I'm currently building a system that does running computations, and every 5 seconds inserts or updates information based on those computations to a few rows in MySQL. I'm working on running this system on a few different servers at once right now with a few agents that are each doing similar processing and then writing on the same set of rows. I already randomize the order in which each agent writes its set of rows, but there's still a lot of deadlock happening. What's the best/fastest way to get through those deadlocks? Should I just rerun the query each time one happens, or do row locks, or something else entirely?
I suggest you try something that won't require more than one client to update your 'few rows.'
For example, you could have each agent that produces results do an INSERT to a staging table with the MEMORY access method.
Then, every five seconds you can run a MySQL event (a stored procedure within the server) that loops through all the rows in that table, posting their results to your 'few rows' and then deleting them. If it's important for the rows in your staging table to be processed in order, then you can use an AUTO_INCREMENT id field. But it might not be important for them to be in order.
If you want to get fancier and more scalable than that, you'll need a queue management system like Apache ActiveMQ.
Related
What is the best way to execute a bulk update of all records in a large table in MySQL?
During the sanitization process, we are updating all rows in the users table, which has 28M rows, to mask a few columns. This is currently taking right around 2 hours to complete in a rake task, and the AWS session expiration is also 2 hours. If the rake task takes longer than the session expiration, the build will fail.
Due to a large number of records, we are updating 25K rows at a time by using find_in_batches, and then update_all in the results. We throttle between each batch by sleeping for 0.1s to avoid high CPU.
So the question is, is there any way we can optimize the bulk update further, or shall we increase the AWS session expiration to 3 hours?
One option could be to batch by id ranges, rather than by exact batch sizes. So update between id 1-100000, followed by 100001-200000, and so on. This avoids the large sets of ids being passed around. As there will be gaps in the ids, each batch would be a different size, but this might not be an issue.
Thanks for your input.
For such large updates overhead of fetching records and instantiating AR objects is very significant (and also there'll be slowdown from GC), fastest way to execute - is to write raw sql query that will do the update (or use update_all to construct it, which is very similar, but allows to use scopes/joins by relations).
I have a mysql table that keep gaining new records every 5 seconds.
The questions are
can I run query on this set of data that may takes more than 5 seconds?
if SELECT statement takes more than 5s, will it affect the scheduled INSERT statement?
what happen when INSERT statement invoked while SELECT is still running, will SELECT get the newly inserted records?
I'll go over your questions and some of the comments you added later.
can I run query on this set of data that may takes more than 5 seconds?
Can you? Yes. Should you? It depends. In a MySQL configuration I set up, any query taking longer than 3 seconds was considered slow and logged accordingly. In addition, you need to keep in mind the frequency of the queries you intend to run.
For example, if you try to run a 10 second query every 3 seconds, you can probably see how things won't end well. If you run a 10 second query every few hours or so, then it becomes more tolerable for the system.
That being said, slow queries can often benefit from optimizations, such as not scanning the entire table (i.e. search using primary keys), and using the explain keyword to get the database's query planner to tell you how it intends to work on that internally (e.g. is it using PKs, FKs, indices, or is it scanning all table rows?, etc).
if SELECT statement takes more than 5s, will it affect the scheduled INSERT statement?
"Affect" in what way? If you mean "prevent insert from actually inserting until the select has completed", that depends on the storage engine. For example, MyISAM and InnoDB are different, and that includes locking policies. For example, MyISAM tends to lock entire tables while InnoDB tends to lock specific rows. InnoDB is also ACID-compliant, which means it can provide certain integrity guarantees. You should read the docs on this for more details.
what happen when INSERT statement invoked while SELECT is still running, will SELECT get the newly inserted records?
Part of "what happens" is determined by how the specific storage engine behaves. Regardless of what happens, the database is designed to answer application queries in a way that's consistent.
As an example, if the select statement were to lock an entire table, then the insert statement would have to wait until the select has completed and the lock has been released, meaning that the app would see the results prior to the insert's update.
I understand that locking database can prevent messing up the SELECT statement.
It can also put a potentially unacceptable performance bottleneck, especially if, as you say, the system is inserting lots of rows every 5 seconds, and depending on the frequency with which you're running your queries, and how efficiently they've been built, etc.
what is the good practice to do when I need the data for calculations while those data will be updated within short period?
My recommendation is to simply accept the fact that the calculations are based on a snapshot of the data at the specific point in time the calculation was requested and to let the database do its job of ensuring the consistency and integrity of said data. When the app requests data, it should trust that the database has done its best to provide the most up-to-date piece of consistent information (i.e. not providing a row where some columns have been updated, but others yet haven't).
With new rows coming in at the frequency you mentioned, reasonable users will understand that the results they're seeing are based on data available at the time of request.
All of your questions are related to locking of table.
Your all questions depend on the way database is configured.
Read : http://www.mysqltutorial.org/mysql-table-locking/
Perform Select Statement While insert statement working
If you want to perform a select statement during insert SQL is performing, you should check by open new connection and close connection every time. i.e If I want to insert lots of records, and want to know that last record has inserted by selecting query. I must have to open connection and close connection in for loop or while loop.
# send a request to store data
insert statement working // take a long time
# select statement in while loop.
while true:
cnx.open()
select statement
cnx.close
//break while loop if you get the result
I have a MySQL server with many innodb tables.
I have a background script that does A LOT a delete/insert with one request : it deletes many millions of rows from table 2, then insert many millions of rows to table 2 using data from table 1 :
INSERT INTO table 2 (date)
SELECT date from table 1 GROUP BY date
(The request is actually more complex but it is to shown what kind of request I am doing).
At the same time, I am going to run a second background script, that does about a million INSERT or UPDATE requests, but separately (I mean, I execute the first update query, then I execute an insert query, etc...) in table 3.
