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The circuit breaker pattern stops a service from trying again to call another service when the previous attempts have failed multiple times. It's similar to electrical circuit breakers that automatically cut off the current when there's abnormal activity.

In a distributed environment, calls to remote resources may fail due to reasons such as application exceptions, timeouts, authentication issues, or overloaded systems. Usually, resilient cloud applications automatically fix these issues over time, and the calling application manages these errors using a retry pattern.

However, in some cases, these failures can persist, like when a service is down or systems are consistently overloaded. Excessive retries can create a cascading effect, overloading the same resource and impacting other resources as well. Repeated calls can impact both cost and performance.

At this stage, circuit breaker patterns come into play to address this issue. When the callee retries, it begins to assess the problem. If there's a specific error or if the error count surpasses the limit, circuit patterns activate, breaking the communication between the caller and the callee.

Circuit Pattern Implementation Details

There are three main stages for the circuit breaker pattern.

Closed

In a healthy state, the circuit breaker remains closed. It routes actions from source to target, mainly observing requests and logging their results.

Open

If a specific error occurs in the previous request, such as a service being down or exceeding the error limit, the circuit breaker transitions to the open state. In this state, the circuit breaker prevents the continuation of the request, immediately returning it to the source application.”

Half Open

The cloud is vast. Azure docs have around a hundred thousand pages, and AWS is just as big. Other cloud providers are out there too. Each gives you lots of apps, different rules, and dozens of integrations, so creating cloud-native ones has its challenges.

They're not identical, but big providers offer similar solutions. For example, Google Cloud Storage is like AWS S3 and Azure's durable function is similar to AWS step functions. Details and rules can differ, but the main idea of the tech is similar, along with the challenges.

Because problems are alike, solutions are too. We can group similar solutions and make templates for each group. Like a cooking recipe guides you to a tasty dish, these templates can be our guide to perfect solutions.

What is a design pattern?

In simple terms, a design pattern is a handy, repeatable solution for a common problem in making software. It's not a complete design you can directly turn into code. Instead, it's like a guide or template, describing how to solve a problem that fits various situations in software design.

What is a cloud design pattern?

In simple words, a cloud design pattern is a solution we can use over and over for common issues in cloud computing. People often use these patterns to make things scalable, reliable, and secure in the cloud.

What are the common challenges in cloud development?

Cloud development has lots of benefits. It makes scaling and adapting to market changes easy for organizations. Plus, it offers better reliability and improved disaster recovery. The pay-as-you-go model also cuts upfront costs.

Moreover, the cloud provides easy access to advanced technologies, even without in-house expertise. This allows organizations to automate IT tasks, easing the load on IT teams and giving them more time.

Cloud development has its challenges. Things like keeping things secure, managing costs, making sure it can grow when needed, and connecting different services can be tricky. Organizations need to tackle these challenges.

Let's explore each topic to understand them better.

Scalability

Scaling is a big plus in the cloud. Organizations adjust resources as needed, paying for what they use, keeping costs low and solutions reliable.

However, it's not always smooth. Complex cloud setups, old systems, or not enough know-how can be challenges. These might affect reliability or lead to cost issues.

Before the cloud, there were stories. Like, organizations aired TV commercials in prime time, and after, customers saw an HTTP 500 error on their computers. Not ideal!

Reliability

Cloud providers set up many data centers in different places, so even if one has a problem, others can keep things going. Also, in the cloud, there are backup components ready to jump in if needed.

However, using the cloud often means dealing with systems spread out everywhere. This makes it tricky to handle communication, consistency, and reliability among different parts. While the cloud lets you grow easily, not planning how to grow or sudden high demand can cause problems, affecting how reliable services are.

Even with backup plans, cloud services can go down sometimes. Things like hardware issues, software problems, or even attacks can happen, impacting how well applications work.

Security

Security is important in cloud development for organizations using cloud-based tools. Even though cloud providers give security features, there are still challenges that organizations must tackle to keep their cloud environments safe.

Challenges include data breaches, misconfigurations, and identity management problems. Steps like encrypting sensitive data, having strong access controls, and clear policies are vital to keep the cloud secure.

Data Management

Handling data in the cloud has lots of benefits over on-prem solutions. It lets organizations be more flexible, scalable, and cost-effective. Automated backups and recovery options make sure data stays safe. Also, cloud services often provide advanced analytics tools, helping in extracting valuable insights from the data. Being scalable helps manage big loads of data easily.

