Archive-name: databases/sybase-faq/part8
URL: http://www.isug.com/Sybase_FAQ
Version: 1.7
Maintainer: David Owen
Last-modified: 2003/03/02
Posting-Frequency: posted every 3rd month
A how-to-find-the-FAQ article is posted on the intervening months.

Performance and Tuning



1.5.1 What are the nitty gritty details on Performance and Tuning?
1.5.2 What is best way to use temp tables in an OLTP environment?
1.5.3 What's the difference between clustered and non-clustered indexes?
1.5.4 Optimistic versus pessimistic locking?
1.5.5 How do I force an index to be used?
1.5.6 Why place tempdb and log on low numbered devices?
1.5.7 Have I configured enough memory for ASE?
1.5.8 Why should I use stored procedures?
1.5.9 I don't understand showplan's output, please explain.
1.5.10 Poor man's sp_sysmon.
1.5.11 View MRU-LRU procedure cache chain.
1.5.12 Improving Text/Image Type Performance

Server Monitoring General Troubleshooting ASE FAQ

-------------------------------------------------------------------------------

1.5.1: Sybase ASE Performance and Tuning

-------------------------------------------------------------------------------

Before going any further, Eric Miner (eric.miner@sybase.com) has made available
two presentations that he made at Techwave 1999. The first covers the use of
optdiag. The second covers features in the way the optimiser works in ASE
11.9.2 and 12. These are Powerpoint slides converted to web pages, so they
might be tricky to read with a text based browser!

All Components Affect Response Time & Throughput

We often think that high performance is defined as a fast data server, but the
picture is not that simple. Performance is determined by all these factors:

* The client application itself:
+ How efficiently is it written?
+ We will return to this later, when we look at application tuning.
* The client-side library:
+ What facilities does it make available to the application?
+ How easy are they to use?
* The network:
+ How efficiently is it used by the client/server connection?
* The DBMS:
+ How effectively can it use the hardware?
+ What facilities does it supply to help build efficient fast
applications?
* The size of the database:
+ How long does it take to dump the database?
+ How long to recreate it after a media failure?

Unlike some products which aim at performance on paper, Sybase aims at solving
the multi-dimensional problem of delivering high performance for real
applications.

OBJECTIVES

To gain an overview of important considerations and alternatives for the
design, development, and implementation of high performance systems in the
Sybase client/server environment. The issues we will address are:

* Client Application and API Issues
* Physical Database Design Issues
* Networking Issues
* Operating System Configuration Issues
* Hardware Configuration Issues
* ASE Configuration Issues

Client Application and Physical Database Design design decisions will
account for over 80% of your system's "tuneable" performance so ... plan
your project resources accordingly !

It is highly recommended that every project include individuals who have taken
Sybase Education's Performance and Tuning course. This 5-day course provides
the hands-on experience essential for success.

Client Application Issues

* Tuning Transact-SQL Queries
* Locking and Concurrency
* ANSI Changes Affecting Concurrency
* Application Deadlocking
* Optimizing Cursors in v10
* Special Issues for Batch Applications
* Asynchronous Queries
* Generating Sequential Numbers
* Other Application Issues

Tuning Transact-SQL Queries

* Learn the Strengths and Weaknesses of the Optimizer
* One of the largest factors determining performance is TSQL! Test not only
for efficient plans but also semantic correctness.
* Optimizer will cost every permutation of accesses for queries involving 4
tables or less. Joins of more than 4 tables are "planned" 4-tables at a
time (as listed in the FROM clause) so not all permutations are evaluated.
You can influence the plans for these large joins by the order of tables in
the FROM clause.
* Avoid the following, if possible:
+ What are SARGS?

