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6 minutes read
Testing Tips for Today’s Distributed Cloud Environments
Written by Serkan Özal
Founder and CTO of Thundra
If you’re taking full advantage of today’s distributed cloud environments for your software development, you may be wasting more time than necessary when testing and debugging your applications.
Distributed environments have been one of the biggest advances in software development and arose in response to a few unique challenges, such as increasing hardware and software complexity, the ubiquity of the cloud, and demand among consumers and enterprises for applications that work seamlessly across a huge range of devices in locations all over the globe.
Distributed architectures solve many problems, bringing increased availability, the ability to scale up or out, flexibility, speed of calculations, and cost savings. The advantages are especially apparent when these benefits are added to the on-demand availability and convenience of the cloud.
But for applications developed around a distributed architecture, testing and debugging have also become far more complex. You’ve got numerous moving parts, all running in different locations, and sometimes testing is difficult, if not impossible, in a local environment.
Let’s look at some of the unique challenges that your team faces when it comes to testing distributed software architectures, then look at three ways you can make debugging easier.
What Makes Testing So Hard?
When we’re talking about scaling our environments to handle increased loads, there are really only two ways to go: scaling up or scaling out. Scaling up involves growing the size of the existing infrastructure—a larger server or higher type of AWS instance, for example. Scaling out refers to adding more infrastructure, for example, adding more servers to spread the load (traditionally) or more instances.
Scaling out gives you a lot more flexibility while also reducing risk, resulting in a more resilient solution overall. However, this type of distributed environment brings with it a trade-off, formally known as the CAP theorem. This states that a distributed system can reliably deliver on just two of the following three aspects: consistency, availability, and “partition tolerance,” meaning its ability to successfully function despite a communication failure.
That doesn’t mean you can’t build and test highly functional applications on distributed systems. It simply means you have to find a balance between high performance, healthy systems availability, and reasonable fault tolerance while also juggling components such as APIs, batch processing, load balancers, message queues, and different storage types.
Essentially, there are a lot of moving parts where something could go wrong. And testing all of this locally is becoming a nearly impossible feat. When every request is potentially traveling through several of these components, it’s far more difficult to trace what’s going on and pinpoint potential culprits. Not to mention that local development may be happening without the entire system being available.
You will also find that some problems may be caused by the distributed system itself, through failures of inter-component communication, such as split brain, when communication is momentarily lost and each node claims master status for itself, causing corruption and inconsistent data.
Let’s explore three of the more common ways to test and debug distributed systems.
1. xUnit Testing: Integrated & Automated
One of the oldest and simplest forms of software verification is unit testing. Today’s xUnit test suites offer an evolution of this tried-and-true strategy.
Probably the greatest appeal of unit testing comes from the ability to automate tests, incorporating them within the CI/CD pipeline where they can be run quickly and easily to confirm the correctness and perform regression testing—in other words, making sure the code you’ve just modified hasn’t caused anything else to break that was previously working.
However, with distributed systems, particularly when it comes to cloud development, almost more important than the correctness of code are the performance and reliability of the app following the code change. And the factors most likely to influence these are those components of the distributed environment mentioned in the previous section.
This means that when a test fails, it might or might not be due to the code itself. The failure could be caused by a dependency, by a configuration change, or any one of a number of other factors, none of which are covered by the unit test, which tends to focus on mimicking the environment. This creates an unrealistic test scenario.
Unit testing becomes even more complicated when microservices are coded in different programming languages because then your team is going to be using a wide array of different unit testing tools.
Overall, while unit testing does provide a way to check the logic of the code itself, it can’t be fully trusted for anything more complex. And with little observability into what’s actually going on and without the ability to trace processes thoroughly across components in a distributed environment, unit testing falls seriously short.
2. APM Solutions: Metrics & Visibility
A slightly different approach to debugging comes from application performance monitoring (APM) solutions. These are third-party solutions that track tangible metrics of software performance, allowing you to find bottlenecks and identify where things are going wrong.
Because APM suites work with the cloud and function across complex distributed systems, they can provide a reasonable complement to the limitations of unit testing. Rather than looking at and testing each service separately, APM solutions can give you visibility into end-to-end interactions among services and allow you to concretely measure the health of these services in real-time.
APM tools can report on metrics such as whether a service is running, the number of errors it is returning, or its load over a given time span. By providing load testing on application components and APIs, developers can help ensure more consistent performance under the stress of heavy load conditions. They can also offer distributed traces correlated with logs, promising code-level insight, and visibility.
