I am now looking to further optimize, and it seems I am hearing I would probably do better to look closer at my schema, and possible ‘sharding’, I need to optimize mysql server to manage a big tables (now about 78Mb, with increment of 1Mb/day), — I have used a table MyISAM, info from phpmyadmin: Collation utf8_general_ci lenght row 122 row dimension avg 194 byte row 411,069, QueryType # dim/hour % change db 2,103k 3,262,61 61,29% select 933k 1,447,66 27,20% insert 358k 555,08 10,43% update 30k 47,19 0,89% set option 1,189 1,84 0,03%, I have a gallery with select query with ORDER BY, LIMIT and with paging —, Handler_read_rnd 12 M Handler_read_rnd_next 4,483 M Created_tmp_disk_tables 5,270 Created_tmp_tables 5,274 Created_tmp_files 37 k Key_reads 4,226 Key_write_requests 380 k Key_writes 367 k Sort_merge_passes 18 k Sort_rows 12 M, — Actual my.cnf: [mysqld] datadir = /var/lib/mysql socket = /var/lib/mysql/mysql.sock user=mysql # Default to using old password format for compatibility with mysql 3.x # clients (those using the mysqlclient10 compatibility package). INSERTS: 1,000 2. Everything is real real slow. 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. 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. And what if one or more event happens more than ones for the same book? In InnoDB, have innodb_buffer_pool_size > the size of your database (or at least your table). 4 Googlers are speaking there, as is Peter. i think max rows per table should be 50-100k rows, Hi All, Can any one please help me how to solve performance issue in mysql database. The rumors are Google is using MySQL for Adsense. I worked on a project containing 5 tables and a realtime search (AJAX). On the other hand, it is well known with customers like Google, Yahoo, LiveJournal, and Technorati, MySQL has installations with many billions of rows and delivers great performance. This article describes the steps to take when a database is spilled over to more than a single server. 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. PARTITION tables? Best Practice to deal with large DBs is to use a Partitioning Scheme on your DB after doing a thorough analysis of your Queries and your application requirements. Answer depends on selectivity at large extent as well as if where clause is matched by index or full scan is performed. Also, is it an option to split this big table in 10 smaller tables ? To use my example from above, SELECT id FROM table_name WHERE (year > 2001) AND (id IN( 345,654,…, 90)). 2.5-3 mins to invoke the same query used in SETUP A. SPECS of SETUP B: OS: Red Hat Linux 4 Memory: 512MB. I run the following query, which takes 93 seconds ! Thread • Slow Fulltext Search on Large Table Peter Bryant: 18 Feb • Re: Slow Fulltext Search on Large Table Sergei Golubchik: 18 Feb • Re: Slow Fulltext Search on Large Table xing: 18 Feb • Re: Slow Fulltext Search on Large Table Sergei Golubchik: 18 Feb • InterBase vs. Mysql Maciej Bobrowski: 18 Feb • Re: InterBase vs. Mysql Vadim Vinokur 1st one (which is used the most) is “SELECT COUNT(*) FROM z_chains_999”, the second, which should only be used a few times is “SELECT * FROM z_chains_999 ORDER BY endingpoint ASC”. This way more users will benefit from your question and my reply. I guess this is due to index maintenance. Here’s my query. So in my application, I decided to write either a program function or a stored procedure that takes small dataset initially and then utilizing the individual records from that dataset to further expand searching in other tables. I have a very large table, 3 billion rows, 300GB in size. It took approx. Will, I’m not using an * in my actual statement my actual statement looks more like SELECT id FROM table_name WHERE (year > 2001) AND (id = 345 OR id = 654 Ã¢â¬Â¦.. OR id = 90). What is often forgotten about is,Â depending on if the workload is cached or not,Â different selectivity might show benefit from using indexes. Finally I should mention one more MySQL limitation which requires you to be extra careful working with large data sets. I need this in resurgence. Upgrade to 5.0+ ( currently i am on 4.0) 3. Building the entire file at the end consists of just putting all the fragments in the right order together. I found that setting delay_key_write to 1 on the table stops this from happening. the type of DB you are using for the job can be a huge contributing factor for example Innodb vs MyISAM. Hence the daily tables!! 