We recently presented a paper about Avatara at VLDB 2012. Avatara is LinkedIn's scalable, low latency, and highly-available OLAP system for "sharded" multi-dimensional queries. It has been powering our analytical products, such as "Who's Viewed My Profile?" and "Who's Viewed This Job?", for two and a half years.
Two years ago we hit a wall. The scale of LinkedIn’s data was growing beyond what we could analyze. At the same time, our members needed their analytics and insights in real-time. We needed a solid solution that would grow with LinkedIn and serve as the platform to power all of our analytics needs across the company at web-scale. In this post, we will share how and why we built a distributed real-
Accepted Papers for the Industrial, Applications and Experience Track 818: "Building User-defined Runtime Adaptation Routines for Stream Processing Applications" by Gabriela Jacques-Silva (IBM Research), Bugra Gedik (IBM Watson), Rohit Wagle (IBM Research), Kun-Lung Wu (IBM Research),Vibhore Kumar (IBM Research) 819: "A Storage Advisor for Hybrid-Store Databases" by Philipp Rösch (SAP AG), Lars Da
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
