Go *

The flag of the Netherlands consists of three colors: red, white and blue. Given balls of these three colors arranged randomly in a line (it does not matter how many balls there are), the task is to arrange them such that all balls of the same color are together and their collective color groups are in the correct order.

For simplicity instead of colors red, white, and blue we will be dealing with ones, twos and zeroes.

Let's start with our intuition. We have an array of zeroth, ones, and twos. How would we sort it? Well, we could put aside all zeroes into some bucket, all ones into another bucket, and all twos into the third. Then we can fetch all items from the first bucket, then from the second, and from the last bucket, and restore all the items. This approach is perfectly fine and has a great performance. We touch all the elements when we iterate through the array, and then we iterate through all the elements once more when we "reassamble" the array. So, the overall time complexity is O(n) + O(n) ~= O(n). The space complexity is also O(n) as we need to store all items in the buckets.

Can we do better than that? There is no way to improve our time complexity. However, we can think of a more efficient algorithm in regard to space complexity. How would we solve the problem without the additional buckets?

Let's make a leap of faith and pretend that somehow we were able to process a part of the array. We iterate through part of the array and put encountered zeroes and ones at the beginning of the array, and twos at the end of the array. Now, we switched to the next index i with some unprocessed value x. What should we do there?

All Prometheus metrics are based on time series - streams of timestamped values belonging to the same metric. Each time series is uniquely identified by its metric name and optional key-value pairs called labels. The metric name specifies some characteristics of the measured system, such as http_requests_total - the total number of received HTTP requests. In practice, you often will be interested in some subset of the values of a metric, for example, in the number of requests received by a particular endpoint; and here is where the labels come in handy. We can partition a metric by adding endpoint label and see the statics for a particular endpoint: http_requests_total{endpoint="api/status"}. Every metric has two automatically created labels: job_name and instance. We see their roles in the next section.

Prometheus provides a functional query language called PromQL. The result of the query might be evaluated to one of four types:

Scalar (aka float)

String (currently unused)

Instant Vector - a set of time series that have exactly one value per timestamp.

Range Vector - a set of time series that have a range of values between two timestamps.

At first glance, Instant Vector might look like an array, and Range Vector as a matrix.

If that would be the case, then a Range Vector for a single time series "downgrades" to an Instant Vector. However, that's not the case:

What is distributed tracing? Distributed tracing is a method used to profile and monitor applications, especially those built using a microservices architecture. Distributed tracing helps pinpoint where failures occur and what causes poor performance.

Let’s have a look at a simple prototype. A user fetches information about a shipment from `logistic` service. logistic service does some computation and fetches the data from a database. logistic service doesn’t know the actual status of the shipment, so it has to fetch the updated status from another service `tracking`. `tracking` service also needs to fetch the data from a database and to do some computation.

In the screenshot below, we see a whole life cycle of the request issued to `logistics` service:

This is the third part of a series covering the implementation of algorithms in Go. In this article, we discuss the Merge Interval algorithm. Usually, when you start learning algorithms you have to deal with some problems like finding the least common denominator or finding the next Fibonacci number. While these are indeed important problems, it is not something that we solve every day. What I like about the Merge Interval algorithm is that we apply it in our everyday life, usually without even noticing that we are solving an algorithmic problem.

Let's say that we need to organize a meeting for our team. We have three colleagues Jay, May, and Ray and their time schedule look as follows (a colored line represents an occupied timeslot):

This is the second part of the article covering the Sliding Window Pattern and its implementation in Go, the first part can be found here.

Let's have a look at the following problem: we have an array of words, and we want to check whether a concatenation of these words is present in the given string. The length of all words is the same, and the concatenation must include all the words without any overlapping. Would it be possible to solve the problem with linear time complexity?

Let's start with string catdogcat and target words cat and dog.

How can we handle this problem?

Let's consider the following problem: we have an array of integers and we need to find out the length of the smallest subarray the sum of which is no less than the target number. If we don't have such a subarray we shall return -1.

We can start with a naive approach and consider every possible subarray in the input:

Introduction

What is Swoft?

Swoft is a PHP high performance microservice coroutine framework. It has been published for many years and has become the best choice for php. It can be like Go, built-in coroutine web server and common coroutine client and is resident in memory, independent of traditional PHP-FPM. There are similar Go language operations, similar to the Spring Cloud framework flexible annotations.

What is Swoft?

Swoft is a PHP high performance microservice coroutine framework. It has been published for many years and has become the best choice for php. It can be like Go, built-in coroutine web server and common coroutine client and is resident in memory, independent of traditional PHP-FPM. There are similar Go language operations, similar to the Spring Cloud framework flexible annotations.

Through three years of accumulation and direction exploration, Swoft has made Swoft the Spring Cloud in the PHP world, which is the best choice for PHP's high-performance framework and microservices management.

Github

https://github.com/swoft-cloud/swoft

This post describes the steps needed for setting up automatic SSL certificates creation and renewal, using Let's Encrypt as the automated Certificate Authority, which provides a well-maintained API.
acme-dns-route53 is the tool to obtain SSL certificates from Let’s Encrypt using DNS-01 challenge with Route53 and Amazon Certificate Manager by AWS. acme-dns-route53 also has the built-in functionality for using this tool inside AWS Lambda, and this is what we are going to do.