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For Older Versions | https://pimcore.com/docs/platform//Data_Importer/Installation#for-older-versions | For Older Versions
To install the Data Importer bundle for older versions of Pimcore, please run the following commands instead:
composer require pimcore/data-importer
bin/console pimcore:bundle:enable PimcoreDataImporterBundle
bin/console pimcore:bundle:install PimcoreDataImporterBundle
Make sure the Datahub bundle's priority is higher than the Data Importer bundle's.
This can be specified as a parameter during bundle enablement or in the Pimcore extension manager.
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Import Execution | https://pimcore.com/docs/platform//Data_Importer/Installation#import-execution | Import Execution
The imports are executed asynchronously in the background. The processing can be done via executing commands on a regular
basis or by utilizing symfony messenger. Either of the two needs to be configured. | [
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] |
Command Based | https://pimcore.com/docs/platform//Data_Importer/Installation#command-based | Command Based
For command based importing, following commands need to be executed on regular base. The actual interval depends
on use cases and system environment.
# Process import queue items that can be executed in parallel
*/5 * * * * php /home/project/www/bin/console datahub:data-importer:process-queue-parallel --processes=5
# Process import queue items that need to be executed sequentially
*/5 * * * * php /home/project/www/bin/console datahub:data-importer:process-queue-sequential
See Import Execution Details for more information about sequential and parallel execution. | [
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Symfony Messenger Based | https://pimcore.com/docs/platform//Data_Importer/Installation#symfony-messenger-based | Symfony Messenger Based
For symfony messenger based importing, at least following configuration needs to be done in symfony configuration:
pimcore_data_importer:
messenger_queue_processing:
activated: true
If activated, the processing is kicked off automatically as soon as an import is prepared.
In addition to that, following settings are available. They all have meaningful default values though:
worker_count_parallel: Count of maximum parallel worker messages for parallel imports.
worker_item_count: Count of items imported per worker message.
worker_count_lifetime: Lifetime of tmp store entry for current worker count entry. After lifetime, the value will be cleared.
Messages are dispatched via pimcore_data_import transport. So make sure, you have
workers processing this transport when activating the messenger based queue processing. | [
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Cron Execution | https://pimcore.com/docs/platform//Data_Importer/Installation#cron-execution | Cron Execution
Import configuration can be setup to be executed on regular base by defining a cron definition. To make sure, the cron
definitions are checked on regular base, following command needs to be executed on regular base. The actual interval depends
on use cases and system environment; the shorter, the more accurate the import execution will take place.
# Check cron configurations and execute necessary import definitions
* * * * * php /home/project/www/bin/console datahub:data-importer:execute-cron
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Data Sources | https://pimcore.com/docs/platform//Data_Importer/Configuration/Data_Sources#data-sources | Data Sources
Every data importer configuration needs a data source. Following data sources are available and can
be configured. | [
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Configuration Options: | https://pimcore.com/docs/platform//Data_Importer/Configuration/Data_Sources#configuration-options: | Configuration Options:
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SFTP | https://pimcore.com/docs/platform//Data_Importer/Configuration/Data_Sources#sftp | SFTP
Loads data from a (remote) sftp location. | [
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Configuration Options: | https://pimcore.com/docs/platform//Data_Importer/Configuration/Data_Sources#configuration-options: | Configuration Options:
Host
Port
Username
Password
Remote Path: Absolute path on remote location.
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Configuration Options: | https://pimcore.com/docs/platform//Data_Importer/Configuration/Data_Sources#configuration-options: | Configuration Options:
Schema: one of http:// or https:// which is prepened to the URL.
Url: Url to file without schema definition (which is added based on schema configuration due
to security reasons).
This implementation uses php wrappers internally, so
encoding username and password is supported as follows: user:[email protected]. | [
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] |
Push | https://pimcore.com/docs/platform//Data_Importer/Configuration/Data_Sources#push | Push
The push data source does not download the data from a remote location, it provides a http endpoint
where data can be pushed to via a POST request.
The data needs to be in the configured file format and provided as raw content of the POST request. The
push data source internally uses php://input to read the content.
The URL for the endpoint is: http(s)://<YOUR_DOMAIN>>/pimcore-datahub-import/<IMPORT_CONFIGURATION_NAME>/push | [
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Configuration Options: | https://pimcore.com/docs/platform//Data_Importer/Configuration/Data_Sources#configuration-options: | Configuration Options:
API Key: API Key that needs to be set as authorization header for every push request.
Ignore not empty queue: By default, imports are only started when import queue is empty
(see also Import Execution Details). Thus pushing data to the
endpoint when import queue is not empty would result in an error. Activating this flag will ignore
existing items in the queue and always adds items to the queue when data is pushed to the endpoint.
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File Formats | https://pimcore.com/docs/platform//Data_Importer/Configuration/File_Formats#file-formats | File Formats
The source data needs to be in an interpretable format for the importer. Following interpreters can
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Configuration Options: | https://pimcore.com/docs/platform//Data_Importer/Configuration/File_Formats#configuration-options: | Configuration Options:
Skip First Row: If activated, first row will be used as column names and not interpreted as data row.
Delimiter
Enclosure
Escape
Internally the adapter uses fgetcsv function of php. | [
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] |
JSON | https://pimcore.com/docs/platform//Data_Importer/Configuration/File_Formats#json | JSON
The source data is interpreted as JSON.
The adapter expects an array of json objects and reads all first level attributes as separate fields.
If one field contains sub objects (like technical_attributes) in the example below, this object
is interpreted as array in one field (and needs to be considered in the transformation pipeline).
Sample File:
[
{
"title_de": "Voluptas et est voluptas.",
"title_en": "Animi ipsam rem et sed vel voluptas.",
...
"technical_attributes": {
"1-6": "value 1",
"2-4": "value 2"
}
},
{
"title_de": "Et alias nesciunt ea mollitia nihil mollitia corporis.",
...
