering Leaders Reject religious debates about architecture patterns - focus on business outcomes Map your state requirements to business value - be stateful where it creates differentiation Start simple but plan for evolution - most successful architectures grow more sophisticated over time Measure what matters - collect baseline performance metrics before making big architectural shifts Build competency in both paradigms - your team needs a diverse toolkit, not a single hammer Invest in testing innovation - consider approaches like HyperTest that transcend the stateful/stateless testing divide Your Experience? I've shared my journey with stateful and stateless architectures over 15+ years, but I'd love to hear about your experiences. What patterns have you found most successful? How are you addressing the testing challenges inherent in your architecture? Dave Winters is a Chief Architect with 15+ years of experience building distributed systems at scale. He has led engineering teams at fintech, adtech, and enterprise SaaS companies, and now advises CIOs and CTOs on strategic architecture decisions. Related to Integration Testing Frequently Asked Questions 1. What is the key difference between stateful and stateless architecture? Stateful architecture retains user session data, while stateless processes each request independently without storing past interactions. 2. When should you choose stateful over stateless architecture? Choose stateful for applications requiring continuous user sessions, like banking or gaming, and stateless for scalable web services and APIs. 3. How does stateless architecture improve scalability? Stateless systems distribute requests across multiple servers without session dependency, enabling easier scaling and load balancing. For your next read Dive deeper with these related posts! 07 Min. Read Choosing the right monitoring tools: Guide for Tech Teams Learn More 09 Min. Read CI/CD tools showdown: Is Jenkins still the best choice? Learn More 08 Min. Read How can engineering teams identify and fix flaky tests? Learn More

REST, GraphQL, or gRPC? Learn how to choose the best API design for your architecture based on performance, flexibility, and scalability needs. 26 February 2025 07 Min. Read REST, GraphQL, or gRPC? Choosing the right API Design WhatsApp LinkedIn X (Twitter) Copy link Test your APIs with HyperTest In the complex landscape of software architecture, choosing the right API (Application Programming Interface) design pattern stands as a critical decision that profoundly affects application performance, scalability, and maintainability. This comprehensive analysis examines three leading API design patterns—REST (Representational State Transfer), GraphQL, and gRPC (Google Remote Procedure Call)—each offering unique advantages for different implementation scenarios. List of Sections : ✅ Introduction ✅ REST: The versatile workhorse ✅ GraphQL: The query powerhouse ✅ gRPC: The high-performance contender ✅ Comparative Analysis ✅ Conclusion Introduction APIs function as crucial intermediaries in software system communications, facilitating feature access and data exchange. The selection between REST, GraphQL, or gRPC as an API design pattern must align with specific application requirements, considering factors such as network performance, data structure complexity, and client heterogeneity. Learn more about API Protocols here 1️⃣ REST: The Versatile Workhorse REST is an architectural style that uses HTTP as its backbone, making it an ideal choice for building APIs that can be easily understood and used by a broad range of clients. It operates on a set of guiding principles, such as statelessness and cacheability, which help in building scalable and performant web services. Pros Cons Uses familiar HTTP methods like GET and POST. Can lead to inefficiencies by returning unnecessary data. Stateless interactions promote system scalability. Statelessness: Each request from client to server must contain all the information the server needs to understand the request, independent of any prior requests. Cacheable: Responses must define themselves as cacheable or not, which helps in reducing the load on the server and improving client performance. Ideal Use Cases: Public APIs: Due to its simplicity and widespread adoption, REST is perfect for public APIs accessed by a diverse set of clients. CRUD Operations: Since REST aligns well with CRUD (Create, Read, Update, Delete) operations, it's well-suited for applications that primarily interact with database entities. Mobile and Web Applications: REST can be efficiently used for applications that do not require real-time data updates but need robust and stable communication over HTTP. Twitter API: Provides broad access to Twitter data, including timelines, tweets, and profiles, using a RESTful architecture. 2️⃣ GraphQL: The Query Powerhouse Developed by Facebook, GraphQL is a query language for APIs and a runtime for executing those queries with your existing data. It dramatically improves the efficiency of web APIs and offers clients the power to ask for exactly what they need and nothing more. Pros Cons Clients request exactly what they need, reducing bandwidth usage. Requires more effort on the server side to resolve queries. Powerful Query Abilities: Clients can request exactly the data they need, nothing less, nothing more, which eliminates over-fetching and under-fetching problems. Real-time Data: GraphQL supports real-time updates through subscriptions, making it ideal for dynamic user interfaces. Ideal Use Cases: Complex Systems: Where multiple entities are interrelated, and the client needs varied subsets of data depending on the context. Real-time Applications: Such as collaborative editing tools where the state must be synced between multiple clients in real-time. Microservices: Where a gateway can aggregate data from multiple services and provide a unified GraphQL endpoint. Shopify: Uses GraphQL to offer a flexible, efficient API for its vast ecosystem of e-commerce sites and apps. 3️⃣ gRPC: The High-Performance Contender gRPC is a modern open-source high performance RPC (Remote Procedure Call) framework that can run in any environment. It uses protocol buffers by default as its interface language and is known for its high performance and suitability in polyglot environments. Pros Cons Leveraging HTTP/2 allows for reduced latency. Not inherently compatible with all web environments. Auto-generates client libraries in various programming languages. HTTP/2 Support: gRPC makes use of HTTP/2 as a transport protocol, which allows for long-lived connections, multiplexing multiple requests over a single connection, and server push capabilities. Protobuf (Protocol Buffers): Highly efficient binary serialization tool that defines a flexible, efficient, automated mechanism for serializing structured data. Learn more about gRPC Vs REST here Ideal Use Cases: Microservices: gRPC is excellent for inter-service communication in a microservices architecture due to its low latency and high throughput. Real-Time Communication: Applications that require real-time data exchange, such as live streaming systems and gaming backends, benefit from gRPC's performance characteristics. Multi-Language Environments: gRPC supports code generation in multiple programming languages, making it a good choice for polyglot environments where different services are written in different languages. Many of Google's own APIs are implemented using gRPC, taking advantage of its performance benefits and broad language support. Testing Challenges with REST, GraphQL, and gRPC Testing different API design paradigms—REST, GraphQL, and gRPC—presents unique challenges due to their varied architectural approaches and communication protocols. Understanding these challenges can help developers create more robust testing strategies. ➡️ Testing Challenges with REST REST's stateless nature means each API request must include all necessary information to be processed independently, complicating tests involving user sessions or states. For example, testing a REST API for a shopping cart requires simulating each step of adding or removing items since the server doesn't remember previous interactions. Additionally, ensuring accurate responses across various scenarios presents challenges, particularly when APIs deliver dynamic or uneven data. For instance, an API fetching user profiles needs extensive testing to handle different types of requests correctly—such as ensuring proper responses for invalid user IDs or unauthorized access attempts. This requires a broad set of test cases to verify that the API behaves as expected under all conditions. ➡️ Testing Challenges with GraphQL Query Complexity: The flexible nature of GraphQL allows for complex queries, which can be challenging to test thoroughly. Each possible query permutation may need individual testing. Nested Data Structures: Deeply nested data requests require complex setup and teardown in test environments, increasing the risk of overlooking edge cases. A test needs to mock users → posts → comments → authors , which increases complexity. const mockResponse = { data: { user: { name: "Alice", posts: [ { title: "GraphQL Testing", comments: [ { content: "Great post!", author: { name: "Bob" } }, { content: "Very informative.", author: { name: "Charlie" } } ] } ] } } }; ➡️ Testing Challenges with gRPC Protocol Buffers: Testing requires familiarity with Protocol Buffers, as you must ensure both serialization and deserialization work as expected across different programming languages. Streaming Capabilities: gRPC's support for bidirectional streaming complicates testing, particularly in ensuring data integrity and order across streams. ✅ How HyperTest can help you test REST, gRPC and GraphQL APIs? HyperTest leverages