diff --git a/solutions/system_design/web_crawler/README.md b/solutions/system_design/web_crawler/README.md index 358cb91322..545ca0d7cb 100644 --- a/solutions/system_design/web_crawler/README.md +++ b/solutions/system_design/web_crawler/README.md @@ -1,104 +1,102 @@ -# Design a web crawler +# 设计一个网页爬虫 -*Note: This document links directly to relevant areas found in the [system design topics](https://github.com/donnemartin/system-design-primer#index-of-system-design-topics) to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.* +**注意：这个文档中的链接会直接指向[系统设计主题索引](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题的索引)中的有关部分，以避免重复的内容。你可以参考链接的相关内容，来了解其总的要点、方案的权衡取舍以及可选的替代方案。** -## Step 1: Outline use cases and constraints +## 第一步：简述用例与约束条件 -> Gather requirements and scope the problem. -> Ask questions to clarify use cases and constraints. -> Discuss assumptions. +> 把所有需要的东西聚集在一起，审视问题。不停的提问，以至于我们可以明确使用场景和约束。讨论假设。 -Without an interviewer to address clarifying questions, we'll define some use cases and constraints. +我们将在没有面试官明确说明问题的情况下，自己定义一些用例以及限制条件。 -### Use cases +### 用例 -#### We'll scope the problem to handle only the following use cases +#### 我们把问题限定在仅处理以下用例的范围中 -* **Service** crawls a list of urls: - * Generates reverse index of words to pages containing the search terms - * Generates titles and snippets for pages - * Title and snippets are static, they do not change based on search query -* **User** inputs a search term and sees a list of relevant pages with titles and snippets the crawler generated - * Only sketch high level components and interactions for this use case, no need to go into depth -* **Service** has high availability +* **服务** 抓取一系列链接： + * 生成包含搜索词的网页倒排索引 + * 生成页面的标题和摘要信息 + * 页面标题和摘要都是静态的，它们不会根据搜索词改变 +* **用户** 输入搜索词后，可以看到相关的搜索结果列表，列表每一项都包含由网页爬虫生成的页面标题及摘要 + * 只给该用例绘制出概要组件和交互说明，无需讨论细节 +* **服务** 具有高可用性 -#### Out of scope +#### 无需考虑 -* Search analytics -* Personalized search results -* Page rank +* 搜索分析 +* 个性化搜索结果 +* 页面排名 -### Constraints and assumptions +### 限制条件与假设 -#### State assumptions +#### 提出假设 -* Traffic is not evenly distributed - * Some searches are very popular, while others are only executed once -* Support only anonymous users -* Generating search results should be fast -* The web crawler should not get stuck in an infinite loop - * We get stuck in an infinite loop if the graph contains a cycle -* 1 billion links to crawl - * Pages need to be crawled regularly to ensure freshness - * Average refresh rate of about once per week, more frequent for popular sites - * 4 billion links crawled each month - * Average stored size per web page: 500 KB - * For simplicity, count changes the same as new pages -* 100 billion searches per month +* 搜索流量分布不均 + * 有些搜索词非常热门，有些则非常冷门 +* 只支持匿名用户 +* 用户很快就能看到搜索结果 +* 网页爬虫不应该陷入死循环 + * 当爬虫路径包含环的时候，将会陷入死循环 +* 抓取 10 亿个链接 + * 要定期重新抓取页面以确保新鲜度 + * 平均每周重新抓取一次，网站越热门，那么重新抓取的频率越高 + * 每月抓取 40 亿个链接 + * 每个页面的平均存储大小：500 KB + * 简单起见，重新抓取的页面算作新页面 +* 每月搜索量 1000 亿次 -Exercise the use of more traditional systems - don't use existing systems such as [solr](http://lucene.apache.org/solr/) or [nutch](http://nutch.apache.org/). +用更传统的系统来练习 —— 不要使用 [solr](http://lucene.apache.org/solr/) 、[nutch](http://nutch.apache.org/) 之类的现成系统。 -#### Calculate usage +#### 计算用量 -**Clarify with your interviewer if you should run back-of-the-envelope usage calculations.** +**如果你需要进行粗略的用量计算，请向你的面试官说明。