dbus

README

dbus

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What is dbus?

dbus = distributed data bus

It is yet another lightweight versatile databus system that transfer/transform pipeline data between plugins.

dbus works by building a DAG of structured data out of the different plugins: from data input, via filter(optional), to the output.

Similar projects

• logstash
• flume
• nifi
• camel
• beats
• kettle
• zapier
• google cloud dataflow
• canal
• storm
• yahoo pipes (dead)

Status

dbus is not yet a 1.0. We're writing more tests, fixing bugs, working on TODOs.

Use Case

• mysql binlog dispatcher
• multiple DC kafka mirror

Features

dbus supports powerful and scalable directed graphs of data routing, transformation and system mediation logic.

• Designed for extension
  • plugin architecture
  • build your own plugins and more
  • enables rapid development and effective testing
• Data Provenance
  • track dataflow from beginning to end
  • visualized dataflow
  • rich metrics feed into tsdb
  • online manual mediation of the dataflow
  • RESTful API
  • monitoring with alert
• Distributed Deployment
  • shard/balance/auto rebalance
  • linear scale
• Delivery Guarantee
  • loss tolerant
  • high throuput vs low latency
  • back pressure
• Robustness
  • race condition detected
  • edge cases fully covered
  • network jitter tested
  • dependent components failure tested
• Systemic Quality
  • hot reload
  • dryrun throughput 1.9M packets/s
• Cluster Support
  • modelling borrowed from helix+kafka controller
  • currently only leader/standby with sharding, without replica
  • easy to write a distributed plugin

Getting Started

1. Installing

To start using dbus, install Go and run go get:

$ go get -u github.com/funkygao/dbus

2. Create config file

Please find sample config files in etc/ directory.

3. Run the server

$ $GOPATH/dbusd -conf $myfile

Dependencies

dbus uses zookeeper for sharding/balance/election.

Plugins

More plugins are listed under dbus-plugin.

Input

• MysqlbinlogInput
• KafkaInput
• MockInput
• StreamInput

Filter

• MysqlbinlogFilter
• MockFilter

Output

• KafkaOutput
• ESOutput
• MockOutput
• StreamOutput

Configuration

• KafkaOutput async mode with batch=1024/500ms, ack=WaitForAll
• KafkaOutput retry=3, retry.backoff=350ms
• Mysql binlog positioner commit every 1s, channal buffer 100

FAQ

mysql binlog

• is it totally data loss tolerant?

  if the binlog exceeds 1MB, it will be discarded(lost)

why not canal?

• no Delivery Guarantee
• no Data Provenance
• no integration with kafka
• only hot standby deployment mode, we need sharding load
• dbus is a dataflow engine, while canal only support mysql binlog pipeline

compared with logstash

• logstash has better ecosystem
• dbus is cluster aware, provides delivery guarantee, data provenance

can there be more than 1 leaders at the same time?

Yes.

For example, 3 participants with 1 being the leader. Then 1 is network partitioned and zk session expires, [2, 3] found this event and re-elect 2 as new leader. Before 1 regain new zk session, [1] and [2] are leaders both. If [1] and [2] both found resources changes, they will both rebalance the cluster.

dbus uses epoch to solve this issue.

what if

• zookeeper crash

  dbus continues to work, but Ack will not be able to persist