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Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
Database Tuning Principles, Experiments and Troubleshooting ...
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  • Goals: Principles of tuning are based on todays infrastructure. Storage is a major aspect of DB architectures. Magnetisk disks should be understood. We should also understand current trends to evaluate the limitations of the tuning principles. For instance, we will see that today the choice of index has a major impact on performance – now the day my 800 Gb database fits in main memory is the choice of an index still important? In some case it doesn’t and in some cases it does. We will come back on this in a few weeks. Goal today: What are the characteristics of secondary and tertiary storage What are the trends that drive the evolution of storage … we will discuss a couple of challenges.
  • Need for an address bit every 18 months Example: IDE uses 28 bits for sector number Barrier at 2^28 sectors of 512 bytes ~ 137 GB
  • Discussion: See disks downstairs: form factor is an element in the evolution Aerial density is main challenge for capacity: How many bits per track (or sector a fraction of a track)? Coat of the platter, bit encoding, process for encoding information. Rotation speed is main challenge for throughput: How fast can the heas read and decode without making too many mistakes? – currently around 10000 RPM is current Actuator and control are key for the access time. - controller with cache and processor.
  • Questions: - is data stored on both sides of a platter? Discussion - implication on performances: minimize seek time (sequential access, prefetching/large pages) shared disk (wait most of the time – minimize disk arm) Question: Given disk characteristics: what are principles for data layout? 1 – hot data in RAM - cache (memory hierarchy) 2 – sequential access vs. random access. Sequential access should be favored (locality for reads and writes should be researched and preserved) 3 – role of controller
  • Internal nodes: B+-tee and B-tree (definition) Links between leaves to speed-up range queries Path from root to leaves Insertion, update, delete (principles: online vs offline reorg and complexity) Search (principles and complexity) Practical considerations: size of key and fanout (example) Key compression
  • Overflow buckets + Offline reorganization Good for point queries
  • Transcript

    • 1. Database Tuning Principles, Experiments and Troubleshooting Techniques http://www.mkp.com/dbtune Dennis Shasha (shasha@cs.nyu.edu) Philippe Bonnet (bonnet@diku.dk)
    • 2. Availability of Materials
      • Power Point presentation is available on my web site (and Philippe Bonnet’s). Just type our names into google 
      • Experiments available from Denmark site maintained by Philippe (site in a few slides)
      • Book with same title available from Morgan Kaufmann.
    • 3. Database Tuning
      • Database Tuning is the activity of making a database application run more quickly. “More quickly” usually means higher throughput, though it may mean lower response time for time-critical applications.
    • 4. Application Programmer (e.g., business analyst, Data architect) Sophisticated Application Programmer (e.g., SAP admin) DBA, Tuner Hardware [Processor(s), Disk(s), Memory] Operating System Concurrency Control Recovery Storage Subsystem Indexes Query Processor Application
    • 5. Outline of Tutorial
      • Basic Principles
      • Tuning the guts
      • Indexes
      • Relational Systems
      • Application Interface
      • Ecommerce Applications
      • Data warehouse Applications
      • Distributed Applications
      • Troubleshooting
    • 6. Goal of the Tutorial
      • To show:
        • Tuning principles that port from one system to the other and to new technologies
        • Experimental results to show the effect of these tuning principles.
        • Troubleshooting techniques for chasing down performance problems.
    • 7. Tuning Principles Leitmotifs
      • Think globally, fix locally (does it matter?)
      • Partitioning breaks bottlenecks (temporal and spatial)
      • Start-up costs are high; running costs are low (disk transfer, cursors)
      • Be prepared for trade-offs (indexes and inserts)
    • 8. Experiments -- why and where
      • Simple experiments to illustrate the performance impact of tuning principles.
      • http://www.diku.dk/dbtune/experiments to get the SQL scripts, the data and a tool to run the experiments.
    • 9. Experimental DBMS and Hardware
      • Results presented throughout this tutorial obtained with:
        • SQL Server 7, SQL Server 2000, Oracle 8i, Oracle 9i, DB2 UDB 7.1
        • Three configurations:
        • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb) 2 Ultra 160 channels, 4x18Gb drives (10000RPM), Windows 2000.
        • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.
        • Pentium III (1 GHz, 256 Kb), 1Gb RAM, Adapter 39160 with 2 channels, 3x18Gb drives (10000RPM), Linux Debian 2.4.
    • 10. Tuning the Guts
      • Concurrency Control
        • How to minimize lock contention?
      • Recovery
        • How to manage the writes to the log (to dumps)?
      • OS
        • How to optimize buffer size, process scheduling, …
      • Hardware
        • How to allocate CPU, RAM and disk subsystem resources?
    • 11. Isolation
      • Correctness vs. Performance
        • Number of locks held by each transaction
        • Kind of locks
        • Length of time a transaction holds locks
    • 12. Isolation Levels
      • Read Uncommitted (No lost update)
        • Exclusive locks for write operations are held for the duration of the transactions
        • No locks for read
      • Read Committed (No dirty retrieval)
        • Shared locks are released as soon as the read operation terminates.
      • Repeatable Read (no unrepeatable reads for read/write )
        • Two phase locking
      • Serializable (read/write/insert/delete model)
        • Table locking or index locking to avoid phantoms
    • 13. Snapshot isolation
      • Each transaction executes against the version of the data items that was committed when the transaction started:
        • No locks for read
        • Costs space (old copy of data must be kept)
      • Almost serializable level:
        • T1: x:=y
        • T2: y:= x
        • Initially x=3 and y =17
        • Serial execution: x,y=17 or x,y=3
        • Snapshot isolation: x=17, y=3 if both transactions start at the same time.
      T1 T2 T3 TIME R(Y) returns 1 R(Z) returns 0 R(X) returns 0 W(Y:=1) W(X:=2, Z:=3) X=Y=Z=0
    • 14. Value of Serializability -- Data
      • Settings:
        • accounts ( number , branchnum, balance);
        • create clustered index c on accounts(number);
        • 100000 rows
        • Cold buffer; same buffer size on all systems.
        • Row level locking
        • Isolation level (SERIALIZABLE or READ COMMITTED)
        • SQL Server 7, DB2 v7.1 and Oracle 8i on Windows 2000
        • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.
    • 15. Value of Serializability -- transactions
      • Concurrent Transactions:
        • T1: summation query [1 thread]
        • select sum(balance) from accounts;
        • T2: swap balance between two account numbers (in order of scan to avoid deadlocks) [N threads]
        • T1:
        • valX:=select balance from accounts where number=X; valY:=select balance from accounts where number=Y; T2:
        • update accounts set balance=valX where number=Y; update accounts set balance=valY where number=X;
    • 16. Value of Serializability -- results
      • With SQL Server and DB2 the scan returns incorrect answers if the read committed isolation level is used (default setting)
      • With Oracle correct answers are returned (snapshot isolation).
    • 17. Cost of Serializability
      • Because the update conflicts with the scan, correct answers are obtained at the cost of decreased concurrency and thus decreased throughput.
    • 18. Locking Overhead -- data
      • Settings:
      • accounts ( number , branchnum, balance);
        • create clustered index c on accounts(number);
        • 100000 rows
        • Cold buffer
        • SQL Server 7, DB2 v7.1 and Oracle 8i on Windows 2000
        • No lock escalation on Oracle; Parameter set so that there is no lock escalation on DB2; no control on SQL Server.
        • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.
    • 19. Locking Overhead -- transactions
      • No Concurrent Transactions:
        • Update [10 000 updates] update accounts set balance = Val;
        • Insert [10 000 transactions], e.g. typical one: insert into accounts values(664366,72255,2296.12);
    • 20. Locking Overhead
      • Row locking is barely more expensive than table locking because recovery overhead is higher than row locking overhead
        • Exception is updates on DB2 where table locking is distinctly less expensive than row locking.
    • 21. Logical Bottleneck: Sequential Key generation
      • Consider an application in which one needs a sequential number to act as a key in a table, e.g. invoice numbers for bills.
      • Ad hoc approach: a separate table holding the last invoice number. Fetch and update that number on each insert transaction.
      • Counter approach: use facility such as Sequence (Oracle)/Identity(MSSQL).
    • 22. Counter Facility -- data
      • Settings:
        • default isolation level: READ COMMITTED; Empty tables
        • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.
      accounts ( number , branchnum, balance); create clustered index c on accounts(number); counter ( nextkey ); insert into counter values (1);
    • 23. Counter Facility -- transactions
      • No Concurrent Transactions:
        • System [100 000 inserts, N threads]
          • SQL Server 7 (uses Identity column)
          • insert into accounts values (94496,2789);
          • Oracle 8i
          • insert into accounts values (seq.nextval,94496,2789);
        • Ad-hoc [100 000 inserts, N threads] begin transaction NextKey:=select nextkey from counter; update counter set nextkey = NextKey+1; insert into accounts values(NextKey,?,?); commit transaction
    • 24. Avoid Bottlenecks: Counters
      • System generated counter (system) much better than a counter managed as an attribute value within a table (ad hoc). Counter is separate transaction.
      • The Oracle counter can become a bottleneck if every update is logged to disk, but caching many counter numbers is possible.
      • Counters may miss ids.
