Data storage and processing

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Kestra's primary purpose is to orchestrate data processing via tasks, so data is central to each flow's execution.

Depending on the task, data can be stored inside the execution context or inside Kestra's internal storage. You can also manually store data inside Kestra's state store by using dedicated tasks.

Some tasks give you the choice of where you want to store the data, usually using a fetchType property or the three fetch/fetchOne/store properties.

For example, using the DynamoDB Query task:

yaml

id: query
type: io.kestra.plugin.aws.dynamodb.Query
tableName: persons
keyConditionExpression: id = :id
expressionAttributeValues:
  :id: "1"
fetchType: FETCH

The fetchType property can have four values:

FETCH_ONE: will fetch the first row and set it in a task output attribute (the row attribute for DynamoDB); the data will be stored inside the execution context.
FETCH: will fetch all rows and set them in a task output attribute (the rows attribute for DynamoDB); the data will be stored inside the execution context.
STORE: will store all rows inside Kestra's internal storage. The internal storage will return a URI usually set in the task output attribute uri and that can be used to retrieve the file from the internal storage.
NONE: will do nothing.

The three fetch/fetchOne/store properties will do the same but using three different task properties instead of a single one.

Storing data

Storing data inside the flow execution context

Data can be stored as variables inside the flow execution context. This can be convenient for sharing data between tasks.

To do so, tasks store data as output attributes that are then available inside the flow via Pebble expressions like {{outputs.taskName.attributeName}}. More information about outputs can be found here.

Be careful that when the size of the data is significant, this will increase the size of the flow execution context, which can lead to slow execution and increase the size of the execution storage inside Kestra's repository.

Depending on the Kestra internal queue and repository implementation, there can be a hard limit on the size of the flow execution context as it is stored as a single row/message. Usually, this limit is around 1MB, so this is important to avoid storing large amounts of data inside the flow execution context.

Storing data inside the internal storage

Kestra has an internal storage that can store data of any size. By default, the internal storage uses the host filesystem, but plugins exist to use other implementations like Amazon S3, Google Cloud Storage, or Microsoft Azure Blobs storage. See internal storage configuration.

When using the internal storage, data is, by default, stored using Amazon Ion format.

Tasks that can store data inside the internal storage usually have an output attribute named uri that can be used to access this file in following tasks.

The following example uses the DynamoDB Query task to query a table and the FTP Upload task to send the retrieved rows to an external FTP server.

yaml

tasks:
- id: query
  type: io.kestra.plugin.aws.dynamodb.Query
  tableName: persons
  keyConditionExpression: id = :id
  expressionAttributeValues:
    :id: "1"
  fetchType: STORE
- id: upload
  type: io.kestra.plugin.fs.ftp.Upload
  host: localhost
  port: 80
  from: "{{ outputs.query.uri }}"
  to: "/upload/file.ion"

If you need to access data from the internal storage, you can use the Pebble read function to read the file's content as a string.

Dedicated tasks allow managing the files stored inside the internal storage:

Concat: concat multiple files.
Delete: delete a file.
Size: get the size of a file.
Split: split a file into multiple files depending on the size of the file or the number of rows.

This should be the main method for storing and carrying large data from task to task. As an example, if you know that a HTTP Request will return a heavy payload, you should consider using HTTP Download along with a Serdes instead of carrying raw data in Flow Execution Context

Storing data inside the state store

Dedicated tasks can store data inside Kestra's sate store. The state store transparently uses Kestra's internal storage as its backend store.

The state store allows storing data that will be shared by all executions of the same flow. You can think of it as a key/value store dedicated to a flow (or a namespace if setting the property namespace: true).

The following tasks are available:

Set: set a state key/value pair.
Get: get a state key/value pair.
Delete: delete a state key/value pair.

Example:

yaml

tasks:
- id: setState
  type: io.kestra.core.tasks.states.Set
  name: myState
  data:
    name: John Doe
- id: getState
  type: io.kestra.core.tasks.states.Get
  name: myState

Processing data

You can make basic data processing thanks to variables processing offered by the Pebble templating engine, see variables basic usage.

But these are limited, and you may need more powerful data processing tools; for this, Kestra offers various data processing tasks like file transformations or scripts.

Converting files

Files from the internal storage can be converted from/to the Ion format to/from another format using the Serdes plugin.

The following formats are currently available: Avro, JSON, XML, and Parquet.

Each format offers a reader to read an Ion serialized data file and write it in the target format and a writer to read a file in a specific format and write it as an Ion serialized data file.

For example, to convert an Ion file to CSV, then back to Ion:

yaml

tasks:
- id: query
  type: io.kestra.plugin.aws.dynamodb.Query
  tableName: persons
  keyConditionExpression: id = :id
  expressionAttributeValues:
    :id: "1"
  fetchType: STORE
- id: convertToCsv
  type: io.kestra.plugin.serdes.csv.CsvWriter
  from: "{{outputs.query.uri}}"
- id: convertBackToIon
  type: io.kestra.plugin.serdes.csv.CsvReader
  from: "{{outputs.convertToCsv.uri}}""

Processing data using scripts

Kestra can launch scripts written in Python, R, Node.js, Shell and Powershell. Depending on the runner, they can run directly in a local process on the host or inside Docker containers.