This is short for search arguments. A search argument is essentially a
constant value such as:
o "My company name"
o 3448

but not:
o 344 + 88
o like "%what you want%"
+ Mathematical Manipulation of SARGs


SELECT name FROM employee WHERE salary * 12 > 100000

+ Use of Incompatible Datatypes Between Column and its SARG


Float & Int, Char & Varchar, Binary & Varbinary are Incompatible;

Int & Intn (allow nulls) OK

+ Use of multiple "OR" Statements - especially on different columns in
same table. If any portion of the OR clause requires a table scan, it
will! OR Strategy requires additional cost of creating and sorting a
work table.
+ Not using the leading portion of the index (unless the query is
completely covered)
+ Substituting "OR" with "IN (value1, value2, ... valueN) Optimizer
automatically converts this to an "OR"
+ Use of Non-Equal Expressions (!=) in WHERE Clause.
* Use Tools to Evaluate and Tune Important/Problem Queries
+ Use the "set showplan on" command to see the plan chosen as "most
efficient" by optimizer. Run all queries through during development and
testing to ensure accurate access model and known performance.
Information comes through the Error Handler of a DB-Library
application.
+ Use the "dbcc traceon(3604, 302, 310)" command to see each alternative
plan evaluated by the optimizer. Generally, this is only necessary to
understand why the optimizer won't give you the plan you want or need
(or think you need)!
+ Use the "set statistics io on" command to see the number of logical and
physical i/o's for a query. Scrutinize those queries with high logical
i/o's.
+ Use the "set statistics time on" command to see the amount of time
(elapsed, execution, parse and compile) a query takes to run.
+ If the optimizer turns out to be a "pessimizer", use the "set forceplan
on" command to change join order to be the order of the tables in the
FROM clause.
+ If the optimizer refuses to select the proper index for a table, you
can force it by adding the index id in parentheses after the table name
in the FROM clause.


SELECT * FROM orders(2), order_detail(1) WHERE ...

This may cause portability issues should index id's vary/change by
site !

Locking and Concurrency

* The Optimizer Decides on Lock Type and Granularity
* Decisions on lock type (share, exclusive, or update) and granularity (page
or table) are made during optimization so make sure your updates and
deletes don't scan the table !
* Exclusive Locks are Only Released Upon Commit or Rollback
* Lock Contention can have a large impact on both throughput and response
time if not considered both in the application and database design !
* Keep transactions as small and short as possible to minimize blocking.
Consider alternatives to "mass" updates and deletes such as a v10.0 cursor
in a stored procedure which frequently commits.
* Never include any "user interaction" in the middle of transactions.
* Shared Locks Generally Released After Page is Read
* Share locks "roll" through result set for concurrency. Only "HOLDLOCK" or
"Isolation Level 3" retain share locks until commit or rollback. Remember
also that HOLDLOCK is for read-consistency. It doesn't block other readers
!
* Use optimistic locking techniques such as timestamps and the tsequal()
function to check for updates to a row since it was read (rather than
holdlock)

ANSI Changes Affecting Concurrency

* Chained Transactions Risk Concurrency if Behavior not Understood
* Sybase defaults each DML statement to its own transaction if not specified
;
* ANSI automatically begins a transaction with any SELECT, FETCH, OPEN,
INSERT, UPDATE, or DELETE statement ;
* If Chained Transaction must be used, extreme care must be taken to ensure
locks aren't left held by applications unaware they are within a
transaction! This is especially crucial if running at Level 3 Isolation
* Lock at the Level of Isolation Required by the Query
* Read Consistency is NOT a requirement of every query.
* Choose level 3 only when the business model requires it
* Running at Level 1 but selectively applying HOLDLOCKs as needed is safest
* If you must run at Level 3, use the NOHOLDLOCK clause when you can !
* Beware of (and test) ANSI-compliant third-party applications for
concurrency

Application Deadlocking

Prior to ASE 10 cursors, many developers simulated cursors by using two or more
connections (dbproc's) and divided the processing between them. Often, this
meant one connection had a SELECT open while "positioned" UPDATEs and DELETEs
were issued on the other connection. The approach inevitably leads to the
following problem:

1. Connection A holds a share lock on page X (remember "Rows Pending" on SQL
Server leave a share lock on the "current" page).
2. Connection B requests an exclusive lock on the same page X and waits...
3. The APPLICATION waits for connection B to succeed before invoking whatever
logic will remove the share lock (perhaps dbnextrow). Of course, that never
happens ...

Since Connection A never requests a lock which Connection B holds, this is NOT
a true server-side deadlock. It's really an "application" deadlock !