    • 25. Insertion Points -- transactions
      • No Concurrent Transactions:
        • Sequential [100 000 inserts, N threads]
          • Insertions into account table with clustered index on ssnum
          • Data is sorted on ssnum
          • Single insertion point
        • Non Sequential [100 000 inserts, N threads]
        • Insertions into account table with clustered index on ssnum
          • Data is not sorted (uniform distribution)
          • 100 000 insertion points
        • Hashing Key [100 000 inserts, N threads]
          • Insertions into account table with extra attribute att with clustered index on (ssnum, att)
          • Extra attribute att contains hash key (1021 possible values)
          • 1021 insertion points
    • 26. Insertion Points
      • Page locking: single insertion point is a source of contention (sequential key with clustered index, or heap)
      • Row locking: No contention between successive insertions.
      • DB2 v7.1 and Oracle 8i do not support page locking.
    • 27. Semantics-Altering Chopping
      • You call up an airline and want to reserve a seat. You talk to the agent, find a seat and then reserve it.
      • Should all of this be a single transaction?
    • 28. Semantics-Altering Chopping II
      • Probably not. You don’t want to hold locks through human interaction.
      • Transaction redesign: read is its own transaction. Get seat is another.
      • Consequences?
    • 29. Atomicity and Durability
      • Every transaction either commits or aborts. It cannot change its mind
      • Even in the face of failures:
        • Effects of committed transactions should be permanent;
        • Effects of aborted transactions should leave no trace.
      ACTIVE (running, waiting) ABORTED COMMITTED COMMIT ROLLBACK Ø BEGIN TRANS
    • 30. LOG DATA DATA DATA STABLE STORAGE UNSTABLE STORAGE WRITE log records before commit WRITE modified pages after commit RECOVERY DATABASE BUFFER LOG BUFFER lri lrj Pj Pi
    • 31. Log IO -- data
      • Settings:
        • lineitem ( L_ORDERKEY, L_PARTKEY , L_SUPPKEY , L_LINENUMBER , L_QUANTITY, L_EXTENDEDPRICE , L_DISCOUNT, L_TAX , L_RETURNFLAG, L_LINESTATUS , L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT , L_SHIPMODE , L_COMMENT );
        • READ COMMITTED isolation level
        • Empty table
        • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.
    • 32. Log IO -- transactions
      • No Concurrent Transactions:
        • Insertions [300 000 inserts, 10 threads], e.g.,
        • insert into lineitem values (1,7760,401,1,17,28351.92,0.04,0.02,'N','O','1996-03-13','1996-02-12','1996-03-22','DELIVER IN PERSON','TRUCK','blithely regular ideas caj');
    • 33. Group Commits
      • DB2 UDB v7.1 on Windows 2000
      • Log records of many transactions are written together
        • Increases throughput by reducing the number of writes
        • at cost of increased minimum response time.
    • 34. Put the Log on a Separate Disk
      • DB2 UDB v7.1 on Windows 2000
      • 5 % performance improvement if log is located on a different disk
      • Controller cache hides negative impact
        • mid-range server, with Adaptec RAID controller (80Mb RAM) and 2x18Gb disk drives.
    • 35. Tuning Database Writes
      • Dirty data is written to disk
        • When the number of dirty pages is greater than a given parameter (Oracle 8)
        • When the number of dirty pages crosses a given threshold (less than 3% of free pages in the database buffer for SQL Server 7)
        • When the log is full, a checkpoint is forced. This can have a significant impact on performance.
    • 36. Tune Checkpoint Intervals
      • Oracle 8i on Windows 2000
      • A checkpoint (partial flush of dirty pages to disk) occurs at regular intervals or when the log is full:
        • Impacts the performance of on-line processing
        • Reduces the size of log
        • Reduces time to recover from a crash
    • 37. Database Buffer Size
      • Buffer too small, then hit ratio too small
      • hit ratio = (logical acc. - physical acc.) / (logical acc.)
      • Buffer too large, paging
      • Recommended strategy: monitor hit ratio and increase buffer size until hit ratio flattens out. If there is still paging, then buy memory.
      LOG DATA DATA RAM Paging Disk DATABASE PROCESSES DATABASE BUFFER
    • 38. Buffer Size -- data
      • Settings:
        • employees ( ssnum , name, lat, long, hundreds1,
        • hundreds2);
        • clustered index c on employees(lat); (unused)
        • 10 distinct values of lat and long, 100 distinct values of hundreds1 and hundreds2
        • 20000000 rows (630 Mb);
        • Warm Buffer
        • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000 RPM), Windows 2000.
    • 39. Buffer Size -- queries
      • Queries:
        • Scan Query
        • select sum(long) from employees;
        • Multipoint query
        • select * from employees where lat = ?;
    • 40. Database Buffer Size
      • SQL Server 7 on Windows 2000
      • Scan query:
        • LRU (least recently used) does badly when table spills to disk as Stonebraker observed 20 years ago.
      • Multipoint query:
        • Throughput increases with buffer size until all data is accessed from RAM.
    • 41. Scan Performance -- data
      • Settings:
        • lineitem ( L_ORDERKEY, L_PARTKEY , L_SUPPKEY , L_LINENUMBER , L_QUANTITY, L_EXTENDEDPRICE , L_DISCOUNT, L_TAX , L_RETURNFLAG, L_LINESTATUS , L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT , L_SHIPMODE , L_COMMENT );
        • 600 000 rows
        • Lineitem tuples are ~ 160 bytes long
        • Cold Buffer
        • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.
    • 42. Scan Performance -- queries
      • Queries:
        • select avg(l_discount) from lineitem;
    • 43. Prefetching
      • DB2 UDB v7.1 on Windows 2000
      • Throughput increases up to a certain point when prefetching size increases.
    • 44. Usage Factor
      • DB2 UDB v7.1 on Windows 2000
      • Usage factor is the percentage of the page used by tuples and auxilliary data structures (the rest is reserved for future)
      • Scan throughput increases with usage factor.
    • 45. Tuning the Storage Subsystem
    • 46. Outline
      • Storage Subsystem Components
        • Moore’s law and consequences
        • Magnetic disk performances
      • From SCSI to SAN, NAS and Beyond
        • Storage virtualization
      • Tuning the Storage Subsystem
        • RAID levels
        • RAID controller cache
    • 47. Exponential Growth
      • Moore’s law
        • Every 18 months:
          • New processing = sum of all existing processing
          • New storage = sum of all existing storage
        • 2x / 18 months ~ 100x / 10 years
      • http://www.intel.com/research/silicon/moorespaper.pdf
    • 48. Consequences of “Moore’s law”
      • Over the last decade:
        • 10x better access time
        • 10x more bandwidth
        • 100x more capacity
        • 4000x lower media price
        • Scan takes 10x longer (3 min vs 45 min)
        • Data on disk is accessed 25x less often (on average)
    • 49. Data Flood
      • Disk Sales double every nine months
        • Because volume of stored data increases
          • Data Warehouses
          • Internet Logs
          • Web Archives
          • Sky Survey
        • Because media price drops much faster than areal density.
      Graph courtesy of Joe Hellerstein Source: J. Porter, Disk/Trend, Inc. http://www.disktrend.com/pdf/portrpkg.pdf
    • 50. Memory Hierarchy Processor cache RAM/flash Disks Tapes / Optical Disks Access Time Price $/ Mb 1 ns x10 x10 6 x10 10 100 10 0.2 0.2 (nearline)
    • 51. Magnetic Disks
      • 1956 : IBM (RAMAC) first disk drive
      • 5 Mb – 0.002 Mb/in2 35000$/year 9 Kb/sec
      • 1980 : SEAGATE
      • first 5.25’’ disk drive
      • 5 Mb – 1.96 Mb/in2 625 Kb/sec
      • 1999 : IBM MICRODRIVE
      • first 1’’ disk drive 340Mb 6.1 MB/sec
      Controller read/write head disk arm tracks platter spindle actuator disk interface
    • 52. Magnetic Disks
      • Access Time (2001)
        • Controller overhead (0.2 ms)
        • Seek Time (4 to 9 ms)
        • Rotational Delay (2 to 6 ms)
        • Read/Write Time (10 to 500 KB/ms)
      • Disk Interface
        • IDE (16 bits, Ultra DMA - 25 MHz)
        • SCSI: width (narrow 8 bits vs. wide 16 bits) - frequency (Ultra3 - 80 MHz).
      • http://www.pcguide.com/ref/hdd/
    • 53. RAID Levels
      • RAID 0: striping (no redundancy)
      • RAID 1: mirroring (2 disks)
      • RAID 5: parity checking
        • Read: stripes read from multiple disks (in parallel)
        • Write: 2 reads + 2 writes
      • RAID 10: striping and mirroring
      • Software vs. Hardware RAID:
        • Software RAID: run on the server’s CPU
        • Hardware RAID: run on the RAID controller’s CPU
    • 54. Why 4 read/writes when updating a single stripe using RAID 5?
      • Read old data stripe; read parity stripe (2 reads)
      • XOR old data stripe with replacing one.
      • Take result of XOR and XOR with parity stripe.
      • Write new data stripe and new parity stripe (2 writes).
