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OBK – An Online High Energy Physics’ Meta-Data Repository

OBK – An Online High Energy Physics’ Meta-Data Repository.

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OBK – An Online High Energy Physics’ Meta-Data Repository

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  1. OBK – An Online High Energy Physics’ Meta-Data Repository List of authors: Dr. I.Alexandrov, Dr. A.Amorim, Ms. E.Badescu, Ms. M.Barczyk, Ms. D.Burckhart-Chromek, Dr. M.Caprini, Dr. M.Dobson, Dr. J.Flammer, Mr. R.Hart, Dr. R.Jones, Mr. A.Kazarov, Mr. S.Kolos, Dr. V.Kotov, Dr. D.Liko, Mr. L.Lucio, Dr. L.Mapelli, Mr. M.Mineev, Dr. L.Moneta, Dr. I.Papadopoulos, Ms. M.Nassiakou, Dr. N.Parrington, Mr. L.Pedro, Mr. A.Ribeiro, Dr. Yu.Ryabov, Mr. D.Schweiger, Mr. I.Soloviev, Dr. H.Wolters Presentation by: Levi Lúcio

  2. Introduction (1) - CERN • Founded in 1954, CERN(European Organization for Nuclear Research)is a wide international collaboration (80 nationalities); • The objective of CERN is the experimental study of physics, in particular the study of matter and the forces that hold it together; • Within CERN’s lifetime, several important physics discoveries have been made, along with technology breakthroughs such as the WWW. Levi Lucio - CERN EP/ATD, FCUL

  3. Introduction (2) - Accelerator • The LHC (Large Hadron Collider) accelerator is now being built at CERN to be ready in 2007. It will be the most powerful particle accelerator in the world and will allow breaking new barriers in HEP (High Energy Physics): Levi Lucio - CERN EP/ATD, FCUL

  4. Introduction (3) - Detectors • Along the accelerator ring, several detectors (4) will be put in place. The ATLAS (A Toroidal LHC ApparatuS) is one of them: Levi Lucio - CERN EP/ATD, FCUL

  5. Introduction (4) - Physics • Two particle beams travelling in the accelerator in opposite senses at 99.9999997% of the light speed meet head on in the detector, producing new particles; • The interaction (collision) of two particles and their final state products is called an event; • For ATLAS, many events need to be collected to have strong statistics that prove the theory - a very rare particle (Higgs boson) is searched for. Levi Lucio - CERN EP/ATD, FCUL

  6. 40 MHz LVL1 100 KHz LVL2 1 KHz HLT 100 Hz (100 M/s) DBMS Introduction (5) - Triggers • The rate of events at ATLAS will be extremely high - 40 MHz; • Only a fraction of those events (1/107) is interesting - a powerful filter (trigger) is necessary; • This still means 100 events of 1Mbyte each per second - 100MByte/s storage; • The ATLAS is expected to produce 1PByte/year of event data. Levi Lucio - CERN EP/ATD, FCUL

  7. ATLAS detector and triggers Online Software OBK DBMS Introduction (6) - OBK Online Book-keeper • Part of the Online Software system - online control, configuration and monitoring of the detector and triggers (thousands of machines); • Records and manageslog data (meta-data) about the detector and trigger chain (diversified information); • Project undertaken in 1996 by the Lisbon FCUL / ATLAS group - L.Lucio, L.Pedro, A.Amorim, A.Ribeiro Levi Lucio - CERN EP/ATD, FCUL

  8. Databases in HEP (1) - History • Before the 1980s - database market not mature to handle size and complexity; in-house solutions in FORTRAN; • 1980s - relational solutions to handle book-keeping data; interest in OO persistent data model; • 1990s - standardization of OO databases (ODMG); investigation and consequent usage of commercial Objectivity/DB by LHC and other HEP experiments; • 2002 - LHC experiments dropped Objectivity/DB and are searching for alternatives - Oracle 9i, homegrown ROOT? Levi Lucio - CERN EP/ATD, FCUL

  9. Databases in HEP (2)Today’s needs • Management of large amounts of data (petabytes); • Support of addition of significative quantities of data on a daily basis; • Support of simultaneous queries; • Support of data access over international networks; • Flexible data model supporting versioning and schema evolution; • Adequate interfacing to tertiary storage. Levi Lucio - CERN EP/ATD, FCUL

  10. Homegrown/ external HEP Databases Data model OO/OR Distribution Databases in HEP (3) Today’s trends • Indecision between homegrown (OO ROOT) or external (OR Oracle 9i) databases; • Not clear what data model to use (pure OO, Object-Relational?); • Heavy research on data distribution - replication, interfacing with GRID; Levi Lucio - CERN EP/ATD, FCUL

  11. The OBK (1) - Definition • Defined in the ATLAS technical proposal as the component that “archives information about the data recorded to permanent storage by the data acquisition system. It records the information to be later used during data analysis1 on a per-run2 basis (run cataloger). It provides interfaces for retrieving and updating the information.” • 1After being collected, event data is analyzed “manually”. • 2A data taking period with a given machine parameterization. Levi Lucio - CERN EP/ATD, FCUL

