Tips for success: Common mistakes in application development with Firebird and how to avoid them
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The document outlines common mistakes in application development with Firebird, emphasizing the importance of proper database design and security measures. It warns against trusting assumptions without verification, highlights the use of natural keys, and offers solutions for character sets and data handling. Additionally, it discusses the significance of managing transactions effectively and avoiding pitfalls related to scalability and excessive security measures.
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Tips for success: Common mistakes in application development with Firebird and how to avoid them
- 1. Tips for success Common mistakes in application development with Firebird and how to avoid them Pavel Císař IBPhoenix Firebird Conference 2011
- 2. Agenda Recipes for disaster Mistakes in database design Wrong approaches to handle data
- 3. Recipes for disaster “ Agile Knowledge Management” Wrong assumptions Unjustified trust to SW, HW, tools etc. “ Thinking only up to the API” Preferring the way of least resistance No prototyping / No testing
- 4. Solution Think, don't just copy! If it walks as duck, squeaks as duck, it could be a beaver in disguise! Don't trust strangers! ...especially those who offer you candy! You don't know until you verify it, so what you're waiting for?
- 5. Database Design Artificial vs. Natural primary keys VARCHAR vs. BLOB Character sets Security
- 6. Artificial vs. Natural keys Artificial keys are always better (at the end) ...but there's hidden bloody price... ...the JOIN HELL Users want to see anything else but keys Users use anything but keys for search You have to join tables to get in important columns even it wouldn't be necessary You have to add more indices
- 7. Solution Use natural keys for most used “leaf” tables Cache the lookup tables in application Full set for small ones MRU for big ones
- 8. VARCHAR & BLOB – The Wrong People decide by feel, not reason Focus on values, not how they're used Oversize just to be safe
- 9. VARCHAR vs. BLOB - Solution There is a great guide from Ivan Přenosil http://www.volny.cz/iprenosil/interbase/ip_ib_strings.htm Quick tips: Anything longer than 150 characters is good candidate for BLOB BLOBs are bad news for search and server-side processing Long VARCHARs are bad news for sorts You can combine several long VARCHARs into single BLOB or refactor them out to 1:1 table More than one BLOB per table is bad idea (Refactor BLOBs out to 1:1 table)
- 10. Golden Rule of Database design Get as much rows on single page as you can BIGINT x INTEGER x SMALLINT DATE x TIMESTAMP Non-empty BLOBs are rows too! Pages smaller than 8K are pointless
- 11. Character sets Not that big problem today, But still some developers are careless or blindly default to UTF-8
- 12. Character sets - Solution Always use one! Consider conversions! What is native data type/encoding for strings your application uses? Minimize number and complexity of conversions: Database <-> Application <-> Input/Output
- 13. Security – The problem Either NO security at all or Extreme security measures
- 14. Security - Solution Use as little from SQL security features as you can Use remote server with restricted access Your application is exclusive gateway to the database Use OWNER account (block SYSDBA if you want) Implement fine-grained security in your application
- 15. Handling data – The wrong “ When I did this in ISQL, Flamerobin, my other app etc., it worked just fine, so what's wrong now?” “ It worked just fine on my development machine, so why it fails in production?” “ Damn, it doesn't scale as I expected...”
- 16. Handling data 101 Get intimate with your connectivity library! Always manage your transactions manually! Always mind the MGA! Never fetch more data than you actually need! One size doesn't fit all: Interactive vs. Machine processing Native vs. Web applications Embedded vs. Department vs. Corporate
- 17. Know your connectivity Identify higher and lower level access paths Learn the steps the access paths use Path complexity Data conversions Storage requirements Algorithm efficiency Points of failure
- 18. Transactions Interactive All data read in single transaction READ_ONLY READ_COMMITTED Writes in separate R/W transaction Machine processing R/W in single SNAPSHOT or READ_COMMITTED No UNDO log Long running “monitoring” transactions READ_ONLY READ_COMMITTED
- 19. MGA Implications Long running transactions block GC Inserts never block other users, update and delete may block Changes create garbage Correlating inserts with update/delete is VERY bad idea Mass update/delete burdens you with GC
- 20. Minimizing data transfers Interactive Less rows Less columns, fetch the rest on demand Cache lookups on client Machine processing Do as much on the server as you can
- 21. Beware the “One code to rule them all” Except the simplest cases, there is direct proportionality between universality and direct cost Universal code that doesn't have an option to cop-out on special case to user code is recipe for disaster
- 22. Questions? Pavel Císař [email_address] IBPhoenix www.ibphoenix.com