Design Alternatives

1. Buffer additional rows in the client that are "nonupdateable". This forces
the shared lock onto a page on which the application will not request an
exclusive lock.
2. Re-code these modules with CT-Library cursors (aka. server-side cursors).
These cursors avoid this problem by disassociating command structures from
connection structures.
3. Re-code these modules with DB-Library cursors (aka. client-side cursors).
These cursors avoid this problem through buffering techniques and
re-issuing of SELECTs. Because of the re-issuing of SELECTs, these cursors
are not recommended for high transaction sites !

Optimizing Cursors with v10.0

* Always Declare Cursor's Intent (i.e. Read Only or Updateable)
* Allows for greater control over concurrency implications
* If not specified, ASE will decide for you and usually choose updateable
* Updateable cursors use UPDATE locks preventing other U or X locks
* Updateable cursors that include indexed columns in the update list may
table scan
* SET Number of Rows for each FETCH
* Allows for greater Network Optimization over ANSI's 1- row fetch
* Rows fetched via Open Client cursors are transparently buffered in the
client:
FETCH -> Open Client <- N rows
Buffers
* Keep Cursor Open on a Commit / Rollback
* ANSI closes cursors with each COMMIT causing either poor throughput (by
making the server re-materialize the result set) or poor concurrency (by
holding locks)
* Open Multiple Cursors on a Single Connection
* Reduces resource consumption on both client and Server
* Eliminates risk of a client-side deadlocks with itself

Special Issues for Batch Applications

ASE was not designed as a batch subsystem! It was designed as an RBDMS for
large multi-user applications. Designers of batch-oriented applications should
consider the following design alternatives to maximize performance :

Design Alternatives :

* Minimize Client/Server Interaction Whenever Possible
* Don't turn ASE into a "file system" by issuing single table / single row
requests when, in actuality, set logic applies.
* Maximize TDS packet size for efficient Interprocess Communication (v10
only)
* New ASE 10.0 cursors declared and processed entirely within stored
procedures and triggers offer significant performance gains in batch
processing.
* Investigate Opportunities to Parallelize Processing
* Breaking up single processes into multiple, concurrently executing,
connections (where possible) will outperform single streamed processes
everytime.
* Make Use of TEMPDB for Intermediate Storage of Useful Data

Asynchronous Queries

Many, if not most, applications and 3rd Party tools are coded to send queries
with the DB-Library call dbsqlexec( ) which is a synchronous call ! It sends a
query and then waits for a response from ASE that the query has completed !

Designing your applications for asynchronous queries provides many benefits:

1. A "Cooperative" multi-tasking application design under Windows will allow
users to run other Windows applications while your long queries are
processed !
2. Provides design opportunities to parallize work across multiple ASE
connections.

Implementation Choices:

* System 10 Client Library Applications:
* True asynchronous behaviour is built into the entire library. Through the
appropriate use of call-backs, asynchronous behavior is the normal
processing paradigm.
* Windows DB-Library Applications (not true async but polling for data):
* Use dbsqlsend(), dbsqlok(), and dbdataready() in conjunction with some
additional code in WinMain() to pass control to a background process. Code
samples which outline two different Windows programming approaches (a
PeekMessage loop and a Windows Timer approach) are available in the
Microsoft Software Library on Compuserve (GO MSL). Look for SQLBKGD.ZIP
* Non-PC DB-Library Applications (not true async but polling for data):
* Use dbsqlsend(), dbsqlok(), and dbpoll() to utilize non-blocking functions.

Generating Sequential Numbers Many applications use unique sequentially
increasing numbers, often as primary keys. While there are good benefits to
this approach, generating these keys can be a serious contention point if not
careful. For a complete discussion of the alternatives, download Malcolm
Colton's White Paper on Sequential Keys from the SQL Server Library of our
OpenLine forum on Compuserve.

The two best alternatives are outlined below.

