chore: sync content to repo (#9564)

Co-authored-by: kamranahmedse <4921183+kamranahmedse@users.noreply.github.com>

Author: github-actions[bot]

src/data/roadmaps/system-design/content/cache-aside@bffJlvoLHFldS0CluWifP.md

@@ -3,18 +3,14 @@
 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
+    
 *   Load entry from the database
+    
 *   Add entry to cache
+    
 *   Return entry
-
-    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
+    
+    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
 
 
 [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.

src/data/roadmaps/system-design/content/database-caching@BeIg4jzbij2cwc_a_VpYG.md

@@ -1,16 +1,9 @@
 # Database Caching
 
-Your database usually includes some level of caching in a default configuration, optimized for a generic use case. Tweaking these settings for specific usage patterns can further boost performance. it's like having a quick-access memory for frequently used data in applications. Here's a simplified explanation:
+Database caching involves storing frequently accessed data from a database in a temporary storage location (the cache) to reduce the load on the database and improve application performance. Instead of repeatedly querying the database for the same data, the application first checks the cache. If the data is present (a cache hit), it's retrieved from the cache, which is much faster than a database query. If the data is not in the cache (a cache miss), the application queries the database, retrieves the data, stores it in the cache for future use, and then returns it to the application.
 
-1.  **Quick Access**: Imagine you're looking up information in a big library (the database). Instead of going to the library every time you need the same book (data), you keep a copy of it on your desk (cache).
-    
-2.  **Faster Retrieval**: When you need that book again, you first check your desk (cache). If it's there, great! You get it right away without going to the library (database) again.
-    
-3.  **Saving Time**: If the book isn't on your desk (cache miss), you go to the library (database) to get it. But you make sure to put a copy on your desk for next time, so you won't have to go to the library again if you need it soon.
-    
-4.  **Different Types**: There are different ways to do this caching. You can cache the results of searches (like bookmarking), whole pieces of information (like keeping a paper copy), or even entire web pages (like saving a snapshot).
-    
-5.  **Benefits**: By keeping frequently used data close by, you save time and reduce the strain on the library (database). It's like having your most-used books right at your fingertips, making your work faster and more efficient.
-    
+Visit the following resources to learn more:
 
-However, it's important to keep the cached data up to date. Otherwise, you might end up with outdated information, like using an old edition of a book instead of the latest one. So, managing this cache properly is key to keeping things running smoothly.
+- [@article@Database Caching](https://aws.amazon.com/caching/database-caching/)
+- [@article@Introduction to database caching](https://www.prisma.io/dataguide/managing-databases/introduction-database-caching)
+- [@article@Database Caching Strategies](https://medium.com/@sesmiat/database-caching-strategies-f5e40c3c9b74)

src/data/roadmaps/system-design/content/databases@5FXwwRMNBhG7LT5ub6t2L.md

@@ -10,5 +10,5 @@ Picking the right database for a system is an important decision, as it can have
 
 Visit the following resources to learn more:
 
-- [@video@Scaling up to your first 10 million users (DB related material begins at 10:33)](https://youtu.be/kKjm4ehYiMs?si=knWdkA2Y5QHM5ZJw&t=633)
-- [@feed@Explore top posts about Backend Development](https://app.daily.dev/tags/backend?ref=roadmapsh)
+- [@video@Scaling up to your first 10 million users](https://www.youtube.com/watch?v=kKjm4ehYiMs)
+- [@feed@Explore top posts about Backend Development](https://app.daily.dev/tags/backend?ref=roadmapsh)

src/data/roadmaps/system-design/content/queue-based-load-leveling@LncTxPg-wx8loy55r5NmV.md

@@ -1,7 +1,7 @@
-# Queue Based Load Leveling
+# Queue-Based Load Leveling
 
 Use a queue that acts as a buffer between a task and a service it invokes in order to smooth intermittent heavy loads that can cause the service to fail or the task to time out. This can help to minimize the impact of peaks in demand on availability and responsiveness for both the task and the service.
 
-Learn more from the following links:
+Visit the following resources to learn more:
 
-- [@article@Queue-Based Load Leveling pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/queue-based-load-leveling)
+- [@article@Queue-Based Load Leveling pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/queue-based-load-leveling)

src/data/roadmaps/system-design/content/sql-tuning@fY8zgbB13wxZ1CFtMSdZZ.md

@@ -11,4 +11,4 @@ Visit the following resources to learn more:
 
 - [@official@Introduction to SQL Tuning - Oracle](https://docs.oracle.com/en/database/oracle/oracle-database/23/tgsql/introduction-to-sql-tuning.html#GUID-B653E5F3-F078-4BBC-9516-B892960046A2)
 - [@article@Query Optimization for Mere Humans in PostgreSQL](https://towardsdatascience.com/query-optimization-for-mere-humans-in-postgresql-875ab864390a/)
-- [@feed@Explore top posts about SQL](https://app.daily.dev/tags/sql?ref=roadmapsh)
+- [@feed@Explore top posts about SQL](https://app.daily.dev/tags/sql?ref=roadmapsh)

src/data/roadmaps/system-design/content/what-is-system-design@idLHBxhvcIqZTqmh_E8Az.md

@@ -6,8 +6,7 @@ It involves taking a problem statement, breaking it down into smaller components
 
 In software engineering, system design is a phase in the software development process that focuses on the high-level design of a software system, including the architecture and components.
 
-It is also one of the important aspects of the interview process for software engineers. Most of the companies have a dedicated system design interview round, where they ask the candidates to design a system for a given problem statement. The candidates are expected to come up with a detailed design of the system, including the architecture, components, and their interactions. They are also expected to discuss the trade-offs involved in their design and the alternatives that they considered.
-
+Visit the following resources to learn more:
 
 - [@article@System Design: Complete Guide with Patterns, Examples, and Techniques](https://swimm.io/learn/system-design/system-design-complete-guide-with-patterns-examples-and-techniques)
-- [@article@A comprehensive guide to system design](https://www.crio.do/blog/a-comprehensive-guide-to-system-design/)
+- [@article@A comprehensive guide to system design](https://www.crio.do/blog/a-comprehensive-guide-to-system-design/)