I've seen many entries about partitioning tables, but there is not a lot of information on when you should make a partition.
Is there a rule of thumb when you should partition tables in SQL Server.
Thanks
My benchmarks indicate that it depends on the query load.
If the queries you perform ALWAYS contain a filter on the partition field the performance benefit is virtually instant (like 1000 records in the table is already beneficial)
If the queries do NOT always contain a filter on the partition field you really have to benchmark with a good sample of the query load before making the decision.
You also have to account for the partition system you use. if you use "static" partitions there is not much harm in creating them immediately. When you use a "sliding window" system you need to take into account the overhead of creating and merging partitions. (which can take a long time on big tables)
#Filip's post is a great topical guide. When your doing your ontologies and estimating how your application will be used, that is, how your users will interact with the application and how that translates to database access, you should have a good idea of the kind of queries that will be performed and how fast certain tables will grow. If your that confident, then you should immediately partition the tables to defer from any maintenance hapzards.
But if your trying to decide on whether to partition populated tables, or you like to perform partitioning lazily like me, here's a nice little nugget from the PostgreSQL docs:
The exact point at which a table will benefit from partitioning depends on the application, although a rule of thumb is that the size of the table should exceed the physical memory of the database server. [src]
Related
I am learning cassandra. Now, I am thinking about SQL's problems that NoSQL addresses, and I have a question about cases of very big data.
About SQL handling very big data, I thought that many pages are saying that tables will be on different servers and queries are slow because of joining tables on different servers. This is a problem of SQL that NoSQL addresses. But, even with NoSQL, if partitions are too big, do not I need to change my data model, make smaller partitions and make multiple queries on them to get the same result? And, is not it slow? Or, you never run out of a space in partition because 2B cells are big enough?
I think your question is mixing several different issues.
First of all, the problem with big data and SQL is usually not that queries become slow, but that the solution cannot scale as the data grows bigger and bigger. If you choose to manually split your tables to several servers, as you suggested, what do you do when you need even more servers - redesign your data model? Also, how do you ensure consistency when an update requires modifying several tables but they are on different hosts?
Second, you mentioned joins, and this is something which NoSQL solutions like Cassandra do not support. You need to manually denormalize the data yourself (i.e., put the already joined data in a table). For some things, Cassandra's new "Materialized Views" feature can come in handy.
Third, and perhaps most importantly, you asked about huge partitions. Indeed Cassandra is not designed to handle huge partitions, and the best practice is far below the 2-billion hard limit which you mentioned: Datastax (the commercial company behind Cassandra's development) suggests in https://docs.datastax.com/en/dse-planning/doc/planning/planningPartitionSize.html that a good rule of thumb is to "keep the maximum number of rows below 100,000 items and the disk size under 100 MB.".
There are several reasons why huge partitions are ill-advised in Cassandra. One of them is that the disk format (sstables and their so-called "promoted index") makes it inefficient to jump to the middle of a huge partition, and you need to do this when you want to read a specific row or iterate through all the rows. Some operations such as compaction and repair work on entire partitions and can become very slow (and in the worst case, also use a lot of memory). E.g., a case that a billion-row partition differs on two nodes by just one row, and the partition-based repair needs to send the entire partition over the network.
Scylla (https://en.wikipedia.org/wiki/Scylla_(database)), a Cassandra clone which is generally more efficient than Apache Cassandra, also has similar issues with huge partitions (as in Cassandra, moderately large partitions are fine), but these issues are actively being worked on, including re-designing the file format, so eventually Scylla should support arbitrary-sized partitions. However, we're still not there yet, and today the recommendation of not letting partitions grow too huge still applies to Scylla as well.
Finally, if you want to get around the problem of too many rows in a single partition, then, yes, you need to tweak your data model to avoid these huge partitions. Sometimes, you just need to fix design mistakes in your model - e.g., I have seen people sticking a lot of unrelated data into the same partition, when it could have easily (and more efficiently!) be put in separate partitions. Sometimes, you need to artificially split your partitions. This is common in so-called "time-series data" modeling in Cassandra, where we (for example) get a new value of some measurement every second and add it as a row to a partition. Here, instead of having one huge partition for all data ever, the accepted practice is to create a separate partition per time window (e.g., a new partition every day, or week, or whatever). Since most queries involve just one time window anyway, they don't even become slower.
