I am wondering at which point would my MySQL table be considered too big.
The table is this:
id
customer_id (int)
value (float)
timestamp_1 (datetime)
tmestampt_2 (datetime)
so the row size is not too great, but would be constantly being added. In my estimation I am looking at around 17000 new rows a day, so about 500,000 a month. The data is likely to be polled quite constantly in large quantities.
Should I be looking at ways to split this or am I still OK at this point?
Thanks,
From http://dev.mysql.com/doc/refman/5.0/en/full-table.html:
The effective maximum table size for MySQL databases is usually determined by operating system constraints on file sizes, not by MySQL internal limits.
From the table in the linked article, on FAT/FAT32 systems and Linux pre 2.4, the maximum file size is 2-4 GB, on all other systems listed, the max file size is at least 2TB.
So long as you index your table correctly, there shouldn't be too much slowdown as your table grows. However, if your table grows to the extent that you do notice any slowdown, it might be an option for you to archive off old records periodically.
What is "Too big" is really going to depend on how big your hardware is. MySQL itself should have no problem managing millions of rows in a table.
Still, I would think about splitting it up to get the best possible performance. Exactly how you do that would depend on how the data is used. Is more recent data used much more frequently? If so, create an archive table with the same structure to store the old data and periodically move data from your main table to the archive table. This would increase the complexity of your application, but could give you better performance in the long run.
It would be too big when your query starts to slow down.
Do you need to keep the entire history in this table or are you only ever looking for the latest values? You could optimise things by archiving off records you don't need onto an archive table.
Other than that, be careful how you plan your indexes. If you put indexes all over the place, inserts may take longer. If you don't have any indexes but need to sort and filter, the retrieval may be too slow.
In MyISAM, the theoretical table size is constrained by the size of data pointer, set by myisam_data_pointer_size.
It can be from 2 to 7 bytes, making the possible table size to be from 2 ^ (8 * 2) = 65k to 2 ^ (8 * 7) = 64P bytes long.
By default, it's 6 bytes (256T).
Of course, since MyISAM tables are held in one file, the maximum size of the file is subject to constraint by the OS and the filesystem.
An InnoDB tablespace can consist of as many as 2^32 pages, which is 4G pages of 16K bytes each, or 64T bytes at most.
Related
Can someone point me in the right direction, i can't find any documentation on this behavior.
We know when you delete rows from a table you end up with "holes" which you can defrag with OPTIMIZE. Do new inserts automatically fill in those holes if left alone? Is there a way to force that behavior if not? Using InnoDB tables for revolving logs, deleting old rows and adding new, would the table roll over or continuously consume disk space? Or would a different engine be better suited for this?
Yes i know of table partitions, i want to explore all options first.
Since this is mostly a non-issue, I will assume you are asking for academic reasons?
InnoDB (you should be using that Engine!) stores the data (and each secondary index) in separate B+Trees.
The data's BTree is ordered by the PRIMARY KEY. The various leaf nodes will be filled to different degrees, based on the order of inserts, deletes, updates (that change the row length), temporary transactional locks on rows, etc, etc.
That last one is because one transaction sees effectively an instantaneous snapshot of the data, possibly different than another transaction's view. This implies that multiple copies of a row may coexist.
The buffer_pool holds 16KB blocks. Each block holds a variable number of rows. This number changes with the changing tides. If too adjacent blocks become "too empty", they will be combined.
Totally empty blocks (say, due to lots of deletes) will be put on a free chain for later reuse by Inserts. But note that the disk used by the table will not shrink.
The failure to shrink is usually not a problem -- most tables grow; any shrinkage is soon followed by a new growth spurt.
PARTITIONs are usually not worth using. However, that is the best way to "keep data for only 90 days", then use DROP PARTITION instead of a big, slow DELETE. (That is about the only use for PARTITION.)
If you add up all the bytes in the INTs (4 bytes each) VARCHARs (pick the average length), etc, etc, you will get what seems like a good estimate for the disk space being used. But due to the things discussed above, you need to multiply that number by 2 to 3 to a better estimate of the disk space actually consumed by the table.