automated test case generation based on real user interactions. This comprehensive approach ensures no test case is missed, offering several key advantages: Automated Test Case Generation from real interactions : HyperTest captures real user interactions with the application. This includes every query, mutation, or API call made in GraphQL, gRPC, or REST. By recording these real-time data flows and user paths, HyperTest automatically generates test cases that reflect actual usage scenarios. This method ensures all possible user interactions are tested, including complex and rare cases that might not be considered in manual test case design. Testing REST APIs in HyperTest Stateful Recording and Mocking : During testing, HyperTest not only captures the requests but also records the state of all components involved in the interaction. This includes databases, external services, and internal states within the application. By recreating the exact state during test playback, HyperTest provides a precise environment for validating how state changes impact the application, identifying potential regressions or errors caused by code changes. Testing gRPC APIs in HyperTest Comprehensive Coverage Across Protocols: Whether it’s a REST API delivering straightforward data, a GraphQL API handling complex, nested queries, or a gRPC service managing streaming data, HyperTest treats all protocols uniformly in terms of testing methodology. It captures, mocks, and replays interactions across all these APIs. This uniform approach simplifies the testing process, ensuring consistency and thorough coverage regardless of the underlying protocol or architecture. Root Cause Analysis : When an error is detected, HyperTest can analyze the recorded test cases and the associated state changes to pinpoint what specific line change or interaction caused the issue. This capability significantly reduces the debugging time for developers by providing clear insights into the source of errors, streamlining the development and maintenance processes. Testing GraphQL APIs in HyperTest This approach not only ensures that every potential user journey is covered but also supports rapid development cycles by automating complex testing workflows and identifying issues at their source. Conclusion Choosing between REST, GraphQL, and gRPC for your API architecture depends on specific project needs, but it's clear that each protocol offers unique advantages. HyperTest enhances the development and maintenance of these APIs as it provides: Comprehensive Coverage Rapid Debugging Stateful Testing Accuracy See how HyperTest helped Zoop.one reduce API test complexity by 60% and cut debugging time by 40% in their GraphQL architecture. Watch the full case study here! With these capabilities, HyperTest supports robust API development, helping teams build efficient, scalable, and reliable applications. This aligns with the goal of choosing the right API design—REST, GraphQL, or gRPC—to best meet the architectural demands of your projects. Get a Demo Related to Integration Testing Frequently Asked Questions 1. What are the key differences between REST, GraphQL, and gRPC? REST uses resource-based endpoints, GraphQL allows flexible queries, and gRPC offers high-performance communication via Protocol Buffers. 2. Which API design is best for microservices architecture? gRPC is often preferred for microservices due to its efficiency, while REST and GraphQL are suitable for broader compatibility and flexibility. 3. When should I choose GraphQL over REST or gRPC? Choose GraphQL when you need precise data fetching, reduced over-fetching, and a flexible schema for frontend applications. For your next read Dive deeper with these related posts! 09 Min. Read gRPC vs. REST: Which is Faster, Efficient, and Better? Learn More 09 Min. Read RabbitMQ vs. Kafka: When to use what and why? Learn More 07 Min. Read Optimize DORA Metrics with HyperTest for better delivery Learn More

Monolithic architecture works best for simple applications because as a single small deployable unit, they are easy to build and maintain. But things do not stay simple all the time. 8 March 2023 06 Min. Read 8 Problems You'll Face with Monolithic Architecture Download the 101 Guide WhatsApp LinkedIn X (Twitter) Copy link Fast Facts Get a quick overview of this blog Understand the differences between monolithic and microservices architectures. Recognize scalability, modularity, deployment, and complexity issues. Explore scalability, agility, easier testing, and alignment with cloud tech. Recognize the importance of microservices for competitiveness. Download the 101 Guide Monolithic architecture is one of the most prevalent software designs. In software engineering, the term " monolithic model ” refers to a single, indivisible unit. The idea behind monolithic software is that all of the parts of an application are put together into a single program like the database, the client/user interface, and the server, all in a single code base. The most important benefit of the monolithic architecture is how simple it is. If you’re just starting out small with your software development, monolithic is easier to test, deploy, debug, and keep an eye on. All of the data is kept in one database, so it doesn't need to be synchronized. Monolithic Design Can’t Keep Up With the Development Needs of Agile teams Monolithic architecture works best for simple applications because as a single small deployable unit, they are easy to build and maintain. But things do not stay simple all the time. As the size and complexity of the app grow, problems start to appear. It becomes harder to make changes to the code without feeling concerned about its cascading effects. Changes to one module can cause other modules to act in unexpected ways, which can cause a chain of errors. Because of how big the monolith is, it takes longer to start up, which slows down development and gets in the way of continuous deployment. Agile teams want to ship new changes quickly and iteratively in short cycles (called sprints) which is difficult to achieve with complex applications built as monoliths. Collaboration is hard because of the complexity of a large code base compressed as a single unit. Quick Question Microservice integration bugs got you down? We can help! Yes Problems with Monolithic Architecture Statista' s 2021 research shows that “ only 1.5% of engineering leaders plan to stick with a monolithic architecture ” . With the demanding need for software expansion, the rise of mobile devices, and the cloud, monolithic applications are not going to help. Worse, since everything in a monolithic architecture is tied to a single codebase, it can be hard to test specific functions or components because it is difficult to separate them, leading to vastly higher costs. 1. Monolithic Architecture Scalability Issues If a monolithic application becomes popular and its user base grows, it can become difficult to scale the application to meet the increased demand. All of the application's features are in a single codebase, so adding more resources requires deploying a whole new version of the application. 2. Lack of modularity Because a monolithic application is a single, cohesive unit, it can be difficult to reuse specific parts of the application in other projects. This can make it hard to update or fix individual parts of an app without changing the whole thing. 3. Slow deployment When a new version of a monolithic application needs to be released, it can take a long time because the whole application needs to be deployed again, even if only a small part of it has changed. 4. Difficulty in identifying and fixing issues When something goes wrong with a monolithic application, it can be hard to figure out why because all of the functionality is in a single codebase. This can make it challenging to fix issues and deploy fixes quickly. 5. Tight coupling Monolithic applications often have tight coupling between different parts of the codebase, which means that changes in one part of the application can have unintended consequences in other parts of the application. 6. Monolithic Architecture Inflexibility Monolithic architecture can be hard to change because it doesn't make it clear which parts of an application are responsible for what. This can make it hard to change or update one part of the application without possibly affecting other parts. 7. Complexity Monolithic applications can become complex over time as they grow and more features are added. This can make it difficult for new developers to understand how the application works and contribute to it. 8. Testing and deployment challenges Testing and releasing a monolithic application can be hard because it can be difficult to test each part of the application separately. This can make it difficult to identify and fix issues before deploying the application. The Emergence of Microservices As software systems got more complicated and had more needs, it became clear that a single-piece architecture couldn't handle everything. As a result, new approaches, such as microservice architecture , have been developed and implemented. However, monolithic architecture is still commonly used, especially for smaller and less complex systems. Again, taking insights from Statista's 2021 research, 81.34% of businesses already use microservices, and 17.34% are planning to make the switch . Microservices, unlike monolithic systems, are designed to scale with changing market demands. Modern businesses are moving away from monolithic systems to microservices so that they can stay competitive . Community Favourite Reads Unit tests passing, but deployments crashing? There's more to the story. Learn More Masterclass on Contract Testing: The Key to Robust Applications Watch Now Related to Integration Testing Frequently Asked Questions 1. What is a monolithic architecture? Monolithic architecture is a traditional software design approach where an entire application is built as a single, interconnected system. In this structure, all components and functions are tightly integrated, making it challenging to modify or scale specific parts independently. It contrasts with microservices architecture, which divides the application into smaller, loosely coupled, and independently deployable components. 