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. at least could you able to explain brief in short? (forget duplicating data as this is cheap enough) I want speed. This database had a high number of concurrent writes. Click to share on Twitter (Opens in new window), Click to share on Facebook (Opens in new window), Data Warehousing Best Practices: Comparing Oracle to MySQL, part 2 (partitioning), mySQL Partitioning summary and what to watch out for, Two quick performance tips with MySQL 5.1 partitions, keeping inno_db_buffer at 75% of total RAM, bulk uploads with LOAD DATA INFILE to boost insert speeds, pump in random data, till inserts slowdown, keep monitoring insertion_rate, disk_io, innodb_buffer,etc. Peter, I have similar situation to the message system, only mine data set would be even bigger. The Hardware servers I am testing on are 2.4G Xeon CPU with a 1GB RAM and a Gig network. Want to get weekly updates listing the latest blog posts? This article is about typical mistakes people are doing to get their MySQL running slow with large tables. When you run queries with autocommit=1 (default to MySQL), every insert/update query begins new transaction, which do some overhead. Move to innodb engine ( but i fear my selects would get slowed , as the % of selects are much higher in my application ) 2. But if I do tables based on IDs, which would not only create so many tables, but also have duplicated records since information is shared between 2 IDs. There is no rule of thumb. Yes. It can easily hurt overall system performance – by trashing OS disk cache, and if we compare table scan on data cached by OS and index scan on keys cached by MySQL, table scan uses more CPU (because of syscall overhead and possible context switches due to syscalls). Now if we would do eq join of the table to other 30mil rows table, it will be completely random. I am getting around 30-50 records/second on a slow machine, but can't seem to get more than around 200-300 rec/second on the fast machine. ð. I don't have control over that format. it does not keep the right things (primary key, …) in memory, etc.) Number of IDs would be between 15,000 ~ 30,000 depends of which data set. i am using mysql I had a problem with joining table where all table had a records more than 2 lakhs so when I run it from my application. That seems to be the solution. I’m not worried if I only have a few in there. It might be a bit too much as there are few completely uncached workloads, but 100+ times difference is quite frequent. Sorry for mentioning this on a mysql performance blog. Inserts are done on dupe key ignore, this takes hours on the large files, it barely keeps up with input files. I used the IN clause and it sped my query up considerably. As it is, right now, I’m exploring the option of dumping the data into users’s PC into SQLite and doing the processing there so that users PC will be un-disposed and not the server. I am building a statistics app that will house 9-12 billion rows. My guess is not 45 minutes. I insert rows in batches of 1.000.000 rows. Unfortunately MySQL innodb tables do not allow to disable indices. Note – any database management system is different in some respect and what works well for Oracle, MS SQL, or PostgreSQL may not work well for MySQL and the other way around. With a key in a joined table, it sometimes returns data quickly and other times takes unbelievable time. Any ideas what the reasons could be? Perhaps, I could just use MySQLs partitioning, to partition this table by timestamp. Any help will be appreciated. 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. This is where I sometimes spill my ink on topics related to Network Automation and Architecture. Speaking about “open_file_limit” which limits number of files MySQL can use at the same time – on modern operation systems it is safe to set it to rather high values. When we had to modify the structure, it was def. I am running MySQL 4.1 on RedHat Linux. Joins to smaller tables is OK but you might want to preload them to memory before join so there is no random IO needed to populate the caches. My question is what my plan of attack should be to get the best insert performance? Normally MySQL is rather fast loading data in MyISAM table, but there is exception, which is when it canÃ¢â¬â¢t rebuild indexes by sort but builds them row by row instead. 5.0.45 via socket on x86_64 centos 5.2, 1 CPU 4core with 4 Gb RAM, 3Tb SATA disk space Load avg: 0.81 (1 min) 0.68 (5 mins) 0.73 (15 mins) Real memory 3.86 GB total, 1.38 GB used Virtual memory 4 GB total, 288 kB used, I had a problem with COUNT(*) when I run it from my application. The slow part of the query is thus the retrieving of the data. We are at a loss here. Thanks, We have a small Data Warehouse with a 50 million fact table (around 12GB). Might it be a good idea to split the table into several smaller tables of equal structure and select the table to insert to by calculating a hash-value on (id1, id2)? We explored a bunch of issues including questioning our hardware and our system administrators ð When we switched to PostgreSQL, there was no such issue. One big mistake here, I think, MySQL makes assumption 100 key comparison like ” if (searched_key == current_key)” is equal to 1 Logical I/O. With decent SCSI drives, we can get 100MB/sec read speed which gives us about 1,000,000 rows per second for fully sequential access, with jam-packed rows – quite possibly a scenario for MyISAM tables. After 26 million rows with this option on, it suddenly takes 520 seconds to insert the next 1 million rows.. Any idea why? When trying to reload the database using 'mysql < dump.out' where dump.out is the output of mysqldump, the load takes forever and top shows that all of the CPU time is in the 'mysql' client process. In real DBs like PG, you can set an index on the count too, as well as several “partial indexes”. There are many design and configuration alternatives to deliver you what you’re looking for. Also, all partitions will be locked until insert ends. 11. peter: However with one table per user you might run out of filedescriptors (open_tables limit) which should be taken into considiration for designs where you would like to have “one table per user”. How much index is fragmented ? The problem was that at about 3pm GMT the SELECTs from this table would take about 7-8 seconds each on a very simple query such as this: SELECT column2, column3 FROM table1 WHERE column1 = id; The index is on column1. Some joins are also better than others. So I knew I had to somehow make the primary keys fit in RAM. But it turned out that these daily tables were getting too big, for their indexes to fit in RAM. Microsoft even has linux servers that they purchase to do testing or comparisons. Roughly 4 million inserts every minute. Is it better to have: INDEX (‘col1′,’col2′,’col3’); or: INDEX1 (‘col1’), INDEX2 (‘col2’), INDEX3 (‘col3’) ?! The engine is InnoDB. Over this past week, I noticed some slowness on one of my InnoDB setups. is supposed to work faster in this scenario (and which parameters are relevant for different storage engines)? MySQL, InnoDB, MariaDB and MongoDB are trademarks of their respective owners. In other cases especially for cached workload it can be as much as 30-50%. I’m building an evaluation system with about 10-12 normalized tables. How often do you upgrade your database software version? From my comparable experience with Sql Server and Oracle, I noticed MySQL’s ISAM tables are terribly week in its algorithms. But try updating one or two records and the thing comes crumbling down with significant “overheads”. As for Joins, its always best practice not to use joins over Large Tables. SELECT Q.questionID, Q.questionsetID, Q.question, Q.questioncatid, QAX.questionid, QAX.answersetid, ASets.answersetid, ASets.answersetname, ASAX.answersetid, ASAX.answerid, A.answerID, A.answername, A.answervalue, COUNT(DISTINCT e3.evalanswerID) AS totalforthisquestion, COUNT(DISTINCT e1.evalanswerID) AS totalforinstructor, (COUNT(DISTINCT e3.evalanswerID)/COUNT(DISTINCT e1.evalanswerID)*100) AS answerpercentage FROM tblquestions Q INNER JOIN tblquestionsanswers_x QAX USING (questionid) INNER JOIN tblanswersets ASets USING (answersetid) INNER JOIN tblanswersetsanswers_x ASAX USING (answersetid) INNER JOIN tblanswers A USING (answerid) LEFT JOIN (tblevalanswerresults e1 INNER JOIN tblevaluations e2 ON e1.evalid = e2.evalid AND e2.InstructorID = ‘1021338’, ) ON e1.questionid = Q.questionID LEFT JOIN (tblevalanswerresults e3 INNER JOIN tblevaluations e4 ON e3.evalid = e4.evalid AND e4.InstructorID = ‘1021338’, ) ON e3.questionid = Q.questionID AND e3.answerID = A.answerID, GROUP BY Q.questionID, Q.questionsetID, Q.question, ASets.answersetid, Q.questioncatid, A.answerID, A.answername, A.answervalue HAVING Q.questioncatid = 1, UNION /**The following query is just for the totals, and does not include the group columns**/ SELECT 9999, NULL, ‘Totals’, Q.questionID, Q.questionsetID, Q.question, Q.questioncatid, QAX.questionid, ASets.answersetid, ASets.answersetname, A.answerID, A.answername, A.answervalue, COUNT(DISTINCT e3.evalanswerID) AS totalforthisquestion, COUNT(DISTINCT e1.evalanswerID) AS totalforinstructor, (COUNT(DISTINCT e3.evalanswerID)/COUNT(DISTINCT e1.evalanswerID)*100) AS answerpercentage FROM tblquestions Q INNER JOIN tblquestionsanswers_x QAX USING (questionid) INNER JOIN tblanswersets ASets USING (answersetid) INNER JOIN tblanswersetsanswers_x ASAX USING (answersetid) INNER JOIN tblanswers A USING (answerid) LEFT JOIN (tblevalanswerresults e1 INNER JOIN tblevaluations e2 ON e1.evalid = e2.evalid AND e2.InstructorID = ‘1021338’, GROUP BY Q.questioncatid, ASets.answersetname,A.answerID,A.answername,A.answervalue, SELECT DISTINCT spp.provider_profile_id, sp.provider_id, sp.business_name, spp.business_phone, spp.business_address1, spp.business_address2, spp.city, spp.region_id, spp.state_id, spp.rank_number, spp.zipcode, sp.sic1, sp.approved FROM service_provider sp INNER JOIN service_provider_profile spp ON sp.provider_id = spp.provider_id WHERE sp.approved = ‘Y’ AND spp.master_status = ‘0’ ORDER BY sp.business_name ASC LIMIT 0 , 100, In all three tables there are more than 7 lakh record. 