Open-sourcing PalDB, a lightweight companion for storing side data
Open Source Open-sourcing PalDB, a lightweight companion for storing side data LinkedIn’s data products give our members recommendations, analytics and insights. As we continue to invent more products that leverage data for our members, we need to push the envelope in our data processing capabilities and do more with less. One issue that often comes up is what to do to improve the usability and me
"Turning the database inside out with Apache Samza" by Martin Kleppmann
Databases are global, shared, mutable state. That's the way it has been since the 1960s, and no amount of NoSQL has changed that. However, most self-respecting developers have got rid of mutable global variables in their code long ago. So why do we tolerate databases as they are? A more promising model, used in some systems, is to think of a database as an always-growing collection of immutable f
Real time insights into LinkedIn's performance using Apache Samza
Real time insights into LinkedIn's performance using Apache Samza It's not easy to quickly gather all the data that goes into a LinkedIn page view, particularly for something like our home page. LinkedIn benefits from a very distributed service-oriented architecture for assembling pages as quickly possible while still being resilient to failures from any particular bit of content. Each bit that en
Garbage Collection Optimization for High-Throughput and Low-Latency Java Applications
Garbage Collection Optimization for High-Throughput and Low-Latency Java Applications High-performance applications form the backbone of the modern web. At LinkedIn, a number of internal high-throughput services cater to thousands of user requests per second. For optimal user experience, it is very important to serve these requests with low latency. For example, a product our members use regularly
Dust.js
The document discusses LinkedIn's adoption of the Dust templating language in 2011. Some key points: - LinkedIn needed a unified view layer as different teams were using different templating technologies like JSP, GSP, ERB. - They evaluated 26 templating options and selected Dust as it best met their criteria like performance, i18n support, and being logic-less. - Dust templates are compiled to Ja
Rest.li: RESTful Service Architecture at Scale
Co-author: Moira Tagle Today we are announcing the open-sourcing of Rest.li, a piece of infrastructure developed and used here at LinkedIn. Rest.li is a REST+JSON framework for building robust, scalable service architectures using dynamic discovery and simple asynchronous APIs. We feel that Rest.li fills a niche for building RESTful service architectures at scale, offering a developer workflow for
SNA Projects Blog : Tech Talk: Michael Deerkoski (Flickr) — “Continuous Deployment at Flickr”
LinkedIn operates the world’s largest professional network with more than 645 million members in over 200 countries and territories. This team builds distributed systems that collect, manage and analyze this digital representation of the world's economy, while our AI experts, data scientists and researchers conduct applied research that fuel LinkedIn’s data-driven products and provide insights tha
Intra-cluster Replication in Apache Kafka
Kafka is a distributed publish-subscribe messaging system. It was originally developed at LinkedIn and became an Apache project in July, 2011. Today, Kafka is used by LinkedIn, Twitter, and Square for applications including log aggregation, queuing, and real time monitoring and event processing. In the upcoming version 0.8 release, Kafka will support intra-cluster replication, which increases both
Leaving JSPs in the dust: moving LinkedIn to dust.js client-side templates
Leaving JSPs in the dust: moving LinkedIn to dust.js client-side templates This is first of the three blog posts talking about how we build scalable, feature-rich, front-end apps for LinkedIn with an emphasis on site speed, performance, and developer productivity. Today, we'll tell you about our move from server-side templates, such as JSPs, ERBs, and GSPs, to client-side JavaScript templates powe
Kafkaによるリアルタイム処理
This document discusses Kafka, a distributed messaging system originally created by LinkedIn and now an Apache project. It provides an overview of how Kafka works and how various companies use it, including for log processing (LinkedIn), analytics (Facebook, Twitter, Google), and integrating with other technologies like Hadoop, Zookeeper, HBase and Storm. It also covers Kafka's scalability, perfor
LinkedIn Data Infrastructure (QCon London 2012)
LinkedIn uses DataBus to replicate data changes from Oracle in real-time. DataBus consists of relay and bootstrap services that capture changes from Oracle and distribute them to various services like search indexes, graphs, and read replicas to keep them updated in real-time. This allows users to immediately see profile updates or new connections in search results and feeds.Read less
Open-sourcing Kafka, LinkedIn's distributed message queue
Open-sourcing Kafka, LinkedIn's distributed message queue Code Alert! This is a part of our continuing series on Engineering at LinkedIn. If this isn’t your cup of Java, check back tomorrow for regular LinkedIn programming. In the meanwhile, check out some of our latest product features, tips and tricks, or user stories. - Ed. We are pleased to open-source another piece of infrastructure software
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LinkedIn Open Sources IndexTank: What Is IndexTank and How Does It Compare to Lucene and Solr