Google Cloud Platform is growing quickly, providing solutions for everything from cloud storage to managed Kubernetes to serverless computing. Since Google App Engine launched in 2008, Google’s suite of serverless products has expanded to help enterprises accelerate application development without having to manage or scale their own infrastructure.
To provide comprehensive visibility into serverless applications running on Google Cloud, we are excited to announce that we have enhanced our Google Cloud Functions, Google Cloud Run, and Google App Engine integrations. In addition to new out-of-the-box dashboards, we’ve also added enhanced latency and resource utilization metrics (p95 and p99) across these integrations, allowing you to effectively troubleshoot performance issues before they impact your users.
Google Cloud Functions is an event-based, asynchronous compute solution that allows you to create small, single-purpose functions. Companies can use Cloud Functions with services like Google Pub/Sub and Google Cloud SQL to automatically scale their systems without provisioning, managing, or upgrading servers.
Datadog’s new Google Cloud Functions dashboard provides a high-level overview of key performance metrics from your functions, including error rate, total invocations, and execution time.
The dashboard also displays function-level metrics that can help you optimize your Cloud Functions usage. For example, if you notice that a function’s memory usage frequently approaches its memory allocation, you may need to provision more memory to those particular functions. Or if certain functions consistently underuse their allocated memory, you can decrease their memory allocation to cut costs.
In the past few years, especially after Amazon Web Services (AWS) introduced its Lambda platform, serverless architecture became the business realm’s buzzword. The increasing popularity of serverless applications saw market leaders like Netflix, Airbnb, Nike, etc., adopting the serverless architecture to handle their backend functions better. Moreover, serverless architecture’s market size is expected to reach a whopping $9.17 billion by the year 2023.
Why use serverless computing?
As a business it is best to approach a professional mobile app development company to build apps that are deployed on various servers; nevertheless, businesses should understand that the benefits of the serverless applications lie in the possibility it promises ideal business implementations and not in the hype created by cloud vendors. With the serverless architecture, the developers can easily code arbitrary codes on-demand without worrying about the underlying hardware.
But as is the case with all game-changing trends, many businesses opt for serverless applications just for the sake of being up-to-date with their peers without thinking about the actual need of their business.
The serverless applications work well with stateless use cases, the cases which execute cleanly and give the next operation in a sequence. On the other hand, the serverless architecture is not fit for predictable applications where there is a lot of reading and writing in the backend system.
Another benefit of working with the serverless software architecture is that the third-party service provider will charge based on the total number of requests. As the number of requests increases, the charge is bound to increase, but then it will cost significantly less than a dedicated IT infrastructure.
Defining serverless software architecture
In serverless software architecture, the application logic is implemented in an environment where operating systems, servers, or virtual machines are not visible. Although where the application logic is executed is running on any operating system which uses physical servers. But the difference here is that managing the infrastructure is the soul of the service provider and the mobile app developer focuses only on writing the codes.
There are two different approaches when it comes to serverless applications. They are
Backend as a service (BaaS)
Function as a service (FaaS)
Moreover, other examples of third-party services are Autho, AWS Cognito (authentication as a service), Amazon Kinesis, Keen IO (analytics as a service), and many more.
FaaS serverless architecture is majorly used with microservices architecture as it renders everything to the organization. AWS Lambda, Google Cloud functions, etc., are some of the examples of FaaS implementation.
Pros of Serverless applications
There are specific ways in which serverless applications can redefine the way business is done in the modern age and has some distinct advantages over the traditional could platforms. Here are a few –
🔹 Highly Scalable
The flexible nature of the serverless architecture makes it ideal for scaling the applications. The serverless application’s benefit is that it allows the vendor to run each of the functions in separate containers, allowing optimizing them automatically and effectively. Moreover, unlike in the traditional cloud, one doesn’t need to purchase a certain number of resources in serverless applications and can be as flexible as possible.
As the organizations don’t need to spend hundreds and thousands of dollars on hardware, they don’t need to pay anything to the engineers to maintain the hardware. The serverless application’s pricing model is execution based as the organization is charged according to the executions they have made.