I have a database with tables that grow every day. I cannot predict which tables are going to grow and which are not as I'm not the one who is putting the data into them.
Is there a way to find tables that need indexes at a particular point in time? Is there a way, in SQL Server, to notify me if a database needs tuning on certain tables?
This is a product we have deployed at different client locations and we cannot go onto their servers every time to check if they have a performance issue. What I was thinking about is something that can notify me if there are performance issues on certain tables, so as the new patches go to the clients we can add these indexes or tuned queries.
After referring to Insertion of data after creating index on empty table or creating unique index after inserting data on oracle? I'm not willing to create indexes while installing databases or when the tables have few rows or are empty.
As per my understanding we must not create indexes on a smaller table as it can affect the write performances.
This is only a real concern if you're bulk loading or otherwise generating a hundred million records each day and write performance is a problem. Indexes do increase write times because they have to be updated when data is written, but unless you're running on a potato or running very high loads it's unlikely to be a problem. You'd know it was a problem before you encountered it.
If we're talking about small tables (less than 100 pages) then it's much more likely that indexes won't be useful because the data set is so small, but you shouldn't be concerned about impacting write performance.
Overall, your application should have indexes that support the queries that you expect should be run in your unit testing and staging. You will need feedback from your customers or clients, but until you really know how people use their data, you're going to have to make a best guess.
The general question of "How do I know what indexes I need when I don't know what queries will be run?" is better suited to DBA Stack Exchange. Briefly, you'll need to use dynamic management views for that. The three missing index dynamic views can be used for this. The example query given isn't horrible:
SELECT mig.*, statement AS table_name,
column_id, column_name, column_usage
FROM sys.dm_db_missing_index_details AS mid
CROSS APPLY sys.dm_db_missing_index_columns (mid.index_handle)
INNER JOIN sys.dm_db_missing_index_groups AS mig
ON mig.index_handle = mid.index_handle
ORDER BY mig.index_group_handle, mig.index_handle, column_id;
You shouldn't just blindly follow what this view says, however. It's a good lead on what to look at, but you have to look at the column order and queries actually being used to tell.
You should also monitor index usage statistics and examine how much and in what way indexes are used compared to how much they have to be updated. Indexes that are updated a million times a day but are used once or twice should be considered for removal.
You will also want to monitor query stats to look for queries that run for a long time. This may be poor development on the part of your client, but can also be a sign of design problems.
This is not even a comprehensive overview of things to look for, however. There's a lot to database maintenance and operations. That's why DBAs make a good living. This is just the tip of the iceberg. Just the tip for indexes, even.
What I'd do if you want to maintain this is consider asking your customers to allow you to send feedback for performance analysis. Set up a broker that monitors the management views and sends compiled and sanitized information back to yourselves. You'll need to be very careful about what you send because you don't want to be sending actual customer data, of course.
Keep in mind that dynamic management views typically reset when the instance does, so the results will not typically represent the entire lifespan of the database.
I'm not a trained DBA, but perform some SQL tasks and have this question:
In SQL databases I've noticed the use archive tables that mimic another table with the exact same fields and which are used to accept rows from the original table when that data is deemed for archiving. Since I've seen examples where those tables reside in the same database and on the same drive, my assumption is that this was done to increase performance. Such tables didn't have more than a about 10 million rows in them...
Why would this be done instead of using a column to designate the status of the row, such as a boolean for an in/active flag?
At what point would this improve performance ?
What would be the best pattern to structure this correctly, given that the data may still need to be queried (or unioned with current data) ?
What else is there to say about this ?
The notion of archiving is a physical, not logical, one. Logically the archive table contains the exact same entity and ought to be the same table.
Physical concerns tend to be pragmatic. The overarching notion is that the "database is getting too (big/slow"). Archiving records makes it easier to do things like:
Optimize the index structure differently. Archive tables can have more indexes without affecting insert/update performance on the working table. In addition, the indexes can be rebuilt with full pages, while the working table will generally want to have pages that are 50% full and balanced.
Optimize storage media differently. You can put the archive table on slower/less expensive disk drives that maybe have more capacity.
Optimize backup strategies differently. Working tables may require hot backups or log shipping while archive tables can use snapshots.
