Yes that is the problem. you could use your master for write queries like, update or insert and the slave for selects. So when I would “REPAIR TABLE table1 QUICK” at about 4pm, the above query would execute in 0.00 seconds. This question is off-topic. Sorry, I should say the the current BTREE index is the same data/order as the columns (Val #1, Val #2, Val #3, Val #4). When the entries goes beyond 1 million the whole system gets too slow. Now it remains on a steady 12 seconds every time i insert 1 million rows. 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!!) Sorry for mentioning this on a mysql performance blog. I think the root of my issue is that the indexes don’t fit into RAM. 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 â€¦.. OR id = 90). If it should be table per user or not depends on numer of users. Are the advanced join methods available now? ), which is what it’s intended, but INSERTing in them is a nightmare. The problem started when I got to around 600,000 rows (table size: 290MB). * also how long would an insert take? Thank you MySQL Table and index statistics. So please suggest me how to reduce this time? My queries are complex and involves a quite a few joins (due to the normalisation) and multiple subqueries (due to nature of the data). Ever wonder how to log slow queries to a MySQL table and set an expire time? Obviously, this gets expensive with huge databases, but you still want to have a good percentage of the db in RAM for good performance. 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. – what parameters i need to insert manually in my.cnf for best performance & low disk usage? * If i run a ‘select from where…’ query, how long is the query likely to take? My my.cnf variables were as follows on a 4GB RAM system, Red Hat Enterprise with dual SCSI RAID: query_cache_limit=1M query_cache_size=32M query_cache_type=1 max_connections=1500 interactive_timeout=25 wait_timeout=10 connect_timeout=5 thread_cache_size=60 key_buffer=750M join_buffer=10M, max_heap_table_size=50M tmp_table_size=64M, max_allowed_packet=16M table_cache=1800 record_buffer=10M sort_buffer_size=24M read_buffer_size=9M max_connect_errors=10 thread_concurrency=4 myisam_sort_buffer_size=950M character-set-server=utf8 default-collation=utf8_unicode_ci set-variable=max_connections=1500 log_slow_queries=/var/log/mysql-slow.log sql-mode=TRADITIONAL concurrent_insert=2 low_priority_updates=1. $_REQUEST[‘USER_PASSWORD’] = mysql_real_escape_string($_REQUEST[‘USER_PASSWORD’]); otherwise some little script kiddy is going to cause you an even bigger problem in the future. When we move to examples where there were over 30 tables and we needed referential integrity and such, MySQL was a pathetic “option”. For large datasets innodb engine is best, myisam can become quite slow with very large database tables. I tried a few things like optimize, putting index on all columns used in any of my query but it did not help that much since the table is still growing… I guess I may have to replicate it to another standalone PC to run some tests without killing my server Cpu/IO every time I run a query. Not kosher. Nothing to be impressed by. 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”. Not to mention keycache rate is only part of the problem – you also need to read rows which might be much larger and so not so well cached. Is this wise .. i.e. Guess no one here would freak out if I said I have a perfectly operational MySQL 5.1.44 database with 21,4 GB of data in a single table and no problems whatsoever running quite hefty queries on it. The server writes less information to the slow query log if you use the --log-short-format option. 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 am building a statistics app that will house 9-12 billion rows. Any help will be appreciated. [mysqld] ... key_buffer = 512M max_allowed_packet = 8M table_cache = 512 sort_buffer_size = 32M read_buffer_size = 32M read_rnd_buffer_size = 128M myisam_sort_buffer_size = 256M thread_cache = 32 query_cache_size = 256M. guess i will have to partition as i used up the maximum 40 indexes and its not speeding things up. 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. I implemented a simple logging of all my web sites access to make some statistics (sites access per day, ip address, search engine source, search queries, user text entries, …) but most of my queries went way too slow to be of any use last year. Thanks for the prompt answer! Of course, this is all RDMS for beginners, but, I guess you knew that. Set slow_query_log to 0 to disable the log or to 1 to enable it. My Max script execution time in PHP is set to 30 Secs. 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. To use my example from above, SELECT id FROM table_name WHERE (year > 2001) AND (id IN( 345,654,…, 90)). You can either flip through all the pages, or you can pull on the right letter tab to quickly locate the name you need. The question I have, is why is this happening, and if there is any kind of query I can preform in order to “focus” the DBMS “attention” to the particular table (in context), since SELECTing data is always faster then INSERTing it. I’ve read the differents comments from this and other forums. long_query_time — To prevent fast-running queries from being logged in the slow query log, specify a value for the shortest query execution time to be logged, in seconds. The problem I have, is regarding some specific tables in the database, which I use for a couple of months duration, minning them with detailed data of a particular task. Maybe the memory is full? However if you have 1000000 users some with only few records you need to have many users in each table. Some operators will control the machines by varying the values in the plc board.We need to collect that values from those machines via wireless communication and store that values into the database server.We need to observe that ,the operator operating the machines correctly or not at server place.Here problem is how we have to create the database for dynamic data. Gee, this is really RDBMS 101. To write general and slow query log entries only to the log tables, use --log_output=TABLE to select tables as the log destination and --general_log and --slow_query_log to enable both logs. Furthermore: If I can’t trust JOINS…doesn’t that go against the whole point about relational databases, 4th normal form and all that? As an example, I've got a process that merges a 6 million row table with a 300 million row table on a daily basis. Shutdown can be long in such case though. QUERY USED: SELECT DISTINCT MachineName FROM LogDetails WHERE NOT MachineName IS NULL AND MachineName !=” ORDER BY MachineName. I guess storing, say, 100K lists and then applying the appropriate join and updating the “statistics-table” (i.e. This is being done locally on a laptop with 2 GB of Ram and a dual core 1.86 Ghz Cpu – while nothing else is happening. 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). The overhead of maintaining an index for an infrequent query will likely cause overall performance degradation. However, if your table has more than 10 rows, they … Speaking about webmail – depending on number of users you’re planning I would go with table per user or with multiple users per table and multiple tables. I tried SQL_BIG_RESULT, analyze table, etc… nothing seems to help. As you probably seen from the article my first advice is to try to get your data to fit in cache. 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. In my proffesion im used to joining together all the data in the query (mssql) before presenting it to the client. Been a while since I’ve worked on MySQL. Hello,pls suggest the solution for my problem. 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)). Whenever I run “SELECT COUNT(*) FROM MYTABLE WHERE status=1” it was tooking only miliseconds from a mysql interface (in 120.000 records). 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). Here are some of the ways I know. Even the count(*) takes over 5 minutes on some queries. What kind of query are you trying to run and how EXPLAIN output looks for that query. Now the Question comes “How can improve performance with large databases.“ See this article http://techathon.mytechlabs.com/performance-tuning-while-working-with-large-database/. -Tom. I just buffer them for processing, but in the end I am not interested in the lists, but only in the mentioned statistics (i.e. Once you know which are the offending queries, you can start exploring what makes them slow. You can’t answer this question that easy. There are also clustered keys in Innodb which combine index access with data access, saving you IO for completely disk-bound workloads. InnoDB is suggested as an alternative. For InnoDB, you may also fix the INNODB specific settings. It might be not that bad in practice, but again, it is not hard to reach 100 times difference. MySQL has a built-in slow query log. “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”. (30min up to 2/3 hours). Do come by my site and let me know your opinion. Secondly, I’m stunned by the people asking questions and begging for help – go to a forum, not a blog. Hence, the larger the table, the more will query last. 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. The lists are actually constantly coming in, kind of in a stream. A database that still has not figured out how to optimize its tables that need anything beyond simple inserts and selects is idiotic. Yahoo uses MySQL for about anything, of course not full text searching itself as it just does not map well to relational database. My original insert script used a mysqli prepared statement to insert each row as we iterate through the file, using the getcsv() funtion. Due to the usage of subqueries, I think this may be the main cause of the slowness. You get free answers to your questions by asking them in this blog (or at MySQL Forums) but other people can benefit from the answers as well. There are many very good articles on optimizing queries on this site. I had 40000 row in database when ever i fire this query in mysql its taking too much time to get data from database. This query works “fine”…some seconds to perform. Under such a heavy load the SELECT and inserts get slowed . Is there a solution then if you must join two large tables? Going to 27 sec from 25 is likely to happen because index BTREE becomes longer. . > > > -- > Dan Nelson > dnelson@stripped It's no use when I create a index on (IP_ADDR,THE_TIME). 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. Why? MySQL has a built-in slow query log. If you started from in-memory data size and expect gradual performance decrease as the database size grows, you may be surprised by a severe drop in performance. Increasing performance of bulk updates of large tables in MySQL. You see a row for each table that was involved in the query: The important pieces here are the table name, the key used, and the number of rows scanned during the execution of the query. Set slow_query_log_file to specify the name of the log file. Hence, I’m considering to actually do a query which just pulls the data out of the main MSSQL server (say every 5 min) and using some scripts to manipulate the resultant csv to a partially de-normalised state and then loading them into the MySQL server. Your MY.CONF settings http://www.notesbit.com/index.php/web-mysql/mysql/mysql-tuning-optimizing-my-cnf-file/. If it is possible you instantly will have half of the problems solved. So rank 1 through to rank 500,000. I also have to add, that once a file has been retrieved on request, the content is cached on the file system, in a manner that calling that file afterwards do not require a query, unless the cached file is deleted. Totally misleading title. Could it, for example, help to increase “key_buffer_size”? I don't expect it to be really fast but is it normal to be that slow? That should improve it somewhat. I have to find records having same value in column5 and another value in column10 of table A equal column6 of table B. I use inner join. I m using php 5 and MySQL 4.1………. I do multifield select on indexed fields, and if row is found, I update the data, if not I insert new row). The large table has 2 indexes on it and totals 3 GB – more than the ram in the machine – this done on Ubuntu 10. I guess this is due to index maintenance. Mysql will only use one > index for a table per query. the join fields are indexed and the selection of the records for join uses primary key. I’m just dealing with the same issue with a message system. So adding ONE JOIN extra, with an additional 75K rows to JOIN, the query went from OK to a DISASTER!!!! Maybe I can rewrite the SQL, since it seems like MySQL handles ONE JOIN, but no way it handles TWO JOINS. SPECS of SETUP A: OS: Windows XP Prof Memory: 512MB. Each row record is approx. oh.. one tip for your readers.. always run ‘explain’ on a fully loaded database to make sure your indexes are being used. This way more users will benefit from your question and my reply. 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