Scenario I have a 10 million row table. I partition it into 10 partitions, which results in 1 million rows per partition but I do not do anything else (like move the partitions to different file groups or spindles)
Will I see a performance increase? Is this in effect like creating 10 smaller tables? If I have queries that perform key lookups or scans, will the performance increase as if they were operating against a much smaller table?
I'm trying to understand how partitioning is different from just having a well indexed table, and where it can be used to improve performance.
Would a better scenario be to move the old data (using partition switching) out of the primary table to a read only archive table?
Is having a table with a 1 million row partition and a 9 million row partition analagous (performance wise) to moving the 9 million rows to another table and leaving only 1 million rows in the original table?
Partitioning is not a performance feature, is for maintenance operations like moving data between tables and dropping data real fast. Partitioning a 10M rows table into 10 partitions of 1M rows each not only that it won't increase most queries performance, will likely make quite a few of them slower.
No query can operate against a smaller set of rows of a single partition unless the query can be determined that it only needs row in that partition alone. But this can be solved, always, and much better, by properly choosing the clustered index on the table, or at least a good covering non-clustered index.
Well first partitioning will only help you if you are using the enterprise version.
I believe it will improve your performance, although the actual benefit you'll get will depend on your specific work load (yhea, it always depends).
It is not exactly as creating 10 smaller tables but if your queries run against the ranges of your partitions the data in that partition will only be "touch". In those cases I think the performance improvement will be noticeable. In cases where queries run across the partition ranges the performance will be worse.
I think you'll have to try what solution fits your data the best, in some cases partition will help you and in some other will do the opposite, another benefit from partitioning is that you won't have to worry about to move your data around.
This is a good article about partitioning - bol
Related
Our team is using Microsoft SQL Server, accessed using Entity Framework Core.
We have a table with 5-40 million records anticipated, which we want to optimize for high-velocity record create, read and update.
Each record is small and efficient:
5 integer (one of which is the indexed primary key)
3 bit
1 datetime
plus 2 varchar(128) - substantially larger than the other columns.
The two varchar columns are populated during creation, but used in only a tiny minority of subsequent reads, and never updated. Assume 10 reads and 4 updates per create.
Our question is: does it improve performance to put these larger columns in a different table (imposing a join penalty for create, but only a tiny minority of reads) versus writing two stored procedures, using one which retrieves the non-varchar columns for the majority of queries, and one which retrieves all columns when required?
Put another way: how much does individual record size affect SQL performance?
does it improve performance to put these larger fields in a different table (imposing a join penalty for create, but only a tiny minority of reads)
A much better alternative is to create indexes which exclude the larger columns and which support frequent access patterns.
The larger columns on the row will have very little cost on single-row operations on the table, but will substantially reduce the row density on the clustered index. So if you have to scan the clustered index, the having large, unused columns drives up the IO cost of your queries. That's where an appropriate non-clustered index can offload any scanning operations away from the clustered index.
But, as always, you should simply test. 40M rows is simple to generate, and then write your top few queries and test their performance with different combinations of indexes.
About: How much does individual record size affect SQL performance?
It depends on the query that is being executed.
For example, in a select statement, if the varchar field is not included in any part of the query, then it is almost not affecting performance.
You could try both models and measure the querys in SSMS, using
SET STATISTICS TIME ON
SET STATISTICS IO ON
GO
Or analyze the query in the Database Engine Tuning Advisor.
Indexing on the three fields which are not NVARCHAR can help
It depends on your mix of reads, writes, updates
It depends if you are appending to the end of a table such as a real-time data collection
Entity Framework is most likely not good for a simple table structure with millions of rows especially if the table is not related to many other tables
I have tables have millions of partitions.
Should I reduce partition count for performance?
As my experience of spark application or hive query system, too many partition was bad for performance.
If you do not have auto clustering on the table, it will not be auto defragmented. So if you write to the table frequently with small row counts, it will be in very bad shape.
Partition count impacts compile time badly, as every partition has metadata that is load to plan/optimize the query. I would suggest doing a rebuild test (select into a new transient table) and run some comparable queries to see the different in compile time.
We have a number of table that sorting (thus auto clustering) does not make sense for as the use pattern is always full-table scan, thus we just rebuild those tables on schedules to keep the partition count down, and for us, that rebuild cost is worth the performance gain.
As with everything Snowflake you should run a test, and see how it is for you. And monitor hot spots as they can and do change.
In Snowflake, there are micro-partitions, and they are managed automatically. Therefore you do not need to worry about the number of micro-partition.
https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions.html#what-are-micro-partitions
It says:
Micro-partitioning is automatically performed on all Snowflake tables.
Tables are transparently partitioned using the ordering of the data as
it is inserted/loaded.
From this page, I understand that micro-partitions are managed by Snowflake, and you do not need to focus on reducing the partition count (this is the original question).
This should also help to understand the difference between clustering and micro-partitions:
https://docs.snowflake.com/en/user-guide/table-considerations.html#when-to-set-a-clustering-key
If you read the above link, you can see that it is not a must to define clustering on even large tables to get a good query performance!
As the original question about reducing the partition count, I also have to say that clustering does not always reduce the number of partitions, but it is another story.
I know that indexes hurt insert/update performance, but I'm trying to troubleshoot and determine the right balance between query performance and insert/update performance.
We've created a number of views (about 20) for some really really complicated queries. They're really slow for seeking by keys (can take 20 seconds to scan for 5 to 10 keys).
