Currently, I am developing a product that does fairly intensive calculations using MS SQL Server 2005. At a high level, the architecture of my product is based on the concept of "runs" where each time I do some analytics it gets stored in a series of run tables (~100 tables per run).
The problem I'm having is that when the number of runs grows to be about 1,000 or so after a few months, performance on the database really seems to drop off, and specifically simple queries like checking for the existence of tables or creating views can take up to a second to two.
I've heard that using multiple filegroups, which I'm not currently doing, could help. Is this true, and if so, why/how would that help? Also, if there are other suggestions, even ones like, use fewer tables, I'm open to them. I just want to speed the database up and hopefully get it in a state where it will scale.
In terms of performance, the big gain in using separate files/filegroups is that it lets you spread your data across multiple physical disks. This is beneficial because with several disks, multiple data requests can be handled simultaneously (parallel is generally faster than serial). All other things being equal, this would tend to benefit performance, but the question of how much depends on your particular data set and the queries you're running.
From your description, the slow operations you're concerned about are creating tables and checking for the existence of tables. If you are generating 100 tables per run, then after 1000 runs you have 100,000 tables. I don't have much experience with creating that many tables in a single database, but you may be pressing the limits of the system tables that track the database schema. In this case, you might see some benefit by spreading your tables across more than one database (these databases could still all live within the same instance of SQL Server).
In general, the SQL Profiler tool is the best starting point for finding slow queries. There are data columns which indicate the CPU and IO cost of each SQL batch, which should point you to the worst offenders. Once you have found the problem queries, I would use the Query Analyzer to generate query plans for each of these queries, and see if you can tell what's making them slow. Do this by opening a query window, entering your query, and hitting Ctrl+L. A complete discussion of what might be slow would fill an entire book, but good things to look for are table scans (very slow for large tables) and inefficient joins.
In the end, you may be able to improve things simply by rewriting your queries, or you may have to make more broad changes to the table schema. For instance, maybe there's a way to create only one or a few tables per run, instead of 1000. More specifics about your particular setup would help us give a more detailed answer.
I also recommend this website for lots of tips on how to make things faster:
http://www.sql-server-performance.com/
When you talk about 100 tables per run, do you actually mean that you're creating new SQL tables? If so, I think that the architecture of your application may be the issue. I can't imagine a situation where you would need that many new tables as opposed to reusing the same few tables multiple times and simply adding a column or two to differentiate between runs.
If you're already reusing the same group of tables and new runs just mean additional rows in those tables, then the issue could simply be that the new data over time is hurting performance in one of several ways. For example:
The tables/indexes could be fragmented after awhile. Make sure that all of your tables have a clustered index. Check for fragmentation using sys.DM_DB_INDEX_PHYSICAL_STATS and issue ALTER INDEX with the REBUILD option if needed to defrag them.
The tables could simply be too large, so that inefficient on small tables are now obvious on the larger tables. Look into proper indexes on the tables to improve performance.
SQL Server will cache query plans (especially for stored procedures), but if the data in a table changes significantly over time that query plan may no longer be appropriate. Look into sp_recompile for your stored procedures to see if that's needed.
#2 is the culprit that I see most often in real world situations. Developers tend to develop using only a small set of test data and overlook proper indexing because you can do almost anything with a table of 20 rows and it will look fast.
Hope this helps
About 1000 of what? Single row writes? Multiple row transactions? Deletes?
A general tip would be to place the data files and log files on separate physical drives. SQL Server keeps track of every write to the log so having those in different drives should give you a general better performance.
But SQL Server tuning depends on what the application is actually doing. There are general tips but you have to measure your own thing...
The file groups being on different physical drives is what will give you the biggest performance boost, can also split up where the indexes are housed so that table writes and index accesses are hitting different disks. There's a lot you can do with partitioning, but that general concept is where the biggest speed impact comes from.