    • 55. RAID Levels -- data
      • Settings:
        • accounts ( number , branchnum, balance);
        • create clustered index c on accounts(number);
        • 100000 rows
        • Cold Buffer
        • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.
    • 56. RAID Levels -- transactions
      • No Concurrent Transactions:
        • Read Intensive:
        • select avg(balance) from accounts;
        • Write Intensive, e.g. typical insert:
        • insert into accounts values (690466,6840,2272.76);
        • Writes are uniformly distributed.
    • 57. RAID Levels
      • SQL Server7 on Windows 2000 (SoftRAID means striping/parity at host)
      • Read-Intensive:
        • Using multiple disks (RAID0, RAID 10, RAID5) increases throughput significantly.
      • Write-Intensive:
        • Without cache, RAID 5 suffers. With cache, it is ok.
    • 58. RAID Levels
      • Log File
        • RAID 1 is appropriate
          • Fault tolerance with high write throughput. Writes are synchronous and sequential. No benefits in striping.
      • Temporary Files
        • RAID 0 is appropriate.
          • No fault tolerance. High throughput.
      • Data and Index Files
        • RAID 5 is best suited for read intensive apps or if the RAID controller cache is effective enough.
        • RAID 10 is best suited for write intensive apps.
    • 59. Controller Prefetching no, Write-back yes.
      • Read-ahead:
        • Prefetching at the disk controller level.
        • No information on access pattern.
        • Better to let database management system do it.
      • Write-back vs. write through:
        • Write back: transfer terminated as soon as data is written to cache.
          • Batteries to guarantee write back in case of power failure
        • Write through: transfer terminated as soon as data is written to disk.
    • 60. SCSI Controller Cache -- data
      • Settings:
        • employees ( ssnum , name, lat, long, hundreds1,
        • hundreds2);
        • create clustered index c on employees(hundreds2);
        • Employees table partitioned over two disks; Log on a separate disk; same controller (same channel).
        • 200 000 rows per table
        • Database buffer size limited to 400 Mb.
        • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.
    • 61. SCSI (not disk) Controller Cache -- transactions
      • No Concurrent Transactions:
        • update employees set lat = long, long = lat where hundreds2 = ?;
        • cache friendly: update of 20,000 rows (~90Mb)
        • cache unfriendly: update of 200,000 rows (~900Mb)
    • 62. SCSI Controller Cache
      • SQL Server 7 on Windows 2000.
      • Adaptec ServerRaid controller:
        • 80 Mb RAM
        • Write-back mode
      • Updates
      • Controller cache increases throughput whether operation is cache friendly or not.
        • Efficient replacement policy!
    • 63. Index Tuning
      • Index issues
        • Indexes may be better or worse than scans
        • Multi-table joins that run on for hours, because the wrong indexes are defined
        • Concurrency control bottlenecks
        • Indexes that are maintained and never used
    • 64. Index
      • An index is a data structure that supports efficient access to data
      Set of Records index Condition on attribute value Matching records (search key)
    • 65. Index
      • An index is a data structure that supports efficient access to data
      Set of Records index Condition on attribute value Matching records (search key)
    • 66. Search Keys
      • A (search) key is a sequence of attributes.
        • create index i1 on accounts(branchnum, balance);
      • Types of keys
        • Sequential: the value of the key is monotonic with the insertion order (e.g., counter or timestamp)
        • Non sequential: the value of the key is unrelated to the insertion order (e.g., social security number)
    • 67. Data Structures
      • Most index data structures can be viewed as trees.
      • In general, the root of this tree will always be in main memory, while the leaves will be located on disk.
        • The performance of a data structure depends on the number of nodes in the average path from the root to the leaf.
        • Data structure with high fan-out (maximum number of children of an internal node) are thus preferred.
    • 68. B+-Tree
      • A B+-Tree is a balanced tree whose leaves contain a sequence of key-pointer pairs.
      96 75 83 107 96 98 103 107 110 120 83 92 95 75 80 81 33 48 69
    • 69. B+-Tree Performance
      • Tree levels
        • Tree Fanout
          • Size of key
          • Page utilization
      • Tree maintenance
        • Online
          • On inserts
          • On deletes
        • Offline
      • Tree locking
      • Tree root in main memory
    • 70. B+-Tree Performance
      • Key length influences fanout
        • Choose small key when creating an index
        • Key compression
          • Prefix compression (Oracle 8, MySQL): only store that part of the key that is needed to distinguish it from its neighbors: Smi, Smo, Smy for Smith, Smoot, Smythe.
          • Front compression (Oracle 5): adjacent keys have their front portion factored out: Smi, (2)o, (2)y. There are problems with this approach:
            • Processor overhead for maintenance
            • Locking Smoot requires locking Smith too.
    • 71. Hash Index
      • A hash index stores key-value pairs based on a pseudo-randomizing function called a hash function .
      The length of these chains impacts performance Hashed key values 0 1 n R1 R5 R3 R6 R9 R14 R17 R21 R25 Hash function key 2341
    • 72. Clustered / Non clustered index
      • Clustered index (primary index)
        • A clustered index on attribute X co-locates records whose X values are near to one another.
      • Non-clustered index (secondary index)
        • A non clustered index does not constrain table organization.
        • There might be several non-clustered indexes per table.
      Records Records
    • 73. Dense / Sparse Index
      • Sparse index
        • Pointers are associated to pages
      • Dense index
        • Pointers are associated to records
        • Non clustered indexes are dense
      P1 Pi P2 record record record
    • 74. Index Implementations in some major DBMS
      • SQL Server
        • B+-Tree data structure
        • Clustered indexes are sparse
        • Indexes maintained as updates/insertions/deletes are performed
      • DB2
        • B+-Tree data structure, spatial extender for R-tree
        • Clustered indexes are dense
        • Explicit command for index reorganization
      • Oracle
        • B+-tree, hash, bitmap, spatial extender for R-Tree
        • clustered index
          • Index organized table (unique/clustered)
          • Clusters used when creating tables.
        • Index-organized tables organize data according to index.
    • 75. Types of Queries
      • Point Query SELECT balance FROM accounts WHERE number = 1023;
      • Multipoint Query SELECT balance FROM accounts WHERE branchnum = 100;
      • Range Query SELECT number FROM accounts WHERE balance > 10000 and balance <= 20000;
      • Prefix Match Query SELECT * FROM employees WHERE name = ‘J*’ ;
    • 76. More Types of Queries
      • Extremal Query SELECT * FROM accounts WHERE balance = max(select balance from accounts)
      • Ordering Query SELECT * FROM accounts ORDER BY balance;
      • Grouping Query SELECT branchnum, avg(balance) FROM accounts GROUP BY branchnum;
      • Join Query SELECT distinct branch.adresse FROM accounts, branch WHERE accounts.branchnum = branch.number and accounts.balance > 10000;
    • 77. Index Tuning -- data
      • Settings:
        • employees ( ssnum , name, lat, long, hundreds1,
        • hundreds2);
        • clustered index c on employees(hundreds1) with fillfactor = 100;
        • nonclustered index nc on employees (hundreds2);
        • index nc3 on employees (ssnum, name, hundreds2);
        • index nc4 on employees (lat, ssnum, name);
        • 1000000 rows ; Cold buffer
        • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.
    • 78. Index Tuning -- operations
      • Operations:
        • Update: update employees set name = ‘XXX’ where ssnum = ?;
        • Insert: insert into employees values (1003505,'polo94064',97.48,84.03,4700.55,3987.2);
        • Multipoint query: select * from employees where hundreds1= ?; select * from employees where hundreds2= ?;
        • Covered query: select ssnum, name, lat from employees;
        • Range Query: select * from employees where long between ? and ?;
        • Point Query: select * from employees where ssnum = ?
    • 79. Clustered Index
      • Multipoint query that returns 100 records out of 1000000.
      • Cold buffer
      • Clustered index is twice as fast as non-clustered index and orders of magnitude faster than a scan.
    • 80. Index “Face Lifts”
      • Index is created with fillfactor = 100.
      • Insertions cause page splits and extra I/O for each query
      • Maintenance consists in dropping and recreating the index
      • With maintenance performance is constant while performance degrades significantly if no maintenance is performed.
    • 81. Index Maintenance
      • In Oracle, clustered index are approximated by an index defined on a clustered table
      • No automatic physical reorganization
      • Index defined with pctfree = 0
      • Overflow pages cause performance degradation
    • 82. Covering Index - defined
      • Select name from employee where department = “marketing”
      • Good covering index would be on (department, name)
      • Index on (name, department) less useful.
      • Index on department alone moderately useful.
    • 83. Covering Index - impact
      • Covering index performs better than clustering index when first attributes of index are in the where clause and last attributes in the select.
      • When attributes are not in order then performance is much worse.
    • 84. Scan Can Sometimes Win
      • IBM DB2 v7.1 on Windows 2000
      • Range Query
      • If a query retrieves 10% of the records or more, scanning is often better than using a non-clustering non-covering index. Crossover > 10% when records are large or table is fragmented on disk – scan cost increases.
    • 85. Index on Small Tables
      • Small table: 100 records, i.e., a few pages.
