keboola/csvmap

Flatten an object into a CSV file(s)

CsvMap

, _(*1)

Installation

composer require keboola/csvmap

Usage

For example, map key key.nested of each $data array item, to CSV column mappedKey., _(*2)

$data = [
    [
        'id => '123',
        'key' => [
            'nested' => 'value1'
        ]
    ] 
];
$mapping = [ 
    'id' => [
        'type' => 'column', 
        'mapping' => [
            'destination' => 'id',
            'primaryKey' => true,
        ] 
    ],
    'key.nested' => 'mappedKey' 
];

$rootType = 'rootName';
$userData = [];
$parser = new Mapper($mapping, $writeHeader, $rootType);
$parser->parse($data, $userData);

$files = $parser->getCsvFiles();
$tempFilePath = $files['rootName']->getPathName();

Mapping

• The mapping is an array.
• The key corresponds to the one root/nested key in the source data. Default delimiter is ., eg. key, key.nested.
• The value is the mapping configuration for the given key. It is string (shorthand notation) or array.
  • There are 3 types of the mapping, defined by the type key:
  • type (optional), column by default.
    • column will store the value from its key into a CSV column
    • user will look into an array in the second argument of the parse function and fill a CSV column with its value
    • table will create a "child" CSV and link through a primary key or a hash, if no primary key is defined

Column mapping

• mapping: Required, must contain destination:
  • destination: Target column in the output CSV file
  • primaryKey: Optional, boolean. If set to true, the column will be included in the primary key
• forceType: Optional, if a value is not scalar, it'll be JSON encoded

Shorthand notation

• If the value is the string, then it is a shorthand notation for the column mapping.
• The string value corresponds to mapping.destination.

Example shorthand notation:, _(*3)

[
     'key.nested' => 'mappedKey' 
]

... is equal to, _(*4)

[
    'key.nested' => [
        'type' => 'column', 
        'mapping' => [
            'destination' => 'mappedKey'
        ] 
    ]
]

Examples

Four different column mappings., _(*5)

[
    'id' => [
        'type' => 'column', 
        'mapping' => [
            'destination' => 'id',
            'primaryKey' => true,
        ] 
    ],
    'name' => 'name',
    'info.url' => 'url,
    'info.tags' => [
        'type' => 'column', 
        'forceType' => true,
        'mapping' => [
            'destination' => 'tags'
        ] 
    ]
]

User mapping

Same as column, except the key of the object is not searched for in the parsed data, but in an array passed to the parser to inject user data, _(*6)

Table mapping

• destination: Required, a target CSV file name
• tableMapping: Required, mapping of all child table's columns
  • Sub-mapping has the same structure as the root $mapping.
• parentKey: Optional, can be used to set the parent/child link as a primary key in the child or override the link's column name in the child
  • primaryKey: boolean, same as in column
  • destination: Name of the link column (if not used, name of the parent table . _pk is used by default)
  • disable: boolean, if set to non-false value, the parent key in the child table, as well as the column in the parent will not be saved

Note:
If the destination is the same as the current parsed 'type' (destination of the parent),
parentKey.disable must be true to preserve consistency of structure of the child and parent., _(*7)

Map scalar items to a separated CSV

• Table mapping is useful when you need to map array of the objects to separate CSV tables.
• But sometimes you need to map an array of the scalar (not object) values, for example a list of tags.
• In this case, you can use an empty key in tableMapping to map a scalar value.

For example, we have this data:, _(*8)

[
    ['id' => 1, 'name' => 'dog', 'tags' => ['useful', 'pet', 'animal']],
    ['id' => 2, 'name' => 'mouse', 'tags' => ['harmful', 'animal']]
]

Example mapping:, _(*9)

[
    'id' => [
        'type' => 'column', 
        'mapping' => [
            'destination' => 'id',
            'primaryKey' => true,
        ]
    ],
    'name' => 'name',
    'tags' => [
        'type' => 'table',
        'destination' => 'tags',
        'tableMapping' => [
            '' => 'tagName' // empty key used to map scalar value
        ]
    ]
]

Results:, _(*10)

root.csv:, _(*11)

"id","name"
"1","dog"
"2","mouse"

tags.csv:, _(*12)

"tagName","root_pk"
"useful","1"
"pet","1"
"animal","1"
"harmful","2"
"animal","2"

Examples

Mixed column and table mappings., _(*13)

[
    'id' => [
        'type' => 'column', 
        'mapping' => [
            'destination' => 'id',
            'primaryKey' => true,
        ]
    ],
    'name' => "name,
    'addresses' => [
        'type' => 'table', 
        'destination' => 'addresses',
        'tableMapping' => [
            'number' => 'number',
            'street' => [
                'type' => 'table',
                'destination' => 'streets',
                'tableMapping' => [
                    'name' => 'name'
                ]        
            ]
         ]
    ]
]

License

MIT licensed, see LICENSE file., _(*14)