It can help with performance. moving certain tables/elemnts to distinct file areas/portions of the disk. this can reduce to a certain extent the amount of external fragmentation impacting the daabase.
I would also look at other factors such as tracesql to determine why queries etc are slowing down - there can be other factors such as query statistics, SP recompiles etc that are easier to fix and can give you greater gains in performance.
Split the tables across separate physical drives. If you have that much disk IO, you need a decent IO solution. Raid 10, fast disks, split the logs and DBs onto separate drives.
Re-examine your architecture - can you use multiple databases? If you create 1000s of tables in a go, you will soon hit some interesting bottlenecks that I've not had to deal with before. Multiple DBs should solve that. Think about having one "Controlling" db containing all your main meta-data, and then satellite DBs containing the actual data.
You don't mention any specs about your server - but we saw a decent increase in performance when we went from 8GB to 20GB RAM.
It could if you place them on separate drives - not logical but physical drives so IO is not slowing you down so much.
Related
We have a web service that pumps data into 3 database tables and a web application that reads that data in aggregated format in a SQL Server + ASP.Net environment.
There is so much data arriving to the database tables and so much data read from them and at such high velocity, that the system started to fail.
The tables have indexes on them, one of them is unique. One of the tables has billions of records and occupies a few hundred gigabytes of disk space; the other table is a smaller one, with only a few million records. It is emptied daily.
What options do I have to eliminate the obvious problem of simultaneously reading and writing from- and to multiple database tables?
I am interested in every optimization trick, although we have tried every trick we came across.
We don't have the option to install SQL Server Enterprise edition to be able to use partitions and in-memory-optimized tables.
Edit:
The system is used to collect fitness tracker data from tens of thousands of devices and to display data to thousands of them on their dashboard in real-time.
Way too broad of requirements and specifics to give a concrete answer. But a suggestion would be to setup a second database and do log shipping over to it. So the original db would be the "write" and the new db would be the "read" database.
Cons
Diskspace
Read db would be out of date by the length of time for log tranfser
Pro
- Could possible drop some of the indexes on "write" db, this would/could increase performance
- You could then summarize the table in the "read" database in order to increase query performance
https://msdn.microsoft.com/en-us/library/ms187103.aspx
Here's some ideas, some more complicated than others, their usefulness depending really heavily on the usage which isn't fully described in the question. Disclaimer: I am not a DBA, but I have worked with some great ones on my DB projects.
[Simple] More system memory always helps
[Simple] Use multiple files for tempdb (one filegroup, 1 file for each core on your system. Even if the query is being done entirely in memory, it can still block on the number of I/O threads)
[Simple] Transaction logs on SIMPLE over FULL recover
[Simple] Transaction logs written to separate spindle from the rest of data.
[Complicated] Split your data into separate tables yourself, then union them in your queries.
[Complicated] Try and put data which is not updated into a separate table so static data indices don't need to be rebuilt.
[Complicated] If possible, make sure you are doing append-only inserts (auto-incrementing PK/clustered index should already be doing this). Avoid updates if possible, obviously.
[Complicated] If queries don't need the absolute latest data, change read queries to use WITH NOLOCK on tables and remove row and page locks from indices. You won't get incomplete rows, but you might miss a few rows if they are being written at the same time you are reading.
[Complicated] Create separate filegroups for table data and index data. Place those filegroups on separate disk spindles if possible. SQL Server has separate I/O threads for each file so you can parallelize reads/writes to a certain extent.
Also, make sure all of your large tables are in separate filegroups, on different spindles as well.
[Complicated] Remove inserts with transactional locks
[Complicated] Use bulk-insert for data
[Complicated] Remove unnecessary indices
Prefer included columns over indexed columns if sorting isn't required on them
That's kind of a generic list of things I've done in the past on various DB projects I've worked on. Database optimizations tend to be highly specific to your situation...which is why DBA's have jobs. Some of the 'complicated' answers could be simple if your architecture supports it already.
