Download Profile Highlight Instagram
Lynelle Bookmiller
The initial profile highlights system was built five years ago to support a relatively smaller number of highlights between two members. Started as a proof of concept, the system was built using a monolithic architecture, where all types of highlights were developed by a single team with limited focus on extensibility and modularity. It worked well for a while. As the company grew to focus on building an active community, new challenges surfaced:
How do we enable a distributed development model where each highlight has its own owners and is strictly separated from others? (The distributed development model would allow other teams to contribute to create a highlight ecosystem.)
These challenges were not well addressed by the old system. A new highlight developer would have to understand a significant part of the service in order to add a new highlight type. Numerous changes would have to be made to different parts of the system, from the entry point of the service all the way to the code where downstream service calls are made. This includes but is not limited to adding a method to an interface, adding an implementation for that, wiring in the new implementation, and creating a LiX control (LinkedIn A/B testing infrastructure). All of these applied to writing testing code as well. Consequently, the existing system was more prone to bugs and iteration speed was impacted.
Microservices architecture
LinkedIn employs the microservices architecture to deliver most of the member experiences. A page view can fan out to a large number of downstream service calls to fetch information, such as profile data, connection information, profile highlights, endorsements, etc., from different services. Each of these calls can further fan out to even more downstream services. For example, the profile highlights service invokes other services to get profile data, shared connections, jobs, etc.
The solution we adopted to address the aforementioned challenges was to re-architect the system into a platform using a plug-in architecture, where each type of highlight can be implemented as a plugin that can then be registered and integrated easily and independently, as depicted in the architectural diagram.
Plugin interface
The plugin interface serves as the hooking point between the profile highlights and the core platform services. Implementation-wise, it uses the classic template method design pattern to provide certain default behaviors while delegating other parts for customization to fetch and assemble individual highlights. Each new type of highlight should implement the provided interface to supply case-specific highlight logic.
Individual highlight plugins
Individual highlight plugins use the plugin interface to integrate with the platform by implementing various methods exposed through the interface. Within the plugin itself, the implementation can vary a lot. Plugins can call different services to fetch different pieces of data and implement complicated logic to compute highlights. They are completely independent of each other by design, and can be under different ownership. The UML diagram below shows the class hierarchy including the plugin interface and example individual plugins.
Task Manager. The task manager component is responsible for bootstrapping the plugins, creating ParSeq tasks to execute the functionalities implemented in individual plugins, and assembling the results. It exempts highlight implementers from dealing with ParSeq task compositions.
Cache Manager. As mentioned earlier, data within LinkedIn is generally identified by a URN. This applies to the profile highlights service as well. When a set of highlights are computed between a profile owner and profile viewer, an ID is created for each highlight and details are stored in a distributed cache, Couchbase, for a configurable period of time.
The Decoration Manager helps highlight plugins invoke remote services to resolve URNs as discussed earlier. Service calls are expensive. In a service-oriented world, this can be exacerbated if a system is not designed with that in mind. The fact that the profile highlights system isolates individual highlight plugins exposes another challenge that must be solved: plugin developers can only optimize the performance at the plugin level. There is no way for them to interact with other plugins. To provide a simple example, when a request arrives, the system needs to compute shared educations and shared experiences. For shared educations, it needs to call profiles service to fetch profiles data, and the schools service to fetch school information. In the meantime, shared experiences needs to call the profiles services, too, for the same reason, and the organization service to fetch company information. Both end up calling the same profiles service for the same information, visualized below in red.
This can quickly deteriorate performance and increase the cost of serve when the number of highlights grows. To solve this problem, we built a performance optimization mechanism within the Decoration Manager. The basic idea is to use LoadingCache to cache requests for all downstream calls initiated from a highlight API call. The key of the cache consists of two parts:
Understandability. Whoever wants to implement a new type of highlight within the service only needs to understand the interface (hooking point) provided by the core platform services. No other knowledge of the system (such as understanding how the highlights will be instantiated, executed, etc.) is required.
Extensibility. Adding a new highlight will be a confined change without the need to touch any other part of the system. All the developer needs to do is 1) implement the creation of the highlight, and 2) register the highlight using annotations.
Reusability. Common functional/non-functional requirements of the system are implemented once and shared by all highlight plugins. Such capabilities include, but are not limited to, timeout management, exception management, default single get implementation, optimization of downstream calls, performance/error metrics, etc.
The performance optimization in the platform has significantly improved latencies and cost to serve compared to the old architecture. The p99, p95, p90, and p50 improved by 29%, 18.6%, 17%, and 7%, respectively, and the cost to serve (including savings from downstream service calls) has been reduced by approximately 50%. We are also set for a much faster iteration of various types of highlights.
Sepsis is an aberrant systemic inflammatory response mediated by the acute activation of the innate immune system. Neutrophils are important contributors to the innate immune response that controls the infection, but harbour the risk of collateral tissue damage such as thrombosis and organ dysfunction. A better understanding of the modulations of cellular processes in neutrophils and other blood cells during sepsis is needed and can be initiated via transcriptomic profile investigations. To that point, the growing repertoire of publicly accessible transcriptomic datasets serves as a valuable resource for discovering and/or assessing the robustness of biomarkers. We employed systematic literature mining, reductionist approach to gene expression profile and empirical in vitro work to highlight the role of a Nudix hydrolase family member, NUDT16, in sepsis. The relevance and implication of the expression of NUDT16 under septic conditions and the putative functional roles of this enzyme are discussed.
Anyone can build a LinkedIn profile but not everyone is going to have a good one. The skills section is one piece of the picture. Ensure that all aspects of the profile tell a cohesive story that stands out. The goal is a profile attractive to recruiters and visitors.
Professionals, recruiters and students use LinkedIn for different reasons. Finding people in the same field, connecting with alumni from college and landing a job can be done on the platform. For job-seeking purposes, highlight skills in the appropriate areas to optimize your profile.
A Linkedin profile with highlighted skills becomes a searchable profile. Job-seekers with developed profiles have better chances of connecting with recruiters. And job offers come to those who take advantage of LinkedIn tools.
Including skills helps build a personal brand. Skills attained from a specific job experience can now be included in the experience section, which is listed above the skills section on LinkedIn profiles.
Assess your skills and credentials. How does your background and experience align with the qualifications outlined in the job description? Select skills, experiences, special knowledge, and accomplishments that you want to highlight in your profile selection.
How can you make your resume stand out to an employer? This resource will help you learn how to use the STAR method to develop strong bullet points that highlight your skills and accomplishments relevant to a position.
The GitHub snapshot series primarily focuses on how repository- and owner-specific metadata sheds light on broader questions regarding software development and proliferation. However, the individual stories of major repositories and owners get lost when tackling such broad questions. To highlight the various aspects and benefits of GitHub discussed throughout the series, this snapshot will focus on one of the most popular machine learning repository owners on the platform: HuggingFace.
Unlike regular Stories that disappear after 24hrs, Stories Highlights can live on your Instagram profile permanently. Highlights offer a great opportunity to drive attention to important content and details about your brand.
One-third of the most-viewed Instagram Stories are produced by businesses. But Stories disappear after 24 hours. With Instagram Highlights, you can give your best-received Stories a permanent place on your Instagram profile.
You can use Instagram Highlights to showcase your best content by creating a literal Highlights reel from the past week. This also allows you to keep ongoing promotions, upcoming events, and other business news on your profile longer.
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