# core-async Published articles for core-async. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Using Clojure channels to increase throughput DevFeed: [Using Clojure channels to increase throughput]() Original publisher: [Read original article]() Author: Abhinav Dubey Published: 2025-05-28T10:07:12Z Content type: tutorial Language: en Sources: [Helpshift]() Topics: [Clojure](), [Concurrency](), [Kafka](), [benchmarking](), [async](), [Grafana]() Tags: [benchmarking](), [channel](), [clojure](), [concurrency](), [core-async](), [grafana](), [kafka](), [multithreading](), [parallelism]() ### AI overview This tutorial explains how to increase throughput in a Clojure-based Kafka consumer by using core.async channels and multiple worker threads. It compares a single-threaded baseline with configurations using three and ten workers, reporting shorter processing times for 1,000 events in the described benchmark. ### Source excerpt When building systems that process large volumes of messages synchronously, performance bottlenecks can quickly become a challenge specially with single-threaded designs. In this post, we'll look at how leveraging worker threads in a Clojure-based Kafka consumer can significantly boost throughput & reduce total processing time. Using simple concurrency primitives, it's possible to achieve parallelism & scale gracefully, all while keeping the codebase clean & maintainable. We'll start with a baseline, introduce worker threads using Clojure's core.async & measure the impact. Setup & Context Kafka & Zookeeper For observability: Grafana Kafka producer: A simple script that sends messages to a Kafka topic at a configurable rate (messages per minute) for a fixed duration. After each event is pushed, a counter metric is emitted Kafka consumer: A simple script that listens to a topic & consumes messages & simulates processing time finding square-root of a number (henceforth, assume that it takes ~1 second to find the square root) . A counter metric is emitted after processing each message The Baseline: Single-Threaded Consumer If each message takes t seconds to process & there are n messages, total processing time becomes n x t seconds. This provides a clean baseline to evaluate the impact of using channel moving forward. Adding workers with core.asyncValues are conveyed on queue-like channels. By default channels require producer and consumer to rendezvous for the transfer of a value through the channel https://clojuredocs.org/clojure.core.async To improve throughput, we introduce parallelism using Clojure's core.async channels. Messages from Kafka are fed into a channel, & multiple worker threads read from this channel to process messages concurrently Here, we used >!! (blocking put) & ) Original publisher: [Read original article]() Published: 2018-02-18T00:00:00Z Content type: tutorial Language: en Sources: [Adam Bard]() Topics: [streaming-data-processing](), [Clojure](), [stream-processing](), [Concurrent Programming](), [distributed-systems]() Tags: [clojure](), [concurrent](), [core-async](), [coroutine](), [distributed-computing](), [distributed-system](), [flink](), [spark](), [stream](), [stream-processing]() ### AI overview This article presents a small-scale stream-processing architecture in Clojure using core.async and component libraries. It describes modular components connected by asynchronous queues and explains how stream-processing design principles can support reusable, loosely coupled systems without requiring a distributed stream processor. ### Source excerpt If you're like me (that is, employed by an ad tech company), stream processing is usually associated with frameworks like Storm, Flink, Spark Streaming, and other such solutions. However, a lot of real-life software can be described as stream processing - data comes in one end, is transformed or aggregated, and goes somewhere else. Many of these workloads don't justify the overhead of a stream processor, but that doesn't mean they can't benefit from some of the lessons of stream processing systems.