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iRODS Automated Ingest Framework

The automated ingest framework gives iRODS an enterprise solution that solves two major use cases: getting existing data under management and ingesting incoming data hitting a landing zone.

Based on the Python iRODS Client and Celery, this framework can scale up to match the demands of data coming off instruments, satellites, or parallel filesystems.

The example diagrams below show a filesystem scanner and a landing zone.

Automated Ingest: Filesystem Scanner Diagram

Automated Ingest: Landing Zone Diagram

Usage options

Redis options

option effect default
redis_host Domain or IP address of Redis host localhost
redis_port Port number for Redis 6379
redis_db Redis DB number to use for ingest 0

S3 options

To scan S3 bucket, minimally requires --s3_keypair and source path of the form /bucket_name/path/to/root/folder.

option effect default
s3_keypair path to S3 keypair file None
s3_endpoint_domain S3 endpoint domain
s3_region_name S3 region name us-east-1
s3_proxy_url URL to proxy for S3 access None
s3_insecure_connection Do not use SSL when connecting to S3 endpoint False

Logging/Profiling options

option effect default
log_filename Path to output file for logs None
log_level Minimum level of message to log None
log_interval Time interval with which to rollover ingest log file None
log_when Type/units of log_interval (see TimedRotatingFileHandler) None

--profile allows you to use vis to visualize a profile of Celery workers over time of ingest job.

option effect default
profile_filename Specify name of profile filename (JSON output) None
profile_level Minimum level of message to log for profiling None
profile_interval Time interval with which to rollover ingest profile file None
profile_when Type/units of profile_interval (see TimedRotatingFileHandler) None

Ingest start options

These options are used at the "start" of an ingest job.

option effect default
job_name Reference name for ingest job a generated uuid
interval Restart interval (in seconds). If absent, will only sync once. None
file_queue Name for the file queue. file
path_queue Name for the path queue. path
restart_queue Name for the restart queue. restart
event_handler Path to event handler file None (see "event_handler methods" below)
synchronous Block until sync job is completed False
progress Show progress bar and task counts (must have --synchronous flag) False
ignore_cache Ignore last sync time in cache - like starting a new sync False

Optimization options

option effect default
exclude_file_type types of files to exclude: regular, directory, character, block, socket, pipe, link None
exclude_file_name a list of space-separated python regular expressions defining the file names to exclude such as "(\S+)exclude" "(\S+).hidden" None
exclude_directory_name a list of space-separated python regular expressions defining the directory names to exclude such as "(\S+)exclude" "(\S+).hidden" None
files_per_task Number of paths to process in a given task on the queue 50
initial_ingest Use this flag on initial ingest to avoid check for data object paths already in iRODS False
irods_idle_disconnect_seconds Seconds to hold open iRODS connection while idle 60

available --event_handler methods

method effect default
pre_data_obj_create user-defined python none
post_data_obj_create user-defined python none
pre_data_obj_modify user-defined python none
post_data_obj_modify user-defined python none
pre_coll_create user-defined python none
post_coll_create user-defined python none
pre_coll_modify user-defined python none
post_coll_modify user-defined python none
character_map user-defined python none
as_user takes action as this iRODS user authenticated user
target_path set mount path on the irods server which can be different from client mount path client mount path
to_resource defines target resource request of operation as provided by client environment
operation defines the mode of operation Operation.REGISTER_SYNC
max_retries defines max number of retries on failure 0
timeout defines seconds until job times out 3600
delay defines seconds between retries 0

Event handlers can use logger to write logs. See structlog for available logging methods and signatures.

Operation mode

operation new files updated files
Operation.REGISTER_SYNC (default) registers in catalog updates size in catalog
Operation.REGISTER_AS_REPLICA_SYNC registers first or additional replica updates size in catalog
Operation.PUT copies file to target vault, and registers in catalog no action
Operation.PUT_SYNC copies file to target vault, and registers in catalog copies entire file again, and updates catalog
Operation.PUT_APPEND copies file to target vault, and registers in catalog copies only appended part of file, and updates catalog
Operation.NO_OP no action no action

--event_handler usage examples can be found in the examples directory.

Character Mapping option

If an application should require that iRODS logical paths produced by the ingest process exclude subsets of the range of possible Unicode characters, we can add a character_map method that returns a dict object. For example:

    class event_handler(Core):
        def character_map():
            return {
        # ...

