I have a large domain set of tables in a database - over 100 tables. Every single one uses a uniqueidentifier as a PK.
I'm realizing now that my mistake is that these are also by default, the clustered index.
Consider a table with this type of structure:
Orders
Id (uniqueidentifier) Primary Key
UserId (uniqueidentifier)
.
.
.
.
Other columns
Most queries are going to be something like "Get top 10 orders for user X sorted by OrderDate".
In this case, would it make sense to create a clustered index on UserId,Id...that way the data is physically stored sorted by UserId?
I'm not too concerned about Inserts and Updates - those will be few enough that performance loss there isn't a big deal. I'm mostly concerned with READs.
A clustered index means that data is physically stored in the order of the values. By default, the primary key is used for the clustered index.
The problem with GUIDs is that they are generated is (essentially) random order. That means that inserts are happening "in the middle" of the table. And, such inserts result in fragmentation.
Without getting into database internals, this is a little hard to explain. But what it means is that inserts require much more work than just inserting the values "at the end" of the table, because new rows go in the middle of a data page so the other rows have to be moved around.
SQL Server offers a solution for this, newsequentialid(). On a given server, this returns a sequential value which is inserted at the end. Often, this is an excellent compromise if you have to use GUIDs.
That said, I have a preference for just plain old ints as ids -- identity columns. These are smaller, so they take up less space. This is particularly true for indexes. Inserts work well because new values go at the "end" of the table. I also find integers easier to work with visually.
Using identity columns for primary keys and foreign key references still allows you to have unique GUID columns for each identity, if that is a requirement for the database (say for interfacing to other applications).
Clustered index is when you want to retrieve rows for a range of values for a given column. As data is physically arranged in that order, the rows can be extracted very efficiently.
a GUID, while excellent for a primary key, could be positively detrimental to performance, as there will be additional cost for inserts and no perceptible benefit on selects.
So yes, don't cluster an index on GUID.
Due to some recent database problems we've been having recently, I've been looking at the state of our indexes and trying to reduce the fragmentation.
The app has several tables of names such as BoysNames and GirlsNames which we use to set attributes on User objects we created. The obvious attribute here would be Gender. These tables can have anywhere from a few hundred to 10,000 rows.
These tables are structured like this:
Name - nvarchar(50) - PK & Clustered Index
DateCreated - datetime
When I tell Sql Server to reorganize or rebuild the indexes on all my tables, most table fragmentaiton goes down to 0%, but some of these Name tables are at 50% fragemented straight away.
I only access these tables in 2 places:
The first is when I select every name from the table and store it in
memory to use against new users coming into the system so I can do something like this: if
(boysNames.Contains(user.Name)) {user.Gender = "M"}; This happens quite
often.
The second is when I'm adding new names to the list, I check for the
existance of a name, if it doesn't exist, I add it. This happens
rarely.
So what I need to know is:
Is this high level of fragmentation going to be causing me problems? How can I reduce the the index fragmentation to 0% when it's being set to 50% straight after a reorganize/rebuild?
Should I have used an int as the primary key and put an index on Name, or was nvarchar the right choice for primary key?
If the index pages are resident in memory, which is likely with that few rows, fragmentation does not matter. You can benchmark that with a count(*) query. Starting with the 2nd execution, you should see in-memory speeds. If you now compare the results for a 100% and a 0% fragmented table, you should see no difference.
I don't think you have a problem. If you insist, you can set a fill factor lower than 100 so that there is room for new rows when you insert rows at random places. Start with 90 and lower it in increments of 5 until you are happy with the rate fragmentation develops.
Clustering on an IDENTITY field would remove the fragmentation for the clustered index, but you'd probably need an index on Name which again fragments. If you do not need any index at all, make this a heap table and be done with it. I recommend very much against this, though.
I am trying to see if using a custom index for a specific type of data might reduce fragmentation in my database.
[Edit: we are using MS SQL Server 2008 R2]
I have an SQL database containing timestamped measurement data. Lots of data is inserted all the time, but once inserted it practically never needs to be updated. These timestamps are, however, not unique, as several devices (around 50 of them) measure the data at the same time.
This means that every 50 rows in the table contain equal timestamp values. This data is received more or less simultaneously, although I could take additional care to ensure that rows are written as sequentially as possible (if that would help), perhaps by keeping them in memory for some time and then writing only when I get the data from all the devices for a single timestamp.