The company that uses the serverless applications is allotted a specific amount of time, and the pricing of the execution depends on the memory required. Different types of costs like presence detection, access authorization, image processing, etc., associated with a physical or virtual server is completely eliminated with the serverless applications.
🔹 Focuses on user experience
As the companies don’t always think about maintaining the servers, it allows them to focus on more productive things like developing and improving customer service features. A recent survey says that about 56% of the users are either using or planning to use the serverless applications in the coming six months.
Moreover, as the companies would save money with serverless apps as they don’t have to maintain any hardware system, it can be then utilized to enhance the level of customer service and features of the apps.
🔹 Ease of migration
It is easy to get started with serverless applications by porting individual features and operate them as on-demand events. For example, in a CMS, a video plugin requires transcoding video for different formats and bitrates. If the organization wished to do this with a WordPress server, it might not be a good fit as it would require resources dedicated to serving pages rather than encoding the video.
Moreover, the benefits of serverless applications can be used optimally to handle metadata encoding and creation. Similarly, serverless apps can be used in other plugins that are often prone to critical vulnerabilities.
Cons of serverless applications
Despite having some clear benefits, serverless applications are not specific for every single use case. We have listed the top things that an organization should keep in mind while opting for serverless applications.
🔹 Complete dependence on third-party vendor
In the realm of serverless applications, the third-party vendor is the king, and the organizations have no options but to play according to their rules. For example, if an application is set in Lambda, it is not easy to port it into Azure. The same is the case for coding languages. In present times, only Python developers and Node.js developers have the luxury to choose between existing serverless options.
Therefore, if you are planning to consider serverless applications for your next project, make sure that your vendor has everything needed to complete the project.
🔹 Challenges in debugging with traditional tools
It isn’t easy to perform debugging, especially for large enterprise applications that include various individual functions. Serverless applications use traditional tools and thus provide no option to attach a debugger in the public cloud. The organization can either do the debugging process locally or use logging for the same purpose. In addition to this, the DevOps tools in the serverless application do not support the idea of quickly deploying small bits of codes into running applications.
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In an ecosystem that has become increasingly integrated with huge chunks of data and information traveling through the airwaves, Big Data has become irreplaceable for establishments.
From day-to-day business operations to detailed customer interactions, many ventures heavily invest in data sciences and data analysis to find breakthroughs and marketable insights.
Plus, surviving in the current era, mandates taking informed decisions and surgical precision based on the projected forecast of current trends to retain profitability. Hence these days, data is revered as the most valuable resource.
According to a recent study by Sigma Computing , the world of Big Data is only projected to grow bigger, and by 2025 it is estimated that the global data-sphere will grow to reach 17.5 Zettabytes. FYI one Zettabyte is equal to 1 million Petabytes.
Moreover, the Big Data industry will be worth an estimate of $77 billion by 2023. Furthermore, the Banking sector generates unparalleled quantities of data, with the amount of data generated by the financial industry each second growing by 700% in 2021.
In light of this information, let’s take a quick look at some of the ways application monitoring can use Big Data, along with its growing importance and impact.
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The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.
The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.
As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.
Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.
The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.
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Many enterprises and SaaS companies depend on a variety of external API integrations in order to build an awesome customer experience. Some integrations may outsource certain business functionality such as handling payments or search to companies like Stripe and Algolia. You may have integrated other partners which expand the functionality of your product offering, For example, if you want to add real-time alerts to an analytics tool, you might want to integrate the PagerDuty and Slack APIs into your application.
If you’re like most companies though, you’ll soon realize you’re integrating hundreds of different vendors and partners into your app. Any one of them could have performance or functional issues impacting your customer experience. Worst yet, the reliability of an integration may be less visible than your own APIs and backend. If the login functionality is broken, you’ll have many customers complaining they cannot log into your website. However, if your Slack integration is broken, only the customers who added Slack to their account will be impacted. On top of that, since the integration is asynchronous, your customers may not realize the integration is broken until after a few days when they haven’t received any alerts for some time.
