If this is your first visit, be sure to check out the FAQ by clicking the link above. You may have to register before you can post: click the register link above to proceed. To start viewing messages, select the forum that you want to visit from the selection below. |
|
|
|
Thread Tools | Display Modes |
#1
|
|||
|
|||
Restated: "Fields are expensive, records are cheap"
Hi,
First let me apolozie for the empty question below. I hit the Post button my mistake and it posted a blank question. Sorry. Hi, I am restating my question because based upon the responses I receive I obviously stated my question incorrectly. So please let me try again. Hopefully I will be a bit more sucessful this time around. In a previous question, I stated that I added 30 fields the membership table (one of many in the system). The initial implementation was SO successful, that the user requested quite a few enhancements resulting in the in a HUGE increase in the scope of the original project. These changes resulted in the addition of 30 new fields to the membership table. The last time people responded to my question, they were concerned about the whether or not these were normalized fields. Please let me state that the row has been normalized to 3rd normal form and these fields are NOT re-occurring fields. One MVP who responded to my original question stated "Fields are expensive, records are cheap". Maybe I am misinterpreting his comment. But to me this means that it is better to have smaller row in multiple linked tables than it is to have one large row that contains all of the normalized data. (IE – it is better to split a large normalize row in a single table into multiple rows in multiple tables). I hope I explained that the right way. My question pertains to the difference in the disk access time it takes to retrieve one record from the disk over a network versus retrieving multiple records versus the overhead involved in Access parsing a lot of fields out of a large row or parsing the same number of fields from multiple smaller rows. I've always been taught the exact opposite - that "Fields are cheap, records are expensive" since going to disk is so SLOW versus accessing data in memory. Is there something different about Access where the statement "Fields are expense, records are cheap" is true? I'm using Access on local machine where the front and backs end reside on the same machine as well as having multiple front ends on each client's machine tied into the back end which resides on a file server over a cat 5 hardwired Ethernet network. My question is strictly concerning the data access time of multiple row over the network versus Access’ overhead of extracting data from multiple small rows versus one large row. And we are assuming a 3rd normal form database design. And it may well be that I am totally misinterpreting the “Fields are expensive, records are cheap” comment. Thank you for your comments. Dennis |
#2
|
|||
|
|||
Restated: "Fields are expensive, records are cheap"
The mantra in the subject line is purely a rule-of-thumb for beginning
database designers. It has no bearing on your question about data access. Your question revolves around how the data is physically stored on the disk. You're saying that there is a delay to move the disk heads to different physical locations to retrieve records, and that delay represents degraded performance. While there's some truth in that simple view, the reality is much more complex than that. Access uses pointers to store the data. If you define a Text field as 255 characters but enter 2 characters in it, it does not use up 510 bytes (unicode) of disk space to store those 2 characters. Internally, the database keeps track of where each field starts in each record. Some fields (memo, OLE, attachments, MVFs) are not actually stored in-line with the other data in the record either. So it's not a matter of a single record being faster to retrieve. There are many other factors at work here, including whether the database has been compacted recently, whether you are using page- or record-locking, whether the disk is badly fragmented, how intelligent are the various layers of caching (physical on the drive, or the controller, or the operating system, or in Access), the Rushmore algorithms in JET, and so on. Then we may have to start another mindset to handle SSD storage issues as well. In the end, you don't really need to consider these as factors when designing JET tables. It makes no sense to break a 60-field table down into one main table with several one-to-one relationships just for performance reasons. The management of those relationships alone represents unnecessary complexity for the database and for developer. You may be forced to go that route if you are subclassing, or if your record is too wide to fit into the 4K page size; otherwise don't even consider it (which is probably what you were thinking in posting.) Having said that, having 60 fields in one table is unusual. The database I happen to be working on right now has 93 tables, and none of them has more than 32 fields (including 6 for tracking when and by whom the record was created, most recently edited, and deactivated.) -- Allen Browne - Microsoft MVP. Perth, Western Australia Tips for Access users - http://allenbrowne.com/tips.html