Add caching strategies

src/roadmaps/system-design/content/112-caching/105-caching-strategies/100-cache-aside.md

@@ -1,6 +1,5 @@
 # Cache-aside
 
-
 The application is responsible for reading and writing from storage. The cache does not interact with storage directly. The application does the following:
 
 - Look for entry in cache, resulting in a cache miss
@@ -8,9 +7,21 @@ The application is responsible for reading and writing from storage. The cache d
 - Add entry to cache
 - Return entry
 
-Memcached is generally used in this manner. Subsequent reads of data added to cache are fast. Cache-aside is also referred to as lazy loading. Only requested data is cached, which avoids filling up the cache with data that isn't requested.
+```python
+def get_user(self, user_id):
+  user = cache.get("user.{0}", user_id)
+  if user is None:
+    user = db.query("SELECT * FROM users WHERE user_id = {0}", user_id)
+      if user is not None:
+        key = "user.{0}".format(user_id)
+        cache.set(key, json.dumps(user))
+  return user
+```
 
-Learn more from the following links:
+[Memcached](https://memcached.org/) is generally used in this manner. Subsequent reads of data added to cache are fast. Cache-aside is also referred to as lazy loading. Only requested data is cached, which avoids filling up the cache with data that isn't requested.
 
-- [Getting started with Cache-aside](https://github.com/donnemartin/system-design-primer#cache-aside)
-- [What is Memcached?](https://memcached.org/)
+![Cache Aside](https://i.imgur.com/Ujf0awN.png)
+
+To learn more, have a look at the following resources:
+
+- [From cache to in-memory data grid](https://www.slideshare.net/tmatyashovsky/from-cache-to-in-memory-data-grid-introduction-to-hazelcast)

src/roadmaps/system-design/content/112-caching/105-caching-strategies/101-write-through.md

@@ -6,8 +6,29 @@ The application uses the cache as the main data store, reading and writing data
 - Cache synchronously writes entry to data store
 - Return
 
+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)
+```
+
 Write-through is a slow overall operation due to the write operation, but subsequent reads of just written data are fast. Users are generally more tolerant of latency when updating data than reading data. Data in the cache is not stale.
 
-To learn more, visit the following links:
+## Disadvantages
 
-- [Getting started with Write-through](https://github.com/donnemartin/system-design-primer#Write-through)
+- When a new node is created due to failure or scaling, the new node will not cache entries until the entry is updated in the database. Cache-aside in conjunction with write through can mitigate this issue.
+- Most data written might never be read, which can be minimized with a TTL.
+
+![Write through](https://i.imgur.com/Ujf0awN.png)
+
+Have a look at the following resources to learn more:
+
+- [Scalability, availability, stability, patterns](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/)

src/roadmaps/system-design/content/112-caching/105-caching-strategies/102-write-behind.md

@@ -10,6 +10,8 @@ In write-behind, the application does the following:
 - There could be data loss if the cache goes down prior to its contents hitting the data store.
 - It is more complex to implement write-behind than it is to implement cache-aside or write-through.
 
+![Scalability, availability, stability, patterns](https://i.imgur.com/XDsb7RS.png)
+
 To learn more, visit the following links:
 
-- [Getting started with Write-behind](https://github.com/donnemartin/system-design-primer#Write-behind)
+- [Scalability, availability, stability, patterns](http://www.slideshare.net/jboner/scalability-availability-stability-patterns/)

src/roadmaps/system-design/content/112-caching/105-caching-strategies/103-refresh-ahead.md

@@ -1,10 +1,14 @@
 # Refresh-ahead
 
-You can configure the cache to automatically refresh any recently accessed cache entry prior to its expiration. Refresh-ahead can result in reduced latency vs read-through if the cache can accurately predict which items are likely to be needed in the future.
+You can configure the cache to automatically refresh any recently accessed cache entry prior to its expiration.
+
+Refresh-ahead can result in reduced latency vs read-through if the cache can accurately predict which items are likely to be needed in the future.
 
 ## Disadvantage of refresh-ahead:
 - Not accurately predicting which items are likely to be needed in the future can result in reduced performance than without refresh-ahead.
 
+![](https://i.imgur.com/sBXb7lb.png)
+
 To learn more, visit the following links:
 
-- [Getting started with Refresh-ahead](https://github.com/donnemartin/system-design-primer#refresh-ahead)
+- [From cache to in-memory data grid](http://www.slideshare.net/tmatyashovsky/from-cache-to-in-memory-data-grid-introduction-to-hazelcast)

src/roadmaps/system-design/content/112-caching/105-caching-strategies/index.md

@@ -1 +1,8 @@
 # Caching Strategies
+
+Since you can only store a limited amount of data in cache, you'll need to determine which cache update strategy works best for your use case. Here is the list of cache update strategies:
+
+- Cache-aside
+- Write-through
+- Write-behind
+- Refresh-ahead