},
]
Internally the adapter uses json_decode($content, true) function of php. | [
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Configuration Options: | https://pimcore.com/docs/platform//Data_Importer/Configuration/File_Formats#configuration-options: | Configuration Options:
Skip First Row: If activated, first row will be used as column names and not interpreted as data row.
Sheet: Name of data sheet to be imported.
Internally the adapter uses phpspreadsheet to read the data. | [
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XML | https://pimcore.com/docs/platform//Data_Importer/Configuration/File_Formats#xml | XML
The source data is interpreted as XML.
The adapter expects a list of data elements at the configued xpath and reads all first level elements
of the data elements as separate fields. If one element contains sub elements (like technical_attributes) in the example
below, this the sub elements are interpreted as array in one field (and needs to be considered in the transformation pipeline). | [
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Configuration Options: | https://pimcore.com/docs/platform//Data_Importer/Configuration/File_Formats#configuration-options: | Configuration Options:
XPath: XPath to the elements to be imported. For the sample below it would be /root/item.
Schema: XSD Schema the import data should be validated against. If not defined, to validation takes place.
Sample File:
<?xml version="1.0"?>
<root>
<item>
<title_de>Et voluptas culpa et incidunt laborum repellat.</title_de>
<title_en>Aliquam et voluptas nemo at excepturi.</title_en>
...
<technical_attributes>
<attribute>
<key>1-6</key>
<value>Myrtle Kovacek</value>
</attribute>
<attribute>
<key>2-4</key>
<value>Ut.</value>
</attribute>
</technical_attributes>
</item>
<item>
...
</item>
</root>
Internally the adapter uses Symfony XmlUtils to read
and validate the data. | [
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Custom File Formats | https://pimcore.com/docs/platform//Data_Importer/Configuration/File_Formats#custom-file-formats | Custom File Formats
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] |
Import Preview | https://pimcore.com/docs/platform//Data_Importer/Configuration/Import_Preview#import-preview | Import Preview
The import configuration allows to show a preview of the data which helps
to setup the mapping configuration and
validate the expected results.
The preview interprets the file the same way as actual import data sources
will be interpreted when executing the import and shows one record of the preview
file at once. The preview does not import any data though.
Any changes in file format settings result in a reloading of the uploaded preview
file. Reloading the preview file or paging the records will also update the
processing result preview of the mapping configuration.
There are two options for loading the preview file:
Upload an extra preview file.
Copy the preview file from the configured data source (not possible with push data source).
For both options, preview files are saved per user and import configuration in Pimcores tmp directory and
should only be a small extract of the actual import files - due to loading performance and used storage space. | [
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Resolver Settings | https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#resolver-settings | Resolver Settings
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Class | https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#class | Class
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Element Loading | https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#element-loading | Element Loading
Define a strategy the importer should use for looking for existing Pimcore data objects in order
to update them instead of creating new data objects.
Following strategies are available: | [
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Loading Strategy: Id | https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#loading-strategy:-id | Loading Strategy: Id
Look for data objects based on their id.
Data Source Index: Field of import that contains the id to look for.
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Loading Strategy: Path | https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#loading-strategy:-path | Loading Strategy: Path
Look for data objects based on their full path.
Data Source Index: Field of import that contains the path to look for.
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Loading Strategy: Attribute | https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#loading-strategy:-attribute | Loading Strategy: Attribute
Look for data objects based on a specific attribute (e.g. Remote Id, EAN, ...).
Data Source Index: Field of import that contains the attribute value to look for.
Attribute Name: Attribute of data object to look for.
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Element Creation | https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#element-creation | Element Creation
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Location Strategy: Static Path | https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#location-strategy:-static-path | Location Strategy: Static Path
Always put new elements to a fixed specific folder.
Path: Folder where to put new elements to.
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] |
Location Strategy: Find Parent | https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#location-strategy:-find-parent | Location Strategy: Find Parent
Find parent based on a strategy.
Find Strategy: Strategy find to parent
Loading Strategy Id: Load based on id.
Loading Strategy Path: Load based on full path.
Loading Strategy Attribute: Load based on data attribute with additional settings:
Class: Data object class to look for (can be different one that the imported data object class)
Attribute Name: Attribute of data object to look for.
Data Source Index: Field of import that contains the value to look for.
Fallback Path: Folder to use if parent cannot be found.
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Element Location Update | https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#element-location-update | Element Location Update
Define location updates of data objects. The importer applies the location update strategy to all imported data objects -
no matter if they are updated or created.
For details on the strategies see Element Creation above. In addition, there is a No Change strategy that does not change
the location of elements at all. | [
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Element Publishing | https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#element-publishing | Element Publishing
Define a strategy to set the published state of data object during import.
Following strategies are available:
Always Publish: Always publishes new created or updated data objects.
Attribute Based: Set publish state based on a field of the import data.
No Change, Publish New: Do not change existing data objects and set new data objects to published.
No Change, Unpublish New: Do not change existing data objects and set new data objects to unpublished.
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Processing Settings | https://pimcore.com/docs/platform//Data_Importer/Configuration/Processing_Settings#processing-settings | Processing Settings
There are a couple of processing settings that allow fine-tuning the import process. | [
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Execution Type | https://pimcore.com/docs/platform//Data_Importer/Configuration/Processing_Settings#execution-type | Execution Type
There are two types of processing - sequential and parallel - which are configured in the import configuration.
For details see Import Execution Details. | [
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Archive File Import | https://pimcore.com/docs/platform//Data_Importer/Configuration/Processing_Settings#archive-file-import | Archive File Import
Define if the importer should archive the imported file for later traceability. When activated,
the importer will create an application logger entry with the imported file as file object attached.
Be aware of needed disk space for archiving the data and that the importer creates a log entry of every
single data row in application logger anyway. For details also see
Import Process and Logging. | [
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Delta Check | https://pimcore.com/docs/platform//Data_Importer/Configuration/Processing_Settings#delta-check | Delta Check
This option requires an id field in the import data.
With active delta check, the importer checks if imported data has changed since last import and imports
data only if updates are available. This might speed-up import because only updated data object will be
processed and saved.
