Merge 399b2bd581 into 578e29cff8

solutions/system_design/mint/README.md

@@ -333,7 +333,7 @@ class SpendingByCategory(MRJob):
 
 State you would 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.
 
@@ -347,8 +347,8 @@ 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)
 * [Asynchronism](https://github.com/donnemartin/system-design-primer#asynchronism)
 * [Consistency patterns](https://github.com/donnemartin/system-design-primer#consistency-patterns)
 * [Availability patterns](https://github.com/donnemartin/system-design-primer#availability-patterns)
@@ -375,7 +375,7 @@ We might only want to store a month of `transactions` data in the database, whil
 
 To address the 200 *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.
 
-2,000 *average* transaction writes per second (higher at peak) might be tough for a single **SQL Write Master-Slave**.  We might need to employ additional SQL scaling patterns:
+2,000 *average* transaction writes per second (higher at peak) might be tough for a single **SQL Write Primary-Replica**.  We might 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)

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)

solutions/system_design/query_cache/README.md

@@ -218,7 +218,7 @@ Refer to [When to update the cache](https://github.com/donnemartin/system-design
 
 State you would 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.
 
@@ -247,7 +247,7 @@ To handle the heavy request load and the large amount of memory needed, we'll sc
 
 ### SQL scaling patterns
 
-* [Read replicas](https://github.com/donnemartin/system-design-primer#master-slave-replication)
+* [Read replicas](https://github.com/donnemartin/system-design-primer#primary-replica-replication)
 * [Federation](https://github.com/donnemartin/system-design-primer#federation)
 * [Sharding](https://github.com/donnemartin/system-design-primer#sharding)
 * [Denormalization](https://github.com/donnemartin/system-design-primer#denormalization)

solutions/system_design/sales_rank/README.md

@@ -245,7 +245,7 @@ For internal communications, we could use [Remote Procedure Calls](https://githu
 
 State you would 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.
 
@@ -259,8 +259,8 @@ 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)
 
@@ -270,7 +270,7 @@ We might only want to store a limited time period of data in the database, while
 
 To address the 40,000 *average* read requests per second (higher at peak), traffic for popular content (and their sales rank) 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.  With the large volume of reads, the **SQL Read Replicas** might not be able to handle the cache misses.  We'll probably need to employ additional SQL scaling patterns.
 
-400 *average* writes per second (higher at peak) might be tough for a single **SQL Write Master-Slave**, also pointing to a need for additional scaling techniques.
+400 *average* writes per second (higher at peak) might be tough for a single **SQL Write Primary-Replica**, also pointing to a need for additional scaling techniques.
 
 SQL scaling patterns include:
 

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

@@ -207,7 +207,7 @@
         * 如果你正在配置自己的 **负载均衡器**, 在多个可用区域中设置多台服务器用于 [双活](https://github.com/donnemartin/system-design-primer#active-active) 或 [主被](https://github.com/donnemartin/system-design-primer#active-passive) 将提高可用性
         * 终止在 **负载平衡器** 上的SSL，以减少后端服务器上的计算负载，并简化证书管理
     * 在多个可用区域中使用多台 **Web服务器**
-    * 在多个可用区域的 [**主-从 故障转移**](https://github.com/donnemartin/system-design-primer#master-slave-replication) 模式中使用多个 **MySQL** 实例来改进冗余
+    * 在多个可用区域的 [**主-从 故障转移**](https://github.com/donnemartin/system-design-primer#primary-replica-replication) 模式中使用多个 **MySQL** 实例来改进冗余
 * 分离 **Web 服务器** 和 [**应用服务器**](https://github.com/donnemartin/system-design-primer#application-layer)
     * 独立扩展和配置每一层
     * **Web 服务器** 可以作为 [**反向代理**](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)
@@ -238,7 +238,7 @@
     * 来自 **Web 服务器** 的会话数据
         * **Web 服务器** 变成无状态的, 允许 **自动伸缩**
     * 从内存中读取 1 MB 内存需要大约 250 微秒，而从SSD中读取时间要长 4 倍，从磁盘读取的时间要长 80 倍。<sup><a href=https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know>1</a></sup>
-* 添加 [**MySQL 读取副本**](https://github.com/donnemartin/system-design-primer#master-slave-replication) 来减少写主线程的负载
+* 添加 [**MySQL 读取副本**](https://github.com/donnemartin/system-design-primer#primary-replica-replication) 来减少写主线程的负载
 * 添加更多 **Web 服务器** and **应用服务器** 来提高响应
 
 **折中方案, 可选方案, 和其他细节:**
@@ -344,7 +344,7 @@ SQL 扩展模型包括：
 
 ### SQL 扩展模式
 
-* [读取副本](https://github.com/donnemartin/system-design-primer#master-slave-replication)
+* [读取副本](https://github.com/donnemartin/system-design-primer#primary-replica-replication)
 * [集合](https://github.com/donnemartin/system-design-primer#federation)
 * [分区](https://github.com/donnemartin/system-design-primer#sharding)
 * [反规范化](https://github.com/donnemartin/system-design-primer#denormalization)