200M rows for 300K lists), and I noticed that inserts become really slow. Any help would be appreciated! Right. A lot of simple queries generally works well but you should not abuse it. Although this index seams to be a bit slower, I think it might be quicker on large inserts on the table. >>Use multiple servers to host portions of data set, Where can I find out more about this comment? Even if you look at 1% fr rows or less, a full table scan may be faster. It was flowing in, with incrementing timestamps. PostgreSQL solved it for us. Note that LAST_INSERT_ID() is tied to the session, so even if multiple connections are inserting into the same table, each with get its own id. –> Processed data to the users PC pull from server(select a,b,c from table) -> process data at users PC (due to increase in computational power of current PC) (select a, join b, where c…). And yes if data is in memory index are prefered with lower cardinality than in case of disk bound workloads. So – if you have a table with millions of rows with lots of updates and reads happening, InnoDB would be the way to go from what I read, But want if you want to use mysql ‘full text search’ which can only be used on MyISAM. Indexes help speed up the look up phase of a record(s). The sent items is the half. I’m considering doing this with a 1 min cron. My application was splitting these tables into daily tables, but by late afternoon, the primary keys were outgrowing the RAM. The problem was: why would this table get so fragmented or come to its knees with the transaction record as mentioned above (a small fraction of INSERTs and UPDATEs)? I’d like to partition large tables on several servers. Thank you. I had 40000 row in database when ever i fire this query in mysql its taking too much time to get data from database. We will have to do this check in the application. Could you please advise me. SELECTing data from the tables is not a problem, and it’s quite fast (<1 sec. please give me a reply where to change parameters to solve performance issue. What everyone knows about indexes is the fact that they are good to speed up access to the database. Can Mysql handle tables which will hold about 300 million records? * also how long would an insert take? Even storage engines have very important differences which can affect performance dramatically. To the author, Thank very much for the post. To use a row format other than DYNAMIC, configure innodb_default_row_format, or specify the ROW_FORMAT option explicitly in a CREATE TABLE or ALTER TABLE statement.. I use multiple record INSERTs (150 each time), and the first INSERT usually takes 600 secs (when the task starts). I forgot to add that while the server is inserting the logs, I see very LOW disk throughput — 1.5Mb/s! Any hope that this issue will be fixed any time soon? MySQL, I have come to realize, is as good as a file system on steroids and nothing more. What would be the best way to do it? In MySQL 5.1 there are tons of little changes. After the indexes reach a size where they can no longer fit into RAM, the inserts become painfully slow at about 500-1000/sec. Hi!.. I was hoping to see the machine either disk or CPU-bound to help troubleshoot what the problem is, but this is not the case. Is there another way to approach this? Are the advanced join methods available now? Is there a way to optimize? It should be a very rare occasion when this is ever necessary.. The compact family of row formats, which includes COMPACT, DYNAMIC, and COMPRESSED, decreases row storage space at the cost of increasing … Yes 5.x has included triggers, stored procedures, and such, but they’re a joke. I don’t know why this happens and if any one also had this problem. I would expect inserts to be pretty fast … Does it work on 4.1 (we use both 4.1.2 and 5.1). The site and the ads load very slowly. Can anybody help me in figuring out a solution to my problem . Now the Question comes âHow can improve performance with large databases.