The delta check is based on hashes and compares the import data only. It does not consider any other
changes of the data object if self, that might occur in the meantime between two imports.
Be aware that calculating and saving hashes for delta checking needs additional resources. So use this option
wisely. | [
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] |
Cleanup | https://pimcore.com/docs/platform//Data_Importer/Configuration/Processing_Settings#cleanup | Cleanup
This option requires an id field in the import data.
The importer can cleanup in Pimcore existing data objects if they are not part of the current import data
anymore. There are two cleanup strategies available:
Unpublish: Unpublish data object to be cleaned up.
Delete: Delete data object to be cleaned up.
Only activate this option, if the imports are always full-imports. Otherwise there might be data loss.
For details see also Import Execution Details. | [
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Transformation Pipeline | https://pimcore.com/docs/platform//Data_Importer/Configuration/Mapping_Configuration/Transformation_Pipeline#transformation-pipeline | Transformation Pipeline
The transformation pipeline takes the data read from source data based on the
Source Attributes setting and transforms it to a necessary target format
with one or multiple operators.
The transformation result panels shows information about the current resulting
type of the transformation and - if a preview file is uploaded - also a preview
based on the currently active data record of the preview panel.
The result type is also important for the Data Target setting
as it defines available options there. | [
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Operators | https://pimcore.com/docs/platform//Data_Importer/Configuration/Mapping_Configuration/Transformation_Pipeline#operators | Operators
Operators transform the input data to a desired output format. Every operator
expects certain input formats and delivers certain an output format.
There are two groups of operators
Standard Operators: Do some real processing and transformation with the input data.
Factory Operators: Prepare data for a certain necessary result type, e.g. convert a
date to a Carbon\Date so it can be assigned to a date field etc.
Their name is usually prefixed with As.
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List of available Operators | https://pimcore.com/docs/platform//Data_Importer/Configuration/Mapping_Configuration/Transformation_Pipeline#list-of-available-operators | List of available Operators
As Array: Converts data to an array, if not already.
As Boolean: Converts data to a boolean.
As Date: Converts data to a date, format definition necessary.
As Gallery: Packs a single asset, or an asset array to a gallery.
As Image Advanced: Packs a single asset to an image advanced.
As Input Quantity Value: Converts an array to a quantity value object for
input quanitity value, expects first item in array to be value, second item in array to be unit id.
As Input Quantity Value Array: Same like As Input Quantity Value, but for arrays of values. Expects an array
with sub-arrays where first item to be value and second item to be unit id each and results in an array of quantity value objects.
As Numeric: Converts data to a numeric by using floatval.
As Quantity Value: Converts an array to a quantity value object for
quanitity value, expects first item in array to be value, second item in array to be unit id.
As Quantity Value Array: Same like As Quantity Value, but for arrays of values. Expects an array
with sub-arrays where first item to be value and second item to be unit id each and results in an array of quantity value objects.
As Geopoint: Expects an array, where the first item reflects the value for the latitude attribute
and the second item reflects the value for the longitude attribute.
As Geobounds: Expects an array, where the first and second item reflect the values for the northeastern latitude and longitude, third and fourth item reflect the values for the southwestern latitude and longitude respectively.
As Geopolygon: Expects an array with a variable amount of items. Whereas first and second item reflect latitude and longitude for the first point, third and fourth item for the second point and so on.
As Geopolyline: Same as As Geopolygon.
As Color: Converts an array(R,G,B,A) or a hex-string to a color object.
As Countries: Converts an array of countries into an array of country codes
Combine: Combines an array to a string with using configuerd glue.
Explode: Explodes string to array using configured delimiter. If input is an array, explode is done on every array item recursively.
Depending if Do not flatten sub arrays is set, the operator results in one flat array with all items, or in an array with sub arrays.
Flatten Array: Converts one array with potential sub arrays into one flat array with all items and sub items in one level.
HTML Decode: Applies html_entity_decode to input data.
Import Asset: Expects a URL as input, downloads data via file_get_contents and stores it as new asset in configured
target folder. File name is extracted from URL via basename. If asset with same name already exists, either it is
reused (when Use Existing Asset activated), or a new asset with suffix in file name is generated.
Load Asset: Loads existing asset based on path or id
Load DataObject: Loads existing data object based on id, path or specific attribute. With specific attribute it is possible to find the object with a partial match of the attribute value (e.g. data value %foo% would find an object with attribute value barfoobar)
Reduce Array KeyValue Pairs: Reduces a flat array ['k1', 'v1', 'k2', 'v2']
to key-value pair array ['k1' => 'v1', 'k2' => 'v2'].
Trim: Removes leading and/or tailing white spaces from string.
Static Text: Adds a static text to the value - either prepends or appends it.
Conditional Conversion: String values are converted to other string values (e.g. '0' to '1' or 'csv-value' to 'object-value'). Multiple conversions can be configured by separating the values with pipe symbol ('|') (e.g. '0|1|2' to 'some|other|values'). An asterisk can be used as a wildcard (e.g. '0|*' to 'no value|default' where '0' will be converted to 'no value' and all other values to 'default').
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What Operators are NOT supposed to do: | https://pimcore.com/docs/platform//Data_Importer/Configuration/Mapping_Configuration/Transformation_Pipeline#what-operators-are-not-supposed-to-do: | What Operators are NOT supposed to do:
Complex statistical calculations: you possibly get an idea like that you can explode string to array and then calculate some average value, but it is wrong place to do it. Please keep the import peformance light.
Assign data to data object fields: One import is updating fields of one Data Object. If you need to update another one then please create one more import.
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Classification Store Batch Details | https://pimcore.com/docs/platform//Data_Importer/Configuration/Mapping_Configuration/Data_Target/Classification_Store_Batch_Details#classification-store-batch-details | Classification Store Batch Details
The Classification Store Batch data target allows assigning multiple classification store attributes
with one mapping. There are a couple of aspects important to know about. | [
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Transformation Result Format | https://pimcore.com/docs/platform//Data_Importer/Configuration/Mapping_Configuration/Data_Target/Classification_Store_Batch_Details#transformation-result-format | Transformation Result Format
The Classification Store Batch data target requires an array with <GROUP_ID>-<KEY_ID> as keys, and values
to be assigned as array values.