2. What are microservices? Microservices are a software development approach where an application is divided into small, independent components that perform specific tasks and communicate with each other through APIs. This architecture improves agility, allowing for faster development and scaling. It simplifies testing and maintenance by isolating components. If one component fails, it doesn't impact the entire system. Microservices also align with cloud technologies, reducing costs and resource consumption. 3. What are the Disadvantages of a monolithic architecture? Monolithic architecture has several disadvantages. It faces scalability challenges, as scaling the entire application can be inefficient and costly. Modifications and updates are complex and risky, given their broad impact. Monolithic apps demand substantial resources and can hinder development speed due to collaboration difficulties. Furthermore, they are susceptible to single points of failure, where issues in one part can disrupt the entire application's functionality, making them less resilient. For your next read Dive deeper with these related posts! 10 Min. Read What is Microservices Testing? Learn More 05 Min. Read Testing Microservices: Faster Releases, Fewer Bugs Learn More 07 Min. Read Scaling Microservices: A Comprehensive Guide Learn More

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Regression Testing is the reevaluation of software functionality after updates to ensure new code aligns with and doesn’t break existing features. 20 February 2024 11 Min. Read What is Regression Testing? Tools, Examples and Techniques WhatsApp LinkedIn X (Twitter) Copy link Download the Checklist What Are the Different Types of Regression Testing? Different types of regression testing exist which cater to varying needs of the software development lifecycle. The choice of regression testing type depends on the scope and impact of changes, allowing testing and development teams to strike a balance between thorough validation and resource efficiency. The following are the types of regression testing. 1.Unit Regression Testing: Isolated and focused testing on individual units of the software. Validates that changes made to a specific unit do not introduce regressions in its functionality. The efficiency of this lies in catching issues within a confined scope without testing the entire system. 2. Partial Regression Testing: This involves testing a part of the entire application and focusing on modules and functionalities that are affected by recent changes. The benefit of partial regression testing is that it saves time and resources especially when the modifications are localised. Balances thorough testing with efficiency by targeting relevant areas impacted by recent updates. 3. Complete Regression Testing: This involves regression testing of the entire application thereby validating all modules and functionalities. It is essential when there are widespread changes that impact the software. It ensures overall coverage even though it is time-consuming when compared to partial regression testing. Regression Testing Techniques Now that we know what the different types of regression testing are, let us focus on the techniques used for the same. Regression testing techniques offer flexibility and adaptability that allow development and testing teams to tailor their approach towards testing based on the nature of changes, project size and resource constraints. Specific techniques are selected depending on the project’s requirements which, in turn, ensures a balance between validation and efficient use of testing resources. The following are the techniques teams use for regression testing: 1.Regression Test Selection: It involves choosing a part of the test cases based on the impacted areas of recent changes. Its main focus is on optimising testing efforts by selecting relevant tests for correct validation. 2. Test Case Prioritization: This means that test cases are ranked based on criticality and likelihood of detecting defects. This maximises efficiency as it tests high-priority cases first thereby allowing the early detection of regressions. 3. Re-test All: This requires that the entire suite of test cases be run after each code modification. This can be time-consuming for large projects but is ultimately an accurate means to ensure comprehensive validation. 4. Hybrid: It combines various regression testing techniques like selective testing and prioritisation to optimise testing efforts. It adapts to the specific needs of the project and thus, strikes a balance between thoroughness and efficiency. 5. Corrective Regression Testing: The focus is on validating the measures applied to resolve the defects that have been identified. This verifies that the added remedies do not create new issues or impact existing functionalities negatively. 6. Progressive Regression Testing: This incorporates progressive testing as changes are made during the development process. This allows for continuous validation and thus minimising the likelihood of accumulating regressions. 7. Selective Regression Testing: Specific test cases are chosen based on the areas affected by recent changes. Testing efforts are streamlined by targeting relevant functionalities in projects with limited resources. 8. Partial Regression Testing: It involves testing only a subset of the entire application. This proves it to be efficient in validating localized changes without the need for the entire system to be retested. 5 Top Regression Testing Tools in 2024 Regression testing is one of the most critical phases in software development, ensuring that modifications to code do not inadvertently introduce defects. Using advanced tools can not only significantly enhance the efficiency of regression testing processes but also the accuracy of the same. We have covered both the free and the paid Regression Testing tools. The top 5 best performing Regression Testing Tools to consider for 2024 are: HyperTest Katalon Postman Selenium testRigor 1. HyperTest - Regression Testing Tool: HyperTest is a regression testing tool that is designed for modern web applications. It offers automated testing capabilities, enabling developers and testers to efficiently validate software changes and identify potential regressions. HyperTest auto-generates integration tests from production traffic, so you don't have to write single test cases to test your service integration. For more on how HyperTest can efficiently take care of your regression testing needs, visit their website here . 👉 Try HyperTest Now 2. Katalon - Regression Testing Tool: Katalon is an automation tool that supports both web and mobile applications. Its simplified interface makes regression testing very easy thereby enabling accessibility for both beginners and experienced testers. Know About - Katalon Alternatives and Competitors 3. Postman - Regression Testing Tool: While renowned for Application Programming Interface (API) testing , Postman also facilitates regression testing through its automation capabilities. It allows testers and developers to create and run automated tests , ensuring the stability of APIs and related functionalities. Know About - Postman Vs HyperTest - Which is More Powerful? 4. Selenium - Regression Testing Tool: Selenium is a widely used open-source tool for web application testing. Its support for various programming languages and browsers makes it a go-to choice for regression testing, providing a scalable solution for diverse projects. 5. testRigor - Regression Testing Tool: testRigor employs artificial intelligence to automate regression testing . It excels in adapting to changes in the application, providing an intelligent and efficient approach to regression testing. Regression Testing With HyperTest Imagine a scenario where a crucial financial calculation API, widely used across various services in a fintech application, receives an update. This update inadvertently changes the data type expectation for a key input parameter from an integer (int) to a floating-point number (float). Such a change, seemingly minor at the implementation level, has far-reaching implications for dependent services that are not designed to handle this new data type expectation. The Breakdown The API in question is essential for calculating user rewards based on their transaction amounts. ➡️Previously, the API expected transaction amounts to be sent as integers (e.g., 100 for $1.00, considering a simplified scenario where the smallest currency unit is integrated into the amount, avoiding the need for floating-point arithmetic). ➡️However, after the update, it starts expecting these amounts in a floating-point format to accommodate more precise calculations (e.g., 1.00 for $1.00). ➡️Dependent services, unaware of this change, continue to send transaction amounts as integers. The API, now expecting floats, misinterprets these integers, leading to incorrect reward calculations. ➡️ Some services might even fail to call the API successfully due to strict type checking, causing transaction processes to fail, which in turn leads to user frustration and trust issues. ➡️As these errors propagate, the application experiences increased failure rates, ultimately crashing due to the overwhelming number of incorrect data handling exceptions. This