We have a 4-5TB SQL Server database. The largest table is around 800 GB big containing 100 million rows. 4-5 other comparable tables are 1/3-2/3 of this size. We went through a process to create new indexes to optimize performance. While the performance certainly improved we saw that the newly inserted data was slowest to query.
It's a financial reporting application with a BI tool working on top of the database. The data is loaded overnight continuing in the late morning, though the majority of the data is loaded by 7am. Users start to query data around 8am through the BI tool and are most concerned with the latest (daily) data.
I wanted to know if newly inserted data causes indexes to go out of order. Is there anything we can do so that we get better performance on the newly inserted data than the old data. I hope I have explained the issue well here. Let me know in case of any missing information. Thanks
Edit 1
Let me describe the architecture a bit.
I have a base table (let’s call it Base) with Date,id as clustered index.
It has around 50 columns
Then we have 5 derived tables (Derived1, Derived2,...) , according to different metric types, which also have Date,Id as clustered index and foreign key constraint on the Base table.
Tables Derived1 and Derived2 have 350+ columns. Derived3,4,5 have around 100-200 columns. There is one large view created to join all the data tables due limitations of the BI tool. The date,ID are the joining columns for all the tables joining to form the view (Hence I created clustered index on those columns). The main concern is with regard to BI tool performance. The BI tool always uses the view and generally sends similar queries to the server.
There are other indexes as well on other filtering columns.
The main question remains - how to prevent performance from deteriorating.
In addition I would like to know
If NCI on Date,ID on all tables would be better bet in addition to the clustered index on date,ID.
Does it make sense to have 150 columns as included in NCI for the derived tables?
You have about a 100 million rows, increasing every day with new portions and those new portions are usually selected. I should use partitioned indexes with those numbers and not regular indexes.
Your solution within sql server would be partitioning. Take a look at sql partitioning and see if you can adopt it. Partitioning is a form of clustering where groups of data share a physical block. If you use year and month for example, all 2018-09 records will share the same physical space and easy to be found. So if you select records with those filters (and plus more) it is like the table has the size of 2018-09 records. That is not exactly accurate but its is quite like it. Be careful with data values for partitioning - opposite to standard PK clusters where each value is unique, partitioning column(s) should result a nice set of different unique combinations thus partitions.
If you cannot use partitions you have to create 'partitions' yourself using regular indexes. This will require some experiments. The basic idea is data (a number?) indicating e.g. a wave or set of waves of imported data. Like data imported today and the next e.g. 10 days will be wave '1'. Next 10 days will be '2' and so on. Filtering on the latest e.g. 10 waves, you work on the latest 100 days import effectively skip out all the rest data. Roughly, if you divided your existing 100 million rows to 100 waves and start on at wave 101 and search for waves 90 or greater then you have 10 million rows to search if SQL is put correctly to use the new index first (will do eventually)
This is a broad question especially without knowing your system. But one thing that I would try is manually update your stats on the indexes/table once you are done loading data. With tables that big, it is unlikely that you will manipulate enough rows to trigger an auto-update. Without clean stats, SQL Server won't have an accurate histogram of your data.
Next, dive into your execution plans and see what operators are the most expensive.
Related
I'm a long time programmer who has little experience with DBMSs or designing databases.
I know there are similar posts regarding this, but am feeling quite discombobulated tonight.
I'm working on a project which will require that I store large reports, multiple times per day, and have not dealt with storage or tables of this magnitude. Allow me to frame my problem in a generic way:
The process:
A script collects roughly 300 rows of information, set A, 2-3 times per day.
The structure of these rows never change. The rows contain two columns, both integers.
The script also collects roughly 100 rows of information, set B, at the same time. The
structure of these rows does not change either. The rows contain eight columns, all strings.
I need to store all of this data. Set A will be used frequently, and daily for analytics. Set B will be used frequently on the day that it is collected and then sparingly in the future for historical analytics. I could theoretically store each row with a timestamp for later query.
