Archive for the ‘optimization’ Category

Multi Range Read (MRR) in MySQL 5.6 and MariaDB 5.5

Март 21st, 2012

This is the second blog post in the series of blog posts leading up to the talk comparing the optimizer enhancements in MySQL 5.6 and MariaDB 5.5. This blog post is aimed at the optimizer enhancement Multi Range Read (MRR). Its available in both MySQL 5.6 and MariaDB 5.5

Now let’s take a look at what this optimization actually is and what benefits it brings.

Multi Range Read

With traditional secondary index lookups, if the columns that are being fetched do not belong to the secondary index definition (and hence covering index optimization is not used), then primary key lookups have to be performed for each secondary key entry fetched. This means that secondary key lookups for column values that do not belong to the secondary index definition can result in a lot of Random I/O. The purpose of MRR is to reduce this Random I/O and make it more sequential, by having a buffer in between where secondary key tuples are buffered and then sorted by the primary key values, and then instead of point primary key lookups, a range lookup is performed on the primary key by using the sorted primary key values.

Let me give you a simple example. Suppose you have the following query executed on the InnoDB table:

SELECT non_key_column FROM tbl WHERE key_column=x

This query will roughly be evaluated in following steps, without MRR:

  1. SELECT key_column, pk_column FROM tbl WHERE key_column=x ORDER BY key_column
    (Note that secondary keys in InnoDB contain primary key columns)
  2. For each pk_column value in step 1 do:
    SELECT non_key_column FROM tbl WHERE pk_column=val

As you can see that the values returned from Step 1 are sorted by the secondary key column ‘key_column’, and then for each value of ‘pk_column’ which is a part of the secondary key tuple, a point primary key lookup is made against base table, the number of these point primary key lookups will be depend on the number of rows that match the condition ‘key_column=x’. You can see that there are a lot of random primary key lookups made.

With MRR, then steps above are changed to the following:

  1. SELECT key_column, pk_column FROM tbl WHERE key_column=x ORDER BY key_column
    (Note that secondary keys in InnoDB contain primary key columns)
  2. Buffer each pk_column value fetched from step 1, and when the buffer is full sort them by pk_column, and do a range primary key lookup as follows:
    SELECT non_key_column from tbl WHERE pk_column IN (…)

As you can see by utilizing the buffer for sorting the secondary key tuples by pk_column, we have converted a lot of point primary key lookups to one or more range primary key lookup. Thereby, converting Random access to one or more sequential access. There is one another interesting thing that has come up here, and that is the importance of the size of the buffer used for sorting the secondary key tuples. If the buffer size is large enough only a single range lookup will be needed, however if the buffer size is small as compared to the combined size of the secondary key tuples fetched, then the number of range lookups will be:

CEIL(S/N)
where,
S is the combined size of the secondary key tuples fetched, and
N is the buffer size.

In MySQL 5.6 the buffer size used by MRR can be controlled by the variable read_rnd_buffer_size, while MariaDB introduces a different variable to control the MRR buffer size mrr_buffer_size. Both buffer sizes default to 256K in MySQL 5.6 and MariaDB 5.5 respectively, which might be low depending on your scenario.

You can read more about the MRR optimization available in MySQL 5.6 here:
http://dev.mysql.com/doc/refman/5.6/en/mrr-optimization.html
and as available in MariaDB 5.5 here:
http://kb.askmonty.org/en/multi-range-read-optimization

Now let’s move on to the benchmarks, to see the difference in numbers.

Benchmark results

For the purpose of this benchmark, I have used TPC-H Query #10 and ran it on TPC-H dataset (InnoDB tables) with a Scale Factor of 2 (InnoDB dataset size ~5G). I did not use Scale Factor of 40 (InnoDB dataset size ~95G), because the query was taking far too long to execute, ~11 hours in case of MySQL 5.5 and ~5 hours in case of MySQL 5.6 and MariaDB 5.5. Note that query cache is disabled during these benchmark runs and that the disks are 4 5.4K disks in Software RAID5.

