Related
The problem
Ok, sorry that my question is somewhat abstract and subjective, but will try to make it as specific as possible. So, the situation I am in is simple - I am remaking a very old MS Access application on a new website using ASP.NET MVC. As currently the MVC site is using SQL Server 2008 (for many well known reasons) I need to find a way to migrate the tables AND the data, because the information in the old database will be used in the new application.
Alright, so far so good, however there are a few problems. The old application is written in a different language, meaning that I want to translate table names, field names, and all other names that are there to English. Furthermore, I will be making some changes on the models themselves (change the type of some fields, add additional fields to some tables, remove old unnecessary ones and more). So technically I'll be 'having my way' with everything.
Researched solutions
With those things in mind I researched for the ways to migrate data from Access database to a SQL Server. Of course, there is a lot of information on the matter, in Stack Overflow alone there are more than a few questions and solutions. So why am I struggling to find the answer ? Well I found a few solutions that will be sufficient to some extend (actually will definitely solve my problems) but I am writing to ask if someone experienced has a better perspective on it than I do. Alright, the solutions and why I am still looking for advice: /I'll be listing just a couple of the most common and popular ones that I found, many of the others share the same capabilities and/or results /
Upsize Wizzard (Access) - this is a tool devised specifically for migrating tables and data from Access. It is my most favourite one for the moment as I find it kind of straightforward to work with and it provides good overall results. I was able to migrate the tables to SQL Server (along with the data of course) which more or less is what I am intending to do. It is fast, it seems like it allows you to migrate indexes, primary keys and even to my knowledge foreign keys (table relationships). The downsides of this tool, however, include that it ignores your queries (which I don't really need honestly) and it doesn't provide a way to change the model, names or types of the properties of the table you migrate - which is the thing I kind of prefer, because I will have to make more than a few changes, adding, renaming, deleting, etc. And then continue with the development process (of the application) which will lead to a few additional minor changes. And finally I would need to apply all changes (migration + all changes) on the production server, which overall is prone to mistakes as I will be doing it by hand (and there are more than a few tables).
SQL Server Migration Assistant (SSMA) - ok, this is a separate tool (not included in Access) with again the same idea - to migrate data from Access to ... possibly everywhere, haven't researched that. Overall it offers more functionality and customizing from the Upsize Wizard, but of course it does it in a more complicated way. I haven't put enough effort to make a migration with this tool yet, as it involves a lot of installations and additional work, but according to my research it provides almost all (if not all) of the functionality I require. The downside however comes with the naming. As I mentioned it allows you to apply changes on the tables, schema, fields, indexes, keys and probably everything, but the articles advice that I change the names in Access first, as it will be easier and the migration process will run more smoothly. I am not allowed to make changes on the original Access database, as it will remain functional until the publish of the 'renewed' project, and the data inside it is being used, so a mere copy of the file is a solution I am not particularly fond of, because I might loose new records. Also I cant predict the changes I would want to make in the development process (as I said I believe I would want/need to apply some additional changes later on when I find 'weaknesses' in my data design in the development process) so I find it to be a little half baked solution.
Conclusion
The options presented, the way I see them, are two:
Use the Upsize Wizard to migrate the access tables, then write a script that applies the changes I want to make. Then in the development process add any additional changes to the script. When ready to publish on the production server, reapply the migration with the wizard, run the changes script and pray everything is fine.
Get more involved with the SSMA tool and try producing an updated version of the tables with the migration process. (See how efficient the renaming is and decide whether to use copied file to rename and then find a way to migrate only new records or do it all in the SSMA). Then again write a script for the changes that occur in the development process and re-do and apply it all on the production server when ready and then pray everything is fine.
Option I have not yet seen, apply it and then pray everything is fine.
I have researched the matter for a couple of days now, and found a few more solutions that I do not believe are better by the mentioned. However I include the possibility of missing the 'big red X on the map', a practical and easy solution which seems like it was designed specifically for me (though I doubt that a little). Anyway, reducing all the madness that I have written so far to a few simple questions will look like:
Is anyone aware if my conclusions are correct? I am leaning towards option one as it is easier to accomplish.
Has anyone experienced/found a better way to do that, or just found some 'logic-leaps' in my writings as I am overthinking the entire thing a little and may be doing some obvious miscalculation.
Very sorry for asking a trivial question and one that includes decision making that may involve deeper understanding of my project and situation, yet I am working with rather sensitive data and would appreciate feedback, even if only to improve my confidence into the chosen approach.