1. "Primary Key" Table Storing Last Key Assigned
+ Minimize contention by either using a seperate "PK" table for each user
table or padding out each row to a page. Make sure updates are
"in-place".
+ Don't include the "PK" table's update in the same transaction as the
INSERT. It will serialize the transactions.
BEGIN TRAN

UPDATE pk_table SET nextkey = nextkey + 1
[WHERE table_name = @tbl_name]
COMMIT TRAN

/* Now retrieve the information */
SELECT nextkey FROM pk_table
WHERE table_name = @tbl_name]

+ "Gap-less" sequences require additional logic to store and retrieve
rejected values
2. IDENTITY Columns (v10.0 only)
+ Last key assigned for each table is stored in memory and automatically
included in all INSERTs (BCP too). This should be the method of choice
for performance.
+ Choose a large enough numeric or else all inserts will stop once the
max is hit.
+ Potential rollbacks in long transactions may cause gaps in the sequence
!

Other Application Issues

+ Transaction Logging Can Bottleneck Some High Transaction Environments
+ Committing a Transaction Must Initiate a Physical Write for
Recoverability
+ Implementing multiple statements as a transaction can assist in these
environment by minimizing the number of log writes (log is flushed to
disk on commits).
+ Utilizing the Client Machine's Processing Power Balances Load
+ Client/Server doesn't dictate that everything be done on Server!
+ Consider moving "presentation" related tasks such as string or
mathematical manipulations, sorting, or, in some cases, even
aggregating to the client.
+ Populating of "Temporary" Tables Should Use "SELECT INTO" - balance
this with dynamic creation of temporary tables in an OLTP environment.
Dynamic creation may cause blocks in your tempdb.
+ "SELECT INTO" operations are not logged and thus are significantly
faster than there INSERT with a nested SELECT counterparts.
+ Consider Porting Applications to Client Library Over Time
+ True Asynchronous Behavior Throughout Library
+ Array Binding for SELECTs
+ Dynamic SQL
+ Support for ClientLib-initiated callback functions
+ Support for Server-side Cursors
+ Shared Structures with Server Library (Open Server 10)

Physical Database Design Issues

+ Normalized -vs- Denormalized Design
+ Index Selection
+ Promote "Updates-in-Place" Design
+ Promote Parallel I/O Opportunities

Normalized -vs- Denormalized

+ Always Start with a Completely Normalized Database
+ Denormalization should be an optimization taken as a result of a
performance problem
+ Benefits of a normalized database include :
1. Accelerates searching, sorting, and index creation since tables are
narrower
2. Allows more clustered indexes and hence more flexibility in tuning
queries, since there are more tables ;
3. Accelerates index searching since indexes tend to be narrower and
perhaps shorter ;
4. Allows better use of segments to control physical placement of
tables ;
5. Fewer indexes per table, helping UPDATE, INSERT, and DELETE
performance ;
6. Fewer NULLs and less redundant data, increasing compactness of the
database ;
7. Accelerates trigger execution by minimizing the extra integrity
work of maintaining redundant data.
8. Joins are Generally Very Fast Provided Proper Indexes are Available
9. Normal caching and cindextrips parameter (discussed in Server
section) means each join will do on average only 1-2 physical I/Os.
10. Cost of a logical I/O (get page from cache) only 1-2 milliseconds.
3. There Are Some Good Reasons to Denormalize
1. All queries require access to the "full" set of joined data.
2. Majority of applications scan entire tables doing joins.
3. Computational complexity of derived columns require storage for SELECTs
4. Others ...

Index Selection

+ Without a clustered index, all INSERTs and "out-of-place" UPDATEs go to
the last page. The lock contention in high transaction environments
would be prohibitive. This is also true for INSERTs to a clustered
index on a monotonically increasing key.
+ High INSERT environments should always cluster on a key which provides
the most "randomness" (to minimize lock / device contention) that is
usable in many queries. Note this is generally not your primary key !
+ Prime candidates for clustered index (in addition to the above) include
:
o Columns Accessed by a Range
o Columns Used with Order By, Group By, or Joins
+ Indexes Help SELECTs and Hurt INSERTs
+ Too many indexes can significantly hurt performance of INSERTs and
"out-of-place" UPDATEs.
+ Prime candidates for nonclustered indexes include :
o Columns Used in Queries Requiring Index Coverage
o Columns Used to Access Less than 20% (rule of thumb) of the Data.
+ Unique indexes should be defined as UNIQUE to help the optimizer
+ Minimize index page splits with Fillfactor (helps concurrency and
minimizes deadlocks)
+ Keep the Size of the Key as Small as Possible
+ Accelerates index scans and tree traversals
+ Use small datatypes whenever possible . Numerics should also be used
whenever possible as they compare faster than strings.