      • Two concurrent processes perform updates (each process works for 10ms before it commits)
      • No index: the table is scanned for each update. No concurrent updates.
      • A clustered index allows to take advantage of row locking.
    • 86. Bitmap vs. Hash vs. B+-Tree
      • Settings:
        • employees ( ssnum , name, lat, long, hundreds1,
        • hundreds2);
        • create cluster c_hundreds (hundreds2 number(8)) PCTFREE 0;
        • create cluster c_ssnum(ssnum integer) PCTFREE 0 size 60;
        • create cluster c_hundreds(hundreds2 number(8)) PCTFREE 0 HASHKEYS 1000 size 600;
        • create cluster c_ssnum(ssnum integer) PCTFREE 0 HASHKEYS 1000000 SIZE 60;
        • create bitmap index b on employees (hundreds2);
        • create bitmap index b2 on employees (ssnum);
        • 1000000 rows ; Cold buffer
        • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.
    • 87. Multipoint query: B-Tree, Hash Tree, Bitmap
      • There is an overflow chain in a hash index because hundreds2 has few values
      • In a clustered B-Tree index records are on contiguous pages.
      • Bitmap is proportional to size of table and non-clustered for record access.
    • 88.
      • Hash indexes don’t help when evaluating range queries
      • Hash index outperforms B-tree on point queries
      B-Tree, Hash Tree, Bitmap
    • 89. Tuning Relational Systems
      • Schema Tuning
        • Denormalization, Vertical Partitioning
      • Query Tuning
        • Query rewriting
        • Materialized views
    • 90. Denormalizing -- data
      • Settings:
        • lineitem ( L_ORDERKEY, L_PARTKEY , L_SUPPKEY , L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE , L_DISCOUNT, L_TAX , L_RETURNFLAG, L_LINESTATUS , L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT , L_SHIPMODE , L_COMMENT );
      • region ( R_REGIONKEY , R_NAME, R_COMMENT );
      • nation ( N_NATIONKEY , N_NAME, N_REGIONKEY , N_COMMENT,);
      • supplier ( S_SUPPKEY , S_NAME, S_ADDRESS, S_NATIONKEY , S_PHONE, S_ACCTBAL, S_COMMENT);
        • 600000 rows in lineitem, 25 nations, 5 regions, 500 suppliers
    • 91. Denormalizing -- transactions
      • lineitemdenormalized ( L_ORDERKEY, L_PARTKEY , L_SUPPKEY , L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE , L_DISCOUNT, L_TAX , L_RETURNFLAG, L_LINESTATUS , L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT , L_SHIPMODE , L_COMMENT, L_REGIONNAME );
        • 600000 rows in lineitemdenormalized
        • Cold Buffer
        • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.
    • 92. Queries on Normalized vs. Denormalized Schemas
      • Queries:
      • select L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT, R_NAME
      • from LINEITEM, REGION, SUPPLIER, NATION
      • where
      • L_SUPPKEY = S_SUPPKEY
      • and S_NATIONKEY = N_NATIONKEY
      • and N_REGIONKEY = R_REGIONKEY
      • and R_NAME = 'EUROPE';
      • select L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT, L_REGIONNAME
      • from LINEITEMDENORMALIZED
      • where L_REGIONNAME = 'EUROPE';
    • 93. Denormalization
      • TPC-H schema
      • Query: find all lineitems whose supplier is in Europe.
      • With a normalized schema this query is a 4-way join.
      • If we denormalize lineitem and add the name of the region for each lineitem (foreign key denormalization) throughput improves 30%
    • 94. Vertical Partitioning
      • Consider account(id, balance, homeaddress)
      • When might it be a good idea to do a “vertical partitioning” into account1(id,balance) and account2(id,homeaddress)?
      • Join vs. size.
    • 95. Vertical Partitioning
      • Which design is better depends on the query pattern:
        • The application that sends a monthly statement is the principal user of the address of the owner of an account
        • The balance is updated or examined several times a day.
      • The second schema might be better because the relation (account_ID, balance) can be made smaller:
        • More account_ID, balance pairs fit in memory, thus increasing the hit ratio
        • A scan performs better because there are fewer pages.
    • 96. Tuning Normalization
      • A single normalized relation XYZ is better than two normalized relations XY and XZ if the single relation design allows queries to access X, Y and Z together without requiring a join.
      • The two-relation design is better iff:
        • Users access tend to partition between the two sets Y and Z most of the time
        • Attributes Y or Z have large values
    • 97. Vertical Partitioning and Scan
      • R (X,Y,Z)
        • X is an integer
        • YZ are large strings
      • Scan Query
      • Vertical partitioning exhibits poor performance when all attributes are accessed.
      • Vertical partitioning provides a sped up if only two of the attributes are accessed.
    • 98. Vertical Partitioning and Point Queries
      • R (X,Y,Z)
        • X is an integer
        • YZ are large strings
      • A mix of point queries access either XYZ or XY.
      • Vertical partitioning gives a performance advantage if the proportion of queries accessing only XY is greater than 20%.
      • The join is not expensive compared to a simple look-up.
    • 99. Queries
      • Settings:
        • employee ( ssnum , name, dept , salary, numfriends);
        • student ( ssnum , name, course, grade);
        • techdept ( dept , manager, location);
        • clustered index i1 on employee (ssnum);
        • nonclustered index i2 on employee (name);
        • nonclustered index i3 on employee (dept);
        • clustered index i4 on student (ssnum);
        • nonclustered index i5 on student (name);
        • clustered index i6 on techdept (dept);
        • 100000 rows in employee, 100000 students, 10 departments; Cold buffer
        • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.
    • 100. Queries – View on Join
      • View Techlocation: create view techlocation as select ssnum, techdept.dept, location from employee, techdept where employee.dept = techdept.dept;
      • Queries:
        • Original:
          • select dept from techlocation where ssnum = ?;
        • Rewritten:
          • select dept from employee where ssnum = ?;
    • 101. Query Rewriting - Views
      • All systems expand the selection on a view into a join
      • The difference between a plain selection and a join (on a primary key-foreign key) followed by a projection is greater on SQL Server than on Oracle and DB2 v7.1.
    • 102. Queries – Correlated Subqueries
      • Queries:
        • Original:
          • select ssnum from employee e1 where salary = (select max(salary) from employee e2 where e2.dept = e1.dept);
        • Rewritten:
          • select max(salary) as bigsalary, dept
          • into TEMP
          • from employee group by dept;
          • select ssnum
          • from employee, TEMP
          • where salary = bigsalary
          • and employee.dept = temp.dept;
    • 103. Query Rewriting – Correlated Subqueries
      • SQL Server 2000 does a good job at handling the correlated subqueries (a hash join is used as opposed to a nested loop between query blocks)
        • The techniques implemented in SQL Server 2000 are described in “Orthogonal Optimization of Subqueries and Aggregates” by C.Galindo-Legaria and M.Joshi, SIGMOD 2001.
      > 10000 > 1000
    • 104. Eliminate unneeded DISTINCTs
      • Query: Find employees who work in the information systems department. There should be no duplicates.
        • SELECT distinct ssnum FROM employee WHERE dept = ‘information systems’
      • DISTINCT is unnecessary, since ssnum is a key of employee so certainly is a key of a subset of employee.
    • 105. Eliminate unneeded DISTINCTs
      • Query: Find social security numbers of employees in the technical departments. There should be no duplicates.
          • SELECT DISTINCT ssnum FROM employee, tech WHERE employee.dept = tech.dept
      • Is DISTINCT needed?
    • 106. Distinct Unnecessary Here Too
      • Since dept is a key of the tech table, each employee record will join with at most one record in tech.
      • Because ssnum is a key for employee, distinct is unnecessary.
    • 107. Reaching
      • The relationship among DISTINCT, keys and joins can be generalized:
        • Call a table T privileged if the fields returned by the select contain a key of T
        • Let R be an unprivileged table. Suppose that R is joined on equality by its key field to some other table S, then we say R reaches S.
        • Now, define reaches to be transitive. So, if R1 reaches R2 and R2 reaches R3 then say that R1 reaches R3.
    • 108. Reaches: Main Theorem
      • There will be no duplicates among the records returned by a selection, even in the absence of DISTINCT if one of the two following conditions hold:
        • Every table mentioned in the FROM clause is privileged
        • Every unprivileged table reaches at least one privileged table.
    • 109. Reaches: Proof Sketch
      • If every relation is privileged then there are no duplicates
        • The keys of those relations are in the from clause
      • Suppose some relation T is not privileged but reaches at least one privileged one, say R. Then the qualifications linking T with R ensure that each distinct combination of privileged records is joined with at most one record of T.
    • 110. Reaches: Example 1
      • The same employee record may match several tech records (because manager is not a key of tech), so the ssnum of that employee record may appear several times.
      • Tech does not reach the privileged relation employee.
        • SELECT ssnum FROM employee, tech WHERE employee.manager = tech.manager
    • 111. Reaches: Example 2
      • Each repetition of a given ssnum vlaue would be accompanied by a new tech.dept since tech.dept is a key of tech
      • Both relations are privileged.
      SELECT ssnum, tech.dept FROM employee, tech WHERE employee.manager = tech.manager
    • 112. Reaches: Example 3
      • Student is priviledged
      • Employee does not reach student (name is not a key of employee)
      • DISTINCT is needed to avoid duplicates.