We are using NHibernate with Guid.Comb to avoid index lookups we would have with plain bigint IDs. As opposed to plain GUIDs, this should reduce fragmentation, but for so many inserts, fragmentation nevertheless happens very soon.
Since my data is timestamped, and data is inserted almost sequentially (increasing timestamps), I am wondering if there is a more clever way to create a primary key with a unique clustered index for this table. Timestamp column is basically a bigint number (.NET DateTime ticks).
I have also noticed that a non-clustered index over that same timestamp column also gets pretty fragmented. So what index strategy would you recommend to reduce heap fragmentation in this case?
Maybe take a look at this answer, HiLo looks interesting.
Also, maybe your fragmentation is not result of the discrepancy between the ordering of the index values and the order in which they are added, but natural file growth effect (as explained here)?
A seperate column for a key doesn't make a lot of sense for this table since you won't be updating any of the data. I imagine you'll be doing a lot of queries though, probably based on that timestamp column.
You could try making the primary key a combination of the timestamp column and a device id column. You could try making that clustered. That should allow you to write nearly as fast as possible. If you query by device however, you may need another index on device id and timestamp (the reverse). I wouldn't make the reverse the clustered one though, as that will make the writes happen all over the table rather than on the trailing pages. And if most queries involve a date range and more than one device, clustering on timestamp first should give you the best performance.
It's my understanding that the quickest way to access a particular row is by its ROWID. In INFORMIX-SE 7.3, when I do a SELECT ROWID FROM table I notice that its values are type SERIAL[INT]. In Oracle, they are SERIAL[HEX]. Has anyone ever used ROWID for any practical use? If I wanted to locate the most recent row added to a table, would SELECT MAX(ROWID) FROM table be quicker and more reliable than say SELECT MAX(pk_id) FROM table, where pk_id is a user-created SERIAL column? What other practical use have you ever put ROWID to work for you?
Your understanding is not necessarily correct. The ROWID property in SQL Server is primarily intended for replication as a way to guarantee that the table has a single-field unique index value. This way the replication system does not have to account for any specific primary key semantics that your design might employ, while still being able to identify every row by a single column. No table is required to have a ROWID column unless it is part of a merge replication publication, so it's not something that every table has, unlike Oracle. It also doesn't serve the same purpose (they're Guid's--or uniqueidentifier in T-SQL parlance--on SQL Server and are random, not sequential integers like they are on Oracle).
The quickest way to retrieve a row from a table is by accessing the row via the clustered index. A table can only have one clustered index, as it's what determines the physical ordering of the rows on the disk. Furthermore, if the table has a primary key, the primary key is the clustered index. While it's possible to declare a table without a primary key and assign the clustered index to something else, I can't (off the top of my head) fathom a reason why you'd want to do this (or, for practical purposes, how you can justify having a table without a primary key).
In short, that means that the quickest way to retireve a row is by using the primary key of the table. Unless the ROWID column is the primary key (which is certainly possible to do), then it isn't the fastest way.
Well, I can only really tell how it works in Oracle, using it for 19+ years :-)
Put simply, ROWID is an internel identification, that acts like an physical address. It can be split into database file no, block no, and row no. So obtaining the ROWID makes the db engine able to look the data up in a single direct IO.
In an index the B* tree will have ROWIDs on the leaf nodes pointing directly the location of the data, e.g. in a primary index.
Being an physical address it is submit to change on relocation on disk, which can happen after restoring a backup, rebuilding a table, or export/import of data.
The db engine can do some tricks, e.g. when moving a pluggable tablespace from one instance to another to avoid rebuilding indexes, however this is strickly db engine internals.
So to keep out of trouble leave the ROWID for internal use for the db engine. Storing the ROWID for your own usage will eventually lead to inconsistency.
In Informix-SE, the ROWID is basically the record number within the C-ISAM file that is used to hold the table. SE only deals in fixed size records, of course (no VARCHAR data).
In Informix Dynamic Server, the ROWID is (a) more complex (page number plus slot number) and (b) not always present (fragmented tables do not expose the ROWID, unless the table was created WITH ROWIDS, in which case the ROWID is a physical column that is indexed after all) - be aware!
If no data is ever deleted and you are using SE, then selecting the row with the maximum ROWID will be the most recently added row. If a row is deleted, then that space will eventually be reused, and then the most recently added row ceases to be the one with the maximum ROWID. (IDS does not make that promise for a variety of complex reasons.)
