Convert an ION file into Avro.

yaml
type: "io.kestra.plugin.serdes.avro.iontoavro"

Convert a CSV file to the Avro format.

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_avro
    type: io.kestra.plugin.serdes.avro.IonToAvro
    from: "{{ outputs.convert.uri }}"
    datetimeFormat: "yyyy-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"}
        ]
      }

Source file URI

Pebble expression referencing an Internal Storage URI e.g. {{ outputs.mytask.uri }}.

Default yyyy-MM-dd[XXX]

Format to use when parsing date

Default yyyy-MM-dd'T'HH:mm[:ss][.SSSSSS][XXX]

Format to use when parsing datetime

Default value is yyyy-MM-dd'T'HH: mm[: ss][.SSSSSS]XXX

Default .

Character to recognize as decimal point (e.g. use ‘,’ for European data).

Default value is '.'

SubType string

Default ["f","false","disabled","0","off","no",""]

Values to consider as False

Default false

Try to infer all fields

If true, we try to infer all fields with trueValues, trueValues & nullValues.If false, we will infer bool & null only on field declared on schema as null and bool.

SubType string

Default ["","#N/A","#N/A N/A","#NA","-1.#IND","-1.#QNAN","-NaN","1.#IND","1.#QNAN","NA","n/a","nan","null"]

Values to consider as null

Default 100

Number of row that will be scanned while inferring. The bigger it is, the more precise the output schema will be.

Only use when 'schema' property is empty

The avro schema associated to the data

If empty the task will try to infer the schema from current data, you can use 'numberOfRowsToScan' property if needed

Default false

Whether to consider a field present in the data but not declared in the schema as an error

Default value is false

Default HH:mm[:ss][.SSSSSS][XXX]

Format to use when parsing time

Default Etc/UTC

Timezone to use when no timezone can be parsed on the source.

If null, the timezone will be UTC Default value is system timezone

SubType string

Default ["t","true","enabled","1","on","yes"]

Values to consider as True

Format uri

URI of a temporary result file