RE: 回复： Why is creating indexes faster after inserting massive data rows?
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. · The INDEX will be 5 times as big as you can fit 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_2...@yahoo.com.cn] Sent: Tuesday, May 08, 2012 9:13 PM To: Rick James Cc: mysql@lists.mysql.com 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? 发件人： Rick James rja...@yahoo-inc.commailto:rja...@yahoo-inc.com 收件人： Johan De Meersman vegiv...@tuxera.bemailto:vegiv...@tuxera.be; Zhangzhigang zzgang_2...@yahoo.com.cnmailto:zzgang_2...@yahoo.com.cn 抄送： mysql@lists.mysql.commailto:mysql@lists.mysql.com mysql@lists.mysql.commailto:mysql@lists.mysql.com 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:vegiv...@tuxera.bemailto:vegiv...@tuxera.be] Sent: Monday, May 07, 2012 1:29 AM To: Zhangzhigang Cc: mysql@lists.mysql.commailto:mysql@lists.mysql.com Subject: Re: Why is creating indexes faster after inserting massive data rows? - Original Message - From: Zhangzhigang zzgang_2...@yahoo.com.cnmailto:zzgang_2...@yahoo.com.cn
Re: 回复： Why is creating indexes faster after inserting massive data rows?
This thread is going on and on and on and on, does anyone have time to actually measure I/O? Let's make numbers talk. Claudio 2012/5/9 Rick James rja...@yahoo-inc.com 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 �C bigger than can be cached in RAM �C 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 �C 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 �C 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 �C 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. ・ The INDEX will be 5 times as big as you can fit 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 �C Terrible! Sortmerge is likely to average over 10,000. From: Zhangzhigang [mailto:zzgang_2...@yahoo.com.cn] Sent: Tuesday, May 08, 2012 9:13 PM To: Rick James Cc: mysql@lists.mysql.com 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? 发件人： Rick James rja...@yahoo-inc.commailto:rja...@yahoo-inc.com 收件人： Johan De Meersman vegiv...@tuxera.bemailto:vegiv...@tuxera.be; Zhangzhigang zzgang_2...@yahoo.com.cnmailto:zzgang_2...@yahoo.com.cn 抄送： mysql@lists.mysql.commailto:mysql@lists.mysql.com mysql@lists.mysql.commailto:mysql@lists.mysql.com 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:vegiv...@tuxera.bemailto: vegiv...@tuxera.be] Sent: Monday, May 07, 2012 1:29 AM To: Zhangzhigang Cc:
RE: 回复： Why is creating indexes faster after inserting massive data rows?
Disagree all the way, numbers are numbers, and better than words, always. Claudio On May 9, 2012 7:22 PM, Rick James rja...@yahoo-inc.com wrote: Numbers can be misleading �C one benchmark will show no difference; another will show 10x difference. Recommend you benchmark _*your*_ case. ** ** *From:* Claudio Nanni [mailto:claudio.na...@gmail.com] *Sent:* Wednesday, May 09, 2012 8:34 AM *To:* Rick James *Cc:* Zhangzhigang; mysql@lists.mysql.com *Subject:* Re: 回复： Why is creating indexes faster after inserting massive data rows? ** ** This thread is going on and on and on and on, does anyone have time to actually measure I/O? Let's make numbers talk. ** ** Claudio ** ** 2012/5/9 Rick James rja...@yahoo-inc.com 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 �C bigger than can be cached in RAM �C 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 �C 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 �C 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 �C 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. ・ The INDEX will be 5 times as big as you can fit 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 �C Terrible! Sortmerge is likely to average over 10,000. From: Zhangzhigang [mailto:zzgang_2...@yahoo.com.cn] Sent: Tuesday, May 08, 2012 9:13 PM To: Rick James Cc: mysql@lists.mysql.com 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? 发件人： Rick James rja...@yahoo-inc.commailto:rja...@yahoo-inc.com 收件人： Johan De Meersman vegiv...@tuxera.bemailto:vegiv...@tuxera.be; Zhangzhigang zzgang_2...@yahoo.com.cnmailto:zzgang_2...@yahoo.com.cn 抄送： mysql@lists.mysql.commailto:mysql@lists.mysql.com mysql@lists.mysql.commailto:mysql@lists.mysql.com 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
Re: 回复： Why is creating indexes faster after inserting massive data rows?
