翻译出处： https://academy.datastax.com/planet-cassandra/nosql-performance-benchmarks
Apache Cassandra NoSQL效率标准
Apache Cassandra™ is a leading NoSQL database platform for modern applications. By offering benefits of continuous availability, high scalability & performance, strong security, and operational simplicity — while lowering overall cost of ownership — Cassandra has become a proven choice for both technical and business stakeholders. When compared to other database platforms such as HBase, MongoDB, Redis, MySQL and many others, the linearly scalable database Apache Cassandra™ delivers higher performance under heavy workloads.
Apache Cassandra ™ 是领先的NoSQL应用数据库平台. 提供可持续应用能力，高伸缩和性能，强大安全性和操作简单—同时降低学习成本—Cassandra 已经成为技术和业务两者的最佳选择。对比其他数据库如Hbase，MongoDB,Redis，Mysql和更多其他数据库，这个可伸缩数据库 Apache Cassandra™ 在高负载的情况下能提供高性能服务。
The following benchmark tests provide a graphical, ‘at a glance’ view of how these platforms compare under different scenarios. When selecting a database it is critically important to understand your use case and find the right fit. Below you will find the following three bechmarks; taking a look at write/read performance and performance at scale:
下面的基准测试提供了一个图形对比，‘对比’ 可以看出在不同场景下面数据库的比较。在选择的数据库时，了解你应用数据库使用场景和选择适合的数据库是重要的。你会发现以下三种情况：看下写/读的情况和扫描情况：
University of Toronto Benchmark
多伦多大学的标准
Netflix: Benchmarking Apache Cassandra Scalability
Netflix公司：Apache Cassandra 可扩展性能
End Point Benchmark Configuration and Results
最后得到标准的配置和结果
University of Toronto NoSQL Database Performance
多伦多大学 NoSQL数据库性能
Engineers at the University of Toronto, in 2012, conducted a thorough benchmarking analysis of various NoSQL platforms including: Apache Cassandra, HBase, MySQL, Redis and Voldemort. The testing was extremely thorough and included a view into performance under varying workloads.
在2012年，多伦多大学的工程师们，对于各种的NoSQL数据库做了全面的性能测试：Apache Cassandra，Hbase，MySQL，Redis 和 Voldemort.测试得非常全面，包括在不同的极端工作负载条件下的性能统计。
For a look at the details behind this analysis as well as a complete write up of the benchmark configurations used, the white paperSolving Big Data Challenges for Enterprise Application Performance Managementprovides all of the insight from this test. Overall their results identified Apache Cassandra the “clear winner throughout our experiments”.
来查看一下分析细节背后的一个完整的基准测试，《解决企业应用程序性能管理的大数据挑战》的白皮书提供了这个测试的所有见解.总体来说，Apache Cassandra 是“整个实验当中明显的赢家”.
A summary of throughput and latency results are available here.
吞吐量和延迟结果在汇总如下.
Throughput for workload Read/Write
吞吐量在工作中的读/写
Throughput for workload Read/Scan/Write
工作中的吞吐量 读/扫描/写
Read latency for workload Read/Write
工作中的读等待 读/写
Write latency for workload Read/Write
工作中的写操作 读/写
If this benchmarking data from University of Toronto is interesting,take a 10 minute Cassandra walkthroughand learn more.
多伦多大学对于标准数据得到的有趣结果，十分钟 Cassandra 入门和学习
Netflix
Netflix decided to run a test designed to validate their tooling and automation scalability as well as the performance characteristics of Cassandra. The results of their testing are provided below. For a more thorough write up of the Netflix testing process including configuration settings and commentary, visit their tech blog post titledBenchmarking Cassandra Scalability on AWS – Over a million writes per second.
Netflix 决定运行一个测试来验证它们的工具和自动化可伸缩性以及Cassandra的性能特性.这测试结果提供如下:有关Netflix测试过程的更详细的描述，包括设置配置和评论，请访问他们的技术博客，标题为《对AWS的Casdand可伸缩性进行基准测试-每秒超过一百万次写入》.
End Point Benchmark Configuration and Results Summary
最终基本配置和结果摘要
End Point, a database and open source consulting company, benchmarked the top NoSQL databases — Apache Cassandra, Apache HBase, and MongoDB — using a variety of different workloads on Amazon Web Services EC2 instances. This is an industry-standard platform for hosting horizontally scalable services such as the NoSQL databases that were tested. In order to minimize the effect of AWS CPU and I/O variability, End Point performed each test 3 times on 3 different days. New EC2 instances were used for each test run to further reduce the impact of any “lame instance” or “noisy neighbor” effect on any one test.
最终，一个数据库和开源咨询公司，检测到最好的NoSQL数据库— Apache Cassandra, Apache HBase, and MongoDB — 运用了大量的不同工作负载在亚马逊Web服务 EC2实例上。这是一个可伸缩服务的行业标准平台，如被测试的NoSQL数据库.为了尽量减少AWS CPU和I/O可变性的影响。结束点在3个不同的时间进行了3次测试.新EC2实例能运用每次测试执行进一步减少任何“差劲的实例”或“嘈杂的邻居” 效率对任何一个测试的影响。
A summary of the workload analysis is available below. For a review of the entire testing process with testing environment configuration details, thebenchmarking NoSQL databases white paperby End Point is available.
下面是工作负载分析的总结.对于具有测试环境配置细节的整个测试过程的回顾,可以使用对《NOSQL数据库白皮书》的最终基准测试。
Goals for the Tests
成功的测试
Select workloads that are typical of today’s modern applications
选择当今现代应用中典型的工作负载。
Use data volumes that are representative of ‘big data’ datasets that exceed the RAM capacity for each node
运用数据量可以代表“大数据” 数据集并超过每个节点的RAM容量。
Ensure that all data written was done in a manner that allowed no data loss (i.e. durable writes), which is what most production environments require
确保所有数据已经写入以允许数据丢失的方式(持久化).这也是大多生产环境的需要。
Tested Workloads
测试负载
The following workloads were included in the benchmark:
以下工作负载中包括在基准中：
Read-mostly workload, based on YCSB’s provided workload B: 95% read to 5% update ratio
更多读取工作量，基于YCSB’s 提供工作负载 B：95% 读 5% 更新率
Read/write combination, based on YCSB’s workload A: 50% read to 50% update ratio
基于YCSB’s的工作负荷A：50% 读取50% 更新率
Read-modify-write, based on YCSB workload F: 50% read to 50% read-modify-write
读-修改-写，基于基于YCSB’s的工作负荷 F：50% 读 50% 读-修改-写
Mixed operational and analytical: 60% read, 25% update, 10% insert, and 5% scan
最大操作和解析： 60% 读，25% 修改，10%插入和5%扫描
Insert-mostly combined with read: 90% insert to 10% read ratio
插入和读：90% 插入到10%读
Throughput Results
吞吐量结果：
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