How do you ensure your API integrations are reliable and high performing? After all, if you’re selling a feature real-time alerting, you’re alerts better well be real-time and have at least once guaranteed delivery. Dropping alerts because your Slack or PagerDuty integration is unacceptable from a customer experience perspective.
Specific API integrations that have an exceedingly high latency could be a signal that your integration is about to fail. Maybe your pagination scheme is incorrect or the vendor has not indexed your data in the best way for you to efficiently query.
Average latency only tells you half the story. An API that consistently takes one second to complete is usually better than an API with high variance. For example if an API only takes 30 milliseconds on average, but 1 out of 10 API calls take up to five seconds, then you have high variance in your customer experience. This is makes it much harder to track down bugs and harder to handle in your customer experience. This is why 90th percentile and 95th percentiles are important to look at.
Reliability is a key metric to monitor especially since your integrating APIs that you don’t have control over. What percent of API calls are failing? In order to track reliability, you should have a rigid definition on what constitutes a failure.
While any API call that has a response status code in the 4xx or 5xx family may be considered an error, you might have specific business cases where the API appears to successfully complete yet the API call should still be considered a failure. For example, a data API integration that returns no matches or no content consistently could be considered failing even though the status code is always 200 OK. Another API could be returning bogus or incomplete data. Data validation is critical for measuring where the data returned is correct and up to date.
Not every API provider and integration partner follows suggested status code mapping
While reliability is specific to errors and functional correctness, availability and uptime is a pure infrastructure metric that measures how often a service has an outage, even if temporary. Availability is usually measured as a percentage of uptime per year or number of 9’s.
AVAILABILITY %DOWNTIME PER YEARDOWNTIME PER MONTHDOWNTIME PER WEEKDOWNTIME PER DAY90% (“one nine”)36.53 days73.05 hours16.80 hours2.40 hours99% (“two nines”)3.65 days7.31 hours1.68 hours14.40 minutes99.9% (“three nines”)8.77 hours43.83 minutes10.08 minutes1.44 minutes99.99% (“four nines”)52.60 minutes4.38 minutes1.01 minutes8.64 seconds99.999% (“five nines”)5.26 minutes26.30 seconds6.05 seconds864.00 milliseconds99.9999% (“six nines”)31.56 seconds2.63 seconds604.80 milliseconds86.40 milliseconds99.99999% (“seven nines”)3.16 seconds262.98 milliseconds60.48 milliseconds8.64 milliseconds99.999999% (“eight nines”)315.58 milliseconds26.30 milliseconds6.05 milliseconds864.00 microseconds99.9999999% (“nine nines”)31.56 milliseconds2.63 milliseconds604.80 microseconds86.40 microseconds
Many API providers are priced on API usage. Even if the API is free, they most likely have some sort of rate limiting implemented on the API to ensure bad actors are not starving out good clients. This means tracking your API usage with each integration partner is critical to understand when your current usage is close to the plan limits or their rate limits.
It’s recommended to tie usage back to your end-users even if the API integration is quite downstream from your customer experience. This enables measuring the direct ROI of specific integrations and finding trends. For example, let’s say your product is a CRM, and you are paying Clearbit $199 dollars a month to enrich up to 2,500 companies. That is a direct cost you have and is tied to your customer’s usage. If you have a free tier and they are using the most of your Clearbit quota, you may want to reconsider your pricing strategy. Potentially, Clearbit enrichment should be on the paid tiers only to reduce your own cost.
Monitoring API integrations seems like the correct remedy to stay on top of these issues. However, traditional Application Performance Monitoring (APM) tools like New Relic and AppDynamics focus more on monitoring the health of your own websites and infrastructure. This includes infrastructure metrics like memory usage and requests per minute along with application level health such as appdex scores and latency. Of course, if you’re consuming an API that’s running in someone else’s infrastructure, you can’t just ask your third-party providers to install an APM agent that you have access to. This means you need a way to monitor the third-party APIs indirectly or via some other instrumentation methodology.
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Google Waffle or App Launcher helps you to get access to your usual Google Apps, Manage Google Account, and receive notifications by Google Apps.
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