      SELECT student.ssnum FROM student, employee, tech WHERE student.name = employee.name AND employee.dept = tech.dept;
    • 113. Aggregate Maintenance -- data
      • Settings:
        • orders ( ordernum , itemnum , quantity, storeid, vendorid );
        • create clustered index i_order on orders(itemnum);
        • store ( storeid , name );
        • item ( itemnum , price);
        • create clustered index i_item on item(itemnum);
        • vendorOutstanding ( vendorid , amount);
        • storeOutstanding ( storeid , amount);
        • 1000000 orders, 10000 stores, 400000 items; Cold buffer
        • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.
    • 114. Aggregate Maintenance -- triggers
      • Triggers for Aggregate Maintenance
        • create trigger updateVendorOutstanding on orders for insert as
        • update vendorOutstanding
        • set amount =
        • (select vendorOutstanding.amount+sum(inserted.quantity*item.price)
        • from inserted,item
        • where inserted.itemnum = item.itemnum
        • )
        • where vendorid = (select vendorid from inserted) ;
        • create trigger updateStoreOutstanding on orders for insert as
        • update storeOutstanding
        • set amount =
        • (select storeOutstanding.amount+sum(inserted.quantity*item.price)
        • from inserted,item
        • where inserted.itemnum = item.itemnum
        • )
        • where storeid = (select storeid from inserted)
    • 115. Aggregate Maintenance -- transactions
      • Concurrent Transactions:
        • Insertions
      • insert into orders values (1000350,7825,562,'xxxxxx6944','vendor4');
        • Queries (first without, then with redundant tables)
      • select orders.vendor, sum(orders.quantity*item.price)
      • from orders,item
      • where orders.itemnum = item.itemnum
      • group by orders.vendorid;
      • vs. select * from vendorOutstanding;
      • select store.storeid, sum(orders.quantity*item.price)
      • from orders,item, store
      • where orders.itemnum = item.itemnum
      • and orders.storename = store.name
      • group by store.storeid;
      • vs. select * from storeOutstanding;
    • 116. Aggregate Maintenance
      • SQLServer 2000 on Windows 2000
      • Using triggers for view maintenance
      • If queries frequent or important, then aggregate maintenance is good.
    • 117. Superlinearity -- data
      • Settings:
        • sales ( id , itemid , customerid , storeid , amount, quantity);
        • item ( itemid );
        • customer ( customerid );
        • store ( storeid );
        • A sale is successful if all foreign keys are present.
        • successfulsales ( id , itemid, customerid, storeid , amount, quantity);
        • unsuccessfulsales ( id , itemid, customerid, storeid, amount, quantity);
        • tempsales ( id , itemid, customerid, storeid , amount,quantity);
    • 118. Superlinearity -- indexes
      • Settings (non-clustering, dense indexes):
        • index s1 on item(itemid);
        • index s2 on customer(customerid);
        • index s3 on store(storeid);
        • index succ on successfulsales(id);
        • 1000000 sales, 400000 customers, 40000 items, 1000 stores
        • Cold buffer
        • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.
    • 119. Superlinearity -- queries
      • Queries:
        • Insert/create indexdelete
          • insert into successfulsales
          • select sales.id, sales.itemid, sales.customerid, sales.storeid, sales.amount, sales.quantity
          • from sales, item, customer, store
          • where sales.itemid = item.itemid
          • and sales.customerid = customer.customerid
          • and sales.storeid = store.storeid;
          • insert into unsuccessfulsales
          • select * from sales;
          • go
          • delete from unsuccessfulsales
          • where id in (select id from successfulsales)
    • 120. Superlinearity -- batch queries
      • Queries:
        • Small batches
          • DECLARE @Nlow INT;
          • DECLARE @Nhigh INT;
          • DECLARE @INCR INT;
          • set @INCR = 100000
          • set @NLow = 0
          • set @Nhigh = @INCR
          • WHILE (@NLow <= 500000)
          • BEGIN
          • insert into tempsales
          • select * from sales
          • where id between @NLow and @Nhigh
          • set @Nlow = @Nlow + @INCR
          • set @Nhigh = @Nhigh + @INCR
          • delete from tempsales
            • where id in (select id from successfulsales);
          • insert into unsuccessfulsales
          • select * from tempsales;
          • delete from tempsales;
          • END
    • 121. Superlinearity -- outer join
      • Queries:
        • outerjoin
          • insert into successfulsales
          • select sales.id, item.itemid, customer.customerid, store.storeid, sales.amount, sales.quantity
          • from
          • ((sales left outer join item on sales.itemid = item.itemid)
          • left outer join customer on sales.customerid = customer.customerid)
          • left outer join store on sales.storeid = store.storeid;
          • insert into unsuccessfulsales
          • select *
          • from successfulsales
          • where itemid is null
          • or customerid is null
          • or storeid is null;
          • go
          • delete from successfulsales
          • where itemid is null
          • or customerid is null
          • or storeid is null
    • 122. Circumventing Superlinearity
      • SQL Server 2000
      • Outer join achieves the best response time.
      • Small batches do not help because overhead of crossing the application interface is higher than the benefit of joining with smaller tables.
    • 123. Tuning the Application Interface
      • 4GL
        • Power++, Visual basic
      • Programming language + Call Level Interface
        • ODBC: Open DataBase Connectivity
        • JDBC: Java based API
        • OCI (C++/Oracle), CLI (C++/ DB2), Perl/DBI
      • In the following experiments, the client program is located on the database server site. Overhead is due to crossing the application interface.
    • 124. Looping can hurt -- data
      • Settings:
        • lineitem ( L_ORDERKEY, L_PARTKEY , L_SUPPKEY , L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE , L_DISCOUNT, L_TAX , L_RETURNFLAG, L_LINESTATUS , L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT , L_SHIPMODE , L_COMMENT );
        • 600 000 rows; warm buffer.
        • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.
    • 125. Looping can hurt -- queries
      • Queries:
        • No loop:
          • sqlStmt = “select * from lineitem where l_partkey <= 200;”
          • odbc->prepareStmt(sqlStmt);
          • odbc->execPrepared(sqlStmt);
        • Loop:
          • sqlStmt = “select * from lineitem where l_partkey = ?;”
          • odbc->prepareStmt(sqlStmt);
          • for (int i=1; i<100; i++)
          • {
          • odbc->bindParameter(1, SQL_INTEGER, i);
          • odbc->execPrepared(sqlStmt);
          • }
    • 126. Looping can Hurt
      • SQL Server 2000 on Windows 2000
      • Crossing the application interface has a significant impact on performance.
      • Why would a programmer use a loop instead of relying on set-oriented operations: object-orientation?
    • 127. Cursors are Death -- data
      • Settings:
        • employees ( ssnum , name, lat, long, hundreds1,
        • hundreds2);
        • 100000 rows ; Cold buffer
        • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.
    • 128. Cursors are Death -- queries
      • Queries:
        • No cursor
        • select * from employees;
        • Cursor
        • DECLARE d_cursor CURSOR FOR select * from employees;
        • OPEN d_cursor while (@@FETCH_STATUS = 0)
        • BEGIN
        • FETCH NEXT from d_cursor END
        • CLOSE d_cursor
        • go
    • 129. Cursors are Death
      • SQL Server 2000 on Windows 2000
      • Response time is a few seconds with a SQL query and more than an hour iterating over a cursor.
    • 130. Retrieve Needed Columns Only - data
      • Settings:
        • lineitem ( L_ORDERKEY, L_PARTKEY , L_SUPPKEY , L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE , L_DISCOUNT, L_TAX , L_RETURNFLAG, L_LINESTATUS , L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT , L_SHIPMODE , L_COMMENT );
        • create index i_nc_lineitem on lineitem (l_orderkey, l_partkey, l_suppkey, l_shipdate, l_commitdate);
        • 600 000 rows; warm buffer.
        • Lineitem records are ~ 10 bytes long
        • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.
    • 131. Retrieve Needed Columns Only - queries
      • Queries:
        • All
          • Select * from lineitem;
        • Covered subset
          • Select l_orderkey, l_partkey, l_suppkey, l_shipdate, l_commitdate from lineitem;
    • 132. Retrieve Needed Columns Only
      • Avoid transferring unnecessary data
      • May enable use of a covering index.
      • In the experiment the subset contains ¼ of the attributes.
        • Reducing the amount of data that crosses the application interface yields significant performance improvement.
      Experiment performed on Oracle8iEE on Windows 2000.
    • 133. Bulk Loading Data
      • Settings:
        • lineitem ( L_ORDERKEY, L_PARTKEY , L_SUPPKEY , L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE , L_DISCOUNT, L_TAX , L_RETURNFLAG, L_LINESTATUS , L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT , L_SHIPMODE , L_COMMENT );
        • Initially the table is empty; 600 000 rows to be inserted (138Mb)
        • Table sits one disk. No constraint, index is defined.
        • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.