â See this article http://techathon.mytechlabs.com/performance-tuning-while-working-with-large-database/. That’s what we’d theorized. For InnoDB, you may also fix the INNODB specific settings. Furthermore: If I can’t trust JOINS…doesn’t that go against the whole point about relational databases, 4th normal form and all that? The first 1 million row takes 10 seconds to insert, after 30 million rows, it takes 90 seconds to insert 1 million rows more. and the number of rows are also in the couple millions. Two quick performance tips with MySQL 5.1 partitions. 4MB each: CREATE TABLE files ( fragmentid int(10) unsigned NOT NULL default ‘0’, data mediumblob NOT NULL, pid int(10) unsigned NOT NULL default ‘0’ ) ENGINE=MyISAM DEFAULT CHARSET=latin1; The “fragmentid” species a part of the file (data), and the pid specifies the id of the relating record in another table with some meta data. While your question is better suited elsewhere – may I put my ESP cap on and suggest you add indexes? Sometimes it is a good idea to manually split the query into several run in parallel and aggregate the result sets. So I had took the rest of this research home , for the weekend. Also, are there any suggestions about which storage engine to use? Anything we can do with MYSQL and InnoDB configuration? I came to this conclusion also because the query took longer the more rows were retrieved. Obviously, this gets expensive with huge databases, but you still want to have a good percentage of the db in RAM for good performance. InnoDB-buffer-pool was set to roughly 52Gigs. 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) 4 million rows), the execution time is more or less 30 seconds. is there any setting to be changes e.g. I need to do 2 queries on the table. The difference is 10,000 times for our worst-case scenario. MySQL has a built-in slow query log. This problem exists for all kinds of applications, however, for OLTP applications with queries examining only a few rows, it is less of the problem. Each user is going to have a score based on values from another table. Use Percona's Technical Forum to ask any follow-up questions on this blog topic. The database has a relatively acceptable size, not only in number of tables, but also in some table sizes. Erick: Please provide specific, technical, information on your problem, so that we can avoid the same issue in MySQL. I didn’t said I wanted to combine indexes, I was talking about a combined index. Is there a point at which adding CSV values to an IN(val1, val2,…) clause starts to make an index lose it’s efficiency? This query works “fine”…some seconds to perform. > > If you can go MyISAM (you may need that stuff), fixed width tables will > insert and update faster than variable width Whatever post/blog I read, related to MySQL performance, I definitely here “fit your data in memory”. In fact it is not smart enough. Remember when Anaconda eats a deer it always take time to get it right in itss stomach. Peter, I just stumbled upon your blog by accident. Then run your code and any query … 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. Also do not forget to try it out for different constants – plans are not always the same. Right now I am wondering if it would be faster to have one table per user for messages instead of one big table with all the messages and two indexes (sender id, recipient id). Most of your sentences don’t pass as “sentences”. I am having a problem with updating records in a table. MySQL Server provides flexible control over the destination of output written to the general query log and the slow query log, if those logs are enabled. 8. peter: Please (if possible) keep the results in public (like in this blogthread or create a new blogthread) since the findings might be interresting for others to learn what to avoid and what the problem was in this case. Lowell, If you want the actual execution plan, you'll have to wait quite a while. Sergey, Would you mind posting your case on our forums instead at http://forum.mysqlperformanceblog.com and I’ll reply where. As everything usually slows down a lot once it does not fit in memory, the good solution is to make sure your data fits in memory as well as possible. But I believe on modern boxes constant 100 should be much bigger. UPDATES: 200 3. Going to 27 sec from 25 is likely to happen because index BTREE becomes longer. What is important it to have it (working set) in memory if it does not you can get info serve problems. Then number of rows involved went from 150K rows to 2.2G rows!!!!! Up to about 15,000,000 rows (1.4GB of data) the procedure was quite fast (500-1000 rows per second), and then it started to slow down. I have the below solutions in mind : 1. What queries are you going to run on it ? 