DocsConceptsReplay

Replay​Replay

Replay allows you to re-run a workflow execution from any chosen task run.

What is a Replay

By using Replay, you can re-run a workflow execution from any selected task run. To do that, simply go to the Gantt view of the chosen workflow execution (it doesn't need to be a Failed execution, it can be an execution in any state) and click on the task run you want to re-run.

Why Replay is useful

Replays are extremely useful for iterative development and reprocessing data.

Imagine the following scenario: you have a workflow that extracts a large compressed CSV dataset and you want to transform it into a Parquet file with a specific schema.

yaml
id: divvy_tripdata
namespace: company.team

variables:
  file_id: "{{ execution.startDate | dateAdd(-3, 'MONTHS') | date('yyyyMM') }}"

tasks:
  - id: get_zipfile
    type: io.kestra.plugin.core.http.Download
    uri: "https://divvy-tripdata.s3.amazonaws.com/{{ render(vars.file_id) }}-divvy-tripdata.zip"

  - id: unzip
    type: io.kestra.plugin.compress.ArchiveDecompress
    algorithm: ZIP
    from: "{{ outputs.get_zipfile.uri }}"

  - id: convert
    type: io.kestra.plugin.serdes.csv.CsvToIon
    from: "{{outputs.unzip.files[render(vars.file_id) ~ '-divvy-tripdata.csv']}}"

  - id: to_parquet
    type: io.kestra.plugin.serdes.avro.IonToAvro # render(vars.file_id)
    from: "{{ outputs.convert.uri }}"
    datetimeFormat: "yy-MM-dd' 'HH:mm:ss"
    schema: |
      {
        "type": "record",
        "name": "Ride",
        "namespace": "com.example.bikeshare",
        "fields": [
          {"name": "ride_id", "type": "string"},
          {"name": "rideable_type", "type": "string"},
          {"name": "started_at", "type": {"type": "long", "logicalType": "timestamp-millis"}},
          {"name": "ended_at", "type": {"type": "long", "logicalType": "timestamp-millis"}},
          {"name": "start_station_name", "type": "string"},
          {"name": "start_station_id", "type": "string"},
          {"name": "end_station_name", "type": "string"},
          {"name": "end_station_id", "type": "string"},
          {"name": "start_lat", "type": "double"},
          {"name": "start_lng", "type": "double"},
          {
            "name": "end_lat",
            "type": ["null", "double"],
            "default": null
          },
          {
            "name": "end_lng",
            "type": ["null", "double"],
            "default": null
          },
          {"name": "member_casual", "type": "string"}
        ]
      }

When you run the above workflow, you should see an error in the to_parquet task.

From the logs, you will be able to see that the error is due to a misconfigured date format in the datetimeFormat field — in fact, the date format should have a full year, not just a two-digit year: "yyyy-MM-dd' 'HH:mm:ss".

You correct the error in the workflow code and save it.

Full corrected flow code

Now you can go to the previously failed Execution and click on the to_parquet task run to re-run it (either from the Gantt or from the Logs view).

replay1

Now select the new revision of the flow code that contains the fix and confirm with the OK button.

replay2

This will re-run the task with the new (corrected!) revision of the flow code.

replay3

You can inspect the logs and verify that the task now completes successfully. The Attempt number will be incremented to show that this is a new run of the task.

replay4

The Overview tab will additionally show the new Attempt number and the new revision of the flow code that was used during Replay.

replay5

The replay feature allowed us to re-run a failed task with the corrected version of the flow code. You didn't have to rerun tasks that had already completed successfully. This is a huge time-saver when iterating on your workflows! ⚡️

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