Ross ROSS = Recommend OSS · open-source software intelligence for agents

siddhi-io/siddhi

Stream Processing and Complex Event Processing Engine observed · 2026-08-28

github.com/siddhi-io/siddhi · homepage · Java · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

72/100

  • Activity 80
  • Release rhythm 46
  • Longevity 100
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 4517
  • days_rel: 147
  • days_push: 120
  • n_releases_24m: 1

Full methodology

Adoption not part of the score

1590 stars · 527 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Siddhi is a cloud-native stream processing and complex event processing (CEP) engine that executes Streaming SQL queries to capture events from diverse sources, detect patterns, and publish outputs in real time. It can run embedded as a Java or Python library, as a microservice on Docker/Kubernetes, and integrates with systems like Kafka, NATS, RDBMS, HTTP, and gRPC.

Use cases

  • process real-time event streams with SQL-like queries
  • detect complex event patterns and anomalies in streams
  • build real-time fraud detection pipelines
  • run long-running time-based aggregations over event data
  • deploy a lightweight stream processor on Kubernetes
  • score events in real time with pre-trained ML models
  • integrate and transform events between Kafka, databases, and HTTP services
  • generate real-time alerts and notifications from sensor data

When to choose

  • you need a lightweight, embeddable stream/CEP engine with a SQL-like query language
  • you want to run stream processing natively on Kubernetes with scalable, highly available deployments
  • you need pattern detection, windowed aggregation, and real-time ML inference in one engine
  • you require integrations with Kafka, NATS, JMS, RDBMS, NoSQL, HTTP, and gRPC out of the box

When to avoid

  • you need large-scale batch analytics or heavy offline ETL rather than real-time streaming
  • your team prefers mainstream ecosystems like Apache Flink, Kafka Streams, or Spark Streaming with larger communities
  • you need a managed cloud service rather than self-hosted infrastructure
  • you require very recent active development, since the project has been folded into WSO2 Enterprise Integrator

Facets

library · maturity maintenance

streaming etl analytics machine-learning message-queue monitoring analytics big-data microservices cloud-computing jvm cloud self-hosted cross-platform stream-processing complex-event-processing streaming-sql cep event-driven cloud-native cncf real-time-analytics kafka nats tensorflow pmml data-engineering real-time docker kubernetes

10 sources

Member repositories

RepositoryRoleHealth v2
siddhi-io/siddhimain72

For agents

markdown · JSON · MCP: product_card(name="siddhi-io/siddhi")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem