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Performance by Design,[object Object],Guy Harrison,[object Object],Director, R&D Melbourne,[object Object],www.guyharrison.net,[object Object]
Introductions ,[object Object]
Performance By Design
Save the red-shirt Toad!,[object Object],The Red-shirt Toad is NOT expendable!,[object Object]
Core message,[object Object],Design limits performance,[object Object],Architecture maps requirements to design,[object Object],Make sure performance requirements are specified,[object Object],Make sure architecture allows for performance,[object Object],Make sure performance requirements are realized,[object Object]
Elements of Performance by Design,[object Object]
Methodology,[object Object]
High performance can mean different things,[object Object],Speed: response time ,[object Object]
Efficiency: power consumption,[object Object]
Power: throughput,[object Object]
Not usually easy to change architectures,[object Object]
Poorly defined requirements lead to this:,[object Object]
The twitter lesson ,[object Object]
Twitter growth,[object Object]
“Twitter is, fundamentally, a messaging system. Twitter was not architected as a messaging system, however. For expediency's sake, Twitter was built with technologies and practices that are more appropriate to a content management system.”,[object Object]
Patterns of database performance,[object Object],Hard to distinguish patterns at low levels,[object Object]
Validating performance can’t wait...,[object Object],User adoption and growth,[object Object],UI Layer (HTML, JavaScript, Ajax) ,[object Object],Middleware layer (J2EE),[object Object],SQLs,[object Object],Database (Tables, views, partitions, etc),[object Object]
Performance By Design
Database Design ,[object Object]
Normalize, but not too far!,[object Object]
Other logical design thoughts,[object Object],Artificial keys,[object Object],Generally more efficient than long composite keys,[object Object],Null values,[object Object],Not a good idea if you intend to search for “unknown” or “incomplete” values,[object Object],Null should not mean something,[object Object],But beneficial as long as you don’t need to look for them. ,[object Object],Data types,[object Object],Constraints on precision can sometimes reduce row lengths,[object Object],Variable length strings usually better,[object Object],Carefully consider CLOBs vs long VARCHARs,[object Object]
Logical to Physical: Subtypes,[object Object],“Customers are people too”,[object Object]
Indexing, clustering and weird table types,[object Object],Lots’ of options:,[object Object],B*-Tree index,[object Object],Bitmap index,[object Object],Hash cluster,[object Object],Index Cluster,[object Object],Nested table,[object Object],Index Organized Table,[object Object],Most often useful:,[object Object],B*-Tree (concatenated) indexes,[object Object],Bitmap indexes,[object Object],Hash Clusters,[object Object]
Performance By Design
Concatenated index effectiveness,[object Object],SELECT cust_id,[object Object],FROM sh.customers c,[object Object],WHERE cust_first_name = 'Connor',[object Object],AND cust_last_name = 'Bishop',[object Object],AND cust_year_of_birth = 1976;,[object Object]
Concatenated indexing guidleines,[object Object],Create a concatenated index for columns from a table that appear together in the WHERE clause.,[object Object],If columns sometimes appear on their own in a WHERE clause, place them at the start of the index.,[object Object],The more selective a column is, the more useful it will be at the leading end of the index (better single key lookups),[object Object],But indexes compress better when the leading columns are less selective. (better scans) ,[object Object],Index skip scans can make use of an index even if the leading columns are not specified, but it’s a poor second choice to a “normal” index range scan.,[object Object]
Bitmap indexes,[object Object]
Bitmap indexes ,[object Object]
Performance By Design
Bitmap join performance ,[object Object],SELECT SUM (amount_sold),[object Object],FROM customers JOIN sales s USING (cust_id) WHERE cust_email='flint.jeffreys@company2.com';,[object Object]
Index overhead,[object Object]
Hash Cluster,[object Object],Cluster key determines physical location on disk,[object Object],Single IO lookup by cluster key,[object Object],Misconfiguration leads to overflow or sparse tables ,[object Object]
Hash Cluster vs B-tree index ,[object Object]
Hash cluster table scan ,[object Object]
Denormalization and partitioning,[object Object],Repeating groups – VARRAYS, nested tables,[object Object],Summary tables – Materialized Views, Result cache,[object Object],Horizontal partitioning – Oracle Partition Option ,[object Object],In-line aggregations – Dimensions ,[object Object],Derived columns – Virtual columns,[object Object],Vertical partitioning ,[object Object],Replicated columns - triggers,[object Object]
Summary tables,[object Object],Aggregate queries on big tables often the most expensive,[object Object],Pre-computing them makes a lot of sense,[object Object],Balance accuracy with overhead ,[object Object],Aggregate Query,[object Object],MV on COMMIT,[object Object],Manual Summary,[object Object],Result set cache,[object Object],MV stale tolerated ,[object Object],Accuracy,[object Object],Efficiency,[object Object]
Vertical partitioning,[object Object]
Physical storage options,[object Object],LOB Storage,[object Object],PCTFREE,[object Object],Compression ,[object Object],Block size ,[object Object],Partitioning ,[object Object]
Performance By Design
Application Architecture and implementation,[object Object]
The best SQL is no SQL ,[object Object],Avoid asking for the same data twice. ,[object Object]
11g client side cache ,[object Object],CLIENT_RESULT_CACHE_SIZE: this is the amount of memory each client program will dedicate to the cache.,[object Object],Use RESULT_CACHE hint or (11GR2) table property,[object Object],Optionally set the CLIENT_RESULT_CACHE_LAG,[object Object]
Parse overhead,[object Object],It’s easy enough in most programming languages to create a unique SQL for every query:,[object Object]
Bind variables are preferred,[object Object]
Parse overhead reduction ,[object Object]
Identifying similar SQLs,[object Object],See force_matching.sql at www.guyharrison.net,[object Object]
Transaction design ,[object Object],Optimistic vs. Pessimistic ,[object Object]
Using ORA_ROWSCN,[object Object],Setting ROWDEPENDENCIES will reduce false fails,[object Object]
Network – stored procedures ,[object Object]
Network traffic example ,[object Object]
Array processing - Fetch,[object Object]
Network overhead – Array processing,[object Object]
Array Insert (Java),[object Object]
Array Insert: (.NET),[object Object]
Array Insert – PL/SQL,[object Object]
Thank you,[object Object]
Geek quiz stuff:,[object Object],High probability answers (keep standing if):,[object Object],Know what Alice and Wally have in common,[object Object],You know the next number in this series 3 . 1 4   ,[object Object],Know what “M” is in E=MC2,[object Object]
Know (or can work out)  your age in hex,[object Object],Have an opinion  about  of ST  vs SW ,[object Object],If you know who Leonard McCoy is ,[object Object],Think there is an important distinction between Nerd and Geek ,[object Object],Can quote  Monty Python ,[object Object],…. Other than dead parrot?,[object Object],You’ve ever  watched Jerry Springer,[object Object]
There are more networked devices in your house than people, pets and cars,[object Object],Know the names of two of Thomas the tank engines friends,[object Object],Know the names of any of Angelina and Brad’s babies,[object Object],Low probability answers: (sit down if you):,[object Object],Have a twitter account ,[object Object],# Azure is your new favourite color,[object Object]
You’ve ever played Zork,[object Object],You have a favourite Dr Who companion ,[object Object],Your favourite is  Sarah Jane   ,[object Object],Know your age in binary (or can work it out in your head) ,[object Object],You are proficient in some form of assembler,[object Object]
# You are proficient in some for or English ,[object Object],There is a rubicks cube in your house ,[object Object],Have your own domain,[object Object],Have ever been to Azeroth,[object Object],Who is ,[object Object],Know who said “Dude I am not your nemesis”,[object Object]
Worn a star trek or star wars costume,[object Object],Played a game that uses a non-six  sided dice,[object Object],Get email on my phone – before getting out of bed,[object Object],Calculator watch,[object Object],Binary time piece ,[object Object],Was on the internet prior to the WWW,[object Object]
# Met my current partner on line,[object Object],Know the next thing in this sequence:  Hydrogen, Helium, Lithuim, Berilium, ….,[object Object],Know what a Gigaquads in a megaquad is,[object Object]
Saw a sci-fi movie more than twice at the movies,[object Object],=========================================================,[object Object],You cleaned up at home before going to work,[object Object]

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Performance By Design

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Notas del editor

  1. TelsavsMasseratiLatency vs throughputVolume?Economics?
  2. MethodsRequirementsMeasurementPrototypeBenchmarkData modelOptimize physical model to queryDenormalizeIndexing, clustering, partitionApplicationMinimize database accessOptimize database access
  3. http://www.steveschmidtracing.com/our-team-5.html
  4. Generally not easy to change architectures.....
  5. Normal form is the best starting point for an efficient database design. However, don’t go overboard in eliminating redundancy. A normalized data model is one in which any data redundancy has been eliminatedand in which data and relationships are uniquely identifiable by primaryand foreign keys. Although the normalized data model is rarely the final destinationfrom a performance point of view, the normalized data model is almost alwaysthe best starting point. Indeed, failing to normalize your logical model isfrequently a recipe for a poorly performing physical model.Relational theory provides for various levels of normal form, some of whichare of academic interest only. Third normal form is the most commonly adoptednormalization level, and it has the following characteristics:❏ All data in an entity (table) is dependent on the primary key.❏ There should be no repeating groups of attributes (columns).❏ No data in an entity is dependent on only part of the key.❏ No data in an entity is dependent on any nonkey attribute.These characteristics are often remembered through the adage “the key, thewhole key, and nothing but the key.”
  6. Subtypes categorize or partition a logical entity and help to classify thetypes of information that is within the entity. A subtype usually has a set of attributesthat are held in common with the parent entity (the super-type) andother attributes that are not shared with the super-type or other subtypes. Figure4-1 shows how a PEOPLE entity could be split into subtypes of CUSTOMERSand EMPLOYEES.When translating entity subtypes into tables, we have the following options:❏ Create tables for the super-type and for each subtype. The super-type tablecontains only columns that are common to both subtypes.❏ Create a table for the super-type only. Attributes from all subtypes becomecolumns in this super-table. Typically, columns from subtype attributes willbe nullable, and a category column indicates the subtype in which a row belongs.❏ Create separate tables for each subtype without creating a table for thesuper-type. Attributes from the super-type are duplicated in each table.The three solutions result in very different performance outcomes. In particular,creating tables for the super-type and each subtype is likely to reduce performancein most circumstances, except where only the super-type is subject to afull table scan. Table 4-1 compares the performance of each of the three solutionsfor common database operations.
  7. Remember, you don’t always have control over the network – in particular client side code may sometimes be located anywhere
  8. The further the code is (in network terms) from the database, the more the network effects will magnify. You can’t get any closer to the database than being inde the database as PL/SQL