    • 134. Bulk Loading Queries
      • Oracle 8i
      • sqlldr directpath=true control=load_lineitem.ctl data=E:Datalineitem.tbl
      • load data
      • infile &quot;lineitem.tbl&quot;
      • into table LINEITEM append
      • fields terminated by '|'
      • (
      • L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE DATE &quot;YYYY-MM-DD&quot;, L_COMMITDATE DATE &quot;YYYY-MM-DD&quot;, L_RECEIPTDATE DATE &quot;YYYY-MM-DD&quot;, L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT
      • )
    • 135. Direct Path
      • Direct path loading bypasses the query engine and the storage manager. It is orders of magnitude faster than for conventional bulk load (commit every 100 records) and inserts (commit for each record).
      Experiment performed on Oracle8iEE on Windows 2000.
    • 136. Batch Size
      • Throughput increases steadily when the batch size increases to 100000 records.Throughput remains constant afterwards.
      • Trade-off between performance and amount of data that has to be reloaded in case of problem.
      Experiment performed on SQL Server 2000 on Windows 2000.
    • 137. Tuning E-Commerce Applications
      • Database-backed web-sites:
        • Online shops
        • Shop comparison portals
        • MS TerraServer
    • 138. E-commerce Application Architecture Clients Web servers Application servers Database server Web cache Web cache Web cache DB cache DB cache
    • 139. E-commerce Application Workload
      • Touristic searching (frequent, cached)
        • Access the top few pages. Pages may be personalized. Data may be out-of-date.
      • Category searching (frequent, partly cached and need for timeliness guarantees)
        • Down some hierarchy, e.g., men’s clothing.
      • Keyword searching (frequent, uncached, need for timeliness guarantees)
      • Shopping cart interactions (rare, but transactional)
      • Electronic purchasing (rare, but transactional)
    • 140. Design Issues
      • Need to keep historic information
        • Electronic payment acknowledgements get lost.
      • Preparation for variable load
        • Regular patterns of web site accesses during the day, and within a week.
      • Possibility of disconnections
        • State information transmitted to the client (cookies)
      • Special consideration for low bandwidth
      • Schema evolution
        • Representing e-commerce data as attribute-value pairs (IBM Websphere)
    • 141. Caching
      • Web cache:
        • Static web pages
        • Caching fragments of dynamically created web pages
      • Database cache (Oracle9iAS, TimesTen’s FrontTier)
        • Materialized views to represent cached data.
        • Queries are executed either using the database cache or the database server. Updates are propagated to keep the cache(s) consistent.
        • Note to vendors: It would be good to have queries distributed between cache and server.
    • 142. Ecommerce -- setting
      • Settings:
        • shoppingcart ( shopperid, itemid , price, qty);
        • 500000 rows; warm buffer
        • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.
    • 143. Ecommerce -- transactions
      • Concurrent Transactions:
        • Mix
          • insert into shoppingcart values (107999,914,870,214);
          • update shoppingcart set Qty = 10 where shopperid = 95047 and itemid = 88636;
          • delete from shoppingcart where shopperid = 86123 and itemid = 8321;
          • select shopperid, itemid, qty, price from shoppingcart where shopperid = ?;
        • Queries Only
          • select shopperid, itemid, qty, price from shoppingcart where shopperid = ?;
    • 144. Connection Pooling (no refusals)
      • Each thread establishes a connection and performs 5 insert statements.
      • If a connection cannot be established the thread waits 15 secs before trying again.
      • The number of connection is limited to 60 on the database server.
      • Using connection pooling, the requests are queued and serviced when possible. There are no refused connections.
      Experiment performed on Oracle8i on Windows 2000
    • 145. Indexing
      • Using a clustered index on shopperid in the shopping cart provides:
        • Query speed-up
        • Update/Deletion speed-up
      Experiment performed on SQL Server 2000 on Windows 2000
    • 146. Capacity Planning
      • Arrival Rate
        • A1 is given as an assumption
        • A2 = (0.4 A1) + (0.5 A2)
        • A3 = 0.1 A2
      • Service Time (S)
        • S1, S2, S3 are measured
      • Utilization
        • U = A x S
      • Response Time
        • R = U/(A(1-U)) = S/(1-U)
        • (assuming Poisson arrivals)
      Entry (S1) 0.4 Search (S2) Checkout (S3) 0.5 0.1 Getting the demand assumptions right is what makes capacity planning hard
    • 147. How to Handle Multiple Servers
      • Suppose one has n servers for some task that requires S time for a single server to perform.
      • The perfect parallelism model is that it is as if one has a single server that is n times as fast.
      • However, this overstates the advantage of parallelism, because even if there were no waiting, single tasks require S time.
    • 148. Rough Estimate for Multiple Servers
      • There are two components to response time: waiting time + service time.
      • In the parallel setting, the service time is still S.
      • The waiting time however can be well estimated by a server that is n times as fast.
    • 149. Approximating waiting time for n parallel servers.
      • Recall: R = U/(A(1-U)) = S/(1-U)
      • On an n-times faster server, service time is divided by n, so the single processor utilization U is also divided by n. So we would get: Rideal = (S/n)/(1 – (U/n)).
      • That Rideal = serviceideal + waitideal.
      • So waitideal = Rideal – S/n
      • Our assumption: waitideal ~ wait for n processors.
    • 150. Approximating response time for n parallel servers
      • Waiting time for n parallel processors ~ (S/n)/(1 – (U/n)) – S/n = (S/n) ( 1/(1-(U/n)) – 1) = (S/(n(1 – U/n)))(U/n) = (S/(n – U))(U/n)
      • So, response time for n parallel processors is above waiting time + S.
    • 151. Example
      • A = 8 per second.
      • S = 0.1 second.
      • U = 0.8.
      • Single server response time = S/(1-U) = 0.1/0.2 = 0.5 seconds.
      • If we have 2 servers, then we estimate waiting time to be (0.1/(2-0.8))(0.4) = 0.04/1.2 = 0.033. So the response time is 0.133.
      • For a 2-times faster server, S = 0.05, U = 0.4, so response time is 0.05/0.6 = 0.0833
    • 152. Example -- continued
      • A = 8 per second.
      • S = 0.1 second.
      • U = 0.8.
      • If we have 4 servers, then we estimate waiting time to be (S/(n – U))(U/n) = 0.1/(3.2) * (0.8/4) = 0.02/3.2 = 0.00625 So response time is 0.10625.
    • 153. Datawarehouse Tuning
      • Aggregate (strategic) targeting:
        • Aggregates flow up from a wide selection of data, and then
        • Targeted decisions flow down
      • Examples:
        • Riding the wave of clothing fads
        • Tracking delays for frequent-flyer customers
    • 154. Data Warehouse Workload
      • Broad
        • Aggregate queries over ranges of values, e.g., find the total sales by region and quarter.
      • Deep
        • Queries that require precise individualized information, e.g., which frequent flyers have been delayed several times in the last month?
      • Dynamic (vs. Static)
        • Queries that require up-to-date information, e.g. which nodes have the highest traffic now?
    • 155. Tuning Knobs
      • Indexes
      • Materialized views
      • Approximation
    • 156. Bitmaps -- data
      • Settings:
      • lineitem ( L_ORDERKEY, L_PARTKEY , L_SUPPKEY , L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE , L_DISCOUNT, L_TAX , L_RETURNFLAG, L_LINESTATUS , L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT , L_SHIPMODE , L_COMMENT );
        • create bitmap index b_lin_2 on lineitem(l_returnflag);
        • create bitmap index b_lin_3 on lineitem(l_linestatus);
        • create bitmap index b_lin_4 on lineitem(l_linenumber);
        • 100000 rows ; cold buffer
        • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.
    • 157. Bitmaps -- queries
      • Queries:
        • 1 attribute
          • select count(*) from lineitem where l_returnflag = 'N';
        • 2 attributes
          • select count(*) from lineitem where l_returnflag = 'N' and l_linenumber > 3;
        • 3 attributes
        • select count(*) from lineitem where l_returnflag =
        • 'N' and l_linenumber > 3 and l_linestatus = 'F';
    • 158. Bitmaps
      • Order of magnitude improvement compared to scan, because summing up the 1s is very fast.
      • Bitmaps are best suited for multiple conditions on several attributes, each having a low selectivity.
      A N R l_returnflag O F l_linestatus
    • 159. Vertical Partitioning Revisited
      • In the same settings that bit vectors are useful – lots of attributes, unpredictable combinations queried – so is vertical partitioning more attractive.
      • If a table has 200 attributes, no reason to retrieve 200 field rows if only three attributes are needed.
      • Sybase IQ, KDB, Vertica all use column-oriented tables.
    • 160. Multidimensional Indexes -- data
      • Settings:
      • create table spatial_facts ( a1 int, a2 int, a3 int, a4 int, a5 int, a6 int, a7 int, a8 int, a9 int, a10 int, geom_a3_a7 mdsys.sdo_geometry );
      • create index r_spatialfacts on spatial_facts(geom_a3_a7) indextype is mdsys.spatial_index;
        • create bitmap index b2_spatialfacts on spatial_facts(a3,a7);
        • 500000 rows ; cold buffer
        • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.