** -* 2 PB of stored page content per month - * 500 KB per page * 4 billion links crawled per month - * 72 PB of stored page content in 3 years -* 1,600 write requests per second -* 40,000 search requests per second +* 每月存储 2 PB 页面 + * 每月抓取 40 亿个页面，每个页面 500 KB + * 三年存储 72 PB 页面 +* 每秒 1600 次写请求 +* 每秒 40000 次搜索请求 -Handy conversion guide: +简便换算指南： -* 2.5 million seconds per month -* 1 request per second = 2.5 million requests per month -* 40 requests per second = 100 million requests per month -* 400 requests per second = 1 billion requests per month +* 一个月有 250 万秒 +* 每秒 1 个请求，即每月 250 万个请求 +* 每秒 40 个请求，即每月 1 亿个请求 +* 每秒 400 个请求，即每月 10 亿个请求 -## Step 2: Create a high level design +## 第二步： 概要设计 -> Outline a high level design with all important components. +> 列出所有重要组件以规划概要设计。 ![Imgur](http://i.imgur.com/xjdAAUv.png) -## Step 3: Design core components +## 第三步：设计核心组件 -> Dive into details for each core component. +> 对每一个核心组件进行详细深入的分析。 -### Use case: Service crawls a list of urls +### 用例：爬虫服务抓取一系列网页 -We'll assume we have an initial list of `links_to_crawl` ranked initially based on overall site popularity. If this is not a reasonable assumption, we can seed the crawler with popular sites that link to outside content such as [Yahoo](https://www.yahoo.com/), [DMOZ](http://www.dmoz.org/), etc +假设我们有一个初始列表 `links_to_crawl`（待抓取链接），它最初基于网站整体的知名度来排序。当然如果这个假设不合理，我们可以使用 [Yahoo](https://www.yahoo.com/)、[DMOZ](http://www.dmoz.org/) 等知名门户网站作为种子链接来进行扩散 。 -We'll use a table `crawled_links` to store processed links and their page signatures. +我们将用表 `crawled_links` （已抓取链接 ）来记录已经处理过的链接以及相应的页面签名。 -We could store `links_to_crawl` and `crawled_links` in a key-value **NoSQL Database**. For the ranked links in `links_to_crawl`, we could use [Redis](https://redis.io/) with sorted sets to maintain a ranking of page links. We should discuss the [use cases and tradeoffs between choosing SQL or NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql). +我们可以将 `links_to_crawl` 和 `crawled_links` 记录在键-值型 **NoSQL 数据库**中。对于 `crawled_links` 中已排序的链接，我们可以使用 [Redis](https://redis.io/) 的有序集合来维护网页链接的排名。我们应当在 [选择 SQL 还是 NoSQL 的问题上，讨论有关使用场景以及利弊 ](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-还是-nosql)。 -* The **Crawler Service** processes each page link by doing the following in a loop: - * Takes the top ranked page link to crawl - * Checks `crawled_links` in the **NoSQL Database** for an entry with a similar page signature - * If we have a similar page, reduces the priority of the page link - * This prevents us from getting into a cycle - * Continue - * Else, crawls the link - * Adds a job to the **Reverse Index Service** queue to generate a [reverse index](https://en.wikipedia.org/wiki/Search_engine_indexing) - * Adds a job to the **Document Service** queue to generate a static title and snippet - * Generates the page signature - * Removes the link from `links_to_crawl` in the **NoSQL Database** - * Inserts the page link and signature to `crawled_links` in the **NoSQL Database** +* **爬虫服务**按照以下流程循环处理每一个页面链接： + * 选取排名最靠前的待抓取链接 + * 在 **NoSQL 数据库**的 `crawled_links` 中，检查待抓取页面的签名是否与某个已抓取页面的签名相似 + * 若存在，则降低该页面链接的优先级 + * 这样做可以避免陷入死循环 + * 继续（进入下一次循环） + * 若不存在，则抓取该链接 + * 在**倒排索引服务**任务队列中，新增一个生成[倒排索引](https://en.wikipedia.org/wiki/Search_engine_indexing)任务。 + * 在**文档服务**任务队列中，新增一个生成静态标题和摘要的任务。 + * 生成页面签名 + * 在 **NoSQL 数据库**的 `links_to_crawl` 中删除该链接 + * 在 **NoSQL 数据库**的 `crawled_links` 中插入该链接以及页面签名 -**Clarify with your interviewer how much code you are expected to write**. +**向面试官了解你需要写多少代码**。 -`PagesDataStore` is an abstraction within the **Crawler Service** that uses the **NoSQL Database**: +`PagesDataStore` 是**爬虫服务**中的一个抽象类，它使用 **NoSQL 数据库**进行存储。 ``` class PagesDataStore(object): @@ -108,31 +106,31 @@ class PagesDataStore(object): ... def add_link_to_crawl(self, url): - """Add the given link to `links_to_crawl`.""" + """将指定链接加入 `links_to_crawl`。""" ... def remove_link_to_crawl(self, url): - """Remove the given link from `links_to_crawl`.""" + """从 `links_to_crawl` 中删除指定链接。""" ... def reduce_priority_link_to_crawl(self, url) - """Reduce the priority of a link in `links_to_crawl` to avoid cycles.""" + """在 `links_to_crawl` 中降低一个链接的优先级以避免死循环。""" ... def extract_max_priority_page(self): - """Return the highest priority link in `links_to_crawl`.""" + """返回 `links_to_crawl` 中优先级最高的链接。""" ... def insert_crawled_link(self, url, signature): - """Add the given link to `crawled_links`.""" + """将指定链接加入 `crawled_links`。""" ... def crawled_similar(self, signature): - """Determine if we've already crawled a page matching the given signature""" + """判断待抓取页面的签名是否与某个已抓取页面的签名相似。""" ... ``` -`Page` is an abstraction within the **Crawler Service** that encapsulates a page, its contents, child urls, and signature: +`Page` 是**爬虫服务**的一个抽象类，它封装了网页对象，由页面链接、页面内容、子链接和页面签名构成。 ``` class Page(object): @@ -144,7 +142,7 @@ class Page(object): self.signature = signature ``` -`Crawler` is the main class within **Crawler Service**, composed of `Page` and `PagesDataStore`. +`Crawler` 是**爬虫服务**的主类，由`Page` 和 `PagesDataStore` 组成。 ``` class Crawler(object): @@ -155,7 +153,7 @@ class Crawler(object): self.doc_index_queue = doc_index_queue def create_signature(self, page): - """Create signature based on url and contents.""" + """基于页面链接与内容生成签名。""" ... def crawl_page(self, page): @@ -176,16 +174,16 @@ class Crawler(object): self.crawl_page(page) ``` -### Handling duplicates +### 处理重复内容 -We need to be careful the web crawler doesn't get stuck in an infinite loop, which happens when the graph contains a cycle. +我们要谨防网页爬虫陷入死循环，这通常会发生在爬虫路径中存在环的情况。 -**Clarify with your interviewer how much code you are expected to write**. +**向面试官了解你需要写多少代码**. -We'll want to remove duplicate urls: +删除重复链接： -* For smaller lists we could use something like `sort | unique` -* With 1 billion links to crawl, we could use **MapReduce** to output only entries that have a frequency of 1 +* 假设数据量较小，我们可以用类似于 `sort | unique` 的方法。（译注： 先排序，后去重） +* 假设有 10 亿条数据，我们应该使用 **MapReduce** 来输出只出现 1 次的记录。 ``` class RemoveDuplicateUrls(MRJob): @@ -199,38 +197,38 @@ class RemoveDuplicateUrls(MRJob): yield key, total ``` -Detecting duplicate content is more complex. We could generate a signature based on the contents of the page and compare those two signatures for similarity. Some potential algorithms are [Jaccard index](https://en.wikipedia.org/wiki/Jaccard_index) and [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity). +比起处理重复内容，检测重复内容更为复杂。我们可以基于网页内容生成签名，然后对比两者签名的相似度。可能会用到的算法有 [Jaccard index](https://en.wikipedia.org/wiki/Jaccard_index) 以及 [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity)。 -### Determining when to update the crawl results +### 抓取结果更新策略 -Pages need to be crawled regularly to ensure freshness. Crawl results could have a `timestamp` field that indicates the last time a page was crawled. After a default time period, say one week, all pages should be refreshed. Frequently updated or more popular sites could be refreshed in shorter intervals. +要定期重新抓取页面以确保新鲜度。抓取结果应该有个 `timestamp` 字段记录上一次页面抓取时间。每隔一段时间，比如说 1 周，所有页面都需要更新一次。对于热门网站或是内容频繁更新的网站，爬虫抓取间隔可以缩短。 -Although we won't dive into details on analytics, we could do some data mining to determine the mean time before a particular page is updated, and use that statistic to determine how often to re-crawl the page. +尽管我们不会深入网页数据分析的细节，我们仍然要做一些数据挖掘工作来确定一个页面的平均更新时间，并且根据相关的统计数据来决定爬虫的重新抓取频率。 -We might also choose to support a `Robots.txt` file that gives webmasters control of crawl frequency. +当然我们也应该根据站长提供的 `Robots.txt` 来控制爬虫的抓取频率。 -### Use case: User inputs a search term and sees a list of relevant pages with titles and snippets +### 用例：用户输入搜索词后，可以看到相关的搜索结果列表，列表每一项都包含由网页爬虫生成的页面标题及摘要 -* The **Client** sends a request to the **Web Server**, running as a [reverse proxy](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server) -* The **Web Server** forwards the request to the **Query API** server -* The **Query API** server does the following: - * Parses the query - * Removes markup - * Breaks up the text into terms - * Fixes typos - * Normalizes capitalization - * Converts the query to use boolean operations - * Uses the **Reverse Index Service** to find documents matching the query - * The **Reverse Index Service** ranks the matching results and returns the top ones - * Uses the **Document Service** to return titles and snippets +* **客户端**向运行[反向代理](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#反向代理web-服务器)的 **Web 服务器**发送一个请求 +* **Web 服务器** 发送请求到 **Query API** 服务器 +* **查询 API** 服务将会做这些事情： + * 解析查询参数 + * 删除 HTML 标记 + * 将文本分割成词组 （译注： 分词处理） + * 修正错别字 + * 规范化大小写 + * 将搜索词转换为布尔运算 + * 使用**倒排索引服务**来查找匹配查询的文档 + * **倒排索引服务**对匹配到的结果进行排名，然后返回最符合的结果 + * 使用**文档服务**返回文章标题与摘要 -We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest): +我们使用 [**REST API**](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#表述性状态转移rest) 与客户端通信： ``` $ curl https://search.com/api/v1/search?query=hello+world ``` -Response: +响应内容: ``` { @@ -250,104 +248,109 @@ Response: }, ``` -For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc). +对于服务器内部通信，我们可以使用 [远程过程调用协议（RPC）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#远程过程调用协议rpc) -## Step 4: Scale the design -> Identify and address bottlenecks, given the constraints. +## 第四步：架构扩展 + +> 根据限制条件，找到并解决瓶颈。 ![Imgur](http://i.imgur.com/bWxPtQA.png) -**Important: Do not simply jump right into the final design from the initial design!** +**重要提示：不要直接从最初设计跳到最终设计！** + +现在你要 1) **基准测试、负载测试**。2) **分析、描述**性能瓶颈。3) 在解决瓶颈问题的同时，评估替代方案、权衡利弊。4) 重复以上步骤。请阅读[设计一个系统，并将其扩大到为数以百万计的 AWS 用户服务](../scaling_aws/README.md) 来了解如何逐步扩大初始设计。 -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. +讨论初始设计可能遇到的瓶颈及相关解决方案是很重要的。例如加上一套配备多台 **Web 服务器**的**负载均衡器**是否能够解决问题？**CDN**呢？**主从复制**呢？它们各自的替代方案和需要**权衡**的利弊又有哪些呢？ -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? +我们将会介绍一些组件来完成设计，并解决架构规模扩张问题。内置的负载均衡器将不做讨论以节省篇幅。 -We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter. +**为了避免重复讨论**，请参考[系统设计主题索引](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#系统设计主题的索引)相关部分来了解其要点、方案的权衡取舍以及替代方案。 -*To avoid repeating discussions*, refer to the following [system design topics](https://github.com/donnemartin/system-design-primer#index-of-system-design-topics) for main talking points, tradeoffs, and alternatives: +* [DNS](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#域名系统) +* [负载均衡器](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#负载均衡器) +* [水平扩展](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#水平扩展) +* [Web 服务器（反向代理）](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#反向代理web-服务器) +* [API 服务器（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) +* [NoSQL](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#nosql) +* [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) -* [DNS](https://github.com/donnemartin/system-design-primer#domain-name-system) -* [Load balancer](https://github.com/donnemartin/system-design-primer#load-balancer) -* [Horizontal scaling](https://github.com/donnemartin/system-design-primer#horizontal-scaling) -* [Web server (reverse proxy)](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server) -* [API server (application layer)](https://github.com/donnemartin/system-design-primer#application-layer) -* [Cache](https://github.com/donnemartin/system-design-primer#cache) -* [NoSQL](https://github.com/donnemartin/system-design-primer#nosql) -* [Consistency patterns](https://github.com/donnemartin/system-design-primer#consistency-patterns) -* [Availability patterns](https://github.com/donnemartin/system-design-primer#availability-patterns) +有些搜索词非常热门，有些则非常冷门。