Currently I am designing a database for use in our company. We are using SQL Server 2008. The database will hold data gathered from several customers. The goal of the database is to acquire aggregate benchmark numbers over several customers.
Recently, I have become worried with the fact that one table in particular will be getting very big. Each customer has approximately 20.000.000 rows of data, and there will soon be 30 customers in the database (if not more). A lot of queries will be done on this table. I am already noticing performance issues and users being temporarily locked out.
My question, will we be able to handle this table in the future, or is it better to split this table up into smaller tables for each customer?
Update: It has now been about half a year since we first created the tables. Following the advices below, I created a handful of huge tables. Since then, I have been experimenting with indexes and decided on a clustered index on the first two columns (Hospital code and Department code) on which we would have partitioned the table had we had Enterprise Edition. This setup worked fine until recently, as Galwegian predicted, performance issues are springing up. Rebuilding an index takes ages, users lock each other out, queries frequently take longer than they should, and for most queries it pays off to first copy the relevant part of the data into a temp table, create indices on the temp table and run the query. This is not how it should be. Therefore, we are considering to buy Enterprise Edition for use of partitioned tables. If the purchase cannot go through I plan to use a workaround to accomplish partitioning in Standard Edition.
Start out with one large table, and then apply 2008's table partitioning capabilities where appropriate, if performance becomes an issue.
Datawarehouses are supposed to be big (the clue is in the name). Twenty million rows is about medium by warehousing standards, although six hundred million can be considered large.
The thing to bear in mind is that such large tables have a different physics, like black holes. So tuning them takes a different set of techniques. The other thing is, users of a datawarehouse must understand that they are dealing with huge amounts of data, and so they must not expect sub-second response (or indeed sub-minute) for every query.
Partitioning can be useful, especially if you have clear demarcations such as, as in your case, CUSTOMER. You have to be aware that partitioning can degrade the performance of queries which cut across the grain of the partitioning key. So it is not a silver bullet.
Splitting tables for performance reasons is called sharding. Also, a database schema can be more or less normalized. A normalized schema has separate tables with relations between them, and data is not duplicated.
I am assuming you have your database properly normalized. It shouldn't be a problem to deal with the data volume you refer to on a single table in SQL Server; what I think you need to do is review your indexes.
Since you've tagged your question as 'datawarehouse' as well I assume you know some things about the subject. Depending on your goals you could go for a star-schema (a multidemensional model with a fact and dimensiontables). Store all fastchanging data in 1 table (per subject) and the slowchaning data in another dimension/'snowflake' tables.
An other option is the DataVault method by Dan Lindstedt. Which is a bit more complex but provides you with full flexibility.
http://danlinstedt.com/category/datavault/
In a properly designed database, that is not a huge anmout of records and SQl server should handle with ease.
A partioned single table is usually the best way to go. Trying to maintain separate indivudal customer tables is very costly in termas of time and effort and far more probne to errors.
Also examine you current queries if you are experiencing performance issues. If you don't have proper indexing (did you for instance index the foreign key fields?) queries will be slow, if you don't have sargeable queries they will be slow if you used correlated subqueries or cursors, they will be slow. Are you returning more data than is striclty needed? If you have select * anywhere in your production code, get rid of it and only return the fields you need. If you used views that call views that call views or if you used EAV table, you willhave performance iisues at this level. If you allowed a framework to autogenerate SQl code, you may well have badly perforimng queries. Remember Profiler is your friend. Of course you could also have a hardware issue, you need a pretty good sized dedicated server for that number of records. It won't work to run this on your web server or a small box.
I suggest you need to hire a professional dba with performance tuning experience. It is quite complex stuff. Databases desigend by application programmers often are bad performers when they get a real number of users and records. Database MUST be designed with data integrity, performance and security in mind. If you didn't do that the changes of having them are slim indeed.
