Data scanner

A small Java application that listens to data ready for registration and performs several pre-registration checks before moving the dataset to an openBIS ETL routine.

Note

Requires Java SE 17 or newer.

The application builds can be found on our Sonatype Maven Repository.

Run the app

Checkout the latest code from main and run the Maven goal spring-boot:run:

mvn spring-boot:run

You have to set different environment variables first to configure the individual process parts. Have a look at the configuration setting to learn more .

What the app does

The following figure gives an overview of the process building blocks and flow:

The basic process flow can be best described with:

1. Scanning step
2. Registration step (preparation)
3. 1 to N processing steps

The last processing step usually hands the dataset over to the actual registration system, in our case it is several openBIS ETL dropboxes. In the current implementation, a marker file is created after successful transfer into the target folder: .MARKER_is_finished_[task ID]

The current implementation consists of 4 steps: scanning, registration, processing, evaluation and are described in the following subsections.

Scanning

In this step, the application scans a pre-defined path and looks for existing registration folders. If a registration folder is present, it is recorded and will be investigated. All other files in a user's directory will be ignored.

Note

It is important that the move operation of any dataset in the registration folder is atomic! Otherwise, data corruption will occur. Ideally the dataset is staged into the user's home folder first (e.g. a copy operation, an upload via SFTP or SSH) and then moved into the registration folder.

Moving operations on the same file system are basically a rename of the file path and atomic.

Within a user's registration directory, the application expect a registration task to be bundled in one folder, e.g.:

|- myuser/registration       // registration folder for user `myuser`
   |- my-registration-batch  // folder name is irrelevant
        |- file1_1.fastq.gz
        |- file1_2.fastq.gz
        |- file2_1.fastq.gz
        |- file2_2.fastq.gz
        |- metadata.txt  // mandatory!

The folder my-registration-batch represents an atomic registration unit and must contain the metadata.txt with information about the measurement ID and the files belonging to this measurement dataset.

Following the previous example, the content of a matching metadata.txt would look like this:

NGSQTEST001AE-1234512312   file1_1.fastq.gz
NGSQTEST001AE-1234512312   file1_2.fastq.gz
NGSQTEST002BC-3321314441   file2_1.fastq.gz
NGSQTEST002BC-3321314441   file2_2.fastq.gz

Make sure that the columns are TAB-separated (\t)!

Once a new registration unit is detected, it gets queued for registration and the next step will take over.

A registration request gets only submitted once to the registration queue and will subsequently get ignored by the scanning process, as long as the folder name or modification timestamp does not change.

If the application quits or stops unexpectedly, on re-start they will get detected and resubmitted again.

Registration

This process step is preparing the dataset registration for subsequent pre-registration task, to guarantee a unified structure and processing model, other steps can build on and take actions accordingly (e.g. harmonised error handling).

Its configuration parameters can be set via environment variables, see the registration step config section to learn more.

In the current implementation, the registration step does several things:

1. Validating the registration metadata file
2. Aggregate all measurement files per measurement ID
3. Assign every task (measurement) a unique task ID
4. Provide provenance information

The task id is just a randomly generated UUID-4 to ensure that datasets with the same name do not get overwritten during the processing.

The provenance information will be written into the task directory in an own file next to the dataset and is of type JSON.

The final task directory structure looks then like this (task dir name is an example):

 |- 74c5d26f-b756-42c3-b6f4-2b4825670a2d
        |- file1_1.fastq.gz
        |- file1_2.fastq.gz
        |- provenance.json

Here is an example of the provenance file:

{
  "origin": "/Users/myuser/registration",
  "user": "/Users/myuser",
  "measurementId": "NGSQTEST001AE-1234512312",
  "datasetFiles" : [ "file1_1.fastq.gz", "file1_2.fastq.gz" ],
  "taskId": "74c5d26f-b756-42c3-b6f4-2b4825670a2d",
  "history": [
    "/opt/scanner-app/scanner-processing-dir/74c5d26f-b756-42c3-b6f4-2b4825670a2d"
  ]
}

Note

The following properties can be expected after all process steps have been executed:

origin: from which path the dataset has been detected during scanning

user: from which user directory the dataset has been picked up

measurementId: any valid QBiC measurement ID that has been found in the dataset (this might be null) in case the evaluation has not been done yet.

datasetFiles: a list of relative file paths belonging to the dataset

taskId: a unique task ID assigned by the application to track the dataset registration progress

history: a list of history items, which steps have been performed. The list is ordered by first processing steps being at the start and the latest at the end.

