streaming

The count-min sketch estimates how often an item occurred using a fixed grid of counters, and it never underestimates. This lesson builds it, states the error bound that makes it usable, shows where it is useless, and ends on the property shared by all three structures that explains why they run distributed systems.

Counting distinct items exactly needs memory proportional to the count. HyperLogLog answers the same question in a fixed twelve kilobytes, for cardinalities into the billions, by measuring an improbable event rather than storing anything. This lesson builds that idea from the leading-zeros intuition up.

Answering set questions exactly costs memory proportional to the data, which fails once the data does not fit. Probabilistic data structures accept a quantified error in exchange for memory that stays constant. This lesson establishes what that trade buys, and why the shape of the error matters more than its size.

The RSC mental model: server-by-default, the 'use client' boundary, what can cross it, Server Actions with 'use server', streaming with Suspense, and a Postgres-to-Client end-to-end example.

Dive into Apache Kafka, a powerful distributed streaming platform designed for high-throughput, fault-tolerant data pipelines. Understand its core components, architecture, and how it enables real-time data processing for modern applications.