On 2012/05/07 10:53, Zhangzhigang wrote: johan 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.. Ok, Creating the index *after* the inserts, the index gets created in a single operation. But the indexes has to be updating row by row after the data rows has all been inserted. Does it work in this way? So i can not find the different overhead about two ways. My simplified 2c. When inserting rows with active indexes one by one (insert), mysql has to 1) lock the space for the data to be added, 2) write the data, 3) lock the index, 4) write the index key(s), 5) unlock the index, 6)unlock the data This happens for each row When first doing all data without index, only 1, 2, and 6 happen. When you then create an index, it can lock the index, read all the data and write all index keys in one go and then unlock the index. If you make an omelet, do you fetch your eggs from the fridge one by one, or all at the same time? :) HTH, Alex -- MySQL General Mailing List For list archives: http://lists.mysql.com/mysql To unsubscribe:http://lists.mysql.com/mysql
Re: 回复： Why is creating indexes faster after inserting massive data rows?
Creating the index in one time is one macro-sort operation, updating the index at every row is doing the operation on and on again. If you do not understand the difference I recommend you to read some basics about sorting algorithms, very interesting read anyway. Claudio 2012/5/7 Zhangzhigang zzgang_2...@yahoo.com.cn johan 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.. Ok, Creating the index *after* the inserts, the index gets created in a single operation. But the indexes has to be updating row by row after the data rows has all been inserted. Does it work in this way? So i can not find the different overhead about two ways. 发件人： Johan De Meersman vegiv...@tuxera.be 收件人： Zhangzhigang zzgang_2...@yahoo.com.cn 抄送： mysql@lists.mysql.com 发送日期： 2012年5月7日, 星期一, 下午 4:28 主题: Re: Why is creating indexes faster after inserting massive data rows? - Original Message - From: Zhangzhigang zzgang_2...@yahoo.com.cn 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 -- Claudio
Re: 回复： Why is creating indexes faster after inserting massive data rows?
- Original Message - From: Zhangzhigang zzgang_2...@yahoo.com.cn Ok, Creating the index *after* the inserts, the index gets created in a single operation. But the indexes has to be updating row by row after the data rows has all been inserted. Does it work in this way? No, when you create an index on an existing table (like after a mass insert), what happens is that the engine does a single full tablescan and builds the index in a single pass, which is a lot more performant than updating a single disk block for every record, for the simple reason that a single disk block can contain dozens of index entries. Imagine that you insert one million rows, and you have 100 index entries in a disk block (random numbers, to make a point. Real numbers will depend on storage, file system, index, et cetera). Obviously there's no way to write less than a single block to disk - that's how it works. You can update your index for each record in turn. That means you will need to do 1 million index - and thus block - writes; plus additional reads for those blocks you don't have in memory - that's the index cache. Now, if you create a new index on an existing table, you are first of all bypassing any index read operations - there *is* no index to read, yet. Then the system is going to do a full tablescan - considered slow, but you need all the data, so there's no better way anyway. The index will be built - in-memory as much as possible - and the system will automatically prefer to write only complete blocks - 10.000 of them. That's the exact same number of index blocks, but you only write each block once, so that's only 10.000 writes instead of 1.000.000. Now there's a lot more at play, things like B-tree balancing and whatnot, but that's the basic picture. -- Bier met grenadyn Is als mosterd by den wyn Sy die't drinkt, is eene kwezel Hy die't drinkt, is ras een ezel
RE: 回复： Why is creating indexes faster after inserting massive data rows?
