I have a table "data" which holds around 100,000,000 records.
I have added a new column to it "batch_id" (Integer).
On the application layer, I'm updating the batch_id in batches of 10,000 records for each of the 100,000,000 records (the batch_id is always the same for 10k).
I'm doing something like this (application layer pseudo code):
loop {
$batch_id = $batch_id + 1;
mysql.query("UPDATE data SET batch_id='$batch_id' WHERE batch_id IS NULL LIMIT 10000");
}
I have an index on the batch_id column.
In the beginning, this update statement took ~30 seconds. I'm now halfway through the Table and it's getting slower and slower. At the moment the same statement takes around 10 minutes(!). It reached a point where this is no longer feasible as it would take over a month to update the whole table at the current speed.
What could I do to speed it up, and why is MySQL Getting slower towards the end of the table?
Could an index on the primary key help?
Is the primary key automatically indexed in MySQL? The answer is Yes
So instead one index for batch_id will help.
The problem is without index the engine do a full table scan. At first is easy find 10k with null values, but when more and more records are updated the engine have to scan much more to find those nulls.
But should be easier create batch_id as an autonumeric column
OTHER OPTION: Create a new table and then add the index and replace old table.
CREATE newTable as
SELECT IF(#newID := #newID + 1,
#newID DIV 10000,
#newID DIV 10000) as batch_id,
<other fields>
FROM YourTable
CROSS JOIN (SELECT #newID :=0 ) as v
Insert auto increment primary key to existing table
Do you have a monotonically increasing id in the table? And all rows for a "batch" have 'consecutive' ids? Then don't add batch_id to the table, instead, create another table Batches with one row per batch: (batch_id (PK), id_start, id_end, start_time, end_time, etc).
If you stick to exact chunks of 10K, then don't even materialize batch_id. Instead, compute it from id DIV 10000 whenever you need it.
If you want to discuss this further, please provide SHOW CREATE TABLE for the existing table, and explain what you will be doing with the "batches".
To answer your question about "slow near the end": It is having to scan farther and farther in the table to find the NULLs. You would be better to walk through the table once, fiddling with each 10K chunk as you go. Do this using the PRIMARY KEY, whatever it is. (That is, even if it is not AUTO_INCREMENT.) More Details .
I've been running a website, with a large amount of data in the process.
A user's save data like ip , id , date to the server and it is stored in a MySQL database. Each entry is stored as a single row in a table.
Right now there are approximately 24 million rows in the table
Problem 1:
Things are getting slow now, as a full table scan can take too many minutes but I already indexed the table.
Problem 2:
If a user is pulling a select data from table it could potentially block all other users (as the table is locked) access to the site until the query is complete.
Our server
32 Gb Ram
12 core with 24 thread cpu
table use MyISAM engine
EXPLAIN SELECT SUM(impresn), SUM(rae), SUM(reve), `date` FROM `publisher_ads_hits` WHERE date between '2015-05-01' AND '2016-04-02' AND userid='168' GROUP BY date ORDER BY date DESC
Lock to comment from #Max P. If you write to MyIsam Tables ALL SELECTs are blocked. There is only a Table lock. If you use InnoDB there is a ROW Lock that only locks the ROWs they need. Aslo show us the EXPLAIN of your Queries. So it is possible that you must create some new one. MySQL can only handle one Index per Query. So if you use more fields in the Where Condition it can be useful to have a COMPOSITE INDEX over this fields
According to explain, query doesn't use index. Try to add composite index (userid, date).
If you have many update and delete operations, try to change engine to INNODB.
Basic problem is full table scan. Some suggestion are:
Partition the table based on date and dont keep more than 6-12months data in live system
Add an index on user_id
I'm trying to run what I believe to be a simple query on a fairly large dataset, and it's taking a very long time to execute -- it stalls in the "Sending data" state for 3-4 hours or more.
