Clustered Indexes On Non-identity Columns To Speed Up Bulk Inserts?
Solution 1:
Well, I've put it to the test, and putting a clustered index on the two "chunk-defining" columns increases the performance of my table.
Inserting a chunk is now relatively fast compared to the situation where I had a clustered IDENTITY key, and about as fast as when I did not have any clustered index. Deleting a chunk is faster than with or without clustered index.
I think the fact that all the records I want to delete or insert are guaranteed to be all together on a certain part of the harddisk makes the tables faster - it would seem logical to me.
Update: After a year of experience with this design I can say that for this approach to work, it is necessary to schedule regular rebuilding of all the indexes (we do it once a week). Otherwise, the indexes become fragmented very soon and performance is lost. Nevertheless, we are in a process of migration to a new database design with partitioned tables, which is basically better in every way - except for the Enterprise Server license cost, but we've already forgotten about it by now. At least I have.
Solution 2:
A clustered index is a physical index, a physical data structure, a row order. If you insert in the middle of the clustered index, the data will be physically inserted in the middle of the present data. I imagine a serious performance issue in this case. I only know this from theory, because if I do this in practice, it will be a mistake according to my theoretical knowledge.
Therefore, I only use (and advise the use) of clustered indexes on fields that are always, physically, inserted at the end, preserving the order.
A clustered index can be placed on a datetime field which marks the moment of insertion or something like that, because physically they will be ordered after appending a row. Identity is a good clustered index also, but not always relevant for querying.
In your solution you place a [uniquifier] field, but why do this when you can put an identity that will do just that? It will be unique, physically ordered, small (for foreign keys in other tables means smaller index), and in some cases faster.
Can't you try this, experiment? I have a similar situation here, where I have 4 billion rows, constantly more are inserting (up to 100 per second), the table has no primary key and no clustered index, so the propositions in this topic are VERY interesting for me too.
Solution 3:
Can I use clustered indexes to speed up bulk inserts in big tables?
Never! Imagine another million rows that you need to put in that table and have them physically ordered it is a colossal loss in performance in the long run.
Can I then still efficiently use foreign key relationships if my IDENTITY column is not the clustered index anymore?
Absolutely. By the way, clustered index is no silver bullet and may be slower than your ordinary index.
Solution 4:
Have a look at the System.Data.SqlClient.SqlBulkCopy API. Given your requirements to write signficant numbers of rows in and out of the database, it might be what you need?
Bulk copy streams the data into the table in a single operation then performs the index check once. I use it to copy 500,000 rows in and out of a database table and it's performance is an order of magnitude better than any other technique I've tried, assuming that your application can be structured to take use of the API?
Solution 5:
i've been playing around with some etl stuff the last little bit. i went through jsut regularly inserting into the table, then removing and readding indexes before and after the insert, tried merge statements, then i finally tried ssis. I'm sold on ssis. Just yesterday i managed to cut an etl process (~24 million records, ~6gb) from ~1-1 1/2 hours per run to ~24 minutes, jsut by letting ssis handle the inserts.
i believe with advanced services you should be able to use ssis.
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