Because performance is so essential in a range of fields, APM solutions are often targeted across an entire organization, from ops to management, to help identify all types of performance issues. This makes these tools not only expensive in terms of licensing and server costs, but also very complicated. And beyond the initial investment, the complexity of APM tooling often requires larger organizations that have made the move to APM to hire dedicated personnel to work on these systems.
3. Microservices: Small & Nimble
Moving to a microservices architecture seems to offer an appealing solution for developing a distributed system.
Microservices—and hexagonal—architectures inherently solve some of the problems of distributed systems because these are built around the concept of loose coupling. This refers to a situation in which each component has little or no knowledge of and dependency on other components around it.
Microservices architectures are built around a number of basic design principles, starting with the single responsibility principle (SRP), which dictates that service should do as little as possible, ideally only one thing. Other principles that mainly flow from SRP include maintainability (since its logic is fairly simple), reusability, and replaceability (since it can be swapped out with another service that has the same interface).
The most important principle that stems from all of these is increased testability in microservices. This is because the inputs and outputs are so well defined by the logic built into the microservice.
All of these principles make components more easily interchangeable and can create robustness in distributed systems. However, they also come with an inherent disadvantage: Microsystems can make it harder to keep data in sync and consistent. And the microsystems architecture may add problems, such as different programming languages, making tracking and tracing difficult.
One method for dealing with end-to-end testing of microservices is creating additional environments and rolling changes out across these environments once testing is complete.
Alternatively, deployment can be automated through the CI/CD pipeline, cutting costs with smaller and/or scheduled resources for lower environments. But because this is only a model of the production environment, the tests will not perfectly parallel the actual distributed system and may reflect different results, calling the entire test approach into question.
A final option is testing directly in production. Let’s acknowledge upfront that this comes with some degree of risk because your test environment is not fully isolated. However, in some cases, especially with tools designed specifically for production debugging, you can mitigate the risk through the use of tenants for testing, redirecting the flow of test requests so you have full control and a limited form of isolation.
When you can deploy rapidly into the real production environment, and then have full observability into the application’s performance, you’ll probably find that your team’s increased agility—not to mention the cost savings that come with not having to recreate production environments—far outweighs the risks.
Thundra: Distributed Debugging Simplified
Today’s distributed systems, especially when they’re coupled with microservices architectures, make tracking and tracing the performance of your applications costly and time-consuming. Tracing requests through different services and understanding the processing they undergo from end to end can be difficult, especially when it is hard or impossible to reproduce the exact environment.
Thundra Sidekick is a cloud-debugging tool created to simplify this entire process for developers. Its non-breaking tracepoints let you work within the actual production environment, debugging the process without blocking the service or affecting customers or downstream services. You set tracepoints to automatically snapshot all variables along with the call stack, giving you total insight into the application state so you can figure out what isn’t working and get it fixed fast.
Thundra Sidekick works right within your IDE using the IntelliJ IDEA Plugin, meaning you never have to leave your comfort zone to get debugging done right. Plus, it saves you time by deploying a hotfix directly to the cloud environment, so you can make sure it actually works before rolling it out through your usual CI/CD process.
Get your development team working more efficiently with remote debugging for microservices. Click to get started with Thundra Sidekick in just minutes.
If you’re taking full advantage of today’s distributed cloud environments for your software development, you may be wasting more time than necessary when testing and debugging your applications.
Distributed environments have been one of the biggest advances in software development and arose in response to a few unique challenges, such as increasing hardware and software complexity, the ubiquity of the cloud, and demand among consumers and enterprises for applications that work seamlessly across a huge range of devices in locations all over the globe.
Distributed architectures solve many problems, bringing increased availability, the ability to scale up or out, flexibility, speed of calculations, and cost savings. The advantages are especially apparent when these benefits are added to the on-demand availability and convenience of the cloud.
But for applications developed around a distributed architecture, testing and debugging have also become far more complex. You’ve got numerous moving parts, all running in different locations, and sometimes testing is difficult, if not impossible, in a local environment.
Let’s look at some of the unique challenges that your team faces when it comes to testing distributed software architectures, then look at three ways you can make debugging easier.
What Makes Testing So Hard?
When we’re talking about scaling our environments to handle increased loads, there are really only two ways to go: scaling up or scaling out. Scaling up involves growing the size of the existing infrastructure—a larger server or higher type of AWS instance, for example. Scaling out refers to adding more infrastructure, for example, adding more servers to spread the load (traditionally) or more instances.
Scaling out gives you a lot more flexibility while also reducing risk, resulting in a more resilient solution overall. However, this type of distributed environment brings with it a trade-off, formally known as the CAP theorem. This states that a distributed system can reliably deliver on just two of the following three aspects: consistency, availability, and “partition tolerance,” meaning its ability to successfully function despite a communication failure.