But, dealing with data in the cloud comes with challenges. Organizations need to ensure data integrity and address concerns about security, and compliance. Making sure rules about data are the same across different cloud setups can be hard, especially if an organization uses many clouds or a mix of cloud and old-school systems. To keep sensitive data safe, there need to be strong security measures, like controls, encryption, and tools to prevent data loss. Bringing data from old systems or other clouds into the cloud can also be challenging, needing careful planning and syncing.

Integration

Cloud integration brings numerous benefits to organizations, helping them link different apps, smoothen data flows, and enhance overall business processes. It's like having a well-organized toolbox for projects, making things run more smoothly, scalable, and efficient.

While offering numerous benefits, there are challenges. Getting old systems into the mix, especially if they're outdated, needs time and effort. This might mean custom integrations to connect them to cloud apps. Cloud integration also involves linking apps and systems that use different ways of talking and different rules. This can make it hard to map data, ensure things work together, and ensure compatibility, and maintain data integrity during integration. Managing data ownership, governance policies, and data lineage across multiple cloud environments can be challenging as well.

Monitoring

Monitoring cloud operations is crucial in modern cloud development. It gives organizations the info they need to keep things running well, find problems, and make sure everything in their cloud apps and systems is healthy and safe. It's like having a set of eyes always looking out for you.

But, monitoring comes with challenges. Bringing in data from different sources, including cloud systems and external sources, can be complex. Monitoring systems produce a lot of data, leading to alert fatigue and difficulty in identifying critical issues. Plus, monitoring systems themselves need to be secured from unauthorized access and data breaches.

What is Next?

In the next article, I'll explain cloud design patterns one by one. I'll use real-world examples to make things clear. We'll connect each pattern with one of the challenges we talked about. By the end, this will give us more know-how on each topic and a better understanding of the challenges.

#Tech #Cloud #CloudDesignPatterns

In late August, southern California faced a mosquito invasion. These tiny pests seemed to appear out of nowhere, leaving their itchy marks on unsuspecting victims. Whether you had a bug screen or not, they found their way into your home, and you'd wake up with irritating mosquito bites. At first, you might not notice them, but as time passed, the incessant itching would become undeniable.

These mosquitoes were notorious for targeting ankles, leaving small, pink, and itchy bites. Sometimes, you'd wake up with not just one or two but possibly three or more bites. The constant itching would make you want to scratch your ankles, especially when walking. It was nearly impossible to scratch them all the time, like when driving, where you'd long for relief.

By the end of the day, you'd have scratched your ankles raw, and the next day, it would only get worse. Congratulations, you'd have itchy scars and mosquito bites.

After a few weeks, I had to confront the mosquito problem plaguing us. The constant itching was unbearable, and even my precious puppies were not spared. Seeing my dog helplessly bothered by these mosquitoes, I decided it was time to take action.

I began wearing socks before sleeping and opted to use the air conditioner instead of opening windows. It made a difference, and though I still spotted the occasional mosquito, I became adept at swatting them before they could bite. Sleeping in socks and long sleeves also helped.

One day, I had a fantastic night out with friends, celebrating something special. I returned home late, exhausted, and in my tired state, I forgot about the mosquito problem. I kept the window open to enjoy the ocean breeze, and by morning, I was covered in fresh mosquito bites. These pests had taken advantage of my lapse in vigilance.

I realized I needed to confront this problem head-on. Passive measures weren't effective. So, I declared war. I ordered bug zappers and mosquito-killing sprays. Before sleeping, I abandoned socks and long sleeves, choosing instead to check the lights and eliminate any mosquitoes. There were a few nights I forgot, but the bug zappers and previous efforts paid off. It was initially a bit scary and required effort, but I learned that ignoring the problem was more costly. My quality of life improved, and I woke up without disturbances. Occasionally, there were accidents, especially when I ventured outside my “castle.” I went shopping and returned with a couple of bites, but this taught me to be prepared. I learned to identify areas prone to mosquitoes and took precautions with repellent body spray.

The key was accepting that I had a problem, taking proactive steps to combat it, and arming myself with solutions to alleviate the bites during my ongoing battle with these pesky insects.

#mosquitoes #personal

Since the beginning of the pandemic, I've noticed a repetitive pattern of content on the internet across various platforms. Whether it's on Reddit, different social media sites, or news websites and popular blogs, the same or similar content keeps circulating. Additionally, Google searches have taken a peculiar turn. When searching in English, the results often differ from the same websites, and in other languages like Turkish, you might encounter clickbait websites with strong SEO but weak content.

I appreciated the user-friendly interface and the quality of the community on Hashnode. I read some excellent articles. After a few articles, my job changed, and life became busier, leaving me with little time to write more.

