high_scalability high_scalability-2012 high_scalability-2012-1293 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: This post on Prismatic ’s Architecture is adapted from an email conversation with Prismatic programmer Jason Wolfe . What should you read on the web today? Any thoroughly modern person must solve this dilemma every day, usually using some occult process to divine what’s important in their many feeds: Twitter, RSS, Facebook, Pinterest, G+, email, Techmeme, and an uncountable numbers of other information sources. Jason Wolfe from Prismatic has generously agreed to describe their thoroughly modern solution for answering the “what to read question” using lots of sexy words like Machine Learning, Social Graphs, BigData, functional programming, and in-memory real-time feed processing. The result is possibly even more occult, but this or something very much like it will be how we meet the challenge of finding interesting topics and stories hidden inside infinitely deep pools of information. A couple of things stand out about Prismatic. They want you to know that Prismatic is being built
sentIndex sentText sentNum sentScore
1 Within seconds of a user signing up for Prismatic enough of their historical activity is fetched and analyzed to make pretty solid topic and publisher suggestions. [sent-30, score-0.551]
2 Most of data flows through a backend pipeline from one service to the next without any round-trips to disk. [sent-40, score-0.283]
3 For each piece of content it must be known who is sharing it and what they are saying about it so relevant commentary can be shown alongside an article and so a user can be shown content shared by their friends or people with similar tastes. [sent-50, score-0.283]
4 At the top of the ingest pipeline are pollers: A RSS poller loops through feeds looking for new articles Twitter and Facebook pollers connect to the corresponding APIs and fetch comments/tweets from users and their friends. [sent-52, score-0.66]
5 A queue of URLs is kept, each of which runs through a 'graph' that successively elaborates the URL, fetching its HTML, applying machine learning algorithms to extract the text of the article, identify the best images, extract the publisher, label with applicable topics, and so on. [sent-61, score-0.291]
6 Onboarding - Backend Onboarding is ingesting data about new users so they can be provided with a great personalized experience within seconds of signing up for the app. [sent-65, score-0.367]
7 This is fast enough that suggestions can start being computed after users OAuth in, and usually be ready with personalized suggestions by the time the user has finished creating their account (selecting a handle and password) and reached in walkthrough. [sent-69, score-0.329]
8 This fingerprint is quite large, and constantly changes as the user views and interacts with content on the site. [sent-79, score-0.285]
9 The remaining problem is how to efficiently serve up feeds for a user without incurring the IO cost and latency of fetching and updating the fingerprint on each request. [sent-85, score-0.463]
10 When a machine comes down the data is flushed and the user is moved to another machine -- in the worst case a few minutes of non-critical data will be lost for some users, which is consider worth it for the benefits in simplicity and scalability. [sent-88, score-0.527]
11 Other services - Client facing There are a few other separate client-facing services: Public-feeds - which does smart caching of topic and publisher feeds for non-logged in users, fetching them from the regular API on demand and allowing multiple ages of each feed to be paged through. [sent-90, score-0.514]
12 Mostly a thin layer in front of the SQL database, which stores critical user data that needs to be periodically snapshotted and backed up. [sent-92, score-0.356]
13 An URL-shortener Batch and other services Other services for machine language training, data archival, and event tracking/analytics. [sent-93, score-0.35]
14 MongoDB is used to store server metrics and user analytics, largely because it supports a nice low-hassle story for sending raw events of different shapes, maintaining the right indices, and keeping online roll-ups for counts. [sent-94, score-0.297]
15 It is used all over the place; for instance, the doc-analysis pipeline is a graph, where each elaboration of a document may depend on the previous (e. [sent-97, score-0.283]
16 , identifying topics for a document depends on having already extracted its text); the newsfeed generation process is a graph composed of many query and ranking steps; and each of the production services is itself a graph, where each resource (e. [sent-99, score-0.338]
17 If a user removes 5 articles from a given publisher in a row, then stop showing them articles from that publisher right now, not tomorrow. [sent-118, score-0.659]
18 This allows rerruning later the user interest ML over the raw events, in case any data is lost through slightly loose write-back caches on this data when a machine goes down or something like that. [sent-121, score-0.479]
19 Think very carefully about the entire pipeline and all of the data that flows through it. [sent-124, score-0.283]
20 The onboarding process, for example, ingests data about new users so they can be provided with a great personalized experience within seconds of signing up for the app. [sent-130, score-0.443]
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