Ultimately, both the Latin and Arabic are graceful designs based on classic proportions, prioritising the beauty, tranquility, and fluid nature of the wordsmith’s art. Finally, the spacing and connections in the Arabic were considered to achieve comparable colour as the Latin in a block of text. First, the Arabic letter sizes were readjusted so as to not appear larger next to the Latin, then weight and contrast were changed in the same way. So it was decided to give Athelas Arabic a thorough reworking to make them appropriate companions while maintaining the natural aesthetic qualities of Arabic. Originally designed independently, it worked entirely on its own and yet already seemed a good fit for Athelas. Katts’s soft-vowel diacritic marks (harakat) are positioned with most of them appearing on similar lower or upper positions to emphasize they are not part of letters.Īthelas Arabic, created by talented Iranian designer Sahar Afshar, is an elegant typeface for fine digital and printed books - perfect for Arabic literature’s captivating forms. Khatt includes the required Lam-Alif ligatures in addition to all vowel diacritic ligatures. The Mutamathil Taqlidi type style uses one glyph per every basic Arabic Unicode character or letter, as defined by the Unicode Standards, and one additional final form glyph, for each freely-connecting letter in an Arabic text. The script design of this font family follows the Arabetics Mutamathil Taqlidi style and utilizes varying x-heights. Each weight has normal and left-slanted “italic” styles. It comes with five weights, regular, medium, bold, light, and ultra-light. Khatt supports all Arabetic scripts covered by Unicode 6.1, and the latest Arabic Supplement and Extended-A Unicode blocks, including support for Quranic texts. Even though Khatt is a cursive style font it offers clearly distinguished and visually unified letter shapes in every position of a word. The word Khatt is also the word for calligraphy in the Arabic language. Khatt tries to mimic the concept behind the meaning of the Arabic word Khatt: a straight horizontal line.
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The ascendant seems to behave unpredictably in polar regions, i.e., in areas above the arctic circle and below the antarctic circle. For example, a system might average 100 HTTPS requests per second. If a team wants to scan a back-end system for bottlenecks, but without including the backlog that still resides externally, the USE metrics might be suitable. Whether or not these distinctions are worth considering depends on the system you work with - more importantly, how it fails. However, the Golden Signals approach doesn't lend itself to distinguishing customer errors from internal errors. The "saturation" metric found in the Golden Signals corresponds to USE's utilization metric. Utilization indicates the time an application service is processing workloads, while saturation is the backlog of unprocessed work that resides within the application's internal systems. USE stands for utilization, saturation and errors. They're similar to the Golden Signals, but the perspective they provide aligns to a different goal. Those studying various monitoring techniques are likely to encounter another set of application performance metrics: The USE metrics. Each section of the histogram groups requests together by the amount of time they take to complete, ranging from 0 ms to 150 ms in increments of five. The histogram below documents the latency of 1,000 requests made to a service with an expected response time of under 80 milliseconds (ms). But, in the Golden Signals, we want to look at the latency over a period of time, which we can visualize with something like a frequency-distribution histogram. It is tempting to think of this metric as a single "average" latency, or perhaps an established "mean" latency that can be used to guide service-level agreements. This metric is the time that elapses between when a system receives a request and when it sends a response. To consistently keep track of end-user experiences, Google's team of software reliability engineers (SREs) created a standard set of four metrics known as the Golden Signals: latency, traffic, errors and saturation. Many performance measures revolve around things like CPU utilization, memory allocation, disk-drive read times or internal bandwidth capacity. Let's examine this approach to systems monitoring, including the four specific metrics it targets, the unique details these metrics reveal, and a quick look at how the Golden Signals might come into play in a microservices environment. These metrics primarily revolve around outward-facing concerns like user request/response times, resource utilization and memory allocation. The Golden Signals have evolved to address the factors that most impact distributed systems like microservice-based architectures. |
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