My issue is that when a script is running, it is fast, like let's say it takes 30minutes each, so 1h total. But when the two scripts are running at the same time, it is VERY slow, like it will take 5h, instead of 1h.
So first, I would like to know what can cause this ? Is it because of IO performance ? (like mysql is writing in two different tables so it is slow to switch between the two ?)
And how could I fix this ? If I could say that the big INSERT query is paused while my second background script is running, it would be great, for example... But I can't find a way to do something like this.
I am not an expert at MySQL administration.. If you need more information, please let me know !
Thank you !!
30 minutes for million INSERT is not fast. Do you have an index on date column? (or whatever column you are using to pivot on)
Regarding your original question.It's difficult to say much without knowing the details of both your scripts and the table structures, but one possible reason why the scripts are running reasonably quickly separately is because you are doing similar kinds of SELECT queries, which might be getting cached by MySQL and then reused for subsequent queries. But if you are running two queries in parallel, then the SELECT's for the corresponding query might not stay in the cache (because there are two concurrent processes which send new queries all the time).
You might want to explicitly disable cache for some queries which you are sure you only run once (using SQL_NO_CACHE modifier) and see if it changes anything. But I'd look into indexing and into your table structure first, because 30 minutes seems to be extremely slow :) E.g. you might also want to introduce partitioning by date for your tables, if you know that you always choose entries in a given period (say by month). The exact tricks depend on your data.
UPDATE: Another issue might be that both your queries work with the same table (table 1), and the default transaction isolation level in MySQL is REPEATABLE READS afair. So it might be that one query is waiting until the other is done with the table to satisfy the transaction isolation level. You might want to lower the transaction isolation level if you are sure that your table 1 is not changed when scripts are working on it.
You can use an event scheduler so you can set mysql to launch this queries at different hours of the day, in another stackoverflow related question you have an exmaple of how to do it: MySQL Event Scheduler on a specific time everyday
Another thing to have in mind is to use the explain plan to see what could be the reason the query is that slow.
I have a MySQL table with 237 million rows. I want to process all of these rows and update them with new values.
I do have sequential ID's, so I could just use a lot of select statements:
where id = '1'
where id = '2'
This is the method mentioned in Sequentially run through a MYSQL table with 1,000,000 records?.
But I'd like to know if there is a faster way using something like a cursor that would be used to sequentially read a big file without needing to load the full set into memory. The way I see it, a cursor would be much faster than running millions of select statements to get the data back in manageable chunks.
Ideally, you get the DBMS to do the work for you. You make the SQL statement so it runs solely in the database, not returning data to the application. All else apart, this saves the overhead of 237 million messages to the client and 237 million messages back to the server.
Whether this is feasible depends on the nature of the update:
Can the DBMS determine what the new values should be?
Can you get the necessary data into the database so that the DBMS can determine what the new values should be?
Will every single one of the 237 million rows be changed, or only a subset?
Can the DBMS be used to determine the subset?
Will any of the id values be changed at all?
If the id values will never be changed, then you can arrange to partition the data into manageable subsets, for any flexible definition of 'manageable'.
You may need to consider transaction boundaries; can it all be done in a single transaction without blowing out the logs? If you do operations in subsets rather than as a single atomic transaction, what will you do if your driving process crashes at 197 million rows processed? Or the DBMS crashes at that point? How will you know where to resume operations to complete the processing?
I've got a theoretical question and can't find a good solution for this on the net:
For a tblA with 100,000 recs.
I want to have multiple processes/apps running, each of which accesses tblA.
I don't want the apps to access the same recs. ie, I want appA to access the 1st 50 rows, with appB accessing the next 50, and appC accessing the next 50 after that..
So basically I want the apps to do a kind of fetch on the next "N" recs in the table. I'm looking for a way to access/process the row data as fast as possible, essentially running the apps in a simultaneous manner. but I don't want the apps to process the same rows.
So, just how should this kind of process be set up?
Is it simply doing a kind of:
select from tblA limit 50
and doing some kind of row locking for each row (which requires innodb)
Pointers/psuedo code would be useful.
Here is some posts from the DBA StackExchange on this
https://dba.stackexchange.com/q/10017/877
https://dba.stackexchange.com/a/4470/877
It discusses SELECT ... LOCK IN SHARE MODE and potential headcahes that comes with it.
Percona wrote a nice article on this along with SELECT ... FOR UPDATE
Your application should handle what data it wants to access. Create a pointer in that. If you're using stored procedures, use another table to store the pointers. Each process would "reserve" a set of rows before beginning processing. Every process should check for the max of that and also see if it is greater than the length of the table.
If you are specifically looking for processing first set, second set, etc. The you can use LIMIT # (i.e. 0,50 51,100 101,150) with an ORDER BY. Locking is not necessary since the processes won't even try to access each others record sets. But I can't imagine a scenario where that would be a good implementation.
An alternative is to just to use update with a limit, then select the records that were updated. You can use the process ID, random number or something else that is almost guaranteed to be unique across processes. Add a "status" field to your table indicating if the record is available for processing (i.e. value is NULL). Then each process would update the status field to "own" the record for processing.
UPDATE tblA SET status=1234567890 WHERE status IS NULL LIMIT 50;
SELECT * FROM tblA WHERE status=1234567890;
This would work for MyISAM or Innodb. With Innodb you would be able to have multiple updates running at once, improving performance.
The problem with these solutions is lag time. If process A executes at 12:00:00 and proccess B also executes at precisely the same time, and in an application, there are several blocks of distinct code leading up to the locks/DMLs, the process time for each would vary. So process A may complete first, or it may be process B. If process A is setting the lock, and process B modifies the record first, you're in trouble. This is the trouble with forking.