Say you have a call center that needs to do X amount of calls in a given day. Each customer can be called for Y amount of products that they need to pay. The business would like to assign the calls to the operators at the start of the day and then be able to evaluate daily the results of those calls.
For those reasons, I'm thinking about deviding the structure into two with one table with the customers and another with the products.
What I am having problem with is deciding the primary key to put in the master scheduling table. Since there will be a good number of records every day, part of me wants to write the data in sequence for each day:
DateOfRecords date
Sequence int
OperatorID int
CustomerID int
Rest of the columns…
With DateOfRecords and sequence being the primary key.
I know that many recommend establishing an integer as a primary key and then index on the other columns. Again, my question comes because of the amount of records to be saved daily given the fact that this will be a historic table.
Any suggestions?
You can have just one clustered key per table. In most cases - but not necessarily - this will be the primary key. There are some things to consider:
The clustered key is the physical table itself. It includes all columns automatically.
The best clustered key is one which will never get fragmented (e.g. a sequence). The worst clustered key is an ordinary Guid.
If there is a clustered key, it will serve as a look-up for other indexes. if the clustered key is bad (e.g. fragmenting), other indexes will get an impact.
If your clustered index is running in fragmentation, you will have to repair this from time to time. This needs a huge physical shifting of data and should be avoided if possible.
Normal indexes are rather slim. They cover - to put it easy - a sorted list, together with the PK for a fast access to the actual row (with all the columns).
So - to put all this together: You should have a clustered primary key index using a non-fragmenting column. Additionally you can use as many indexes as you need, covering (and including) columns you need in queries.
There can be good reasons for multi-column PKs, Sometimes this can be the best choice. But the general advise is: Use a sequence as the (clustered) PK and place additional indexes to support your queries.
This will not be the absolutely fastest approach in each and any case, but a very good serves-for-all approach.
And one more point: Many programming approaches are best supported, if the tables have some things in common. E.g. an ID column or something like InsertDateTime. This allows for more generic programming approaches.
UPDATE: To add to your comment...
In a comment above you state: "...and then having problems later on when there are millions of records later on". Such issues are best solved with partitioned tables, filtered indexes and/or archiving strategies...
UPDATE2: Good to think about sargability...
In your question you write "Since there will be a good number of records every day, part of me wants to write the data in sequence for each day". Most operations you do against a column will break the usage of indexes (read about sargability). But a CAST() of a DATETIME to a DATE will allow to filter for calendar days without performance losses. Something like WHERE CAST(InsertDateTime AS DATE) ='20190909' will work lightning fast with an index on InsertDateTime.
Related
I've been researching best practices for creating clustered indexes and I'm just trying to totally understand these two suggestions that's listed with pretty much every BLOG or article on the matter
Columns that contain a large number of distinct values.
Queries that return large result sets.
These seem to be slightly contrary or I'm guessing maybe it just depends on how you're accessing the table.. Or my interpretation of what "large result sets" mean is wrong....
Unless you're doing range queries over the clustered column it seems like you typically won't be getting large result sets that matter. So in cases where SQL Server defaults the clustered indexes on the PK you're rarely going to fulfill the large result set suggestion but of course it does the large number of distinct values..
To give the question a little more context. This quetion stems from a vertical auditing table we have that has a column for TABLE.... Every single query that's written against this table has a
WHERE TABLE = 'TABLENAME'
But the TableName is highly non distinct... Each result set of tablenames is rather large which seems to fulfill that second conditon but it's definitely not largerly unique.... Which means all that other stuff happens with having to add the 4 byte Uniquifer (sp?) which makes the table a lot larger etc...
This situation has come up a few times for me when I've come upon DBs that have say all the contact or some accounts normalized into a single table and they are only separated by a TYPE parameter. Which is on every query....
In the case of the audit table the queries are typically not that exciting either they are just sorted by date modified, sometimes filtered by column, user that made the change etc...
My other thought with this auditing scenario was to just make the auditing table a HEAP so that inserting is fast so there's not contention between tables being audited and then to generate indexed views over the data ...
Index design is just as much art as it is science.
