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The HERA-B detector The database problem The Architecture The Berkeley-DB DBMS

The HERA-B database services detector configuration, calibration, alignment, slow control, data classification. A. Amorim, Vasco Amaral, Umberto Marconi, Tome Pessegueiro, Stefan Steinbeck, Antonio Tome, Vicenzo Vagnoni and Helmut Wolters. The HERA-B detector The database problem

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The HERA-B detector The database problem The Architecture The Berkeley-DB DBMS

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  1. The HERA-B database servicesdetector configuration, calibration, alignment, slow control, data classification A. Amorim A. Amorim, Vasco Amaral, Umberto Marconi, Tome Pessegueiro, Stefan Steinbeck, Antonio Tome, Vicenzo Vagnoni and Helmut Wolters • The HERA-B detector • The database problem • The Architecture • The Berkeley-DB DBMS • The client/server integration • The domains and solutions • Conclusions and Outlook

  2. B/B-tagging B0/B0 J/y KS HERA-B Experiment A. Amorim Vertex Detector Si strip 12 mm resolution MUON (m/h) tube,pad and gas pixel chambers RICH (p/K) multianode PMT ECAL(g+e/h) W/Pb scintillator shashlik TRD (e/h) straw tubes +thin fibers C4F10 HiPt trigger pad/gas pixel Magnet: 2 Tm Tracking: - ITR(<20cm): MSGC-GEM - OTR(>20cm): 5+10mm drift cells

  3. The main challenge: Selecting A. Amorim

  4. HERA-B DAQ Detector Front End Electronics A. Amorim FCS 1000 SHARC (DSP) Event Control DSP SWITCH DSP SWITCH Trigger PC Trigger PC Trigger PC SLT/TLT INTERNET SWITCH 4LT PC 4LT PC 4LT Logger PC L2-farm: 240 PC’s L4-farm 200 PC’s

  5. To provide persistence services (including online-offline replication) to: • Detector configuration • Common accepted schema • Calibration and alignment • Distributing information to the reconstruction and trigger farms • Associate each event with the corresponding database information • Slow control • Manage updates without data redundancy • Data set and event classification • Online Bookkeeping • Detector Configuration • Calibration and Alignment • Slow Control • Data Set and Event Classification • Online Bookkeeping The HERA-B database problem A. Amorim

  6. Characterizing the context A. Amorim

  7. Key= name+ version Machine independent blub of DATA /PM/ Descrip. field1 ; field 2; ... Db: /RICH/HV/ .2 .5 -.1 56892 ... versions Keys, objects and client/server A. Amorim client/server at the SDB level +RPM -> an UDP based communication package.

  8. The Berkeley DB A. Amorim See http://www.sleepycat.com/ • Embedded transactional store with: logging, locking, commit and roll back, disaster recovery. • Intended for: high-concurrency read-write workloads, transactions and recoverability. • Cursors to speed access from many clients.. • Open Source policyThe license is free for non-commercial purposes - rather nice support • No client/server support is provided

  9. Slow Control Interface A. Amorim Metadata Object Data Object Update Update Pmt1000 Pmt1003 Pmt2000 1.2 ... 1 ... 1.5 ... 2 ... 1.6 ... 2.3 ... time Optimized Queries

  10. Associations to Events A. Amorim ... ... ... Index Obj. Index Obj. Index Obj. Revision 0 - online Calibrating 1 - offline Index Obj. Active server interface Index Objects (referenced by events) Client/server Dynamic Associations Index Obj. Created in active Servers

  11. key’ Keykey’ Key Basic n-n associations (LEDA) A. Amorim • Associations are navigated with iterators • Using hash tables. • Keys as OID’s with the scope of classes. • Explicitly loaded or saved (as containers) LEDA - Object Manager (hash table implemented associations) Active server interface Key objects (referenced by events) Client/server

  12. GUI for editing and drawing A. Amorim From R&D: JAVA, TCL/TK, gtk Reusing and extending widget. Data hidden from TCL/TK ROOT database Binding Socket: Client/ Server

  13. General Architecture 109 Evt./y A. Amorim

  14. Conclusions A. Amorim • ONLINE: • Large number of Clients => Gigabytes per Update • broadcast simultaneously to SLT • tree of cache database servers to the 4LT • Correlates (dynamically) each event with the databases objects • 600 k SLC parameters using data and update objects • parameter history is re-clustered on the database servers • The online database system has been successfully commissioned • OFFLINE: • Replication mechanism decouples online from offline • also provides incremental backup of the data • TCP/IP gateways and proxies • “data warehousing” for data-set classification -> MySQL • Relation to event tag under evaluation • Also providing persistency to ROOT objects • Using Open Source external packages has been extremely useful.

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