  12. Requirements gathering High level design Implementation Testing Integration The OBK (2)Development approach • Prototypical spiral (3 prototypes - OBK/Objectivity, OBK/OKS and OBK/MySQL); • Well defined software development process + documentation production; • Usage of development support tools: CVS, CMT (platform management), Perl, Rose, documentation templates, etc. Requirements document DB and code diagrams Developer and user manuals Test report Levi Lucio - CERN EP/ATD, FCUL

  13. LVL1 Run Control Messaging Monitoring Databases Ancilliary Online Sw. Detector DataFlow SCADA LVL2 EF The OBK (3)Online Software context • The OBK is part of the Databases super-component of the Online Software: OBK Levi Lucio - CERN EP/ATD, FCUL

  14. Requirements gathering Main Use Cases • Data acquisition:After being started with the Online Software, the OBK will acquire the specified data in an automatically without human intervention; • Information updating:Users will want to add their own annotations to the acquired data; • Data access:It will be possible for several kinds of clients, such as humans, applications or offline data analysis frameworks to access the database adequately; • Data administration:Users will want to manage and administrate the OBK database. Levi Lucio - CERN EP/ATD, FCUL

  15. Online Software IS MRS ConfDB OBK acquisition software DBMS C++ API Web Browser Administrative tools High level design (1) Package overview • ISInformation System • MRSMessage Reporting System • ConfDBConfiguration Databases Levi Lucio - CERN EP/ATD, FCUL

  16. Partition n-1 Partition n Partition n+1 ... ... Run n-1 Run n Run n+1 ... ... IS Messages MRS Messages Annotations IS Meta-info Configuration Data High level design (2)Logical database structure • Partition:subset of the detector and triggers that can acquire data independently. Levi Lucio - CERN EP/ATD, FCUL

  17. Implementation Languages and tools • C++ programming languageUsed to code all OBK acquisition engines (including connections to the DBMSs) and API software; • STL (Standard Template Library)Data containers and algorithm templates used as building blocks for C++ applications; • Objectivity/DBCommercial distributed object oriented database management system; • OKSIn-memory persistent object manager implemented in-house to satisfy ATLAS’ needs in terms of configuration databases; • MySQLOpen source relational database management system; • PHPGeneral purpose scripting language, specially adequate for web programming; • PerlGeneral purpose scripting language; • ApacheWidely used HTTP server. Levi Lucio - CERN EP/ATD, FCUL

  18. 0..* OBKISInfo OBKAnnotation OBKSLCRun OBKRun OBKAuthor OBKConfFiles OBKRunWEvents OBKMRSMessage OBKISAttribute OBKISAttrBasic OBKISAttrArray OBKISDocument OBKMRSParam Partition Federation 1 1..* 1 1 Run Database 1 1 1 Container Object 1..* Object 0..* 0..* class OBKRun : public ooContObj { public: Run (); Run (uint32 runNumb); void setRunNumb (uint32 runNumb); uint32 getRunNumb (); ooRef(OBKComment) runComms[] <-> commToRun[]; ooRef(Coordinator) runCoordinator <-> rCoordinated[]; ooRef(LockedStatus) runToLStat[] <-> lStatOfRun[]; ooRef(OBKConfdb)hasConfig <-> appliesToRuns[]; protected: uint32 m_runNumb; d_Timestamp m_startDate; d_Timestamp m_endDate; }; Inherits from Objectivity class 0..* 1 1 0..* References to access persistent objects Implementation Objectivity prototype(1) Levi Lucio - CERN EP/ATD, FCUL

  19. Implementation Objectivity prototype(2) • Comments • Objectivity/DB makes available specialized engines to handle connections and concurrency; • Very good integration between code and DMBS - minimal difference between persistent and transient objects; • The prototype makes use of Objectivity/DB transactions. A new transaction is started for each new run; • Objectivity/DB’s locking mechanism is used explicitely in the code to avoid incoherent reads/writes. MROW (Multiple Readers One Writer) facility used to read data as soon as it is written. Levi Lucio - CERN EP/ATD, FCUL

  20. Federation data file DB Root (File System) Partition data files Partition 1 Partition 2 Partition 3 Run data files Run 21 Run 22 Run 23 Implementation OKS prototype(1) • Data model includes XML data files and objects; • A data file is either in-memory or in disk (atomic loads); • Database schema equivalent to OBK/Objectivity’s one. Definition of a new OKS “object” OksClass *PartitionInfo = new OksClass( "PartitionInfo", false); { OksAttribute *partitionName = new OksAttribute( "partitionName", OksAttribute::string_type, false, "unknown", true); PartitionInfo->add(partitionName); PartitionInfo->add(inUse); } Levi Lucio - CERN EP/ATD, FCUL

  21. Implementation OKS prototype(2) • Comments • OKS is a persistency C++ library. No services other than the ones included in the library are made available; • No concurrency management is available. In the OBK case concurrency was implemented using OS mechanisms; • No transactions are available. At the beginning of each run new data files are opened and at the end of the run closed; • The prototype includes optimized accesses to certain parameters which are very requested. They are kept in a special central data file (cache). Levi Lucio - CERN EP/ATD, FCUL