Promote "Update-in-Place" Design

+ "Update-in-Place" Faster by Orders of Magnitude
+ Performance gain dependent on number of indexes. Recent benchmark (160
byte rows, 1 clustered index and 2 nonclustered) showed 800%
difference!
+ Alternative ("Out-of-Place" Update) implemented as a physical DELETE
followed by a physical INSERT. These tactics result in:
1. Increased Lock Contention
2. Increased Chance of Deadlock
3. Decreased Response Time and Throughput
+ Currently (System 10 and below), Rules for "Update-in-Place" Behavior
Include :
1. Columns updated can not be variable length or allow nulls
2. Columns updated can not be part of an index used to locate the row
to update
3. No update trigger on table being updated (because the inserted and
deleted tables used in triggers get their data from the log)


In v4.9.x and below, only one row may be affected and the
optimizer must know this in advance by choosing a UNIQUE index.
System 10 eliminated this limitation.

Promote Parallel I/O Opportunities

+ For I/O-bound Multi-User Systems, Use A lot of Logical and Physical
Devices
+ Plan balanced separation of objects across logical and physical
devices.
+ Increased number of physical devices (including controllers) ensures
physical bandwidth
+ Increased number of logical Sybase devices ensures minimal contention
for internal resources. Look at SQL Monitor's Device I/O Hit Rate for
clues. Also watch out for the 128 device limit per database.
+ Create Database (in v10) starts parallel I/O on up to 6 devices at a
time concurrently. If taken advantage of, expect an 800% performance
gain. A 2Gb TPC-B database that took 4.5 hours under 4.9.1 to create
now takes 26 minutes if created on 6 independent devices !
+ Use Sybase Segments to Ensure Control of Placement


This is the only way to guarantee logical seperation of objects on
devices to reduce contention for internal resources.

+ Dedicate a seperate physical device and controller to the transaction
log in tempdb too.
+ optimize TEMPDB Also if Heavily Accessed
+ increased number of logical Sybase devices ensures minimal contention
for internal resources.
+ systems requiring increased log throughput today must partition
database into separate databases

Breaking up one logical database into multiple smaller databases
increases the number number of transaction logs working in parallel.

Networking Issues

+ Choice of Transport Stacks
+ Variable Sized TDS Packets
+ TCP/IP Packet Batching

Choice of Transport Stacks for PCs

+ Choose a Stack that Supports "Attention Signals" (aka. "Out of Band
Data")
+ Provides for the most efficient mechanism to cancel queries.
+ Essential for sites providing ad-hoc query access to large databases.
+ Without "Attention Signal" capabilities (or the urgent flag in the
connection string), the DB-Library functions DBCANQUERY ( ) and
DBCANCEL ( ) will cause ASE to send all rows back to the Client
DB-Library as quickly as possible so as to complete the query. This can
be very expensive if the result set is large and, from the user's
perspective, causes the application to appear as though it has hung.
+ With "Attention Signal" capabilities, Net-Library is able to send an
out-of-sequence packet requesting the ASE to physically throw away any
remaining results providing for instantaneous response.
+ Currently, the following network vendors and associated protocols
support the an "Attention Signal" capable implementation:
1. NetManage NEWT
2. FTP TCP
3. Named Pipes (10860) - Do not use urgent parameter with this Netlib
4. Novell LAN Workplace v4.1 0 Patch required from Novell
5. Novell SPX - Implemented internally through an "In-Band" packet
6. Wollongong Pathway
7. Microsoft TCP - Patch required from Microsoft

Variable-sized TDS Packets

Pre-v4.6 TDS Does Not Optimize Network Performance Current ASE TDS packet
size limited to 512 bytes while network frame sizes are significantly
larger (1508 bytes on Ethernet and 4120 bytes on Token Ring).

The specific protocol may have other limitations!