If stored linearly, both sets of data in their own table, using a DBMS, the data will reach ~300k rows per year. Having little experience with DBMSs, this sounds high for two tables to manage.
I feel as though throwing this information into a database with each pass of the script will lead to slow read times and general responsiveness. For example, generating an Access database and tossing this information into two tables seems like too easy of a solution.
I suppose my question is: how many rows is too many rows for a table in terms of performance? I know that it would be in very poor taste to create tables for each day or month.
Of course this only melts into my next, but similar, issue, audit logs...
300 rows about 50 times a day for 6 months is not a big blocker for any DB. Which DB are you gonna use? Most will handle this load very easily. There are a couple of techniques for handling data fragmentation if the data rows exceed more than a few 100 millions per table. But with effective indexing and cleaning you can achieve the performance you desire. I myself deal with heavy data tables with more than 200 million rows every week.
Make sure you have indexes in place as per the queries you would issue to fetch that data. Whats ever you have in the where clause should have an appropriate index in db for it.
If you row counts per table exceed many millions you should look at partitioning of tables DBs store data in filesystems as files actually so partitioning would help in making smaller groups of data files based on some predicates e.g: date or some unique column type. You would see it as a single table but on the file system the DB would store the data in different file groups.
Then you can also try table sharding. Which actually is what you mentioned....different tables based on some predicate like date.
Hope this helps.
You are over thinking this. 300k rows is not significant. Just about any relational database or NoSQL database will not have any problems.
Your design sounds fine, however, I highly advise that you utilize the facility of the database to add a primary key for each row, using whatever facility is available to you. Typically this involves using AUTO_INCREMENT or a Sequence, depending on the database. If you used a nosql like Mongo, it will add an id for you. Relational theory depends on having a primary key, and it's often helpful to have one for diagnostics.
So your basic design would be:
Table A tableA_id | A | B | CreatedOn
Table B tableB_id | columns… | CreatedOn
The CreatedOn will facilitate date range queries that limit data for summarization purposes and allow you to GROUP BY on date boundaries (Days, Weeks, Months, Years).
Make sure you have an index on CreatedOn, if you will be doing this type of grouping.
Also, use the smallest data types you can for any of the columns. For example, if the range of the integers falls below a particular limit, or is non-negative, you can usually choose a datatype that will reduce the amount of storage required.
I'm looking to manage a large dataset of log files. There is an average of 1.5 million new events per month that I'm trying to keep. I've used access in the past, though it's clearly not meant for this, and managing the dataset is a nightmare, because I'm having to split the datasets into months.
For the most part, I just need to filter event types and count the number. But before I do a bunch of work on the data import side of things, I wanted to see if anyone can verify that this SQL Server is a good choice for this. Is there an entry limit I should avoid and archive entries? Is there a way of archiving entries?
The other part is that I'm entering logs from multiple sources, with this amount of entries, is it wise to put them all into the same table, or should each source have their own table, to make queries faster?
edit...
There would be no joins, and about 10 columns. Data would be filtered through a view, and I'm interested to see if the results from a select query that filter based on one or more columns would have a reasonable response time? Does creating a set of views speed things up for frequent queries?
In my experience, SQL Server is a fine choice for this, and you can definitely expect better performance from SQL Server than MS-Access, with generally more optimization methods at your disposal.
I would probably go ahead and put this stuff into SQL Server Express as you've said, hopefully installed on the best machine you can use (though you did mention only 2GB of RAM). Use one table so long as it only represents one thing (I would think a pilot's flight log and a software error log wouldn't be in the same "log" table, as an absurdly contrived example). Check your performance. If it's an issue, move forward with any number of optimization techniques available to your edition of SQL Server.
