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CERN/IT/DB
ORACLE 9i
for Physics Data
CERN Testplan
Database Group, IT
DB Workshop 11-13 July 2001
CERN/IT/DB
Main Areas of Interest
 VLDB & Oracle database architecture
 C++ Binding (OCCI)
 Data consistency and concurrent access
 Database performance
CERN/IT/DB
VLDB & Oracle Database
Architecture
 Scalability
 Maximum database size
 Maximum size and number of DB entities
 Runtime limits
 Storage Overhead
 Mass Storage System integration
 Data Import/Export
CERN/IT/DB
Maximum Database Size
ORACLE stores data in files or disk partitions
 Similar characteristics - in most places in this
presentation datafile can be replaced by disk
partition
Maximum number of datafiles per database
 Oracle 8i – 216 files
 Oracle 9 – maybe more
 Maximum datafile size
 4 million blocks, up to 64GB
Oracle 8i database: ~100TB
Oracle 9i …?
CERN/IT/DB
Limitations of DB Entities
 Size limitations of most likely used database types:
 Objects
 LOBS
 VArrays
 How big can they be and how many can we have of:
 Tables, columns, rows
 Table partitions
 Indexes
 How many classes in the schema?
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Runtime Limits
Server side
 Instances per
database
 Locks
 SGA size
 Sessions
 Open files
Client side
 Cache size
 Max. # opened
objects
 Single object larger
than cache
CERN/IT/DB
Storage overhead
What storage overhead is introduced by
the various levels in the Oracle storage
hierarchy?
 Motivation: O(PB) -> even factor 2 is a lot
 Can we reliably estimate the storage
consumption?
 Which overheads are most relevant for our
particular data models?
CERN/IT/DB
Storage overhead
(cont’d)
Physical & logical storage hierarchy
 database, tablespace, extent, segment, block
 table, partition, column, row
Relational data types
 NUMBER, VARCHAR, LOB, index, nested table,
REF
OO data types
 OID, VArray
CERN/IT/DB
MSS/HSM Interface
Too much data (many PBs) to keep it all
online at the same time
 Need an integration with a MSS
Only files that are not in use (closed) can be
moved to MSS and removed from disk
 ORACLE instance does not close files when the
client closes connection
Changing tablespace status to offline will
close all files that belong to it
Moving tablespaces offline/online needs to be
done manually
CERN/IT/DB
Data Export/Import
 Data distribution to other computing sites
 needs to be done efficiently
 in self-consistent portions
 Transportable tablespaces seem to be the best solution
 Experience in the DB group already exists
 Need more testing with object tables
• How the restrictions for “self containment” relate to object
references?
 Compare with the standard ORACLE data export tools
 See if transportable tablespaces can be used with MSS
 Is is possible to import back the same tablespace?
 Is it worth working around the “self containment” restrictions in
order to have the schema in MSS?
CERN/IT/DB
C++ Binding (OCCI)
Persistent objects
Client-side object cache
Large collections
Complex Object Retrieval
Object Type Translator (OTT)
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Object Persistency
 C++ object persistency is implemented using
ORACLE SQL object types (user defined types)
 C++ object attributes are stored as ORACLE native types
 Which Oracle data types are of central importance for
physics data models?
 NUMBER
 VARRAY
 Large Objects: LOBs, BFILEs
 Is their current implementation and interface
adequate for our use cases?
CERN/IT/DB
Client-side Object Cache
Cache size management
 control size to adapt to access patterns
Object pinning
 creation, navigation
 C pointers, REFs
Object caching, garbage collection,
refreshing…
CERN/IT/DB
Large Collections
How to create & handle large collections of
O(109)?
Oracle supports:
 ordered collections: VARRAYs
 unordered collections: nested tables
OCCI maps both to STL vectors
 Hierarchical structure needed for scalability
Adequate for physics data use cases?
 What about index-organized tables?
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Group fetching
OO data models
 interrelated objects forming graphs
 navigate/traverse:
fetch & pin each object individually
 costly network round-trips (client/server)
Oracle’s Complex Object Retrieval
 pre-fetching (depth level)
 does it fit to physics data models?
CERN/IT/DB
Object Type Translator
OTT translates an object data model from
ORACLE SQL object types into C++ classes
 Each type is translated 1-1
 The inheritance tree is preserved
Areas of focus:




How to convert existing C++ designs to SQL?
Which C++ types are allowed?
Which SQL types are usefull?
Can OTT handle complicated data models?
CERN/IT/DB
Using OTT
Does OTT support many developers working
simultaneously on a shared data store?
Does OTT support continuous development?
Creating OTT conventions in HEP
 Type naming conventions
• Each persistent class requires several type names
– SQL, OCCI generated, user
 File naming conventions
 Makefile rules (a la HepODBMS makefiles)
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General Database Features
Concurrent access
Data consistency
Performance
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Concurrency
Shared access to a large collections of
complex objects
 by many (hundreds) simultaneous readers
• Need to support hundreds of readers…
• … with an aggregate rate “close” to disk rate for user
data
 by many writers ( e.g. a reconstruction farm)
• Some concurrency problems in the past
– with shared and updated meta data (e.g. registry of
collections)
– excessive reload of unchanged data after transaction
commits
 by both at the same time?
CERN/IT/DB
Transaction Model
Understand how Oracle’s transaction model
affects our (existing) applications
Objy:
 Default: read and write locks
 Optionally: MROW read locks
Oracle: no read locks
 does promise better concurrency
• Could we allow our (high priority) writers to work
concurrently with (low priority) readers?
 navigational applications with the intend to update
may have to do a “late refresh”
CERN/IT/DB
Data Consistency
In the presence of faulty application software
 How do to recover from a SEGV in our
reconstruction software?
 Can we insure that faulty client programs can not
corrupt shared data or database metadata?
but also…
 What is the impact of redo and undo logging on
the data loading rate?
• Could/should we switch off logging?
CERN/IT/DB
Hardware and Media Faults
 How to recover from a temporary or
permanent Hardware Fault
–
–
–
–
–
a single disk sector
an offline file (e.g. Tape)
a disk
a complete node
a complete site
 What manual cleanup is required in our
application?
 How fast can we get online again?
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Availability
How close can we get to 24x7 access
to our store?
 Which DBA activities require to bring an
instance down?
• Backup?
 Any activities would require all instances,
tablespaces, files, … to be accessible?
CERN/IT/DB
Performance – Data Transfer
• What is the achievable “sequential” user data
rate?
 How much CPU is required per MB/s transfer rate?
• On DB client and server?
 How does the storage overhead of “Number” affect us
in I/O bandwidth and CPU?
• How do different object sizes affect the transfer rate?
• Blocking may influence storage overheads
• Number of objects affects CPU requirements
• What rate for sparse (“random”) access?
CERN/IT/DB
Performance – Cache Efficiency
• Repeat above measurements…
• … with hot and cold server side buffer cache
• Navigate through a cyclic object list of
increasing length using PL/SQL
• Repeat with hot and cold client side object
cache
• Do the same using OCCI
• How much cache memory do we need per
MB of user data?
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Other Test Topics
Data clustering
Object naming
Database links
Server-side queries
Stored procedures
Data replication
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Final Goals
Create a VLDB: 10-100TB
 With MSS interface?
Implement a real life data model in OCCI
 Working on the CMS data model
Prove that OCCI can be used in HEP
applications
 Cache management, performance, reliability