Example code demonstrating how to use the Janrain /entity.bulkCreate API to bulk load user profiles into the Janrain platform.
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Janrain Data Loader

Example code demonstrating how to use the Janrain /entity.bulkCreate API to bulk load user profiles into the Janrain platform.


Data Format

The script consumes CSV files formatted with the following rules:

  • UTF-8 encoded
  • Comma delimited
  • Unix-style line endings
  • First row (column headers) must match Janrain schema attribute names
    • Object attributes expressed in "dot notation" such as primaryAddress.city
  • Plural attributes represented as JSON strings (don't forget to use double-quotes)

See sample_data.csv for an example.

Quick Start

Install Dependencies

The dependencies are specified in the requirements.txt and can be installed using pip:

pip install -r requirements.txt

Dry Run

A "dry run" will validate the file encoding (UTF-8) and ensure that all of the records can be processed without making any API calls. Set the rate limit to 0 since the API is not actually being called.

python3 janrain-dataload.py --apid_uri=https://my_application.dev.janraincapture.com --client_id=REDACTED --client_secret=REDACTED --rate-limit=0 --dry-run my_data.csv

A successfull run will log a message to the screen as each batch is processed by a worker thread:

INFO janrain-dataload.py: Loading data from /home/rsmith/my_data.csv into the 'user' entity type.
INFO dataload.reader: Validating UTF-8 encoding
INFO janrain-dataload.py: Batch #1 (lines 2-11)
INFO janrain-dataload.py: Batch #63108 (lines 631072-631074)
INFO janrain-dataload.py: Checking results
INFO janrain-dataload.py: Done!

UTF-8 validation will occur prior to processing the records. If any data is encountered which is not UTF-8 encoded the script will terminate and indicate which line needs to be corrected.

INFO janrain-dataload.py: Loading data from /home/rsmith/my_data.csv into the 'user' entity type.
INFO dataload.reader: Validating UTF-8 encoding
ERROR dataload.reader: Line 23116: 'utf-8' codec can't decode byte 0xbf in position 7: invalid start byte

Live Run

Note: Always coordinate a production data migration with Janrain to ensure application rate limits and monitoring have been configured appropriately.

The live run will be making API calls to Janrain and thus must be limited to ensure that it runs well below API rate limits. The --workers and --rate-limit arguments can be used to fine-tune the rate at which records are loaded.

python3 janrain-dataload.py --apid_uri=https://my_application.dev.janraincapture.com --client_id=REDACTED --client_secret=REDACTED --rate-limit=1 --workers=4 my_data.csv

Result Logs

While the data is being loaded, 2 separate CSV log files are used to store the results per record. You can watch each of these logs in a separate terminal to keep an eye on errors as they are returned from the API.

The failure log stores the batch number, line number, and error message:

1,2,Attempted to update a duplicate value

The success log stores the batch number, line number, and UUID of the newly created user:


Note: The result logs are overwritten with each run.

Command-line Arguments

The utility is invoked from the command-line as a Python 3 script. The --help flag is used to obtain usage information:

python3 janrain-dataload.py --help

usage: janrain-dataload.py [-h] [-u APID_URI] [-i CLIENT_ID] [-s CLIENT_SECRET]
                    [-k CONFIG_KEY] [-d] [-t TYPE_NAME] [-b BATCH_SIZE]
                    [-a START_AT] [-w WORKERS] [-q QUEUE_SIZE] [-o TIMEOUT]
                    [-r RATE_LIMIT] [-x]

positional arguments:
  DATA_FILE             full path to the data file being loaded

optional arguments:
  -h, --help            show this help message and exit
  -u APID_URI, --apid_uri APID_URI
                        Full URI to the Capture API domain
  -i CLIENT_ID, --client-id CLIENT_ID
                        authenticate with a specific client_id
  -s CLIENT_SECRET, --client-secret CLIENT_SECRET
                        authenticate with a specific client_secret
  -k CONFIG_KEY, --config-key CONFIG_KEY
                        authenticate using the credentials defined at a
                        specific path in the configuration file (eg.
  -d, --default-client  authenticate using the default client defined in the
                        configuration file
  -t TYPE_NAME, --type-name TYPE_NAME
                        entity type name (default: user)
  -b BATCH_SIZE, --batch-size BATCH_SIZE
                        number of records per batch (default: 10)
  -a START_AT, --start-at START_AT
                        record number to start at (default: 1)
  -w WORKERS, --workers WORKERS
                        number of worker threads (default: 4)
  -q QUEUE_SIZE, --queue-size QUEUE_SIZE
                        max number of batches queued (default: 25000)
  -o TIMEOUT, --timeout TIMEOUT
                        timeout in seconds for API calls (default: 10)
  -r RATE_LIMIT, --rate-limit RATE_LIMIT
                        max API calls per second (default: 1)
  -x, --dry-run         process data without making any API calls

Data Transformations

Some data within the CSV source will need to be transformed before it can be consumed by the Janrain API. A transformation function can be defined in dataload/transformations.py and associated with a column in the CSV file in janrain-dataload.py.

Available Transformations

  • transform_password - Transforms hashed password from a legacy system into JSON objects which specify the hashing algorithm to use.
  • transform_date - Transforms dates from known formats into the Janrain's UTC date format.
  • transform_plural - Transforms a JSON string representation of a plural into JSON object representation.
  • transform_booleans - Transforms boolean strings into true Boolean types (blank values become NULL)

Custom Transformations

To create a custom transformation, define a new function in dataload/transformations.py. For example, if you need to convert apples to oranges:

def transform_apples(value):
    if value == "apples":
        return "oranges"
        return value

You can then add that transformation to a CSV column using the add_transformation method of the CSV reader object in janrain-dataload.py:

reader.add_transformation("favoriteFruit", transform_apples)


The utility uses the standard Python logging module for both the application log and the result logs.

  • stdout - Application log at the INFO log level.
  • log.txt - Application log (same as stdout) at the DEBUG log level.
  • fail.csv - Result log for records which failed to get imported.
  • janrain-dataload.log - Result log for records which were successfully imported.

The formatting, filenames, log level, and other parameters can be configured for the various loggers using the configuration file logging_config.json. See the Python Logging HowTo for details.

Tips and Best Practices

  • Use automation to generate the test data files to ensure that the exact same processes can generate data files for a production run.
  • Adjust date/time values into UTC.
  • Fine tune batch sizes with the --batch-size argument rather than increasing the --timeout argument. API calls should not take longer than 10 seconds.
  • Keep the rate of the data load well under the application rate limit so as not to impact API usage from other sources (especially on production environments).
  • Use the tail -f command on the result logs to keep an eye on API calls which are failing. If you see the 510 (rate limit) error code you need to reduce the --rate-limit and/or --workers arguments.
  • Running the utility on a very large set of data may take a while. Try running the script from screen session on a server.