Executive Overview



Jason Coposky


Executive Director, iRODS Consortium

Executive Overview



October 15, 2019

Agriculture Victoria

Melbourne, Australia

What is iRODS

Distributed - runs on a laptop, a cluster, on premises or geographically distributed

Open Source - BSD-3 Licensed, install it today and try before you buy

Metadata Driven & Data Centric - Insulate both your users and your data from your infrastructure

iRODS as the Integration Layer

iRODS Core Competencies

The underlying technology categorized into four areas

Data Virtualization

Combine various distributed storage technologies into a Unified Namespace

  • Existing file systems
  • Cloud storage
  • On premises object storage
  • Archival storage systems

iRODS provides a logical view into the complex physical representation of your data, distributed geographically, and at scale.

Projection of the Physical into the Logical

Logical Path

Physical Path(s)

Data Discovery

Attach metadata to any first class entity within the iRODS Zone

  • Data Objects
  • Collections
  • Users
  • Storage Resources
  • The Namespace

iRODS provides automated and user-provided metadata which makes your data and infrastructure more discoverable, operational and valuable.

Metadata Everywhere

Workflow Automation

Integrated scripting language which is triggered by any operation within the framework

  • Authentication
  • Storage Access
  • Database Interaction
  • Network Activity
  • Extensible RPC API 

The iRODS rule engine provides the ability to capture real world policy as computer actionable rules which may allow, deny, or add context to operations within the system.

Dynamic Policy Enforcement

  • restrict access
  • log for audit and reporting
  • provide additional context
  • send a notification

The iRODS rule may:

Dynamic Policy Enforcement

A single API call expands to many plugin operations all of which may invoke policy enforcement

  • Authentication
  • Database
  • Storage
  • Network
  • Rule Engine
  • Microservice

Plugin Interfaces:

Secure Collaboration

iRODS allows for collaboration across administrative boundaries after deployment

  • No need for common infrastructure
  • No need for shared funding
  • Affords temporary collaborations

iRODS provides the ability to federate namespaces across organizations without pre-coordinated funding or effort.

iRODS as a Service Interface

Federation - Shared Data and Services

Ingest to Institutional repository

As data matures and reaches a broader community, data management policy must also evolve to meet these additional requirements.

iRODS Capabilities

Automated Ingest - Landing Zone

Automated Ingest - Filesystem Scanning

Storage Tiering

Core Competencies




Core Competencies




Deployment Patterns

Data to Compute

Compute to Data

Filesystem Synchronization

Filesystem Synchronization

Data to Compute

Compute to Data

The Data Management Model

Use Cases


The Wellcome Sanger Institute

Sanger - Replication

  • Data preferentially placed on resource servers in the green data center (fallback to red)
  • Data replicated to the other room.
  • Checksums applied
  • Green and red centers both used for read access.

Sanger - Metadata

attribute: library

attribute: total_reads

attribute: type

attribute: lane

attribute: is_paired_read

attribute: study_accession_number

attribute: library_id

attribute: sample_accession_number

attribute: sample_public_name

attribute: manual_qc

attribute: tag

attribute: sample_common_name

attribute: md5

attribute: tag_index

attribute: study_title

attribute: study_id

attribute: reference

attribute: sample

attribute: target

attribute: sample_id

attribute: id_run

attribute: study

attribute: alignment

  • Example metadata attributes
  • Users query and access data from local compute clusters
  • Users access iRODS locally via the command line interface

Sanger - Federation

Maastricht DataHub

Maastricht DataHub

SURF Scale Out Pilot

University Zone


University Zone


Server Hosting Environment

Resource Server

Resource Server

Tape Archive

Disk Storage

Object Storage


External Community Zones




Local Storage


Tape Library

EUDAT  University Zone


EUDAT University Zone


B2SAFE iRODS Federation

EUDAT Centers

iRODS Federation


GridFTP Data Movement


iRODS Proof of Concept

Initial Goals

  1. Upload existing AVR data as example content into S3 bucket avr-irods-data
  2. Get S3 files/folders registered to iRODS catalog
  3. Extract salient metadata - eg EXIF tags in .TIF files
  4. Tag Data Objects and Collections to make them Actionable and Discoverable

The Infrastructure

Catalog Service Provider

Catalog Service


Automated Ingest







S3 Buckets










The Content

  • Ingest policy registers object in place then extracts metadata
  • Apply metadata to the object in the catalog
    • metadata headers available in the files
    • contextual metadata : LZ directory, instrument, etc.
  • Demonstrate
    • ingest
    • discovery
    • data egress
    • graphical presentation
    • file system presentation : WebDAV

Automated Ingest

S3 buckets scanned

  • avr_irods_data
  • possibly many others

Any data that is discovered during a scan

  • Automatically registered to a storage resource
  • Metadata extracted and applied to the object in the catalog
  • Event possibly generated for audit trail

Users can view and access data and metadata from any client

Data Discovery with Metalnx

Automated Ingest has provided metadata for data discovery


The metadata can be directly inspected in Metalnx


The query builder can be used to identify data sets of interest via Attribute, Value, Unit matches


Queries to the system metadata may also be performed, searching on values such as file name, collection path, user, etc.

File System Presentations: DAVRods

DAVRods provides both a simple web based interface as well as the ability to mount a folder on the desktop


DAVRods is an Apache Module implemented in C using the native iRODS POSIX API


DAVRods can be used to edit data in-place, or to copy data to/from a users collections

Data Discovery with Command Line

Query using imeta, a command-line iRODS client utility:

imeta qu -C TYPE = pix4d and EXPER like '%wonwondah%' and YMD '>='  2018-09 and YMD '<'  2019-01 and STRUCT like '%ortho%mosaic%' | grep -v "\-\-\-\-"

Upcoming Goals

Upcoming Goals

  1. Index images, reflectivity and vegetation index geo-databases to allow quick search
  2. Enable collaboration/cross-organizational search via federation
  3. Enable visualization and interaction via the web


Agriculture Victoria - iRODS Executive Overview and Demonstration

By jason coposky

Agriculture Victoria - iRODS Executive Overview and Demonstration

An executive overview of iRODS, its technology, capabilities and deployment patterns as well as a demonstration of capabilities.

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