In the last few months, I've had some spare time and planned to write more. However, I encountered a hurdle. I realized I didn't need a development-centric blog; I wanted a space to share my thoughts on various topics. Unfortunately, this didn't align with Hashnode's rules, and it would feel out of place among the tech-focused blogs. Consequently, I decided to migrate my blog from Hashnode to Write.as.

Initially, I experimented with hosting my own instance on one of my servers and even on a Raspberry Pi, which turned out to be surprisingly easy. I opted not to use my EC2 instances due to cost concerns. While the Raspberry Pi experiment went well, I didn't want to rely on my home internet to host a blog. So here I am, writing this post with my Write.as account. While I haven't made my first payment yet, I've already updated the CNAME record. After this post, I plan to transfer my old posts from Hashnode and import them into my new blog. I've already exported all my posts to a JSON file, although I'm unsure about the importing process; I'll figure that out after completing this post.

#Writefreely #personal

Window Sliding Technique is a strategy that aims to reduce nested loops for solving problems where you need to analyze a sequence of elements, like an array or a string. The technique reduces the use of a nested loop and replaces it with a single loop, reducing the time complexity.

The sliding window technique is efficient because it avoids unnecessary computations. By moving the window only one step at a time, you avoid repeating calculations already done for the previous window. This can save a lot of time and make the algorithm more efficient.

This approach is useful in solving problems that involve finding a subarray or substring that meets a certain condition, such as the maximum sum of a subarray or the longest substring without repeating characters. By sliding the window over the input sequence, the algorithm can efficiently explore all possible subarrays or substrings and identify the ones that meet the given condition.

The longest sub-array having a sum is less than k.

Given an array of positive integers nums and an integer k, find the
length of the longest subarray whose sum is less than or equal to k.

The problem supposes you have an array of positive numbers and a target number, k. You want to find the longest possible subarray (a contiguous sequence of elements) in the array whose sum is less than or equal to k.

Input: arr[] = { 3, 1, 2, 4, 5, 9 }, k = 10 Output: 4 Explanation: The sub-array is {3, 1, 2, 4}.

Solution

Create a window of elements by moving the right pointer to the right until the desired size or condition is met. If the sum of the elements in the window exceeds the given integer k, we need to adjust the window to the right. We do this by moving the left end of the window one step to the right and subtracting the element that was previously at the left end of the window from current. We repeat this process as many times as needed until the sum of the elements in the window is less than or equal to k.

At each iteration, we update the answer variable with the maximum length of the subarray seen so far. We calculate this as the difference between the current right index and the left index. We continue iterating over the array until we reach the end.

public int FindLengthOfLongestSubarray(int[] nums, int k) {
    int left = 0;
    int current = 0;
    int answer = 0;

    for (int right = 0; right < nums.Length; right++) {
        current += nums[right];
        while (current > k) {
            current -= nums[left];
            left++;
        }
        answer = Math.Max(answer, right - left + 1);
    }
    return answer;
}

Fixed Size Sliding Window

The fixed sliding window problem is a specific type of problem that requires finding a solution within a fixed-size window of elements in an array or sequence. This means that the size of the window remains constant throughout the problem.

The maximum sum of any subarray of size k

Given an array of integers and a fixed window size of k, find the
maximum sum of any subarray of size k.

Solution

To solve this problem using the sliding window technique, we would start by initializing two pointers, left and right, to the beginning of the array. We would then create a window of elements by moving the right pointer to the right by the size of the window.

Next, we would calculate the sum of the elements in the window. We would then store this sum in a variable, say max_sum.

We would then slide the window to the right by incrementing the left and right pointers by one, subtracting the element at the left end of the old window from the sum and adding the element at the right end of the new window to the sum. We would then update the max_sum as necessary by comparing it to the sum of the new window:

public int MaxSumSubarray(int[] arr, int k)
{
    int left = 0;
    int right = k - 1;
    int maxSum = 0;
    int currSum = 0;

    // Calculate the sum of the first window
    for (int i = 0; i <= right; i++)
    {
        currSum += arr[i];
    }
    maxSum = currSum;

    // Slide the window and update the maximum sum as necessary
    while (right < arr.Length - 1)
    {
        currSum -= arr[left];
        left++;
        right++;
        currSum += arr[right];

        if (currSum > maxSum)
        {
            maxSum = currSum;
        }
    }

    return maxSum;
}

Conclusion

In conclusion, the sliding window technique is a useful algorithmic pattern that can be applied to a wide range of problems in which we need to find a subarray or substring of a given array or string that satisfies certain constraints.

The technique involves creating a “window” of a fixed size or a variable size that slides through the input data, with the goal of finding the optimal solution or the longest/shortest subarray/substring that satisfies a specific condition.