solutions/system_design/scaling_aws/README.md

@@ -207,7 +207,7 @@ Our **Benchmarks/Load Tests** and **Profiling** show that our single **Web Serve
         * If you are configuring your own **Load Balancer**, setting up multiple servers in [active-active](https://github.com/donnemartin/system-design-primer#active-active) or [active-passive](https://github.com/donnemartin/system-design-primer#active-passive) in multiple availability zones will improve availability
         * Terminate SSL on the **Load Balancer** to reduce computational load on backend servers and to simplify certificate administration
     * Use multiple **Web Servers** spread out over multiple availability zones
-    * Use multiple **MySQL** instances in [**Master-Slave Failover**](https://github.com/donnemartin/system-design-primer#master-slave-replication) mode across multiple availability zones to improve redundancy
+    * Use multiple **MySQL** instances in [**Primary-Replica Failover**](https://github.com/donnemartin/system-design-primer#primary-replica-replication) mode across multiple availability zones to improve redundancy
 * Separate out the **Web Servers** from the [**Application Servers**](https://github.com/donnemartin/system-design-primer#application-layer)
     * Scale and configure both layers independently
     * **Web Servers** can run as a [**Reverse Proxy**](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)
@@ -238,7 +238,7 @@ Our **Benchmarks/Load Tests** and **Profiling** show that we are read-heavy (100
     * Session data from the **Web Servers**
         * The **Web Servers** become stateless, allowing for **Autoscaling**
     * Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.<sup><a href=https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know>1</a></sup>
-* Add [**MySQL Read Replicas**](https://github.com/donnemartin/system-design-primer#master-slave-replication) to reduce load on the write master
+* Add [**MySQL Read Replicas**](https://github.com/donnemartin/system-design-primer#primary-replica-replication) to reduce load on the write master
 * Add more **Web Servers** and **Application Servers** to improve responsiveness
 
 *Trade-offs, alternatives, and additional details:*
@@ -313,7 +313,7 @@ We'll continue to address scaling issues due to the problem's constraints:
     * A data warehouse such as Redshift can comfortably handle the constraint of 1 TB of new content per month
 * With 40,000 average read requests per second, read traffic for popular content can be addressed by scaling the **Memory Cache**, which is also useful for handling the unevenly distributed traffic and traffic spikes
     * The **SQL Read Replicas** might have trouble handling the cache misses, we'll probably need to employ additional SQL scaling patterns
-* 400 average writes per second (with presumably significantly higher peaks) might be tough for a single **SQL Write Master-Slave**, also pointing to a need for additional scaling techniques
+* 400 average writes per second (with presumably significantly higher peaks) might be tough for a single **SQL Write Primary-Replica**, also pointing to a need for additional scaling techniques
 
 SQL scaling patterns include:
 
@@ -344,7 +344,7 @@ We can further separate out our [**Application Servers**](https://github.com/don
 
 ### SQL scaling patterns
 
-* [Read replicas](https://github.com/donnemartin/system-design-primer#master-slave-replication)
+* [Read replicas](https://github.com/donnemartin/system-design-primer#primary-replica-replication)
 * [Federation](https://github.com/donnemartin/system-design-primer#federation)
 * [Sharding](https://github.com/donnemartin/system-design-primer#sharding)
 * [Denormalization](https://github.com/donnemartin/system-design-primer#denormalization)

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

@@ -289,7 +289,7 @@ $ curl https://social.com/api/v1/friend_search?person_id=1234
 
 ### SQL 扩展模式
 
-* [读取副本](https://github.com/donnemartin/system-design-primer#master-slave-replication)
+* [读取副本](https://github.com/donnemartin/system-design-primer#primary-replica-replication)
 * [集合](https://github.com/donnemartin/system-design-primer#federation)
 * [分区](https://github.com/donnemartin/system-design-primer#sharding)
 * [反规范化](https://github.com/donnemartin/system-design-primer#denormalization)

solutions/system_design/social_graph/README.md

@@ -256,7 +256,7 @@ For internal communications, we could use [Remote Procedure Calls](https://githu
 
 State you would 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.
 
@@ -290,7 +290,7 @@ Below are further optimizations:
 
 ### SQL scaling patterns
 
-* [Read replicas](https://github.com/donnemartin/system-design-primer#master-slave-replication)
+* [Read replicas](https://github.com/donnemartin/system-design-primer#primary-replica-replication)
 * [Federation](https://github.com/donnemartin/system-design-primer#federation)
 * [Sharding](https://github.com/donnemartin/system-design-primer#sharding)
 * [Denormalization](https://github.com/donnemartin/system-design-primer#denormalization)

solutions/system_design/twitter/README.md

@@ -229,7 +229,7 @@ The response would be similar to that of the home timeline, except for tweets ma
 
 State you would 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.
 
@@ -243,8 +243,8 @@ 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)
 
@@ -267,7 +267,7 @@ We'll also want to address the bottleneck with the **SQL Database**.
 
 Although the **Memory Cache** should reduce the load on the database, it is unlikely the **SQL Read Replicas** alone would be enough to handle the cache misses.  We'll probably need to employ additional SQL scaling patterns.
 
-The high volume of writes would overwhelm a single **SQL Write Master-Slave**, also pointing to a need for additional scaling techniques.
+The high volume of writes would overwhelm a single **SQL Write Primary-Replica**, also pointing to a need for additional scaling techniques.
 
 * [Federation](https://github.com/donnemartin/system-design-primer#federation)
 * [Sharding](https://github.com/donnemartin/system-design-primer#sharding)

solutions/system_design/web_crawler/README.md

@@ -262,7 +262,7 @@ For internal communications, we could use [Remote Procedure Calls](https://githu
 
 State you would 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.
 
@@ -294,7 +294,7 @@ Below are a few other optimizations to the **Crawling Service**:
 
 ### SQL scaling patterns
 
-* [Read replicas](https://github.com/donnemartin/system-design-primer#master-slave-replication)
+* [Read replicas](https://github.com/donnemartin/system-design-primer#primary-replica-replication)
 * [Federation](https://github.com/donnemartin/system-design-primer#federation)
 * [Sharding](https://github.com/donnemartin/system-design-primer#sharding)
 * [Denormalization](https://github.com/donnemartin/system-design-primer#denormalization)