Partioning is definately something to look into. I had a database that had 2 tables sharded. Each table contained around 30-35million records. I have since merged this into one large table and assigned some good indexes. So far, I've not had to partition this table as it's working a treat, but I'm keep partitioning in mind. One thing that I have noticed, compared to when the data was sharded, and that's the data import. It is now slower, but I can live with that as the Import tool can be re-written ;o)
One table and use table partitioning.
I think the advice to use NOLOCK is unjustified based on the information given. NOLOCK means you will get inaccurate and unreliable results from your queries (dirty and phantom reads). Before using NOLOCK you need to be sure that's not going to be a problem for your customers.
Is this a single flat table (no particular model)? Typically in data warehouses, you either have a normalized data model (third normal form at least - usually in an entity-relationship-model) or you have dimensional data (Kimball method or variations - usually fact tables with associated dimension tables in a set of stars).
In both cases, indexes play a large part, and partitioning can also play a part in getting queries to perform (but partitioning is not usually about performance but about maintenance being able to add and drop partitions quickly) over very large data sets - but it really depends on the order of aggregation and the types of queries.
One table, then worry about performance. That is, assuming you are collecting the exact same information for each customer. That way, if you have to add/remove/modify a column, you are only doing it in one place.
If you're on MS SQL server and you want to keep the single table, table partitioning could be one solution.
Keep one table - 20M rows isn't huge, and customers aren't exactly the kind of table that you can easily 'archive off', and the aggrevation of searching multiple tables to find a customer isn't worth the effort (SQL is likely to be much more efficient at BTree searching than your own invention is)
You will need to look into the performance and locking issues however - this will prevent your db from scaling.
You can also create supplemental tables that hold already calculated details on historical information if there are common queries.
Every time I find out that the performance of data retrieval from my database is slow. I try to figure out which part of my SQL query has the problem and I try to optimize it and also add some indexes to the table. But this does not always solve the problem.
My question is :
Are there any other tricks to make SQL server performance better?
What are the other reason which can make SQL server performance worse?
Inefficient query design
Auto-growing files
Too many indexes to be maintained on a table
Too few indexes on a table
Not properly choosing your clustered index
Index fragmentation due to poor maintenance
Heap fragmentation due to no clustered index
Too high FILLFACTORs used on indexes, causing excessive page splitting
Too low of a FILLFACTOR used on indexes, causing excessive space usage and increased scanning time
Not using covered indexes where appropriate
Non-selective indexes being used
Improper maintenance of statistics (out of date statistics)
Databases not normalized properly
Transaction logs and data sharing the same drive spindles
The wrong memory configuration
Too little memory
Too little CPU
Slow hard drives
Failing hard drives or other hardware
A 3D screensaver on your database server chewing up your CPU
Sharing the database server with other processes which compete for CPU and memory
Lock contention between queries
Queries which scan entire large tables
Front end code which searches data in an inefficent manner (nested loops, row by row)
CURSORS which are not necessary and/or are not FAST_FORWARD
Not setting NOCOUNT when you have large tables being cursored through.
Using a transaction isolation level which is too high (such as using SERIALIZABLE when it's not necessary)
Too many round trips between the client and the SQL Server (a chatty interface)
An unnecessary linked server query
A linked server query which targets a table on a remote server with no primary or candidate key defined
Selecting too much data
Excessive query recompilations
oh and there might be some others, too.
When I talk to new developers that have this problem I usually find that it is because of one of two problems. Both of them are fixed if you follow these 2 rules.
First, don’t retrieve any data that you don’t need. For example, if you are doing paging then don’t bring back 100 rows and then calculate which ones belong on the page. Have the stored proc figure it out and only retrieve the 10 you need.
Second, nothing is faster than work you don’t do. For example, I worked on a system where the full roles and rights for a user were retrieved with every page requested – this was 100’s of rows for some users. Even just saving this to session state on the first request and then using it from there for subsequent requests took a meaningful weight off of the database.
Suggest you get a good book on Performance tuning for the database you use (this is very much database specific). This is an extremely complex subject and cannot really be answered other than in generalities on the web.