There is one other tool/method you might want to consider that seems to cater to your specific needs more. This would be to use the data import/export tool that ships with sqlserver to do a complete copy of all data into a temporary location within sql server and then write custom queries to reorganize the names and other changes you want to make. Is a bit more work but you could use the end product as a seed method for your migrations ;) (if you are doing code first anyway)
I'm working on an ASP.net web application that uses SQL as a database back-end. One issue that I have is that it sometimes takes a while to get my DBA to create or modify tables in the database which under no circumstance am I allowed to modify on my own.
Here is something that I do is when I expect users to upload files with their data.
Suppose the user uploads a new record for a table called Student_Records. The user uploads a record with fname Bob and lname Smith. The record is assigned primary key 123 The user also uploads two files: attendance_record.pdf and homework_record.pdf. Let's suppose that I have a network share: \\foo\bar where the files are saved.
One way of handling this situtation would be to have a table Student_Records_Files that associates the key 123 with Bob Smith. However, since I have trouble getting tables created, I've gone and done something different: When I save the files on the server, I call them 123_attendance_record.pdf and 123_homework_record.pdf. That way, I can easily identify what table record each file is associated with without having to create a new SQL table. I am, in essence, using the file system itself as a join table (Obviously, the file system is a type of database).
In my code for retrieving the files, I scan the directory \\foo\bar and look for files that begin with each primary key number from Student_Records.
It seems to work very well, but is it good practice?
There is nothing wrong with using the file system to store files. It's what it is used for.
There are a few things to keep in mind though.
I would consider a better method of storing the files - perhaps a directory for each user, rather than simply appending the user id to the filename.
Ensure that the file store is resilient and backed up with the same regularity as your database. If your database is configured to give you a backup every 10 minutes, but your file store only does a backup every day (or worse week) then you might be in for a world of pain.
Also consider what would happen if the user uploads two documents that are the same name.
First of all, I think it's a bad practice, in general, to design your architecture based on how responsive your DBA is. Any given compromise based on this approach may or may not be a big deal, but over time it will result in a poorly designed system.
Second, making the file name this critical seems dangerous to me; there's no protection against a person or application modifying the filename without realizing its importance.
Third, one of the advantages of having a table to maintain the join between the person and the file is that you can add additional data, such as: when was the file uploaded, what is the MIME type, has the file been read by anyone through the system, is this file a newer version of a previous file, etc. etc. Metadata can be very powerful, and the filesystem offers only limited ways to store it.
There are really two questions here. One is, given that for administrative reasons you cannot get changes made to the database schema, is it acceptable to devise some workaround. To that I'd have to say yes. What else can you do? In theory, if it takes two weeks to get the DBA to make a schema change for you, then this two weeks should be added to any deadline that you are given. In practice, this almost never happens. I've often worked places where some paperwork or whatever required two weeks before I could even begin work, and then I'd be given two weeks and one day to do the project. Sometimes you just have to put it together with rubber bands and bandaids.
Two is, is it a good idea to build a naming convention into file names and use this to identify files and their relationship to other data. I've done this at times and it's generally worked for me, though I have a perhaps irrational emotional feeling that it's not a good idea.
On the plus side, (a) By building information into a file name, you make it easy for both the computer and a human being to identify file associations. (Human readable as long as the naming convention is straightforward enough, anyway.) (b) By eliminating the separate storage of a link, you eliminate the possibility of a bad link. A file with the appropriate name may not exist, of course, but a database record with appropriate keys may not exist, or the file reference in such a record may be null or invalid. So it seems to solve one problem there without creating any new problems.
Potential minuses are: (a) You may have characters in the key that are not legal in file names. You may be able to just strip such characters out, or this may cause duplicates. The only safe thing to do is to escape them in some way, which is a pain. (b) You may exceed the legal length of a file name. Not as much of an issue as it was in the bad old 8.3 days. (c) You can't share files. If a database record points to a file, then two db records could point to the same file. If you must make two copies of a file, not only does this waste disk space, but it also means that if the file is updated, you must be sure to update all copies. If in your application it would make no sense to share files, than this isn't an issue.
You have to manage the files in some way, but you had to do that anyway.
I really can't think of any over-riding minuses. As I say, I've done this on occassion and didn't run into any particular problems. I'm interested in seeing others' responses.
I think it is not good practice because you are making your working application very dependent on specific implementation details and it would make it pretty hard to work with in the future to maintain, or if other people later needed access to your code/api.