Processing

In the current implementation, this process step only does some simple checks, and can be extended to e.g. perform checksum validation. Feel free to use it as template for subsequent process steps.

Evaluation

Last but not least, this step validates the QBiC measurement ID via a configurable regex pattern.

In case of invalid measurement ID formats, the process moves the task directory into the user's home error folder. After the user has provided a valid QBiC measurement id, they can move the dataset into registration again.

In case of a successful ID validation, the dataset will be moved to the configured destination folder. If multiple destination folders are provided in the configuration, the assignment of the next target directory is based on a round-robin approach, to balance any downstream task load (e.g. openBIS dropbox registration).

Configuration

Global settings

#------------------------
# Global settings
#------------------------
# Directory name that will be used for the manual intervention directory
# Created in the users' home folders
# e.g. /home/<user1>/error
users.error.directory.name=error
# Directory name that will be used for the detecting dropped datasets
# Needs to be present in the users' home folders
# e.g. /home/<user1>/registration
users.registration.directory.name=registration

Configure the names of the two application directories for error handling and registration.

Note

The registration folder needs to be present, the application is not creating it automatically, no prevent accidental dataset overwrite.

Scanner step config

#--------------------------------------
# Settings for the data scanning thread
#--------------------------------------
# Path to the directory that contains all user directories
# e.g. /home in Linux or /Users in macOS
scanner.directory=${SCANNER_DIR:/home}
# The time interval (milliseconds) the scanner thread iterates through the scanner directory
# Value must be an integer > 0
scanner.interval=1000

Sets the applications top level scanning directory and considers every folder in it as an own user directory.

The scanner interval is set to 1 second by default is not yet supposed to be configured via environment variables (if required, override it with command line arguments).

Registration step config

Sets the number of threads per process, its working directory and the target directory, to where finished tasks are moved to after successful operation.

#----------------
# Settings for the registration worker threads
#----------------
registration.threads=${REGISTRATION_THREADS:2}
registration.metadata.filename=metadata.txt
registration.working.dir=${WORKING_DIR:}
registration.target.dir=${PROCESSING_DIR:}

Processing step config

Sets the number of threads per process, its working directory and the target directory, to where finished tasks are moved to after successful operation.

#------------------------------------
# Settings for the 1. processing step
# Proper packaging and provenance data, some simple checks
#------------------------------------
processing.threads=${PROCESSING_THREADS:2}
processing.working.dir=${PROCESSING_DIR}
processing.target.dir=${EVALUATION_DIR}

Evaluation step config

Sets the number of threads per process, its working directory and the target directory, to where finished tasks are moved to after successful operation.

#----------------------------------
# Setting for the 2. processing step:
# Measurement ID evaluation
# ---------------------------------
evaluations.threads=${EVALUATION_THREADS:2}
evaluation.working.dir=${EVALUATION_DIR}
# Define one or more target directories here
# Example single target dir:
#    evaluation.target.dirs=/my/example/target/dir
# Example multiple target dir:
#   evaluation.target.dirs=/my/example/target/dir1,/my/example/target/dir2,/my/example/target/dir3
evaluation.target.dirs=${OPENBIS_ETL_DIRS}
evaluation.measurement-id.pattern=${MEASUREMENT_ID_PATTERN:^(MS|NGS)Q[A-Z0-9]{4}[0-9]{3}[A-Z0-9]{2}-[0-9]*}

Note

You can define multiple target directories for this process! You just have to provide a ,-separated list of target directory paths. The implementation will assign the target directories based on a round-robin draw.