  22. Implementation MySQL prototype(1) • Relational model: completely different database schema from previous OO approaches. MYSQL *sock,mysql; MYSQL_RES *res; MYSQL_ROW tmp; string selectqbuf; char * date; selectqbuf = ("SELECT MAX(StartDate) FROM run WHERE PartitionID =" + PartitionId); if(mysql_query(sock,selectqbuf.c_str())) { userMessaging->m_obkErr(new string("Query: " + selectqbuf + " failed! " + (string)mysql_error(sock)),2); } if(!(res = mysql_store_result(sock))) { userMessaging->m_obkErr(new string ("Couldn't get result from query: " + (string)mysql_error(sock)),2); } tmp = mysql_fetch_row(res); date =tmp[0]; } Query execution request to the engine Levi Lucio - CERN EP/ATD, FCUL

  23. Implementation MySQL prototype(2) • Comments • As Objectivity/DB, MySQL also makes available an engine to deal with queries; • Concurrency issues are managed transparently by the MySQL engine; • Transactions and atomic operations are made available by the MySQL engine - not used by the OBK though; • Indexes on certain key tables were created to accelerate queries (up to a factor of 45 speed difference); • XML used to deal with the difficulty of storing collection types. Levi Lucio - CERN EP/ATD, FCUL

  24. Implementation - Data Access • Command line dumpDebug situations, not many available resources; • C++ APIShared library; uses STL for return structures; • Web-based browserMore sophisticated, includes administrative tools. Heavier on resources than previous solutions. Levi Lucio - CERN EP/ATD, FCUL

  25. Performance & Scalability (1) • While for the OBK/Objy and the OBK/OKS store times rise (check if the run already exists), the OBK/MySQL presents low and constant store times. Time to store an MRS start of run message OBK/OKS OBK/Objy All tests performed in unloaded linux7.1/gcc2.96 PIII/800MHz OBK/MySQL Levi Lucio - CERN EP/ATD, FCUL

  26. Performance & Scalability (2) • The OBK/OKS is the fastest - the operation takes place in memory, no I/O accesses. Time to store a typical IS message OBK/OKS OBK/Objy OBK/MySQL Levi Lucio - CERN EP/ATD, FCUL

  27. Performance & Scalability (3) Concurrent access from multiple IS servers • While being accessed simultaneously by multiple IS servers the OBK/OKS presents the best performance - fastest IS storing time. Also, the Online Software is affected by OBK’s performance; • Worst performance in storage space by OBK/Objy - a container always allocates a predefined number of fixed size pages, even if they are not used. Disk space to store a 1000 run database Levi Lucio - CERN EP/ATD, FCUL

  28. Performance & Scalability (4) • Best results by OBK/MySQL, due to the efficiency of the MySQL query engine, faster than hand-coded queries in the OO prototypes; • In query 2 the OBK/Objy presents the worse performance - the parameters which are searched for are cached in the case of OBK/Objy and OBK/OKS; Query 1 - retrieving all the information about a single run Query 2 - Select all the runs that fulfil a given criteria Levi Lucio - CERN EP/ATD, FCUL

  29. Performance & ScalabilityOverall Results • Best overall results by the MySQL OBK prototype; • Strong results also from the OKS OBK prototype, mainly due to its in-memory features; • Less optimal results achieved by the Objectivity/ DB prototype - requires deep know-how to be properly tuned. Levi Lucio - CERN EP/ATD, FCUL

  30. Deployment • Large scale tests of the Online Software (simulated environment): • 2001 (OBK/OKS): 111 nodes running on 111 machines; • 2002 (OBK/MySQL): 210 nodes running on 210 machines. • Testbeams (real data acquisition with parts of the detector running): • 2000 (OBK/Objy): 2 Gbytes acquired; • 2001 (OBK/OKS): 3 Gbytes acquired; • 2002 (OBK/MySQL): 5 Gbytes acquired (still running). Levi Lucio - CERN EP/ATD, FCUL

  31. Some metrics Requirements gathering: 2 man/month, 2 documents produced. Testing Design and implementation Documentation: 3 man/month, User & Developer’s manual,Test report. Levi Lucio - CERN EP/ATD, FCUL

  32. Lessons learnt • Software Development ProcessFollowing a formal approach to the development of the three prototypes yielded: easy comparison of the OBKs; diminishment of the effort to build the latter prototypes; delivery of a quality OBK product; • TechnologyOO DBMS technology is very flexible in terms of data mapping and provides natural integration with programming languages. It is possible to follow an OO development approach both for application and database. RDBMS technology is less elegant but very efficient… • Interaction with usersGood and constant interaction with the final users of the system makes development simpler and faster. Continuous enhancement of the knowledge about the systems and the people the software interacts with is essencial while putting the problem under perspective. Levi Lucio - CERN EP/ATD, FCUL

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