Executive Overview

Jason Coposky

@jason_coposky

Executive Director, iRODS Consortium

Executive Overview

 

Our Membership

Consortium

Member

Consortium

Member

Consortium

Member

Consortium

Member

Our Business Model

Start with proof of concept

  • Use Case Driven
  • Hands on
  • Service and Support Contract
  • Master Services Agreement

 

Consortium Membership

  • Four Levels - $10k to $150k
  • 10 to 300 hours of support
  • Participation in Software roadmap
  • Discounted hourly rate

 

Tier 3 Support

  • Systems Integrators
  • Compute Vendors
  • Storage Vendors

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

The iRODS Data Management Model

Core Competencies

Policy

Capabilities

Patterns

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
  • RPC API

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.

What is a Policy

A Definition of Policy

 

 

A set of ideas or a plan of what to do in particular situations that has been agreed to officially by a group of people...

 

 

So how does iRODS do this?

iRODS Policies

The reflection of real world data management decisions in computer actionable code.

 

(a plan of what to do in particular situations)

Possible Policies - The What

  • Data Movement
  • Data Verification
  • Data Retention
  • Data Replication
  • Data Placement
  • Checksum Validation
  • Metadata Extraction
  • Metadata Application
  • Metadata Conformance
  • Replica Verification
  • Vault to Catalog Verification
  • Catalog to Vault Verification
  • ...

Policy Composition

Consider Storage Tiering:

  • Violating Object Identification
  • Data Movement
    • Data Replication
    • Data Verification
  • Data Retention
  • Packaged and supported solutions
  • Require configuration not code
  • Derived from the majority of use cases observed in the user community

iRODS Capabilities

Automated Ingest - Landing Zone

Automated Ingest - Filesystem Scanning

Storage Tiering

Core Competencies

Policy

Capabilities

Indexing

Core Competencies

Policy

Capabilities

Publishing

Deployment Patterns

Data to Compute

Compute to Data

Data Transfer Nodes

Filesystem Synchronization

Filesystem Synchronization

Data to Compute

Compute to Data

Data Transfer Nodes

The Data Management Model

Use Cases

iRODS

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

Catalog

University Zone

Catalog

Server Hosting Environment

Resource Server

Resource Server

Tape Archive

Disk Storage

Object Storage

SURF EUDAT CDI

External Community Zones

Catalog

Zone

Catalog

Local Storage

CXFS

Tape Library

EUDAT  University Zone

Catalog

EUDAT University Zone

Catalog

B2SAFE iRODS Federation

EUDAT Centers

iRODS Federation

ARCHIVE

GridFTP Data Movement

Questions?

Overview

iRODS Proof of Concept

Initial Goals

  1. Automatically Ingest data from a 'Landing Zone'
  2. Extract salient metadata - eg EXIF tags
  3. Tag Data Objects and Collections to make them Actionable and Discoverable
  4. Discover and interact with data on the command line
  5. Discover and interact with data via Metalnx
  6. Share data via Metalnx
  7. Interact with data via NFS and WebDAV

Automated Ingest

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

File System Presentations: NFSRODS

NFSRODS leverages the Java iRODS Client 'Jargon' and is implemented with NFS4J

 

NFSRODS acts as a Mid-Tier client to iRODS

 

NFSRODS projects iRODS ACLs into NFS extended ACLs

 

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

Data Discovery with Command Line

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

imeta qu -d "Image Make" = Apple

iquest "%s/%s" "SELECT COLL_NAME, DATA_NAME WHERE META_DATA_ATTR_NAME = 'Image Make' AND META_DATA_ATTR_VALUE = 'Apple'"

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

Questions?

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