AMQP
AMQP (Advanced Message Queuing Protocol) Standard is a commonly used messaging protocol used in the open-source application development process.
Integrate the AMQP API with the Python API
Setup the AMQP API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate AMQP and Python remarkably fast. Free for developers.
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Popular AMQP and Python Triggers#
Emit new event for each new message in an AMQP 1.0 queue. See the library example here.
Popular AMQP and Python Actions#
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Send a new message to an AMQP 1.0 queue. See the library example here.
Overview of AMQP#
AMQP (Advanced Message Queuing Protocol) is a flexible protocol designed for high-performance messaging. Integrating the AMQP API within Pipedream workflows allows for robust messaging capabilities between various systems and services. You can use it to queue tasks, run asynchronous job processing, and facilitate communication between different parts of your application or different applications altogether. AMQP's reliability and standardization make it a go-to choice for enterprise-level messaging patterns.
Connect AMQP#
export default defineComponent({
props: {
aqmp: {
type: "app",
app: "amqp",
},
},
async run({ steps, $ }) {
// Access required authentication info via:
// this.aqmp.$auth.host
// this.aqmp.$auth.post
// this.aqmp.$auth.username
// this.aqmp.$auth.password
},
})Overview of Python#
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.
This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
Connect Python#
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}Trusted by 1,000,000+ developers from startups to Fortune 500 companies