Also note that the following changes were made in the MySQL config:
optimizer_switch=’index_condition_pushdown=off’
optimizer_switch=’mrr=on’
optimizer_switch=’mrr_sort_keys=on’ (only on MariaDB 5.5)
optimizer_switch=’mrr_cost_based=off’
read_rnd_buffer_size=4M (only on MySQL 5.6)
mrr_buffer_size=4M (only on MariaDB 5.5)

We have turned off ICP optimization for the purpose of this particular benchmark, because we want to see the individual affect of an optimization (where possible). Also note that we have turned off mrr_cost_based, this is because the cost based algorithm used to calculate the cost of MRR when the optimizer is choosing the query execution plan, is not sufficiently tuned and it is recommended to turn this off.

The query used is:

select
        c_custkey,
        c_name,
        sum(l_extendedprice * (1 - l_discount)) as revenue,
        c_acctbal,
        n_name,
        c_address,
        c_phone,
        c_comment
from
        customer,
        orders,
        lineitem,
        nation
where
        c_custkey = o_custkey
        and l_orderkey = o_orderkey
        and o_orderdate >= '1993-08-01'
        and o_orderdate < date_add( '1993-08-01' ,interval '3' month)
        and l_returnflag = 'R'
        and c_nationkey = n_nationkey
group by
        c_custkey,
        c_name,
        c_acctbal,
        c_phone,
        n_name,
        c_address,
        c_comment
order by
        revenue desc
LIMIT 20;

In-memory workload

Now let's see how effective is MRR when the workload fits entirely in memory. For the purpose of benchmarking in-memory workload, the InnoDB buffer pool size is set to 6G and the buffer pool was warmed up, so that the relevant pages were already loaded in the buffer pool. Note that as mentioned at the start of the benchmark results section, the InnoDB dataset size is ~5G. Ok so now let's take a look at the graph:

MRR doesn't really make any positive difference to the query times for MySQL 5.6, when the workload fits entirely in memory, because there is no extra cost for memory access at random locations versus memory access at sequential locations. In fact there is extra cost added by the buffering step introduced by MRR, and hence, there is a slight increase in query time for MySQL 5.6, increase of 0.02s. But the query times for MariaDB 5.5 are greater than both MySQL 5.5 and MySQL 5.6

IO bound workload

Now let's see how effective is MRR when the workload is IO bound. For the purpose of benchmarking IO bound workload, the InnoDB buffer pool size is set to 1G and the buffer pool was not warmed up, so that it does not have the relevant pages loaded up already:

MRR does make a lot of difference when the workload is IO bound, the query time is decreased from ~11min to under a minute. The query time is reduced further when the buffer size is set to 4M. Note also that query time for MariaDB is still a little higher by a couple of seconds, when compared to MySQL 5.6.

Now let's take a look at the status counters.

MySQL Status Counters

These status counters were captured when performing the benchmark on IO bound workload, mentioned above.