I'm learning how to optimize my database by re choosing the correct data types for the columns and I want to know how much size I will save if I choose MEDIUMINT (3 Bytes) instead of INT(4 Bytes)
AFAIK -and correct me if I'm wrong- I need the database size to be as small as possible to fit in RAM to reduce the hard-desk requests. The size of the database consists of the tables sizes + index sizes
giving that I have an INT column that has 10'000'000 rows and a B-Tree index on it, how much size In MBs I will save if I changed the datatype of the column from INT to MEDIUMINT at
table data size ?
index size ?
note: I know MySQL will not reduce the actual size on disk unless I OPTIMIZE TABLE
EDIT: My situation is that I will finish my first serious system in my life shortly -it's an ERP system that I plan to sell in the Arab region market - . Plans 1, 2, 3, 4 databases are supposed to be about 2GB, 4GB, 10GB, 40GB respectively, so If I could reduce the size of each database without sacrificing performance/features, why not ? If I could make a 32GB RAM machine serve 4 clients instead of 2, why not ?
Just use INT unless you have a specific, measurable problem. You're only going to make a mess of things if you fret over every single byte in an era where even the most thrifty of smart phones has a billion of them for memory alone.
I need the database size to be as small as possible to fit in RAM to reduce the hard-desk requests.
No you don't. You need the database to be easy to work with and perform adequately. In an era of SSD-backed databases, I/O will not be a problem until you're operating at large scale, and when and if that day comes then you can take measurements and understand the specific problems you're having.
Shaving a single byte off your INT field is unlikely to make anything better since three byte integer values are not something your CPU can directly deal with. These will be converted to four bytes and aligned properly so they can be understood, a process that's messy compared to reading a plain old 32-bit integer.
Remember, MySQL comes from an era where a high-end server had 64 megabytes of memory and a 9 gigabyte hard disk was considered huge. Back then you did have to shave bytes off because you only had a handful of them.
Now we have other concerns, like will you accidentally exhaust your 24-bit integer space like Slashdot did where their site went down because of exactly the sort of "optimizing" you're intending to do here.
Be careful. Optimize when you have a concrete reason to, not just because you think you need to. Avoiding premature optimization is a constant struggle in development, but if you're disciplined you can avoid it.
(I disagree with some of the other Answers/Comments. I will try to answer all the questions, plus address all the points that I disagree with.)
MEDIUMINT is 3 bytes, saving 1 byte per row over INT.
TINYINT is 1 bytes, saving 3 bytes per row over INT.
In both cases, there is another 1 or 3 bytes saved per occurrence in any INDEX other than the PRIMARY KEY.
If you are likely to have more data+index than space in RAM, then it is wise to shrink the datatypes but be conservative.
Use MEDIUMINT UNSIGNED (etc) if the value is non-negative, such as for AUTO_INCREMENT. That gives you a limit of 16M instead of 8M. (Yeah, yeah, that's a tiny improvement.)
Beware of "burning" AUTO_INCREMENT ids -- INSERT IGNORE (and several other commands) will allocate the next auto_inc before checking whether it will be used.
Even if data+index exceeds RAM size (actually innodb_buffer_pool_size), it may not slow down to disk speed -- it depends on access patterns of the data. Beware of UUIDs, they are terribly random. Using UUIDs when you can't cache the entire index is deadly. The buffer_pool is a cache. (I have seen a 1TB dataset run fast enough with only 32GB of RAM and a spinning disk.)
Using ALTER TABLE to change a datatype probably (I am not sure) rebuilds the table, thereby performing the equivalent of OPTIMIZE TABLE.
If the table was created with innodb_file_per_table = OFF and you turn it ON before doing the ALTER, you get a separate file for the table, but ibdata1 will not shrink (instead it will have lots more free space).
Alignment of 3-byte numbers -- not an issue. Powers of 2 is not relevant here. MySQL assumes all columns are at poor boundaries, and of poor sizes. All numbers are converted to a generic format (64-bit numbers) for operating on. This conversion is an insignificant part of the total time -- fetching the row (even if cached) is the most costly part.
When I/O-bound, shrinking datatypes leads to more rows per block, which leads to fewer disk hits (except in the UUID case). When I/O-bound, hitting the disk is overwhelming the biggest performance cost.
"NULLS take no space" -- https://dev.mysql.com/doc/internals/en/innodb-field-contents.html . So, again, less I/O. But, beware, if this leads to an extra check for NULL in a SELECT, that could lead to a table scan instead of using an index. Hitting 10M rows is a lot worse than hitting just a few.
As for how many clients you can fit into 32GB -- Maybe 6 or more. Remember, the buffer_pool is a cache; data and indexes are cached on a block-by-block basis. (An InnoDB block is 16KB.)
One more thing... It is a lot easier to shrink the datatypes before going into production. So, do what you can safely do now.