For example:
+ IPX is limited to 576 bytes in a routed network.
+ SPX requires acknowledgement of every packet before it will send
another. A recent benchmark measured a 300% performance hit over TCP in
"large" data transfers (small transfers showed no difference).
+ Open Client Apps can "Request" a Larger Packet Shown to have
significant performance improvement on "large" data transfers such as
BCP, Text / Image Handling, and Large Result Sets.
o clients:
# isql -Usa -Annnnn
# bcp -Usa -Annnnn
# ct_con_props (connection, CS_SET, CS_PACKETSIZE, &packetsize,
sizeof(packetsize), NULL)
o An "SA" must Configure each Servers' Defaults Properly
# sp_configure "default packet size", nnnnn - Sets default packet
size per client connection (defaults to 512)
# sp_configure "maximum packet size", nnnnn - Sets maximum TDS
packet size per client connection (defaults to 512)
# sp_configure "additional netmem", nnnnn - Additional memory for
large packets taken from separate pool. This memory does not
come from the sp_configure memory setting.

Optimal value = ((# connections using large packets large
packetsize * 3) + an additional 1-2% of the above calculation
for overhead)

Each connection using large packets has 3 network buffers: one
to read; one to write; and one overflow.
@ Default network memory - Default-sized packets come from
this memory pool.
@ Additional Network memory - Big packets come this memory
pool.

If not enough memory is available in this pool, the server
will give a smaller packet size, down to the default

TCP/IP Packet Batching

+ TCP Networking Layer Defaults to "Packet Batching"
+ This means that TCP/IP will batch small logical packets into one larger
physical packet by briefly delaying packets in an effort to fill the
physical network frames (Ethernet, Token-Ring) with as much data as
possible.
+ Designed to improve performance in terminal emulation environments
where there are mostly only keystrokes being sent across the network.
+ Some Environments Benefit from Disabling Packet Batching
+ Applies mainly to socket-based networks (BSD) although we have seen
some TLI networks such as NCR's benefit.
+ Applications sending very small result sets or statuses from sprocs
will usually benefit. Benchmark with your own application to be sure.
+ This makes ASE open all connections with the TCP_NODELAY option.
Packets will be sent regardless of size.
+ To disable packet batching, in pre-Sys 11, start ASE with the 1610
Trace Flag.


$SYBASE/dataserver -T1610 -d /usr/u/sybase/master.dat ...

Your errorlog will indicate the use of this option with the message:

ASE booted with TCP_NODELAY enabled.

Operating System Issues

+ Never Let ASE Page Fault
+ It is better to configure ASE with less memory and do more physical
database I/O than to page fault. OS page faults are synchronous and
stop the entire dataserver engine until the page fault completes. Since
database I/O's are asynchronous, other user tasks can continue!
+ Use Process Affinitying in SMP Environments, if Supported
+ Affinitying dataserver engines to specific CPUs minimizes overhead
associated with moving process information (registers, etc) between
CPUs. Most implementations will preference other tasks onto other CPUs
as well allowing even more CPU time for dataserver engines.
+ Watch out for OS's which are not fully symmetric. Affinitying
dataserver engines onto CPUs that are heavily used by the OS can
seriously degrade performance. Benchmark with your application to find
optimal binding.
+ Increase priority of dataserver engines, if supported
+ Give ASE the opportunity to do more work. If ASE has nothing to do, it
will voluntarily yield the CPU.
+ Watch out for OS's which externalize their async drivers. They need to
run too!
+ Use of OS Monitors to Verify Resource Usage
+ The OS CPU monitors only "know" that an instruction is being executed.
With ASE's own threading and scheduling, it can routinely be 90% idle
when the OS thinks its 90% busy. SQL Monitor shows real CPU usage.
+ Look into high disk I/O wait time or I/O queue lengths. These indicate
physical saturation points in the I/O subsystem or poor data
distribution.
+ Disk Utilization above 50% may be subject to queuing effects which
often manifest themselves as uneven response times.
+ Look into high system call counts which may be symptomatic of problems.
+ Look into high context switch counts which may also be symptomatic of
problems.
+ Optimize your kernel for ASE (minimal OS file buffering, adequate
network buffers, appropriate KEEPALIVE values, etc).
+ Use OS Monitors and SQL Monitor to Determine Bottlenecks
+ Most likely "Non-Application" contention points include:
Resource Where to Look
--------- --------------
CPU Performance SQL Monitor - CPU and Trends

Physical I/O Subsystem OS Monitoring tools - iostat, sar...