Counter Name MySQL 5.5 MySQL 5.6 MySQL 5.6 w/ read_rnd_bufer_size=4M MariaDB 5.5 MariaDB 5.5 w/ mrr_buffer_size=4M
Created_tmp_disk_tables 1 1 1 1 1
Created_tmp_tables 1 1 1 1 1
Handler_mrr_init N/A 0 0 1 1
Handler_mrr_rowid_refills N/A N/A N/A 1 0
Handler_read_key 508833 623738 622184 508913 507516
Handler_read_next 574320 574320 572889 574320 572889
Handler_read_rnd_next 136077 136094 136366 136163 136435
Innodb_buffer_pool_read_ahead 0 20920 23669 20920 23734
Innodb_buffer_pool_read_requests 1361851 1264739 1235472 1263290 1235781
Innodb_buffer_pool_reads 120548 102948 76882 102672 76832
Innodb_data_read 1.84G 1.89G 1.53G 1.89G 1.53G
Innodb_data_reads 120552 123872 100551 103011 77213
Innodb_pages_read 120548 123868 100551 123592 100566
Innodb_rows_read 799239 914146 912318 914146 912318
Select_scan 1 1 1 1 1
Sort_scan 1 1 1 1 1
  • As you can see from the status counters above that both MySQL 5.6 and MariaDB 5.5 are reporting high numbers for Innodb_buffer_pool_read_ahead which shows that the access pattern was sequential and hence InnoDB decided to do read_ahead, while in MySQL 5.5 no read_ahead was done because the access pattern was not sequential. Another thing to note is that more read_ahead is done when the buffer size used by MRR, is set to 4M, which obviously means that the more index tuples that can fit in the buffer the more sequential the access pattern will be.
  • There is one MRR related variable introduced in MySQL 5.6 and MariaDB 5.5 Handler_mrr_init and another additional one introduced in MariaDB 5.5 Handler_mrr_rowid_refills. Handler_mrr_init is incremented when a MRR range scan is performed, but we can see its only incremented in MariaDB 5.5 and not in MySQL 5.6, is that because of a bug in MySQL 5.6 code? As MRR was used in both MySQL 5.6 and MariaDB 5.5. Handler_mrr_rowid_refills counts how many times the buffer used by MRR had to be reinitialized, because the buffer was small and not all index tuples could fit in the buffer. If this is > 0 then it means Handler_mrr_rowid_refills + 1 MRR range scans had to be performed. As in the table above you can with default buffer size of 256K, MariaDB 5.5 shows that Handler_mrr_rowid_refills = 1, which means the buffer is small and there were 2 MRR range scans needed. But with a buffer size of 4M, we can see that Handler_mrr_rowid_refills = 0, which means that the buffer was big enough and only 1 MRR range scan was needed, which is as efficient as it can be. This is also evident in the query times, which is lower by a couple of seconds when buffer size of 4M is used.
  • Another interesting thing to note is that MySQL 5.6 and MariaDB 5.5 are both reading more rows than MySQL 5.5, as can be seen by the numbers reported for the status counter Innodb_rows_read. While MySQL 5.6 is also reporting increased numbers for the counter Handler_read_key. This is because of how status counter values are incremented when index lookup is performed. As I explained at the start of the post that traditional index lookup (for non-index-only columns) involves, reading an index record, and then using the PK column value in the index record to make a lookup in the PK. Both these lookups are performed in a single call to the storage engine and the counters Handler_read_key and Innodb_rows_read are incremented by ONE. However, when MRR is used then there are two separate calls made to the storage engine to perform the index record read and then to perform the MRR range scan on the PK. This causes the counters Handler_read_key and Innodb_rows_read to be incremented by TWO. It does not actually mean that queries with MRR are performing badly. The interesting thing is that though both MariaDB and MySQL 5.6 are reporting high numbers for Innodb_rows_read, which is completely in line with how the counters behave with MRR, but the value for counter Handler_read_key is more or less the same for MariaDB 5.5 when compared to MySQL 5.5, and this does not make sense to me. Probably its due to a bug in how counter is calculated inside MariaDB?

Other Observations

Sometimes both for MariaDB 5.5 and MySQL 5.6, the optimizer chooses the wrong query execution plan. Let's take a look at what are the good and bad query execution plans.

a. Bad Plan

id      select_type     table   type    possible_keys   key     key_len ref     rows    filtered        Extra
1       SIMPLE  nation  ALL     PRIMARY NULL    NULL    NULL    25      100.00  Using temporary; Using filesort
1       SIMPLE  customer        ref     PRIMARY,i_c_nationkey   i_c_nationkey   5       dbt3.nation.n_nationkey 2123    100.00
1       SIMPLE  orders  ref     PRIMARY,i_o_orderdate,i_o_custkey       i_o_custkey     5       dbt3.customer.c_custkey 7       100.00  Using where
1       SIMPLE  lineitem        ref     PRIMARY,i_l_orderkey,i_l_orderkey_quantity      PRIMARY 4       dbt3.orders.o_orderkey  1       100.00  Using where

b. Good Plan

id      select_type     table   type    possible_keys   key     key_len ref     rows    filtered        Extra
1       SIMPLE  orders  range   PRIMARY,i_o_orderdate,i_o_custkey       i_o_orderdate   4       NULL    232722  100.00  Using where; Rowid-ordered scan; Using temporary; Using filesort
1       SIMPLE  customer        eq_ref  PRIMARY,i_c_nationkey   PRIMARY 4       dbt3.orders.o_custkey   1       100.00  Using where
1       SIMPLE  nation  eq_ref  PRIMARY PRIMARY 4       dbt3.customer.c_nationkey       1       100.00
1       SIMPLE  lineitem        ref     PRIMARY,i_l_orderkey,i_l_orderkey_quantity      PRIMARY 4       dbt3.orders.o_orderkey  2       100.00  Using where