Here's how I would probably do it initially:
Create your table with a non-clustered primary key, if you use a PK on your log table -- I normally use an identity column to give me a guaranteed order of events (unlike duplicate datetimes) and show possible log insert failures (missing identities). Set a clustered index on the main datetime column (you mentioned that your're already splitting into separate tables by month, so I assume you'll query this way, too). If you have a few queries that you run on this table routinely, by all means make views of them but don't expect a speedup by simply doing so. You'll more than likely want to look at indexing your table based upon the where clauses in those queries. This is where you'll be giving SQL server the information it needs to run those queries efficiently.
If you're unable to get your desired performance through optimizing your queries, indexes, using the smallest possible datatypes (especially on your indexed columns) and running on decent hardware, it may be time to try partitioned views (which require some form of ongoing maintenance) or partitioning your table. Unfortunately, SQL Server Express may limit you on what you can do with partitioning, and you'll have to decide if you need to move to a more feature-filled edition of SQL Server. You could always test partitioning with the Enterprise evaluation or Developer editions.
Update:
For the most part, I just need to filter event types and count the number.
Since past logs don't change (sort of like past sales data), storing the past aggregate numbers is an often-used strategy in this scenario. You can create a table which simply stores your counts for each month and insert new counts once a month (or week, day, etc.) with a scheduled job of some sort. Using the clustered index on your datetime column, SQL Server could much more easily aggregate the current month's numbers from the live table and add them to the stored aggregates for displaying the current values of total counts and such.
Sounds like one table to me, that would need indexes on exactly the sets of columns you will filter. Restricting access through views is generally a good idea and ensures your indexes will actually get used.
Putting each source into their own table will require UNION in your queries later, and SQL-Server is not very good optimizing UNION-queries.
"Archiving" entries can of course be done manually, by moving entries in a date-range to another table (that can live on another disk or database), or by using "partitioning", which means you can put parts of a table (e.g. defined by date-ranges) on different disks. You have to plan for the partitions when you plan your SQL-Server installation.
Be aware that Express edition is limited to 4GB, so at 1.5 million rows per month this could be a problem.
I have a table like yours with 20M rows and little problems querying and even joining, if the indexes are used.
I'm currently using a MySQL table for an online game under LAMP.
One of the table is huge (soon millions of rows) and contains only integers (IDs,timestamps,booleans,scores).
I did everything to never have to JOIN on this table. However, I'm worried about the scalability. I'm thinking about moving this single table to another faster database system.
I use intermediary tables to calculate the scores but in some cases, I have to use SUM() or AVERAGE() directly on some filtered rowsets of this table.
For you, what is the best database choice for this table?
My requirements/specs:
This table contains only integers (around 15 columns)
I need to filter by certain columns
I'd like to have UNIQUE KEYS
It could be nice to have "INSERT ... ON DUPLICATE UPDATE" but I suppose my scripts can manage it by themselves.
i have to use SUM() or AVERAGE()
thanks
Just make sure you have the correct indexes on so selecting should be quick
Millions of rows in a table isn't huge. You shouldn't expect any problems in selecting, filtering or upserting data if you index on relevant keys as #Tom-Squires suggests.
Aggregate queries (sum and avg) may pose a problem though. The reason is that they require a full table scan and thus multiple fetches of data from disk to memory. A couple of methods to increase their speed:
If your data changes infrequently then caching those query results in your code is probably a good solution.
If it changes frequently then the quickest way to improve their performance is probably to ensure that your database engine keeps the table in memory. A quick calculation of expected size: 15 columns x 8 bytes x millions =~ 100's of MB - not really an issue (unless you're on a shared host). If your RDBMS does not support tuning this for a specific table, then simply put it in a different database schema - shouldn't be a problem since you're not doing any joins on this table. Most engines will allow you to tune that.
I have an sql server 2008 database along with 30000000000 records in one of its major tables. Now we are looking for the performance for our queries. We have done with all indexes. I found that we can split our database tables into multiple partitions, so that the data will be spread over multiple files, and it will increase the performance of the queries.