This format has to be prepared by the transformation pipeline in advance.
The simpliest way to archive that is using a JSON data source like
[
{
"remote-id": 1,
"datastore_input": {
"1-1": "some value object 1",
"1-2": "some other value object 1"
}
},
{
"remote-id": 2,
"datastore_input": {
"1-1": "some value"
}
}
]
and then just use the As Array operator to import the data.
As an alternative, it is also possible to encode the data into one data field (e.g. for CSVs) and use more operators
of the transformation pipiline to archive the required format.
An example data could be 1-6#value 1||2-4#value 2. The corresponding transformation pipeline would then be something
like the following
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-0.19045902788639069,
-0.6042674779891968,
0.397957980632782,
-0.24903081357479095,
0.7636746168136597,
-0.37218067049980164,
-0.28307923674583435,
-0.6579918265342712,
-0.6703292727470398,
0.05389493703842163,
-0.684611976146698,
0.5492775440216064,
0.6421960592269897,
-0.5714630484580994,
0.4410707354545593,
-0.7785536050796509,
-0.007518399506807327,
-0.862188994884491,
-0.7926574349403381,
0.08618846535682678,
-0.3425074815750122,
-0.5439746975898743,
0.49092912673950195,
0.45462021231651306,
0.08330520987510681,
0.3651457130908966,
-0.271784245967865,
0.3861997425556183,
-0.04476074501872063,
0.2412962168455124,
-0.42572927474975586,
0.5331923365592957,
0.26186954975128174,
-0.4641077220439911,
-0.09060730785131454,
-0.27293074131011963,
-0.11659904569387436,
-0.38654008507728577,
0.3948993682861328,
0.1241915225982666,
-0.29734230041503906,
0.2270183563232422,
-0.15498915314674377,
-0.9594030380249023,
0.19076287746429443,
-0.2842237949371338,
-0.11636611074209213,
-0.4718362092971802,
0.10426703095436096,
0.8949939608573914,
-0.23160138726234436,
0.34037965536117554,
0.21570931375026703,
-0.08796586096286774,
0.8698019981384277,
0.5900156497955322,
-0.6422522664070129,
0.18836644291877747,
-0.2674522399902344
] |
Assign Complex Data Values | https://pimcore.com/docs/platform//Data_Importer/Configuration/Mapping_Configuration/Data_Target/Classification_Store_Batch_Details#assign-complex-data-values | Assign Complex Data Values
Assigning more complex values (like date or quantityValue) to the classification store requires some more operators
to be added to the transformation pipeline, as the values need to be transformed to a date or quantityValue.
This is possible, with one restriction: One transformation pipeline can result in one complex value type only. It is not
possible to mix values in one mapping with one transformation pipeline.
When importing different complex data types to a classification store, they need to be splitted by type. An import file
could look as follows:
[
{
"remote-id": 1,
"datastore_input": {
"1-1": "some value object 1",
"1-2": "some other value object 1"
},
"datastore_date": {
"1-4": "2021-07-22"
},
"datastore_unit": {
"1-3": "234 km"
}
},
{
"remote-id": 2,
"datastore_input": {
"1-1": "some value"
},
"datastore_date": {
"1-4": "2021-07-21"
},
"datastore_unit": {
"1-3": "54 km"
}
}
]
The mapping needs to contain a mapping entry for each datastore_* which all assign the data to the same classification
store field.
Mappings for date and quantityValue attributes can look as follows (please notice that in that context, As Quantity Value Array is used).
| [
-0.12972185015678406,
0.3716003894805908,
0.12893792986869812,
0.1024947538971901,
-0.49427568912506104,
-0.0691688284277916,
0.6599692702293396,
-0.15777961909770966,
-0.29635724425315857,
1.1101499795913696,
0.44559937715530396,
-0.059457775205373764,
0.5124831795692444,
-0.33893346786499023,
-0.11363948881626129,
-0.1580422967672348,
-0.1650771200656891,
0.13317975401878357,
-1.0743696689605713,
-0.15215303003787994,
0.5763734579086304,
0.23609261214733124,
-1.3949750661849976,
-0.41824662685394287,
-0.5883448123931885,
0.5251897573471069,
0.2666209638118744,
0.06611853837966919,
1.4615477323532104,
1.0794564485549927,
-0.6804654002189636,
-0.1534518003463745,
0.22342565655708313,
-0.4418809115886688,
0.19755776226520538,
0.6408942341804504,
0.4038735032081604,
0.09911889582872391,
-0.30130264163017273,
-0.9190019965171814,
0.28780969977378845,
-0.14465871453285217,
0.4681643843650818,
-0.18325339257717133,
-0.8939235210418701,
0.18801330029964447,
-0.1188918948173523,
-0.4208068549633026,
-0.36395150423049927,
-1.2153726816177368,
-0.2961832880973816,
-0.12604716420173645,
0.5150434970855713,
0.012021091766655445,
-0.28248539566993713,
0.0052678752690553665,
0.2330629825592041,
-0.3441348075866699,
-0.6007920503616333,
-0.4516526758670807,
-0.09481492638587952,
0.011307118460536003,
-0.30875933170318604,
-0.3540603518486023,
0.546039879322052,
0.47978904843330383,
-0.7042551636695862,
-0.7230845093727112,
-0.33868610858917236,
-0.9216789603233337,
-0.9633024334907532,
0.20090064406394958,
0.027265524491667747,
-0.5892284512519836,
-0.41257989406585693,
-0.20087549090385437,
-0.3756767213344574,
-0.07585634291172028,
-0.12900680303573608,
0.2077382355928421,
0.6666261553764343,
0.4072897434234619,
0.14311225712299347,
-0.11047834903001785,
-0.4486331045627594,
0.5626680850982666,
0.47568804025650024,
-0.16260367631912231,
0.4401819407939911,
-0.45427295565605164,
-0.6843424439430237,
0.5768724083900452,
0.1579572558403015,
-0.13452331721782684,
1.1005266904830933,
0.7971640825271606,
0.14471884071826935,
0.6905393600463867,
0.3923743665218353,
-0.45127278566360474,
0.59959477186203,
1.3119152784347534,
0.14908365905284882,
1.0044277906417847,
-0.09565287083387375,
0.7920578718185425,
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0.5083212852478027,
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0.7883962988853455,
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-0.44083166122436523,
-0.03756655007600784,
0.23278647661209106,
-0.2168344408273697,
-0.14599332213401794,
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-0.477112352848053,
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-0.279168963432312,
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0.04864702746272087,