That doesn’t mean you can’t build and test highly functional applications on distributed systems. It simply means you have to find a balance between high performance, healthy systems availability, and reasonable fault tolerance while also juggling components such as APIs, batch processing, load balancers, message queues, and different storage types.
Essentially, there are a lot of moving parts where something could go wrong. And testing all of this locally is becoming a nearly impossible feat. When every request is potentially traveling through several of these components, it’s far more difficult to trace what’s going on and pinpoint potential culprits. Not to mention that local development may be happening without the entire system being available.
You will also find that some problems may be caused by the distributed system itself, through failures of inter-component communication, such as split brain, when communication is momentarily lost and each node claims master status for itself, causing corruption and inconsistent data.
Let’s explore three of the more common ways to test and debug distributed systems.
1. xUnit Testing: Integrated & Automated
One of the oldest and simplest forms of software verification is unit testing. Today’s xUnit test suites offer an evolution of this tried-and-true strategy.
Probably the greatest appeal of unit testing comes from the ability to automate tests, incorporating them within the CI/CD pipeline where they can be run quickly and easily to confirm the correctness and perform regression testing—in other words, making sure the code you’ve just modified hasn’t caused anything else to break that was previously working.
However, with distributed systems, particularly when it comes to cloud development, almost more important than the correctness of code are the performance and reliability of the app following the code change. And the factors most likely to influence these are those components of the distributed environment mentioned in the previous section.
This means that when a test fails, it might or might not be due to the code itself. The failure could be caused by a dependency, by a configuration change, or any one of a number of other factors, none of which are covered by the unit test, which tends to focus on mimicking the environment. This creates an unrealistic test scenario.
Unit testing becomes even more complicated when microservices are coded in different programming languages because then your team is going to be using a wide array of different unit testing tools.
Overall, while unit testing does provide a way to check the logic of the code itself, it can’t be fully trusted for anything more complex. And with little observability into what’s actually going on and without the ability to trace processes thoroughly across components in a distributed environment, unit testing falls seriously short.
2. APM Solutions: Metrics & Visibility
A slightly different approach to debugging comes from application performance monitoring (APM) solutions. These are third-party solutions that track tangible metrics of software performance, allowing you to find bottlenecks and identify where things are going wrong.
Because APM suites work with the cloud and function across complex distributed systems, they can provide a reasonable complement to the limitations of unit testing. Rather than looking at and testing each service separately, APM solutions can give you visibility into end-to-end interactions among services and allow you to concretely measure the health of these services in real-time.
APM tools can report on metrics such as whether a service is running, the number of errors it is returning, or its load over a given time span. By providing load testing on application components and APIs, developers can help ensure more consistent performance under the stress of heavy load conditions. They can also offer distributed traces correlated with logs, promising code-level insight, and visibility.
Because performance is so essential in a range of fields, APM solutions are often targeted across an entire organization, from ops to management, to help identify all types of performance issues. This makes these tools not only expensive in terms of licensing and server costs, but also very complicated. And beyond the initial investment, the complexity of APM tooling often requires larger organizations that have made the move to APM to hire dedicated personnel to work on these systems.
3. Microservices: Small & Nimble
Moving to a microservices architecture seems to offer an appealing solution for developing a distributed system.
Microservices—and hexagonal—architectures inherently solve some of the problems of distributed systems because these are built around the concept of loose coupling. This refers to a situation in which each component has little or no knowledge of and dependency on other components around it.
Microservices architectures are built around a number of basic design principles, starting with the single responsibility principle (SRP), which dictates that service should do as little as possible, ideally only one thing. Other principles that mainly flow from SRP include maintainability (since its logic is fairly simple), reusability, and replaceability (since it can be swapped out with another service that has the same interface).
The most important principle that stems from all of these is increased testability in microservices. This is because the inputs and outputs are so well defined by the logic built into the microservice.
All of these principles make components more easily interchangeable and can create robustness in distributed systems. However, they also come with an inherent disadvantage: Microsystems can make it harder to keep data in sync and consistent. And the microsystems architecture may add problems, such as different programming languages, making tracking and tracing difficult.
One method for dealing with end-to-end testing of microservices is creating additional environments and rolling changes out across these environments once testing is complete.
Alternatively, deployment can be automated through the CI/CD pipeline, cutting costs with smaller and/or scheduled resources for lower environments. But because this is only a model of the production environment, the tests will not perfectly parallel the actual distributed system and may reflect different results, calling the entire test approach into question.
