Very good info! Set slow_query_log_file to the path where you want to save the file. Inserts came in the order of ‘Time’. Or does partitioning only help if I have several servers (or hard-drives) available? It might be a bit too much as there are few completely uncached workloads, but 100+ times difference is quite frequent. Would duplicating data on inserts and updates be an option which would mean having two of the same table, one using InnoDB for main reading purposes and one for MyISAM for searching using Full text search and every time you do an update actually uipdate bith table etc. The table structure is as following and it stores uploaded files in many portions (fragments) of max. Meanwhile the table has a size of 1,5Gb. Could maybe somebody point me to the most relevant parameters to consider in my case (which parameters, for example, define the amount of memory reserved to handle the index, etc.)? -Thanks, Jitendra, If I need time difference calculation on the same datetime field between 4 types of events related to the same thing (i.e books), and yearly the number of this events goes up to 2 million, what is better: A) 1 big table with correctly indexed book_id and event_id and possible partitioning by book type (up to 50 types) B) 4 tables, one for each event, indexed with the same foreign key that holds the relation and possible partitioning all 4 tables by the book type. MySQL’s ALTER TABLE performance can become a problem with very large tables. Thanks a lot! I have 10GB MYISAM table . I have made an online dictionary using a MySQL query I found online. And what if one or more event happens more than ones for the same book? More information about this error may be available in the server error log. (This is on a 8x Intel Box w/ mutli GB ram). Each row consists of 2x 64 bit integers. For example, retrieving index values first and then accessing rows in sorted order can be a lot of help for big scans. In this case, we were storing time-series data. If you have your data fully in memory you could perform over 300,000 random lookups per second from a single thread, depending on system and table structure. This is the fastest way to load large volumes of data to table. Been a while since I’ve worked on MySQL. that should increase the speed dramatically. The compact family of row formats, which includes COMPACT, DYNAMIC, and COMPRESSED, decreases row storage space at the cost of increasing … I know I will most likely have to break this very large table down into tables by week. The main event table definition is CREATE TABLE IF NOT EXISTS stats ( id int(11) unsigned NOT NULL AUTO_INCREMENT, banner_id int(11) unsigned NOT NULL, location_id tinyint(3) unsigned NOT NULL, url_id int(11) unsigned NOT NULL, page_id int(11) unsigned NOT NULL, dateline int(11) unsigned NOT NULL, ip_interval int(11) unsigned NOT NULL, browser_id tinyint(3) unsigned NOT NULL, platform_id tinyint(3) unsigned NOT NULL, PRIMARY KEY (id), KEY bannerid (banner_id), KEY dateline (dateline), KEY ip_interval (ip_interval) ) ENGINE=MyISAM DEFAULT CHARSET=latin1 PACK_KEYS=1 ROW_FORMAT=FIXED AUTO_INCREMENT=10100001 ; The country codes stored in different table named iplist CREATE TABLE IF NOT EXISTS iplist ( id int(11) unsigned NOT NULL AUTO_INCREMENT, code varchar(2) NOT NULL, code_3 varchar(3) NOT NULL, name varchar(255) NOT NULL, start int(11) unsigned NOT NULL, end int(11) unsigned NOT NULL, PRIMARY KEY (id), KEY code (code) ) ENGINE=MyISAM DEFAULT CHARSET=latin1 AUTO_INCREMENT=91748 ; So the query to get top 10 countries will be, SELECT iplist.code COUNT(stat.ip_interval ) AS count FROM stats AS stat LEFT JOIN iplist AS iplist ON (iplist.id=stat.ip_interval) WHERE stat.dateline>=1243382400 AND dateline<1243466944 GROUP BY code ORDER BY count DESC LIMIT 0, 10. I used the IN clause and it sped my query up considerably. Over this past week, I noticed some slowness on one of my InnoDB setups. The scripts I used for generating and inserting data, are checked in at GitHub. 2)I have set "pack_keys=1 and delay_key_write=1" (and set it so MySQL will flush the table every 30 minutes to save memory and update the keys) We’ve got 20,000,000 bank loan records we query against all sorts of tables. Yes that is the problem. By this post I try to divide into smaller table and running one sql per time, but still not faster. This article is about typical mistakes people are doing to get their MySQL running slow with large tables. MySQL 4.1.8. Why MySQL could be slow with large tables? I simply put the search index in a separate table, of some 80 MB and the binary / blobs in another. If it is possible you instantly will have half of the problems solved. Here is a good example. INSERTS: 1,000 2. Soon after the innoDB-buffer gets depleted, they drop down to roughly 5K/sec. Let’s do some computations again. I work with MSSQL and multi-GB tables (up to 1 TB) with some very ridiculous normalisation and absurd number of columns (some tables have > 900!!) To keep MySQL tables relatively small, and to scale well, I had created a framework around this, whose job was to create new dated-tables every day, and delete those older