Fixed-size sliding window problems are easier to solve as we only need to maintain a window of a fixed size, while variable-size sliding window problems require us to adjust the window size dynamically based on the problem constraints.

Overall, the sliding window technique provides a simple and efficient way to solve a variety of problems, particularly those that involve searching for a continuous subset of data that meets specific criteria.

#Tech

What Is The Two Pointer Technique?

The two-pointer is an easy technique used to solve some array-related problems. It involves using two pointers, one starting from the beginning of the array and the other starting from the end of the array, to traverse the array and find a solution. This technique is helpful because it reduces the time complexity of the algorithm and increases its efficiency.

The two-pointer technique is used in various solutions such as finding the sum of two numbers in an array that equals a given target, finding the length of the longest subarray with a given sum, and finding the shortest subarray with a given sum. The basic idea behind this technique is to start from the two ends of the array and move the pointers towards each other until a solution is found or it becomes clear that a solution does not exist.

Two Sum Problem

Two Sum, one of the famous interview questions can be solved with two pointers technique.

Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target.

You may assume that each input would have exactly one solution, and you may not use the same element twice.

You can return the answer in any order.

The Solution of Two Sum Problem with Two Pointers Technique

We initialize two pointers left and right to the start and end of the array, respectively. We calculate the sum of the numbers at left and right and compare it with the target. If the sum is equal to the target, we have found two numbers that add up to the target, so we return their indices. If the sum is less than the target, we increment the left pointer. If the sum is greater than the target, we decrement the right pointer. If the loop ends without finding a solution, we throw an exception.

public int[] TwoSum(int[] nums, int target) 
{
    int left = 0, right = nums.Length - 1;
    while (left < right) 
    {
        int sum = nums[left] + nums[right];
        if (sum == target) 
        {
            return new int[] { left, right };
        } 
        else if (sum < target) 
        {
            left++;
        } 
        else 
        {
            right--;
        }
    }
    throw new ArgumentException("No two sum solution");
}

“Two-pointer technique” and its application to problems involving two arrays

The two-pointer technique can also be applied to problems involving two arrays. In this scenario, the two pointers are used to traverse the two arrays simultaneously, with one pointer moving through each array. The goal is to find a solution that satisfies a given condition, such as finding a pair of elements with a given sum or finding the common elements between two arrays.

The Intersection Question

Let's consider an example of finding the common elements between two sorted arrays. In this problem, we are given two sorted arrays, and our goal is to find the elements that are common to both arrays. To solve this problem using the two-pointer technique, we start with two pointers, one at the beginning of each array, and compare the elements pointed to by the two pointers. If the elements are equal, we have found a common element, and both pointers are advanced one step. If the element in one array is greater than the element in the other array, the pointer pointing to the greater element is advanced one step. The process continues until one of the pointers reaches the end of its array.

The Solution of Intersection Problem with Two Pointers Technique

public List<int> Intersection(int[] nums1, int[] nums2)
{
    int i = 0, j = 0;
    List<int> result = new List<int>();
    while (i < nums1.Length && j < nums2.Length)
    {
        if (nums1[i] == nums2[j])
        {
            result.Add(nums1[i]);
            i++;
            j++;
        }
        else if (nums1[i] < nums2[j])
        {
            i++;
        }
        else
        {
            j++;
        }
    }
    return result;
}

Note: Assumes that the input arrays are already sorted.

Initialize two-pointers i and j: The two-pointers, i and j, are initialized to zero. They will be used to traverse the arrays nums1 and nums2, respectively. Create a result list result: A list result is created to store the elements that belong to both arrays. Start a while loop: The loop starts with the condition that both i and j are less than the length of their respective arrays. Compare the elements: Inside the loop, the elements pointed by i and j are compared. If they are equal, the element is added to the result list and both pointers are incremented. If the element pointed by i is less than the element pointed by j, only i is incremented. If the element pointed by j is less, only j is incremented. Repeat the loop: The loop continues until either i or j reaches the end of the array. Return the result: Finally, the result list is returned as the result.

Conclusion

In conclusion, the two-pointer technique is a highly effective algorithm for solving a variety of problems in computer science. It involves using two pointers that traverse a data structure, such as an array or list, in opposite directions to search for a solution. By utilizing this technique, developers can efficiently find solutions to problems with linear time complexity, making it an ideal method for optimizing performance and streamlining problem-solving. With its versatility and ease of use, the two-pointer technique is an indispensable tool for any C# developer, allowing them to quickly and effectively tackle complex challenges with confidence.

#Tech