not only disrupts the service but also tarnishes the application's reputation due to the apparent unreliability and financial inaccuracies. The Role of HyperTest in Preventing Regression Bugs HyperTest , with its advanced regression testing capabilities, is designed to catch such regressions before they manifest as bugs or errors in the production environment, thus preventing potential downtime or crashes. Here's how HyperTest could prevent the scenario from unfolding: Automated Regression Testing : HyperTest would automatically run a comprehensive suite of regression tests as soon as the API update is deployed in a testing or staging environment. These tests include verifying the data types of inputs and outputs to ensure they match expected specifications. Data Type Validation : Specifically, HyperTest would have test cases that validate the type of data the API accepts. When the update changes the expected data type from int to float, HyperTest would flag this as a potential regression issue because the dependent services' test cases would fail, indicating they are sending integers instead of floats. Immediate Feedback : Developers receive immediate feedback on the regression issue, highlighting the discrepancy between expected and actual data types. This enables a quick rollback or modification of the dependent services to accommodate the new data type requirement before any changes are deployed to production. Continuous Integration and Deployment (CI/CD) Integration : Integrated into the CI/CD pipeline , HyperTest ensures that this validation happens automatically with every build. This integration means that no update goes into production without passing all regression tests, including those for data type compatibility. Comprehensive Coverage : HyperTest provides comprehensive test coverage, ensuring that all aspects of the API and dependent services are tested, including data types, response codes, and business logic. This thorough approach catches issues that might not be immediately obvious, such as the downstream effects of a minor data type change. By leveraging HyperTest's capabilities, the fintech application avoids the cascading failures that could lead to a crash and reputational damage. Instead of reacting to issues post-deployment, the development team proactively addresses potential problems, ensuring that updates enhance the application without introducing new risks. HyperTest thus plays a crucial role in maintaining software quality, reliability, and user trust, proving that effective regression testing is indispensable in modern software development workflows. 💡 Schedule a demo here  to learn about this approach better Conclusion We now know how important regression testing is to software development and the stability required for applications during modifications. The various tools employed ensure that software is constantly being tested to detect unintended side effects thus safeguarding against existing functionalities being compromised. The examples of regression testing scenarios highlight why regression testing is so important and at the same time, versatile! Embracing these practices and tools contributes to the overall success of the development lifecycle, ensuring the delivery of high-quality and resilient software products. If teams can follow best practices the correct way, there is no stopping what regression testing can achieve for the industry. Please do visit HyperTest to learn more about the same. Frequently Asked Questions 1. What is regression testing with examples? Regression testing ensures new changes don't break existing functionality. Example: Testing after software updates. 2. Which tool is used for regression? Tools: HyperTest, Katalon, Postman, Selenium, testRigor 3. Why is it called regression testing? It's called "regression testing" to ensure no "regression" or setbacks occur in previously working features. For your next read Dive deeper with these related posts! 07 Min. Read FinTech Regression Testing Essentials Learn More 08 Min. Read What is API Test Automation?: Tools and Best Practices Learn More 07 Min. 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Learn how to streamline end-to-end testing by eliminating the need for test data preparation in our insightful webinar. E2E Testing 40 min. How to do End-to-End testing without preparing test data? Learn how to streamline end-to-end testing by eliminating the need for test data preparation in our insightful webinar. Get Access Speakers Shailendra Singh Founder HyperTest Karan Raina Co-Founder, CTO HyperTest Prevent Logical bugs in your database calls, queues and external APIs or services Book a Demo

Integration testing involves logically integrating software modules and testing them as a unified group to reduce bugs, errors, or issues in their interaction. 27 November 2023 13 Min. Read What Is Integration Testing? Types, Tools & Examples Download the Checklist WhatsApp LinkedIn X (Twitter) Copy link Fast Facts Get a quick overview of this blog Delve into integration testing's role in validating communication between software modules. Understand the strategic use of Big Bang and Incremental approaches for optimal results. Recognize how integration testing enhances collaboration among diverse system components. Explore HyperTest’s unique integration testing approach, eliminating manual mocking for streamlined testing. Checklist to Implement Integration Testing Imagine a jigsaw puzzle. Each puzzle piece represents a module of the software. Integration testing is like putting these pieces together to see if they fit correctly and form the intended picture. Just like how a misaligned puzzle piece can disrupt the overall image, a single module not properly integrated can cause problems in the software. What is Integration Testing? The testing pyramid comprises three tiers: the base , representing unit testing; the middle layer , which involves integration testing; and the top layer , dedicated to end-to-end testing. HyperTest is evolving the way integration tests are created and performed, with uniquely recording all the traffic that's coming your application's way and using that to create test cases for your APIs, avoiding the burden of keeping all the services up and running with its auto-mock capability. In the integration layer, interface testing occurs, examining the interactions between various components or services within an application. After individual system units or functions undergo independent testing, integration testing aims to assess their collective performance as a unified system and pinpoint any defects that may arise. Integration testing concentrates on testing and validating the interactions and data interchange between two different services/components. Its objective is to detect issues or defects that may surface when various components are integrated and interact with one another. By pinpointing and addressing integration issues early in the development process, integration testing reduces the likelihood of encountering more serious and expensive problems in later stages. What is the need of Integration Testing? Integration testing is an essential phase in the software development process, designed to ensure that individual software modules work together as a unit. 1. Early Detection of Interface Issues : Integration testing focuses on the points where modules interact. It helps identify problems in the way these modules communicate and share data. For example , if two modules that perform different functions need to exchange data, integration testing can reveal if there are mismatches in data formats or protocols , which might not be apparent in unit testing. Integration testing can reduce interface errors by up to 50% compared to projects that skip this phase. 2. Facilitates Systematic Verification : This testing approach allows for a systematic examination of the system’s functionality and performance. It ensures that the complete system meets the specified requirements. 3. Reduces Risk of Regression : When new modules are integrated with existing ones, there's a risk that changes could break previously working functionality. Integration testing helps catch such regression errors early. For instance , an update in an e-commerce application’s payment module should not disrupt the product selection process. Regular integration testing can decrease regression errors by approximately 30%. 4. Improves Code Reliability and Quality : By testing the interactions between modules, developers can identify and fix bugs that might not be evident during unit testing. This leads to higher code quality and reliability. Integration testing can improve overall code quality by up to 35%. 5. Saves Time and Cost in the Long Run : Although integration testing requires time and resources upfront, it ultimately saves time and cost by catching and fixing issues early in the development cycle. It's generally more expensive to fix bugs in later stages of development or post-deployment. Don't keep all your services up and running--That's what companies like Nykaa, Skaud, Yellow.ai, Fyers etc are doing to keep up with the fast-moving competitive world today, steal their approach here. What are the benefits of Integration testing? We've already seen the benefits of integration testing in the above section, but just to summarize it for you all: ✔️detects all the errors early in the development process, ✔️software modules/services work together correctly, ✔️no or low risk of facing integration issues later. Here's a video that can help you with knowing all the integration testing benefits. 