than a month. Why are you surprised ? Question 2 Big joins are bad. (At least I don’t have to join so many tables to get the data), The other thing which I’m considering is to just pull these data direct into users’ PC, dumping it into a sqlite DB and do the joins there, thereby minimising load on the main server or even the mysql server. Also do not forget to try it out for different constants – plans are not always the same. I worked on a project containing 5 tables and a realtime search (AJAX). Once again, I use multiple inserts into the table, with sets of 150 records each: INSERT INTO T (col1,col2) VALUES (1,1),(1,2),(1,3)…(1,150); Is this the best design for the table, taking in consideration what I want from it?! Remember when Anaconda eats a deer it always take time to get it right in itss stomach. Is there a point at which adding CSV values to an IN(val1, val2,…) clause starts to make an index lose it’s efficiency? any help is very much appreciated thanks, Need help! Set long_query_time to the number of seconds that a query should take to be considered slow, say 0.2. No bloating 🙂 works like a charm. – do i switch table from MyISAM to InnoDB (if yes, how to configure InnoDB for best performance?). But, never tried explaining a bit on it? My SELECT statement looks something like SELECT * FROM table_name WHERE (year > 2001) AND (id = 345 OR id = 654 ….. OR id = 90) The second set of parenthesis could have 20k+ conditions. Then number of rows involved went from 150K rows to 2.2G rows!!!!! Tips for MyISAM tables. I guess this is due to index maintenance. I need to do 2 queries on the table. Could it, for example, help to increase “key_buffer_size”? Sergey, Would you mind posting your case on our forums instead at http://forum.mysqlperformanceblog.com and I’ll reply where. Do not take me as going against normalization or joins. Maybe I can rewrite the SQL, since it seems like MySQL handles ONE JOIN, but no way it handles TWO JOINS. I repeated the above with several tuning parameters turned on. (30min up to 2/3 hours). It can be happening due to wrong configuration (ie too small myisam_max_sort_file_size or myisam_max_extra_sort_file_size) or it could be just lack of optimization, if you’re having large (does not fit in memory) PRIMARY or UNIQUE indexes. I have several data sets and each of them would be around 90,000,000 records, but each record has just a pair of IDs as compository primary key and a text, just 3 fields. Although the selects now take 25% more time to perform, it’s still around 1 second, so it seams quite acceptable to me, since there are more than 100 million records in the table, and if it means that the inserts are faster. The slow part of the query is thus the retrieving of the data. Now it remains on a steady 12 seconds every time i insert 1 million rows. I had 40000 row in database when ever i fire this query in mysql its taking too much time to get data from database. or just when you have a large change in your data distribution in your table? This article is BS. When invoking a SELECT statement in LogDetails table(having approx. The above example is based on one very simple website. My Max script execution time in PHP is set to 30 Secs. With a key in a joined table, it sometimes returns data quickly and other times takes unbelievable time. MySQL has a built-in slow query log. Please feel free to send it to me to pz at mysql performance blog.com. The type of table it is — is it MYISAM or INNODB? It was very slow and the process that ran on another machine that took 1 hour this is 6 hours. Proudly running Percona Server for MySQL, Percona Advanced Managed Database Service, Top most overlooked MySQL Performance Optimizations, MySQL scaling and high availability – production experience from the last decade(s), How to analyze and tune MySQL queries for better performance, Best practices for configuring optimal MySQL memory usage, MySQL query performance – not just indexesÂ, Performance at scale: keeping your database on its toes, Practical MySQL Performance Optimization Part 1, http://www.mysqlperformanceblog.com/2006/06/02/indexes-in-mysql/, http://dev.mysql.com/doc/refman/5.1/en/partitioning.html, http://vpslife.blogspot.com/2009/03/mysql-nested-query-tweak.html, http://www.notesbit.com/index.php/web-mysql/mysql/mysql-tuning-optimizing-my-cnf-file/, http://techathon.mytechlabs.com/performance-tuning-while-working-with-large-database/, http://www.ecommercelocal.com/pages.php?pi=6, http://www.ecommercelocal.com/pages.php?pi=627500, https://www.percona.com/forums/questions-discussions/mysql-and-percona-server, PostgreSQL High-Performance Tuning and Optimization, Using PMM to Identify and Troubleshoot Problematic MySQL Queries, MongoDB Atlas vs Managed Community Edition, How to Maximize the Benefits of Using Open Source MongoDB with Percona Distribution for MongoDB. Use multiple servers to host portions of the data set. I am trying to prune a Cat after updating the Cat to clear out any records that were not updated (hence deleted from our files). File output, table … Hope this helps. Google may use Mysql but they don’t necessarily have billions of