|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Flink Learning||13,801||17 days ago||8||apache-2.0||Java|
|flink learning blog. http://www.54tianzhisheng.cn/ 含 Flink 入门、概念、原理、实战、性能调优、源码解析等内容。涉及 Flink Connector、Metrics、Library、DataStream API、Table API & SQL 等内容的学习案例，还有 Flink 落地应用的大型项目案例（PVUV、日志存储、百亿数据实时去重、监控告警）分享。欢迎大家支持我的专栏《大数据实时计算引擎 Flink 实战与性能优化》|
|Redpanda||7,787||1||6 hours ago||343||April 25, 2021||1,570||C++|
|Redpanda is a streaming data platform for developers. Kafka API compatible. 10x faster. No ZooKeeper. No JVM!|
|Benthos||6,644||9||3 days ago||45||July 02, 2023||391||mit||Go|
|Fancy stream processing made operationally mundane|
|Faust||6,501||14||16||7 months ago||46||February 25, 2020||280||other||Python|
|Python Stream Processing|
|Watermill||6,093||182||7 days ago||45||July 16, 2023||84||mit||Go|
|Building event-driven applications the easy way in Go.|
|Ksql||5,645||8 hours ago||1,277||other||Java|
|The database purpose-built for stream processing applications.|
|Materialize||5,317||6 hours ago||50||August 09, 2023||2,230||other||Rust|
|Materialize is an Operational Data Warehouse: A cloud data warehouse with streaming internals, built for work that needs action on what’s happening right now.|
|Risingwave||5,225||7 hours ago||9||August 03, 2023||868||apache-2.0||Rust|
|The streaming database: SQL stream processing with Postgres-like experience 🪄. 10X more cost-efficient than Apache Flink 🚀.|
|Arroyo||2,571||15 hours ago||43||apache-2.0||Rust|
|Distributed stream processing engine in Rust|
|Memgraph||1,777||7 hours ago||248||other||C++|
|Open-source graph database, built for real-time streaming data, compatible with Neo4j.|
Benthos is a high performance and resilient stream processor, able to connect various sources and sinks in a range of brokering patterns and perform hydration, enrichments, transformations and filters on payloads.
It comes with a powerful mapping language, is easy to deploy and monitor, and ready to drop into your pipeline either as a static binary, docker image, or serverless function, making it cloud native as heck.
Benthos is declarative, with stream pipelines defined in as few as a single config file, allowing you to specify connectors and a list of processing stages:
input: gcp_pubsub: project: foo subscription: bar pipeline: processors: - mapping: | root.message = this root.meta.link_count = this.links.length() root.user.age = this.user.age.number() output: redis_streams: url: tcp://TODO:6379 stream: baz max_in_flight: 20
Delivery guarantees can be a dodgy subject. Benthos processes and acknowledges messages using an in-process transaction model with no need for any disk persisted state, so when connecting to at-least-once sources and sinks it's able to guarantee at-least-once delivery even in the event of crashes, disk corruption, or other unexpected server faults.
This behaviour is the default and free of caveats, which also makes deploying and scaling Benthos much simpler.
AWS (DynamoDB, Kinesis, S3, SQS, SNS), Azure (Blob storage, Queue storage, Table storage), GCP (Pub/Sub, Cloud storage, Big query), Kafka, NATS (JetStream, Streaming), NSQ, MQTT, AMQP 0.91 (RabbitMQ), AMQP 1, Redis (streams, list, pubsub, hashes), Cassandra, Elasticsearch, HDFS, HTTP (server and client, including websockets), MongoDB, SQL (MySQL, PostgreSQL, Clickhouse, MSSQL), and many more.
Connectors are being added constantly, if something you want is missing then open an issue.
If you want to dive fully into Benthos then don't waste your time in this dump, check out the documentation site.
For guidance on how to configure more advanced stream processing concepts such as stream joins, enrichment workflows, etc, check out the cookbooks section.
For guidance on building your own custom plugins in Go check out the public APIs.
Grab a binary for your OS from here. Or use this script:
curl -Lsf https://sh.benthos.dev | bash
Or pull the docker image:
docker pull jeffail/benthos
Benthos can also be installed via Homebrew:
brew install benthos
For more information check out the getting started guide.
benthos -c ./config.yaml
Or, with docker:
# Using a config file docker run --rm -v /path/to/your/config.yaml:/benthos.yaml jeffail/benthos # Using a series of -s flags docker run --rm -p 4195:4195 jeffail/benthos \ -s "input.type=http_server" \ -s "output.type=kafka" \ -s "output.kafka.addresses=kafka-server:9092" \ -s "output.kafka.topic=benthos_topic"
Benthos serves two HTTP endpoints for health checks:
/pingcan be used as a liveness probe as it always returns a 200.
/readycan be used as a readiness probe as it serves a 200 only when both the input and output are connected, otherwise a 503 is returned.
Benthos exposes lots of metrics either to Statsd, Prometheus or for debugging purposes an HTTP endpoint that returns a JSON formatted object.
Benthos provides lots of tools for making configuration discovery, debugging and organisation easy. You can read about them here.
Build with Go (any currently supported version):
git clone [email protected]:benthosdev/benthos cd benthos make
Benthos uses golangci-lint for linting, which you can install with:
curl -sSfL https://raw.githubusercontent.com/golangci/golangci-lint/master/install.sh | sh -s -- -b $(go env GOPATH)/bin
And then run it with
It's pretty easy to write your own custom plugins for Benthos in Go, for information check out the API docs, and for inspiration there's an example repo demonstrating a variety of plugin implementations.
By default Benthos does not build with components that require linking to external libraries, such as the
zmq4 input and outputs. If you wish to build Benthos locally with these dependencies then set the build tag
# With go go install -tags "x_benthos_extra" github.com/benthosdev/benthos/v4/cmd/benthos@latest # Using make make TAGS=x_benthos_extra
Note that this tag may change or be broken out into granular tags for individual components outside of major version releases. If you attempt a build and these dependencies are not present you'll see error messages such as
ld: library not found for -lzmq.
There's a multi-stage
Dockerfile for creating a Benthos docker image which results in a minimal image from scratch. You can build it with:
Then use the image:
docker run --rm \ -v /path/to/your/benthos.yaml:/config.yaml \ -v /tmp/data:/data \ -p 4195:4195 \ benthos -c /config.yaml