As a side note, TokuDB uses what it calls fractal technology to somewhat improve the performance of incremental INDEXing. They delay some of the BTree work so that they can better batch stuff. While waiting for that to finish, queries are smart enough to look in more than one place for the index info. InnoDB does something similar, but it is limited to the size of the buffer_pool. -Original Message- From: Johan De Meersman [mailto:vegiv...@tuxera.be] Sent: Monday, May 07, 2012 8:06 AM To: Zhangzhigang Cc: mysql@lists.mysql.com Subject: Re: 回复： Why is creating indexes faster after inserting massive data rows? - Original Message - From: Zhangzhigang zzgang_2...@yahoo.com.cn Ok, Creating the index *after* the inserts, the index gets created in a single operation. But the indexes has to be updating row by row after the data rows has all been inserted. Does it work in this way? No, when you create an index on an existing table (like after a mass insert), what happens is that the engine does a single full tablescan and builds the index in a single pass, which is a lot more performant than updating a single disk block for every record, for the simple reason that a single disk block can contain dozens of index entries. Imagine that you insert one million rows, and you have 100 index entries in a disk block (random numbers, to make a point. Real numbers will depend on storage, file system, index, et cetera). Obviously there's no way to write less than a single block to disk - that's how it works. You can update your index for each record in turn. That means you will need to do 1 million index - and thus block - writes; plus additional reads for those blocks you don't have in memory - that's the index cache. Now, if you create a new index on an existing table, you are first of all bypassing any index read operations - there *is* no index to read, yet. Then the system is going to do a full tablescan - considered slow, but you need all the data, so there's no better way anyway. The index will be built - in-memory as much as possible - and the system will automatically prefer to write only complete blocks - 10.000 of them. That's the exact same number of index blocks, but you only write each block once, so that's only 10.000 writes instead of 1.000.000. Now there's a lot more at play, things like B-tree balancing and whatnot, but that's the basic picture. -- Bier met grenadyn Is als mosterd by den wyn Sy die't drinkt, is eene kwezel Hy die't drinkt, is ras een ezel
Re: 回复： Why is creating indexes faster after inserting massive data rows?
Hi, A couple cents to this. There isn't really a million of block writes. The record gets added to the block, but that gets modified in OS cache if we assume MyISAM tables and in the Innodb buffer if we assume InnoDB tables. In both cases, the actual writing does not take place and does not slow down the process.What does however happen for each operation, is processing the statement, locating the entries to update in the index, index block splits and , for good reason, committing. When it comes to creating an index, what needs to happen, is to read the whole table and to sort all rows by the index key. The latter process will be the most determining factor in answering the original question, because for the large tables the sort will have to do a lot of disk I/O.The point I am trying to make is there will be situations when creating indexes and then inserting the rows will be faster than creating an index afterwards. If we try to determine such situations, we could notice that the likelihood of the sort going to disk increases with the amount of distinct values to be sorted. For this reason, my choice would be to create things like primary/unique keys beforehand unless I am certain that everything will fit in the available memory. Peace Karen On May 7, 2012, at 8:05 AM, Johan De Meersman wrote: - Original Message - From: Zhangzhigang zzgang_2...@yahoo.com.cn Ok, Creating the index *after* the inserts, the index gets created in a single operation. But the indexes has to be updating row by row after the data rows has all been inserted. Does it work in this way? No, when you create an index on an existing table (like after a mass insert), what happens is that the engine does a single full tablescan and builds the index in a single pass, which is a lot more performant than updating a single disk block for every record, for the simple reason that a single disk block can contain dozens of index entries. Imagine that you insert one million rows, and you have 100 index entries in a disk block (random numbers, to make a point. Real numbers will depend on storage, file system, index, et cetera). Obviously there's no way to write less than a single block to disk - that's how it works. You can update your index for each record in turn. That means you will need to do 1 million index - and thus block - writes; plus additional reads for those blocks you don't have in memory - that's the index cache. Now, if you create a new index on an existing table, you are first of all bypassing any index read operations - there *is* no index to read, yet. Then the system is going to do a full tablescan - considered slow, but you need all the data, so there's no better way anyway. The index will be built - in-memory as much as possible - and the system will automatically prefer to write only complete blocks - 10.000 of them. That's the exact same number of index blocks, but you only write each block once, so that's only 10.000 writes instead of 1.000.000. Now there's a lot more at play, things like B-tree balancing and whatnot, but that's the basic picture. -- 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 To unsubscribe:http://lists.mysql.com/mysql
Re: 回复： Why is creating indexes faster after inserting massive data rows?