👇 Quick Question Are you Planning to Automate your Integration Testing? Yes Types of Integration testing Revealing defects takes center stage in integration testing, emphasizing the interaction time between integrated units. As for integration test methods, there exist four types, which are as follows: 1.Big Bang Integration Testing: In this approach, all or most of the developed modules are integrated simultaneously, and then tested as a whole. This method is straightforward but can be challenging if there are many modules, as identifying the exact source of a defect can be difficult. ➡️Example: Imagine a simple application comprising three modules: User Interface (UI), Database (DB), and Processing Logic (PL). In Big Bang Integration Testing, all these modules are integrated at once and tested together. 2. Incremental Integration Testing: This method involves integrating modules one by one and testing each integration step. It helps in isolating defects related to interfacing. Incremental Integration Testing can be further divided into: Top-Down Integration Testing : Starts from the top-level modules and progresses downwards, integrating and testing one module at a time. Stubs (dummy modules) are often used to simulate lower-level modules not yet integrated. Example : In a layered application, the top layer (e.g., User Interface) is tested first with stubs replacing the lower layers. Gradually, real modules replace the stubs. Bottom-Up Integration Testing : Begins with the integration of the lowest-level modules and moves upwards. Here, drivers (temporary modules) are used to simulate higher-level modules not yet integrated. Example : In the same layered application, integration might start with the database layer, using drivers to simulate the upper layers. 3. Sandwich (Hybrid) Integration Testing: Combines both top-down and bottom-up approaches. It is useful in large projects where different teams work on various segments of the application. Example: While one team works on the top layers using a top-down approach, another could work on the lower layers using a bottom-up approach. Eventually, the two are merged. 4. Functional Incremental Integration Testing: In this method, the integration is based on the functionality or functionality groups, rather than the structure of the software. Example: If a software has functionalities A, B, and C, functional incremental integration might first integrate and test A with B, then add C. Key steps in Integration testing Here's a concise step-by-step approach to perform integration testing: If you want to skip the traditional work-around with Integration testing, then simply implement HyperTest's SDK and get started with Integration Testing easily. ✅No need to manage dedicated environment ✅No test data preparation required ✅No services required to be kept up and running, auto-mocks to save you Get started with HyperTest now or you don't want your teams to work faster, smarter and save 10x more the time, then here's the steps involved in performing integration testing the old way. Define Integration Test Plan : Outline the modules to be tested, goals, and integration sequence. Prepare Testing Environment : Set up the necessary hardware and software for testing. Develop Test Cases : Create test scenarios focusing on module interactions, covering functional, performance, and error-handling aspects. Execute Test Cases : Run the tests either manually or using automated tools. Record and Analyze Results : Document outcomes, identify bugs or discrepancies. Regression Testing : After fixing bugs, retest to ensure no new issues have arisen. Performance Testing : Verify the system meets performance criteria like load and stress handling. Review and Documentation : Review the process and document findings and best practices. Get a demo Challenges in Integration testing Although Integration testing is a critical phase in the software development lifecycle, but it also comes with its fair share of challenges or hurdles: 1. Complex Interdependencies Software modules often have complex interdependencies, making it challenging to predict how changes in one module will affect others. This complexity can lead to unexpected behaviors during testing, making it difficult to isolate and fix issues. 2. Environment Differences Integration tests may pass in a development environment but fail in a production-like environment due to differences in configurations, databases, or network settings. These inconsistencies can lead to a false sense of security regarding the system's stability and functionality. 3. Test Data Management Managing test data for integration testing can be challenging, especially when dealing with large datasets or needing to simulate specific conditions. Inadequate test data can lead to incomplete testing, overlooking potential issues that might occur in real-world scenarios. 4. Interface Compatibility Ensuring compatibility between different modules, especially when they are developed by separate teams or include third-party services. Incompatibility issues can lead to system failures or reduced functionality. 5. Time and Resource Constraints Integration testing can be time-consuming and resource-intensive, particularly for large and complex systems. This can lead to a trade-off between thorough testing and meeting project deadlines, potentially impacting software quality. 6. Automating Integration Tests Automating integration tests is challenging due to the complexity of interactions between different software components. Limited automation can result in increased manual effort, longer testing cycles, and the potential for human error. 7. Regression Issues New code integrations can unintentionally affect existing functionalities, leading to regression issues. Identifying and fixing these issues can be time-consuming, impacting the overall project timeline. How unit testing, integration testing and end-to-end testing are different from each other? Unit Testing , Integration Testing, and End-to-End Testing are three distinct levels of software testing , each serving a specific purpose in the software development lifecycle. Unit Testing focuses on individual components in isolation. Integration Testing concentrates on the interaction and integration between different components. End-to-End Testing validates the complete flow of an application, from start to finish, mimicking real-world user scenarios. Aspect Unit Testing Integration Testing End-to-End Testing Definition Testing individual units or components of the software in isolation. Testing how multiple units or components work together. Testing the entire application in a setup that simulates real-world use. Scope Very narrow; focuses on a single function, method, or class. Broader than unit testing; focuses on the interaction between units or modules. Broadest; covers the entire application and its interaction with external systems and interfaces. Purpose To ensure that each unit of the software performs as designed. To test the interface between units and detect interface errors. To verify the complete system and workflow of the application. Level of Testing Lowest level of testing. Middle level comes after unit testing. Highest level, often the final phase before the product release. Testing Conducted By Usually by developers. Both by developers and test engineers. Testers, sometimes with the involvement of end-users. Tools Used JUnit, NUnit, Mockito, etc. JUnit, Postman, HyperTest etc. Selenium, Cypress, Protractor, etc. Execution Speed Fastest among the three types. Slower than unit testing but faster than end-to-end testing. Slowest due to its comprehensive nature. Dependency Handling Often uses mocks and stubs to isolate the unit being tested. Tests real modules but may use stubs for external services. Uses real data and integrates with external interfaces and services. Automated Integration testing with HyperTest HyperTest , specializes in integration testing to maintain a consistently bug-free system. With automated tools boasting lower error rates, HyperTest can cut production bugs by up to 90%, offering a fail-proof solution. It caters to developers, streamlining test case planning without the need for extra tools and even your testers. It monitors the network traffic 24/7 and auto-generates tests to keep your application sane and working. Read how HyperTest has helped a growing FinTech with half a million users to achieve zero schema failures Community Favourite Reads Confidently implement effective mocks for accurate tests. Learn More How to do End-to-End testing without preparing test data? Watch Now Related to Integration Testing Frequently Asked Questions 1. What is integration testing in short? Integration testing ensures that different parts of a software application work seamlessly when combined. It focuses on detecting and resolving issues that arise from the interactions between modules or subsystems. Approaches include top-down, bottom-up, big bang, and incremental testing. Early identification of integration issues reduces the risk of defects, enhancing the overall reliability of the software. Automated testing tools are often employed for efficient and consistent validation. 2. What's the difference between Integration and API testing? Integration testing examines the collaboration of different modules within a system, ensuring they work harmoniously. API testing, on the other hand, specifically evaluates the communication and data exchange between different software systems. While integration testing focuses on the overall functionality, API testing hones in on the seamless interaction of application programming interfaces. Picture integration testing as checking if all instruments play together in an orchestra, while API testing is like examining how musicians exchange sheet music. 3. What are the types of integration testing? Integration testing includes top-down, bottom-up, big bang, and incremental approaches. Each assesses how components collaborate within a system. For example, incremental testing integrates and tests individual components in small increments to identify issues systematically. For your next read Dive deeper with these related posts! 08 Min. Read Best Integration Testing Tools in Software Testing Learn More 07 Min. Read Integration Testing Best Practices in 2024 Learn More 05 Min. Read Boost Dev Velocity with Automated Integration Testing Learn More

