I just learned about the wonders of columnstore indexes and how you can "Use the columnstore index to achieve up to 10x query performance gains over traditional row-oriented storage, and up to 7x data compression over the uncompressed data size."
With such sizable performance gains, is there really any reason to NOT use them?
The main disadvantage is that you'll have a hard time reading only a part of the index if the query contains a selective predicate. There are ways to do it (partitioning, segment elimination) but those are neither particularly easy to reliably implement nor do they scale to complex requirements.
For scan-only workloads columnstore indexes are pretty much ideal.
Columnstore Indexes is especially beneficial for DataWarehousing (DW). Meaning that you will only perform updates or deletes at certain times.
This is due to their special design with delta loading and more features. This video will show great detail and a nice basic overview of what the exact difference is Columnstore Index.
Traditional
If you however have a high I/O (input and output) of the application; Columnstore Index is not ideal since traditional row indexing will find and manipulate (using the rows found through the index) on that specific target. An example of this would be a ATM application which frequently changes the values of the rows of the given persons accounts.
ColumnStore
Columnstore Indexing indexes throughout the COLUMNS which is not ideal in this case since the row values will be spread throughout the segmentations (columnsindexes).
I highly recommend the video!
I want to also elaborate on the non-clustered vs clustered columnstore:
Non-clustered Columnstore (update in 2012) saves the WHOLE data again meaning (2X data) twice the data.
Where as Clustered Columnstore index (update in 2014) only takes up 5MB for about 16GB of data. This is due to the RTE (runtime encoding) which saves the amount of duplicate data in each column. Making the index take up less extra storage.
Hello A very detailed explanation of columns store index can be found here.
ColumnStore Index
A columnstore index is a technology for storing, retrieving and managing data by using a columnar data format, called a columnstore.
This feature has been introduced with SQL Server 2012 which intends to significantly speed-up the processing time of common data warehousing queries. The main objectives of columnstore indexes is appropriate for typical data warehousing data sets and improve the performance of the query whenever data is pulled from the huge datasets.
They are column based indexes which are capable to transform the data warehousing experience for users by enabling faster performance for common data warehousing queries such as filtering, aggregating, grouping and star-join queries. They store the data column-wise instead of row-wise, as indexes currently do.
Related
I have a large table with 10 million records and is used for one of our existing applications. we are working on a new application wherein it requires only filtered result set of large table with 7000 records.
My question is will there be any performance gain going for a smaller table with 7000 records vs querying large table with filter condition(and it will joined to few other tables in the schema which are completely independant from existing application)? or should I avoid redundancy maintaining all the data in one table? This is the design in data warehouse. Please suggest!
Thank you!
For almost any database, using a sample table will be noticeably faster. This is because reading the records will require loading fewer data pages.
In addition, if the base table is being updated, then a "snapshot" is isolated from page, table, and row locks that occur on the main table. This is good from a performance perspective, but it means that the versions can get out-of-synch, which may be bad.
And, from a querying perspective, the statistics on the sample would be more accurate. This helps the optimizer choose the best query plans.
I can think of two cases where performance might not improve significantly. The first is if your database supports clustered indexes and the rows that you want are defined by a range of index keys (or a single key). These will be "adjacent", so the clustered index would scan about the same number of pages. There is a slight overhead for the actual index structure.
Similarly, if your records were so large that there was one record per data page, then the advantage of a second table would be less. It would eliminate the index access overhead, but not reduce the number of reads.
None of these considerations say whether or not you should use a separate table. You should test in your environment. The overhead of managing a separate table (and there is a cost to creating and deleting it both in terms of performance and application complexity) may outweigh small performance gains.
We are working with U-SQL tables and have questions related to Clustered Index. In U-SQL table, parallelism is managed by how data is partitioned and distributed. Does Clustered Index impact parallelism as well in U-SQL table? Secondly how it manages data skew in a distribution bucket?
Clustered index is not impacting parallelism per se, but it may impact if you read the data using an index seek or index scan depending on the query predicate. So it impacts the performance of accessing the data inside a vertex.
