Twitter has enforced very strict rate limiting. Some people cannot even see their own tweets.
Rate limiting is a very important yet often overlooked topic. Let's use this opportunity to take a look at what it is and the most popular algorithms.
A thread.
#RateLimitExceeded
What is rate limiting? Rate limiting controls the rate at which users or services can access a resource. Here are some examples:
- A user can send a message no more than 2 per second
- One can create a maximum of 10 accounts per day from the same IP address
Fixed Window Counter
The algorithm divides the timeline into fixed-size time windows and assigns a counter for each window. Each request increments the counter by some value. Once the counter reaches the threshold, subsequent requests are blocked until the new time window begins
Sliding Window Log
The Sliding Window Log algorithm fixes the issue with the Fixed Window Counter algorithm where it allows more requests to slip through at the edges of a time window.
Sliding Window Counter
The Sliding Window Counter algorithm is a more efficient variation of the Sliding Window Log algorithm. It is a hybrid that combines the fixed window counter and sliding window log.
Token Bucket
The Token Bucket algorithm is widely used for rate limiting. It is simple, well understood and commonly used by large tech companies. Both Amazon and Stripe use this algorithm to throttle their API requests.
Leaky Bucket
The algorithm uses a "bucket" metaphor but processes requests differently. Requests enter the bucket and are processed at a fixed rate, simulating a "leak" in the bucket. If the bucket becomes full, new requests are discarded until there is space available.
Model Context Protocol (MCP) is a new system introduced by Anthropic to make AI models more powerful.
It is an open standard (also being run as an open-source project) that allows AI models (like Claude) to connect to databases, APIs, file systems, and other tools without needing custom code for each new integration.
MCP follows a client-server model with 3 key components:
1 - Host: AI applications like Claude that provide the environment for AI interactions so that different tools and data sources can be accessed. The host runs the MCP Client.
1 - Collaboration Tools
Software development is a social activity. Learn to use collaboration tools like Jira, Confluence, Slack, MS Teams, Zoom, etc.
2 - Programming Languages
Pick and master one or two programming languages. Choose from options like Java, Python, JavaScript, C#, Go, etc.
3 - API Development
Learn the ins and outs of API Development approaches such as REST, GraphQL, and gRPC.
4 - Web Servers and Hosting
Know about web servers as well as cloud platforms like AWS, Azure, GCP, and Kubernetes
5 - Authentication and Testing
Learn how to secure your applications with authentication techniques such as JWTs, OAuth2, etc. Also, master testing techniques like TDD, E2E Testing, and Performance Testing
6 - Databases
Learn to work with relational (Postgres, MySQL, and SQLite) and non-relational databases (MongoDB, Cassandra, and Redis).
/1 What is the difference between “pull” and “push” payments?
The diagram below shows how the pull and push payments work.
/2 🔹 When we swipe a credit/debit card at a merchant, it is a pull payment, where the money is sent from the cardholder to the merchant. The merchant pulls money from the cardholder’s account, and the cardholder approves the transaction.
/3 🔹 With Visa Direct or Mastercard Send, the push payments enable merchant, corporate, and government disbursements.
Step 1: The merchant initiates the push payment through a digital channel. It can be a mobile phone or a bank branch etc.
/2 The Netflix Engineering team selects a variety of databases to empower streaming at scale.
Relational databases: Netflix chooses MySql for billing transactions, subscriptions, taxes, etc. They use CockroachDB to support a multi-region active-active architecture.
/3 Columnar databases: Netflix primarily uses them for analytics purposes. They utilize Redshift and Druid for structured data storage, Spark and data pipeline processing, and Tableau for data visualization.