The table looks like this:
CREATE TABLE `transaction` (
`id` bigint(20) unsigned NOT NULL AUTO_INCREMENT,
`uuid` varchar(36) NOT NULL,
`userId` varchar(64) NOT NULL,
`protocol` int(11) NOT NULL,
... A few other fields: ints and small varchars
`created` datetime NOT NULL,
PRIMARY KEY (`id`),
KEY `uuid` (`uuid`),
KEY `userId` (`userId`),
KEY `protocol` (`protocol`),
KEY `created` (`created`)
) ENGINE=InnoDB AUTO_INCREMENT=61 DEFAULT CHARSET=utf8 ROW_FORMAT=COMPRESSED KEY_BLOCK_SIZE=4 COMMENT='Transaction audit table'
And the query is here:
select protocol, count(distinct userId) as count from transaction
where created > '2012-01-15 23:59:59' and created <= '2012-02-14 23:59:59'
group by protocol;
The table has approximately 222 million rows, and the where clause in the query filters down to about 20 million rows. The distinct option will bring it down to about 700,000 distinct rows, and then after grouping, (and when the query finally finishes), 4 to 5 rows are actually returned.
I realize that it's a lot of data, but it seems that 4-5 hours is an awfully long time for this query.
Thanks.
Edit: For reference, this is running on AWS on a db.m2.4xlarge RDS database instance.
Why don't you profile a query and see what exactly is happening?
SET PROFILING = 1;
SET profiling_history_size = 0;
SET profiling_history_size = 15;
/* Your query should be here */
SHOW PROFILES;
SELECT state, ROUND(SUM(duration),5) AS `duration (summed) in sec` FROM information_schema.profiling WHERE query_id = 3 GROUP BY state ORDER BY `duration (summed) in sec` DESC;
SET PROFILING = 0;
EXPLAIN /* Your query again should appear here */;
I think this will help you in seeing where exactly query takes time and based on result you can perform optimization operations.
This is a really heavy query. To understand why it takes so long you should understand the details.
You have a range condition on the indexed field, that is MySQL finds the smallest created value in the index and for each value it gets the corresponding primary key from the index, retrieves the row from disk, and fetches the required fields (protocol, userId) missing in the current index record, puts them in a "temporary table", making the groupings on those 700000 rows. The index can actually be used and is used here only for speeding up the range condition.
The only way to speed it up, is to have an index that contains all the necessary data, so that MySQL would not need to make on disk lookups for the rows. That is called a covering index. But you should understand that the index will reside in memory and will contain ~ sizeOf(created+protocol+userId+PK)*rowCount bytes, that may become a burden as itself for the queries that update the table and for other indexes. It is easier to create a separate aggregates table and periodically update the table using your query.
Both distinct and group by will need to sort and store temporary data on the server. With that much data that might take a while.
Indexing different combinations of userId, created and protocol will help, but I can't say how much or what index will help the most.
Starting from a certain version of MariaDB (maybe since 10.5), I noticed that after importing a dump with
mysql dbname < dump.sql
the optimizer thinks things different from how they are, making the wrong decisions about indexes.
In general even listing tables innodb with phpmyadmin becomes very very slow.
I noticed that running
ANALYZE TABLE myTable;
fixes.
So after each import I run, that it's equal to run ANALYZE on each table
mysqlcheck -aA
I'm a relative novice when it comes to databases. We are using MySQL and I'm currently trying to speed up a SQL statement that seems to take a while to run. I looked around on SO for a similar question but didn't find one.
The goal is to remove all the rows in table A that have a matching id in table B.
I'm currently doing the following:
DELETE FROM a WHERE EXISTS (SELECT b.id FROM b WHERE b.id = a.id);
There are approximately 100K rows in table a and about 22K rows in table b. The column 'id' is the PK for both tables.
This statement takes about 3 minutes to run on my test box - Pentium D, XP SP3, 2GB ram, MySQL 5.0.67. This seems slow to me. Maybe it isn't, but I was hoping to speed things up. Is there a better/faster way to accomplish this?
EDIT:
Some additional information that might be helpful. Tables A and B have the same structure as I've done the following to create table B:
CREATE TABLE b LIKE a;
Table a (and thus table b) has a few indexes to help speed up queries that are made against it. Again, I'm a relative novice at DB work and still learning. I don't know how much of an effect, if any, this has on things. I assume that it does have an effect as the indexes have to be cleaned up too, right? I was also wondering if there were any other DB settings that might affect the speed.
Also, I'm using INNO DB.
Here is some additional info that might be helpful to you.