Data skew is not managed. If you have skew you will have to either find a better distribution key, use a skewfactor hint or use ROUND ROBIN distribution.
I would like to add clustered columnstore indexes to some tables in a SQL Server 2014 database. Before doing so, I need to gather a good estimate of required memory. How can I predict clustered columnstore memory usage?
Things I know:
The size of the tables on disk
How the tables will be queried
The growth rate of these tables on disk
You will find an answer here - source - under the title "Memory Usage".
I'd rather not copy & paste the relevant section as the Terms Of Use of sqlservercentral.com state "You are not permitted to copy or use any of the Redgate Materials for any purpose." Though presumably the Terms Of Use themselves are exempt from this condition. :-)
My company is moving to SQL Server 2008 R2. We have a table with tons of archive data. Majority of the queries that uses this table employs DateTime value in the where statement. For example:
Query 1
SELECT COUNT(*)
FROM TableA
WHERE
CreatedDate > '1/5/2010'
and CreatedDate < '6/20/2010'
I'm making the assumption that partitions are created on CreatedDate and each partition is spread out across multiple drives, we have 8 CPUs, and there are 500 million records in the database that are evenly spread out across the dates from 1/1/2008 to 2/24/2011 (38 partitions). This data could also be portioned in to quarters of a year or other time durations, but lets keep the assumptions to months.
In this case I would believe that the 8 CPU's would be utilized, and only the 6 partitions would be queried for dates between 1/5/2010 and 6/20/2010.
Now what if I ran the following query and my assumptions are the same as above.
Query 2
SELECT COUNT(*)
FROM TableA
WHERE State = 'Colorado'
Questions?
1. Will all partitions be queried? Yes
2. Will all 8 CPUs be used to execute the query? Yes
3. Will performance be better than querying a table that is not partitoned? Yes
4. Is there anything else I'm missing?
5. How would Partition Index help?
I answer the first 3 questions above, base on my limited knowledge of SQL Server 2008 Partitioned Table & Parallelism. But if my answers are incorrect, can you provide feedback any why I'm incorrect.
Resource:
Video: Demo SQL Server 2008 Partitioned Table Parallelism (5 minutes long)
MSDN: Partitioned Tables and Indexes
MSDN: Designing Partitions to Manage Subsets of Data
MSDN: Query Processing Enhancements on Partitioned Tables and Indexes
MSDN: Word Doc: Partitioned Table and Index Strategies Using SQL Server 2008 white paper
BarDev
Partitioning is never an option for improving performance. The best you can hope for is to have on-par performance with non-partitioned table. Usually you get a regression that increases with the number of partitions. For performance you need indexes, not partitions. Partitions are for data management operations: ETL, archival etc. Some claim that partition elimination is possible performance gain, but for anything partition elimination can give placing the leading index key on the same column as the partitioning column will give much better results.
Will all partitions be queried?
That query needs an index on State. Otherwise is a table scan, and will scan the entire table. A table scan over a partitioned table is always slower than a scan over the same size non-partitioned table. The index itself can be aligned on the same partition scheme, but the leading key must be State.
Will all 8 CPUs be used to execute the query?
Parallelism has nothing to do with partitioning, despite the common misconception of the contrary. Both partitioned and non-partitioned range scans can be use a parallel operator, it will be the Query Optimizer decision.
Will performance be better than querying a table that is not
partitioned?
No
How would Partition Index help?
An index will help. If the index has to be aligned, then it must be partitioned. A non-partitioned index will be faster than a partitioned one, but the index alignment requirement for switch-in/switch-out operations cannot be circumvented.
If you're looking at partitioning, it should be because you need to do fast switch-in switch-out operations to delete old data past retention policy period or something similar. For performance, you need to look at indexes, not at partitioning.
Partitioning can increase performance--I have seen it many times. The reason partitioning was developed was and is performance, especially for inserts. Here is an example from the real world:
I have multiple tables on a SAN with one big ole honking disk as far as we can tell. The SAN administrators insist that the SAN knows all so will not optimize the distribution of data. How can a partition possibly help? Fact: it did and does.