0.1967073380947113,
0.01680406741797924,
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0.38346701860427856,
0.16721303761005402,
0.8323886394500732,
-0.11152519285678864,
-0.14870040118694305,
-0.7077369689941406,
0.022412611171603203,
0.476484090089798,
-0.11614858359098434,
-0.47914862632751465,
0.1103566586971283,
-0.6309771537780762,
0.5594847202301025,
-0.7357460260391235,
-0.27479812502861023,
-0.048055194318294525,
0.7181925177574158,
0.41023513674736023,
0.21955962479114532,
0.007923142053186893,
0.44342824816703796,
0.14552325010299683,
0.3082033693790436,
0.42533019185066223,
0.15033379197120667,
-0.13754168152809143,
-0.21551471948623657,
-0.3337187170982361,
-0.37973663210868835,
-0.3549611270427704,
0.4899246394634247,
0.6766350269317627,
0.09421482682228088,
0.344639390707016,
0.2942848801612854,
0.1864335685968399,
0.7426881790161133,
0.49162790179252625,
0.46379613876342773,
0.5120291709899902,
0.8477863669395447,
0.2831943929195404,
1.211011528968811,
0.057683806866407394,
0.21091364324092865,
0.6459258198738098,
0.049110449850559235,
0.2788843512535095,
-0.23898380994796753,
0.28903424739837646,
0.2125168740749359,
-0.20115546882152557,
0.05493919178843498,
-0.6931666731834412,
-0.059527456760406494,
0.09906784445047379,
-0.1863555610179901,
-0.4203270971775055,
1.3530707359313965,
-0.613400936126709,
0.29680630564689636,
0.0656074732542038,
0.17511411011219025,
-0.7261089086532593,
-0.3015497028827667,
-0.1352740079164505,
0.4196421802043915,
-0.7526205778121948,
-0.12384145706892014,
0.6425980925559998,
0.058493174612522125,
0.4126910865306854,
0.38091903924942017,
0.5305604338645935,
0.2907011806964874,
0.0898352563381195,
-0.11576707661151886,
0.09922266751527786,
-0.7484064698219299,
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-0.5434886813163757,
-0.4911007881164551,
-0.3879591226577759,
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-0.8178946375846863,
0.49153462052345276,
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0.21100489795207977,
0.4408360719680786,
0.6443191170692444,
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0.04638522490859032,
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Data Target | https://pimcore.com/docs/platform//Data_Importer/Configuration/Mapping_Configuration/Data_Target#data-target | Data Target
Data target definition assigns the result of the transformation pipeline to a data object field. The available data object
fields for assignment depend on the transformation result of the transformation pipeline.
Three different data target types are available: | [
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] |
Direct | https://pimcore.com/docs/platform//Data_Importer/Configuration/Mapping_Configuration/Data_Target#direct | Direct
Assign data directly to fields defined directly in the data object class, object bricks or localized fields. The naming
schema for Object brick fields is <OBJECT_BRICK_FIELD>.<OBJECT_BRICK_TYPE>.<ATTRIBUTE>.
The availability of target fields depends on the settings and the result type of the transformation pipeline.
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Classification Store | https://pimcore.com/docs/platform//Data_Importer/Configuration/Mapping_Configuration/Data_Target#classification-store | Classification Store
Assign data to specific keys in a classification store.
The availability of target fields depends on the settings and the result type of the transformation pipeline.
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Classification Store Batch | https://pimcore.com/docs/platform//Data_Importer/Configuration/Mapping_Configuration/Data_Target#classification-store-batch | Classification Store Batch
Assign multiple classification store attributes with one mapping. For this to work, the transformation result has to be
an array with <GROUP_ID>-<KEY_ID> as keys, and values as array values.
For more details and examples see Classification Store Batch Details. | [
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Mapping Configuration | https://pimcore.com/docs/platform//Data_Importer/Configuration/Mapping_Configuration#mapping-configuration | Mapping Configuration
The mapping configuration defines what data fields from the import data should be imported where and how to
the Pimcore data object.
The mapping configuration consists of multiple mapping entries. Each mapping entry provides settings for:
Label: Defines a name for the mapping entry. The name is used as title of the mapping entry only and has no further
functionality.
Source Attributes: Define one or multiple fields from the import data as source for the mapping entry. Multiple fields
are passed as an array to transformation pipeline and data target. The field list is based on the preview file uploaded,
but also other fields can be defined by just writing the data index or field name.
Transformation Pipeline: The transformation pipeline transforms the data read from the source attribute(s) with operators.
For details see Transformation Pipeline.
Data Target: Data target definition assigns the result of the transformation pipeline to a data object field.
For details see Data Target.
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Execution Configuration | https://pimcore.com/docs/platform//Data_Importer/Configuration/Execution_Configuration#execution-configuration | Execution Configuration
Depending on the configuration there are different options to start an actual import.
When data source is push, the import process starts every time data is pushed to the corresponding
endpoint. There is no further configuration possible or needed.
For all the other data sources, Pimcore needs a trigger to start the import process. Following triggers
are available. | [
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Manual Execution | https://pimcore.com/docs/platform//Data_Importer/Configuration/Execution_Configuration#manual-execution | Manual Execution
Clicking on the Manual Execution Start button in the configuration editor manually starts the import.
