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|From:||Rick James||Date:||May 9 2012 3:21pm|
|Subject:||RE: 回复： Why is creating indexes faster |
after inserting massive data rows?
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A BTree that is small enough to be cached in RAM can be quickly maintained. Even the “block splits” are not too costly without the I/O. A big file that needs sorting – bigger than can be cached in RAM – is more efficiently done with a dedicated “sort merge” program. A “big” INDEX on a table may be big enough to fall into this category. I/O is the most costly part of any of these operations. My rule of thumb for MySQL SQL statements is: If everything is cached, the query will run ten times as fast as it would if things have to be fetched from disk. Sortmerge works this way: 1. Sort as much of the file as you can in RAM. Write that sorted piece to disk. 2. Repeat for the next chunk of the file. Repeat until the input file is broken into sorted chunks. 3. Now, “merge” those chunks together – take the first row from each, decide which is the “smallest”, send it to the output 4. Repeat until finished with all the pieces. For a really big task, there may have to be more than on “merge” pass. Note how sort merge reads the input sequentially once, writes the output sequentially once, and has sequential I/O for each merge chunk. “Sequential” I/O is faster than “random” I/O – no arm motion on traditional disks. (SSDs are a different matter; I won’t go into that.) The “output” from the sortmerge is fed into code that builds the BTree for the table. This building of the BTree is sequential – fill the first block, move on to the next block, and never have to go back. BTrees (when built randomly), if they need to spill to disk, will involve random I/O. (And we are talking about an INDEX that is so big that it needs to spill to disk.) When a block “splits”, one full block becomes two half-full blocks. Randomly filling a BTree leads to, on average, the index being 69% full. This is not a big factor in the overall issue, but perhaps worth noting. How bad can it get? Here’s an example. · You have an INDEX on some random value, such as a GUID or MD5. in RAM. · MySQL is adding to the BTree one row at a time (the non-sortmerge way) When it is nearly finished, only 1 of 5 updates to the BTree can be done immediately in RAM; 4 out of 5 updates to the BTree will have to hit disk. If you are using normal disks, that is on the order of 125 rows per second that you can insert – Terrible! Sortmerge is likely to average over 10,000. From: Zhangzhigang [mailto:zzgang_2008@stripped] Sent: Tuesday, May 08, 2012 9:13 PM To: Rick James Cc: mysql@stripped Subject: 回复： Why is creating indexes faster after inserting massive data rows? James... >* By doing all the indexes after building the table (or at least all the non-UNIQUE indexes), "sort merge" can be used. This technique had been highly optimized over the past half-century, and is more efficient. I have a question about "sort merge": Why does it do the all "sort merge"? In my opinion, it just maintains the B tree and inserts one key into a B tree node which has fewer sorted keys, so it is good performance. If it only does the "sort merge", the B tree data structure have to been created separately. it wastes some performance. Does it? ________________________________ 发hoo-inc.com>> 收件人： Johan De Meersman <firstname.lastname@example.org<mailto:vegivamp@stripped>>; Zhangzhigang <zzgang_2008@stripped<mailto:zzgang_2008@stripped>> om>" <mysql@stripped<mailto:mysql@stripped>> 发送日期： 2012年5月8日, 星期二, 上午 12:35 主题: RE: Why is creating indexes faster after inserting massive data rows? * Batch INSERTs run faster than one-row-at-a-time, but this is unrelated to INDEX updating speed. * The cache size is quite important to dealing with indexing during INSERT; see http://mysql.rjweb.org/doc.php/memory <http://mysql.rjweb.org/doc.php/memory%0A> * Note that mysqldump sets up for an efficient creation of indexes after loading the data. This is not practical (or necessarily efficient) when incremental INSERTing into a table. As for the original question... * Updating the index(es) for one row often involves random BTree traversals. When the index(es) are too big to be cached, this can involve disk hit(s) for each row inserted. * By doing all the indexes after building the table (or at least all the non-UNIQUE indexes), "sort merge" can be used. This technique had been highly optimized over the past half-century, and is more efficient. > -----Original Message----- > From: Johan De Meersman [mailto:vegivamp@stripped<mailto:vegivamp@stripped>] > Sent: Monday, May 07, 2012 1:29 AM > To: Zhangzhigang > Cc: email@example.com<mailto:mysql@stripped> > Subject: Re: Why is creating indexes faster after inserting massive > data rows? > > ----- Original Message ----- > > From: "Zhangzhigang" <zzgang_2008@stripped<mailto:zzgang_2008@stripped>> > > > > Creating indexes after inserting massive data rows is faster than > > before inserting data rows. > > Please tell me why. > > Plain and simple: the indices get updated after every insert statement, > whereas if you only create the index *after* the inserts, the index > gets created in a single operation, which is a lot more efficient. > > I seem to recall that inside of a transaction (thus, InnoDB or so) the > difference is markedly less; I might be wrong, though. > > > -- > Bier met grenadyn > Is als mosterd by den wyn > Sy die't drinkt, is eene kwezel > Hy die't drinkt, is ras een ezel > > -- > MySQL General Mailing List > For list archives: http://lists.mysql.com/mysql <http://lists.mysql.com/mysql%0A> > To unsubscribe: http://lists.mysql.com/mysql <http://lists.mysql.com/mysql%0A>