Co-authors: Suruchi Shah and Hari Shankar The rise of instant messaging has changed how we communicate. Compared to the back-and-forth of email, we send and receive messages at much higher volumes and speeds. In the instant conversations we have, we also expect to be able to search for important phrases, moments, or references with ease. This rapid increase in...
Performance Articles
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- Topics:
- Performance,
- infrastructure,
- Architecture,
- Search
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Co-authors: Xiang Zhang and Chuck Jerian Espresso is LinkedIn’s document-oriented, highly available, and timeline-consistent distributed datastore. Clients, such as services fetching profile data, read a subset fields of whole documents from Espresso in different use cases for reasons related to performance and costs. For example, one use case is reading only a...
- Topics:
- Performance,
- ESPRESSO
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Pinot, an open source scalable distributed columnar OLAP data store, is used extensively across almost all areas of LinkedIn. It drives member-facing use cases such as Feed Item Popularity and Who Viewed My Profile, supports recruiter tools such as Talent Intelligence, and holds the data for our internal unified metrics platform. Pinot adoption has grown...
- Topics:
- Performance,
- Pinot,
- linux
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Monitor, profile, optimize, and ramp. This is the cycle of performance operations that we rely on to improve our Android app. Part 1...
- Topics:
- Performance,
- Mobile,
- Android,
- RUM,
- site speed
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Defined as the speed at which an app loads and responds to a member, app performance is critical to an app’s success. When an app...
- Topics:
- Performance,
- Mobile,
- Android
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One of the core aspirations of the engineering teams at LinkedIn is to delight our members with instantaneous page load experiences....
- Topics:
- Performance,
- site speed