The SE implementation of ROWID does not store the ROWID in the table, and does not create an index on it, but it does not need an index because it knows the formula for where to go to find the data (offset in data file = ROWID * RowSize), give or take a plus one on RowSize or a minus one ROWID or both.
As to practical use for ROWID, the style that was used before fragmentation was added to IDS was to select a list of ROWID values for the records of interest in the table, maintaining that list in memory:
SELECT ROWID
FROM InterestingTable
WHERE SomeColumn = xxx
AND AnotherColumn < yyy;
Then, the program could present these rows one at time, fetching the current data via the stored ROWID. The ROWID for a record would not change while a program was running. This ensured that the current data - whether edits from the current user or someone else - was shown when the record was displayed.
There's a program you're familiar with, ISQL Perform, that behaves like this. And it does not work with fragmented tables (necessarily in IDS; SE does not support fragmented tables) unless they are created with a physical ROWID column with the WITH ROWIDS clause.
Perhaps the term "RDBMS" rather than "an SQL server"?
Attaching any purpose to a ROWID is a bad idea. Particularly if you're in the habit of dropping and recreating tables. If your table needs a SERIAL PK, then that's what it should have. No good can come of using ROWIDs within your application.
I recently asked this question:
MS SQL share identity seed amongst tables
(Many people wondered why)
I have the following layout of a table:
Table: Stars
starId bigint
categoryId bigint
starname varchar(200)
But my problem is that I have millions and millions of rows. So when I want to delete stars from the table Stars it is too intense on SQL Server.
I cannot use built in partitioning for 2005+ because I do not have an enterprise license.
When I do delete though, I always delete a whole category Id at a time.
I thought of doing a design like this:
Table: Star_1
starId bigint
CategoryId bigint constaint rock=1
starname varchar(200)
Table: Star_2
starId bigint
CategoryId bigint constaint rock=2
starname varchar(200)
In this way I can delete a whole category and hence millions of rows in O(1) by doing a simple drop table.
My question is, is it a problem to have hundreds of thousands of tables in your SQL Server? The drop in O(1) is extremely desirable to me. Maybe there's a completely different solution I'm not thinking of?
Edit:
Is a star ever modified once it is inserted? No.
Do you ever have to query across star categories? I never have to query across star categories.
If you are looking for data on a particular star, would you know which table to query? Yes
When entering data, how will the application decide which table to put the data into? The insertion of star data is done all at once at the start when the categoryId is created.
How many categories will there be? You can assume there will be infinite star categories. Let's say up to 100 star categories per day and up to 30 star categories not needed per day.
Truly do you need to delete the whole category or only the star that the data changed for? Yes the whole star category.
Have you tried deleting in batches? Yes we do that today, but it is not good enough.
od enough.
Another technique is mark the record for deletion? There is no need to mark a star as deleted because we know the whole star category is eligible to be deleted.
What proportion of them never get used? Typically we keep each star category data for a couple weeks but sometimes need to keep more.
When you decide one is useful is that good for ever or might it still need to be deleted later?
Not forever, but until a manual request to delete the category is issued.
If so what % of the time does that happen? Not that often.
What kind of disc arrangement are you using? Single filegroup storage and no partitioning currently.
Can you use sql enterprise ? No. There are many people that run this software and they only have sql standard. It is outside of their budget to get ms sql enterprise.
My question is, is it a problem to have hundreds of thousands of tables in your SQL Server?
Yes. It is a huge problem to have this many tables in your SQL Server. Every object has to be tracked by SQL Server as metadata, and once you include indexes, referential constraints, primary keys, defaults, and so on, then you are talking about millions of database objects.
While SQL Server may theoretically be able to handle 232 objects, rest assured that it will start buckling under the load much sooner than that.
And if the database doesn't collapse, your developers and IT staff almost certainly will. I get nervous when I see more than a thousand tables or so; show me a database with hundreds of thousands and I will run away screaming.
Creating hundreds of thousands of tables as a poor-man's partitioning strategy will eliminate your ability to do any of the following:
Write efficient queries (how do you SELECT multiple categories?)
Maintain unique identities (as you've already discovered)
Maintain referential integrity (unless you like managing 300,000 foreign keys)
Perform ranged updates
Write clean application code
Maintain any sort of history
Enforce proper security (it seems evident that users would have to be able to initiate these create/drops - very dangerous)
Cache properly - 100,000 tables means 100,000 different execution plans all competing for the same memory, which you likely don't have enough of;
Hire a DBA (because rest assured, they will quit as soon as they see your database).