    • 161. Multidimensional Indexes -- queries
      • Queries:
        • Point Queries
          • select count(*) from fact where a3 = 694014 and a7 = 928878;
          • select count(*) from spatial_facts where SDO_RELATE(geom_a3_a7, MDSYS.SDO_GEOMETRY(2001, NULL, MDSYS.SDO_POINT_TYPE(694014,928878, NULL), NULL, NULL), 'mask=equal querytype=WINDOW') = 'TRUE';
        • Range Queries
          • select count(*) from spatial_facts where SDO_RELATE(geom_a3_a7, mdsys.sdo_geometry(2003,NULL,NULL, mdsys.sdo_elem_info_array(1,1003,3),mdsys.sdo_ordinate_array(10,800000,1000000,1000000)), 'mask=inside querytype=WINDOW') = 'TRUE';
          • select count(*) from spatial_facts where a3 > 10 and a3 < 1000000 and a7 > 800000 and a7 < 1000000;
    • 162. Multidimensional Indexes
      • Oracle 8i on Windows 2000
      • Spatial Extension:
        • 2-dimensional data
        • Spatial functions used in the query
      • R-tree does not perform well because of the overhead of spatial extension.
    • 163. Multidimensional Indexes
      • R-Tree
      • SELECT STATEMENT
      • SORT AGGREGATE
      • TABLE ACCESS BY INDEX ROWID SPATIAL_FACTS
      • DOMAIN INDEX R_SPATIALFACTS
      • Bitmaps
      • SELECT STATEMENT
      • SORT AGGREGATE
      • BITMAP CONVERSION COUNT
      • BITMAP AND
      • BITMAP INDEX SINGLE VALUE B_FACT7
      • BITMAP INDEX SINGLE VALUE B_FACT3
    • 164. Materialized Views -- data
      • Settings:
        • orders ( ordernum , itemnum , quantity, storeid, vendor );
        • create clustered index i_order on orders(itemnum);
        • store ( storeid , name );
        • item ( itemnum , price);
        • create clustered index i_item on item(itemnum);
        • 1000000 orders, 10000 stores, 400000 items; Cold buffer
        • Oracle 9i
        • Pentium III (1 GHz, 256 Kb), 1Gb RAM, Adapter 39160 with 2 channels, 3x18Gb drives (10000RPM), Linux Debian 2.4.
    • 165. Materialized Views -- data
      • Settings:
        • create materialized view vendorOutstanding
        • build immediate
        • refresh complete
        • enable query rewrite
        • as
        • select orders.vendor, sum(orders.quantity*item.price)
        • from orders,item
        • where orders.itemnum = item.itemnum
        • group by orders.vendor;
    • 166. Materialized Views -- transactions
      • Concurrent Transactions:
        • Insertions
      • insert into orders values (1000350,7825,562,'xxxxxx6944','vendor4');
        • Queries
      • select orders.vendor, sum(orders.quantity*item.price)
      • from orders,item
      • where orders.itemnum = item.itemnum
      • group by orders.vendor;
      • select * from vendorOutstanding;
    • 167. Materialized Views
      • Graph:
        • Oracle9i on Linux
        • Total sale by vendor is materialized
      • Trade-off between query speed-up and view maintenance:
        • The impact of incremental maintenance on performance is significant.
        • Rebuild maintenance achieves a good throughput.
        • A static data warehouse offers a good trade-off.
    • 168. Materialized View Maintenance
      • Problem when large number of views to maintain.
      • The order in which views are maintained is important:
        • A view can be computed from an existing view instead of being recomputed from the base relations (total per region can be computed from total per nation).
      • Let the views and base tables be nodes v_i
      • Let there be an edge from v_1 to v_2 if it possible to compute the view v_2 from v_1. Associate the cost of computing v_2 from v_1 to this edge.
      • Compute all pairs shortest path where the start nodes are the set of base tables.
      • The result is an acyclic graph A. Take a topological sort of A and let that be the order of view construction.
    • 169. Materialized View Example
      • Detail(storeid, item, qty, price, date)
      • Materialized view1(storeid, category, qty, month)
      • Materializedview2(city, category, qty, month)
      • Materializedview3(storeid, category, qty, year)
    • 170. Approximations -- data
      • Settings:
        • TPC-H schema
        • Approximations
        • insert into approxlineitem
        • select top 6000 *
          • from lineitem
          • where l_linenumber = 4;
          • insert into approxorders
          • select O_ORDERKEY, O_CUSTKEY, O_ORDERSTATUS, O_TOTALPRICE, O_ORDERDATE, O_ORDERPRIORITY, O_CLERK, O_SHIPPRIORITY, O_COMMENT
          • from orders, approxlineitem
          • where o_orderkey = l_orderkey;
    • 171. Approximations -- queries
        • insert into approxsupplier
        • select distinct S_SUPPKEY,
        • S_NAME ,
        • S_ADDRESS,
        • S_NATIONKEY,
        • S_PHONE,
        • S_ACCTBAL,
        • S_COMMENT
        • from approxlineitem, supplier
        • where s_suppkey = l_suppkey;
        • insert into approxpart
        • select distinct P_PARTKEY,
        • P_NAME ,
        • P_MFGR ,
        • P_BRAND ,
        • P_TYPE ,
        • P_SIZE ,
        • P_CONTAINER ,
        • P_RETAILPRICE ,
        • P_COMMENT
        • from approxlineitem, part
        • where p_partkey = l_partkey;
        • insert into approxpartsupp
        • select distinct PS_PARTKEY,
        • PS_SUPPKEY,
        • PS_AVAILQTY,
        • PS_SUPPLYCOST,
        • PS_COMMENT
        • from partsupp, approxpart, approxsupplier
        • where ps_partkey = p_partkey and ps_suppkey = s_suppkey;
        • insert into approxcustomer
        • select distinct C_CUSTKEY,
        • C_NAME ,
        • C_ADDRESS,
        • C_NATIONKEY,
        • C_PHONE ,
        • C_ACCTBAL,
        • C_MKTSEGMENT,
        • C_COMMENT
        • from customer, approxorders
        • where o_custkey = c_custkey;
        • insert into approxregion select * from region;
        • insert into approxnation select * from nation;
    • 172. Approximations -- more queries
      • Queries:
        • Single table query on lineitem
        • select l_returnflag, l_linestatus, sum(l_quantity) as sum_qty, sum(l_extendedprice) as sum_base_price,
        • sum(l_extendedprice * (1 - l_discount)) as sum_disc_price,
        • sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge,
        • avg(l_quantity) as avg_qty, avg(l_extendedprice) as avg_price, avg(l_discount) as avg_disc, count(*) as count_order
        • from lineitem
        • where datediff(day, l_shipdate, '1998-12-01') <= '120'
        • group by l_returnflag, l_linestatus
        • order by l_returnflag, l_linestatus;
    • 173. Approximations -- still more
      • Queries:
        • 6-way join
        • select n_name, avg(l_extendedprice * (1 - l_discount)) as revenue
        • from customer, orders, lineitem, supplier, nation, region
        • where c_custkey = o_custkey
        • and l_orderkey = o_orderkey
        • and l_suppkey = s_suppkey
        • and c_nationkey = s_nationkey
        • and s_nationkey = n_nationkey
        • and n_regionkey = r_regionkey
        • and r_name = 'AFRICA'
        • and o_orderdate >= '1993-01-01'
        • and datediff(year, o_orderdate,'1993-01-01') < 1
        • group by n_name
        • order by revenue desc;
    • 174. Approximation accuracy
      • Good approximation for query Q1 on lineitem
      • The aggregated values obtained on a query with a 6-way join are significantly different from the actual values -- for some applications may still be good enough.
    • 175. Approximation Speedup
      • Aqua approximation on the TPC-H schema
        • 1% and 10% lineitem sample propagated.
      • The query speed-up obtained with approximated relations is significant.
    • 176. Approximating Count Distinct
      • Suppose you have one or more columns with duplicates. You want to know how many distinct values there are.
      • Imagine that you could hash into (0,1). (Simulate this by a very large integer range).
      • Hashing will put duplicates in the same location and, if done right, will distribute values uniformly.
      • How can we use this?
    • 177. Approximating Count Distinct
      • Suppose you took the minimum 1000 hash values.
      • Suppose that highest value of the 1000 is f.
      • What is a good approximation of the number of distinct values?
    • 178. Tuning Distributed Applications
      • Queries across multiple databases
        • Federated Datawarehouse
        • IBM’s DataJoiner now integrated at DB2 v7.2
          • The source should perform as much work as possible and return as few data as possible for processing at the federated server
      • Processing data across multiple databases
    • 179. A puzzle
      • Two databases X and Y
        • X records inventory data to be used for restocking
        • Y contains delivery data about shipments
      • Improve the speed of shipping by sharing data
        • Certain data of X should be postprocessed on Y shortly after it enters X
        • You want to avoid losing data from X and you want to avoid double-processing data on Y, even in the face of failures.
    • 180. Two-Phase Commit X Y commit commit Source (coordinator & participant) Destination (participant)
      • Commits are coordinated between the source and the destination
      • If one participant fails then blocking can occur
      • Not all db systems support prepare-to-commit interface
    • 181. Replication Server
      • Destination within a few seconds of being up-to-date
      • Decision support queries can be asked on destination db
      • Administrator is needed when network connection breaks!
