I am using Azure SQL. I found the select performance of table partition is slower than the original. Can Sql Server table partition actually improve the select performance? As the Partition count increases the query performance degrades (i.e. 36 partition, 915 partition).
Also, what's the method to avoid partition scan and do normal table heap scan in a partitioned table?
1.About Partitioned Tables.
If your table has large data, you can use the table and index partitioning.
One of the benefits of partitioning is that:
You may improve query performance, based on the types of queries you frequently run and on your hardware configuration. For example, the query optimizer can process equi-join queries between two or more partitioned tables faster when the partitioning columns in the tables are the same, because the partitions themselves can be joined.
Here is the documentation:Partitioned Tables and Indexes
When you run the select query of a partition table, the query will search from your partitions until the data you need is found. So the select performance may be slower than original sometimes.
2.When your table is creating, you must need to specify if it is the partition table. Once the table is created as the partition table, you could not change it. I didn't find the method for you, I guess maybe that's not supported.
I hope it can helps you.
Related
I have a large table consisting of 4 Billion+ rows and 50 columns, most of which are either datetime or numeric except a few which are varchar.
Data will be inserted into the table on a weekly basis (about 20 million rows).
I expect queries with where clauses on some of the datetime columns, and a couple of the the varchar columns. There is no primary key in the table.
There are no indexes, nor the table is partitioned. I am using SQL Server 2016.
I understand that I need to partition or index the table, but I am not sure which approach to take or both in-fact.
Since the table is large, should I create the indexes first or should I create the partitions first? If I do create the indexes and then create the partitions, what should I do to maintain these with new data coming in weekly.
EDIT: Also, minimal updates and deletes are expected on the table
I understand that I need to partition or index the table
You need to understand what you gain from partitioning. It is not at all the case that SQL Server requires partitioning on big tables to function adequately. SQL Server scales to arbitrary tables sizes without any inherent issues.
Common benefits of partitioning are:
Mass deletion in constant time
Different storage for older partitions
Not backing up old partitions
Sometimes in special situations (e.g. columnstore), partitioning can help as a strategy to speed up queries. Normally, indexing is better for that.
Essentially, partitioning splits the table physically into multiple sub tables. Most often this has a negative effect on query plans. Indexes are perfectly capable of restricting the set of data that needs to be touched. Partitions are worse for that.
Most of the queries will be filtering on the datetime columns and on some of the varchar columns. Like, get data for a certain daterange for a certain entity. With the indexes, it will be fragmented a lot because of new inserts and rebuilding/reorganising the indexes will also consume a lot of time. I can do it but again not sure which approach.
It seems you can best solve this by indexing:
Index according to the queries you expect.
Maintain the indexes properly. This is not too hard. For example, rebuild them after the weekly load.
Since the table is large, should I create the indexes first or should I create the partitions first?
Set up that partitioning objects first. Then, create or rebuild the clustered index on the new partitioning scheme. If possible drop other indexes first and recreate them afterwards (might not work due to availability restrictions).
what should I do to maintain these with new data coming in weekly.
What concerns do you have? New data will be stored in the appropriate partitions automatically. Make sure to create new partitions before loading the data. Keep partitions ready for 2 weeks in advance. The latest partitions must always be empty to avoid costly splits.
There is no primary key in the table.
Most often this is a not a good design. Most tables should have a primary key and a clustered index. If there is no natural key use an artifical one such as a bigint identity.
You definitely can apply partitioning but my feeling is that it will not gain you what you maybe expect. But it will force you to take on additional maintenance burdens, possibly reduce performance and there is risk of making mistakes that threaten availability. Simplicity is important.
I have a table that is having approximately 450,000 records per month going into it. It is an audit table of sorts that tracks changes to other tables in the database. That is, inserts, updates and deletes of records. Typically this table is not queried (perhaps only 2-3 times per month to examine how data in other tables changed and under very specific circumstances)
It has been put to me that we should consider partitioning this table to help improve database performance. If the table is only being inserted to 99.9% of the time and rarely queried, would there be any tangible benefit to this partitioning this table?
Thanks.
If the table is only being inserted to 99.9% of the time and rarely
queried, would there be any tangible benefit to this partitioning this
table?