Now weather you should do this or not is a whole different question. If you are really taking that much of a performance hit and it is significantly easier to work with how you have it, then I would say go ahead and break the rules. Ideally its good to follow best practice methods, but sometimes you have to bend the rules a little to make things work.
First, why is this a table change as opposed to a data change? Once you have the tables set up you should only need to update rows in that table every time that a user adds new files. If you have to put up with this one-time, two-week delay then bite the bullet and just get it done right.
Second, instead of trying to work around the problem why don't you try to fix the problem? Why is the process of implementing table changes so slow? Are you at least able to work on a development database (in which you have control to test and try out these changes)? Even if it's your own laptop you can at least continue on with development. Work with your manager, the DBA, and whoever else you need to, in order to improve the process. Would it help to speed things up if your scripts went through a formal testing process before you handed them off to the DBA so that he doesn't need to test the scripts, etc. himself?
Third, if this is a production database then you should probably be building in this two-week delay into your development cycle. You know that it takes two weeks for the DBA to review and implement changes in production, so make sure that if you have a deadline for releasing functionality that you have enough lead time for it.
Building this kind of "data" into a filename has inherent problems as others have pointed out. You have no relational integrity guarantees and the "data" can be changed without knowledge of the rest of the application/database.
It's best to keep everything in the database.
Network file I/O is spotty at best. In addition, its slower than the DB I/O.
If the DBA is difficult in getting small changes into the database, you
may be dealing with:
A political control issue. Maybe he just knows DB stuff and is threatened
when he perceives others moving in on his turf. Whatever his reasons, you need
to GET WORK DONE. Period. Document all the extra time / communication / work
you need to do for each small change and take that up with the management.
If the first level of management is unwilling to see things your way,
(it does not matter what their reasons are), escalate the issue
to the next level of management. In the past, I've gotten results this way.
It was more of a political territory problem than a technical problem.
The DBA eventually gave up and gave me full access to the TEST system BUT
he also stipulated that I would need to learn his testing process,
naming convention, his DB standards and practices, his way of testing, etc.
I was game.
I would also need to fix any database problems arising from changes I introduced.
This was fair and I got to wear the DBA hat in addition to the developer hat.
I got the freedom I needed and he got one less thing to worry about.
A process issue. Maybe the DBA needs to put every small DB change you submit
through a gauntlet of testing and performance analysis. Maybe he has a highly
normalized DB schema and because he has the big picture, he needs to normalize or
denormalize your requested DB changes to fit into the existing schema.
Ask to work with him. Ask him for a full DB design diagram.
Get a good sense of his DB design philosophy. Implement your DB changes with
his DB design philosophy in mind. Show that you understand that he's trying
to keep the DB in good order (understand normalization, relational constraints,
check constraints) Give him less to worry about. He needs to trust that you
will not muck up his database.
Accumulate all the small changes into a lengthy script and submit them to the DBA.
This way, you won't have to wait for each small change to go through all of his
process / testing. In addition, you're giving him a bigger picture view of your
development planning (that is in step with his DB design philosophy) instead of
just the play by play.
I have some pretty complex reports to write. Some of them... I'm not sure how I could write an sql query for just one of the values, let alone stuff them in a single query.
Is it common to just pull a crap load of data and figure it all via code instead? Or should I try and find a way to make all the reports rely on sql?
I have a very rich domain model. In fact, parts of code can be expanded on to calculate exactly what they want. The actual logic is not all that difficult to write - and it's nicer to work my domain model than with SQL. With SQL, writing the business logic, refactoring it, testing it and putting it version control is a royal pain because it's separate from your actual code.
For example, one the statistics they want is the % of how much they improved, especially in relation to other people in the same class, the same school, and compared to other schools. This requires some pretty detailed analysis of how they performed in the past to their latest information, as well as doing a calculation for the groups you are comparing against as a whole. I can't even imagine what the sql query would even look like.
The thing is, this % improvement is not a column in the database - it involves a big calculation in of itself by analyzing all the live data in real-time. There is no way to cache this data in a column as doing this calculation for every row it's needed every time the student does something is CRAZY.
I'm a little afraid about pulling out hundreds upon hundreds of records to get these numbers though. I may have to pull out that many just to figure out 1 value for 1 user... and if they want a report for all the users on a single screen, it's going to basically take analyzing the entire database. And that's just 1 column of values of many columns that they want on the report!
Basically, the report they want is a massive performance hog no matter what method I choose to write it.
Anyway, I'd like to ask you what kind of solutions you've used to these kind of a problems.
