Connectors

onestep 1.x uses Source for input and Sink for output. Many connectors implement both, so they can be consumed by tasks and also receive upstream task return values.

Built-in Connectors

In-Memory

Connector	Source	Sink	Description
Memory	Yes	Yes	In-memory queue, suitable for development and testing

Timers

Connector	Source	Sink	Description
Interval	Yes	No	Fixed interval trigger
Cron	Yes	No	Cron expression trigger

Message Queues

Connector	Source	Sink	Description
Redis Streams	Yes	Yes	Redis Streams message queue, install onestep-redis
RabbitMQ	Yes	Yes	RabbitMQ queue, install onestep-mq
AWS SQS	Yes	Yes	AWS SQS managed queue, install onestep-sqs
AWS SNS	No	Yes	AWS SNS topic fan-out sink, install onestep-sqs
Kafka	Yes	Yes	Kafka topic source/sink, install onestep-kafka
Cloudflare Queues	Yes	Yes	Cloudflare managed queue pull/push, install onestep-cf-queues

Databases

Connector	Source	Sink	Description
SQL (MySQL / PostgreSQL)	Yes	Yes	Table queue/incremental sync/binlog CDC/table sink/tracked execution, install onestep-sql[mysql] or onestep-sql[postgres]
MongoDB	Yes	Yes	Collection polling/Change Stream/table sink, install onestep-mongodb
Elasticsearch / OpenSearch	No	Yes	Async bulk Sink, install onestep-elasticsearch
ClickHouse	No	Yes	Async confirmed table output Sink, install onestep-clickhouse
Feishu Bitable	Yes	Yes	Feishu Bitable incremental sync/table sink, install onestep-feishu-bitable

Web

Connector	Source	Sink	Description
Webhook	Yes	No	HTTP request reception
HTTP Sink	No	Yes	HTTP JSON request output

Custom

Connector	Source	Sink	Description
Custom	Yes	Yes	Implement any data source

Selection Guide

Development & Testing

from onestep import MemoryQueue

source = MemoryQueue("test")

Production - Distributed Tasks

from onestep_rabbitmq import RabbitMQConnector

rmq = RabbitMQConnector("amqp://...")
source = rmq.queue("jobs")

Production - Cloud Native

from onestep_sqs import SQSConnector

sqs = SQSConnector(region_name="us-east-1")
source = sqs.queue("https://sqs...")

Database Driven

from onestep_sql.mysql import MySQLConnector

db = MySQLConnector("mysql+pymysql://...")
source = db.table_queue(
    table="tasks",
    key="id",
    where="status = 0",
    claim={"status": 1},
    ack={"status": 2},
    nack={"status": 0},
)

Scheduled Tasks

from onestep import CronSource, IntervalSource

# Fixed interval
source = IntervalSource.every(minutes=5)

# Specific time
source = CronSource("0 9 * * *")

External Integration

from onestep import HttpSink, WebhookSource

# Receive external system push
source = WebhookSource(path="/webhooks/github")

# Send processing results to external HTTP endpoint
sink = HttpSink("notify", url="https://example.com/hooks/events")

YAML Configuration

resources:
  memory:
    type: memory
  
  timer:
    type: interval
    minutes: 5
  
  cron:
    type: cron
    expression: "0 9 * * *"
  
  rmq:
    type: rabbitmq
    url: "amqp://..."
  
  jobs:
    type: rabbitmq_queue
    connector: rmq
    queue: "jobs"
  
  db:
    type: mysql
    dsn: "mysql+pymysql://..."
  
  tasks:
    type: mysql_table_queue
    connector: db
    table: "tasks"
  
  webhook:
    type: webhook
    path: "/webhook"
    port: 8080

  notify:
    type: http_sink
    url: "https://example.com/hooks/events"

tasks:
  - name: process_jobs
    source: jobs
    emit: notify
    handler:
      ref: myapp:process_jobs

YAML registers resource types through installed plugins. Before using rabbitmq, redis_stream, sqs_queue, mysql_table_queue, postgres_incremental, mongodb_polling, elasticsearch_bulk_sink, clickhouse_table_sink, kafka_topic or feishu_bitable_*, install the corresponding plugin in the worker environment.

Custom Source/Sink

Refer to Custom Broker to implement custom data sources.

Next Steps

• Memory - In-memory queue
• RabbitMQ - RabbitMQ queue
• Kafka - Kafka topic source/sink
• Feishu Bitable - Feishu Bitable sync
• SQL (MySQL / PostgreSQL) - Database integration
• Webhook - HTTP reception
• HTTP Sink - HTTP output
• Custom - Custom implementation