Reply to group, rather than allenbrowne at mvps dot org. "Dennis" wrote in message ... Hi, First let me apolozie for the empty question below. I hit the Post button my mistake and it posted a blank question. Sorry. Hi, I am restating my question because based upon the responses I receive I obviously stated my question incorrectly. So please let me try again. Hopefully I will be a bit more sucessful this time around. In a previous question, I stated that I added 30 fields the membership table (one of many in the system). The initial implementation was SO successful, that the user requested quite a few enhancements resulting in the in a HUGE increase in the scope of the original project. These changes resulted in the addition of 30 new fields to the membership table. The last time people responded to my question, they were concerned about the whether or not these were normalized fields. Please let me state that the row has been normalized to 3rd normal form and these fields are NOT re-occurring fields. One MVP who responded to my original question stated "Fields are expensive, records are cheap". Maybe I am misinterpreting his comment. But to me this means that it is better to have smaller row in multiple linked tables than it is to have one large row that contains all of the normalized data. (IE – it is better to split a large normalize row in a single table into multiple rows in multiple tables). I hope I explained that the right way. My question pertains to the difference in the disk access time it takes to retrieve one record from the disk over a network versus retrieving multiple records versus the overhead involved in Access parsing a lot of fields out of a large row or parsing the same number of fields from multiple smaller rows. I've always been taught the exact opposite - that "Fields are cheap, records are expensive" since going to disk is so SLOW versus accessing data in memory. Is there something different about Access where the statement "Fields are expense, records are cheap" is true? I'm using Access on local machine where the front and backs end reside on the same machine as well as having multiple front ends on each client's machine tied into the back end which resides on a file server over a cat 5 hardwired Ethernet network. My question is strictly concerning the data access time of multiple row over the network versus Access’ overhead of extracting data from multiple small rows versus one large row. And we are assuming a 3rd normal form database design. And it may well be that I am totally misinterpreting the “Fields are expensive, records are cheap” comment. Thank you for your comments. Dennis |
#3
|
|||
|
|||
Restated: "Fields are expensive, records are cheap"
On Wed, 24 Feb 2010 20:51:01 -0800, Dennis
wrote: The last time people responded to my question, they were concerned about the whether or not these were normalized fields. Please let me state that the row has been normalized to 3rd normal form and these fields are NOT re-occurring fields. One MVP who responded to my original question stated "Fields are expensive, records are cheap". That was me, and that (non-normal structure) was my concern. It is impossible to tell from a brief post how knowledgable the poster might be about relational design. As you well know, wide-flat, spreadsheetish, non-normalized designs are a very common trap for beginners. You're obviously not a beginner (now that I know more about your background!) so my reply was out of line. My only excuse is that at the time I did NOT know your level of skill. I apologize for jumping the gun. What I consider "expensive" is that an improperly normalized table structure will require far more work in getarounds, contorted queries, form and report maintenance, and code than would a properly normalized design. Given that you have (it seems) valid Entities with sixty or more discrete, atomic, non-interdependent Attributes, I'll just express my mixed admiration and sympathy and bow out. -- John W. Vinson [MVP] |
#4
|
|||
|
|||
Restated: "Fields are expensive, records are cheap"
Allen,
Thank you very much for the answering my question. I understand the issue of disk access quite complex given all those issues and SCSI versus SATA, non-raid vs different level of raids, speed of the controller, network switches, routers, and all of the other issues that you brought up just causes my head to hurt when I even try to think about them. So you are correct, I did take a VERY simplistic approach in my question. I did not realize that database actually kept track of where each field starts and stop. The other variable length record databases I have worked with just buried a start and stop marker in the record. So while the "presentation" layer of the database that knew where each field started and ended, the actual engine had no idea. I will have to go back and check if it would be possible for all of the fields to be filled out and possible exceeds the 4k record / row limit. I did not know about the 4 k limit. Thank you for bringing that up!!!!! You may have saved me again. You are correct when you say I was thinking about breaking my 60 field record into multiple tables. I interpreted the comment from the MVP as suggesting I should break up 60 field record up into multiple