A final option is testing directly in production. Let’s acknowledge upfront that this comes with some degree of risk because your test environment is not fully isolated. However, in some cases, especially with tools designed specifically for production debugging, you can mitigate the risk through the use of tenants for testing, redirecting the flow of test requests so you have full control and a limited form of isolation.
When you can deploy rapidly into the real production environment, and then have full observability into the application’s performance, you’ll probably find that your team’s increased agility—not to mention the cost savings that come with not having to recreate production environments—far outweighs the risks.
Thundra: Distributed Debugging Simplified
Today’s distributed systems, especially when they’re coupled with microservices architectures, make tracking and tracing the performance of your applications costly and time-consuming. Tracing requests through different services and understanding the processing they undergo from end to end can be difficult, especially when it is hard or impossible to reproduce the exact environment.
Thundra Sidekick is a cloud-debugging tool created to simplify this entire process for developers. Its non-breaking tracepoints let you work within the actual production environment, debugging the process without blocking the service or affecting customers or downstream services. You set tracepoints to automatically snapshot all variables along with the call stack, giving you total insight into the application state so you can figure out what isn’t working and get it fixed fast.
Thundra Sidekick works right within your IDE using the IntelliJ IDEA Plugin, meaning you never have to leave your comfort zone to get debugging done right. Plus, it saves you time by deploying a hotfix directly to the cloud environment, so you can make sure it actually works before rolling it out through your usual CI/CD process.
Get your development team working more efficiently with remote debugging for microservices. Click to get started with Thundra Sidekick in just minutes.
If you’re taking full advantage of today’s distributed cloud environments for your software development, you may be wasting more time than necessary when testing and debugging your applications.
Distributed environments have been one of the biggest advances in software development and arose in response to a few unique challenges, such as increasing hardware and software complexity, the ubiquity of the cloud, and demand among consumers and enterprises for applications that work seamlessly across a huge range of devices in locations all over the globe.
Distributed architectures solve many problems, bringing increased availability, the ability to scale up or out, flexibility, speed of calculations, and cost savings. The advantages are especially apparent when these benefits are added to the on-demand availability and convenience of the cloud.
But for applications developed around a distributed architecture, testing and debugging have also become far more complex. You’ve got numerous moving parts, all running in different locations, and sometimes testing is difficult, if not impossible, in a local environment.
Let’s look at some of the unique challenges that your team faces when it comes to testing distributed software architectures, then look at three ways you can make debugging easier.
What Makes Testing So Hard?
When we’re talking about scaling our environments to handle increased loads, there are really only two ways to go: scaling up or scaling out. Scaling up involves growing the size of the existing infrastructure—a larger server or higher type of AWS instance, for example. Scaling out refers to adding more infrastructure, for example, adding more servers to spread the load (traditionally) or more instances.
Scaling out gives you a lot more flexibility while also reducing risk, resulting in a more resilient solution overall. However, this type of distributed environment brings with it a trade-off, formally known as the CAP theorem. This states that a distributed system can reliably deliver on just two of the following three aspects: consistency, availability, and “partition tolerance,” meaning its ability to successfully function despite a communication failure.
That doesn’t mean you can’t build and test highly functional applications on distributed systems. It simply means you have to find a balance between high performance, healthy systems availability, and reasonable fault tolerance while also juggling components such as APIs, batch processing, load balancers, message queues, and different storage types.
Essentially, there are a lot of moving parts where something could go wrong. And testing all of this locally is becoming a nearly impossible feat. When every request is potentially traveling through several of these components, it’s far more difficult to trace what’s going on and pinpoint potential culprits. Not to mention that local development may be happening without the entire system being available.
You will also find that some problems may be caused by the distributed system itself, through failures of inter-component communication, such as split brain, when communication is momentarily lost and each node claims master status for itself, causing corruption and inconsistent data.
Let’s explore three of the more common ways to test and debug distributed systems.
1. xUnit Testing: Integrated & Automated
One of the oldest and simplest forms of software verification is unit testing. Today’s xUnit test suites offer an evolution of this tried-and-true strategy.
Probably the greatest appeal of unit testing comes from the ability to automate tests, incorporating them within the CI/CD pipeline where they can be run quickly and easily to confirm the correctness and perform regression testing—in other words, making sure the code you’ve just modified hasn’t caused anything else to break that was previously working.
However, with distributed systems, particularly when it comes to cloud development, almost more important than the correctness of code are the performance and reliability of the app following the code change. And the factors most likely to influence these are those components of the distributed environment mentioned in the previous section.