There are many things to consider, including:
How the table will be accessed most often: mostly inserts? any updates? more SELECTs than DML statements? Any audit table will likely have mostly inserts, no updates, rarely deletes unless there is a time-limit on the data, and some SELECTs.
For Clustered indexes, keep in mind that the data in each column of the clustered index will be copied into each non-clustered index (though not for UNIQUE indexes, I believe). This is helpful as those values are available to queries using the non-clustered index for covering, etc. But it also means that the physical space taken up by the non-clustered indexes will be that much larger.
Clustered indexes generally should either be declared with the UNIQUE keyword or be the Primary Key (though there are exceptions, of course). A non-unique clustered index will have a hidden 4-byte field called a uniqueifier that is required to make each row with a non-unique key value addressable, and is just wasted space given that the order of your rows within the non-unique groupings is not apparently obvious so trying to narrow down to a single row is still a range.
As is mentioned everywhere, the clustered index is the physical ordering of the data so you want to cater to what needs the best I/O. This relates also to the point directly above where non-unique clustered indexes have an order but if the data is truly non-unique (as opposed to unique data but missing the UNIQUE keyword when the index was created) then you miss out on a lot of the benefit of having the data physically ordered.
Regardless of any information or theory, TEST TEST TEST. There are many more factors involved that pertain to your specific situation.
So, you mentioned having a Date field as well as the TableName. If the combination of the Date and TableName is unique then those should be used as a composite key on a PK or UNIQUE CLUSTERED index. If they are not then find another field that creates the uniqueness, such as UserIDModified.
While most recommendations are to have the most unique field as the first one (due to statistics being only on the first field), this doesn't hold true for all situations. Given that all of your queries are by TableName, I would opt for putting that field first to make use of the physical ordering of the data. This way SQL Server can read more relevant data per read without having to seek to other locations on disk. You would likely also being ordering on the Date so I would put that field second. Putting TableName first will cause higher fragmentation across INSERTs than putting the Date first, but upon an index rebuild the data access will be faster as the data is already both grouped ( TableName ) and ordered ( Date ) as the queries expect. If you put Date first then the data is still ordered properly but the rows needed to satisfy the query are likely spread out across the datafile(s) which would require more I/O to get. AND, more data pages to satisfy the same query means more pages in the Buffer Pool, potentially pushing out other pages and reducing Page Life Expectancy (PLE). Also, you would then really need to inculde the Date field in all queries as any queries using only TableName (and possibly other filters but NOT using the Date field) will have to scan the clustered index or force you to create a nonclustered index with TableName being first.
I would be weary of the Heap plus Indexed View model. Yes, it might be optimized for the inserts but the system still needs to maintain the data in the indexed view across all DML statements against the heap. Again you would need to test, but I don't see that being materially better than a good choice of fields for a clustered index on the audit table.
I'm working in SQL Server 2008 R2
As part of a complete schema rebuild, I am creating a table that will be used to store advertising campaign performance by zipcode by day. The table setup I'm thinking of is something like this:
CREATE TABLE [dbo].[Zip_Perf_by_Day] (
[CampaignID] int NOT NULL,
[ZipCode] int NOT NULL,
[ReportDate] date NOT NULL,
[PerformanceMetric1] int NOT NULL,
[PerformanceMetric2] int NOT NULL,
[PerformanceMetric3] int NOT NULL,
and so on... )
Now the combination of CampaignID, ZipCode, and ReportDate is a perfect natural key, they uniquely identify a single entity, and there shouldn't be 2 records for the same combination of values. Also, almost all of my queries to this table are going to be filtered on 1 or more of these 3 columns. However, when thinking about my clustered index for this table, I run into a problem. These 3 columns do not increment over time. ReportDate is OK, but CampaignID and Zipcode are going to be all over the place while inserting rows. I can't even order them ahead of time because results come in from different sources during the day, so data for CampaignID 50000 might be inserted at 10am, and CampaignID 30000 might come in at 2pm. If I use the PK as my clustered index, I'm going to run into fragmentation problems.
So I was thinking that I need an Identity ID column, let's call it PerformanceID. I can see no case where I would ever use PerformanceID in either the select list or where clause of any query. Should I use PerformanceID as my PK and clustered index, and then set up a unique constraint and non-clustered indexes on CampaignID, ZipCode, and ReportDate? Should I keep those 3 columns as my PK and just have my clustered index on PerformanceID? (<- This is the option I'm leaning towards right now) Is it OK to have a slightly fragmented table? Is there another option I haven't considered? I am looking for what would give me the best read performance, while not completely destroying write performance.