Indexing these views (with both clustered and non-clustered indexes on the various key columns) speeds up their performance in the area of 80x to 100x. It also hurts insert/update performance to the point that a script which inserts about 100 rows into various related tables takes about 45 seconds to run instead of being instantaneous.
I'd prefer not to go the OLAP route for these views (it would add a whole new layer of complexity....and the views are currently updatable, which would pose a reverse synchronization problem)...so I'm trying to figure out how to balance query performance with insert/update performance.
Can someone please suggest how to diagnose the specific problem indexes - and potential ways of reducing their impact on inserts/updates?
I've already tried using covering indexes, indexes with INCLUDEs and composite clustered indexes as alternatives to see if it makes a difference (it doesn't really).
Thanks.
For this scenario please use single column else filtered indexes and avoid composite ones which have more than two columns.
I am working on a badly designed database in MS SQL 2008. It has a table with "60,000,000" records and it increases by about "4,000,000" records per week.
So if I use "SQL 2008 table partitioning", will it help? If so, please suggest the steps to follow.
Couple of points you need to check before arriving at Table Partitioning Strategy
Total Records "60,000,000" records and it increases by about "4,000,000" records per week
Question - Can you Archive Remove any Data on Weekly basis. Reason is to know if you use queries for OLTP or OLAP you can think of having another machine where data is replicated and retained
Do you know statics of how queries look like, Indexes that are being used
Objective of partitioning is to reduce the number of partitioning to look for required data. Example - Suppose your query where clause filter is based on month number. Then in that case if you have parition done every month, then when you have a query for month = 5, then only that particular partition would be queried
You have to provide little more information on schema, query usage patterns, indexes
Help with what? You did not explain the problem. Are certain queries slow? Do you have problems with ETL? Do you have problems deletion of obsolete data? Do you have problems with maintenance and backups?
As a general rule table partitioning is a feature for easing ETL via fast partition switch operations and for data location administration (distribute tables across several filegroups and leverage filegroup features like piece-meal restore).
One area partitioning does not help is performance. Performance issues are addressed with indexes and the best you can hope for is on-par performance with a non-partitioned case. There is a misconception going around that partition elimination will help performance, see also Introduction to Partitioned Tables. What is usually missed is that for performance a much much better alternative is to simply move the partitioning key as the leftmost key in the clustered key, which will cause a range scan with, in the worse case, at least on-par, if not better, performance than compared with the partition elimination. Partition elimination helps queries that have to handle partitioned tables when partitioning was required due to the other reasons (ETL, fast deletes, filegroup management).
I have a scenario in which there's a huge amount of status data about an item.
The item's status is updated from minute to minute, and there will be about 50,000 items in the near future. So that, in one month, there will be about 2,232,000,000 rows of data. I must keep at least 3 months in the main table, before archieving older data.
I must plan to achieve quick queries, based on a specific item (its ID) and a data range (usually, up to one month range) - e.g. select A, B, C from Table where ItemID = 3000 and Date between '2010-10-01' and '2010-10-31 23:59:59.999'
So my question is how to design a partitioning structure to achieve that?
Currently, I'm partitioning based on the "item's unique identifier" (an int) mod "the number of partitions", so that all partitions are equally distributed. But it has the drawback of keeping one additional column on the table to act as the partition column to the partition function, therefore, mapping the row to its partition. All that add a little bit of extra storage. Also, each partition is mapped to a different filegroup.
Partitioning is never done for query performance. With partitioning the performance will always be worse, the best you can hope for is no big regression, but never improvement.
For query performance, anything a partition can do, and index can do better, and that should be your answer: index appropriately.
Partitioning is useful for IO path control cases (distribute on archive/current volumes) or for fast switch-in switch-out scenarios in ETL loads. So I would understand if you had a sliding window and partition by date so you can quickly switch out the data that is no longer needed to be retained.
Another narrow case for partitioning is last page insert latch contention, like described in Resolving PAGELATCH Contention on Highly Concurrent INSERT Workloads.
Your partition scheme and use case does not seem to fit any of the scenarios in which it would benefit (maybe is the last scenario, but is not clear from description), so most likely it hurts performance.
I do not really agree with Remus Rusanu. I think the partitioning may improve performance if there's a logical reason (related to your use cases). My guess is that you could partition ONLY on the itemID. The alternative would be to use the date as well, but if you cannot predict that a date range will not cross the boundaries of a given partition (no queries are sure to be with a single month), then I would stick to itemId partitioning.
If there are only a few items you need to compute, another option is to have a covering index: define an INDEX on you main differentiation field (the itemId) which INCLUDEs the fields you need to compute.
CREATE INDEX idxTest ON itemId INCLUDE quantity;
Applicative partitioning actually CAN be beneficial for query performance. In your case you have 50K items and 2G rows. You could for example create 500 tables, each named status_nnn where nnn is between 001 and 500 and "partition" your item statuses equally among these tables, where nnn is a function of the item id. This way, given an item id, you can limit your search a priori to 0.2% of the whole data (ca. 4M rows).
This approach has a lot of disadvantages, as you'll probably have to deal with dynamic sql and a other unpleasant issues, especially if you need to aggregate data from different tables. BUT, it will definitely improve performance for certain queries, s.a. the ones you mention.
Essentially applicative partitioning is similar to creating a very wide and flat index, optimized for very specific queries w/o duplicating the data.
Another benefit of applicative partitioning is that you could in theory (depending on your use case) distribute your data among different databases and even different servers. Again, this depends very much on your specific requirements, but I've seen and worked with huge data sets (billions of rows) where applicative partitioning worked very well.