Optimize replication differently, if you are using it. If an archive table is only updated once per day via nightly batch, you can use snapshot as opposed to transactional replication.
Different levels of access. Perhaps you want different security access levels for the archive table.
Lock contention. If you working table is very hot you'd rather have your MIS developers access the archive table where they are less likely to halt your operations when they run something and forget to specify dirty read semantics.
The best practice would not to use archive tables but to move the data from the OLTP database to an MIS database, data warehouse, or data marts with denormalized data. But some organizations will have trouble justifying the cost of an additional DB system (which aren't cheap). There are far fewer hurdles to adding an additional table to an existing DB.
I say this frequently, but...
Multiple tables of identical structure almost never makes sense.
A status flag is a much better idea. There are proper ways to increase performance (partitioning/indexing) without denormalizing data or otherwise creating redundancies. 10 million records is pretty small in the world of modern rdbms, so what you're seeing is the product of poor planning or misunderstanding of databases.
I found a posting on the MySQL forums from 2005, but nothing more recent than that. Based on that, it's not possible. But a lot can change in 3-4 years.
What I'm looking for is a way to have an index over a view but have the table that is viewed remain unindexed. Indexing hurts the writing process and this table is written to quite frequently (to the point where indexing slows everything to a crawl). However, this lack of an index makes my queries painfully slow.
I don't think MySQL supports materialized views which is what you would need, but it wouldn't help you in this situation anyway. Whether the index is on the view or on the underlying table, it would need to be written and updated at some point during an update of the underlying table, so it would still cause the write speed issues.
Your best bet would probably be to create summary tables that get updated periodically.
Have you considered abstracting your transaction processing data from your analytical processing data so that they can both be specialized to meet their unique requirements?
The basic idea being that you have one version of the data that is regularly modified, this would be the transaction processing side and requires heavy normalization and light indexes so that write operations are fast. A second version of the data is structured for analytical processing and tends to be less normalized and more heavily indexed for fast reporting operations.
Data structured around analytical processing is generally built around the cube methodology of data warehousing, being composed of fact tables that represent the sides of the cube and dimension tables that represent the edges of the cube.
Flexviews supports materialized views in MySQL by tracking changes to underlying tables and updating the table which functions as a materialized view. This approach means that SQL supported by the view is a bit restricted (as the change logging routines have to figure out which tables it should track for changes), but as far as I know this is the closest you can get to materialized views in MySQL.
Do you only want one indexed view? It's unlikely that writing to a table with only one index would be that disruptive. Is there no primary key?
If each record is large, you might improve performance by figuring out how to shorten it. Or shorten the length of the index you need.
If this is a write-only table (i.e. you don't need to do updates), it can be deadly in MySQL to start archiving it, or otherwise deleting records (and index keys), requiring the index to start filling (reusing) slots from deleted keys, rather than just appending new index values. Counterintuitive, but you're better off with a larger table in this case.
In a recent project the "lead" developer designed a database schema where "larger" tables would be split across two separate databases with a view on the main database which would union the two separate database-tables together. The main database is what the application was driven off of so these tables looked and felt like ordinary tables (except some quirky things around updating). This seemed like a HUGE performance problem. We do see problems with performance around these tables but nothing to make him change his mind about his design. Just wondering what is the best way to do this, or if it is even worth doing?
I don't think that you are really going to gain anything by partitioning the table across multiple databases in a single server. All you have essentially done there is increased the overhead in working with the "table" in the first place by having several instances (i.e. open in two different DBs) of it under a single SQL Server instance.
How large of a dataset do you have? I have a client with a 6 million row table in SQL Server that contains 2 years worth of sales data. They use it transactionally and for reporting without any noticiable speed problems.
Tuning the indexes and choosing the correct clustered index is crucial to performance of course.
If your dataset is really large and you are looking to partition, you will get more bang for your buck partitioning the table across physical servers.
Partitioning is not something to be undertaken lightly as there can be many subtle performance implications.
My first question is are you referring simply to placing larger table objects in separate filegroups (on separate spindles) or are you referring to data partitioning inside of a table object?