For instance, Dave markle tell you inefficient queries can cause the problem and there are many many ways to write inefficient queries and many more ways to fix them.
If you're new to the database and you have access to the database engine tuning advisor, you can heuristically tune your database.
You basically capture the SQL queries being run against your DB in the SQL Profiler, then feed those to DETA. DETA effectively runs the queries (without altering your data) and then works out what information your database is missing (views, indexes, partitions, statistics etc.) to do the queries better.
It can then apply them for you and monitor them in the future. I'm not saying to assume that DETA is always right or to do things without understanding, but I've found that it's definately a good way to see what your queries are doing, how long they take, and how you can index the DB appropriately.
PS: With all that said, it's much better to invest in a good DBA at the start of a project so that you have good structures and indexing to start with. But thats not the position that you're in right now...
This is a very wide question. And there is a ton of answers already. Still I would like to add one important factor - Page Split. The problem is – there are good splits and bad splits. Following are good articles explaining how to use transaction_log extended event for identifying bad/nasty page splits
Tracking Problematic Pages Splits in SQL Server 2012 Extended Events - Jonathan Kehayias
Tracking page splits using the transaction log - Paul Randal
You mentioned:
I try to optimize it and also add some indexes
But, sometimes removing unused non-clustered indexes may help to improve performance as it help to reduce transaction logs. Read Top Reasons for Log Performance Problems
Wait statistics, or please tell me where it hurts gives an idea about using wait statistics for performance analysis.
To see some fresh ideas for performance, take a look at
Performance Considerations - sqlmag.com
Separate tables in joins to different disks (for parallel disk I/O - filegroups).
Avoid joins on columns with few unique values.
To understand JOIN, read Advanced JOIN Techniques
I've been asked to troubleshoot performance problems in a SQL Server 2005 database.
The challenge is not a huge amount of data, but the huge number of tables. There are more than 30,000 tables in a single database. The total data size is about 650 GB.
I don't have any control over the application that creates all those tables. The application uses roughly 2,500 tables per "division" on a larger company with 10-15 divisions.
How do you even start to check for performance problems? All the articles you find on VLDB (Very Large DB) are about the amount of data, not the amount of tables.
Any ideas? Pointers? Hints?
Start like any other kind of performance tuning. Among other things, you should not assume that the large number of tables constitutes a performance problem. It may be a red herring.
Instead, ask the users "what's slow"? Even if you measured the performance (using the Profiler, perhaps), your numbers might not match the perceived performance problem.
As others have noted, the number of tables is probably indicative of a bad design, but it is far from a slam dunk that it is the source of the performance problems.
The best advice I can give you for any performance optimization is to stop guessing about the source of the problem and go look for it. Above all else, don't start optimizing until you have positively identified the source of the problem.
I'd start by running some traces on the database and identify the poor performing queries. This would also tell you which tables are getting used the most by the application. In all likelihood a large number of those tables are probably either: A) leftover temp tables; B) no longer used; or C) working tables someone didn't clean up.
Putting the poor DB design aside, if no users are reporting slow response times then you don't currently have a performance problem.
If you do have a performance problem:
1) Check for fragmentation (dbcc showcontig)
2) Check the hardware specs, RAID/drive/file placement. Check the SQL server error logs.
If hardware seems underspecified or poorly designed, run Performance counters (see PAL
tool)
3) Gather trace data during a normal query work load and identify expensive queries (see this SO answer: How Can I Log and Find the Most Expensive Queries?)
Is the software creating all these tables? If so, maybe the same errors are being repeated over and over. Do all the tables have a primary key? Do they all have a clustered index? Are all the necessary non-clustered indexes present (those columns that are used for filtering and joins) etc etc etc.
Is upgrading the SQL Server 2008 an option? If so, you could take advantage of the new Policy Based Management feature to enforce best practice for this large amount of tables.