But unfortunatly this functionality is only available in the sql server enterprise edition, which is unaffordable for us.
Is there any way to opimize for the query performance? For example, the query
select * from mymajortable where date between '2000/10/10' and '2010/10/10'
takes around 15 min to retrieve around 10000 records.
A SELECT * will obviously be less efficiently served than a query that uses a covering index.
First step: examine the query plan and look for and table scans and the steps taking the most effort(%)
If you don’t already have an index on your ‘date’ column, you certainly need one (assuming sufficient selectivity). Try to reduce the columns in the select list, and if ‘sufficiently’ few, add these to the index as included columns (this can eliminate bookmark lookups into the clustered index and boost performance).
You could break your data up into separate tables (say by a date range) and combine via a view.
It is also very dependent on your hardware (# cores, RAM, I/O subsystem speed, network bandwidth)
Suggest you post your table and index definitions.
First always avoid Select * as that will cause the select to fetch all columns and if there is an index with just the columns you need you are fetching a lot of unnecessary data. Using only the exact columns you need to retrieve lets the server make better use of indexes.
Secondly, have a look on included columns for your indexes, that way often requested data can be included in the index to avoid having to fetch rows.
Third, you might try to use an int column for the date and convert the date into an int. Ints are usually more effective in range searches than dates, especially if you have time information to and if you can skip the time information the index will be smaller.
One more thing to check for is the Execution plan the server uses, you can see this in management studio if you enable show execution plan in the menu. It can indicate where the problem lies, you can see which indexes it tries to use and sometimes it will suggest new indexes to add.
It can also indicate other problems, Table Scan or Index Scan is bad as it indicates that it has to scan through the whole table or index while index seek is good.
It is a good source to understand how the server works.
If you add an index on date, you will probably speed up your query due to an index seek + key lookup instead of a clustered index scan, but if your filter on date will return too many records the index will not help you at all because the key lookup is executed for each result of the index seek. SQL server will then switch to a clustered index scan.
To get the best performance you need to create a covering index, that is, include all you columns you need in the "included columns" part of your index, but that will not help you if you use the select *
another issue with the select * approach is that you can't use the cache or the execution plans in an efficient way. If you really need all columns, make sure you specify all the columns instead of the *.
You should also fully quallify the object name to make sure your plan is reusable
you might consider creating an archive database, and move anything after, say, 10-20 years into the archive database. this should drastically speed up your primary production database but retains all of your historical data for reporting needs.
What type of queries are we talking about?
Is this a production table? If yes, look into normalizing a bit more and see if you cannot go a bit further as far as normalizing the DB.
If this is for reports, including a lot of Ad Hoc report queries, this screams data warehouse.
I would create a DW with seperate pre-processed reports which include all the calculation and aggregation you could expect.
I am a bit worried about a business model which involves dealing with BIG data but does not generate enough revenue or even attract enough venture investment to upgrade to enterprise.
We have a very large table (> 77M records and growing) runing on SQL Server 2005 64bit Standard edition and we are seeing some performance issues. There are up to a hundred thousand records added daily.
Does anyone know if there is a limit to the number of records SQL server Standard edition can handle? Should be be considering moving to Enterprise edition or are there some tricks we can use?
Additional info:
The table in question is pretty flat (14 columns), there is a clustered index with 6 fields, and two other indexes on single fields.
We added a fourth index using 3 fields that were in a select in one problem query and did not see any difference in the estimated performance (the query is part of a process that has to run in the off hours so we don't have metrics yet). These fields are part of the clustered index.
Agreeing with Marc and Unkown above ... 6 indexes in the clustered index is way too many, especially on a table that has only 14 columns. You shouldn't have more than 3 or 4, if that, I would say 1 or maybe 2. You may know that the clustered index is the actual table on the disk so when a record is inserted, the database engine must sort it and place it in it's sorted organized place on the disk. Non clustered indexes are not, they are supporting lookup 'tables'. My VLDBs are laid out on the disk (CLUSTERED INDEX) according to the 1st point below.
