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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.
For Older Versions
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https://pimcore.com/docs/platform//Data_Importer/Installation#bundle-configuration
Bundle Configuration
Bundle Configuration
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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.
Import Execution
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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.
Command Based
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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.
Symfony Messenger Based
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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
Cron Execution
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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.
Data Sources
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https://pimcore.com/docs/platform//Data_Importer/Configuration/Data_Sources#asset
Asset Loads data from an asset stored within the Pimcore instance.
Asset
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https://pimcore.com/docs/platform//Data_Importer/Configuration/Data_Sources#configuration-options:
Configuration Options: Path to asset
Configuration Options:
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https://pimcore.com/docs/platform//Data_Importer/Configuration/Data_Sources#sftp
SFTP Loads data from a (remote) sftp location.
SFTP
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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.
Configuration Options:
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https://pimcore.com/docs/platform//Data_Importer/Configuration/Data_Sources#http
HTTP Loads data from a (remote) http location.
HTTP
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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].
Configuration Options:
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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
Push
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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.
Configuration Options:
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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 be configured:
File Formats
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https://pimcore.com/docs/platform//Data_Importer/Configuration/File_Formats#csv
CSV The source data is interpreted as CSV.
CSV
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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.
Configuration Options:
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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.
JSON
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https://pimcore.com/docs/platform//Data_Importer/Configuration/File_Formats#xlsx-(excel)
XLSX (Excel) The source data is interpreted as XLSX Excel file.
XLSX (Excel)
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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.
Configuration Options:
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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).
XML
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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.
Configuration Options:
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https://pimcore.com/docs/platform//Data_Importer/Configuration/File_Formats#custom-file-formats
Custom File Formats You can import any file format using custom adapters.
Custom File Formats
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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.
Import Preview
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https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#resolver-settings
Resolver Settings Resolver settings are responsible to define to which Pimcore data imported data should be imported to and consists of following parts:
Resolver Settings
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https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#class
Class Define the Pimcore data object class of the imported data.
Class
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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:
Element Loading
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https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#loading-strategy:-no-loading
Loading Strategy: No Loading Does not look for any existing Pimcore data objects. Import always creates new data objects.
Loading Strategy: No Loading
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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.
Loading Strategy: Id
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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.
Loading Strategy: Path
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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.
Loading Strategy: Attribute
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https://pimcore.com/docs/platform//Data_Importer/Configuration/Resolver_Settings#element-creation
Element Creation Define location of new created data objects.
Element Creation
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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.
Location Strategy: Static Path
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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.
Location Strategy: Find Parent
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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.
Element Location Update
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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.
Element Publishing
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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.
Processing Settings
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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.
Execution Type
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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.
Archive File Import
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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.
Delta Check
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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.
Cleanup
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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.
Transformation Pipeline
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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.
Operators
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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').
List of available Operators
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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.
What Operators are NOT supposed to do:
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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.
Classification Store Batch Details
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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
Transformation Result Format
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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).
Assign Complex Data Values
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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:
Data Target
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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.
Direct
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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.
Classification Store
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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.
Classification Store Batch
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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.
Mapping Configuration
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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.
Execution Configuration
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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.
Manual Execution
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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.
Cron-based Execution
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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.
Command-based Execution
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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.
Configuration
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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
Configuration Chapters
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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
Import Execution Details
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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.
1) Preparation
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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.
2) Processing
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https://pimcore.com/docs/platform//Data_Importer/Import_Progress_and_Logging#import-progress-and-logging
Import Progress and Logging
Import Progress and Logging
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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).
Import Progress
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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.
Logging
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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
Extend via Custom Strategies
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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.
Extending Schema
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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.
1) PHP Implementation
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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" }
2) Registering implementation as symfony service
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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.
3) JavaScript Implementation
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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 ;-) .
Events
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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
Extending
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https://pimcore.com/docs/platform//Data_Importer/Troubleshooting_FAQ#troubleshooting--faq
Troubleshooting / FAQ
Troubleshooting / FAQ
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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.
1) Status in the Execution isn´t progressing
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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.
2) "ERROR: The command is already running." while running the process queue command
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https://pimcore.com/docs/platform//Data_Importer#data_importer
Data_Importer
Data_Importer
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https://pimcore.com/docs/platform//Data_Importer#title:-data-importer
title: Data Importer
title: Data Importer
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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.
Pimcore Data Importer
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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
Features in a Nutshell
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https://pimcore.com/docs/platform//Data_Importer#documentation-overview
Documentation Overview Installation Configuration Import Execution Details Import Progress and Logging Extending Troubleshooting/FAQ
Documentation Overview
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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
Further Information
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https://pimcore.com/docs/platform//Datahub_File_Export/Installation/Upgrade#upgrade-information
Upgrade Information Following steps are necessary during updating to newer versions.
Upgrade Information
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https://pimcore.com/docs/platform//Datahub_File_Export/Installation/Upgrade#upgrade-to-2.0.0
Upgrade to 2.0.0 Make sure all running exports are done prior upgrading to version 2.0.0.
Upgrade to 2.0.0
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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.
Upgrade to 1.1.0
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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.
Upgrade to Pimcore X
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https://pimcore.com/docs/platform//Datahub_File_Export/Installation#installation
Installation
Installation
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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.
Required Bundles
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https://pimcore.com/docs/platform//Datahub_File_Export/Installation#installation-process
Installation Process
Installation Process
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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
For Pimcore >= 10.5
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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
For Older Versions
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https://pimcore.com/docs/platform//Datahub_File_Export/Basic_Configuration#basics
Basics
Basics
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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.
General settings
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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.
Schema definition
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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.
Workspace definition
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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.
Delivery destination
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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.
Delivery Trigger and Execution
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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).
Export setup
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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.
Manual execution directly in Configuration
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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.
Automatic execution via Cron Definition
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