*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.*
We could store the user's own tweets to populate the user timeline (activity from the user) in a [relational database](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms). We should discuss the [use cases and tradeoffs between choosing SQL or NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql).
Delivering tweets and building the home timeline (activity from people the user is following) is trickier. Fanning out tweets to all followers (60 thousand tweets delivered on fanout per second) will overload a traditional [relational database](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms). We'll probably want to choose a data store with fast writes such as a **NoSQL database** or **Memory Cache**. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.<sup><ahref=https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know>1</a></sup>
* The **Client** posts a tweet to the **Web Server**, running as a [reverse proxy](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)
* [Scatter gathers](https://github.com/donnemartin/system-design-primer#under-development) each server in the cluster to determine if there are any results for the query
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](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/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?
We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter.
*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:
The **Fanout Service** is a potential bottleneck. Twitter users with millions of followers could take several minutes to have their tweets go through the fanout process. This could lead to race conditions with @replies to the tweet, which we could mitigate by re-ordering the tweets at serve time.
We could also avoid fanning out tweets from highly-followed users. Instead, we could search to find tweets for high-followed users, merge the search results with the user's home timeline results, then re-order the tweets at serve time.
Additional optimizations include:
* Keep only several hundred tweets for each home timeline in the **Memory Cache**
* Keep only active users' home timeline info in the **Memory Cache**
* If a user was not previously active in the past 30 days, we could rebuild the timeline from the **SQL Database**
* Query the **User Graph Service** to determine who the user is following
* Get the tweets from the **SQL Database** and add them to the **Memory Cache**
* Store only a month of tweets in the **Tweet Info Service**
* Store only active users in the **User Info Service**
* The **Search Cluster** would likely need to keep the tweets in memory to keep latency low
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.
* External communication with clients - [HTTP APIs following REST](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest)
