Enable syntax highlighting in all python code snippets.

README-ja.md

@@ -1166,7 +1166,7 @@ Redisはさらに以下のような機能を備えています:
 * エントリをキャッシュに追加します
 * エントリを返します
 
-```
+```python
 def get_user(self, user_id):
     user = cache.get("user.{0}", user_id)
     if user is None:
@@ -1209,7 +1209,7 @@ set_user(12345, {"foo":"bar"})
 
 キャッシュコード:
 
-```
+```python
 def set_user(user_id, values):
     user = db.query("UPDATE Users WHERE id = {0}", user_id, values)
     cache.set(user_id, user)

README-zh-Hans.md

@@ -1180,7 +1180,7 @@ Redis 有下列附加功能：
 - 将查找到的结果存储到缓存中
 - 返回所需内容
 
-```
+```python
 def get_user(self, user_id):
     user = cache.get("user.{0}", user_id)
     if user is None:
@@ -1223,7 +1223,7 @@ set_user(12345, {"foo":"bar"})
 
 缓存代码：
 
-```
+```python
 def set_user(user_id, values):
     user = db.query("UPDATE Users WHERE id = {0}", user_id, values)
     cache.set(user_id, user)

README-zh-TW.md

@@ -1167,7 +1167,7 @@ Redis 還有以下額外的功能：
 * 將該筆記錄儲存到快取
 * 將資料返回
 
-```
+```python
 def get_user(self, user_id):
     user = cache.get("user.{0}", user_id)
     if user is None:
@@ -1210,7 +1210,7 @@ set_user(12345, {"foo":"bar"})
 
 快取程式碼：
 
-```
+```python
 def set_user(user_id, values):
     user = db.query("UPDATE Users WHERE id = {0}", user_id, values)
     cache.set(user_id, user)

README.md

@@ -1164,7 +1164,7 @@ The application is responsible for reading and writing from storage.  The cache
 * Add entry to cache
 * Return entry
 
-```
+```python
 def get_user(self, user_id):
     user = cache.get("user.{0}", user_id)
     if user is None:
@@ -1201,13 +1201,13 @@ The application uses the cache as the main data store, reading and writing data
 
 Application code:
 
-```
+```python
 set_user(12345, {"foo":"bar"})
 ```
 
 Cache code:
 
-```
+```python
 def set_user(user_id, values):
     user = db.query("UPDATE Users WHERE id = {0}", user_id, values)
     cache.set(user_id, user)

solutions/system_design/mint/README.md

@@ -182,7 +182,7 @@ For the **Category Service**, we can seed a seller-to-category dictionary with t
 
 **Clarify with your interviewer how much code you are expected to write**.
 
-```
+```python
 class DefaultCategories(Enum):
 
     HOUSING = 0
@@ -199,7 +199,7 @@ seller_category_map['Target'] = DefaultCategories.SHOPPING
 
 For sellers not initially seeded in the map, we could use a crowdsourcing effort by evaluating the manual category overrides our users provide.  We could use a heap to quickly lookup the top manual override per seller in O(1) time.
 
-```
+```python
 class Categorizer(object):
 
     def __init__(self, seller_category_map, self.seller_category_crowd_overrides_map):
@@ -219,7 +219,7 @@ class Categorizer(object):
 
 Transaction implementation:
 
-```
+```python
 class Transaction(object):
 
     def __init__(self, created_at, seller, amount):
@@ -232,7 +232,7 @@ class Transaction(object):
 
 To start, we could use a generic budget template that allocates category amounts based on income tiers.  Using this approach, we would not have to store the 100 million budget items identified in the constraints, only those that the user overrides.  If a user overrides a budget category, which we could store the override in the `TABLE budget_overrides`.
 
-```
+```python
 class Budget(object):
 
     def __init__(self, income):
@@ -273,7 +273,7 @@ user_id   timestamp   seller  amount
 
 **MapReduce** implementation:
 
-```
+```python
 class SpendingByCategory(MRJob):
 
     def __init__(self, categorizer):

solutions/system_design/pastebin/README.md

@@ -130,7 +130,7 @@ To generate the unique url, we could:
     * Base 64 is another popular encoding but provides issues for urls because of the additional `+` and `/` characters
     * The following [Base 62 pseudocode](http://stackoverflow.com/questions/742013/how-to-code-a-url-shortener) runs in O(k) time where k is the number of digits = 7:
 
-```
+```python
 def base_encode(num, base=62):
     digits = []
     while num > 0
@@ -142,7 +142,7 @@ def base_encode(num, base=62):
 
 * Take the first 7 characters of the output, which results in 62^7 possible values and should be sufficient to handle our constraint of 360 million shortlinks in 3 years:
 
-```
+```python
 url = base_encode(md5(ip_address+timestamp))[:URL_LENGTH]
 ```
 