Unsure if your software is working correctly? Learn the differences between sanity testing and smoke testing to ensure your application functions as expected. 18 June 2024 09 Min. Read Sanity Testing Vs. Smoke Testing: What Are The Differences? WhatsApp LinkedIn X (Twitter) Copy link Checklist for best practices What is Smoke Testing? Smoke testing is a preliminary set of tests conducted on a new software build to verify its basic functions and stability . 💡 It is more like buying a new electronic appliance. Before plugging it in, you are likely to perform a basic check — is it plugged in properly? Does the power light turn on? Smoke testing is a lightweight software process that is undertaken by both testers and developers with the goal being the identification of any major showstopper bugs that would prevent further testing from proceeding effectively. It is a health check for the software build — if the software fails these basic tests, it is typically considered ‘’ unsmokable ’’ and returned to developers for bug fixes before proceeding with more in-depth testing. Smoke testing, thus, serves as a critical first line of defense, ensuring only stable builds progress to further testing stages. Here's what smoke testing typically involves: Core Functionality Checks: Can users log in successfully? Do basic actions like data entry and navigation work as expected? These basic verification checks ensure the software is in a minimally functional state before dedicating time and resources to further testing. Integration Checks: In applications with multiple components, smoke testing might involve verifying basic communication and data exchange between these components. This ensures a foundational level of integration before moving on to more complex testing scenarios. Regression Checks (Basic): While not a substitute for comprehensive regression testing , smoke testing might include some basic checks to identify any regressions (reintroduced bugs) from recent code changes. This helps catch critical regressions early on, preventing wasted effort on testing a potentially broken build. What is Sanity Testing? Sanity testing focuses on a specific set of checks designed to verify core functionalities and basic user flows, unlike comprehensive testing procedures. It is a quick health check for your software after code changes. Sanity testing is essentially a gatekeeper that ensures the build is stable for further, more rigorous testing. Sanity testing prioritizes speed and efficiency, allowing testers to assess the build’s stability and identify critical problems early on. Here's what sanity testing typically involves: Verifying Key Functionalities: The core functionalities that keep the software running smoothly are the primary focus. This involves testing logins, data entry and basic navigation to ensure these essential functions have not been broken by recent code changes. Quick Smoke Test Integration: Sanity testing incorporates basic smoke test elements, focusing on verifying the most fundamental functionalities of the software to identify any major showstopper bugs. Regression Checks (Limited): While not a replacement for comprehensive regression testing , sanity testing might include limited checks to ensure critical functionalities have not regressed (introduced new bugs) due to recent changes. Sanity Testing vs. Smoke Testing: Core Differences Both smoke testing and sanity testing act as initial quality checks for new software builds. However, they differ in their scope, goals and execution. Here's a breakdown of sanity testing vs. smoke testing: Focus: Smoke Testing: The focus is on verifying the absolute basics — Can users log in? Do core functionalities like data entry and saving work as expected? The goal is to identify any major roadblocks that would prevent further testing altogether. Sanity Testing: Sanity testing delves a bit deeper while still prioritising core functionalities. Its aim is to ensure not only basic functionality but also the stability of key user flows and core features after code changes. It is a more in-depth health check compared to the basic smoke test. Scope: Smoke Testing: Smoke testing has a narrower scope. It typically involves a small set of pre-defined tests designed to catch showstopper bugs. The idea is to quickly identify major issues before investing time and resources in further testing. Sanity Testing: Sanity testing has a slightly broader scope than smoke testing. It involves additional checks beyond the core functionalities, ensuring basic user journeys and interactions function as intended. This provides a more complete picture of the build's stability. Execution: Smoke Testing: Smoke testing is designed for speed and efficiency. It involves testers or developers running a pre-defined set of automated tests to quickly assess the build's basic functionality. Sanity Testing: Sanity testing is more flexible in its execution. While some level of automation might be employed, testers often design test cases based on their knowledge of the recent code changes and the application's core functionalities. Smoke testing acts as the initial hurdle, ensuring the build is minimally functional before further testing commences. Sanity testing builds upon this foundation by providing a more in-depth check of core functionalities and user flows. Development teams can use both techniques for a more efficient and effective testing strategy, ultimately leading to the delivery of high-quality software by understanding the core differences in sanity testing vs. smoke testing. Feature Smoke Testing Sanity Testing Purpose Identify critical issues preventing basic functionality Validate new features/bug fixes and their impact Goal Ensure minimal viability for further testing Determine stability for in-depth testing Focus Core functionalities across the entire application Specific functionalities or features impacted by recent changes Depth Shallow check Dives deeper into targeted areas Scope Broad Narrow Timing Performed first on initial builds Performed after some build stability Documentation Often documented or scripted Usually not documented or scripted Execution Can be automated or manual Typically manual Analogy Smoke check to see if the engine starts Targeted inspection of new parts before a full drive Smoke and Sanity Testing Both smoke and sanity testing play important roles in software development, but their applications to specific things differ. Here's a closer look at examples illustrating the key distinctions between them: 1. Smoke Testing Example: Imagine a new build for a social media application is released. Here's how smoke testing might be implemented: Test Case 1: User Login: The smoke test would verify if users can log in successfully using their existing credentials. A failed login could indicate issues with user authentication or database connectivity, thus preventing further testing. Test Case 2: Creating a New Post: The main function of the application is creating new posts. The smoke test would check if users can successfully create a new post with text and an image. Failure here could signify problems with data entry, content storage or image upload functionality which ultimately requires further investigation before proceeding. Test Case 3: Basic Navigation: Smoke testing would involve verifying if users can navigate through the main sections of the application, such as the home feed, profile page and messaging section. Inability to navigate smoothly could indicate issues with the user interface or underlying routing mechanisms. These smoke tests are designed to be quick and automated whenever possible. If any of these basic functionalities fail, the build would be considered " unsmokable " and returned to developers for bug fixing before further testing commences. 2. Sanity Testing Example: Let's consider the same social media application after a code change that has focused on improving the newsfeed algorithm. Here's how sanity testing might be applied: Test Case 1: Login and Feed Display: Sanity testing would include a basic login check, similar to smoke testing. Then, it would verify if the user's newsfeed displays content after logging in, thus ensuring core functionality is not broken. Test Case 2: Newsfeed Content Relevance: Since the code change focused on the newsfeed algorithm, sanity testing would delve deeper. It usually would involve checking if the content displayed in the newsfeed is somewhat relevant to the user's interests or past interactions (a basic test of the new algorithm). This ensures the main functionality of the newsfeed has not been entirely broken by the code changes. Test Case 3: Basic User Interactions: Sanity testing might involve checking if users can still perform basic actions like liking posts, commenting and sharing content within the newsfeed. This ensures that core user interactions have not been unintentionally impacted by the algorithm update. While not as comprehensive as full regression testing, sanity testing provides a more in-depth check compared to smoke testing. It focuses on core functionalities and user flows likely to be affected by the recent code changes, allowing for early detection of regressions or unintended side effects. Advantages of Smoke Testing and Sanity Testing Advantages of Smoke Testing: Early Bug Detection: Smoke testing is the first line of defense, identifying