热门的搜索词可以通过诸如 Redis 或者 Memcached 之类的**内存缓存**来缩短响应时间，避免**倒排索引服务**以及**文档服务**过载。**内存缓存**同样适用于流量分布不均匀以及流量短时高峰问题。从内存中读取 1 MB 连续数据大约需要 250 微秒，而从 SSD 读取同样大小的数据要花费 4 倍的时间，从机械硬盘读取需要花费 80 倍以上的时间。1 -Some searches are very popular, while others are only executed once. Popular queries can be served from a **Memory Cache** such as Redis or Memcached to reduce response times and to avoid overloading the **Reverse Index Service** and **Document Service**. The **Memory Cache** is also useful for handling the unevenly distributed traffic and traffic spikes. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.1 -Below are a few other optimizations to the **Crawling Service**: +以下是优化**爬虫服务**的其他建议： -* To handle the data size and request load, the **Reverse Index Service** and **Document Service** will likely need to make heavy use sharding and replication. -* DNS lookup can be a bottleneck, the **Crawler Service** can keep its own DNS lookup that is refreshed periodically -* The **Crawler Service** can improve performance and reduce memory usage by keeping many open connections at a time, referred to as [connection pooling](https://en.wikipedia.org/wiki/Connection_pool) - * Switching to [UDP](https://github.com/donnemartin/system-design-primer#user-datagram-protocol-udp) could also boost performance -* Web crawling is bandwidth intensive, ensure there is enough bandwidth to sustain high throughput +* 为了处理数据大小问题以及网络请求负载，**倒排索引服务**和**文档服务**可能需要大量应用数据分片和数据复制。 +* DNS 查询可能会成为瓶颈，**爬虫服务**最好专门维护一套定期更新的 DNS 查询服务。 +* 借助于[连接池](https://en.wikipedia.org/wiki/Connection_pool)，即同时维持多个开放网络连接，可以提升**爬虫服务**的性能并减少内存使用量。 + * 改用 [UDP](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#用户数据报协议udp) 协议同样可以提升性能 +* 网络爬虫受带宽影响较大，请确保带宽足够维持高吞吐量。 -## Additional talking points +## 其它要点 -> Additional topics to dive into, depending on the problem scope and time remaining. +> 是否深入这些额外的主题，取决于你的问题范围和剩下的时间。 -### SQL scaling patterns +### SQL 扩展模式 -* [Read replicas](https://github.com/donnemartin/system-design-primer#master-slave) -* [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) -* [SQL Tuning](https://github.com/donnemartin/system-design-primer#sql-tuning) +* [读取复制](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#主从复制) +* [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) +* [非规范化](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#非规范化) +* [SQL 调优](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-调优) #### NoSQL -* [Key-value store](https://github.com/donnemartin/system-design-primer#key-value-store) -* [Document store](https://github.com/donnemartin/system-design-primer#document-store) -* [Wide column store](https://github.com/donnemartin/system-design-primer#wide-column-store) -* [Graph database](https://github.com/donnemartin/system-design-primer#graph-database) -* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql) +* [键-值存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#键-值存储) +* [文档类型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#文档类型存储) +* [列型存储](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#列型存储) +* [图数据库](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#图数据库) +* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#sql-还是-nosql) + + +### 缓存 -### Caching +* 在哪缓存 + * [客户端缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#客户端缓存) + * [CDN 缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#cdn-缓存) + * [Web 