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Kestra is an infinitely scalable orchestration and scheduling platform, creating, running, scheduling, and monitoring millions of complex pipelines.
- 🔀 Any kind of workflow: Workflows can start simple and progress to more complex systems with branching, parallel, dynamic tasks, flow dependencies
- 🎓 Easy to learn: Flows are in simple, descriptive language defined in YAML—you don't need to be a developer to create a new flow.
- 🔣 Easy to extend: Plugins are everywhere in Kestra, many are available from the Kestra core team, but you can create one easily.
- 🆙 Any triggers: Kestra is event-based at heart—you can trigger an execution from API, schedule, detection, events
- 💻 A rich user interface: The built-in web interface allows you to create, run, and monitor all your flows—no need to deploy your flows, just edit them.
- ⏩ Enjoy infinite scalability: Kestra is built around top cloud native technologies—scale to millions of executions stress-free.
Example flow:
id: my-first-flow
namespace: my.company.teams
inputs:
- type: FILE
name: uploaded
description: A Csv file to be uploaded through API or UI
tasks:
- id: archive
type: io.kestra.plugin.gcp.gcs.Upload
description: Archive the file on Google Cloud Storage bucket
from: "{{ inputs.uploaded }}"
to: "gs://my_bucket/archives/{{ execution.id }}.csv"
- id: csvReader
type: io.kestra.plugin.serdes.csv.CsvReader
from: "{{ inputs.uploaded }}"
- id: fileTransform
type: io.kestra.plugin.scripts.nashorn.FileTransform
description: This task will anonymize the contactName with a custom nashorn script (javascript over jvm). This show that you able to handle custom transformation or remapping in the ETL way
from: "{{ outputs.csvReader.uri }}"
script: |
if (row['contactName']) {
row['contactName'] = "*".repeat(row['contactName'].length);
}
- id: avroWriter
type: io.kestra.plugin.serdes.avro.AvroWriter
description: This file will convert the file from Kestra internal storage to avro. Again, we handling ETL since the conversion is done by Kestra before loading the data in BigQuery. This allow you to have some control before loading and to reject wrong data as soon as possible.
from: "{{ outputs.fileTransform.uri }}"
schema: |
{
"type": "record",
"name": "Root",
"fields":
[
{ "name": "contactTitle", "type": ["null", "string"] },
{ "name": "postalCode", "type": ["null", "long"] },
{ "name": "entityId", "type": ["null", "long"] },
{ "name": "country", "type": ["null", "string"] },
{ "name": "region", "type": ["null", "string"] },
{ "name": "address", "type": ["null", "string"] },
{ "name": "fax", "type": ["null", "string"] },
{ "name": "email", "type": ["null", "string"] },
{ "name": "mobile", "type": ["null", "string"] },
{ "name": "companyName", "type": ["null", "string"] },
{ "name": "contactName", "type": ["null", "string"] },
{ "name": "phone", "type": ["null", "string"] },
{ "name": "city", "type": ["null", "string"] }
]
}
- id: load
type: io.kestra.plugin.gcp.bigquery.Load
description: Simply load the generated from avro task to BigQuery
avroOptions:
useAvroLogicalTypes: true
destinationTable: kestra-prd.demo.customer_copy
format: AVRO
from: "{{outputs.avroWriter.uri }}"
writeDisposition: WRITE_TRUNCATE
- id: aggregate
type: io.kestra.plugin.gcp.bigquery.Query
description: Aggregate some data from loaded files
createDisposition: CREATE_IF_NEEDED
destinationTable: kestra-prd.demo.agg
sql: |
SELECT k.categoryName, p.productName, c.companyName, s.orderDate, SUM(d.quantity) AS quantity, SUM(d.unitPrice * d.quantity * r.exchange) as totalEur
FROM `kestra-prd.demo.salesOrder` AS s
INNER JOIN `kestra-prd.demo.orderDetail` AS d ON s.entityId = d.orderId
INNER JOIN `kestra-prd.demo.customer` AS c ON c.entityId = s.customerId
INNER JOIN `kestra-prd.demo.product` AS p ON p.entityId = d.productId
INNER JOIN `kestra-prd.demo.category` AS k ON k.entityId = p.categoryId
INNER JOIN `kestra-prd.demo.rates` AS r ON r.date = DATE(s.orderDate) AND r.currency = "USD"
GROUP BY 1, 2, 3, 4
timePartitioningField: orderDate
writeDisposition: WRITE_TRUNCATETo get a local copy up and running, please follow these simple steps.
Make sure you have already installed:
- Download the compose file here and save it with the name
docker-compose.yml, for linux and macos, you can runwget https://raw.githubusercontent.com/kestra-io/kestra/develop/docker-compose.yml - Run
docker-compose pull - Run
docker-compose up -d - Open
http://localhost:8080on your browser - Follow this tutorial to create your first flow.
- Read the documentation to understand how to
- Develop your flows
- Deploy Kestra
- Use our terraform provider
- Develop your own plugins
Kestra is built on plugin systems. You can find your plugin to interact with your provider; alternatively, you can follow simple steps to develop your own plugin. Here are the official plugins that are available:
This list is growing quickly as we are actively building more plugins, and we welcome contributions!
Join our community if you need help, want to chat or have any other questions for us:
- GitHub - Discussion forums and updates from the Kestra team
- Twitter - For all the latest Kestra news
- Discord - Join the conversation! Get all the latest updates and chat to the devs
See the open issues for a list of proposed features (and known issues) or look at the project board.
We love contributions big or small, check out our guide on how to get started.
See our Plugin Developer Guide for developing Kestra plugins.
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