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Informations about the package data-importer-extensions-bundle
title: Data Importer
Torq IT Data Importer Extensions
This extension adds a number of additional features to the Pimcore Data Importer bundle.
Path Syntax
A number of our extensions make use of the Path syntax that allows for Paths to be created based on values in the import.
Take for example an Excel File: | Year | Make | Model | Color |
---|---|---|---|---|
2015 | GMC | Sierra | White | |
2001 | Chevrolet | Silverado | Blue |
To build the Path /Products/Cars/GMC/Sierra/2015
using Path Syntax would be /Products/Cars/$[1]/$[2]/$[0]
. The numerical values correspond to the indexes of the values in the Excel file (starting at 0).
For an XML file:
the Path Syntax would use the Attribute names instead /Products/Cars/$[Make]/$[Model]/$[Year]
Data Interpreters
Data Interpreters are the supported "File Formats" that the Data Importer bundle can use. We've added a few of our own.
Advanced XLSX Interpreter
The Advanced XLSX interpreter makes a few improvements over the default XLSX interpreter.
This interpreter uses openspout
as the Excel parser. Open Spout XLSX parsing uses much less memory than the default XLSX parses which makes use of PHPOffice
. We've seen files that required >4GB RAM on PHPOffice use less than 50MB with openspout. We've also detected a memory leak in some cases with the PHPOffice implementation where RAM gets allocated on the server and never released.
Configuration Option | Description |
---|---|
Unique Column Indexes | Accepts a comma separated list of column indexes to treat as unique values. Used to filter the rows in an excel file. For example an excel file with the headers Brand,Model,SubModel and you want to import a unique Brand object for each new Brand you encounter. In this case, use value 0 to only take unique values from the first column in the Excel file. If you want to create a data object for each Brand and Model use 0,1 as the value. |
Row Filter | This accepts a Symfony Expression to be applied to the rows of the Excel file. Each row in the Excel file get's handed to the expression evaluator as a variable named row . The expression row[0] == 'Apple' would only process rows where the value of the first column is Apple. |
Bulk XLSX Interpreter
This Bulk XLSX Interpreter has the same options as the Advanced XLSX Interpreter. The main difference is that the Excel file gets converted to a CSV and loaded into the Data Importer queue using LOAD LOCAL INFILE
. This VERY DRASTICALLY improves the performance of loading the queue table. We've seen 200K rows loaded in <5s. Our experience with a 16GB RAM server shows that Excel files over 30K rows often are not imported successfully by the default XLSX Interpreter.
This Feature Requires the Database Server to be configured to permit local infile / infile permissions!
See MySQL Documentation regarding LOCAL INFILE
.
Also in your database connection you'll need to add the Bulk option (1001:true) in example:
Bulk CSV Interpreter
The Bulk CSV Interpreter has the same options as the regular CSV interpreter but like the Bulk XLSX Interpreter it uses LOAD LOCAL INFILE
to queue data rows.
Please see the Bulk XLSX Interpreter Section for limitions and requirments.
SQL Interpreter
This Interpreter is to be used when using the SQL Data Loader. Behind the scenes this uses the Bulk CSV Interpreter as it is very fast. If you run into errors please see the Bulk XLSX Interpreter Section for limitions and requirments.
XML Schema Based Preview Interpreter
This Interpreter is an expansion upon the default XML based interpreter that will load all fields defined by the provided Xsd file for use in the preview screen.
Data Loaders
SQL Data Loader
The SQL Data Loader uses DBAL to allow data to be loaded from a SQL source. Connections to any database supported by DBAL will work provided they are configured correctly inside of database.yaml
. (Database configuration can be placed in any valid Symfony config file, provided its in the correct format as can be seen in database.yaml
).
To set up a SQL loader
- Create a new connection in
database.yaml
or if using the Pimcore database skip this step. - Select the correct connection from the Connection Name dropdown
- Provide a valid query using the Select, where, from, Group By, and Limit fields.
- Ensure to select SQL under File Format! This loader produces a CSV file as part of loading the SQL.
Data Targets
Data Targets control where data flows as its being mapped to Data Objects.
Advanced Classification Store
This is the same as the Classification Store Data Target except it adds the Overwrite
options as seen on the Direct
Data Target.
Image Gallery Appender
This can be used to add an image into an Image Gallery field.
Property
This is used to set a property on a Data Object.
Operators
Constants
This operator simply returns a constant string. Useful if wanting to control OBJECT_TYPE
object or variant.
SafeKey
This ensure that a value is cleaned to be a valid Key value.
Import Asset Advanced
This allows two additional pieces of functionality when importing an asset:
Path Uses the Path Syntax described above to store the asset in a specified folder.
URL Property Specifies the name of the property on the asset to store the source URL the asset was captured from.
Arithmetic
This allows you to apply addition, subtraction, multiplication, or division with a defined constant and your value.
Regex Replace
This allows you to do string replaces using preg_replace
Element Loading
Advanced Path Strategy
This allows loading objects using the Path syntax described earlier in this ReadMe.
Using the example Excel file in the Path section you could load the Data Object at /Products/Cars/GMC/Sierra/2015
using Path Syntax /Products/Cars/$[1]/$[2]/$[0]
.
Property
This allows a data object to be loaded based on the value of a property stored on it.
This assumes that the property value is unique. If a non-unique value exists, it'll be a random object returned that matches the criteria.
Element Creation
Advanced Parent Strategy
This allows locating objects using the Path syntax described earlier in this ReadMe.
Using the example Excel file in the Path section you could create a Data Object with parent /Products/Cars/GMC/Sierra/2015
using Path Syntax /Products/Cars/$[1]/$[2]/$[0]
.
All versions of data-importer-extensions-bundle with dependencies
openspout/openspout Version ^4.0
pimcore/pimcore Version ^11.0
pimcore/compatibility-bridge-v10 Version ^1.0
pimcore/admin-ui-classic-bundle Version ^1.1