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] |
Cron-based Execution | https://pimcore.com/docs/platform//Data_Importer/Configuration/Execution_Configuration#cron-based-execution | Cron-based Execution
Besides the Manual Execution Start button there is also the possibility to configure a cron definition
based on which the import starts on a regular base, e.g. every 10 Minutes, once a day, etc.
See for example Crontab Guru for possibilities and a generator for generating
cron definitions.
Make sure datahub:data-importer:execute-cron command is setup properly during installation.
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] |
Command-based Execution | https://pimcore.com/docs/platform//Data_Importer/Configuration/Execution_Configuration#command-based-execution | Command-based Execution
It is also possible to imports with executing the datahub:data-importer:prepare-import command on the commandline.
For further details on import execution also see Import Execution Details. | [
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] |
Configuration | https://pimcore.com/docs/platform//Data_Importer/Configuration#configuration | Configuration
This section gives your details about all the configuration options of an import. Also, have a look
at Import Execution Details to understand how imports are
actually executed based on the import configuration. | [
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Configuration Chapters | https://pimcore.com/docs/platform//Data_Importer/Configuration#configuration-chapters | Configuration Chapters
Data Sources
File Formats
Import Preview
Resolver Settings
Processing Settings
Mapping Configuration
Execution Configuration
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Import Execution Details | https://pimcore.com/docs/platform//Data_Importer/Import_Execution_Details#import-execution-details | Import Execution Details
Every import execution consists of two major parts
Preparation
Processing
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] |
1) Preparation | https://pimcore.com/docs/platform//Data_Importer/Import_Execution_Details#1)-preparation | 1) Preparation
Import preparation loads import source from configured data source, interprets the file based on the file format settings
and splits import source into single data rows. These data rows are added to a queue to be processed during import processing.
Preparation starts when 'import is starte', that means ...
when clicking manual execution in Execution tab of import configuration UI.
based on cron definition for import when datahub:data-importer:execute-cron command is executed. Every command execution calls
preparation of all import configuration, that are or were due since the last command execution.
when datahub:data-importer:prepare-import is executed for a certain import configuration.
when data is pushed to the corresponding endpoint via http when push data source is configured.
For starting imports see also Execution Configuration.
Steps to be executed while preparation are:
Loading data from data source.
Interpret source data, spilt into data rows and create queue items.
If activated: check if existing elements in Pimcore need to be cleaned up and create corresponding cleanup queue items.
Preparation is executed only when the queue for corresponding import configuration is empty to prevent race conditions
during import. | [
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] |
2) Processing | https://pimcore.com/docs/platform//Data_Importer/Import_Execution_Details#2)-processing | 2) Processing
There are two types of processing - sequential and parallel - which are configured in the import configuration.
Sequential imports process the queue items one by one in the order they were added. This might be necessary when data elements
depend on each other, e.g. when building up hierarchies etc.
Parallel imports process the queue items in a parallelized way. This speed things up, but queue items will not be processed
in their exact order.
For executing processing of sequential and parallel queue items, two commands
(datahub:data-importer:process-queue-sequential and datahub:data-importer:process-queue-parallel) are available or
processing can be done via the symfony messenger.
The actual processing is the same for both types and consists of following steps for import jobs:
Look for existing data element based on loading strategy or create new data element.
Update location of data element based on location update strategy.
Update published state of element based on publish strategy.
Process mappings and transformation pipelines for each mapping.
Assign transformation result to data element based on data target definition.
Cleanup jobs are simpler and consist of following steps:
Look for existing data element based on loading strategy.
Cleanup element (unpublish or delete) based on cleanup strategy.
To learn about all different configuration options and strategies in detail, see the Configuration Docs. | [
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Import Progress and Logging | https://pimcore.com/docs/platform//Data_Importer/Import_Progress_and_Logging#import-progress-and-logging | Import Progress and Logging | [
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Import Progress | https://pimcore.com/docs/platform//Data_Importer/Import_Progress_and_Logging#import-progress | Import Progress
The execution tab of the import configuration shows current status and progress information of an import. It shows,
if there are queue items currently processed, the progress of the current processing, and it also allows to cancel the
current import run (and remove all queue items).
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Logging | https://pimcore.com/docs/platform//Data_Importer/Import_Progress_and_Logging#logging | Logging
The importer logs overview information into Pimcore application logger and detailed debugging information into standard
Pimcore/symfony loggers.
The Import Logs tab of the importer configuration shows a prefiltered list of the application logger containing only
entries belonging to the current import configuration.
The application logger contains ...
information about started imports.
information about each imported element including the original imported data as linked file object.
archived original import files if activated.
information about any problems and errors happened.
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] |
Extend via Custom Strategies | https://pimcore.com/docs/platform//Data_Importer/Extending/Extend_Custom_Strategies#extend-via-custom-strategies | Extend via Custom Strategies
The bundle architecture easily allows extension and customization of many
parts of the importing process.
Following strategies and configuration options can be extended all following the same schema:
Data Sources
File Formats
Resolver Loading Strategies
Element Location Strategies
Element Publishing Strategies
Cleanup Strategies
Transformation Pipeline Operators
Data Targets
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Extending Schema | https://pimcore.com/docs/platform//Data_Importer/Extending/Extend_Custom_Strategies#extending-schema | Extending Schema
Extending one of the listed strategies and configuration options needs following steps. Also, have a look at the current
implementations to see how things are working. | [
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1) PHP Implementation | https://pimcore.com/docs/platform//Data_Importer/Extending/Extend_Custom_Strategies#1)-php-implementation | 1) PHP Implementation
PHP implementations always need to implement an interface (e.g. DataLoaderInterface for data sources). Sometimes there
is also an abstract base class, that already implements certain functionality. Try to use them if possible
(e.g. AbstractInterpreter for FileFormats). It is of course also possible to extend existing implementations. | [
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] |
2) Registering implementation as symfony service | https://pimcore.com/docs/platform//Data_Importer/Extending/Extend_Custom_Strategies#2)-registering-implementation-as-symfony-service | 2) Registering implementation as symfony service
The php implementation needs to be registered as symfony service and tagged accordingly.