tables. When an MVP takes time to comment on something I doing, I do try to follow their advice. It has not yet led me astray. I have since been informed that the comment "Fields are expensive...” refers to the dollar cost of added the field to the forms, reports, and the future maintenance cost associated with that field. But that is another story. My background is with large commercial insurance companies. All of the companies have had relational databases designed to the 3rd normal form. Between the policy information, reinsurance tracking, state, federal, rate development information we have to maintain for each policy, the typical policy master table had well over 200 fields in it. The policy master table just had fields that were common to all policies! Any data that applied to a specified line (auto, homeowners, etc) is stored in line specific master policy field. Our coverage tables have over 100 fields. Our Claim master table had over 100 fields in it. So for me, 60 fields are actually pretty small. However, I will go back and re-examine my design. There are things that I could put in a separate table, but they really are properties of the member. Thank you for responding and supplying all that wonderful information. It gives me a better understanding of how Access works internally. Thanks once again, Dennis |
#5
|
|||
|
|||
Restated: "Fields are expensive, records are cheap"
John, I know it was you, but I did not feel it was appropriate for me to use your name in my questions, hence “an MVP commented”. John, no offence was taken by your comment. I did go back and re-read my original posting and realized that I did not provide anywhere near enough information for you to think of anything else. That was entirely my fault for not being more detailed. I’m still having a problem drawing the line between not enough detail and too much detail. Sorry about that. I agree that when you read a brief posting, it is very difficult to tell what a person’s background is. In trying to payback you MVP’s for answering my questions, I have been trying to answer forum questions that I have previously asked and received answers. I know it is impossible to tell from a brief posting how knowledgeable a poster might be. Fully agree there. I also agree with your comment about wide-flat spreadsheetish non-normalized designs are. I responded to someone tonight about the training database. They had quite a few issues with the tables. I fully understand the issue. I had NO problem what so ever with your response and you have not reason at all to apologize. Personally, I feel bad that you feel the way you do. I never took offence at your comments. While I have a lot of db experience, I am still a newbie when it comes to Access. I thought that Access was like other database engines. While that is somewhat true, it is mostly a false statement because the VBA code and events work so differently from anything I’ve used. I’m still beating my head against that learning cliff. I interpreted your comment as implying that it was better to have a couple of small master tables that one large master table. Since it was coming from an MVP, I figured that I had better ask some more questions. Since I did not know how to ask you directly, I just put it on the forum. I agree with comment about a field being expensive in an improperly normalized table structure resulting in all sorts of workarounds. 10 of the 30 new fields had to do with funeral information for a deceased member. Since all of the information is specific to the individual member, I included on the member table. You might be able to argue that it should go in it own table, but for data entry and reporting simplicity I included it my member table. Actually, your comment did help me because I was reminded about Access 4k record size limitation. I’m not sure if a completely filled out record will excess 4k or not, I will have to check. So that alone will prevent a problem from occurring. If the potential record size will exceed 4k, I will break on the funeral information into a separate table as that information is quite lengthy. I do have one question for you about your “non-interdependent” field. What do you mean by that? To me, a policy effective date and expiration date are interdependent because the effective date has to be before the expiration date. Also City, St, and zip seem to be interdependent. So, what do you mean by non-interdependent? Please, never bow out of one my questions. I have learned so much from you and the other MVP that I would be on the loosing end of that deal. Thank you for all of your assistance. I have no way to express my appreciation except to say if you are ever in the Orlando, Fl area I would be glad to give you and airboat ride in the whiles of the Florida swamps. I’ll show you the real Florida. Thanks again for all of your help. Dennis |
#6
|
|||
|
|||
Restated: "Fields are expensive, records are cheap"
"Allen Browne" wrote in
