Format Options

Hydrolix can import a number of different message and file types into the platform. To define the encoding of a file this is done in the format_details section within the settings object. The current list includes:

• CSV
• JSON

CSV

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

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:

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

CSV Example

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

JSON

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:

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.

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

JSON Flattening Example

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
}

Example including depth: 1:

"settings": {
    "format_details": {
        "flattening": {
            "active": true,
            "depth": 1,
            "map_flattening_strategy": {
                "left": ".",
                "right": ""
            },
            "slice_flattening_strategy": {
                "left": "[",
                "right": "]"
            }
        }
    },
    ...
}

After applying the transform and limiting the depth to 1 level we would end up with the following JSON:

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

This is useful when you want to leverage map datatype and just flatten at a specific level.

Compression

The compression property in a transform describes one or more compression algorithms that Hydrolix should expect to find already applied to the data package as a whole, and which it will need to uncompress prior to working with the data.

For example, setting the transform's compression property to "gzip" means that you expect the source data, in its entirety, to have had the GZIP compression method applied to it prior to its receipt by Hydrolix.

Compression algorithms

Valid values for the compression property include the following:

Note that, in streaming ingestion, the request document may have compression represented via the content-encoding header, but the data has its own compression potentially.

Compression Layering

To define multiple layers of compression, specify them in a comma-and-space-separated list:

"compression": "gzip, bzip2, zip"

The order matters: Hydrolix will attempt to apply decompression algorithms in the order specified, right-to-left.

In the above example, Hydrolix would apply zlib decompression to all received data, then further apply bzip2 decompression, and end with applying gzip decompression.