Partitioning is mostly a manageability feature. I would expect no difference in insert performance with or without able partitioning. For SELECT queries, partitioning may improve performance of large scans if partitions can be eliminated (i.e. partitioning column specified in WHERE clause, but indexing and query tuning is usually the key to performance.
Partitioning can improve performance of purge operations. For example, you could use a monthly sliding window to purge an entire month of data at once rather than individual row deletes. I don't know if that's with the trouble with only 450K rows/month, though.
I think you want to get fast access to your recent data.
Add date column as first column in primary key clustered instead of partioning
My understanding in an Azure SQL Data Warehouse table, each column is indexed in a column store table (maybe I'm wrong). If that is the case, why is there ability to create additional indexes (Create Index statement). I was thinking maybe due to composite index (Multiple field indexes).
But in a query with a single field join, after creating an index on that field, the performance got much better.
Is there some general rule to go by when creating indexes in Azure Sql Data Warehouse?
Generally we should create index on the column which are more used in any query. Index are also a burden on a database because Index take some space in the disk. Creating Index on all the column of a table is not a good idea. we should make Index on the basis of query.
Besides index we can use Partition or table space to boost query performance.
I have a table on a MS Azure SQL DB with 60,000 rows that is starting to take longer to execute with a SELECT statement. The first column is the "ID" column which is the primary key. As of right now, there is no other indexes. The thing about this table is the rows are based on recent news articles, therefore the last rows in the table are always going to be accessed more than the older rows.
If possible, how can I tell SQL Server to start querying at the end of the table working backwards when I do a SELECT operation?
Also, what can I do with indexes to make reading from the table faster with the last rows as the priority?
Typically, the SQL Server query optimizer will choose the data access strategy based on the available indexes, data distribution statistics & query. For example, SQL Server can scan an index forward, backward, physical order & so on. The choice is determined based on many variables.
In your example, if there is a date/time column in the table then you can index and use that in your predicate(s). This will automatically enable use of that index if that is the most selective one.
Alternatively, you can partition the table based on a column and access most recent data based on the partitioning key. This is common use of partitioning with a rolling window. With this approach, the predicate in your queries will specify the partitioning column which will help the optimizer pick the correct set of partitions to scan. This will dramatically reduce the amount of data that needs to be searched since partition elimination happens before execution depending on the query plan.
Microsoft in its MSDN entry about altering SQL 2005 partitions, listed a few possible approaches:
Create a new partitioned table with the desired partition function, and then insert the data from the old table into the new table by using an INSERT INTO...SELECT FROM statement.
Create a partitioned clustered index on a heap
Drop and rebuild an existing partitioned index by using the Transact-SQL CREATE INDEX statement with the DROP EXISTING = ON clause.
Perform a sequence of ALTER PARTITION FUNCTION statements.
Any idea what will be the most efficient way for a large scale DB (millions of records) with partitions based on the dates of the records (something like monthly partitions), where data spreads over 1-2 years?
Also, if I mostly access (for reading) recent information, will it make sense to keep a partition for the last X days, and all the rest of the data will be another partition? Or is it better to partition the rest of the data too (for any random access based on date range)?
I'd recommend the first approach - creating a new partitioned table and inserting into it - because it gives you the luxury of comparing your old and new tables. You can test query plans against both styles of tables and see if your queries are indeed faster before cutting over to the new table design. You may find there's no improvement, or you may want to try several different partitioning functions/schemes before settling on your final result. You may want to partition on something other than date range - date isn't always effective.
I've done partitioning with 300-500m row tables with data spread over 6-7 years, and that table-insert approach was the one I found most useful.
You asked about how to partition - the best answer is to try to design your partitions so that your queries will hit a single partition. If you tend to concentrate queries on recent data, AND if you filter on that date field in your where clauses, then yes, have a separate partition for the most recent X days.
Be aware that you do have to specify the partitioned field in your where clause. If you aren't specifying that field, then the query is probably going to hit every partition to get the data, and at that point you won't have any performance gains.
Hope that helps! I've done a lot of partitioning, and if you want to post a few examples of table structures & queries, that'll help you get a better answer for your environment.