Reduce your clustered index to 1 or 2. The best field choices are the IDENTITY (INT), if you have one, or a date field in which the fields are being added to the database, or some other field that is a natural sort of how your data is being added to the database. The point is you are trying to keep that data at the bottom of the table ... or have it laid out on the disk in the best (90%+) way that you'll read the records out. This makes it so that there is no reorganzing going on or that it's taking one and only one hit to get the data in the right place for the best read. Be sure to put the removed fields into non-clustered indexes so you don't lose the lookup efficacy. I have NEVER put more than 4 fields on my VLDBs. If you have fields that are being update frequently and they are included in your clustered index, OUCH, that's going to reorganize the record on the disk and cause COSTLY fragmentation.
Check the fillfactor on your indexes. The larger the fill factor number (100) the more full the data pages and index pages will be. In relation to how many records you have and how many records your are inserting you will change the fillfactor # (+ or -) of your non-clustered indexes to allow for the fill space when a record is inserted. If you change your clustered index to a sequential data field, then this won't matter as much on a clustered index. Rule of thumb (IMO), 60-70 fillfactor for high writes, 70-90 for medium writes, and 90-100 for high reads/low writes. By dropping your fillfactor to 70, will mean that for every 100 records on a page, 70 records are written, which will leave free space of 30 records for new or reorganized records. Eats up more space, but it sure beats having to DEFRAG every night (see 4 below)
Make sure the statistics exist on the table. If you want to sweep the database to create statistics using the "sp_createstats 'indexonly'", then SQL Server will create all the statistics on all the indexes that the engine has accumulated as requiring statistics. Don't leave off the 'indexonly' attribute though or you'll add statistics for every field, that would then not be good.
Check the table/indexes using DBCC SHOWCONTIG to see which indexes are getting fragmented the most. I won't go into the details here, just know that you need to do it. Then based on that information, change the fillfactor up or down in relation to the changes the indexes are experiencing change and how fast (over time).
Setup a job schedule that will do online (DBCC INDEXDEFRAG) or offline (DBCC DBREINDEX) on individual indexes to defrag them. Warning: don't do DBCC DBREINDEX on this large of a table without it being during maintenance time cause it will bring the apps down ... especially on the CLUSTERED INDEX. You've been warned. Test and test this part.
Use the execution plans to see what SCANS, and FAT PIPES exist and adjust the indexes, then defrag and rewrite stored procs to get rid of those hot spots. If you see a RED object in your execution plan, it's because there are not statistics on that field. That's bad. This step is more of the "art than the science".
On off peak times, run the UPDATE STATISTICS WITH FULLSCAN to give the query engine as much information about the data distributions as you can. Otherwise do the standard UPDATE STATISTICS (with standard 10% scan) on tables during the weeknights or more often as you see fit with your observerations to make sure the engine has more information about the data distributions to retrieve the data for efficiently.
Sorry this is so long, but it's extremely important. I've only give you here minimal information but will help a ton. There's some gut feelings and observations that go in to strategies used by these points that will require your time and testing.
No need to go to Enterprise edition. I did though in order to get the features spoken of earlier with partitioning. But I did ESPECIALLY to have much better mult-threading capabilities with searching and online DEFRAGING and maintenance ... In Enterprise edition, it is much much better and more friendly with VLDBs. Standard edition doesn't handle doing DBCC INDEXDEFRAG with online databases as well.
The first thing I'd look at is indexing. If you use the execution plan generator in Management Studio, you want to see index seeks or clustered index seeks. If you see scans, particularly table scans, you should look at indexing the columns you generally search on to see if that improves your performance.
You should certainly not need to move to Enterprise edition for this.
[there is a clustered index with 6 fields, and two other indexes on single fields.]