      X Y Source Destination
    • 182. Staging Tables
      • No specific mechanism is necessary at source or destination
      • Coordination of transactions on X and Y
      X Y Source Destination Staging table
    • 183. Issues in Solution
      • A single thread can invoke a transaction on X, Y or both, but the thread may fail in the middle. A new thread will regain state by looking at the database.
      • We want to delete data from the staging tables when we are finished.
      • The tuples in the staging tables will represent an operation as well as data.
    • 184. States
      • A tuple (operation plus data) will start in state unprocessed, then state processed, and then deleted.
      • The same transaction that processes the tuple on the destination site also changes its state. This is important so each tuple’s operation is done exactly once.
    • 185. Staging Tables Database X Database Y Table S Table I Yunprocessed Yunprocessed Yunprocessed M1 Read from source table M2 Write to destination Table S Yunprocessed Yunprocessed Yunprocessed Database Y Table I unprocessed unprocessed unprocessed Database X STEP 1 STEP 2
    • 186. Staging Tables Database X Database Y Table S Table I Yunprocessed Yunprocessed Yunprocessed Table S Yunprocessed Yunprocessed Yunprocessed Database Y Table I processed unprocessed unprocessed Database X STEP 3 STEP 4 M3 Update on destination processed unprocessed unprocessed M4 Delete source; then dest Y Xact: Update source site Xact:update destination site
    • 187. What to Look For
      • Transactions are atomic but threads, remember, are not. So a replacement thread will look to the database for information.
      • In which order should the final step do its transactions? Should it delete the unprocessed tuple from X as the first transaction or as the second?
    • 188. Troubleshooting Techniques (*)
      • Extraction and Analysis of Performance Indicators
        • Consumer-producer chain framework
        • Tools
          • Query plan monitors
          • Performance monitors
          • Event monitors
      • (*) From Alberto Lerner’s chapter
    • 189. A Consumer-Producer Chain of a DBMS’s Resources PARSER OPTIMIZER EXECUTION SUBSYSTEM DISK SYBSYSTEM CACHE MANAGER LOGGING SUBSYSTEM LOCKING SUBSYSTEM NETWORK DISK/ CONTROLLER CPU MEMORY sql commands High Level Consumers Intermediate Resources/ Consumers Primary Resources probing spots for indicators
    • 190. Recurrent Patterns of Problems
      • An overloading high-level consumer
      • A poorly parameterized subsystem
      • An overloaded primary resource
      Effects are not always felt first where the cause is!
    • 191. A Systematic Approach to Monitoring
      • Question 1: Are critical queries being served in the most efficient manner?
      • Question 2: Are subsystems making optimal use of resources?
      • Question 3: Are there enough primary resources available?
      Extract indicators to answer the following questions
    • 192. Investigating High Level Consumers
      • Answer question 1: “Are critical queries being served in the most efficient manner?”
        • Identify the critical queries
        • Analyze their access plans
        • Profile their execution
    • 193. Identifying Critical Queries
      • Critical queries are usually those that:
      • Take a long time
      • Are frequently executed
      • Often, a user complaint will tip us off.
    • 194. Event Monitors to Identify Critical Queries
      • If no user complains...
      • Capture usage measurements at specific events (e.g., end of each query) and then sort by usage
      • Less overhead than other type of tools because indicators are usually by-product of events monitored
      • Typical measures include CPU used, IO used, locks obtained etc.
    • 195. An example Event Monitor
      • CPU indicators sorted by Oracle’s Trace Data Viewer
      • Similar tools: DB2’s Event Monitor and MSSQL’s Server Profiler
    • 196. An example Plan Explainer
      • Access plan according to MSSQL’s Query Analyzer
      • Similar tools: DB2’s Visual Explain and Oracle’s SQL Analyze Tool
    • 197. Finding Strangeness in Access Plans
      • What to pay attention to in a plan
      • Access paths for each table
      • Sorts or intermediary results (join, group by, distinct, order by)
      • Order of operations
      • Algorithms used in the operators
    • 198. To Index or not to index? select c_name, n_name from CUSTOMER join NATION on c_nationkey=n_nationkey where c_acctbal > 0 Which plan performs best? (nation_pk is an non-clustered index over n_nationkey, and similarly for acctbal_ix over c_acctbal)
    • 199. Non-clustering indexes can be trouble For a low selectivity predicate, each access to the index generates a random access to the table – possibly duplicate! It ends up that the number of pages read from the table is greater than its size, i.e., a table scan is way better Table Scan Index Scan 5 sec 143,075 pages 6,777 pages 136,319 pages 7 pages 76 sec 272,618 pages 131,425 pages 273,173 pages 552 pages CPU time data logical reads data physical reads index logical reads index physical reads
    • 200. An example Performance Monitor (query level)
      • Buffer and CPU consumption for a query according to DB2’s Benchmark tool
      • Similar tools: MSSQL’s SET STATISTICS switch and Oracle’s SQL Analyze Tool
      Statement number: 1 select C_NAME, N_NAME from DBA.CUSTOMER join DBA.NATION on C_NATIONKEY = N_NATIONKEY where C_ACCTBAL > 0 Number of rows retrieved is: 136308 Number of rows sent to output is: 0 Elapsed Time is: 76.349 seconds … Buffer pool data logical reads = 272618 Buffer pool data physical reads = 131425 Buffer pool data writes = 0 Buffer pool index logical reads = 273173 Buffer pool index physical reads = 552 Buffer pool index writes = 0 Total buffer pool read time (ms) = 71352 Total buffer pool write time (ms) = 0 … Summary of Results ================== Elapsed Agent CPU Rows Rows Statement # Time (s) Time (s) Fetched Printed 1 76.349 6.670 136308 0
    • 201. An example Performance Monitor (system level)
      • An IO indicator’s consumption evolution (qualitative and quantitative) according to DB2’s System Monitor
      • Similar tools: Window’s Performance Monitor and Oracle’s Performance Manager
    • 202. Investigating High Level Consumers: Summary Find critical queries Found any? Investigate lower levels Answer Q1 over them Overcon- sumption? Tune problematic queries yes yes no no
    • 203. Investigating Primary Resources
      • Answer question 3: “Are there enough primary resources available for a DBMS to consume?”
      • Primary resources are: CPU, disk & controllers, memory, and network
      • Analyze specific OS-level indicators to discover bottlenecks.
      • A system-level Performance Monitor is the right tool here
    • 204. CPU Consumption Indicators at the OS Level 100% CPU % of utilization 70% time Sustained utilization over 70% should trigger the alert. System utilization shouldn’t be more than 40%. DBMS (on a non- dedicated machine) should be getting a decent time share. total usage system usage
    • 205. Disk Performance Indicators at the OS Level Wait queue New requests Disk Transfers /second Should be close to zero Wait times should also be close to zero Idle disk with pending requests? Check controller contention. Also, transfers should be balanced among disks/controllers Average Queue Size
    • 206. Memory Consumption Indicators at the OS Level pagefile real memory virtual memory Page faults/time should be close to zero. If paging happens, at least not DB cache pages. % of pagefile in use will tell you how much memory is “needed.”
    • 207. Investigating Intermediate Resources/Consumers
      • Answer question 2: “Are subsystems making optimal use of resources?”
      • Main subsystems: Cache Manager, Disk subsystem, Lock subsystem, and Log/Recovery subsystem
      • Similarly to Q3, extract and analyze relevant Performance Indicators
    • 208. Cache Manager Performance Indicators Table scan readpage () Free Page slots Page reads/ writes Pick victim strategy Data Pages Cache Manager If page is not in the cache, readpage (logical) generates an actual IO (physical). Fraction of readpages that did not generate physical IO should be 90% or more (hit ratio) Pages are regularly saved to disk to make free space. # of free slots should always be > 0
    • 209. Disk Manager Performance Indicators rows page extent file Storage Hierarchy (simplified) disk Displaced rows (moved to other pages) should be kept under 5% of rows Free space fragmentation: pages with little space should not be in the free list Data fragmentation: ideally files that store DB objects (table, index) should be in one or few (<5) contiguous extents File position: should balance workload evenly among all disks
    • 210. Lock Manager Performance Indicators Lock request Lock List Locks pending list Deadlocks and timeouts should seldom happen (no more then 1% of the transactions) Lock wait time for a transaction should be a small fraction of the whole transaction time. Number of lock waits should be a small fraction of the number of locks on the lock list. Object Lock Type TXN ID
    • 211. Investigating Intermediate and Primary Resources: Summary Answer Q3 Problems at OS level? Answer Q2 Tune low-level resources yes no Problematic subsystems? Tune subsystems Investigate upper level yes no
    • 212. Troubleshooting Techniques
      • Monitoring a DBMS’s performance should be based on queries and resources.
        • The consumption chain helps distinguish problems’ causes from their symptoms
        • Existing tools help extracting relevant performance indicators
    • 213. Recall Tuning Principles
      • Think globally, fix locally (troubleshoot to see what matters)
      • Partitioning breaks bottlenecks (find parallelism in processors, controllers, caches, and disks)
      • Start-up costs are high; running costs are low (batch size, cursors)
      • Be prepared for trade-offs (unless you can rethink the queries)

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