A Bullhorn Platform SDK tool. Quickly import CSV data into your Bullhorn CRM.

There's an App for This!

Check out the Data Loader Desktop App for Windows and Mac that wraps the Data Loader in a convenient, user friendly app. Check out the Getting Started Video.

The app handles loading data only. For features such as exporting and deleting data, parsing and uploading resumes and files, continue on.

Quick Start Guide

1. Install Java if you don't have it already (you won't need the development kit for Windows) - Windows | Mac

   1. Verify that you have a compatible version of java on the command line by typing: java -version, which should show: java version "1.8.x"

2. Download the dataloader.zip file from the downloads section of the Latest Release

3. Extract contents of dataloader.zip file (will extract into a dataloader folder)

4. Edit the file dataloader/dataloader.properties to enter your account information

5. Place your source CSV files in the dataloader/data folder. Rename them to be the name of the entity being loaded, for example: Candidate.csv, Placement.csv, etc.

6. Open a command prompt in the dataloader folder

7. Get familiar with the available fields (column header names) from the example files in the dataloader/examples folder or use the template command to generate an example file containing all possible fields for the entity, for example: > dataloader template Candidate

8. Ensure that the column names in your source CSV exist in the example and/or template CSV files

9. Run dataloader on the command line, for example: > dataloader load data/Candidate.csv

10. Check for status on the command line, check for error records in the file: dataloader/results/<InputFileName>_<Timestamp>_failure.csv, check for successful records in the file: dataloader/results/<InputFileName>_<Timestamp>_success.csv, and check for full output in the dataloader/log directory

Releases

• Latest Release
• All Releases

Documentation

• DataLoader Wiki
• DataLoader Best Practices
• Bullhorn Open Source
• Bullhorn Website

Requirements

• 8GB RAM Minimum, 16GB RAM Recommended

Configure

Edit the file: dataloader.properties to specify the login credentials, data configuration and more.

The following list of properties can be set in the DataLoader.properties file:

Credentials

• username, password : this is a Bullhorn admin or API user credentials

• clientId/clientSecret: To retrieve your clientId and clientSecret, please call Support.

Environment URL

• authorizeUrl
  • This is the location of your Bullhorn authorization server.
  • For staging/non-production environment, use: https://auth9.bullhornstaffing.com/oauth/authorize
  • If you are a live Bullhorn customer and loading data in production, use: https://auth.bullhornstaffing.com/oauth/authorize
• tokenUrl
  • This is the location of your Bullhorn REST token server.
  • For staging/non-production environment, use: https://auth9.bullhornstaffing.com/oauth/token
  • If you are a live Bullhorn customer and loading data in production, use: https://auth.bullhornstaffing.com/oauth/token
• loginUrl
  • This is the location of your Bullhorn REST login server.
  • For staging/non-production environment, use: https://rest9.bullhornstaffing.com/rest-services/login
  • If you are a live Bullhorn customer and loading data in production, use: https://rest.bullhornstaffing.com/rest-services/login

New vs Updates

If you would like to update existing Bullhorn records, use this section to specify what Bullhorn entity field should be used to match the record to be loaded with a Bullhorn record. The entries should be in the following format:

<entityName>ExistField=<fieldName>

If you are inserting all new records, leave this field blank or comment out the entries listed here. For additional details, see Load Insert vs Update.

Formatting

• dateFormat
  • Default value is MM/dd/yy HH:mm
  • Documentation can be found here: http://docs.oracle.com/javase/8/docs/api/java/text/SimpleDateFormat.html
• listDelimiter
  • A list delimiter would be used when a field supports multiple values.
  • For example, if I was to use “|” as a list delimiter, I could insert a single candidate with multiple categories into the CRM by saying “Category A|Category B|Category C”.
  • Default value is ;
  • Commas can also be used as the list delimiter value, provided quotes are used around the value, such as: "A,B,C",”D,E,F”.

Performance

• numThreads
  • Number of rows to process at a time.
  • Default value is 0 to allow the program to select the optimal number of threads based off system properties.
  • Maximum is 15.
  • We suggest that users do not edit this value.

Generate CSV Template

dataloader template <EntityName>

This will generate the file: <EntityName>Example.csv in the current directory. This file will contain all of the available fields in the entity record as columns in the CSV. The first row of data will contain the data type (string, integer, etc). The first row data must be replaced with the data of the first record. For more information, see the wiki

Load

dataloader load path/to/<EntityName>.csv

Inserts or updates records. Requires a column for each data field to load. These column names must match the names in the entity template. The provided CSV file must start with the name of the entity. If the <EntityName>ExistField property in the dataloader.properties file is enabled, that field (column) will be used to determine if the record already exists. If a record with the ExistField value exists, that record will be updated, otherwise a new record will be inserted. If the <EntityName>ExistField property is disabled, a new record will always be created. ExistFields are disabled by default in the properties file. For more information, see the wiki.

dataloader load path/to/directory

Performs load for every valid CSV file in the given directory. The order that entities are loaded in will respect dependencies. The provided directory must contain valid CSV files that start with the name of the entity.

Load Insert vs Update

The load command allows users to load data as new records or update existing records depending on the dataloader.properties.

