I was inspired by Marcel and others writing about life of SRE at Instana and how our SRE team uses Instana in their daily work to keep the Instana SAAS platform running smoothly. But Instana is not just a tool for Site Reliability Engineering. It’s also a tool for developers. With this blog post, I want to give you a glimpse of how Instana developers use Instana to continuously improve Instana.
The events of 2020 have accelerated ecommerce, increasing demand for and traffic on online marketplaces. Analyst eMarketer predicts that ecommerce sales in the United States will grow 18% in 2020, against an overall fall in total retail sales of 10.5% for the year. Likewise, our business—Japan-headquartered consumer-to-consumer marketplace Mercari Inc—is growing rapidly. In the United States alone, we have seen 74% year-on-year growth in monthly average users to 3.4 million.
These days, the internal workings of Linux applications involve many different moving parts. Sometimes, it can be rather difficult to debug them when things go wrong or run slower than expected. Tracing an application’s execution is one way of understanding potential issues without diving into the source code. To this end, we wrote an app-tracing tool called etrace, designed to detect performance bottlenecks and runtime issues in snaps.
PHP profiling is used by developers to identify performance issues or bottlenecks in their code. Profilers enable developers to drill into individual lines of code to ascertain which ones are running slow or are resource intensive. There are several reasons why a program might consume more CPU resources than it is expected to. To troubleshoot and/or optimize a PHP application’s consumption of CPU resources, a PHP CPU profiler is necessary.
According to the Stackoverflow survey of 2019, Python programming language garnered 73.1% approval among developers. It ranks second to Rust and continues to dominate in Data Science and Machine Learning(ML). Python is a developers’ favorite. It is a high-level language known for its robustness and its core philosophy―simplicity over complexity. However, Python application’s performance is another story. Just like any other application, it has its share of performance issues.
Profiling is an essential part of application development, where optimizing performance and resource efficiency are important. It is also useful for troubleshooting performance and crash issues. Profilers provide details about code execution, which are otherwise not available through logging and code instrumentation. A typical profile provides a statistic, e.g. CPU usage or allocated memory, for every line of code highlighting the hot spots.
To complement distributed tracing, runtime metrics, log analytics, Synthetic Monitoring, and Real User Monitoring, we’ve made another addition to the application developer’s toolkit to make troubleshooting performance issues even faster and simpler. Continuous Profiler is an always-on, production code profiler that enables you to analyze code-level performance across your entire environment, with minimal overhead.