Transaction Log SQL Monitor - Device I/O and
Device Hit Rate
on Log Device

ASE Network Polling SQL Monitor - Network and Benchmark
Baselines

Memory SQL Monitor - Data and Cache
Utilization

+ Use of Vendor-support Striping such as LVM and RAID
+ These technologies provide a very simple and effective mechanism of
load balancing I/O across physical devices and channels.
+ Use them provided they support asynchronous I/O and reliable writes.
+ These approaches do not eliminate the need for Sybase segments to
ensure minimal contention for internal resources.
+ Non-read-only environments should expect performance degradations when
using RAID levels other than level 0. These levels all include fault
tolerance where each write requires additional reads to calculate a
"parity" as well as the extra write of the parity data.

Hardware Configuration Issues

+ Number of CPUs
+ Use information from SQL Monitor to assess ASE's CPU usage.
+ In SMP environments, dedicate at least one CPU for the OS.
+ Advantages and scaling of VSA is application-dependent. VSA was
architected with large multi-user systems in mind.
+ I/O Subsystem Configuration
+ Look into high Disk I/O Wait Times or I/O Queue Lengths. These may
indicate physical I/O saturation points or poor data distribution.
+ Disk Utilization above 50% may be subject to queuing effects which
often manifest themselves as uneven response times.
+ Logical Volume configurations can impact performance of operations such
as create database, create index, and bcp. To optimize for these
operations, create Logical Volumes such that they start on different
channels / disks to ensure I/O is spread across channels.
+ Discuss device and controller throughput with hardware vendors to
ensure channel throughput high enough to drive all devices at maximum
rating.

General ASE Tuning

+ Changing Values with sp_configure or buildmaster


It is imperative that you only use sp_configure to change those
parameters that it currently maintains because the process of
reconfiguring actually recalculates a number of other buildmaster
parameters. Using the Buildmaster utility to change a parameter
"managed" by sp_configure may result in a mis-configured server and
cause adverse performance or even worse ...

+ Sizing Procedure Cache
o ASE maintains an MRU-LRU chain of stored procedure query plans. As
users execute sprocs, ASE looks in cache for a query plan to use.
However, stored procedure query plans are currently not re-entrant!
If a query plan is available, it is placed on the MRU and execution
begins. If no plan is in memory, or if all copies are in use, a new
copy is read from the sysprocedures table. It is then optimized and
put on the MRU for execution.
o Use dbcc memusage to evaluate the size and number of each sproc
currently in cache. Use SQL Monitor's cache statistics to get your
average cache hit ratio. Ideally during production, one would hope
to see a high hit ratio to minimize the procedure reads from disk.
Use this information in conjuction with your desired hit ratio to
calculate the amount of memory needed.
+ Memory
o Tuning memory is more a price/performance issue than anything else
! The more memory you have available, the greater than probability
of minimizing physical I/O. This is an important goal though. Not
only does physical I/O take significantly longer, but threads doing
physical I/O must go through the scheduler once the I/O completes.
This means that work on behalf of the thread may not actually
continue to execute for quite a while !
o There are no longer (as of v4.8) any inherent limitations in ASE
which cause a point of diminishing returns on memory size.
o Calculate Memory based on the following algorithm :


Total Memory = Dataserver Executable Size (in bytes) +
Static Overhead of 1 Mb +
User Connections x 40,960 bytes +
Open Databases x 644 bytes +
Locks x 32 bytes +
Devices x 45,056 bytes +
Procedure Cache +
Data Cache

+ Recovery Interval
o As users change data in ASE, only the transaction log is written to
disk right away for recoverability. "Dirty" data and index pages
are kept in cache and written to disk at a later time. This
provides two major benefits:
1. Many transactions may change a page yet only one physical write
is done
2. ASE can schedule the physical writes "when appropriate"
o ASE must eventually write these "dirty" pages to disk.
o A checkpoint process wakes up periodically and "walks" the cache
chain looking for dirty pages to write to disk
o The recovery interval controls how often checkpoint writes dirty
pages.
+ Tuning Recovery Interval
o A low value may cause unnecessary physical I/O lowering throughput
of the system. Automatic recovery is generally much faster during
boot-up.
o A high value minimizes unnecessary physical I/O and helps
throughput of the system. Automatic recovery may take substantial
time during boot-up.