On the other hand, it's not a problem at all to have hundreds of thousands of rows, or even millions of rows, in a single table - that's the way SQL Server and other SQL RDBMSes were designed to be used and they are very well-optimized for this case.
The drop in O(1) is extremely desirable to me. Maybe there's a completely different solution I'm not thinking of?
The typical solution to performance problems in databases is, in order of preference:
Run a profiler to determine what the slowest parts of the query are;
Improve the query, if possible (i.e. by eliminating non-sargable predicates);
Normalize or add indexes to eliminate those bottlenecks;
Denormalize when necessary (not generally applicable to deletes);
If cascade constraints or triggers are involved, disable those for the duration of the transaction and blow out the cascades manually.
But the reality here is that you don't need a "solution."
"Millions and millions of rows" is not a lot in a SQL Server database. It is very quick to delete a few thousand rows from a table of millions by simply indexing on the column you wish to delete from - in this case CategoryID. SQL Server can do this without breaking a sweat.
In fact, deletions normally have an O(M log N) complexity (N = number of rows, M = number of rows to delete). In order to achieve an O(1) deletion time, you'd be sacrificing almost every benefit that SQL Server provides in the first place.
O(M log N) may not be as fast as O(1), but the kind of slowdowns you're talking about (several minutes to delete) must have a secondary cause. The numbers do not add up, and to demonstrate this, I've gone ahead and produced a benchmark:
Table Schema:
CREATE TABLE Stars
(
StarID int NOT NULL IDENTITY(1, 1)
CONSTRAINT PK_Stars PRIMARY KEY CLUSTERED,
CategoryID smallint NOT NULL,
StarName varchar(200)
)
CREATE INDEX IX_Stars_Category
ON Stars (CategoryID)
Note that this schema is not even really optimized for DELETE operations, it's a fairly run-of-the-mill table schema you might see in SQL server. If this table has no relationships, then we don't need the surrogate key or clustered index (or we could put the clustered index on the category). I'll come back to that later.
Sample Data:
This will populate the table with 10 million rows, using 500 categories (i.e. a cardinality of 1:20,000 per category). You can tweak the parameters to change the amount of data and/or cardinality.
SET NOCOUNT ON
DECLARE
#BatchSize int,
#BatchNum int,
#BatchCount int,
#StatusMsg nvarchar(100)
SET #BatchSize = 1000
SET #BatchCount = 10000
SET #BatchNum = 1
WHILE (#BatchNum <= #BatchCount)
BEGIN
SET #StatusMsg =
N'Inserting rows - batch #' + CAST(#BatchNum AS nvarchar(5))
RAISERROR(#StatusMsg, 0, 1) WITH NOWAIT
INSERT Stars2 (CategoryID, StarName)
SELECT
v.number % 500,
CAST(RAND() * v.number AS varchar(200))
FROM master.dbo.spt_values v
WHERE v.type = 'P'
AND v.number >= 1
AND v.number <= #BatchSize
SET #BatchNum = #BatchNum + 1
END
Profile Script
The simplest of them all...
DELETE FROM Stars
WHERE CategoryID = 50
Results:
This was tested on an 5-year old workstation machine running, IIRC, a 32-bit dual-core AMD Athlon and a cheap 7200 RPM SATA drive.
I ran the test 10 times using different CategoryIDs. The slowest time (cold cache) was about 5 seconds. The fastest time was 1 second.
Perhaps not as fast as simply dropping the table, but nowhere near the multi-minute deletion times you mentioned. And remember, this isn't even on a decent machine!
But we can do better...
Everything about your question implies that this data isn't related. If you don't have relations, you don't need the surrogate key, and can get rid of one of the indexes, moving the clustered index to the CategoryID column.
Now, as a rule, clustered indexes on non-unique/non-sequential columns are not a good practice. But we're just benchmarking here, so we'll do it anyway:
CREATE TABLE Stars
(
CategoryID smallint NOT NULL,
StarName varchar(200)
)
CREATE CLUSTERED INDEX IX_Stars_Category
ON Stars (CategoryID)
Run the same test data generator on this (incurring a mind-boggling number of page splits) and the same deletion took an average of just 62 milliseconds, and 190 from a cold cache (outlier). And for reference, if the index is made nonclustered (no clustered index at all) then the delete time only goes up to an average of 606 ms.
Conclusion:
If you're seeing delete times of several minutes - or even several seconds then something is very, very wrong.