Without knowing any details about the fields, I would try to find a way to make the clustered index smaller.
With SQL Server, all the clustered-key fields will also be included in all the non-clustered indices (as a way to do the final lookup from non-clustered index to actual data page).
If you have six fields at 8 bytes each = 48 bytes, multiply that by two more indices times 77 million rows - and you're looking at a lot of wasted space which translates into a lot
of I/O operations (and thus degrades performance).
For the clustered index, it's absolutely CRUCIAL for it to be unique, stable, and as small as possible (preferably a single INT or such).
Marc
Do you really need to have access to all 77 million records in a single table?
For example, if you only need access to the last X months worth of data, then you could consider creating an archiving strategy. This could be used to relocate data to an archive table in order to reduce the volume of data and subsequently, query time on your 'hot' table.
This approach could be implemented in the standard edition.
If you do upgrade to the Enterprise edition you can make use of table partitioning. Again depending on your data structure this can offer significant performance improvements. Partitioning can also be used to implement the strategy previously mentioned but with less administrative overhead.
Here is an excellent White paper on table partitioning in SQL Server 2005
http://msdn.microsoft.com/en-us/library/ms345146.aspx
I hope what I have detailed is clear and understandable. Please do feel to contact me directly if you require further assistance.
Cheers,
http://msdn.microsoft.com/en-us/library/ms143432.aspx
You've got some room to grow.
As far as performance issues, that's a whole other question. Caching, sharding, normalizing, indexing, query tuning, app code tuning, and so on.
Standard should be able to handle it. I would look at indexing and the queries you use with the table. You want to structure things in such a way that your inserts don't cause too many index recalcs, but your queries can still take advantage of the index to limit lookups to a small portion of the table.
Beyond that, you might consider partitioning the table. This will allow you to divide the table into several logical groups. You can do it "behind-the-scenes", so it still appears in sql server as one table even though it stored separately, or you can do it manually (create a new 'archive' or yearly table and manually move over rows). Either way, only do it after you looked at the other options first, because if you don't get that right you'll still end up having to check every partition. Also: partitioning does require Enterprise Edition, so that's another reason to save this for a last resort.
In and of itself, 77M records is not a lot for SQL Server. How are you loading the 100,000 records? is that a batch load each day? or thru some sort of OLTP application? and is that the performance issue you are having, i.e adding the data? or is it the querying that giving you the most problems?
If you are adding 100K records at a time, and the records being added are forcing the cluster-index to re-org your table, that will kill your performance quickly. More details on the table structure, indexes and type of data inserted will help.
Also, the amount of ram and the speed of your disks will make a big difference, what are you running on?
maybe these are minor nits, but....
(1) relational databases don't have FIELDS... they have COLUMNS.
(2) IDENTITY columns usually mean the data isn't normalized (or the designer was lazy). Some combination of columns MUST be unique (and those columns make up the primary key)
(3) indexing on datetime columns is usually a bad idea; CLUSTERING on datetime columns is also usually a bad idea, especially an ever-increasing datetime column, as all the inserts are contending for the same physical space on disk. Clustering on datetime columns in a read-only table where that column is part of range restrictions is often a good idea (see how the ideas conflict? who said db design wasn't an art?!)
What type of disks do you have?
You might monitor some disk counters to see if requests are queuing.
You might move this table to another drive by putting it in another filegroup. You can also to the same with the indexes.
Initially I wanted to agree with Marc. The width of your clustered index seems suspect, as it will essentially be used as the key to perform lookups on all your records. The wider the clustered index, the slower the access, generally. And a six field clustered index feels really, really suspect.
Uniqueness is not required for a clustered index. In fact, the best candidates for fields that should be in the clustered index are ones that are not unique and used in joins. For example, in a Persons table where each Person belongs to one Group and you frequently join Persons to Groups, while accessing batches of people by group, Person.group_id would be an ideal candidate, for this particular use case.