Table A has a structure similar to this (I've sanitized this a bit):
DROP TABLE IF EXISTS `frobozz`.`a`;
CREATE TABLE `frobozz`.`a` (
`id` bigint(20) unsigned NOT NULL auto_increment,
`fk_g` varchar(30) NOT NULL,
`h` int(10) unsigned default NULL,
`i` longtext,
`j` bigint(20) NOT NULL,
`k` bigint(20) default NULL,
`l` varchar(45) NOT NULL,
`m` int(10) unsigned default NULL,
`n` varchar(20) default NULL,
`o` bigint(20) NOT NULL,
`p` tinyint(1) NOT NULL,
PRIMARY KEY USING BTREE (`id`),
KEY `idx_l` (`l`),
KEY `idx_h` USING BTREE (`h`),
KEY `idx_m` USING BTREE (`m`),
KEY `idx_fk_g` USING BTREE (`fk_g`),
KEY `fk_g_frobozz` (`id`,`fk_g`),
CONSTRAINT `fk_g_frobozz` FOREIGN KEY (`fk_g`) REFERENCES `frotz` (`g`)
) ENGINE=InnoDB AUTO_INCREMENT=179369 DEFAULT CHARSET=utf8 ROW_FORMAT=DYNAMIC;
I suspect that part of the issue is there are a number of indexes for this table.
Table B looks similar to table B, though it only contains the columns id and h.
Also, the profiling results are as follows:
starting 0.000018
checking query cache for query 0.000044
checking permissions 0.000005
Opening tables 0.000009
init 0.000019
optimizing 0.000004
executing 0.000043
end 0.000005
end 0.000002
query end 0.000003
freeing items 0.000007
logging slow query 0.000002
cleaning up 0.000002
SOLVED
Thanks to all the responses and comments. They certainly got me to think about the problem. Kudos to dotjoe for getting me to step away from the problem by asking the simple question "Do any other tables reference a.id?"
The problem was that there was a DELETE TRIGGER on table A which called a stored procedure to update two other tables, C and D. Table C had a FK back to a.id and after doing some stuff related to that id in the stored procedure, it had the statement,
DELETE FROM c WHERE c.id = theId;
I looked into the EXPLAIN statement and rewrote this as,
EXPLAIN SELECT * FROM c WHERE c.other_id = 12345;
So, I could see what this was doing and it gave me the following info:
id 1
select_type SIMPLE
table c
type ALL
possible_keys NULL
key NULL
key_len NULL
ref NULL
rows 2633
Extra using where
This told me that it was a painful operation to make and since it was going to get called 22500 times (for the given set of data being deleted), that was the problem. Once I created an INDEX on that other_id column and reran the EXPLAIN, I got:
id 1
select_type SIMPLE
table c
type ref
possible_keys Index_1
key Index_1
key_len 8
ref const
rows 1
Extra
Much better, in fact really great.
I added that Index_1 and my delete times are in line with the times reported by mattkemp. This was a really subtle error on my part due to shoe-horning some additional functionality at the last minute. It turned out that most of the suggested alternative DELETE/SELECT statements, as Daniel stated, ended up taking essentially the same amount of time and as soulmerge mentioned, the statement was pretty much the best I was going to be able to construct based on what I needed to do. Once I provided an index for this other table C, my DELETEs were fast.
Postmortem:
Two lessons learned came out of this exercise. First, it is clear that I didn't leverage the power of the EXPLAIN statement to get a better idea of the impact of my SQL queries. That's a rookie mistake, so I'm not going to beat myself up about that one. I'll learn from that mistake. Second, the offending code was the result of a 'get it done quick' mentality and inadequate design/testing led to this problem not showing up sooner. Had I generated several sizable test data sets to use as test input for this new functionality, I'd have not wasted my time nor yours. My testing on the DB side was lacking the depth that my application side has in place. Now I've got the opportunity to improve that.
Reference: EXPLAIN Statement
Deleting data from InnoDB is the most expensive operation you can request of it. As you already discovered the query itself is not the problem - most of them will be optimized to the same execution plan anyway.
While it may be hard to understand why DELETEs of all cases are the slowest, there is a rather simple explanation. InnoDB is a transactional storage engine. That means that if your query was aborted halfway-through, all records would still be in place as if nothing happened. Once it is complete, all will be gone in the same instant. During the DELETE other clients connecting to the server will see the records until your DELETE completes.