To create new records:

• In the dataloader.properties file, do not include or set the ExistField setting, or comment out the existing settings.

To update existing records:

• Several of the Bullhorn entities have an externalID available although any field could be used to compare against existing records.
• For other entities that do not have the externalID predefined, one of the available custom or standard fields can be used.
• To match against the Bullhorn-assigned ID, use id.
• The external ID must be unique for each entity type. For example, having more than one candidate with the same external ID is not allowed and the system will not be able to update or delete the record if needed through the data loader tool.
• Sample external ID configuration in dataloader.properties:
  • candidateExistField=externalID
  • clientContactExistField=externalID
  • clientCorporationExistField=externalID
  • jobOrderExistField=externalID
  • leadExistField=customText1
  • opportunityExistField=externalID
  • placementExistField=customText1
  • leadExistField=customText1
  • housingComplexExistField=id (this is an example for matching against the BH-assigned ID)

Export

dataloader export path/to/<EntityName>.csv

Export is a backup, or undo button for mass updating data. Saves the current state of existing records that will be updated by the Load command. Requires the same CSV input file or directory as load, and requires that duplicate checking using the<EntityName>ExistField property in the dataloader.properties file is enabled.

Rows that exist as records in the ATS will have their current state output to the _success results file. Rows that do not have a corresponding record in the ATS (for which load would perform an insert) will be output to the _failure results file. To learn more, see the wiki.

dataloader export path/to/directory

Performs export for every valid CSV file in the given directory.

Delete

dataloader delete path/to/<EntityName>.csv

The provided CSV file must contain an id column. This column will contain the Bullhorn internal IDs of the records to delete. The provided CSV file must start with the name of the entity.

dataloader delete path/to/directory

Performs delete for every valid CSV file in the given directory. The order that entities are deleted in will respect dependencies. The provided directory must contain valid CSV files that start with the name of the entity. For more information, see the wiki.

Convert Attachments

dataloader convertAttachments path/to/<EntityName>.csv

Converts locally stored files (txt, doc/x, opend, odt, rtf, html or pdf) to html. Writes out converted attachments to: convertedAttachments\<EntityName>\<ExternalID> in the current working directory. ExternalID is the value of the <entityName>.externalID column in the CSV input file. This action is to be followed by the load command, which will check the convertedAttachments folder that was created and load any converted attachments that match the externalID in the CSV input file. All converted attachments will be set as the description field for the entity. Currently supports Candidate, ClientContact, and ClientCorporation. For more information, see the wiki.

Load Attachments

dataloader loadAttachments path/to/<EntityName>.csv

Attaches files to preexisting records. Attachment file paths are mapped with provided CSV file. Only attaches files for one entity at a time. The provided CSV file must start with the name of the entity. For more information, see the wiki.

Delete Attachments

dataloader deleteAttachments <path/to/<EntityName>.csv

The provided CSV file must contain an id column and a parentEntityID column. The id column will contain the Bullhorn internal IDs of the files to delete and the parentEntityID column will contain the Bullhorn internal IDs of the entity the file is attached to. The provided CSV file must start with the name of the entity. For more information, see the wiki.

Console Output

DataLoader provides run process status for the console. Every 111 records, there will be an output of Processed: # records. When the run is complete, total status of all actions will display. Such as:

Results of DataLoader run
Total records processed: #
Total records inserted: #
Total records updated: #
Total records deleted: #
Total records failed: #

Results Files

DataLoader divides all processed rows from the given CSV file into two output files, one for sucessfully uploaded rows and one for failures. Results files are saved in the folder results/ inside the current working directory. If no results/ directory exists, one will be created at runtime. The success file will be named: results/<InputFileName>_<Timestamp>_success.csv and each row will be a copy of the original row prepended with two extra columns: an id column with the bullhorn ID of the record and an action column that will be either INSERT or UPDATE. The failure file will be named: results/<InputFileName>_<Timestamp>_failure.csv and each row will be a copy of the original row prepended with a reason column containing the error text.

Log File Output

DataLoader logs all operations to file. Logfiles are saved in the folder log/ inside the current working directory. If no log/ directory exists, one will be created at runtime. The name of the most recently generated logfile will be dataloader.log. All historical logfiles will have timestamps appended to the filename, such as: dataloader_yyyy-MM-dd_HH:MM.ss.log.

Examples

Minimal example files for learning purposes for High-Level Entities are provided in the examples folder and are covered in detail on the Examples Wiki Page.

Property File Input (Optional)

By default DataLoader will attempt to load the file dataloader.properties from the current working directory. To use a different properties file, use the -Dpropertyfile argument.

java -Dpropertyfile=path/to/my/dataloader.properties -jar target/dataloader-{version}.jar template <EntityName>

Contribute

There are multiple ways to contribute to Bullhorn DataLoader:

• Submit bugs and help us verify fixes as they are checked in.
• Review source code changes.
• Contribute bug fixes.

Building From Source Code

1. Install Prerequisites

• Apache Maven 3.0
• Java SDK 1.8
• Node.js

2. Clone the repo:

git clone https://github.com/bullhorn/dataloader.git

3. Change to the DataLoader directory:

cd dataloader

4. Build the jar:

mvn clean package

This will produce dataloader-{version}.jar in /target, which includes all required dependencies.