Those script tasks are available in the Scripts Plugin. Here is documentation for each of them:

The Python task will run a Python script in a Docker container or in a local process.
The Node task will run a Node.js script in a Docker container or in a local process.
The R task will run an R script in a Docker container or in a local process.
The Shell task will execute a single Shell command, or a list of commands that you provide.
The PowerShell task will execute a single PowerShell command, or a list of commands that you provide.

The following example will query the BigQuery public dataset with Wikipedia page views to find the top 10 pages, convert it to CSV, and use the CSV file inside a Python task for further transformations using Pandas.

yaml

id: wikipedia-top-ten-python-panda
namespace: dev
description: analyze top 10 Wikipedia pages

tasks:
  - id: query
    type: io.kestra.plugin.gcp.bigquery.Query
    sql: |
      SELECT DATETIME(datehour) as date, title, views FROM `bigquery-public-data.wikipedia.pageviews_2023`
      WHERE DATE(datehour) = current_date() and wiki = 'en'
      ORDER BY datehour desc, views desc
      LIMIT 10
    store: true
    projectId: geller
    serviceAccount: "{{envs.gcp_creds}}"

  - id: write-csv
    type: io.kestra.plugin.serdes.csv.CsvWriter
    from: "{{outputs.query.uri}}"

  - id: wdir
    type: io.kestra.core.tasks.flows.WorkingDirectory
    tasks:
    - id: file
      type: io.kestra.core.tasks.storages.LocalFiles
      inputs:
        data.csv: "{{outputs['write-csv'].uri}}"
    
    - id: pandas
      type: io.kestra.plugin.scripts.python.Script
      warningOnStdErr: false
      docker: 
        image: ghcr.io/kestra-io/pydata:latest
      script: |
        import pandas as pd
        from kestra import Kestra
        
        df = pd.read_csv("data.csv")
        views = df['views'].sum()
        Kestra.outputs({'views': int(views)})

Kestra offers several plugins for ingesting and transforming data — check the Plugin list for more details.

Make sure to also check:

The Script documentation for a detailed overview of how to work with Python, R, Node.js, Shell and Powershell scripts and how to integrate them with Git and Docker.
The Blueprints catalog — simply search for the relevant language (e.g. Python, R, Rust) or use case (ETL, Git, dbt, etc.) to find the relevant examples.

Processing data using file transform

Kestra can process data row by row using file transform tasks. The transformation will be done with a small script written in Python, JavaScript, or Groovy.

The Jython FileTransform task allows transforming rows with Python.
The Nashorn FileTransform task allows transforming rows with JavaScript.
The Groovy FileTransform task allows transforming rows with Groovy.

The following example will query the BigQuery public dataset for Wikipedia pages, convert it row by row with the Nashorn FileTransform, and write it in a CSV file.

yaml

id: wikipedia-top-ten-file-transform
namespace: io.kestra.tests
description: A flow that loads wikipedia top 10 EN pages
tasks:
  - id: query-top-ten
    type: io.kestra.plugin.gcp.bigquery.Query
    sql: |
      SELECT DATETIME(datehour) as date, title, views FROM `bigquery-public-data.wikipedia.pageviews_2023`
      WHERE DATE(datehour) = current_date() and wiki = 'en'
      ORDER BY datehour desc, views desc
      LIMIT 10
    store: true
  - id: file-transform
    type: io.kestra.plugin.scripts.nashorn.FileTransform
    from: "{{outputs['query-top-ten'].uri}}"
    script: |
      logger.info('row: {}', row)

      if (row['title'] === 'Main_Page' || row['title'] === 'Special:Search' || row['title'] === '-') {
        // remove un-needed row
        row = null
      } else {
        // add a 'time' column
        row['time'] = String(row['date']).substring(11)
        // modify the 'date' column to only keep the date part
        row['date'] = String(row['date']).substring(0, 10)
      }
  - id: write-csv
    type: io.kestra.plugin.serdes.csv.CsvWriter
    from: "{{outputs['file-transform'].uri}}"

The script can access a logger to log messages. Each row is available in a row variable where each column is accessible using the dictionary notation row['columnName'].

Purging data

The PurgeExecution task can purge all the files stored inside the internal context by a flow execution.

It can be used at the end of a flow to purge all its generated files.

yaml

tasks:
  - id: "purge-execution"
    type: "io.kestra.core.tasks.storages.PurgeExecution"

The execution context itself will not be available after the end of the execution and will be automatically deleted from Kestra's repository after a retention period (by default, seven days) that can be changed; see configurations.

Also, the Purge task can be used to purge storages, logs, executions of previous execution. For example, this flow will purge all of these every day:

yaml

id: purge
namespace: io.kestra.tests

tasks:
  - id: "purge"
    type: "io.kestra.core.tasks.storages.Purge"
    endDate: "{{ now() | dateAdd(-1, 'MONTHS') }}"

triggers:
  - id: schedule
    type: io.kestra.core.models.triggers.types.Schedule
    cron: "0 0 * * *"

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​Data storage and processing

Data storage and processing