Haha. The Async Http Client library purpose is to allow Java applications to easily execute HTTP requests and asynchronously process the HTTP responses. In this blog I will explain how to use the library and what features are supported. Executing request synchronously or asynchronously. The first thing to decide when using the library is if your application can handle asynchronous response or not. If not, the library has been designed using the Future API, hence you can always execute synchronous call by blocking on the Future.get() method: AsyncHttpClient client = new AsyncHttpClient(); Response response = client.prepareGet((" .execute().get(); The above means the request will block until the full Response has been received. AsyncHttpClient client = new AsyncHttpClient(); Response response = client.preparePut((" A better way than above would consist of using an AsyncHandler. Creating a Request object Creating a Response object or.
Using Flat Files So Elections Don't Break Your Server. In the Times Interactive News Department, we pay careful attention to caching strategy to make sure it’s a good fit for the project at hand. Publishing live election results requires a carefully tuned system: the setup must be able to withstand some of the most intense traffic levels seen all year at NYTimes.com, but at the same time, it needs to get information to our readers quickly as we receive updates from our editors and the Associated Press. Most of our projects live as Rails applications deployed behind Varnish. They typically run on Amazon’s EC2, backed by MySQL on an Amazon RDS. We knew we wanted to build out the election results as part of our usual stack, but we also knew the setup — given its scale and profile — needed to be resilient against the following: Varnish failure — varnishd, so far, has never crashed on us.
We also wanted the system to have these characteristics: Trading Varnish for Flat Files The Setup The election night setup at NYTimes.com. Just Make It Faster. As a user, how often have you thought “I wish this web service was faster.” As a CEO, how often have you said “just make it faster.” Or, more simply, “why is this damn thing so slow?” This is a not a new question. I’ve been thinking about this since I first started writing code (APL) when I was 12 (ahem – 33 years ago) on a computer in the basement of a Frito-Lay data center in Dallas. This morning, as part of my daily information routine, I came across a brilliant article by Carlos Bueno, an engineer at Facebook, titled “The Full Stack, Part 1.” “A “full-stack programmer” is a generalist, someone who can create a non-trivial application by themselves.
He then dissects a simple SQL query (DELETE FROM some_table WHERE id = 1234;) and gives several quick reasons why performance could vary widely when this query is executed. It reminded me of a client situation from my first company, Feld Technologies. We had one big problem. I recall having a very stressful month. Pregel. Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. In this paper we present a computational model suitable for this task. Programs are expressed as a sequence of iterations, in each of which a vertex can receive messages sent in the previous iteration, send messages to other vertices, and modify its own state and that of its outgoing edges or mutate graph topology. Facebook: Why our 'next-gen' comms ditched MySQL. About a year ago, when Facebook set out to build its email-meets-chat-meets-everything-else messaging system, the company knew its infrastructure couldn't run the thing.
"[The Facebook infrastructure] wasn't really ready to handle a bunch of different forms of messaging and have it happen in real time," says Joel Seligstein, a Facebook engineer who worked on the project. So Seligstein and crew mocked up a multifaceted messaging prototype, tossed it onto various distributed storage platforms, and ran a Big Data bake off. The winner was HBase, the open source distributed database modeled after Google's proprietary BigTable platform. Facebook was already using MySQL for message storage, the open source Cassandra platform for inbox search, and the proprietary Haystack platform for storing photos.
But in the company's mind, HBase was better equipped to handle a new-age messaging system that would seek to seamlessly juggle email, chat, and SMS as well as traditional on-site Facebook messages. Home | VlexoFree Hosting. Web Hosting Services, Reseller Hosting, and Dedicated Servers by HostGator.