solutions/system_design/query_cache/README.md

@@ -97,7 +97,7 @@ The cache can use a doubly-linked list: new items will be added to the head whil
 
 **Query API Server** implementation:
 
-```
+```python
 class QueryApi(object):
 
     def __init__(self, memory_cache, reverse_index_service):
@@ -121,7 +121,7 @@ class QueryApi(object):
 
 **Node** implementation:
 
-```
+```python
 class Node(object):
 
     def __init__(self, query, results):
@@ -131,7 +131,7 @@ class Node(object):
 
 **LinkedList** implementation:
 
-```
+```python
 class LinkedList(object):
 
     def __init__(self):
@@ -150,7 +150,7 @@ class LinkedList(object):
 
 **Cache** implementation:
 
-```
+```python
 class Cache(object):
 
     def __init__(self, MAX_SIZE):

solutions/system_design/sales_rank/README.md

@@ -102,7 +102,7 @@ We'll use a multi-step **MapReduce**:
 * **Step 1** - Transform the data to `(category, product_id), sum(quantity)`
 * **Step 2** - Perform a distributed sort
 
-```
+```python
 class SalesRanker(MRJob):
 
     def within_past_week(self, timestamp):

solutions/system_design/social_graph/README.md

@@ -62,7 +62,7 @@ Handy conversion guide:
 
 Without the constraint of millions of users (vertices) and billions of friend relationships (edges), we could solve this unweighted shortest path task with a general BFS approach:
 
-```
+```python
 class Graph(Graph):
 
     def shortest_path(self, source, dest):
@@ -117,7 +117,7 @@ We won't be able to fit all users on the same machine, we'll need to [shard](htt
 
 **Lookup Service** implementation:
 
-```
+```python
 class LookupService(object):
 
     def __init__(self):
@@ -132,7 +132,7 @@ class LookupService(object):
 
 **Person Server** implementation:
 
-```
+```python
 class PersonServer(object):
 
     def __init__(self):
@@ -151,7 +151,7 @@ class PersonServer(object):
 
 **Person** implementation:
 
-```
+```python
 class Person(object):
 
     def __init__(self, id, name, friend_ids):
@@ -162,7 +162,7 @@ class Person(object):
 
 **User Graph Service** implementation:
 
-```
+```python
 class UserGraphService(object):
 
     def __init__(self, lookup_service):

solutions/system_design/web_crawler/README.md

@@ -100,7 +100,7 @@ We could store `links_to_crawl` and `crawled_links` in a key-value **NoSQL Datab
 
 `PagesDataStore` is an abstraction within the **Crawler Service** that uses the **NoSQL Database**:
 
-```
+```python
 class PagesDataStore(object):
 
     def __init__(self, db);
@@ -134,7 +134,7 @@ class PagesDataStore(object):
 
 `Page` is an abstraction within the **Crawler Service** that encapsulates a page, its contents, child urls, and signature:
 
-```
+```python
 class Page(object):
 
     def __init__(self, url, contents, child_urls, signature):
@@ -146,7 +146,7 @@ class Page(object):
 
 `Crawler` is the main class within **Crawler Service**, composed of `Page` and `PagesDataStore`.
 
-```
+```python
 class Crawler(object):
 
     def __init__(self, data_store, reverse_index_queue, doc_index_queue):
@@ -187,7 +187,7 @@ We'll want to remove duplicate urls:
 * 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
 
-```
+```python
 class RemoveDuplicateUrls(MRJob):
 
     def mapper(self, _, line):