So during cold query runs the optimizer would switch to using plan 'a', which does not involve MRR, and the query time for MySQL 5.6 and MariaDB 5.5 jumps to ~11min (this is the query time for MySQL 5.5) While when it sticks to plan 'b' for MySQL 5.6 and MariaDB 5.5, then query times remain under a minute. So when the correct query execution plan is not used, there is no difference in query times between MySQL 5.5 and MySQL 5.6/MariaDB 5.5 This is another area of improvement in the optimizer, as it is clearly a part of the optimizer's job to select the best query execution plan. I had noted a similar thing when benchmarking ICP, the optimizer made a wrong choice. It looks like that there is still improvement and changes needed in the optimizer's cost estimation algorithm.

MariaDB 5.5 expands the concept of MRR to improve the performance of secondary key lookups as well. But this works only with joins and specifically with Block Access Join Algorithms. So I am not going to cover it here, but will cover it in my next post which will be on Block Access Join Algorithms.

Conclusion

There is a huge speedup when the workload is IO bound, the query time goes down from ~11min to under a minute. The query time is reduced further when buffer size is set large enough so that the index tuples fit in the buffer. But there is no performance improvement when the workload is in-memory, in fact MRR adds extra sorting overhead which means that the queries are just a bit slower as compared to MySQL 5.5 MRR clearly changes the access pattern to sequential, and hence InnoDB is able to do many read_aheads. Another thing to take away is that MariaDB is just a bit slower as compared to MySQL 5.6, may be something for the MariaDB guys to look at.


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Profiling your slow queries using pt-query-digest and some love from Percona Server

Декабрь 28th, 2011
This guide will get you up and running with how to identify the bottleneck queries using the excellent tool pt-query-digest. You will learn how to use and analyze the output returned by pt-query-digest. You will also learn some differences between slow query logging in various MySQL versions. Later on in the post I will also show you how to make use of the extra diagnostic data available with Percona Server.
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Flag based COUNT using MySQL

Октябрь 27th, 2011

I was asked for help in optimizing a MySQL query where flags are stored in a database and references should be counted based on the flag value. Sounds not complicated at first, but there are several flags that should be counted and also just once per reference. A lot of food for GROUP BY you may think. Having said this, search and group for flags in this table would be really slow due to a very poor cardinality. But let's start with the actual problem. The example is fictitious, but I did my best to find a general use case for this problem.

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Optimized Pagination using MySQL

Октябрь 24th, 2011

Dealing with large data sets makes it necessary to pick out only the newest or the hottest elements and not displaying everything. In order to have older items still available, Pagination navigation's have become established. However, implementing a Pagination with MySQL is one of those problems that can be optimized poorly with MySQL and certainly other RDBM systems. However, knowing the underlying database can also help in optimizing pagination queries, because there is no real copy and paste solution.

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OR conditions considered bad… Or? And a workaround.

Июль 19th, 2011
Some things are known to be just bad. GOTOs used to be one such thing (something I still use them, but only where appropriate, which isn't that many places). Maybe it is just so, that some things are useful, but not for everything, so maybe the issue is that they are used inappropriately. Or?

The OR condition is one such things in MySQL circles! Oh, you have an OR condition! That is going to be so slow! sort of. And the reason an OR is "slow" is that as MySQL will use only one index for each statement, only one "side" or the or condition can use an index. Or sometimes even worse, MySQL will consider using an index that is common to the two "sides" or is outside the OR conditition, despite that fact that there are perfectly fine, highly selective indexes on both sides of the OR condition.

