You have 100 partitions. So it's likely that your hash values modulus 100 are all even numbers.
This is a general risk of hashing. If the hashing function produces only values with a pattern like this, you can get imbalanced partitions.
One way to fix this is to use a prime number for the number of partitions. For example 101 is a prime number close to your ...
Do you have any advice or suggestion?
All the operations you described are metadata-only, so I wouldn't expect any significant difference in performance.
It is strange that you don't have Foreign Keys on your [App] schema. You've already paid the cost of checking the DRI, and if you have validated foreign keys in your database the Query Optimizer can take ...
That is a pretty hefty database to shrink. I'd just like to point out two things you want to check for, before you start your shrink operations:
LOB data. This will take forever, since there are no backpointers for the LOB data. I.e., a LOB page was moved and all SQL Server know is what table it belongs to. It has to do a table scan to find the row, one for ...
One possibility would be to use Transactional replication and add static row filters to filter out the older data, this would also have the advantage of a shorter cut over time to the new server (I'm assuming you're doing a side by side migration not an in place upgrade).
you can read about this here - https://www.brentozar.com/archive/2017/12/whats-bad-shrinking-databases-dbcc-shrinkdatabase/
and about when shrinking is a must what can you do here - https://www.brentozar.com/archive/2020/07/what-if-you-really-do-need-to-shrink-a-database/
When we shrink our database, it introduces both external and internal fragmentation, it ...
You say that that no combination of columns is "unique"?
bbaird rightly suggests PRIMARY KEY (SerialNumber, ItemCode, ArrivalDate) to help with performance, but it won't work because of lack of uniqueness?
In that case, do this:
PRIMARY KEY (SerialNumber, ItemCode, ArrivalDate, ID)
This gives you
The clustering benefit all your ...
So going down your list of questions:
Do this partitioning do any good in terms of performance with my
existing table structures and indexes.
No! If you always query on date, maybe.
Do this partitioning do any good for selects statements I have
Only your third query, and that's a maybe.
What this partitioning will impact on other three ...
Partitions have a lot of restrictions
You can'_t use HASH with multiple columns
If you want multiple columns they have to be in the primary key, like shown in the example.
For your time you see in the second example. you have to make even more sacrifices and if that is what you seek, you have to test it
CREATE TABLE abc
itemid bigint(20) NOT NULL ...
You should specify which database system and version you're using as this will affect your actual choices available for how you structure and manage the data.
You mentioned large in your context means 100,000 new records a day, so let's plan for something an order of magnitude bigger, 1 million records a day. In a month that's 30 million new records, in a ...
For partition pruning to work, it is important that your partition key is simple and part of the where clause of your queries. In your example you can simplify to
check((extract(hour from "timestamp")) = 0 # equality
check((extract(hour from "timestamp")) = 1 # etc.
check((extract(hour from "timestamp")) = 23
At this time it ...
Your query is asking a bad question. As-is, the optimizer has no idea if the value 1001 exists in the table. The predicate <> 1000 does not mean "everything under 1000", unless you give it more information.
CREATE PARTITION FUNCTION
AS RANGE RIGHT FOR VALUES (1000);
Another way you can handle this that would minimize downtime (assuming the majority of the data is being deleted) is you can INSERT everything within the last month into a new Table. DROP the old Table, and use sp_rename to instantly rename your new Table to the old Table's name.
WHERE OldTable.[date] >= '...
You need to delete rows in batches to avoid lock escalation. Lock escalation happens when SQL Server switches from row or page locks to locking the entire table. Lock escalation conserves memory when SQL Server detects a large number of row or page locks have been taken, and more are needed to complete the operation. This may be why you're having blocking ...