Possible factors are:
Statistics aren't up to date (shouldn't be an issue here, but if it is, just run sp_updatestats);
Lack of indexing (although, curiously, removing the IX_Stars_Category index in the first example actually leads to a faster overall delete, because the clustered index scan is faster than the nonclustered index delete);
Improperly-chosen data types. If you only have millions of rows, as opposed to billions, then you do not need a bigint on the StarID. You definitely don't need it on the CategoryID - if you have fewer than 32,768 categories then you can even do with a smallint. Every byte of unnecessary data in each row adds an I/O cost.
Lock contention. Maybe the problem isn't actually delete speed at all; maybe some other script or process is holding locks on Star rows and the DELETE just sits around waiting for them to let go.
Extremely poor hardware. I was able to run this without any problems on a pretty lousy machine, but if you're running this database on a '90s-era Presario or some similar machine that's preposterously unsuitable for hosting an instance of SQL Server, and it's heavily-loaded, then you're obviously going to run into problems.
Very expensive foreign keys, triggers, constraints, or other database objects which you haven't included in your example, which might be adding a high cost. Your execution plan should clearly show this (in the optimized example above, it's just a single Clustered Index Delete).
I honestly cannot think of any other possibilities. Deletes in SQL Server just aren't that slow.
If you're able to run these benchmarks and see roughly the same performance I saw (or better), then it means the problem is with your database design and optimization strategy, not with SQL Server or the asymptotic complexity of deletions. I would suggest, as a starting point, to read a little about optimization:
SQL Server Optimization Tips (Database Journal)
SQL Server Optimization (MSDN)
Improving SQL Server Performance (MSDN)
SQL Server Query Processing Team Blog
SQL Server Performance (particularly their tips on indexes)
If this still doesn't help you, then I can offer the following additional suggestions:
Upgrade to SQL Server 2008, which gives you a myriad of compression options that can vastly improve I/O performance;
Consider pre-compressing the per-category Star data into a compact serialized list (using the BinaryWriter class in .NET), and store it in a varbinary column. This way you can have one row per category. This violates 1NF rules, but since you don't seem to be doing anything with individual Star data from within the database anyway anyway, I doubt you'd be losing much.
Consider using a non-relational database or storage format, such as db4o or Cassandra. Instead of implementing a known database anti-pattern (the infamous "data dump"), use a tool that is actually designed for that kind of storage and access pattern.
Must you delete them? Often it is better to just set an IsDeleted bit column to 1, and then do the actual deletion asynchronously during off hours.
Edit:
This is a shot in the dark, but adding a clustered index on CategoryId may speed up deletes. It may also impact other queries adversely. Is this something you can test?
This was the old technique in SQL 2000 , partitioned views and remains a valid option for SQL 2005. The problem does come in from having large quantity of tables and the maintenance overheads associated with them.
As you say, partitioning is an enterprise feature, but is designed for this large scale data removal / rolling window effect.
One other option would be running batched deletes to avoid creating 1 very large transaction, creating hundreds of far smaller transactions, to avoid lock escalations and keep each transaction small.
Having separate tables is partitioning - you are just managing it manually and do not get any management assistance or unified access (without a view or partitioned view).
Is the cost of Enterprise Edition more expensive than the cost of separately building and maintaining a partitioning scheme?
Alternatives to the long-running delete also include populating a replacement table with identical schema and simply excluding the rows to be deleted and then swapping the table out with sp_rename.
I'm not understanding why whole categories of stars are being deleted on a regular basis? Presumably you are having new categories created all the time, which means your number of categories must be huge and partitioning on (manually or not) that would be very intensive.
Maybe on the Stars table set the PK to non-clustered and add a clustered index on categoryid.
Other than that, is the server setup well done regarding best practices for performance? That is using separate physical disks for data and logs, not using RAID5, etc.
When you say deleting millions of rows is "too intense for SQL server", what do you mean? Do you mean that the log file grows too much during the delete?
All you should have to do is execute the delete in batches of a fixed size:
DECLARE #i INT
SET #i = 1
WHILE #i > 0
BEGIN
DELETE TOP 10000 FROM dbo.SuperBigTable
WHERE CategoryID = 743
SELECT #i = ##ROWCOUNT
END
If your database is in full recovery mode, you will have to run frequent transaction log backups during this process so that it can reuse the space in the log. If the database is in simple mode, you shouldn't have to do anything.
