Developing a Data Management System for the ATLAS Experiment

1. Developing a Data Management System for the ATLAS Experiment September 20, 2005 Miguel Branco miguel.branco@cern.ch ATLAS DDM

2. Outline • ‘Data Challenges 2’ and ‘Rome Production’ • Lessons Learned • DQ2 • Design • Implementation • Data model • Services • Conclusion ATLAS DDM

3. DC2 and Rome Production • Production started Spring 2004 and finished recently • ProdSys: • Data Management (DQ): high-level service that interacted with all ATLAS Grid catalogs and storages • File-based: relied on backend RLS (Globus RLS, EDG RLS) • Also implemented a simple reliable file transfer (FIFO queue) Supervisors: collect jobs from production database dispatch to executors Executors (per ‘Grid’): translate physics definition to a Grid job and launch it DQ: All components interacted with data management

4. Lessons learned • Catalogs were provided by Grid providers and used “as-is” • Granularity: file-level. No datasets, no “file collections” • No scoping of queries (difficult to find data, slow) • No bulk operations • No managed and transparent data access, unreliable GridFTP • SRM also unreliable; Problems with mass storage • Difficult to handle different mass storage stagers from Grid • Metadata support not usable; too slow • Logical Collection Name as metadata string field: /datafiles/rome/… • Catalogs not always geographically distributed • Single point of failure (middleware, people/timezones) • No “ATLAS resources information system” (with known/negotiated QoS) … and unreliable information systems from Grid providers • Operational problems • Timezones, lack of people, experience, communication ATLAS DDM

5. DQ2 Design rationale • Evolve from past experience • Scalability • Administrative, Geographical, Load • Interoperability • Grid m/w components • Replica Catalog, Storage Management, Reliable File Transfer • Global != Site != Local != Clients • Production and User Analysis • Security • Datasets, not files… • Bulk • Datasets and Datablocks (a immutable collection of files) ATLAS DDM

6. DQ2 • Moves from a file based system to one based on datasets • Hides file level granularity from users • A hierarchical structure makes cataloging more manageable • However file level access is still possible • Scalable global data discovery and access via a catalog hierarchy • No global physical file replica catalog • but global dataset replica catalog and global dataset location catalog Files Files Datasets Sites Files Files Dataset Files

7. Catalog architecture and interactions ATLAS DDM

8. ‘Global’ catalogs Dataset Repository Holds all dataset names and unique IDs (+ system metadata) Dataset Hierarchy Maintains versioning information and information on ‘container datasets’, datasets consisting of other datasets Dataset Content Catalog Maps each dataset to its constituent files This one holds info on every logical file so must be highly scalable, however it can be highly partitioned using metadata etc.. Dataset Location Catalog Stores locations of each dataset All logically global but may be distributed physically ATLAS DDM

9. ‘Local’ Catalogs Local Replica Catalog Per grid/site/tier providing logical to physical file name mapping. Implementations of this catalog are Grid specific but must use a standard interface. Claims Catalog Per site storage, keeping user claims on datasets. Claims are used to manage stage lifetime, resources and provide accounting. Currently all ‘Local’ catalogs are deployed per ATLAS site ATLAS DDM

10. Implementation • Architectural Style • REST-style (not entirely RESTful) • Communication: intend to migrate non-performance critical payload (monitoring, real-time status reporting) to XML soon • vocabularies will emerge from experience of running system • Development • First usable prototype deployed 47 days after project started • Technology choices • Python; servers hosted on Apache (mod_python, mod_gridsite); clients using PyCurl • POOL File Catalog interface gives us choice of back-end for catalogs • File movement: SRM, GridFTP, gLite FTS, HTTP, dccp, cp • Security • Use HTTPS (with Globus proxy certs) for POST/PUT/DELETE and HTTP for GETs, ie world-readable data, best performance (can be made secure to ATLAS VO if required)