Some actual usage information. This table will get written to in batches. Feeds come in at various times during the day, they get processed, and this table gets written to. It's going to get heavily read, as by-day performance is important around here. When I fill this table, it should have about 5 million rows, and will grow at a pace of about 8,000 - 10,000 rows per day.
In my experience, you probably do want to use another INT Identity field as your clustered index key. I would also add a UNIQUE constraint to that one (it helps with execution plans).
A big part of the reason is space - if you use a 3 field key for your clustered index, you will have all 3 fields in every row of every non-clustered index on that table (as your clustered index row identifier). If you only plan to have a couple of indexes that isn't a big deal, but if you have a lot of them it can make a big difference. The more data per row, the more pages needed and the more IO you have.
Fragmentation is a very real issue that can cause major performance problems, especially as the table grows.
Having that additional cluster key will also mean writes will be faster for your inserts. All new rows will go to the end of your table, which means existing rows won't be touched or rearranged.
If you want to use those three fields as a FK in other tables, then by all means have them as your PK.
For the most part it doesn't really matter if you ever directly reference your clustered index key. As long as it is narrow, increasing, and unique you should be in good shape.
EDIT:
As Damien points out in the comments, if you will be filtering on single fields of your PK, you will need to have an index on each one (or always use the first field in the covering index).
On the information given (ReportDate, CampaignID, ZipCode) or (ReportDate, ZipCode, CampaignID) seem like better candidates for the clustered index than a surrogate key. Defragmentation would be a potential concern if the time taken to rebuild indexes became prohibitive but given the sizes I would expect for this table (10s or 1000s rather than 1,000,000s of rows per day) that seems unlikely to be an issue.
If I understood all you have written correctly you are opting out of natural clustering due to fragmentation penalties.
For this purpose you consider meaningless IDs which will:
avoid insert penalties for clustered index when inserting out of order batches (great for write performance)
guarantee that your data is fragmented for reads that put conditions on the natural key (not so good for read performance)
JNK point's out that fragmentation can be a real issue, however you need to establish a baseline against which you will measure and you need to establish if reading or writing is more important to you (or how important they are in measurable terms).
There's nothing that will beat a good test case - so finally that is the best recommendation I can give.
With databases it is often relatively easy to build scripts that will create real benchmarks with real workloads and realistic data quantities.
I recently became involved with a new software project which uses SQL Server 2000 for its data storage.
In reviewing the project, I discovered that one of the main tables uses a clustered index on its primary key which consists of four columns:
Sequence numeric(18, 0)
Date datetime
Client varchar(9)
Hash tinyint
This table experiences a lot of inserts in the course of normal operation.
Now, I'm a C++ developer, not a DB Admin, but my first impression of this table design was that that having these fields as a clustered index would be very detrimental to insert performance, since the data would have to be physically reordered on each insert.
In addition, I can't really see any benefit to this since one would have to be querying all of these fields frequently to justify the clustered index, right?
So basically I need some ammunition for when I go to the powers that be to convince them that the table design should be changed.
The clustered index should contain the column(s) most queried by to give the greatest chance of seeks or of making a nonclustered index cover all the columns in the query.
The primary key and the clustered index do not have to be the same. They are both candidate keys, and tables often have more than one such key.
You said
In addition, I can't really see any benefit to this since one would have to be querying all of these fields frequently to justify the clustered index, right?
That's not true. A seek can be had just by using the first column or two of the clustered index. It may be a range seek, but it's still a seek. You don't have to specify all the columns of it in order to get that benefit. But the order of the columns does matter a lot. If you're predominantly querying on Client, then the Sequence column is a bad choice as the first in the clustered index. The choice of the second column should be the item that is most queried in conjunction with the first (not by itself). If you find that a second column is queried by itself almost as often as the first column, then a nonclustered index will help.
As others have said, reducing the number of columns/bytes in the clustered index as much as possible is important.