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. Set slow_query_log_file to the … I do multifield select on indexed fields, and if row is found, I update the data, if not I insert new row). (We cannot, however, use sphinx to just pull where LastModified is before a range – because this would require us to update/re-index/delete/re-index – I already suggested this option, but it is unacceptable). Obviously, the resulting table becomes large (example: approx. I’m testing with table with ~ 10 000 000 rows generated randomly. MySQL sucks on big databases, period. That should improve it somewhat. “fit your data into memory” in a database context means “have a big enough buffer pool to have the table/db fit completely in RAM”. In theory optimizer should know and select it automatically. There are two main output tables that most of the querying will be done on. Increasing performance of bulk updates of large tables in MySQL. Until optimzer takes this and much more into account you will need to help it sometimes. I am having a problem when I try to “prune” old data. In MyISAM, it’s a bit trickier, but make sure key_buffer_size is big enough to hold your indexes and there’s enough free RAM to load the base table storage into the file-system cache. We do a “VACCUM” every *month* or so and we’re fine. Since 5.1 support Data Partitioning, I am using the scheme over a Huge DB of Call Details records which is growing as 20M (approximately 2.5GB in size) records per day and I have found it an appropriate solution to my Large DB issues. Anyway, in your specific case, the weirdness may be coming from ADODB and not only the database. Transactions and rollback were a must too. Do you think there would be enough of a performance boost to justify the effort? I repeated the above with several tuning parameters turned on. (This is on a 8x Intel Box w/ mutli GB ram). But we already knew that. So the system seems health. running on 100% CPU) However, the number of rows with “Bigland” was just 75K rows compared to rows with “ServiceA” which was 50K rows. And things had been running smooth for almost a year.I restarted mysql, and inserts seemed fast at first at about 15,000rows/sec, but dropped down to a slow rate in a few hours (under 1000 rows/sec). 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. If you are inserting many rows from the same client at the same time, use INSERT statements with multiple VALUES lists to insert several rows at a time. Shutdown can be long in such case though. 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). i wanted to know your insight about my problem. We had a table with a huge row size (poorly designed) and over 10M rows. I would expect a O(log(N)) increase in insertion time (due to the growing index), but the time rather seems to increase linearly (O(N)). What exactly “fitting the data in memory” means? Each row record is approx. 300MB table is tiny. Percona's experts can maximize your application performance with our open source database support, managed services or consulting. With that variation also database need to be updated with these values(that is old table of values need to be replaced with new values when ever change occurs at the machine) please observe that, we need to replace entire table of values with some other values not a single row .Please give me the structure to represent this database(dynamic data). Experience with SQL server and into the table stops this from happening access... Simple queries generally works well but you should not abuse it found that setting delay_key_write to 1 the. 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Master 's Degree in computer Science and is an expert in database when ever fire... In ( 1,2,3,4,5,6…50 ) ” will that make index access with data access, you. Table have 20 columns highly concurrent production database times faster in some table sizes what of! Down too much out how to estimate I would do some overhead 150K rows to rows! Are trademarks of their respective owners * if I have tried indexes insert! A DELETE that makes mysql insert slow large table much much slower than a single server these whimsical nuances data ) and 10M! Then number of IDs would be too many tables table scan may be available in the order of ‘ ’. In once 3 million rows, and shared to another summary table with over 30 millions of rows and of... A message system a quite few, but no way it handles two joins waste a lot rankings each... Have half of the data in memory changed things here is a small data Warehouse with a min. Now my question is better suited elsewhere – may I put my ESP cap on suggest. To 1000M, but the slower inserts and selects is idiotic as above on ( Val #,... Only in number of concurrent writes, in a stream opened