Audit Performance Tuning for v10.0

+ Potentially as Write Intensive as Logging
+ Isolate Audit I/O from other components.
+ Since auditing nearly always involves sequential writes, RAID Level 0
disk striping or other byte-level striping technology should provide
the best performance (theoretically).
+ Size Audit Queue Carefully
+ Audit records generated by clients are stored in an in memory audit
queue until they can be processed.
+ Tune the queue's size with sp_configure "audit queue size", nnnn (in
rows).
+ Sizing this queue too small will seriously impact performance since all
user processes who generate audit activity will sleep if the queue
fills up.
+ Size Audit Database Carefully
+ Each audit row could require up to 416 bytes depending on what is
audited.
+ Sizing this database too small will seriously impact performance since
all user processes who generate audit activity will sleep if the
database fills up.

Back to top

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1.5.2: Temp Tables and OLTP

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(Note from Ed: It appears that with ASE 12, Sybase have solved the problem of
select/into locking the system tables for the duration of the operation. The
operation is now split into two parts, the creation of the table followed byt
the insert. The system tables are only locked for the first part, and so, to
all intents and purposes, the operation acts like a create/insert pair whilst
remaining minimally logged.

Our shop would like to inform folks of a potential problem when using temporary
tables in an OLTP environment. Using temporary tables dynamically in a OLTP
production environment may result in blocking (single-threading) as the number
of transactions using the temporary tables increases.

Does it affect my application?

This warning only applies for SQL that is being invoked frequently in an OLTP
production environment, where the use of "select into..." or "create table #
temp" is common. Application using temp tables may experience blocking problems
as the number of transactions increases.

This warning does not apply to SQL that may be in a report or that is not used
frequently. Frequently is defined as several times per second.

Why? Why? Why?

Our shop was working with an application owner to chase down a problem they
were having during peak periods. The problem they were having was severe
blocking in tempdb.

What was witnessed by the DBA group was that as the number of transactions
increased on this particular application, the number of blocks in tempdb also
increased.

We ran some independent tests to simulate a heavily loaded server and
discovered that the data pages in contention were in tempdb's syscolumns table.

This actually makes sense because during table creation entries are added to
this table, regardless if it's a temporary or permanent table.

We ran another simulation where we created the tables before the stored
procedure used it and the blocks went away. We then performed an additional
test to determine what impact creating temporary tables dynamically would have
on the server and discovered that there is a 33% performance gain by creating
the tables once rather than re-creating them.

Your mileage may vary.

How do I fix this?

To make things better, do the 90's thing -- reduce and reuse your temp tables.
During one application connection/session, aim to create the temp tables only
once.

Let's look at the lifespan of a temp table. If temp tables are created in a
batch within a connection, then all future batches and stored procs will have
access to such temp tables until they're dropped; this is the reduce and reuse
strategy we recommend. However, if temp tables are created in a stored proc,
then the database will drop the temp tables when the stored proc ends, and this
means repeated and multiple temp table creations; you want to avoid this.

Recode your stored procedures so that they assume that the temporary tables
already exist, and then alter your application so that it creates the temporary
tables at start-up -- once and not every time the stored procedure is invoked.

That's it! Pretty simple eh?

Summary

The upshot is that you can realize roughly a 33% performance gain and not
experience the blocking which is difficult to quantify due to the specificity
of each application.

Basically, you cannot lose.

Solution in pseudo-code

If you have an application that creates the same temp table many times within
one connection, here's how to convert it to reduce and reuse temp table
creations. Raymond Lew has supplied a detailed example for trying this.

Old

open connection
loop until time to go
exec procedure vavoom_often
/* vavoom_often creates and uses #gocart for every call */
/* eg: select * into #gocart from gocart */
go