It's too bad that the Sequence is a random value instead of incrementing, but that may not be able to be helped. The answer isn't to throw in an identity column unless your application can start using it as the primary query condition on this table (unlikely). Now, since you're stuck with this random Sequence column (presuming it IS the most often queried), let's look at another of your statements:
having these fields as a clustered index would be very detrimental to insert performance, since the data would have to be physically reordered on each insert.
That's not entirely true.
The physical location on the disk is not really what we're talking about here, but it does come into play in terms of fragmentation, which is a performance implication.
The rows inside each 8k page are not ordered. It's just that all the rows in each page are less than the next page and more than the previous one. The problem occurs when you insert a row and the page is full: you get a page split. The engine has to copy all the rows after the inserted row to a new page, and this can be expensive. With a random key you're going to get a lot of page splits. You can ameliorate the problem by using a lower fillfactor when rebuilding the index. You'd have to play with it to get the right number, but 70% or 60% might serve you better than 90%.
I believe that having datetime as the second CI column could be beneficial, since you'd still be dealing with pages needing to be split between two different Sequence values, but it's not nearly as bad as if the second column in the CI was also random, since you'd be guaranteed to page split on every insert, where with an ascending value you can get lucky if the row can be added to a page because the next Sequence number starts on the next page.
Shortening the data types and number of all columns in a table as well as its nonclustered indexes can boost performance too, since more rows per page = fewer page reads per request. Especially if the engine is forced to do a table scan. Moving a bunch of rarely-queried columns to a separate 1-1 table could do wonders for some of your queries.
Last, there are some design tweaks that could help as well (in my opinion):
Change the Sequence column to a bigint to save a byte for every row (8 bytes instead of 9 for the numeric).
Use a lookup table for Client with a 4-byte int identity column instead of a varchar(9). This saves 5 bytes per row. If possible, use a smallint (-32768 to 32767) which is 2 bytes, an even greater savings of 7 bytes per row.
Summary: The CI should start with the column most queried on. Remove any columns from the CI that you can. Shorten columns (bytes) as much as you can. Use a lower fillfactor to mitigate the page splits caused by the random Sequence column (if it has to stay first because of being queried the most).
Oh, and get your online defragging going. If the table can't be changed, at least it can be reorganized frequently to keep it in best possible shape. Don't neglect statistics, either, so the engine can pick appropriate execution plans.
UPDATE
Another strategy to consider is if the composite key used in the table can be converted to an int, and a lookup table of the values is created. Let's say some combination of less than all 4 columns is repeated in over 100 rows, for example, Sequence + Client + Hash but only with varying Date values. Then an insert to a separate SequenceClientHash table with an identity column could make sense, because then you could look up the artificial key once and use it over and over again. This would also get your CI to add new rows only on the last page (yay) and substantially reduce the size of the CI as repeated in all nonclustered indexes (yippee). But this would only make sense in certain narrow usage patterns.
Now, marc_s suggested just adding an additional int identity column as the clustered index. It is possible that this could help by making all the nonclustered indexes get more rows per page, but it all depends on exactly where you want the performance to be, because this would guarantee that every single query on the table would have to use a bookmark lookup and you could never get a table seek.
About "tons of page splits and bad index fragmentation": as I already said this can be ameliorated somewhat with a lower fill factor. Also, frequent online index reorganization (not the same as rebuilding) can help reduce the effect of this.
Ultimately, it all comes down to the exact system and its unique pattern of data access combined with decisions about which parts you want optimized. For some systems, having a slower insert isn't bad as long as selects are always fast. For others, having consistent but slightly slower select times is more important than having slightly faster but inconsistent select times. For others, the data isn't really read until it's pushed to a data warehouse anyway so the inserts need to be as fast as possible. And adding into the mix is the fact that performance isn't just about user wait time or even query response time but also about server resources especially in the case of massive parallelism, so that total throughput (say, in client responses per time unit) matters more than any other factor.
Clustered indexes (CI) work best over ever-increasing, narrow, rarely changing values. You'll want your CI to cover the column(s) that get hit the most often in queries with >=, <=, or BETWEEN statements.
