2. Updates from Database Services at CERN Andrei Dumitru CERN IT Department / Database Services

3. Credit: Mariusz Piorkowski

4. Databases at CERN ~100 Oracle databases, most of them RAC Mostly NAS storage plus some SAN with ASM More than 500 TB of data files for production DBs in total Example of critical production DBs: LHC logging database ~170 TB, expected growth up to ~70 TB / year But also as DBaaS, as single instances MySQL Open community databases PostgreSQL databases Oracle 11g 4

5. Accelerator logging 5 ~ 95% data reduction Millions of records/min ~ 250000 signals ~ 15 data loading processes ~ 5.5 billion records/day ~ 275 GB/day  100 TB/year throughput ~ 1 million signals ~ 300 data loading processes ~ 4 billion records/day ~ 160 GB/day  52 TB/year stored Credit: C. Roderick

6. Administrative systems All applications are based on Oracle as database Oracle Weblogic server manages numerous HR and administrative Java-based web applications used by CERN Java EE and Apex Oracle HR is E-Business Suite R12 6

7. Engineering & equipment data Managing a million components over a lifecycle of 30 years Integrated PLM platform by linking together commercial systems 7 3D CAD CATIA Design data management Manufacturing follow-up Installation follow-up Maintenance management Data publishing Workflow actions Credit: D. Widegren

8. Experiment systems Online Data-taking operations Rely on SCADA system to store and monitor the detector parameters (temperatures, voltages, …) Up to 150.000 changes/second stored in Oracle databases Offline Post data-taking analysis, reconstruction and reprocessing, logging of critical operations, … Database replication: Oracle Streams, Oracle Golden Gate, active standby databases 8

9. New DBs Services QPSR Quench Protection System will store ~150 K rows/second (64GB per redo log) 1 000 000 rows/second achieved during catchup tests need to keep data for a few days (~ 50 TB) Doubtful that previous HW could handle that SCADAR Consolidated WinCC/PVSS archive repository Will store ~50-60K rows/second (may increase in the future) the data retention varies depending on the application (from a few days to 5 years) 9

10. Service Lifecycle 10 HW MigrationSW upgrade Stable services Decomission New systems Installation Every ~3-4 years

11. Preparation for LHC Run 2 Requirement: changes had to fit LHC schedule New HW installation on critical power Decommission of some old HW Critical power move from current location to new location Keep up with Oracle SW evolution Applications’ evolution - more resources needed Integration with Agile Infrastructure @CERN LS1: no stop DB services 11

12. Service Evolution during LS1 Hardware evolution New DB servers Storage evolution New generation of Storage servers Refresh cycle of OS and OS related Puppet RHEL 6 Database Software evolution Upgrade to newer Oracle version 12

13. Oracle Real Application Clusters - Overview 13 Operating System Oracle RAC Instance 1 Operating System Oracle RAC Instance 2 Oracle Clusterware Public Network a.k.a Cluster Interconnect Oracle Database on Shared Storage Node 1 Node 2 Oracle Clusterware Storage Network Private Network Example of 2-node DB cluster

14. Our Deployment Model Database Clusters with RAC Servers Running Red Hat Enterprise Linux Storage NAS (Network-attached Storage) from NetApp High capacity SATA + SSD cache Network 10 Gig Ethernet - for Storage, Interconnect, Users Number of nodes: 2 – 5 14

15. New Hardware Consolidation of HW 100 Production Servers Dual 8 core XEON e5-2650 128GB/256GB RAM 3x10Gb interfaces Specific network requirements 1. IP1 (cs network) 2. ATLAS Pit 3. Technical network 4. Routed network accessible from outside of CERN, 5. Non-routed network only internal to CERN 15 Credit: Paul Smith

16. Storage Evolution NetApp FAS3240NetApp FAS8060 NVRAM1.0 GB8.0 GB System memory8GB64GB CPU1 x 64-bit 4-core 2.33 Ghz2 x 64-bit 8-core 2.10 Ghz SSD layer (maximum)512GB8TB Aggregate size180TB400TB OS controllerData ONTAP® 7-mode Data ONTAP® C-mode* 16 scaling up scaling out * Cluster made of 8 controllers. Shared with other services. Credit: Ruben Gaspar

17. Storage Evolution Centrally managed storage Monitored: Netapp + home made tools Enables consolidation Thin provisioning on file systems Transparent volume move More capacity for growth More SSD => Performance gains for DB service ~2-3 times more of overall performance 17 Credit: Ruben Gaspar

18. Advantage of the New Hardware More memory & more CPU MEM: RAM 48GB -> 128GB / 256GB DB Cache: 20GB -> 86GB / 197GB Faster storage Storage cache 18 HW Migration

19. Available Software releases Production databases on Oracle 11.2.0.3 before LHC LS1 All databases were upgraded and migrated to new hardware Oracle 11g – version 11.2.0.4 Terminal patch set of Oracle 11g Extended support ends January 2018 Oracle 12c - versions 12.1.0.1 and 12.1.0.2 First release of 12c and the subsequent patch set Users of 12.1.0.1 will have to upgrade to 12.1.0.2 or higher by 2016 No current Oracle version fits well the entire LHC Run 2. Preparation for LHC Run 2 - software 19

20. Consolidation Schema based consolidation Many applications share the same RAC cluster Consolidation - per customer and/or functionality Host based consolidation Run different DB services on the same machine Support for different Oracle homes (versions) on the same host RAC clusters Load Balancing and possibility to growth High Availability: cluster survives node failures Maintenance: scheduled rolling interventions 20

21. Replication Disaster Recovery Data Guard Databases in Wigner Active Data Guard available to users for read-only operations Streams to Golden Gate migration completed! Improved scalability – performance better than Streams ATLAS ONLINE – OFFLINE (condDB) ATLAS OFFLINE – TIER1s RAL IN2P3 TRIUMF LHCb ONLINE-OFFLIN 21

22. Scalable Databases Goal: open and scalable analytic platform for the data currently stored in traditional databases LHClog / control systems archives / other monitoring and auditing systems Solution - Hadoop cluster shared nothing - scalable open systems - many approaches on storing and processing the data Conclusions Data processing with Hadoop scales-out - no matter what engine you will use Choosing the right data format for storing certain data is a key to deliver high performance All the details in “Evaluation of distributed open source solutions in CERN database use cases” by Kacper Surdy (Tuesday) 22

23. Database on Demand https://cern.ch/DBOnDemand

24. Database on Demand Covers a demand from CERN community not addressed by the Oracle service Different RDBMS: MySQL, PostgreSQL and Oracle Follows a DBaaS paradigm Making users database owners - full DBA privileges No access to underlying hardware No DBA support or application support No vendor support (except for Oracle) Foreseen as single instance service It provides tools to manage DBA actions: configuration, start/stop, upgrades, backups & recoveries, instance monitoring 24

25. Database on Demand Evolution of the amount of MySQL, Oracle and PostgreSQL instances in the DBoD service 25

26. Database on Demand instances per Database Management System 26

27. Enhanced Monitoring now available 27

28. Acknowledgements Work presented here on behalf of the CERN Database Services group In particular key contributions to this presentation from: Marcin Blaszczyk, Ruben Gaspar, Zbigniew Baranowski, Lorena Lobato Pardavila, Luca Canali, Eric Grancher 28