To achieve this, InnoDB uses a technique called MVCC (Multi Version Concurrency Control). What it basically does is to give each connection a snapshot view of the whole database as it was when the first statement of the transaction started. To achieve this, every record in InnoDB internally can have multiple values - one for each snapshot. This is also why COUNTing on InnoDB takes some time - it depends on the snapshot state you see at that time.
For your DELETE transaction, each and every record that is identified according to your query conditions, gets marked for deletion. As other clients might be accessing the data at the same time, it cannot immediately remove them from the table, because they have to see their respective snapshot to guarantee the atomicity of the deletion.
Once all records have been marked for deletion, the transaction is successfully committed. And even then they cannot be immediately removed from the actual data pages, before all other transactions that worked with a snapshot value before your DELETE transaction, have ended as well.
So in fact your 3 minutes are not really that slow, considering the fact that all records have to be modified in order to prepare them for removal in a transaction safe way. Probably you will "hear" your hard disk working while the statement runs. This is caused by accessing all the rows.
To improve performance you can try to increase InnoDB buffer pool size for your server and try to limit other access to the database while you DELETE, thereby also reducing the number of historic versions InnoDB has to maintain per record.
With the additional memory InnoDB might be able to read your table (mostly) into memory and avoid some disk seeking time.
Try this:
DELETE a
FROM a
INNER JOIN b
on a.id = b.id
Using subqueries tend to be slower then joins as they are run for each record in the outer query.
This is what I always do, when I have to operate with super large data (here: a sample test table with 150000 rows):
drop table if exists employees_bak;
create table employees_bak like employees;
insert into employees_bak
select * from employees
where emp_no > 100000;
rename table employees to employees_todelete;
rename table employees_bak to employees;
drop table employees_todelete;
In this case the sql filters 50000 rows into the backup table.
The query cascade performs on my slow machine in 5 seconds.
You can replace the insert into select by your own filter query.
That is the trick to perform mass deletion on big databases!;=)
Your time of three minutes seems really slow. My guess is that the id column is not being indexed properly. If you could provide the exact table definition you're using that would be helpful.
I created a simple python script to produce test data and ran multiple different versions of the delete query against the same data set. Here's my table definitions:
drop table if exists a;
create table a
(id bigint unsigned not null primary key,
data varchar(255) not null) engine=InnoDB;
drop table if exists b;
create table b like a;
I then inserted 100k rows into a and 25k rows into b (22.5k of which were also in a). Here's the results of the various delete commands. I dropped and repopulated the table between runs by the way.
mysql> DELETE FROM a WHERE EXISTS (SELECT b.id FROM b WHERE a.id=b.id);
Query OK, 22500 rows affected (1.14 sec)
mysql> DELETE FROM a USING a LEFT JOIN b ON a.id=b.id WHERE b.id IS NOT NULL;
Query OK, 22500 rows affected (0.81 sec)
mysql> DELETE a FROM a INNER JOIN b on a.id=b.id;
Query OK, 22500 rows affected (0.97 sec)
mysql> DELETE QUICK a.* FROM a,b WHERE a.id=b.id;
Query OK, 22500 rows affected (0.81 sec)
All the tests were run on an Intel Core2 quad-core 2.5GHz, 2GB RAM with Ubuntu 8.10 and MySQL 5.0. Note, that the execution of one sql statement is still single threaded.
Update:
I updated my tests to use itsmatt's schema. I slightly modified it by remove auto increment (I'm generating synthetic data) and character set encoding (wasn't working - didn't dig into it).