I'm not sure how your data normally gets hit. Most often you'll see a CI on an IDENTITY column or another narrow column (because this column will also be returned "tacked on" to all non-clustered indexes, and we don't want a ton of data added on to every fetch if it isn't needed). It's possible the data might be getting queried most often on date, and that may be a good choice, but all four columns is likely not correct (I stress likely, because I don't know the set-up; this may not have anything wrong with it). There are some pointers here: http://msdn.microsoft.com/en-us/library/aa933131%28SQL.80%29.aspx
There are a few things you are misunderstanding about how SQL creates and uses indexes.
Clustered indexes aren't necessarily physically ordered on disk by the clustered index, at least not in real-time. They are just a logical ordering.
I wouldn't expect a major performance hit based on this structure and removing the clustered index before you have actually identified a performance issue related to that index is clearly premature optimization.
Also, an index can be useful (especially one with several fields in it) even for searches that don't sort or get queried on all columns included in it.
Obviously, there should be a justification for creating a multi-part clustered index, just like any index, so it makes sense to ask for that if you think it was added capriciously.
Bottom line: Don't optimize the indexes for insert performance until you have actually detected a performance problem with inserts. It usually isn't worth it.
If you have only that single clustered index on your table, that might not be too bad. However, the clustering index is also used for looking up the real data page for any hit in a non-clustered index - therefor, the clustered index (all its columns) are also part of each and every non-clustered index you might have on your table.
So if you have a few nonclustered indices on your table, then you're definitely a) wasting a lot of space (and not just on disk - also in your server's RAM!), and b) your performance will be bad.
A good clustered index ought to be:
small (best bet: a 4-byte INT) - yours is pretty bad with up to 28 bytes per entry
unique
stable (never change)
ever-increasing
I would bet your current setup violates at least two if not more of those requirements. Not following these recommendations will lead to waste of space, and as you rightfully say, lots of page and index fragmentation and page splits (having to "rearrange" the data when an insert happens somewhere in the middle of the clustered index).
Quite honestly: just add a surrogate ID INT IDENTITY(1,1) to your table and make that the primary clustered key - you should see quite a nice boost in performance, just from that, if you have lots of INSERT (and UPDATE) operations going on!
See some more background info on what makes a good clustering key, and what is important about them, here:
GUIDs as PRIMARY KEYs and/or the clustering key
The Clustered Index Debate Continues...
Ever-increasing clustering key - the Clustered Index Debate..........again!
I ultimately agree with Erik's last paragraph:
"Ultimately, it all comes down to the exact system and its unique pattern of data access combined with decisions about which parts you want optimized..."
This is the basic thing I force people to learn: there's no universal solution.
You have to know your data and the actions performed against it. You have to know how frequent different type of actions are and their impact and expected execution times (you don't have to hard tune some rarely executed query and impact everything else if the end user agrees the query execution time is not so important--let's say waiting for few minutes for some report once per week is okay). Of course, as Erik said
"performance isn't just about user wait time or even query response time but also about server resources"
If such a query affects overall server performance, it should be considered as a serious candidate for optimization, even if execution time is fine. I've seen some very fast queries that used huge amount of CPU on multiprocessor servers, while slightly slower solution were incomparable "lighter" from resource utilization point of view. In that case I almost always go for the slower one.
Once you know what is your goal you can decide how many indexes you need and which one should be clustered. Unique constraints, filtered indexes, indexes with included columns are quite powerful tools for tuning. Choosing proper columns is important, but often choosing proper order of columns is even more important. And at the end, don't kill insert/update performance with tons of indexes if the table is frequently modified.
The database I'm working with is currently over 100 GiB and promises to grow much larger over the next year or so. I'm trying to design a partitioning scheme that will work with my dataset but thus far have failed miserably. My problem is that queries against this database will typically test the values of multiple columns in this one large table, ending up in result sets that overlap in an unpredictable fashion.
Everyone (the DBAs I'm working with) warns against having tables over a certain size and I've researched and evaluated the solutions I've come across but they all seem to rely on a data characteristic that allows for logical table partitioning. Unfortunately, I do not see a way to achieve that given the structure of my tables.
Here's the structure of our two main tables to put this into perspective.
Table: Case
Columns:
Year
Type
Status
UniqueIdentifier
PrimaryKey
etc.