This means that when a test fails, it might or might not be due to the code itself. The failure could be caused by a dependency, by a configuration change, or any one of a number of other factors, none of which are covered by the unit test, which tends to focus on mimicking the environment. This creates an unrealistic test scenario.
Unit testing becomes even more complicated when microservices are coded in different programming languages because then your team is going to be using a wide array of different unit testing tools.
Overall, while unit testing does provide a way to check the logic of the code itself, it can’t be fully trusted for anything more complex. And with little observability into what’s actually going on and without the ability to trace processes thoroughly across components in a distributed environment, unit testing falls seriously short.
2. APM Solutions: Metrics & Visibility
A slightly different approach to debugging comes from application performance monitoring (APM) solutions. These are third-party solutions that track tangible metrics of software performance, allowing you to find bottlenecks and identify where things are going wrong.
Because APM suites work with the cloud and function across complex distributed systems, they can provide a reasonable complement to the limitations of unit testing. Rather than looking at and testing each service separately, APM solutions can give you visibility into end-to-end interactions among services and allow you to concretely measure the health of these services in real-time.
APM tools can report on metrics such as whether a service is running, the number of errors it is returning, or its load over a given time span. By providing load testing on application components and APIs, developers can help ensure more consistent performance under the stress of heavy load conditions. They can also offer distributed traces correlated with logs, promising code-level insight, and visibility.
Because performance is so essential in a range of fields, APM solutions are often targeted across an entire organization, from ops to management, to help identify all types of performance issues. This makes these tools not only expensive in terms of licensing and server costs, but also very complicated. And beyond the initial investment, the complexity of APM tooling often requires larger organizations that have made the move to APM to hire dedicated personnel to work on these systems.
3. Microservices: Small & Nimble
Moving to a microservices architecture seems to offer an appealing solution for developing a distributed system.
Microservices—and hexagonal—architectures inherently solve some of the problems of distributed systems because these are built around the concept of loose coupling. This refers to a situation in which each component has little or no knowledge of and dependency on other components around it.
Microservices architectures are built around a number of basic design principles, starting with the single responsibility principle (SRP), which dictates that service should do as little as possible, ideally only one thing. Other principles that mainly flow from SRP include maintainability (since its logic is fairly simple), reusability, and replaceability (since it can be swapped out with another service that has the same interface).
The most important principle that stems from all of these is increased testability in microservices. This is because the inputs and outputs are so well defined by the logic built into the microservice.
All of these principles make components more easily interchangeable and can create robustness in distributed systems. However, they also come with an inherent disadvantage: Microsystems can make it harder to keep data in sync and consistent. And the microsystems architecture may add problems, such as different programming languages, making tracking and tracing difficult.
One method for dealing with end-to-end testing of microservices is creating additional environments and rolling changes out across these environments once testing is complete.
Alternatively, deployment can be automated through the CI/CD pipeline, cutting costs with smaller and/or scheduled resources for lower environments. But because this is only a model of the production environment, the tests will not perfectly parallel the actual distributed system and may reflect different results, calling the entire test approach into question.
A final option is testing directly in production. Let’s acknowledge upfront that this comes with some degree of risk because your test environment is not fully isolated. However, in some cases, especially with tools designed specifically for production debugging, you can mitigate the risk through the use of tenants for testing, redirecting the flow of test requests so you have full control and a limited form of isolation.
When you can deploy rapidly into the real production environment, and then have full observability into the application’s performance, you’ll probably find that your team’s increased agility—not to mention the cost savings that come with not having to recreate production environments—far outweighs the risks.
Thundra: Distributed Debugging Simplified
Today’s distributed systems, especially when they’re coupled with microservices architectures, make tracking and tracing the performance of your applications costly and time-consuming. Tracing requests through different services and understanding the processing they undergo from end to end can be difficult, especially when it is hard or impossible to reproduce the exact environment.
Thundra Sidekick is a cloud-debugging tool created to simplify this entire process for developers. Its non-breaking tracepoints let you work within the actual production environment, debugging the process without blocking the service or affecting customers or downstream services. You set tracepoints to automatically snapshot all variables along with the call stack, giving you total insight into the application state so you can figure out what isn’t working and get it fixed fast.
Thundra Sidekick works right within your IDE using the IntelliJ IDEA Plugin, meaning you never have to leave your comfort zone to get debugging done right. Plus, it saves you time by deploying a hotfix directly to the cloud environment, so you can make sure it actually works before rolling it out through your usual CI/CD process.
Get your development team working more efficiently with remote debugging for microservices. Click to get started with Thundra Sidekick in just minutes.