Sometimes a report can be generated by a single query. Sometimes some procedural code has to be written. And sometimes, even though a single query CAN be used, it's much better/faster/clearer to write a bit of procedural code.
Case in point - another developer at work wrote a report that used a single query. That query was amazing - turned a table sideways, did some amazing summation stuff - and may well have piped the output through hyperspace - truly a work of art. I couldn't have even conceived of doing something like that and learned a lot just from readying through it. It's only problem was that it took 45 minutes to run and brought the system to its knees in the process. I loved that query...but in the end...I admit it - I killed it. ((sob!)) I dismembered it with a chainsaw while humming "Highway To Hell"! I...I wrote a little procedural code to cover my tracks and...nobody noticed. I'd like to say I was sorry, but...in the end the job ran in 30 seconds. Oh, sure, it's easy enough to say "But performance matters, y'know"...but...I loved that query... ((sniffle...)) Anybody seen my chainsaw..? >;->
The point of the above is "Make Things As Simple As You Can, But No Simpler". If you find yourself with a query that covers three pages (I loved that query, but...) maybe it's trying to tell you something. A much simpler query and some procedural code may take up about the same space, page-wise, but could possibly be much easier to understand and maintain.
Share and enjoy.
Sounds like a challenging task you have ahead of you. I don't know all the details, but I think I would go at it from several directions:
Prioritize: You should try to negotiate with the "customer" and prioritize functionality. Chances are not everything is equally useful for them.
Manage expectations: If they have unrealistic expectations then tell them so in a nice way.
IMHO SQL is good in many respects, but it's not a brilliant programming language. So I'd rather just do calculations in the application rather than in the database.
I think I'd go for some delay in the system .. perhaps by caching calculated results for some minutes before recalculating. This is with a mind towards performance.
The short answer: for analysing large quantities of data, a SQL database is probably the best tool around.
However, that does not mean you should analyse this straight off your production database. I suggest you look into Datawarehousing.
For a one-off report, I'll write the code to produce it in whatever I can best reason about it in.
For a report that'll be generated more than once, I'll check on who is going to be producing it the next time. I'll still write the code in whatever I can best reason about it in, but I might add something to make it more attractive to use to that other person.
People usually use a third party report writing system rather than writing SQL. As an application developer, if you're spending a lot of time writing complex reports, I would severely question your manager's actions in NOT buying an off-the-shelf solution and letting less-skilled people build their own reports using some GUI.
I have been working on sql server and front end coding and have usually faced problem formulating queries.
I do understand most of the concepts of sql that are needed in formulating queries but whenever some new functionality comes into the picture that can be dont using sql query, i do usually fails resolving them.
I am very comfortable with select queries using joins and all such things but when it comes to DML operation i usually fails
For every query that i never done before I usually finds uncomfortable with that while creating them. Whenever I goes for an interview I usually faces this problem.
Is it their some concept behind approaching on formulating sql queries.
Eg.
I need to create an sql query such that
A table contain single column having duplicate record. I need to remove duplicate records.
I know i can find the solution to this query very easily on Googling, but I want to know how everyone comes to the desired result.
Is it something like Practice Makes Man Perfect i.e. once you did it, next time you will be able to formulate or their is some logic or concept behind.
I could have get my answer of solving above problem simply by posting it on stackoverflow and i would have been with an answer within 5 to 10 minutes but I want to know the reason. How do you work on any new kind of query. Is it a major contribution of experience or some an implementation of concepts.
Whenever I learns some new thing in coding section I tries to utilize it wherever I can use it. But here scenario seems to be changed because might be i am lagging in some concepts.
EDIT
How could I test my knowledge and
concepts in Sql and related sql
queries ?
Typically, the first time you need to open a child proof bottle of pills, you have a hard time, but after that you are prepared for what it might/will entail.
So it is with programming (me thinks).
You find problems, research best practices, and beat your head against a couple of rocks, but in the process you will come to have a handy set of tools.
Also, reading what others tried/did, is a good way to avoid major obsticles.
All in all, with a lot of practice/coding, you will see patterns quicker, and learn to notice where to make use of what tool.
I have a somewhat methodical method of constructing queries in general, and it is something I use elsewhere with any problem solving I need to do.
The first step is ALWAYS listing out any bits of information I have in a request. Information is essentially anything that tells me something about something.
A table contain single column having
duplicate record. I need to remove
duplicate
I have a table (I'll call it table1)
I have a
column on table table1 (I'll call it col1)
I have
duplicates in col1 on table table1
I need to remove
duplicates.