showstopper bugs early in the software development cycle. This prevents wasted time and resources on further testing an unstable build. This also helps save associated costs. If users cannot even log in, further testing becomes irrelevant. Improved Efficiency: Smoke testing prioritizes a streamlined approach. It typically involves a pre-defined set of automated tests designed to assess basic functionalities quickly. This allows for rapid feedback on a build's stability, enabling developers to address issues promptly and testers to focus their efforts on more in-depth testing procedures for builds that pass the smoke test. Reduced Risk of Regression: Even though it is not a substitute for regression testing, smoke testing often includes basic checks for functionalities to ensure they have not regressed (reintroduced bugs) due to recent code changes. This helps catch regressions early, preventing them from slipping through the cracks and causing problems later in the development process. Advantages of Sanity Testing: Deeper Focus on Core Functionalities: While smoke testing verifies the absolute basics, sanity testing delves a bit deeper. It ensures not only basic functionality but also the stability of key user flows and core features after code changes. This provides a more complete picture of the build's health, identifying issues that might have slipped through smoke testing. Faster Development Cycles: By identifying critical issues early through both smoke and sanity testing, development teams can address them promptly and prevent wasted effort on testing unstable builds. This streamlined approach ultimately contributes to faster development cycles, allowing teams to iterate, fix issues and deliver software features at a more rapid pace. Reduced Release Risks: Software releases riddled with bugs can damage user experience and brand reputation. Smoke and sanity testing work together to minimize the risk of major issues reaching production. These testing techniques provide a vital layer of confidence before deploying software to a wider audience by ensuring core functionalities and basic user flows remain operational after code changes. Disadvantages of Smoke Testing and Sanity Testing Disadvantages of Smoke Testing: Limited Scope: Smoke testing focuses on verifying the absolute essentials. This is its strength for rapid feedback, but also its weakness. Complex functionalities, edge cases and non-core features might be overlooked, thereby leading to regressions or bugs in these areas remaining undetected. False Sense of Security: A successful smoke test does not guarantee a bug-free application. Its limited scope can create a false sense of security, leading to overlooking issues that might surface during later testing stages. Testers and developers tend to have a sense of accomplishment after a successful smoke test, neglecting the need for thorough follow-up testing. Reliance on Pre-defined Tests: Smoke testing often relies on pre-defined sets of automated tests. These tests usually do not adapt well to changes in the user interface or application behavior, missing newly introduced bugs. Maintaining a set of smoke tests can be time-consuming and require ongoing updates as the software evolves. Disadvantages of Sanity Testing: Subjectivity and Bias: Sanity testing often involves testers designing test cases on the fly based on their knowledge of the application and recent code changes. This flexibility can be advantageous, but it also introduces subjectivity and bias. Testers prioritize functionalities they are more familiar with, overlooking less prominent areas or edge cases. Limited Regression Coverage: Sanity testing is not a replacement for regression testing. Its focus on core functions ensures stability after code changes, but it does not guarantee the regression of functionalities that are not specifically tested. Additional regression testing procedures are imperative to ensure the overall quality and stability of the software. Documentation Overhead: Maintaining clear documentation of functionalities tested during sanity checks is necessary even though it is not as extensive as formal test scripts. This ensures consistency and facilitates knowledge sharing among testers, but it adds an overhead compared to entirely unscripted testing approaches. Finding the right balance between documentation and efficiency is key. Conclusion Smoke testing and sanity testing serve distinct yet complementary roles in the software development process. While smoke testing acts as a swift gatekeeper, sanity testing delves deeper into core functionalities. Understanding these differences allows teams to use both techniques for a more efficient and effective testing strategy. Related to Integration Testing Frequently Asked Questions 1. What is the purpose of Sanity Testing? Sanity testing acts as a quick checkpoint in software development. Its purpose is to confirm that new additions or bug fixes haven't disrupted the software's core functionality. By running a small set of tests focused on the impacted areas, sanity testing helps determine if the build is stable enough for more in-depth testing. It's a like a preliminary scan to ensure further testing efforts aren't wasted on a fundamentally broken build. 2. What is the best software testing tool? Smoke testing aims to identify major roadblocks early on, before delving into more detailed testing. The goal is to ensure the software is minimally functional and stable enough to warrant further investment in testing resources. Imagine it as a quick smoke check to see if the engine sputters to life before taking the car for a full diagnostic. 3. What is difference between smoke testing and sanity testing? Smoke testing is a quick thumbs-up/thumbs-down on core functionality, while sanity testing ensures new changes haven't broken existing features. Imagine smoke testing as a car starting and sanity testing as checking new parts. For your next read Dive deeper with these related posts! 09 Min. Read What is Smoke Testing? and Why Is It Important? Learn More 07 Min. Read Types of Testing : What are Different Software Testing Types? Learn More Add a Title What is Integration Testing? A complete guide Learn More

Learn how to easily compare JSON files and find differences using tools and techniques for efficient analysis and debugging. 19 March 2025 07 Min. Read The Developer's Guide to JSON Comparison: Tools and Techniques WhatsApp LinkedIn X (Twitter) Copy link Try JSON Comparison Tool Now Ever deployed a breaking change that was just a missing comma? It's Monday morning. Your team just deployed a critical update to production. Suddenly, Slack notifications start flooding in—the application is down. After frantic debugging, you discover the culprit: a single misplaced key in a JSON configuration file. What should have been "apiVersion": "v2" was accidentally set as " apiVerison": "v2 " . A typo that cost your company thousands in downtime and your team countless stress-filled hours. This scenario is all too familiar to developers working with JSON data structures. The reality is that comparing JSON files effectively isn't just a nice-to-have skill—it's essential for maintaining system integrity and preventing costly errors. Stack Overflow's 2024 Developer Survey shows 83% of developers prefer JSON over XML or other data formats for API integration. What is a JSON File? JSON (JavaScript Object Notation) is a lightweight data interchange format that has become the lingua franca of web applications and APIs. It's human-readable, easily parsable by machines, and versatile enough to represent complex data structures. A simple JSON object looks like this: { "name": "John Doe", "age": 30, "city": "New York", "active": true, "skills": ["JavaScript", "React", "Node.js"] } JSON files can contain: Objects (enclosed in curly braces) Arrays (enclosed in square brackets) Strings (in double quotes) Numbers (integer or floating-point) Boolean values (true or false) Null values The nested and hierarchical nature of JSON makes it powerful but also introduces complexity when comparing files for differences. Why comparing JSON files is critical? JSON comparison is essential in numerous development scenarios: Scenario Why JSON Comparison Matters API Development Ensuring consistency between expected and actual responses Configuration Management Detecting unintended changes across environments Version Control Tracking modifications to data structures Database Operations Validating data before and after migrations Debugging Isolating the exact changes that caused an issue Quality Assurance Verifying that changes meet requirements Without effective comparison tools, these tasks become error-prone and time-consuming, especially as JSON structures grow in complexity. Common JSON Comparison Challenges Before diving into solutions, let's understand what makes JSON comparison challenging: Order Sensitivity : JSON objects don't guarantee key order, so {"a":1,"b":2} and {"b":2,"a":1} are semantically identical but may be flagged as different by naive comparison tools. Whitespace and Formatting : Differences in indentation or line breaks shouldn't affect comparison results. Type Coercion : String "123" is not the same as number 123, and comparison tools need to respect this distinction. Nested Structures : Deeply nested objects make visual comparison nearly impossible. Array Order : Sometimes array order matters ([1,2,3] vs. [3,2,1]), but other times it doesn't (lists of objects where only the content matters). Methods for Comparing JSON Files 1. Visual Inspection The most basic approach is manually comparing JSON files side-by-side in your editor. This works for small files but quickly becomes impractical as complexity increases. Pros: No tools required Good for quick checks on small files Cons: Error-prone Impractical for large files Difficult to spot subtle differences With microservices now powering 85% of enterprise applications, JSON has become the standard interchange format, with an average enterprise managing over 100,000 JSON payloads daily. 