If you ask me, this is not a fault with the OR condition but rather a problem with the MySQL optimizer. Why in heavens name can't a single statement use two indexes, if that is what it takes? And let me let you in on a little secret: MySQL can use multiple indexes for one statement! But that depends on what you mean with a statement. And MySQL means something slightly different than many of us do!

Without further ado, lets have a look at an example. We work at a retail store, and a package from us has been stuck at the post office. We want to check what product this is, but we don't know the product id. What the guy who called us from the post-office said was something that looked like a brand name, that I can map to a brand ID, the number of units in the package and the weight. But to be honest, the last two weren't terribly reliable. OK, lets find the product in the product table, which looks like this:
CREATE TABLE `product` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`brand_id` int(11) NOT NULL,
`quantity` int(11) NOT NULL,
`weight` int(11) NOT NULL,
PRIMARY KEY (`id`),
KEY `ix_brand` (`brand_id`),
KEY `ix_weight_brand` (`weight`,`brand_id`),
KEY `ix_quantity_brand` (`quantity`,`brand_id`)
) ENGINE=InnoDB AUTO_INCREMENT=2321295 DEFAULT CHARSET=utf8

I know for certain that brand_id is 6, I already looked that up. But there are millions of products in the product table! Luckily, looking for approriate products using brand_id and either quantity or weight should be easy, right? We know now that the weight is 41 and quantity is 78. And we have approriate indexes, this should not be a big deal, right:
SELECT id FROM product WHERE brand_id = 6 AND (weight = 41 OR quantity = 78)

Well, although this works, it is a big sluggish, real slow actually. Lets look at what mySQL does with this statement:
EXPLAIN SELECT id FROM product WHERE brand_id = 6 AND (weight = 41 OR quantity = 78)
And what we get is this:
+----+-------------+---------+------+--------------------------------------------+----------+---------+-------+------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+---------+------+--------------------------------------------+----------+---------+-------+------+-------------+
| 1 | SIMPLE | product | ref | ix_brand,ix_weight_brand,ix_quantity_brand | ix_brand | 4 | const | 4291 | Using where |
+----+-------------+---------+------+--------------------------------------------+----------+---------+-------+------+-------------+
That wasn't so good. Let's try a different way:
EXPLAIN SELECT id FROM product WHERE (brand_id = 6 AND weight = 41) or (brand_id = 6 AND quantity = 78);

And that will result in the same query path. Only one index can be used, and there is one index that fits with both paths, that on brand_id, so MySQL picks that. Using FORCE_INDEX will work, but still only 1 index will be used, and the result may well be even worse, as a FORCE_INDEX on, say the ix_weight_brand index, will make the other path, on quantity, dead slow! What you would like MySQL to do, which doesn't seem so complicated, is to realize that there are two distinct paths here which can be looked up using an index real easy, execute them both and merge the results. But no, MySQL will not DO that! Only 1 index per statement, that's it. Or?

Well, when you understand that MySQL will only use one index per statement, consider what MySQL means with statement here. For a SELECT it is the individual SELECT statement that is the statement, which sounds reasonable until you consider a UNION! Each and every statement in a UNION is considered a separate statement (in this particular case that is, but it is a but messy, UNIONs in MySQL are a bit of a kludge, really)! So if we rewrite the statement above as a UNION, which is easily done for many queries involving OR-conditions, you get something like this:
SELECT id FROM product
WHERE brand_id = 6 AND weight = 41
UNION
SELECT id FROM product
WHERE brand_id = 6 AND quantity = 78;

What are we saying here? We are telling MySQL that these are actually two separate paths, which is what we did with the OR condition, but in this case, MySQL can use two indexes, and will nicely merge the results, so an explain looks like this:
+------+--------------+------------+------+----------------------------+-------------------+---------+-------------+------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+------+--------------+------------+------+----------------------------+-------------------+---------+-------------+------+-------------+
| 1 | PRIMARY | product | ref | ix_brand,ix_weight_brand | ix_weight_brand | 8 | const,const | 31 | Using index |
| 2 | UNION | product | ref | ix_brand,ix_quantity_brand | ix_quantity_brand | 8 | const,const | 1 | Using index |
| NULL | UNION RESULT | <union1,2> | ALL | NULL | NULL | NULL | NULL | NULL | |
+------+--------------+------------+------+----------------------------+-------------------+---------+-------------+------+-------------+
This latter query is often so much faster than the alternatives, and we have tricked MySQL into using two indexes and merge the result. But for some reason, MySQL is unable to figure this one out for itself. Is this an important tip I am giving you here? Is this a neat optimization trick that I am handing out? Short-term, the answer is yes.