The next step of my query construction is identifying the action I'll take from the information I have.
I'll look for certain keywords (e.g. remove, create, edit, show, etc...) along with the standard insert, update, delete to determine the action.
In the example this would be DELETE because of remove.
The next step is isolation.
Asnwer the question "the action determined above should only be valid for ______..?" This part is almost always the most difficult part of constructing any query because it's usually abstract.
In the above example you're listing "duplicate records" as a piece of information, but that's really an abstract concept of something (anything where a specific value is not unique in usage).
Isolation is also where I test my action using a SELECT statement.
Every new query I run gets thrown through a select first!
The next step is execution, or essentially the "how do I get this done" part of a request.
A lot of times you'll figure the how out during the isolation step, but in some instances (yours included) how you isolate something, and how you fix it is not the same thing.
Showing duplicated values is different than removing a specific duplicate.
The last step is implementation. This is just where I take everything and make the query...
Summing it all up... for me to construct a query I'll pick out all information that I have in the request. Using the information I'll figure out what I need to do (the action), and what I need to do it on (isolation). Once I know what I need to do with what I figure out the execution.
Every single time I'm starting a new "query" I'll run it through these general steps to get an idea for what I'm going to do at an abstract level.
For specific implementations of an actual request you'll have to have some knowledge (or access to google) to go further than this.
Kris
I think in the same way I cook dinner. I have some ingredients (tables, columns etc.), some cooking methods (SELECT, UPDATE, INSERT, GROUP BY etc.) then I put them together in the way I know how.
Sometimes I will do something weird and find it tastes horrible, or that it is amazing.
Occasionally I will pick up new recipes from the internet or friends, then use parts of these in my own.
I also save my recipes in handy repositories, broken down into reusable chunks.
On the "Delete a duplicate" example, I'd come to the result by googling it. This scenario is so rare if the DB is designed properly that I wouldn't bother keeping this information in my head. Why bother, when there is a good resource is available for me to look it up when I need it?
For other queries, it really is practice makes perfect.
Over time, you get to remember frequently used patterns just because they ARE frequently used. Rare cases should be kept in a reference material. I've simply got too much other stuff to remember.
Find a good documentation to your software. I am using Mysql a lot and Mysql has excellent documentation site with decent search function so you get many answers just by reading docs. If you do NOT get your answer at least you are learning something.
Than I set up an example database (or use the one I am working on) and gradually build my SQL. I tend to separate the problem into small pieces and solve it step by step - this is very successful if you are building queries including many JOINS - it is best to start with some particular case and "polute" your SQL with many conditions like WHEN id = "123" which you are taking out as you are working towards your solution.
The best and fastest way to learn good SQL is to work with someone else, preferably someone who knows more than you, but it is not necessarry condition. It can be replaced by studying mature code written by others.
Your example is a test of how well you understand the DISTINCT keyword and the GROUP BY clause, which are SQL's ways of dealing with duplicate data.
Examples and experience. You look at other peoples examples and you create your own code and once it groks, you don't need to think about it again.
I would have a look at the Mere Mortals book - I think it's the one by Hernandez. I remember that when I first started seriously with SQL Server 6.5, moving from manual ISAM databases and Access database systems using VB4, that it was difficult to understand the syntax, the joins and the declarative style. And the SQL queries, while powerful, were very intimidating to understand - because typically, I was looking at generated code in Microsoft Access.
However, once I had developed a relatively systematic approach to building queries in a consistent and straightforward fashion, my skills and confidence quickly moved forward.
From seeing your responses you have two options.
Have a copy of the specification for whatever your working on (SQL spec and the documentation for the SQL implementation (SQLite, SQL Server etc..)
Use Google, SO, Books, etc.. as a resource to find answers.
You can't formulate an answer to a problem without doing one of the above. The first option is to become well versed into the capabilities of whatever you are working on.
The second option allows you to find answers that you may not even fully know how to ask. You example is fairly simplistic, so if you read the spec/implementation documentaion you would know the answer right away. But there are times, where even if you read the spec/documentation you don't know the answer. You only know that it IS possible, just not how to do it.
Remember that as far as jobs and supervisors go, being able to resolve a problem is important, but the faster you can do it the better which can often be done with option 2.
Say I offer user to check off languages she speaks and store it in a db. Important side note, I will not search db for any of those values, as I will have some separate search engine for search.