11. Datablocks • Datablocks are defined as immutable and unbreakable collections of files • They are a special case of datasets • A site cannot hold partial datablocks • There are no versions for datablocks • Used to aggregate files for convenient distribution • Files grouped together by physics properties, run number etc.. • Much more scalable than file level distribution • Useful for provenance: immutable sets of data • The principal means of data distribution and data discovery • immutability avoids consistency problems when distributing data • moving data in blocks improves data distribution (bulk SRM requests) ATLAS DDM

12. Subscriptions • A site can subscribe to data • When a new version is available, this latest version of the dataset is automatically made available through site-local specific services carrying out the required replication - Automated movement • Subscriptions can be made to datasets (for file distribution) or container datasets (for datablock distribution) • Use cases: • Automatic distribution of datasets holding a variable collection of datablocks (container datasets) • Automatic replication of files by subscribing to a mutable dataset (eg file-based calibration data distribution) Site ‘X’: Dataset ‘A’ Subscriptions: File1 File2 Dataset ‘A’ | Site ‘X’ Dataset ‘B’ | Site ‘Y’ (Container) Dataset ‘B’ Site ‘Y’: Data block1 Data block2

13. File1 DS1 File2 File3 DB1 CDS1 Subscribes to CDS1 DB2 DB3 DB1 “Subscribes” (temporarily) to DB1 Subscriptions • Various data movement use cases… • Datasets: • latest version of a dataset (triggers automatic updates whenever a new version appears) • Container Datasets: • which in turn contain datablocks or datasets • supports subscriptions to the latest version of a container dataset (automatically triggers updates whenever e.g. the set of datablocks making up the container dataset changes) • Datablocks (single copy of immutable set of files) • Databuckets (diagram next slide) • replication of a set of files using notification model (whenever new content appears on the databucket, the replication is triggered) Subscribes to DS1 Dataset Location Catalog updated

14. Remote Site File 2 File 1 File 1 Data “bucket” (file-based data bucket) Data buckets • Data must be replicated (quickly) not by the appearance of a new version but by new content • alternative would be constantly defining new versions of datasets! • Will use notification model: • Whenever new content appears on a data bucket, sites subscribing to it are notified and data is moved accordingly • Data buckets can contain files • Data buckets can contain datablocks ATLAS DDM

15. Summary of Services • Global services • Dataset catalogs • Requirements: grid environment, database, Apache services • Site services • Subscriptions, Databuckets, Claims and minimal information system (monitoring, real-time reporting) • Requirements: grid environment, database, Apache services, DQ2 agents for moving data, grid-specific data movement clients, Python, PyCURL, grid certificate • Local worker node client • Contact local LRC, get and put data to local Storage • Requirements: grid environment • Clients • Define datasets and datablocks, subscribe them to sites • Associate files with new dataset versions • Query dataset definition, contents, location • … • Requirements: Python, PyCURL, grid certificate for writing

16. Detail on Subscriptions Function Agents State Machine Fetcher Finds incomplete datasets unknownSURL ReplicaResolver Finds remote SURL knownSURL MoverPartitioner Assigns Mover agents assigned Mover Moves file toValidate ReplicaVerifier Verifies local replica validated BlockVerifier Verifies whole dataset complete done List of software required to handle subscriptions. Requires minimal deployment effort (laptop support!)

17. Claims • Claims catalog manages the usage of datasets • User requests have a lifetime • Claim is assigned • User may add claims on existing datasets • Claim owner may (should) release claim when done • Claim owner may extend lifetime of claim • Automatically handled by user client tools • Behavior • Each claim has an expiration time (now plus lifetime) • Claim is active until released or expired • Datasets may have multiple active claims for different users • Cache-turnover relies on expired claims • Claims provide mechanism for accounting, policy enforcement and dealing with Mass Storage (claim triggers SRM stage request) ATLAS DDM

18. Conclusion • Evolve the model based on past experience • based on proven technologies • Appears to scale so far • load, geographic and very important administrative scalability • It is running now across some US ATLAS and LCG sites • Ramping up (starting now!) to the full set of LCG and US ATLAS resources. http://uimon.cern.ch/twiki/bin/view/Atlas/DDM ATLAS DDM