Message/File Encoding

Hydrolix can import a number of different message and file types into the platform. The current list includes:

  • CSV
  • JSON
  • Parquet

To define the encoding of a file this is done in the format_details section within the settings object.


CSV in Hydrolix terms is defined as "Character" rather than Comma Separated encoded files or messages.

Format details

To create a transform schema that handles CSV-formatted incoming data, Set its type property to "csv", and its format_details object to include the following configuration options:






number, string

The delimiter substring.


number, string


The escape character.




The number of rows to skip before ingestion starts


number, string


The quote character.


number, string

The comment character. Only single characters are supported.




If true, then the ingester will not process lines beginning with the comment character.




If true, then Hydrolix will expect incoming data to use Windows-style (CR-LF) line endings.

(Note that Hydrolix recognizes "\t" as a tab character for the purposes of CSV configuration.)


    "name": "my_special_transform",
    "type": "csv",
    "settings": {
        "format_details": {
            "skip_head": 2,
            "delimiter": ","


When ingesting rows formatted as JSON objects, Hydrolix uses the names of the objects' top-level keys to establish the mapping between output_columns and the source data. That is, if your source data contains a top-level property named "employees" that you wish to ingest, then you must name corresponding column definition in your transform "employees" as well.

This also applies to JSON flattening: your output columns must share the full names of any flattened data field whose value you wish to copy into them. So, if your flattened incoming data structure has a relevant property named "employees.departments[0]", and you wish to copy its values into your Hydrolix table, then one of your transform's output_columns must also have its name property set to the string "employees.departments[0]".

JSON Flattening

When accepting JSON-formatted source data, you may optionally flatten each incoming object as a pre-processing step prior to ingesting it. This can transform complex, multi-level JSON structures into simple objects comprising one level of key/value pairs, ready for storage in a single table row.

To do this, define a flattening property within your transform's format_details. Set its value an object with the following properties:

activeIf 1 (or any other true value), Hydrolix will flatten incoming JSON objects before ingesting them as rows.
map_flattening_strategyConfiguration for flattening any JSON objects within each row's main object.
slice_flattening_strategyConfiguration for flattening any JSON arrays within each row's main object.

The two "strategy" properties accept an object that defines the rules that Hydrolix should follow to create new key names for the resulting, flattened JSON object.

leftThe substring to use when concatenating an element's key with its parent's key.
rightThe substring to use when concatenating an element's key with its child's key.

Not defining (or defining as null) either of the "strategy" properties will deactivate flattening for either objects or arrays, respectively.

An example of JSON flattening

Consider the following JSON object, which we wish to ingest as a single row:

  "date": "2020-01-01",
  "data": {
    "oranges": [ 1, 2, 3 ],
    "apples": [
        "cortland": 6,
        "honeycrisp": [ 7, 8, 9 ]
      [ 10, 11, 12 ]

Imagine that the transform handling it contains the following flattening configuration:

"settings": {
    "format_details": {
        "flattening": {
            "active": true,
            "map_flattening_strategy": {
                "left": ".",
                "right": ""
            "slice_flattening_strategy": {
                "left": "[",
                "right": "]"

After applying these JSON flattening strategies, Hydrolix would end up ingesting the following, single-level JSON object:

    "date": "2020-01-01"
    "data.oranges[0]": 1
    "data.oranges[1]": 2
    "data.oranges[2]": 3
    "data.apples[0].cortland": 6
    "data.apples[0].honeycrisp[0]": 7
    "data.apples[0].honeycrisp[1]": 8
    "data.apples[0].honeycrisp[2]": 9
    "data.apples[1][0]": 10
    "data.apples[1][1]": 11
    "data.apples[1][2]": 12


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