Replace master/slave with primary/replica

After further feedback I've not updated the images and just made
alterations to the READMEs for mentions of master-slave to
primary-replica

solutions/system_design/pastebin/README-zh-Hans.md

@@ -239,7 +239,7 @@ class HitCounts(MRJob):
 
 说明您将迭代地执行这样的操作：1)**Benchmark/Load 测试**，2)**Profile** 出瓶颈，3)在评估替代方案和权衡时解决瓶颈，4)重复前面，可以参考[在 AWS 上设计一个可以支持百万用户的系统](../scaling_aws/README.md)这个用来解决如何迭代地扩展初始设计的例子。
 
-重要的是讨论在初始设计中可能遇到的瓶颈，以及如何解决每个瓶颈。比如，在多个 **Web 服务器** 上添加 **负载平衡器** 可以解决哪些问题？ **CDN** 解决哪些问题？**Master-Slave Replicas** 解决哪些问题? 替代方案是什么和怎么对每一个替代方案进行权衡比较？
+重要的是讨论在初始设计中可能遇到的瓶颈，以及如何解决每个瓶颈。比如，在多个 **Web 服务器** 上添加 **负载平衡器** 可以解决哪些问题？ **CDN** 解决哪些问题？**Primary-Replica Replicas** 解决哪些问题? 替代方案是什么和怎么对每一个替代方案进行权衡比较？
 
 我们将介绍一些组件来完成设计，并解决可伸缩性问题。内部的负载平衡器并不能减少杂乱。
 
@@ -253,7 +253,7 @@ class HitCounts(MRJob):
 * [应用层](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用层)
 * [缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存)
 * [关系型数据库管理系统 (RDBMS)](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#关系型数据库管理系统rdbms)
-* [SQL write master-slave failover](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#故障切换)
+* [SQL write primary-replica failover](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#故障切换)
 * [主从复制](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#主从复制)
 * [一致性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#一致性模式)
 * [可用性模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#可用性模式)
@@ -264,7 +264,7 @@ class HitCounts(MRJob):
 
 要处理 *平均* 每秒 40 读请求(峰值更高)，其中热点内容的流量应该由 **内存缓存** 处理，而不是数据库。**内存缓存** 对于处理分布不均匀的流量和流量峰值也很有用。只要副本没有陷入复制写的泥潭，**SQL Read Replicas** 应该能够处理缓存丢失。
 
-对于单个 **SQL Write Master-Slave**，*平均* 每秒 4paste 写入 (峰值更高) 应该是可以做到的。否则，我们需要使用额外的 SQL 扩展模式:
+对于单个 **SQL Write Primary-Replica**，*平均* 每秒 4paste 写入 (峰值更高) 应该是可以做到的。否则，我们需要使用额外的 SQL 扩展模式:
 
 * [联合](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#联合)
 * [分片](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#分片)

solutions/system_design/pastebin/README.md

@@ -241,7 +241,7 @@ To delete expired pastes, we could just scan the **SQL Database** for all entrie
 
 State you would do this iteratively: 1) **Benchmark/Load Test**, 2) **Profile** for bottlenecks 3) address bottlenecks while evaluating alternatives and trade-offs, and 4) repeat.  See [Design a system that scales to millions of users on AWS](../scaling_aws/README.md) as a sample on how to iteratively scale the initial design.
 
-It's important to discuss what bottlenecks you might encounter with the initial design and how you might address each of them.  For example, what issues are addressed by adding a **Load Balancer** with multiple **Web Servers**?  **CDN**?  **Master-Slave Replicas**?  What are the alternatives and **Trade-Offs** for each?
+It's important to discuss what bottlenecks you might encounter with the initial design and how you might address each of them.  For example, what issues are addressed by adding a **Load Balancer** with multiple **Web Servers**?  **CDN**?  **Primary-Replica Replicas**?  What are the alternatives and **Trade-Offs** for each?
 
 We'll introduce some components to complete the design and to address scalability issues.  Internal load balancers are not shown to reduce clutter.
 
@@ -255,8 +255,8 @@ We'll introduce some components to complete the design and to address scalabilit
 * [API server (application layer)](https://github.com/donnemartin/system-design-primer#application-layer)
 * [Cache](https://github.com/donnemartin/system-design-primer#cache)
 * [Relational database management system (RDBMS)](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms)
-* [SQL write master-slave failover](https://github.com/donnemartin/system-design-primer#fail-over)
-* [Master-slave replication](https://github.com/donnemartin/system-design-primer#master-slave-replication)
+* [SQL write primary-replica failover](https://github.com/donnemartin/system-design-primer#fail-over)
+* [Primary-replica replication](https://github.com/donnemartin/system-design-primer#primary-replica-replication)
 * [Consistency patterns](https://github.com/donnemartin/system-design-primer#consistency-patterns)
 * [Availability patterns](https://github.com/donnemartin/system-design-primer#availability-patterns)
 
@@ -266,7 +266,7 @@ An **Object Store** such as Amazon S3 can comfortably handle the constraint of 1
 
 To address the 40 *average* read requests per second (higher at peak), traffic for popular content should be handled by the **Memory Cache** instead of the database.  The **Memory Cache** is also useful for handling the unevenly distributed traffic and traffic spikes.  The **SQL Read Replicas** should be able to handle the cache misses, as long as the replicas are not bogged down with replicating writes.
 
-4 *average* paste writes per second (with higher at peak) should be do-able for a single **SQL Write Master-Slave**.  Otherwise, we'll need to employ additional SQL scaling patterns:
+4 *average* paste writes per second (with higher at peak) should be do-able for a single **SQL Write Primary-Replica**.  Otherwise, we'll need to employ additional SQL scaling patterns:
 
 * [Federation](https://github.com/donnemartin/system-design-primer#federation)
 * [Sharding](https://github.com/donnemartin/system-design-primer#sharding)