To start tuning now, I would use profiler to find those statements with the longest duration, then see what you can do to improve them (add indexes is usually the simplest way).
This question is related to another:
Will having multiple filegroups help speed up my database?
The software we're developing is an analytical tool that uses MS SQL Server 2005 to store relational data. Initial analysis can be slow (since we're processing millions or billions of rows of data), but there are performance requirements on recalling previous analyses quickly, so we "save" results of each analysis.
Our current approach is to save analysis results in a series of "run-specific" tables, and the analysis is complex enough that we might end up with as many as 100 tables per analysis. Usually these tables use up a couple hundred MB per analysis (which is small compared to our hundreds of GB, or sometimes multiple TB, of source data). But overall, disk space is not a problem for us. Each set of tables is specific to one analysis, and in many cases this provides us enormous performance improvements over referring back to the source data.
The approach starts to break down once we accumulate enough saved analysis results -- before we added more robust archive/cleanup capability, our testing database climbed to several million tables. But it's not a stretch for us to have more than 100,000 tables, even in production. Microsoft places a pretty enormous theoretical limit on the size of sysobjects (~2 billion), but once our database grows beyond 100,000 or so, simple queries like CREATE TABLE and DROP TABLE can slow down dramatically.
We have some room to debate our approach, but I think that might be tough to do without more context, so instead I want to ask the question more generally: if we're forced to create so many tables, what's the best approach for managing them? Multiple filegroups? Multiple schemas/owners? Multiple databases?
Another note: I'm not thrilled about the idea of "simply throwing hardware at the problem" (i.e. adding RAM, CPU power, disk speed). But we won't rule it out either, especially if (for example) someone can tell us definitively what effect adding RAM or using multiple filegroups will have on managing a large system catalog.
Without first seeing the entire system, my first recommendation would be to save the historical runs in combined tables with a RunID as part of the key - a dimensional model may also be relevant here. This table can be partitioned for improvement, which will also allow you to spread the table into other filegroups.
Another possibility it to put each run in its own database and then detach them, only attaching them as needed (and in read-only form)
CREATE TABLE and DROP TABLE are probably performing poorly because the master or model databases are not optimized for this kind of behavior.
I also recommend talking to Microsoft about your choice of database design.
Are the tables all different structures? If they are the same structure you might get away with a single partitioned table.
If they are different structures, but just subsets of the same set of dimension columns, you could still store them in partitions in the same table with nulls in the non-applicable columns.
If this is analytic (derivative pricing computations perhaps?) you could dump the results of a computation run to flat files and reuse your computations by loading from the flat files.
This seems to be a very interesting problem/application that you are working with. I would love to work on something like this. :)
You have a very large problem surface area, and that makes it hard to start helping. There are several solution parameters that are not evident in your post. For example, how long do you plan to keep the run analysis tables? There's a LOT other questions that need to be asked.
You are going to need a combination of serious data warehousing, and data/table partitioning. Depending on how much data you want to keep and archive you may need to start de-normalizing and flattening the tables.
This would be pretty good case where contacting Microsoft directly can be mutually beneficial. Microsoft gets a good case to show other customers, and you get help directly from the vendor.
We ended up splitting our database into multiple databases. So the main database contains a "databases" table that refers to one or more "run" databases, each of which contains distinct sets of analysis results. Then the main "run" table contains a database ID, and the code that retrieves a saved result includes the relevant database prefix on all queries.
This approach allows the system catalog of each database to be more reasonable, it provides better separation between the core/permanent tables and the dynamic/run tables, and it also makes backups and archiving more manageable. It also allows us to split our data across multiple physical disks, although using multiple filegroups would have done that too. Overall, it's working well for us now given our current requirements, and based on expected growth we think it will scale well for us too.
We've also noticed that SQL 2008 tends to handle large system catalogs better than SQL 2000 and SQL 2005 did. (We hadn't upgraded to 2008 when I posted this question.)