: While there's some truth in that simple view, the reality is much more complex than that. Access uses pointers to store the data. If you define a Text field as 255 characters but enter 2 characters in it, it does not use up 510 bytes (unicode) of disk space to store those 2 characters. Internally, the database keeps track of where each field starts in each record. Some fields (memo, OLE, attachments, MVFs) are not actually stored in-line with the other data in the record either. So it's not a matter of a single record being faster to retrieve. There are many other factors at work here, including whether the database has been compacted recently, whether you are using page- or record-locking, whether the disk is badly fragmented, how intelligent are the various layers of caching (physical on the drive, or the controller, or the operating system, or in Access), the Rushmore algorithms in JET, and so on. Then we may have to start another mindset to handle SSD storage issues as well. And none of this considers the issue of disk caching, such that there's not that much difference between data loaded into RAM and data that is being read from/written to disk, since the latter usually takes place through the caching mechanism, and is not going to be limited by the speed of the actual storage medium, but by RAM. This has been the case in Windows since at least c. 1991-2, when Windows 3.1 was released with disk caching turned on by default. It was essential for decent performance in Windows, but it also meant that your databases were going to be speeded up because of the disk cache, too (although back then it was largely a read-only improvement, as lazy writes and such had not been implemented in the DOS disk cache; any modern version of Windows, though, caches both reads and writes). -- David W. Fenton http://www.dfenton.com/ usenet at dfenton dot com http://www.dfenton.com/DFA/ |
#7
|
|||
|
|||
Restated: "Fields are expensive, records are cheap"
=?Utf-8?B?RGVubmlz?= wrote in
: My background is with large commercial insurance companies. All of the companies have had relational databases designed to the 3rd normal form. The determination of whether or not those tables were actually normalized depends on the chosen definition of the entity being modelled in the table. I would consder the 200-field table you mention later to be unquestionably denormalized, even though I know nothing at all about the content/function of those fields. That sounds like a table that has a bunch of fields that are used only for a single record type, so that an auto insurance policy has one set of fields, but a renter's insurance policy has a different set of fields. Any time you're using some fields for some records and not for others, it's an indication to me that the entity has been misdefined, and should probably be broken into at least two tables, with a narrow header table and a long child table, where each row stores what was formerly a field in the wide table. All that said, my conclusion could be wrong for any particular application. But "fields are expensive, rows are cheap" is a generalized rule of thumb, not a hard-and-fast law of nature. It allows for exceptions for certain purposes, but is a starting point for evaluating a schema design. -- David W. Fenton http://www.dfenton.com/ usenet at dfenton dot com http://www.dfenton.com/DFA/ |
#8
|
|||
|
|||
Restated: "Fields are expensive, records are cheap"
=?Utf-8?B?RGVubmlz?= wrote in
: I agree with comment about a field being expensive in an improperly normalized table structure resulting in all sorts of workarounds. 10 of the 30 new fields had to do with funeral information for a deceased member. Since all of the information is specific to the individual member, I included on the member table. You might be able to argue that it should go in it own table, but for data entry and reporting simplicity I included it my member table. That sounds like the type of data I'd put in a separate 1:1 table, as it only applies once a particular threshold has been crossed. A record in that table also means the person is deceased (though you may not have the information and might still need to store a date of death in the main record). I wouldn't call that denormalized, but I have always found a certain utility in using the 1:1 table for things that apply only after the entity has reached a certain milestone. However, I would likely avoid having multiple 1:1 records, though, as it then becomes complicated to get a single editable row, unless the 1:1 records are related in a logical chain that is modelled in the database in that way. -- David W. Fenton http://www.dfenton.com/ usenet at dfenton dot com http://www.dfenton.com/DFA/ |
#9
|
|||
|
|||
Restated: "Fields are expensive, records are cheap"
David,
I can see where disk caching would help in a sequential process, but does disk caching really help in a randomly accessed database during data entry? The first data entry might access the first record, the next the 1,000th record, then next on the 5,000th record, and so on and so on. So, unless the entire table is cached, does it really help? Dennis |
#10
|
|||
|
|||
Restated: "Fields are expensive, records are cheap"
David,
Hmmm, I see your point and kind of agree with it. My back ground in on large and midrnage computers where it is nothing to have a 200 field, 30K record. However, I realize that Access is a different beast and I'm having to learn to adjust for it restrictions. Thanks for the insight. Just more to think about. But then I learn something new also. Thanks, Dennis |
|
Thread Tools | |
Display Modes | |
|
|