The returned dictionary, in this case, indicates that the ingest process should replace all non-alphanumeric (as well as non-ASCII) characters with an underscore wherever they may occur in an otherwise normally generated logical path. The substition process also applies to the intermediate (ie collection name) elements in a logical path, and a suffix is appended to affected path elements to avoid potential collisions with other remapped object names.

Each key of the returned dictionary indicates a character or set of characters needing substitution. Possible key types include:

  1. character
    # substitute backslashes with underscores
    '\\': '_'
  1. tuple of characters
    # any character of the tuple is replaced by a Unicode small script x
    ('\\','#','-'): '\u2093'
  1. regular expression
    # any character outside of range 0-256 becomes an underscore
    re.compile('[\u0100-\U0010ffff]'): '_'
  1. callable accepting a character argument and returning a boolean
    # ASCII codes above 'z' become ':'
    (lambda c: ord(c) in range(ord('z')+1,128)): ':'

In the event that the order-of-substitution is significant, the method may instead return a list of key-value tuples.


Any file whose path in the filesystem whose ingest results in a UnicodeEncodeError exception being raised (e.g. by the inclusion of an unencodable UTF8 sequence) will be automatically renamed using a base-64 sequence to represent the original, unmodified vault path.

Additionally, data objects that have had their names remapped, whether pro forma or via a UnicodeEncodeError, will be annotated with an AVU of the form

Attribute: "irods::automated_ingest::" + ANNOTATION_REASON Value: A PREFIX plus the base64-converted "bad filepath" Units: "python3.base64.b64encode(full_path_of_source_file)"

Where :

  • ANNOTATION_REASON is either "UnicodeEncodeError" or "character_map" depending on why the remapping occurred.
  • PREFIX is either "irods_UnicodeEncodeError_" or blank(""), again depending on the re-mapping cause.

Note that the UnicodeEncodeError type of remapping is unconditional, whereas the character remapping is contingent on an event handler's character_map method being defined. Also, if a UnicodeEncodeError-style ingest is performed on a given object, this precludes character mapping being done for the object.

Manual Deployment

Configure python-irodsclient environment

python-irodsclient (PRC) is used by the Automated Ingest tool to interact with iRODS. The configuration and client environment files used for a PRC application applies here as well.

If you are using PAM authentication, remember to use the Client Settings File.

iRODSSessions are instantiated using an iRODS client environment file. The client environment file used can be controlled with the IRODS_ENVIRONMENT_FILE environment variable. If no such environment variable is set, the file is expected to be found at ${HOME}/.irods/irods_environment.json. A secure connection can be made by making the appropriate configurations in the client environment file.

Starting Redis Server

Install Redis server:

Starting the Redis server with package installation:


Or, dameonized:

sudo service redis-server start
sudo systemctl start redis

The Redis GitHub page also describes how to build and run Redis:

The Redis documentation also recommends an additional step:

Make sure to set the Linux kernel overcommit memory setting to 1. Add vm.overcommit_memory = 1 to /etc/sysctl.conf and then reboot or run the command sysctl vm.overcommit_memory=1 for this to take effect immediately.

This allows the Linux kernel to overcommit virtual memory even if this exceeds the physical memory on the host machine. See documentation for more information.

Note: If running in a distributed environment, make sure Redis server accepts connections by editing the bind line in /etc/redis/redis.conf or /etc/redis.conf.

Setting up virtual environment

You may need to upgrade pip:

pip install --upgrade pip

Install virtualenv:

pip install virtualenv

Create a virtualenv with python3:

virtualenv -p python3 rodssync

Activate virtual environment:

source rodssync/bin/activate

Install this package

pip install irods_capability_automated_ingest

Set up environment for Celery:

export CELERY_BROKER_URL=redis://<redis host>:<redis port>/<redis db> # e.g. redis://
export PYTHONPATH=`pwd`

Start celery worker(s):

celery -A irods_capability_automated_ingest.sync_task worker -l error -Q restart,path,file -c <num workers> 

Note: Make sure queue names match those of the ingest job (default queue names shown here).

Using the sync script

Start sync job

python -m irods_capability_automated_ingest.irods_sync start <source dir> <destination collection>

List jobs

python -m irods_capability_automated_ingest.irods_sync list

Stop jobs

python -m irods_capability_automated_ingest.irods_sync stop <job name>

Watch jobs (same as using --progress)

python -m irods_capability_automated_ingest.irods_sync watch <job name>

Run tests

Note: The tests start and stop their own Celery workers, and they assume a clean Redis database.

python -m irods_capability_automated_ingest.test.test_irods_sync

See docker/ingest-test/ for how to run tests with Docker Compose.