We partitioned multiple tables using the same scheme (FileID%200) with 200 partitions ALL on primary. What use would that be if the only reason to have a partitioning scheme is for "swapping"? None, but the purpose of partitioning is performance. You see, each of those partitions has its own paging scheme. I can write data to all of them at once and there is no possibility of a deadlock. The pages cannot be locked because each writing process has an unique ID that equates to a partition. 200 partitions increased performance 2000x (fact) and deadlocks dropped from 7500 per hour to 3-4 per day. This for the simple reason that page lock escalation always occurs with large amounts of data and a high volume OLTP system and page locks are what cause deadlocks. Partitioning, even on the same volume and file group, places the partitioned data on different pages and lock escalation has no effect since processes are not attempting to access the same pages.
THe benefit is there, but not as great, for selecting data. But typically the partitioning scheme would be developed with the purpose of the DB in mind. I am betting Remus developed his scheme with incremental loading (such as daily loads) rather than transactional processing in mind. Now if one were frequently selecting rows with locking (read committed) then deadlocks could result if processes attempted to access the same page simultaneously.
But Remus is right--in your example I see no benefit, in fact there may be some overhead cost in finding the rows across different partitions.
the very first question i have is if your table has a clustered index on it. if not, you'll want one.
Also, you'll want a covering index for your queries. Covering Indexes
If you have a lot of historical data you might look into an archiving process to help speed up your oltp applications.
So, it seems to me like a query on a table with 10k records and a query on a table with 10mil records are almost equally fast if they are both fetching roughly the same number of records and making good use of simple indexes(auto increment, record id type indexed field).
My question is, will this extend to a table with close to 4 billion records if it is indexed properly and the database is set up in such a way that queries always use those indexes effectively?
Also, I know that inserting new records in to a very large indexed table can be very slow because all the indexes have to be recalculated, if I add new records only to the end of the table can I avoid that slow down, or will that not work because the index is a binary tree and a large chunk of the tree will still have to be recalculated?
Finally, I looked around a bit for a FAQs/caveats about working with very large tables, but couldn't really find one, so if anyone knows of something like that, that link would be appreciated.
Here is some good reading about large tables and the effects of indexing on them, including cost/benefit, as you requested:
http://www.dba-oracle.com/t_indexing_power.htm
Indexing very large tables (as with anything database related) depends on many factors, incuding your access patterns, ratio of Reads to Writes and size of available RAM.
If you can fit your 'hot' (i.e. frequently accessed index pages) into memory then accesses will generally be fast.
The strategy used to index very large tables, is using partitioned tables and partitioned indexes. BUT if your query does not join or filter on the partition key then there will no improvement in performance over an unpartitioned table i.e. no partition elimination.
SQL Server Database Partitioning Myths and Truths
Oracle Partitioned Tables and Indexes
It's very important to keep your indexes as narrow as possible.
Kimberly Tripp's The Clustered Index Debate Continues...(SQL Server)
Accessing the data via a unique index lookup will slow down as the table gets very large, but not by much. The index is stored as a B-tree structure in Postgres (not binary tree which only has two children per node), so a 10k row table might have 2 levels whereas a 10B row table might have 4 levels (depending on the width of the rows). So as the table gets ridiculously large it might go to 5 levels or higher, but this only means one extra page read so is probably not noticeable.
When you insert new rows, you cant control where they are inserted in the physical layout of the table so I assume you mean "end of the table" in terms of using the maximum value being indexed. I know Oracle has some optimisations around leaf block splitting in this case, but I dont know about Postgres.
If it is indexed properly, insert performance may be impacted more than select performance. Indexes in PostgreSQL have vast numbers of options which can allow you to index part of a table or the output of an immutable function on tuples in the table. Also size of the index, assuming it is usable, will affect speed much more slowly than will the actual scan of the table. The biggest difference is between searching a tree and scanning a list. Of course you still have disk I/O and memory overhead that goes into index usage, and so large indexes don't perform as well as they theoretically could.