Table: Case_Participant
Columns:
Case.PrimaryKey
LastName
FirstName
SSN
DLN
OtherUniqueIdentifiers
Note that any of the columns above can be used as query parameters.
Rather than guess, measure. Collect statistics of usage (queries run), look at the engine own statistics like sys.dm_db_index_usage_stats and then you make an informed decision: the partition that bests balances data size and gives best affinity for the most often run queries will be a good candidate. Of course you'll have to compromise.
Also don't forget that partitioning is per index (where 'table' = one of the indexes), not per table, so the question is not what to partition on, but which indexes to partition or not and what partitioning function to use. Your clustered indexes on the two tables are going to be the most likely candidates obviously (not much sense to partition just a non-clustered index and not partition the clustered one) so, unless you're considering redesign of your clustered keys, the question is really what partitioning function to choose for your clustered indexes.
If I'd venture a guess I'd say that for any data that accumulates over time (like 'cases' with a 'year') the most natural partition is the sliding window.
If you have no other choice you can partition by key module the number of partition tables.
Lets say that you want to partition to 10 tables.
You will define tables:
Case00
Case01
...
Case09
And partition you data by UniqueIdentifier or PrimaryKey module 10 and place each record in the corresponding table (Depending on your unique UniqueIdentifier you might need to start manual allocation of ids).
When performing a query, you will need to run same query on all tables, and use UNION to merge the result set into a single query result.
It's not as good as partitioning the tables based on some logical separation which corresponds to the expected query, but it's better then hitting the size limit of a table.
Another possible thing to look at (before partitioning) is your model.
Are you in a normalized database? Are there further steps which could improve performance by different choices in the normalization/de-/partial-normalization? Are there options to transform the data into a Kimball-style dimensional star model which is optimal for reporting/querying?
If you aren't going to drop partitions of the table (sliding window, as mentioned) or treat different partitions differently (you say any columns can be used in the query), I'm not sure what you are trying to get out of the partitioning that you won't already get out of your indexing strategy.
I'm not aware of any table limits on rows. AFAIK, the number of rows is limited only by available storage.
I have several tables whose only unique data is a uniqueidentifier (a Guid) column. Because guids are non-sequential (and they're client-side generated so I can't use newsequentialid()), I have made a non-primary, non-clustered index on this ID field rather than giving the tables a clustered primary key.
I'm wondering what the performance implications are for this approach. I've seen some people suggest that tables should have an auto-incrementing ("identity") int as a clustered primary key even if it doesn't have any meaning, as it means that the database engine itself can use that value to quickly look up a row instead of having to use a bookmark.
My database is merge-replicated across a bunch of servers, so I've shied away from identity int columns as they're a bit hairy to get right in replication.
What are your thoughts? Should tables have primary keys? Or is it ok to not have any clustered indexes if there are no sensible columns to index that way?
When dealing with indexes, you have to determine what your table is going to be used for. If you are primarily inserting 1000 rows a second and not doing any querying, then a clustered index is a hit to performance. If you are doing 1000 queries a second, then not having an index will lead to very bad performance. The best thing to do when trying to tune queries/indexes is to use the Query Plan Analyzer and SQL Profiler in SQL Server. This will show you where you are running into costly table scans or other performance blockers.
As for the GUID vs ID argument, you can find people online that swear by both. I have always been taught to use GUIDs unless I have a really good reason not to. Jeff has a good post that talks about the reasons for using GUIDs: https://blog.codinghorror.com/primary-keys-ids-versus-guids/.
As with most anything development related, if you are looking to improve performance there is not one, single right answer. It really depends on what you are trying to accomplish and how you are implementing the solution. The only true answer is to test, test, and test again against performance metrics to ensure that you are meeting your goals.
[Edit]
#Matt, after doing some more research on the GUID/ID debate I came across this post. Like I mentioned before, there is not a true right or wrong answer. It depends on your specific implementation needs. But these are some pretty valid reasons to use GUIDs as the primary key:
For example, there is an issue known as a "hotspot", where certain pages of data in a table are under relatively high currency contention. Basically, what happens is most of the traffic on a table (and hence page-level locks) occurs on a small area of the table, towards the end. New records will always go to this hotspot, because IDENTITY is a sequential number generator. These inserts are troublesome because they require Exlusive page lock on the page they are added to (the hotspot). This effectively serializes all inserts to a table thanks to the page locking mechanism. NewID() on the other hand does not suffer from hotspots. Values generated using the NewID() function are only sequential for short bursts of inserts (where the function is being called very quickly, such as during a multi-row insert), which causes the inserted rows to spread randomly throughout the table's data pages instead of all at the end - thus eliminating a hotspot from inserts.