I suspect that the situation described is an attempt to have the physical storage of certain large tables on different spindles from the rest of the tables. In this case, adding the extra overhead of separate databases, losing any ability to enforce referential integrity across databases, and the security implications of enabling cross-database ownership chaining does not provide any benefit over using multiple filegroups within a single database. If, as is quite possible, the separate databases you refer to in your question are not even stored on separate spindles but are all stored on the same spindle then you negate even the slight performance benefit you could have gained by physically separating your disk activity and have received absolutely no benefit.
I would suggest instead of using additional databases to hold large tables you look into the Filegroup topic in SQL Server Books Online or for a quick review see this article:
If you are interested in data partitioning (including partitioning into multiple file groups) then I recommend reading articles by Kimberly Tripp, who gave an excellent presentation at the time SQL Server 2005 came out about the improvements available there. A good place to start is this whitepaper
Which version of SQL Server are you using? SQL Server 2005 has partitioned tables, but in 2000 (or 7.0) you needed to use partition views.
Also, what was the reasoning for putting the table partitions in a separate database?
When I've had to partition tables in the past (pre-2005), it's usually by a date column or something similar, with a view over the various partitions. Books Online has a section that talks about how to do this and all of the rules around it. You need to follow the rules to make it work how it's supposed to work.
The key thing to remember is that your partitioning column must be part of the primary key and you want to try to always use that column in any access against the table so that the optimizer can ignore partitions that shouldn't be affected by the query.
Look up "partitioned table" in MSDN and you should be able to find a more complete tutorial for SQL Server 2005 partitioned tables as well as advice on how to set them up for maximum performance.
Are you asking about best practices in terms of database design, or convincing your lead to change his mind? :)
In terms of design... Back in the goode olde days, vertical partitioning was sometimes needed to work around database engine limitations, where the number of columns in a table was a hard limit, like 255 columns. These days the main benefits are purely for performance: putting rarely used columns, or blobs on a separate disk array. But if you're regularly pulling things from both tables it will likely be a loss. It sounds like your lead is suffering from a case of premature optimisation.
In terms of telling your lead is wrong... that requires diplomacy. If he's aware of mutterings of discontent in terms of performance, a benchmark is probably the best way to show the difference.
Create a new physical table somewhere with 'create table t1 as select * from view1' and then run some lengthy batch with the vertically partitioned table and your new table. If it's as bad as you say, the difference should be evident.
But this too may be premature optimisation. Find out what the end-users think of the performance. If the performance is good enough, for some definition of good, then don't fix what ain't broke.
There is a definite benefit for table partitioning (regardless whether it's on same or different filegroups /disks). If the partition column is correctly selected, you'll realize that your queries will hit only the required partition. So imagine if you have 100 million records (I've partitioned tables much bigger than that - about 20+ Billion rows) and if for the most part, more than 70% of your data access is only a certain category or timeline or type of data then it helps to keep the most accessed data in a separate partition. Plus you can align the partition with separate file groups with various type of disks (SATA, Fiber channel, SSDs) so that the most accessed/busy data are on the fastest storage and the least/rarely accessed are virtually on slower disks.
Although, in SQL Server there's limited partitioning ability, unlike Oracle. You can choose only one column for partitioning (even in SQL 2008). So you've to choose a column wisely where that column also is part of most of your frequent queries. For the most part, people find it easy to choose to partition by a date column. However although it seems logical to partition that way, if your queries do not have that column as part of the condition, you won't be gaining sufficient benefits from partitioning (in other words, your query will hit all the partition regardless).
It's much easier to partition for data warehouse/data mining type databases than OLTP as most DW database queries are limited by time period.
That's why these days due to the volume of data being handled by databases, it's wise to design the application in such a way that ever query is limited by some broader group such as time, geographical location or such so that when such columns are chosen for partitioning you'll gain maximum benefits.
I would disagree with the assumption that nothing can be gained by partitioning.
If the partition data is physically and logically aligned, then the potential IO of queries should be dramatically reduced.
For example, We have a table which has the batch field as an INT representing an INT.
If we partition the data by this field and then re-run a query for a particular batch, we should be able to run set statistics io ON before and after partitioning and see a reduction in IO,
If we have a million rows per partition and each partition is written to a separate device. The query should be able to eliminate the nonessential partitions.
I've not done a lot of partitioning on SQL Server, but I do have experience of partitioning on Sybase ASE, and this is known as partition eliminiation. When I have time I'm going to test out the scenario on a SQL Server 2005 machine.