Now, the obvious way of storing these values is to create a table like
UserLanguages
(
UserID nvarchar(50),
LookupLanguageID int
)
but the site will be high load and we are trying to eliminate any overhead where possible, so in order to avoid joins with main member table when showing results on UI, I was thinking of storing languages for a user in the main table, having them comma separated, like "12,34,65"
Again, I don't search for them so I don't worry about having to do fulltext index on that column.
I don't really see any problems with this solution, but am I overlooking anything?
Thanks,
Andrey
Don't.
You don't search for them now
Data is useless to anything but this one situation
No data integrity (eg no FK)
You still have to change to "English,German" etc for display
"Give me all users who speak x" = FAIL
The list is actually a presentation issue
It's your system, though, and I look forward to answering the inevitable "help" questions later...
You might not be missing anything now, but when you're requirements change you might regret that decision. You should store it normalized like your first instinct suggested. That's the correct approach.
What you're suggesting is a classic premature optimization. You don't know yet whether that join will be a bottleneck, and so you don't know whether you're actually buying any performance improvement. Wait until you can profile the thing, and then you'll know whether that piece needs to be optimized.
If it does, I would consider a materialized view, or some other approach that pre-computes the answer using the normalized data to a cache that is not considered the book of record.
More generally, there are a lot of possible optimizations that could be done, if necessary, without compromising your design in the way you suggest.
This type of storage has almost ALWAYS come back to haunt me. For one, you are not even in first normal form. For another, some manager or the other will definitely come back and say.. "hey, now that we store this, can you write me a report on... "
I would suggest going with a normalized design. Put it in a separate table.
Problems:
You lose join capability (obviously).
You have to reparse the list on each page load / post back. Which results in more code client side.
You lose all pretenses of trying to keep database integrity. Just imagine if you decide to REMOVE a language later on... What's the sql going to be to fix all of your user profiles?
Assuming your various profile options are stored in a lookup table in the DB, you still have to run "30 queries" per profile page. If they aren't then you have to code deploy for each little change. bad, very bad.
Basing a design decision on something that "won't happen" is an absolute recipe for failure. Sure, the business people said they won't ever do that... Until they think of a reason they absolutely must do it. Today. Which will be promptly after you finish coding this.
As I stated in a comment, 30 queries for a low use page is nothing. Don't sweat it, and definitely don't optimize unless you know for darn sure it's necessary. Guess how many queries SO does for it's profile page?
I generally stay away at the solution you described, you asking for troubles when you store relational data in such fashion.
As alternative solution:
You could store as one bitmasked integer, for example:
0 - No selection
1 - English
2 - Spanish
4 - German
8 - French
16 - Russian
--and so on powers of 2
So if someone selected English and Russian the value would be 17, and you could easily query the values with Bitwise operators.
Premature optimization is the root of all evil.
EDIT: Apparently the context of my observation has been misconstrued by some - and hence the downvotes. So I will clarify.
Denormalizing your model to make things easier and/or 'more performant' - such as creating concatenated columns to represent business information (as in the OP case) - is what I refer to as a "premature optimization".
While there may be some extreme edge cases where there is no other way to get the necessary performance necessary for a particular problem domain - one should rarely assume this is the case. In general, such premature optimizations cause long-term grief because they are hard to undo - changing your data model once it is in production takes a lot more effort than when it initially deployed.
When designing a database, developers (and DBAs) should apply standard practices like normalization to ensure that their data model expresses the business information being collected and managed. I don't believe that proper use of data normalization is an "optimization" - it is a necessary practice. In my opinion, data modelers should always be on the lookout for models that could be restructured to (at least) third normal form (3NF).
If you're not querying against them, you don't lose anything by storing them in a form like your initial plan.
If you are, then storing them in the comma-delimited format will come back to haunt you, and I doubt that any speed savings would be significant, especially when you factor in the work required to translate them back.
You seem to be extremely worried about adding in a few extra lookup table joins. In my experience, the time it takes to actually transmit the HTML response and have the browser render it far exceed a few extra table joins. Especially if you are using indexes for your primary and foreign keys (as you should be). It's like you are planning a multi-day cross-country trip and you are worried about 1 extra 10 minute bathroom stop.
The lack of long-term flexibility and data integrity are not worth it for such a small optimization (which may not be necessary or even noticeable).
Nooooooooooooooooo!!!!!!!!
As stated very well in the above few posts.
If you want a contrary view to this debate, look at wordpress. Tables are chocked full of delimited data, and it's a great, simple platform.