Open Source Java Stream Processing Tools

Reactor Core is a foundational library for building reactive applications in Java, providing a powerful API for asynchronous, non-blocking programming.

ActiveInsight provides real-time detection and reaction to events and patterns. It is a platform that enables the detection of meaningful events within multiple, high frequency, event streams.

A Middleware for Distrubted Data Stream Processing

LogsGrep is a unique, grep-like utility designed specifically to target log files containing multi-line entries. The primary target is Java log files (Log4J, common, ...), where it is very common to have multiline log entries (for example log entries with a stacktrace). It follows Unix philosophy, does only its primary job and expects its input to be generated by other more advanced tools (tail, cat, type, find...); There is no goal to be compatible with Unix grep. LogsGrep is written in the Java programming langue having performance and low resource usage in mind (no strings, no object creation, stream-processing).

MXQuery is a low-footprint implementation of XQuery 1.0, XQuery Update 1.0, XQuery Fulltext 1.0 and XQuery Scripting 1.0 as well as a subset of XQuery 1.1 (windowing, try/catch). It provides extensions to do data stream processing/CEP and SOAP/REST

Apache Pulsar is a cloud-native, distributed messaging and streaming platform originally created at Yahoo! and now a top-level Apache Software Foundation project. Easy to deploy, lightweight compute process, developer-friendly APIs, no need to run your own stream processing engine. Run in production at Yahoo! scale for over 5 years, with millions of messages per second across millions of topics. Expand capacity seamlessly to hundreds of nodes. Low publish latency (< 5ms) at scale with strong durability guarantees. Configurable replication between data centers across multiple geographic regions. Built from the ground up as a multi-tenant system. Supports isolation, authentication, authorization and quotas. Persistent message storage based on Apache BookKeeper. IO-level isolation between write and read operations. Flexible messaging models with high-level APIs for Java, Go, Python, C++, Node.js, WebSocket and C#.

Fully open source, cloud-native, scalable, micro streaming, and complex event processing system capable of building event-driven applications for use cases such as real-time analytics, data integration, notification management, and adaptive decision-making. Event processing logic can be written using Streaming SQL queries via graphical and source editors, to capture events from diverse data sources, process and analyze them, integrate with multiple services and data stores, and publish output to various endpoints in real time. Agile development experience with SQL-like query language and graphical drag-and-drop editor supporting event simulation. Lightweight runtime that can natively run on Kubernetes, Docker, VM, or bare metal, and embedded in any Java or Python application. Scalable, and highly available distributed event processing on Kubernetes, with NATS Streaming and Siddhi Kubernetes Operator.

Build applications that respond immediately to events. Craft materialized views over streams. Receive real-time push updates, or pull current state on demand. Seamlessly leverage your existing Apache Kafka® infrastructure to deploy stream-processing workloads and bring powerful new capabilities to your applications. Use a familiar, lightweight syntax to pack a powerful punch. Capture, process, and serve queries using only SQL. No other languages or services are required. ksqlDB enables you to build event streaming applications leveraging your familiarity with relational databases. Three categories are foundational to building an application: collections, stream processing, and queries. Streams are immutable, append-only sequences of events. They're useful for representing a series of historical facts. Tables are mutable collections of events. They let you represent the latest version of each value per key.