vitess/primecache.go at master · youtube/vitess · GitHub
// primecache primes the MySQL buffer cache with the rows that are
// going to be modified by the replication stream. It only activates
// if we're falling behind on replication.
db  vitess  mysql 
Sidekiq is a full-featured background processing framework for Ruby. It aims to be simple to integrate with any modern Rails application and much higher performance than other existing solutions.
[1406.2294] A Fast, Minimal Memory, Consistent Hash Algorithm
We present jump consistent hash, a fast, minimal memory, consistent hash algorithm that can be expressed in about 5 lines of code. In comparison to the algorithm of Karger et al., jump consistent hash requires no storage, is faster, and does a better job of evenly dividing the key space among the buckets and of evenly dividing the workload when the number of buckets changes. Its main limitation is that the buckets must be numbered sequentially, which makes it more suitable for data storage applications than for distributed web caching.
research  caching  hashing  algorithm 
6 weeks ago
Evolution of the Netflix API
Ben Christensen describes how the Netflix API evolved from a typical one-size-fits-all RESTful API designed to support public developers into a web service platform optimized to handle the diversity and variability of each device and user experience. The talk will also address the challenges involving operations, deployment, performance, fault-tolerance, and rate of innovation at massive scale.
netflix  video 
7 weeks ago
A parallelizing combination of ping/traceroute
tools  networking 
9 weeks ago
HCF: Soundtrack - SuperMusicVision
hacf  tv  music 
10 weeks ago
Passive TCP Performance Analysis
             In the last two decades the Internet has changed a lot in terms of its bandwidth, types of applications and number of users, however, what has not changed is the fact that TCP is still the most popular transport protocol, accounting for more than 80% of the Internet traffic. Given TCP's wide-spread usage, its performance fundamentally impacts the performance of Internet transfers. Packet losses are known to affect TCP performance by impacting two important aspects of TCP - reliable delivery and congestion control. However, what is not well understood is exactly how much they affect TCP performance. The extent to which losses affect TCP performance depends on how well TCP deals with these losses. Current analysis techniques for TCP lack the level of details or accuracy required to illuminate the above issues. To achieve the required level of details and accuracy, We have developed a new analysis tool and have used it to study two different interactions between TCP and losses -- (i) TCP's efficiency in detecting packet losses when they occur and (ii) TCP's efficiency in avoiding losses when possible. We describe both our methodology and analysis below.
net  tcp 
april 2014
Big Data Benchmark
Several analytic frameworks have been announced in the last year. Among them are inexpensive data-warehousing solutions based on traditional Massively Parallel Processor (MPP) architectures (Redshift), systems which impose MPP-like execution engines on top of Hadoop (Impala, HAWQ) and systems which optimize MapReduce to improve performance on analytical workloads (Shark, Stinger/Tez). This benchmark provides quantitative and qualitative comparisons of five systems. It is entirely hosted on EC2 and can be reproduced directly from your computer.
april 2014
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