Here's my new table definitions:
drop table if exists a;
drop table if exists b;
drop table if exists c;
create table c (id varchar(30) not null primary key) engine=InnoDB;
create table a (
id bigint(20) unsigned not null primary key,
c_id varchar(30) not null,
h int(10) unsigned default null,
i longtext,
j bigint(20) not null,
k bigint(20) default null,
l varchar(45) not null,
m int(10) unsigned default null,
n varchar(20) default null,
o bigint(20) not null,
p tinyint(1) not null,
key l_idx (l),
key h_idx (h),
key m_idx (m),
key c_id_idx (id, c_id),
key c_id_fk (c_id),
constraint c_id_fk foreign key (c_id) references c(id)
) engine=InnoDB row_format=dynamic;
create table b like a;
I then reran the same tests with 100k rows in a and 25k rows in b (and repopulating between runs).
mysql> DELETE FROM a WHERE EXISTS (SELECT b.id FROM b WHERE a.id=b.id);
Query OK, 22500 rows affected (11.90 sec)
mysql> DELETE FROM a USING a LEFT JOIN b ON a.id=b.id WHERE b.id IS NOT NULL;
Query OK, 22500 rows affected (11.48 sec)
mysql> DELETE a FROM a INNER JOIN b on a.id=b.id;
Query OK, 22500 rows affected (12.21 sec)
mysql> DELETE QUICK a.* FROM a,b WHERE a.id=b.id;
Query OK, 22500 rows affected (12.33 sec)
As you can see this is quite a bit slower than before, probably due to the multiple indexes. However, it is nowhere near the three minute mark.
Something else that you might want to look at is moving the longtext field to the end of the schema. I seem to remember that mySQL performs better if all the size restricted fields are first and text, blob, etc are at the end.
You're doing your subquery on 'b' for every row in 'a'.
Try:
DELETE FROM a USING a LEFT JOIN b ON a.id = b.id WHERE b.id IS NOT NULL;
Try this out:
DELETE QUICK A.* FROM A,B WHERE A.ID=B.ID
It is much faster than normal queries.
Refer for Syntax : http://dev.mysql.com/doc/refman/5.0/en/delete.html
I know this question has been pretty much solved due to OP's indexing omissions but I would like to offer this additional advice, which is valid for a more generic case of this problem.
I have personally dealt with having to delete many rows from one table that exist in another and in my experience it's best to do the following, especially if you expect lots of rows to be deleted. This technique most importantly will improve replication slave lag, as the longer each single mutator query runs, the worse the lag would be (replication is single threaded).
So, here it is: do a SELECT first, as a separate query, remembering the IDs returned in your script/application, then continue on deleting in batches (say, 50,000 rows at a time).
This will achieve the following:
each one of the delete statements will not lock the table for too long, thus not letting replication lag to get out of control. It is especially important if you rely on your replication to provide you relatively up-to-date data. The benefit of using batches is that if you find that each DELETE query still takes too long, you can adjust it to be smaller without touching any DB structures.
another benefit of using a separate SELECT is that the SELECT itself might take a long time to run, especially if it can't for whatever reason use the best DB indexes. If the SELECT is inner to a DELETE, when the whole statement migrates to the slaves, it will have to do the SELECT all over again, potentially lagging the slaves because it has to do the long select all over again. Slave lag, again, suffers badly. If you use a separate SELECT query, this problem goes away, as all you're passing is a list of IDs.
Let me know if there's a fault in my logic somewhere.
For more discussion on replication lag and ways to fight it, similar to this one, see MySQL Slave Lag (Delay) Explained And 7 Ways To Battle It
P.S. One thing to be careful about is, of course, potential edits to the table between the times the SELECT finishes and DELETEs start. I will let you handle such details by using transactions and/or logic pertinent to your application.
DELETE FROM a WHERE id IN (SELECT id FROM b)
Maybe you should rebuild the indicies before running such a hugh query. Well, you should rebuild them periodically.
REPAIR TABLE a QUICK;
REPAIR TABLE b QUICK;
and then run any of the above queries (i.e.)
DELETE FROM a WHERE id IN (SELECT id FROM b)
The query itself is already in an optimal form, updating the indexes causes the whole operation to take that long. You could disable the keys on that table before the operation, that should speed things up. You can turn them back on at a later time, if you don't need them immediately.
Another approach would be adding a deleted flag-column to your table and adjusting other queries so they take that value into account. The fastest boolean type in mysql is CHAR(0) NULL (true = '', false = NULL). That would be a fast operation, you can delete the values afterwards.