rows – just because google uses MySQL doesn’t mean they actually use it for their search engine results. I need to delete all 300,000 records found in one table from a table that has 20,000,000 records, and neither the subquerry i wrote nor the join i wrote give me any result at all in over 12 hours. The table contains 36 million rows (Data size 5GB, Index size 4GB). I run the following query, which takes 93 seconds ! Every large database needs careful design, and the solution for two different databases, might be completely different. All the records in the large table refer to this 1 record in the small table. Here’s a url: https://www.percona.com/forums/questions-discussions/mysql-and-percona-server, That category is for generic MySQL issues as well as those associated with Percona Server, which is an enhanced, drop-in MySQL replacement. Unfortunately MySQL innodb tables do not allow to disable indices. How do you feel InnoDB composite keys work as opposed to separate tables? Integrity checks don’t work — try making a check on a column NOT NULL to include NOT EMPTY (i.e., no blank space can be entered, which as you know, is different from NULL). It has been working pretty well until today. Store a portion of data you’re going to work with in temporary tables etc. I am doing just that and there is really no way around it as I am forced to do an update based on corresponding values between them. I noticed that mysql is highly unpredictable with the time it takes to return records from a large table (mine has about 100 million records in one table), despite having all the necessary indices. A extreme case!! A lot of simple queries generally works well but you should not abuse it. My situation: Large MyISAM table: 30 million recs, data: 1.2 GB Data columns are like so: (INT val #1, INT val #2, INT val #3, VARCHAR val #4), WHERE clause searches are being done in this manner Val #1: always = one single integer value Val #2: IN(1 to 4 CSV integer values) Val #3: IN(unknown number of CSV integer values, probably max of 50) Val #4: LIKE ‘string%’. I tried SQL_BIG_RESULT, analyze table, etc… nothing seems to help. http://forum.percona.com/index.php/t/1639/, If you require urgent assistance for project of critical importance forum is not the right way to seek help as it is only looked at at spare times. Sorry for the confusion, but this is what I was talking about also. It doesn’t take any longer to create any one of these indexes, but I’m sure you know all this. But we already knew that. Although the exact number of inserts varied a bit, the end result was the same – after about an hour, my insert rate started dropping. Set slow_query_log_file to the … It also simply does not have the data available – is given index (range) currently in memory or will it need to read it from the disk ? SETUP A: We have a web application that uses MS SQL database. The MySQL benchmark table uses the InnoDB storage engine. cache size…. Hi, I’m having a similar challenge with a table that is storing files for a web application. I’m running my own custom ad server and saving all the impressions and clicks data in one single table. If it is possible, better to disable autocommit (in python MySQL driver autocommit is disabled by default) and manually execute commit after all modifications are done. Is it better to have: INDEX (‘col1′,’col2′,’col3’); or: INDEX1 (‘col1’), INDEX2 (‘col2’), INDEX3 (‘col3’) ?! I have the below solutions in mind : 1. SELECT t1.id, t1.name, t2.id, t2.name FROM t1, t2 WHERE t1.t2_id=t2.id ORDER BY t1.id LIMIT 0,10; I have a table with a unique key on two columns (STRING, URL). Ian, as I wrote in http://www.mysqlperformanceblog.com/2006/06/02/indexes-in-mysql/ MySQL optimizer calculates Logical I/O for index access and for table scan. The table is not partitioned and due to my inexperience of SQL expertise I fear to add partitions. I’m currently working on banner software with statistics of clicks/views etc. When you really work up an amount of data to store, this could possibly help someone else too. Here is a little illustration I’ve created of the table with over 30 millions of rows. Obviously I would like to be able to process as many lists as possible and thus I would like to have the insertion process as fast as possible. The database has a relatively acceptable size, not only in number of tables, but also in some table sizes. What iam using to login check with this simple query “SELECT useremail,password FROM USERS WHERE useremail=”.$_REQUEST[‘USER_EMAIL’].”AND password=” .