服务器缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#web-服务器缓存) + * [数据库缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库缓存) + * [应用缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#应用缓存) +* 什么需要缓存 + * [数据库查询级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#数据库查询级别的缓存) + * [对象级别的缓存](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#对象级别的缓存) +* 何时更新缓存 + * [缓存模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#缓存模式) + * [直写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#直写模式) + * [回写模式](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#回写模式) + * [刷新](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#刷新) + +### 异步与微服务 -* Where to cache - * [Client caching](https://github.com/donnemartin/system-design-primer#client-caching) - * [CDN caching](https://github.com/donnemartin/system-design-primer#cdn-caching) - * [Web server caching](https://github.com/donnemartin/system-design-primer#web-server-caching) - * [Database caching](https://github.com/donnemartin/system-design-primer#database-caching) - * [Application caching](https://github.com/donnemartin/system-design-primer#application-caching) -* What to cache - * [Caching at the database query level](https://github.com/donnemartin/system-design-primer#caching-at-the-database-query-level) - * [Caching at the object level](https://github.com/donnemartin/system-design-primer#caching-at-the-object-level) -* When to update the cache - * [Cache-aside](https://github.com/donnemartin/system-design-primer#cache-aside) - * [Write-through](https://github.com/donnemartin/system-design-primer#write-through) - * [Write-behind (write-back)](https://github.com/donnemartin/system-design-primer#write-behind-write-back) - * [Refresh ahead](https://github.com/donnemartin/system-design-primer#refresh-ahead) +* [消息队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#消息队列) +* [任务队列](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#任务队列) +* [背压](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#背压) +* [微服务](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#微服务) -### Asynchronism and microservices +### 通信 -* [Message queues](https://github.com/donnemartin/system-design-primer#message-queues) -* [Task queues](https://github.com/donnemartin/system-design-primer#task-queues) -* [Back pressure](https://github.com/donnemartin/system-design-primer#back-pressure) -* [Microservices](https://github.com/donnemartin/system-design-primer#microservices) +* 可权衡选择的方案： + * 与客户端的外部通信 - [使用 REST 作为 HTTP API](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#表述性状态转移rest) + * 内部通信 - [RPC](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#远程过程调用协议rpc) +* [服务发现](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#服务发现) -### Communications -* Discuss tradeoffs: - * External communication with clients - [HTTP APIs following REST](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest) - * Internal communications - [RPC](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc) -* [Service discovery](https://github.com/donnemartin/system-design-primer#service-discovery) +### 安全性 -### Security +请参阅[安全](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#安全)。 -Refer to the [security section](https://github.com/donnemartin/system-design-primer#security). -### Latency numbers +### 延迟数值 -See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know). +请参阅[每个程序员都应该知道的延迟数](https://github.com/donnemartin/system-design-primer/blob/master/README-zh-Hans.md#每个程序员都应该知道的延迟数)。 -### Ongoing +### 持续探讨 -* Continue benchmarking and monitoring your system to address bottlenecks as they come up -* Scaling is an iterative process +* 持续进行基准测试并监控你的系统，以解决他们提出的瓶颈问题。 +* 架构扩展是一个迭代的过程。