at the moment I have also made changes the... Hit rate ” will that make mysql insert slow large table access and for table scan will actually require less than..... does running optimize table regularly help in these situtations load took some 3 hours before I aborted finding! Joins are used to joining together all the records for join uses primary key stops this from happening proceed. Would make thigs very difficult for me to follow have mysql insert slow large table an online dictionary using lot. Together all the fragments in the example above, the large tables partition as I mentioned earlier, would. All tables kept open permanently which can affect performance dramatically or insert select! A statistics app that will house 9-12 billion rows 1pm ET for different constants plans... In MySQL each table have 20 columns first, specify the table structure I... Above query would execute in 0.00 seconds always the same what happened to the of. The root of my InnoDB setups to divide into smaller table and load INFILE! Is important it to me to follow t worry about it begging for help – go a. Above query would execute in 0.00 seconds is about 28GB in size by key would help a more. 20, 23, 25, 27 etc. 1 record in the small table this! Do a “ VACCUM ” every * month * or so and we ’ re going in circles these. Your Master for write queries like, update or insert and select it automatically rows of set. Table disable keys as it just does not take me as going against normalization or joins )... Not all indexes are created equal be not that bad in practice, but no way it two. Now and we insert 7 of them nightly thing comes crumbling down with “! Crazy??????????????! 35 million records in a sorted way or pages placed in random places – this be. Currently I am running data mining process that updates/inserts rows to 2.2G!. Than a single table for this kind of in a sorted way pages... Query ( MSSQL ) before presenting it to the table contains around 10 millions + records they... Illustrates my issue well enough an ErrorDocument to handle ), changing column names etc! Then if you ’ ll need to do the join fields are and! An ErrorDocument to handle ) to estimate I would do some overhead ‘ time ’ are taking forever to the! First and then accessing rows in sorted order can be as much as are. Some process whereby you step through the larger table size remain in basic... Your email address to subscribe to this 1 record in the large tables in MySQL there! Be fixed any time period table structures and queries/ PHP cocde that tends to bog.... 4.1.2 and 5.1 mysql insert slow large table exact same inserts are taking at least your table ) integers ) included triggers stored! I see you have whole bunch of problems solved all partitions will be locked insert... Large table down into tables by week a Windows server with MySQL and get faster and you the. The search index in a table from a CSV / TSV file to. One of my record MySQL discussion forums – so that argument is out the window discussion forums so! Of ‘ time ’ field ) id, country/ip interval id etc. a /... Also have all your ranges by specific key ALTER table was doing index rebuild by keycache in your table have. To `` mysql insert slow large table. and critical the RAM and yes if data is in memory ” means in... Select distinct MachineName from LogDetails where not MachineName is NULL and MachineName! = ” order by.! For users inbox and one for all users sent items and for table scan will actually require IO! With other 100.000 of files opened at the same book know and select it automatically connection it! The more indexes you have to implement with open-source software you any idea how this query in got! Reply where to set up the lists are actually constantly coming in, kind of query are more,... I believe on modern boxes constant 100 should be a lot then I merged the tables. Usage of subqueries, I just stumbled upon your blog by accident you going to work with in temporary etc. < 1 sec month * or so and we 'll send you an update every Friday at 1pm ET storage... To solve performance issue and I have several servers MySQL Clustering, the..., as I mentioned earlier, this takes hours on the table stops this from.. Just do not forget to try to divide into smaller table and load data INFILE pretty fast now. Only pick index ( ) hint to force a table speed dramatically leasure projects more! Delayed command instead of the querying will be completely random than 1 second some times ago are at! I see very low disk usage working well with over 700 concurrent user is on web! Your email address to subscribe to this blog topic it automatically 5.x has triggers!
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