2. Command Line Tools Command-line utilities offer powerful options for JSON comparison. ➡️ Using diff The standard diff command can compare any text files: diff file1.json file2.json For more readable output, you can use: diff -u file1.json file2.json The diff command in JSON format is particularly valuable for detecting schema drift between model definitions and actual database implementations. The structured output can feed directly into CI/CD pipelines, enabling automated remediation. ➡️ Using jq The jq tool is specifically designed for processing JSON on the command line: # Compare after sorting keys jq --sort-keys . file1.json > sorted1.json jq --sort-keys . file2.json > sorted2.json diff sorted1.json sorted2.json Pros: Scriptable and automatable Works well in CI/CD pipelines Highly customizable Cons: Steeper learning curve Output can be verbose May require additional parsing for complex comparisons 3. Online JSON Comparison Tools Online tools provide visual, user-friendly ways to compare JSON structures. These are particularly helpful for team collaboration and sharing results. Top Online JSON Comparison Tools Tool Highlights HyperTest JSON Comparison Tool -Color-coded diff visualization -Structural analysis -Key-based comparison -Handles large JSON files efficiently JSONCompare - Side-by-side view - Syntax highlighting - Export options JSONDiff - Tree-based visualization - Change statistics CodeBeautify - Multiple formatting options - Built-in validation The HyperTest JSON Comparison Tool stands out particularly for its performance with large files and intuitive visual indicators that make complex structural differences immediately apparent. Let's look at an example of comparing two versions of a user profile with the HyperTest tool: Before: { "name": "John", "age": 25, "location": "New York", "hobbies": [ "Reading", "Cycling", "Hiking" ] } After: { "name": "John", "age": 26, "location": "San Francisco", "hobbies": [ "Reading", "Traveling" ], "job": "Software Developer" } Using the HyperTest JSON Comparison Tool , these differences would be immediately highlighted: Changed: age from 25 to 26 Changed: location from "New York" to "San Francisco" Modified array: hobbies (removed "Cycling", "Hiking"; added "Traveling") Added: job with value "Software Developer" Try the tool here Pros: Intuitive visual interface No installation required Easy to share results Great for non-technical stakeholders Cons: Requires internet connection May have file size limitations Potential privacy concerns with sensitive data NoSQL databases like MongoDB, which store data in JSON-like documents, have seen a 40% year-over-year growth in enterprise adoption. 4. Programming Languages and Libraries For integration into your development workflow, libraries in various programming languages offer JSON comparison capabilities. ➡️ Python Using the jsondiff library: from jsondiff import diff import json with open('file1.json') as f1, open('file2.json') as f2: json1 = json.load(f1) json2 = json.load(f2) differences = diff(json1, json2) print(differences) ➡️ JavaScript/Node.js Using the deep-object-diff package: const { diff } = require('deep-object-diff'); const fs = require('fs'); const file1 = JSON.parse(fs.readFileSync('file1.json')); const file2 = JSON.parse(fs.readFileSync('file2.json')); console.log(diff(file1, file2)); Pros: Fully customizable Can be integrated into existing workflows Supports complex comparison logic Can be extended with custom rules Cons: Requires programming knowledge May need additional work for visual representation Initial setup time 5. IDE Extensions and Plugins Many popular IDEs offer built-in or extension-based JSON comparison: IDE Extension/Feature VS Code Compare JSON extension JetBrains IDEs Built-in file comparison Sublime Text FileDiffs package Atom Compare Files package Pros: Integrated into development environment Works offline Usually supports syntax highlighting Cons: IDE-specific May lack advanced features Limited visualization options Advanced JSON Comparison Techniques ➡️ Semantic Comparison Sometimes you need to compare JSON files based on their meaning rather than exact structure. For example: // File 1 { "user": { "firstName": "John", "lastName": "Doe" } } // File 2 { "user": { "fullName": "John Doe" } } While structurally different, these might be semantically equivalent for your application. Custom scripts or specialized tools like the HyperTest JSON Comparison Tool offer options for rule-based comparison that can handle such cases. ➡️ Schema-Based Comparison Instead of comparing the entire JSON structure, you might only care about changes to specific fields or patterns: // Example schema-based comparison logic function compareBySchema(json1, json2, schema) { const result = {}; for (const field of schema.fields) { if (json1[field] !== json2[field]) { result[field] = { oldValue: json1[field], newValue: json2[field] }; } } return result; } Real-world use cases for JSON Comparison ➡️ API Response Validation When developing or testing APIs, comparing expected and actual responses helps ensure correct behavior: // Test case for user profile API test('should return correct user profile', async () => { const response = await api.getUserProfile(123); const expectedResponse = require('./fixtures/expectedProfile.json'); expect(deepEqual(response, expectedResponse)).toBe(true); }); ➡️ Configuration Management Tracking changes across environment configurations helps prevent deployment issues: # Script to check configuration differences between environments jq --sort-keys . dev-config.json > sorted-dev.json jq --sort-keys . prod-config.json > sorted-prod.json diff sorted-dev.json sorted-prod.json > config-diff.txt ➡️ Database Migration Verification Before and after snapshots ensure data integrity during migrations: # Python script to verify migration results import json from jsondiff import diff with open('pre_migration.json') as pre, open('post_migration.json') as post: pre_data = json.load(pre) post_data = json.load(post) differences = diff(pre_data, post_data) # Expected differences based on migration plan expected_changes = { 'schema_version': ('1.0', '2.0'), 'field_renamed': {'old_name': 'new_name'} } # Verify changes match expectations # ... Best Practices for JSON Comparison Normalize Before Comparing : Sort keys, standardize formatting, and handle whitespace consistently. Use Purpose-Built Tools : Choose comparison tools designed specifically for JSON rather than generic text comparison. Automate Routine Comparisons : Integrate comparison into CI/CD pipelines and testing frameworks. Consider Context : Sometimes structural equivalence matters; other times, semantic equivalence is more important. Document Expected Differences : When comparing across environments or versions, maintain a list of expected variances. Handle Large Files Efficiently : For very large JSON files, use streaming parsers or specialized tools like the HyperTest JSON Comparison Tool that can handle substantial files without performance issues. Future of JSON Comparison As JSON continues to dominate data interchange, comparison tools are evolving: AI-Assisted Comparison : Machine learning algorithms that understand semantic equivalence beyond structural matching. Real-time Collaborative Comparison : Team-based analysis with annotation and discussion features. Integration with Schema Registries : Comparison against standardized schemas for automatic validation. Performance Optimizations : Handling increasingly large JSON datasets efficiently. Cross-Format Comparison : Comparing JSON with other formats like YAML, XML, or Protobuf. Conclusion Effective JSON comparison is an essential skill for modern developers. From simple visual inspection to sophisticated programmatic analysis, the right approach depends on your specific requirements, team structure, and workflow integration needs. By leveraging tools like the HyperTest JSON Comparison Tool for visual analysis and integrating command-line utilities or programming libraries into your development process, you can catch JSON-related issues before they impact your users or systems. Try the Online JSON Comparison tool here Remember that the goal isn't just to identify differences but to understand their implications in your specific context. A minor JSON change might be inconsequential—or it might bring down your entire system. The right comparison strategy helps distinguish between the two. Related to Integration Testing Frequently Asked Questions 1. Why do developers need to compare JSON files? Developers compare JSON files to track changes, debug issues, validate API responses, manage configurations across environments, and ensure data integrity during transformations or migrations. 2. What are the challenges developers face when manually comparing JSON files? Manual comparison becomes challenging due to nested structures, formatting differences, key order variations, and the sheer volume of data in complex JSON files. Human error is also a significant factor. 4. What are the advantages of using online JSON diff tools? Online tools like HyperTest's JSON comparison provide visual, user-friendly interfaces with color-coded differences, side-by-side views, and specialized JSON understanding. For your next read Dive deeper with these related posts! 08 Min. Read Using Blue Green Deployment to Always be Release Ready Learn More 09 Min. Read CI/CD tools showdown: Is Jenkins still the best choice? Learn More 08 Min. Read How can engineering teams identify and fix flaky tests? Learn More