Also, because the inserts are randomly distributed, the chance of page splits is greatly reduced. While a page split here and there isnt too bad, the effects do add up quickly. With IDENTITY, page Fill Factor is pretty useless as a tuning mechanism and might as well be set to 100% - rows will never be inserted in any page but the last one. With NewID(), you can actually make use of Fill Factor as a performance-enabling tool. You can set Fill Factor to a level that approximates estimated volume growth between index rebuilds, and then schedule the rebuilds during off-peak hours using dbcc reindex. This effectively delays the performance hits of page splits until off-peak times.
If you even think you might need to enable replication for the table in question - then you might as well make the PK a uniqueidentifier and flag the guid field as ROWGUIDCOL. Replication will require a uniquely valued guid field with this attribute, and it will add one if none exists. If a suitable field exists, then it will just use the one thats there.
Yet another huge benefit for using GUIDs for PKs is the fact that the value is indeed guaranteed unique - not just among all values generated by this server, but all values generated by all computers - whether it be your db server, web server, app server, or client machine. Pretty much every modern language has the capability of generating a valid guid now - in .NET you can use System.Guid.NewGuid. This is VERY handy when dealing with cached master-detail datasets in particular. You dont have to employ crazy temporary keying schemes just to relate your records together before they are committed. You just fetch a perfectly valid new Guid from the operating system for each new record's permanent key value at the time the record is created.
http://forums.asp.net/t/264350.aspx
The primary key serves three purposes:
indicates that the column(s) should be unique
indicates that the column(s) should be non-null
document the intent that this is the unique identifier of the row
The first two can be specified in lots of ways, as you have already done.
The third reason is good:
for humans, so they can easily see your intent
for the computer, so a program that might compare or otherwise process your table can query the database for the table's primary key.
A primary key doesn't have to be an auto-incrementing number field, so I would say that it's a good idea to specify your guid column as the primary key.
Just jumping in, because Matt's baited me a bit.
You need to understand that although a clustered index is put on the primary key of a table by default, that the two concepts are separate and should be considered separately. A CIX indicates the way that the data is stored and referred to by NCIXs, whereas the PK provides a uniqueness for each row to satisfy the LOGICAL requirements of a table.
A table without a CIX is just a Heap. A table without a PK is often considered "not a table". It's best to get an understanding of both the PK and CIX concepts separately so that you can make sensible decisions in database design.
Rob
Nobody answered actual question: what are pluses/minuses of a table with NO PK NOR a CLUSTERED index.
In my opinion, if you optimize for faster inserts (especially incremental bulk-insert, e.g. when you bulk load data into a non-empty table), such a table: with NO clustered index, NO constraints, NO Foreign Keys, NO Defaults and NO Primary Key, in a database with Simple Recovery Model, is the best. Now, if you ever want to query this table (as opposed to scanning it in its entirety) you may want to add a non-clustered non-unique indexes as needed but keep them to the minimum.
I too have always heard having an auto-incrementing int is good for performance even if you don't actually use it.
A Primary Key needn't be an autoincrementing field, in many cases this just means you are complicating your table structure.
Instead, a Primary Key should be the minimum collection of attributes (note that most DBMS will allow a composite primary key) that uniquely identifies a tuple.
In technical terms, it should be the field that every other field in the tuple is fully functionally dependent upon. (If it isn't you might need to normalise).
In practice, performance issues may mean that you merge tables, and use an incrementing field, but I seem to recall something about premature optimisation being evil...
Since you are doing replication, your are correct identities are something to stear clear of. I would make your GUID a primary key but nonclustered since you can't use newsequentialid. That stikes me as your best course. If you don't make it a PK but put a unique index on it, sooner or later that may cause people who maintain the system to not understand the FK relationships properly introducing bugs.