Search Results for "32"

This crate provides custom de/serialization helpers to use in combination with serde's with-annotation and with the improved serde_as-annotation.

...Featuring automatic serialization and deserialization, request and response type detection, variety of authentications and other useful features, it is being used by hundreds of thousands of projects. RestSharp passed over 32 million downloads on NuGet, with average daily download count of 10,000. It's being used by many popular OSS projects, including Roslyn and Swagger. The main purpose of RestSharp is to make synchronous and asynchronous calls to remote resources over HTTP. As the name suggests, the main audience of RestSharp are developers who use REST APIs. ...

Chunk-based JSON parsing and generation in Objective-C. SBJson's number one feature is stream/chunk-based operation. Feed the parser one or more chunks of UTF8-encoded data and it will call a block you provide with each root-level document or array. Or, optionally, for each top-level entry in each root-level array. With this you can reduce the apparent latency for each download/parse cycle of documents over a slow connection. You can start parsing and return chunks of the parsed document...

...The script is essentially an engine to generate any sort of random data in any format. It currently comes with 30 or so Data Types (types of data it generates), 12 Export Types (formats for the data, like CSV, SQL, JSON), plus around 32 data sets for specific countries (city names, regions, etc). But more importantly, it can be extended in any way you want. Check out the developer documentation for more information on that. The current major version of the script is 4.x, which was a big change over earlier releases. Earlier versions were written in PHP and MYSQL and 3.x offered a REST API to let you generate the data programmatically rather than via an API. ...

...Extended Memory Semantics (EMS) unifies synchronization and storage primitives to address several challenges of parallel programming. A modern multi-core server has 16-32 cores and nearly 1TB of memory, equivalent to an entire rack of systems from a few years ago. As a consequence, jobs formerly requiring a Map-Reduce cluster can now be performed entirely in shared memory on a single server without using distributed programming.