|kafka-node||68.9 kB||MIT||10 Years||4 Nov 2019|
|kafkajs||143.73 kB||MIT||6 Years||27 Feb 2023|
|node-rdkafka||4.31 MB||MIT||7 Years||21 Jul 2023|
|no-kafka||65.36 kB||MIT||7 Years||9 Dec 2018|
|ascoltatori||107.59 kB||MIT||11 Years||6 Aug 2018|
|@kafkajs/confluent-schema-registry||22.66 kB||UNKNOWN||4 Years||4 Oct 2022|
|kafkajs-snappy||32.07 kB||MIT||5 Years||12 Nov 2018|
|kafka-streams||26.35 kB||MIT||6 Years||24 Feb 2020|
|opentelemetry-instrumentation-kafkajs||14.66 kB||Apache-2.0||2 Years||27 Jul 2023|
|prozess||22.32 kB||BSD||11 Years||30 Sep 2014|
|message-queue||16.11 kB||Artistic-2.0||9 Years||1 Oct 2014|
|message-hub-rest||13.6 kB||Apache-2.0||8 Years||6 Jul 2018|
|kafka-rest||22.2 kB||Apache-2||8 Years||20 Dec 2016|
|kafkaesque||25.53 kB||MIT||10 Years||26 Sep 2016|
|express-kafka-producer||11 kB||MIT||8 Years||9 Jul 2015|
Apache Kafka libraries are particularly useful when applications need to process large amounts of data in real-time. They can be applied in system monitoring, real-time analytics, and data processing pipelines for transforming and aggregating data into different systems. Kafka can effectively handle data flow between applications and systems from various sources in a fault-tolerant manner and maintain real-time processing.
Apache Kafka libraries typically provide a rich set of functionalities. Key among them is the ability to publish and subscribe to streams of records (also known as a stream processing). Kafka libraries group these records, called messages, into categories called topics. Furthermore, Kafka libraries allow real-time handling of these message streams using two core APIs: the Producer API and the Consumer API.
Moreover, Kafka libraries help in storing streams of records in a fault-tolerant, durable way and managing streams of records as they occur.
Despite its distinct advantages, there are several potential issues you may encounter with Apache Kafka libraries:
In the context of npm and Node.js, interacting with Kafka libraries may exhibit additional challenges due to the asynchronous, event-driven nature of the environment. This could potentially lead to issues in managing and synchronizing data flow, specifically during high-volume data processing.