My only other recommendation is to make sure that you have an appropriate index in CategoryId. I might even recommend that this be the clustered index.
If you want to optimize on a category delete clustered composite index with category at the first place might do more good than damage.
Also you could describe the relationships on the table.
It sounds like the transaction log is struggling with the size of the delete. The transaction log grows in units, and this takes time whilst it allocates more disk space.
It is not possible to delete rows from a table without enlisting a transaction, although it is possible to truncate a table using the TRUNCATE command. However this will remove all rows in the table without condition.
I can offer the following suggestions:
Switch to a non-transactional database or possibly flat files. It doesn't sound like you need atomicity of a transactional database.
Attempt the following. After every x deletes (depending on size) issue the following statement
BACKUP LOG WITH TRUNCATE_ONLY;
This simply truncates the transaction log, the space remains for the log to refill. However Im not sure howmuch time this will add to the operation.
What do you do with the star data? If you only look at data for one category at any given time this might work, but it is hard to maintain. Every time you have a new category, you will have to build a new table. If you want to query across categories, it becomes more complex and possibly more expensive in terms of time. If you do this and do want to query across categories a view is probably best (but do not pile views on top of views). If you are looking for data on a particular star, would you know which table to query? If not then how are you going to determine which table or are you goign to query them all? When entering data, how will the application decide which table to put the data into? How many categories will there be? And incidentally relating to each having a separate id, use the bigint identities and combine the identity with the category type for your unique identifier.
Truly do you need to delete the whole category or only the star that the data changed for?
And do you need to delete at all, maybe you only need to update information.
Have you tried deleting in batches (1000 records or so at a time in a loop). This is often much faster than deleting a million records in one delete statement. It often keeps the table from getting locked during the delete as well.
Another technique is mark the record for deletion. Then you can run a batch process when usage is low to delete those records and your queries can run on a view that excludes the records marked for deletion.
Given your answers, I think your proposal may be reasonable.
I know this is a bit of a tangent, but is SQL Server (or any relational database) really a good tool for this job? What relation database features are you actually using?
If you are dropping whole categories at a time, you can't have much referential integrity depending on it. The data is read only, so you don't need ACID for data updates.
Sounds to me like you are using basic SELECT query features?
Just taking your idea of many tables - how can you realise that...
What about using dynamic queries.
create the table of categories that have identity category_id column.
create the trigger on insert for this tale - in it create table for stars with the name dynamically made from category_id.
create the trigger on delete - in it drop the corresponding stars table also with the help of dynamically created sql.
to select stars of concrete category you can use function that returns table. It will take category_id as a parameter and return result also through dynamic query.
to insert stars of new category you firstly insert new row in categories table and then insert stars to appropriate table.
Another direction in which I would make some researches is using xml typed column for storing stars data. The main idea here is if you need to operate stars only by categories than why not to store all stars of concrete category in one cell of the table in xml format. Unfortunately I absolutely cannot imaging what will be the performance of such decision.
Both this variants are just like ideas in brainstorm.
As Cade pointed out, adding a table for each category is manually partitioning the data, without the benefits of the unified access.
There will never be any deletions for millions of rows that happen as fast as dropping a table, without the use of partitions.
Therefore, it seems like using a separate table for each category may be a valid solution. However, since you've stated that some of these categories are kept, and some are deleted, here is a solution:
Create a new stars table for each new
category.
Wait for the time period to expire where you decide whether the stars for the category are kept or not.
Roll the records into the main stars table if you plan on keeping them.
Drop the table.
This way, you will have a finite number of tables, depending on the rate you add categories and the time period where you decide if you want them or not.
Ultimately, for the categories that you keep, you're doubling the work, but the extra work is distributed over time. Inserts to the end of the clustered index may be experienced less by the users than deletes from the middle. However, for those categories that you're not keeping, you're saving tons of time.
Even if you're not technically saving work, perception is often the bigger issue.
I didn't get an answer to my comment on the original post, so I am going under some assumptions...
Here's my idea: use multiple databases, one for each category.
You can use the managed ESE database that ships with every version of Windows, for free.
Use the PersistentDictionary object, and keep track of the starid, starname pairs that way. If you need to delete a category, just delete the PersistentDictionary object for that category.
PersistentDictionary<int, string> starsForCategory = new PersistentDictionary<int, string>("Category1");
This will create a database called "Category1", on which you can use standard .NET dictionary methods (add, exists, foreach, etc).