Tag name defines the extension point (e.g. pimcore.datahub.data_importer.loader for data sources) and
type defines the actual type of the extension. It must be unique and is also the link to values in the configuration file
and the JavaScript implementation.
Pimcore\Bundle\DataImporterBundle\DataSource\Loader\HttpLoader:
tags:
- { name: "pimcore.datahub.data_importer.loader", type: "http" }
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] |
3) JavaScript Implementation | https://pimcore.com/docs/platform//Data_Importer/Extending/Extend_Custom_Strategies#3)-javascript-implementation | 3) JavaScript Implementation
Create a JavaScript class which meets following requirements:
Located in a certain namespace depending on the extension point
(e.g. pimcore.plugin.pimcoreDataImporterBundle.configuration.components.loader.* for data sources).
Extend the pimcore.plugin.pimcoreDataImporterBundle.configuration.components.abstractOptionType class.
Define a type attribute that matches the type of the service definition, e.g. type: 'http'
Implement a function buildSettingsForm that creates and returns a form with all necessary setting field for the exension.
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] |
Events | https://pimcore.com/docs/platform//Data_Importer/Extending/Events#events | Events
Another option to customize import behavior is listening for events. The bundle fires following events during the
import process:
Pimcore\Bundle\DataImporterBundle\Event\DataObject\PreSaveEvent: Fired just before an imported data object will be saved
Pimcore\Bundle\DataImporterBundle\Event\DataObject\PostSaveEvent: Fired just after an imported data object is saved
More events to come when needed (just provide PRs ;-) . | [
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Extending | https://pimcore.com/docs/platform//Data_Importer/Extending#extending | Extending
The bundle architecture provides several extension possibilities for developers to customize the behaviour.
Beside standard symfony extension and customization possibilities, there are two more specific options for extending:
Extend via Custom Strategies
Events
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Troubleshooting / FAQ | https://pimcore.com/docs/platform//Data_Importer/Troubleshooting_FAQ#troubleshooting--faq | Troubleshooting / FAQ | [
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] |
1) Status in the Execution isn´t progressing | https://pimcore.com/docs/platform//Data_Importer/Troubleshooting_FAQ#1)-status-in-the-execution-isn´t-progressing | 1) Status in the Execution isn´t progressing
Please be aware that you either need to activate the symfony messenger based or the command based processing for the queue.
See Import Execution Details for details.
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] |
2) "ERROR: The command is already running." while running the process queue command | https://pimcore.com/docs/platform//Data_Importer/Troubleshooting_FAQ#2)-"error:-the-command-is-already-running."-while-running-the-process-queue-command | 2) "ERROR: The command is already running." while running the process queue command
Sometimes it can happen that one of the worker processes terminates unexpectedly. If you try to run the process queue command again,
you may get a "The command is already running." error message.
If that is the case, please check the "lock_keys" table. The key column contains the (SHA256) hashed representation of the command (e.g. datahub:data-importer:process-queue-sequential).
Delete the corresponding key and try to run the command again. | [
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Data_Importer | https://pimcore.com/docs/platform//Data_Importer#data_importer | Data_Importer
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Pimcore Data Importer | https://pimcore.com/docs/platform//Data_Importer#pimcore-data-importer | Pimcore Data Importer
This extension adds a comprehensive import functionality to Pimcore Datahub. It allows importing data from external
sources and adjusting it to Pimcore Data Objects based on a configured mapping without writing any code. | [
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Features in a Nutshell | https://pimcore.com/docs/platform//Data_Importer#features-in-a-nutshell | Features in a Nutshell
Multiple imports configuration directly in Datahub.
Data import from various data sources.
Supported File Formats: csv, xlsx, json, xml.
Strategies configuration for:
loading existing elements for updating data.
defining location for newly imported data.
publishing data.
cleanup of existing data.
Mappings definition for adjusting data to Pimcore Data Objects with:
simple transformations.
preview of imported data.
Imports execution directly in Pimcore Datahub or on a regular base via cron definitions.
Import status updates and extensive logging information.
Mapping and preview of data | [
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] |
Documentation Overview | https://pimcore.com/docs/platform//Data_Importer#documentation-overview | Documentation Overview
Installation
Configuration
Import Execution Details
Import Progress and Logging
Extending
Troubleshooting/FAQ
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Further Information | https://pimcore.com/docs/platform//Data_Importer#further-information | Further Information
On other Pimcore Datahub adapters and export solutions:
Datahub (GraphQL API)
Datahub Simple Rest API
Datahub File Export
Datahub Productsup
Datahub CI Hub
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Upgrade Information | https://pimcore.com/docs/platform//Datahub_File_Export/Installation/Upgrade#upgrade-information | Upgrade Information
Following steps are necessary during updating to newer versions. | [
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Upgrade to 1.1.0 | https://pimcore.com/docs/platform//Datahub_File_Export/Installation/Upgrade#upgrade-to-1.1.0 | Upgrade to 1.1.0
Execute all migrations of the bundle.
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Upgrade to Pimcore X | https://pimcore.com/docs/platform//Datahub_File_Export/Installation/Upgrade#upgrade-to-pimcore-x | Upgrade to Pimcore X
Update to latest (allowed) bundle version in Pimcore 6.9 and execute all migrations.
Then update to Pimcore X.
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Installation | https://pimcore.com/docs/platform//Datahub_File_Export/Installation#installation | Installation | [
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Required Bundles | https://pimcore.com/docs/platform//Datahub_File_Export/Installation#required-bundles | Required Bundles
This bundle depends on Pimcore Datahub bundle. This needs to be installed first. | [
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Installation Process | https://pimcore.com/docs/platform//Datahub_File_Export/Installation#installation-process | Installation Process | [
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For Pimcore >= 10.5 | https://pimcore.com/docs/platform//Datahub_File_Export/Installation#for-pimcore->=-10.5 | For Pimcore >= 10.5
To install the File Export bundle for Pimcore 10.5 or higher, follow the three steps below:
Install the required dependencies:
composer require pimcore/data-hub-file-export
Make sure the bundle is enabled in the config/bundles.php file. The following lines should be added:
use Pimcore\Bundle\DataHubFileExportBundle\PimcoreDataHubFileExportBundle;
// ...
return [
// ...