The same thoughts expressed in sql statements:
ALTER TABLE a ADD COLUMN deleted CHAR(0) NULL DEFAULT NULL;
-- The following query should be faster than the delete statement:
UPDATE a INNER JOIN b SET a.deleted = '';
-- This is the catch, you need to alter the rest
-- of your queries to take the new column into account:
SELECT * FROM a WHERE deleted IS NULL;
-- You can then issue the following queries in a cronjob
-- to clean up the tables:
DELETE FROM a WHERE deleted IS NOT NULL;
If that, too, is not what you want, you can have a look at what the mysql docs have to say about the speed of delete statements.
BTW, after posting the above on my blog, Baron Schwartz from Percona brought to my attention that his maatkit already has a tool just for this purpose - mk-archiver. http://www.maatkit.org/doc/mk-archiver.html.
It is most likely your best tool for the job.
Obviously the SELECT query that builds the foundation of your DELETE operation is quite fast so I'd think that either the foreign key constraint or the indexes are the reasons for your extremely slow query.
Try
SET foreign_key_checks = 0;
/* ... your query ... */
SET foreign_key_checks = 1;
This would disable the checks on the foreign key. Unfortunately you cannot disable (at least I don't know how) the key-updates with an InnoDB table. With a MyISAM table you could do something like
ALTER TABLE a DISABLE KEYS
/* ... your query ... */
ALTER TABLE a ENABLE KEYS
I actually did not test if these settings would affect the query duration. But it's worth a try.
Connect datebase using terminal and execute command below, look at the result time each of them, you'll find that times of delete 10, 100, 1000, 10000, 100000 records are not Multiplied.
DELETE FROM #{$table_name} WHERE id < 10;
DELETE FROM #{$table_name} WHERE id < 100;
DELETE FROM #{$table_name} WHERE id < 1000;
DELETE FROM #{$table_name} WHERE id < 10000;
DELETE FROM #{$table_name} WHERE id < 100000;
The time of deleting 10 thousand records is not 10 times as much as deleting 100 thousand records.
Then, except for finding a way delete records more faster, there are some indirect methods.
1, We can rename the table_name to table_name_bak, and then select records from table_name_bak to table_name.
2, To delete 10000 records, we can delete 1000 records 10 times. There is an example ruby script to do it.
#!/usr/bin/env ruby
require 'mysql2'
$client = Mysql2::Client.new(
:as => :array,
:host => '10.0.0.250',
:username => 'mysql',
:password => '123456',
:database => 'test'
)
$ids = (1..1000000).to_a
$table_name = "test"
until $ids.empty?
ids = $ids.shift(1000).join(", ")
puts "delete =================="
$client.query("
DELETE FROM #{$table_name}
WHERE id IN ( #{ids} )
")
end
The basic technique for deleting multiple Row form MySQL in single table through the id field
DELETE FROM tbl_name WHERE id <= 100 AND id >=200;
This query is responsible for deleting the matched condition between 100 AND 200 from the certain table
I use a 1 column memory table to keep track of views on various items in my DB. Each view = INSERT query into the memory table. Every 10 mins, I wanna count() the rows for each item, and commit changes to DB.
The question is.... if I run the query that will get the list of all items, such as
SELECT COUNT(*) AS period_views, `item_id` FROM `-views` GROUP BY `item_id` ORDER BY `item_id`
and then run an update query for each row to add the amount of views in that period, and then truncate the table. This operation might take a few seconds.... and in those few seconds, there is going to be other INSERTS into that table, that didnt make it into the original count. Will they be truncated too once that command executes? or will the table be locked until the entire operation completes, and the the new INSERTs added?
MySQL does not lock the table automatically, and it is possible that you will lose some records in between getting the count and performing the truncate. So two solutions jump out at me:
1) Use table locks to prevent the memory table being updated - depending on the nature of your application, this means that all of your clients might freeze for a few seconds while you are updating, this might be OK.
2) Add a second column to keep track of which records you are currently updating ...
ALTER TABLE `-views` ADD work_in_progress TINYINT NOT NULL DEFAULT 0;
And then when you want to work on the those records
UPDATE `-views` SET work_in_progress = 1;
SELECT COUNT(*) AS period_views, `item_id` FROM `-views` WHERE work_in_progress GROUP BY `item_id` ORDER BY `item_id`;
# [ perform updates as necessary ]
DELETE FROM `-views` WHERE work_in_progress;
This implementation will guarantee that you don't delete any -views which were added while you were updating.
And FWIW, -views is an awful name for a table!