The exact size of your index is going to depend on how many rows you have, but also on how the data in your index looks.
If you shave off 1 byte per record in your data, and you have 10.000.000 records, that'll only save you up to 10MB on disk for the table data. Adding an index is going to add some more, and B-trees have empty space in them, but it depends on the actual data how inefficient it is.
If you want to save space, make sure that the field is not nullable, because even if you fill all rows with data, there is information per record, stating whether the nullable field contains data or not.
What are problems occur when mysql server table contains more than 120 columns ?
from a technical point of view without any consideration on the reasons for which you need 120 columns in a table mysql documentation 5.7 says:
Column Count Limits
MySQL has hard limit of 4096 columns per table, but the effective
maximum may be less for a given table.
https://dev.mysql.com/doc/refman/5.7/en/column-count-limit.html
This is bad practice to have 120 columns in a table better to split into multiple tables.
Since MYSQL is famous for relation database, So make relation based tables structure.
List of issues comes once your application become bigger.
Application gets slow (Since data fetching from table is slowly).
If your internet is slow, then you may not load the application page.
If huge amount of data is loaded at once due to the numbers of columns, then your server require more bandwidth.
Might you may not able you open in mobile, Since mobile better work with small amount of data.
https://dba.stackexchange.com/questions/3972/too-many-columns-in-mysql
There is a hard limit of 4096 columns per table, but the effective maximum may be less for a given table. The exact limit depends on several interacting factors.
Every table (regardless of storage engine) has a maximum row size of 65,535 bytes. Storage engines may place additional constraints on this limit, reducing the effective maximum row size.
The maximum row size constrains the number (and possibly size) of columns because the total length of all columns cannot exceed this size.
...
Individual storage engines might impose additional restrictions that limit table column count. Examples:
InnoDB permits up to 1000 columns.
If the numbers of the record is very big like N*10 M per table , is there any benefit to move the varchar(2xx) column to a nosql storage? The content of the text won't be very long, I　think 200 characters is big enough. And the engine of mysql will be innoDB. The column won't be used as an index.
Moving a specific column won't help performance much and will likely reduce performance because you need to get data from two places instead of one.
In general the slow part of any query is finding the right record - once you find that record, reading a few hundred bytes more doesn't really change anything.
Also, 10 million records of 200 characters is at most 4GB - not much even if your dataset needs to fit in RAM.
What is the maximum size for a MySQL table? Is it 2 million at 50GB? 5 million at 80GB?
At the higher end of the size scale, do I need to think about compressing the data? Or perhaps splitting the table if it grew too big?
I once worked with a very large (Terabyte+) MySQL database. The largest table we had was literally over a billion rows.
It worked. MySQL processed the data correctly most of the time. It was extremely unwieldy though.
Just backing up and storing the data was a challenge. It would take days to restore the table if we needed to.
We had numerous tables in the 10-100 million row range. Any significant joins to the tables were too time consuming and would take forever. So we wrote stored procedures to 'walk' the tables and process joins against ranges of 'id's. In this way we'd process the data 10-100,000 rows at a time (Join against id's 1-100,000 then 100,001-200,000, etc). This was significantly faster than joining against the entire table.
Using indexes on very large tables that aren't based on the primary key is also much more difficult. Mysql stores indexes in two pieces -- it stores indexes (other than the primary index) as indexes to the primary key values. So indexed lookups are done in two parts: First MySQL goes to an index and pulls from it the primary key values that it needs to find, then it does a second lookup on the primary key index to find where those values are.
The net of this is that for very large tables (1-200 Million plus rows) indexing against tables is more restrictive. You need fewer, simpler indexes. And doing even simple select statements that are not directly on an index may never come back. Where clauses must hit indexes or forget about it.
But all that being said, things did actually work. We were able to use MySQL with these very large tables and do calculations and get answers that were correct.
About your first question, the effective maximum size for the database is usually determined by operating system, specifically the file size MySQL Server will be able to create, not by MySQL Server itself. Those limits play a big role in table size limits. And MyISAM works differently from InnoDB. So any tables will be dependent on those limits.
If you use InnoDB you will have more options on manipulating table sizes, resizing the tablespace is an option in this case, so if you plan to resize it, this is the way to go. Give a look at The table is full error page.
I am not sure the real record quantity of each table given all necessary information (OS, Table type, Columns, data type and size of each and etc...) And I am not sure if this info is easy to calculate, but I've seen simple table with around 1bi records in a couple cases and MySQL didn't gave up.