Off late at my work which currently involves moving our platform code from monolithic to microservices architecture, I have come to realize that there is a lot of boilerplate code that not only needs to be implemented in every other service but also involves maintenance pertaining to tribal knowledge within the team. This means that there isn't any standard way of implementing a service, raising the possibility of a lot of code duplication, thereby resulting in a maintenance nightmare.
Maximizing uptime of diagnostic equipment is vital to both patients and healthcare organizations. As medical imaging equipment becomes more sophisticated and the need for healthcare organizations to improve their availability becomes more acute, so does the value of machine log data and advanced analytics. Here, we’ve listed several important ways that hospitals can use machine log data and predictive/prescriptive analytics to optimize operational efficiency and revenues.
Medical imaging or diagnostic equipment such as Computed Tomography (CT), Ultrasound, and Magnetic Resonance Imaging (MRI) devices play a critical role in modern healthcare. But while these devices enable healthcare providers to better diagnosis their patients' and provide an optimal treatment plan, they are also very expensive to maintain.
In this section we will investigate how Glassbeam’s DSL called SPL (Semiotic Parsing Language) helps in parsing multi-structured machine logs.
SPL allows a log file to be treated as a hierarchical document consisting of multiple segments (or sections). Each hierarchical segment is called namespace. This allows for zeroing in on the exact section to parse specific elements from, thus localizing the scope of extracts.
Glassbeam’s business revolves around providing business intelligence on machine data. Intelligence comes from structured data. Machine data is not always structured. So, there is a gap between what is needed and what is produced. As Glassbeam’s head of engineering, I am going to write a two series blog about how Glassbeam bridges this gap.
In my previous blog, I discussed the framework we have built for testing large scale machine log data. In this post, I will share results of our test. Every test run with varying server clusters was executed through our automation framework, and therefore had minimal effort on our side except clicking a button after deciding the number of servers we need.
The scalability of our platform is broadly dependent on 2 things:
While we run must-run performance tests for every release to make sure that the new features being added does not impact the performance of our platform, we (Engineering @ Glassbeam) wanted the ability to run large scale tests periodically too. However, running large scale performance tests is a time consuming and expensive affair. Such tests require spinning up tens of machines which can take days to setup and run the tests.
In modern healthcare, medical imaging devices such as X-ray, Computed Tomography(CT), Ultrasound, and Magnetic Resonance Imaging (MRI) devices play a critical role. These devices allow healthcare professionals to examine the patient and determine the root cause of their symptoms. Imaging devices allow healthcare providers to develop the right treatment plan for their patients.