// make sure PimcoreDataHubBundle is added before to that list
// ...
PimcoreDataHubFileExportBundle::class => ['all' => true],
// ...
];
Install the bundle:
bin/console pimcore:bundle:install PimcoreDataHubFileExportBundle
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For Older Versions | https://pimcore.com/docs/platform//Datahub_File_Export/Installation#for-older-versions | For Older Versions
To install the File Export bundle for older versions of Pimcore, please run the following commands instead:
composer require pimcore/data-hub-file-export
bin/console pimcore:bundle:enable PimcoreDataHubFileExportBundle
bin/console pimcore:bundle:install PimcoreDataHubFileExportBundle
Please make sure, that the bundles are loaded in following order:
PimcoreDataHubBundle
PimcoreDataHubFileExportBundle
Custom Bundles which extend the PimcoreDataHubFileExportBundle
This priority order can be specified as a parameter during bundle enablement or in the Pimcore extension manager.
Please also have a look at Export Setup to see if additional cron jobs need to be configured for
your setup.
After the installation you should see the File Export adapter in the Datahub configuration panel
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General settings | https://pimcore.com/docs/platform//Datahub_File_Export/Basic_Configuration#general-settings | General settings
Active: Enable/Disable the configuration.
Description: Provide a description for the configuration.
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Schema definition | https://pimcore.com/docs/platform//Datahub_File_Export/Basic_Configuration#schema-definition | Schema definition
Configure the exported data with drag & drop as it you do it in the regular grid column definition in Pimcore.
Optionally define image / video thumbnails if you are going to export assets - if no thumbnail is selected, the original asset path is used.
Make sure your column names are unique and if you use JSON or XML as export format the column names have to be
valid for that type. To rename a field, you can use the Operator Alias.
If you use exporter settings where the data has to be read (e.g. File name conflict = Overwrite or
Trigger for delivery = Deliver directly on save) - you have to export the ObjectId field as id because it is used
as identifier.
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Workspace definition | https://pimcore.com/docs/platform//Datahub_File_Export/Basic_Configuration#workspace-definition | Workspace definition
Workspaces - restrict the export to specific folders.
Custom export service - define a custom export service which should execute the export - default is pimcore.datahub.fileExport.exporter.file.
If you define your own service, please make sure it is public, shared and tagged as in the sample below:
app.datahub.my-exporter:
class: AppBundle\DataSyndicator\Exporter\File
public: true
shared: false
tags:
- { name: "pimcore.datahub.fileExport.exporter"}
If you want to filter further items you can use the pimcore.datahub.fileExport.isValidExportItem event or overwrite the
getQueryCondition() of the exporter service.
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] |
Delivery destination | https://pimcore.com/docs/platform//Datahub_File_Export/Basic_Configuration#delivery-destination | Delivery destination
File Type: The export format to use.
Filename: The name of the target file.
Delivery Type: Where the data should be delivered.
File name conflict:
Overwrite: Always creates a new file with just the changed data. Be careful when using Delivery directly on save
with this option as the data will be overwritten.
Update current file: Add all data to the same file. If data exists the new data from the object is added or updated.
Automatic file increment: If a file exists a new one is created (filename with a numeric suffix). The created file contains only new data.
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Delivery Trigger and Execution | https://pimcore.com/docs/platform//Datahub_File_Export/Basic_Configuration#delivery-trigger-and-execution | Delivery Trigger and Execution
The trigger for delivery defines, when the data should be exported:
Disabled: The export ist just done by the export command in the ProcessManger (more details later on). Each export is a full export.
Deliver directly on save: When the data object is saved, the data of this particular data object is directly written to the target destination. This should
only be used if you have simple objects and inheritance is not enabled, because of possible save performance impacts.
Populate to queue: When an object is saved, only a queue item is created. The export ist done later on (asynchronously).
So there is no performance drawback when you save the object because the "heavy work" is done in the background later on.
For details about the execution settings, see Export Setup. | [
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Export setup | https://pimcore.com/docs/platform//Datahub_File_Export/Export_Setup#export-setup | Export setup
The bundle allows manual and automatic execution of exports. The export can be setup in different ways. For all ways,
there are two export modes:
Full Export: Does a full export regardless of items that might be in the queue.
Only Queue Items: Only exports items that are in the queue (populated via the Delivery Trigger settings).
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Manual execution directly in Configuration | https://pimcore.com/docs/platform//Datahub_File_Export/Export_Setup#manual-execution-directly-in-configuration | Manual execution directly in Configuration
The export can be executed directly by the user in the configuration dialog. For the two export modes, two different buttons
exist. When clicking the corresponding button, the export is executed asynchronously via
Symfony messenger queue.
The progress of the export is also visualized directly in the configuration dialog, the Export Logs tab shows all log
messages from the export.
Please make sure to set up following thing:
Setup of Symfony messenger queue and the transport pimcore_datahub_fileexport is processed.
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Automatic execution via Cron Definition | https://pimcore.com/docs/platform//Datahub_File_Export/Export_Setup#automatic-execution-via-cron-definition | Automatic execution via Cron Definition
In addition to the manual execution, a recurring export based on cron definitions can be configured directly in the configuration.
Thus, it is possible to easily configure exports e.g. every 10 Minutes, once a day, etc.
See for example Crontab Guru for possibilities and a generator for generating
cron definitions.
For the two export modes, two cron definitions can be configured. If cron definition is empty, export is not executed.
The actual export then is processed via Symfony messenger queue.
Please make sure to set up following things:
Execution of datahub:export:file:execute-cron on regular basis, e.g. every minute. It checks, if any cron definitions require execution of an export.
Setup of Symfony messenger queue and the transport pimcore_datahub_fileexport is processed.
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