Good point about key buffer. I was only thinking about the table updates for MyISAM, not indexes. The being stuck waiting for buffer flush could also happen. However, for the table blocks this would be the same issue as with load followed by index rebuild, and for the indexes, it will have to be compared, performance-wise, with an expense of sorting an equally sized index. On May 7, 2012, at 10:40 AM, Rick James wrote: (Correction to Karen's comments) * MyISAM does all its index operations in the key_buffer, similar to InnoDB and its buffer_pool. * Yes, writes are delayed (in both engines), but not forever. If the table is huge, you will eventually be stuck waiting for blocks to be flushed from cache. * If the table is small enough, all the I/O can be delayed, and done only once. So yes, the in-memory cache may be faster. Based on this discussion, you should note that random indexes, such as GUIDs, MD5s, etc, tend to -Original Message- From: Karen Abgarian [mailto:a...@apple.com] Sent: Monday, May 07, 2012 10:31 AM To: mysql@lists.mysql.com Subject: Re: 回复： Why is creating indexes faster after inserting massive data rows? Hi, A couple cents to this. There isn't really a million of block writes. The record gets added to the block, but that gets modified in OS cache if we assume MyISAM tables and in the Innodb buffer if we assume InnoDB tables. In both cases, the actual writing does not take place and does not slow down the process.What does however happen for each operation, is processing the statement, locating the entries to update in the index, index block splits and , for good reason, committing. When it comes to creating an index, what needs to happen, is to read the whole table and to sort all rows by the index key. The latter process will be the most determining factor in answering the original question, because for the large tables the sort will have to do a lot of disk I/O.The point I am trying to make is there will be situations when creating indexes and then inserting the rows will be faster than creating an index afterwards. If we try to determine such situations, we could notice that the likelihood of the sort going to disk increases with the amount of distinct values to be sorted. For this reason, my choice would be to create things like primary/unique keys beforehand unless I am certain that everything will fit in the available memory. Peace Karen On May 7, 2012, at 8:05 AM, Johan De Meersman wrote: - Original Message - From: Zhangzhigang zzgang_2...@yahoo.com.cn Ok, Creating the index *after* the inserts, the index gets created in a single operation. But the indexes has to be updating row by row after the data rows has all been inserted. Does it work in this way? No, when you create an index on an existing table (like after a mass insert), what happens is that the engine does a single full tablescan and builds the index in a single pass, which is a lot more performant than updating a single disk block for every record, for the simple reason that a single disk block can contain dozens of index entries. Imagine that you insert one million rows, and you have 100 index entries in a disk block (random numbers, to make a point. Real numbers will depend on storage, file system, index, et cetera). Obviously there's no way to write less than a single block to disk - that's how it works. You can update your index for each record in turn. That means you will need to do 1 million index - and thus block - writes; plus additional reads for those blocks you don't have in memory - that's the index cache. Now, if you create a new index on an existing table, you are first of all bypassing any index read operations - there *is* no index to read, yet. Then the system is going to do a full tablescan - considered slow, but you need all the data, so there's no better way anyway. The index will be built - in-memory as much as possible - and the system will automatically prefer to write only complete blocks - 10.000 of them. That's the exact same number of index blocks, but you only write each block once, so that's only 10.000 writes instead of 1.000.000. Now there's a lot more at play, things like B-tree balancing and whatnot, but that's the basic picture. -- 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 To unsubscribe:http://lists.mysql.com/mysql -- MySQL General Mailing List For list archives: http://lists.mysql.com/mysql To unsubscribe:http://lists.mysql.com/mysql