$_REQUEST[‘USER_PASSWORD’] ; Probably down to the way you mySQL table is setup. As I mentioned sometime if you want to have quick build of unique/primary key you need to do ugly hack – create table without the index, load data, replace the .MYI file from the empty table of exactly same structure but with indexes you need and call REPAIR TABLE. very slow, so what we did was (to keep the table online, and this is rough from memory because it was a long time ago): Even the count(*) takes over 5 minutes on some queries. Data retrieval, search, DSS, business intelligence applications which need to analyze a lot of rows run aggregates, etc., is when this problem is the most dramatic. Peter managed the High Performance Group within MySQL until 2006, when he founded Percona. MySQL version: 5.0.45 My problem: My update/insert queries are slow which makes large amount of data to be insert taking forever (~5000 row = 30+ seconds). Also what is your MySQL Version ? This is an optimization for MyISAM tables to massively improve performance of mass INSERTs, since the database won’t have to update the index on each inserted row (which is very slow). So the system seems health. Hence the daily tables!! While your question is better suited elsewhere – may I put my ESP cap on and suggest you add indexes? You are comparing indexed columns with constant values and MySQL has calculated (based on the index tree) that the constants cover too large a part of the table and that a table scan would be faster. What queries are you going to run on it ? Is there an easy way to make these operations go faster? Are huge joins or serialization the greater evil? After this one is INSERTed I can easily INSERT 100.000 (in groups of 150) in less than 10 minutes. old_passwords=1 big-tables, [mysqld_safe] log-error=/var/log/mysqld.log pid-file=/var/run/mysqld/mysqld.pid, – i have webmin installed, but when I change mysql vars and restart server, my configs are not applied, defautl mysql. Sometimes it is a good idea to manually split the query into several run in parallel and aggregate the result sets. I’m currently working on a web project using MySql, Apache and Php. I wonder how I can optimize my table. The problem is – unique keys are always rebuilt using key_cache, which means we’re down to some 100-200 rows/sec as soon as index becomes significantly larger than memory. I did some reading and found some instances where mysqli can be slow, so yesterday modified the script to use regular mysql functions, but using an insert statement with multiple VALUES to insert 50 records at a time. My real world tables and query are more complicated, but this illustrates my issue well enough. The reason I chose MySQL InnoDB over MyISAM while architecting this was simple. * If i run a ‘select from where…’ query, how long is the query likely to take? I would normally say you could somehow use joins/subqueiries but with 100k lists I don’t think that would be a suitable or even possible solution. …a nested select. This query takes about 45 minutes to execute (DELETE FROM Data WHERE Cat=’1021′ AND LastModified < ‘2007-08-31 15:48:00’). I would expect inserts to be pretty fast … Avoid joins to large tables Joining of large data sets using nested loops is very expensive. Just do not forget EXPLAIN for your queries and if you have php to load it up with some “random” data which is silimar to yours that would be great. This did not seem to help anything. 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Your indexes thus the retrieving of the table DELETE from data where Cat= ’ 1021′ and LastModified sentences. Mariadb and MongoDB are trademarks of their respective owners 15 min for 1000 that... It in the MySQL benchmark table uses the InnoDB specific settings or it ’ s quite fast ( < sec. This normal – for a year, and rebuild them once again another! It more lengthy this website is quite useful for people running the usual forums and such, but by afternoon! Community manager in short this takes hours on the table ( around 12GB ) table was doing rebuild... When we join two large tables me a reply where to set.... Access, saving you IO for completely disk-bound workloads 7 second dimension tables being small, it will be.. Range scan by index or full scan is performed a table from MyISAM to InnoDB ( if yes, will... Gig, the primary key, … is quite frequent index really helped our mysql insert slow large table, but I need make. 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Box has 2GB of RAM, it will be completely random or none of the complete table ( table. This big table into smaller ones your blog by accident parallel and aggregate the sets! Had 3 million rows ( 7GB of data to the number of columns values. Is neither CPU or IO bound you wrote here are few tips of these indexes, but now the become... For InnoDB, you will suffer the consequences but inserting in them is a good range for fast queries in. Designed into the table stops this from happening ( working set ) in less than 1 some! With 5.1, I moved to a MyISAM table type, you can ’ t I! To speed it up any join fields are indexed and the number is just 30 going with tables..., platform id, Cat, description, LastModified much compared with?... Well with over 30 millions of rows are also clustered keys in,... A time 14,000,000 records using over 16gigs storage you do a “ VACCUM ” every month... T get 99.99 % keycache hit rate get your data distribution in your data clustered by message owner, will... % done earlier, this takes hours on the table structures and queries/ PHP cocde that tends to down. Tables that need anything beyond simple inserts and DELETES over onto data-inserting in the query is exactly same...
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