But am I with this saying that you should stay clear of OR-conditions? Absolutely not, no way. What I am presenting here is an awkward way of circumventing some obvious flaws with the MySQL optimizer, and this should be fixed! But what I AM saying is this: If you currently have big performance problems with MySQL SELECTs involving OR conditions, you might consider rewriting those statements to UNIONs, sometimes that hels. But do not do this will ALL your OR-conditions, only where you have to and it makes sense. Let's meanwhile wait for the MySQL developers to fix this. (No, I'm not good enough at the optimizer code or most other parts of the MySQL kernel to fix this myself. I'm happy to build things around MySQL, but I do not have the time to get more involved with the kernel).

And before I keave you for now: This was tested with MySQL 5.5.7 on Linux. I have NOT checked for fixes, updates to this, but I do hope it has NOT been fixed? Why? Why do I now want it fixed?? Have I gone bonkers? Yes, I am bonkers, but that's not the issue here, the issue is that such rather involved fixes to the optimizer is NOT something I want introduced in the middle of a GA release! But I'd be really glad to have it fixed in 5.6 or whatever that release is to be called! And yes, I am ware this is not exactly with the optimizer itself, but more so with the query execution, but for now, I have decided to call it the optimimizer anyway, as the sun is shining and the weather is nice and all that, sometime around christmas I might consider changing my mind.

Cheers for now
/Karlsson

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On Covering Indexes and Their Impact on Performance

Июль 3rd, 2011
The purpose of this post is to describe what covering indexes are and how they can be used to improve the performance of queries. People mostly use indexes to filter or sort the results but not much thought is given to actually reduce the disk reads by using proper indexes. So I will show you how to reduce disk reads and hence improve the performance of queries by utilizing indexes properly.
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Optimizing UPDATE and DELETE statements

Февраль 24th, 2011

While most people look at performance optimizations for SELECT statements, UPDATE and DELETE statements are often overlooked. These can benefit from the principles of analyzing the Query Execution Plan (QEP). You can only run an EXPLAIN on a SELECT statement, however it’s possible to rewrite an UPDATE or DELETE statement to perform like a SELECT statement.

To optimize an UPDATE, look at the WHERE clause. If you are using the PRIMARY KEY, no further analysis is necessary. If you are not, it is of benefit to rewrite your UPDATE statement as a SELECT statement and obtain a QEP as previously detailed to ensure optimal indexes are used. For example:

UPDATE t
SET	c1 = ‘x’, c2 = ‘y’, c3 = 100
WHERE c1 = ‘x’
AND	d = CURDATE()

You can rewrite this UPDATE statement as a SELECT statement for using EXPLAIN:

EXPLAIN SELECT c1, c2, c3 FROM	t WHERE c1 = ‘x’ AND	d = CURDATE()

You should now apply the same principles as you would when optimizing SELECT statements.


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MySQL Query Optimization – Tip # 1 – Avoid using wildcard character at the start of a LIKE pattern.

Ноябрь 23rd, 2010
The more I go through others SQL, there are some common mistakes that I see developers making over and over again, so I thought why not start a series of tips that can help developers optimize their queries and avoid common pitfalls. So this post is a part of that series of tips, and this is the first tip "Avoid using a wild card character at the start of a LIKE pattern".
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MySQL Paginated displays – How to kill performance vs How to improve performance!

Октябрь 30th, 2010
Pagination is used very frequently in many websites, be it search results or most popular posts they are seen everywhere. But the way how it is typically implemented is naive and prone to performance degradation. In this article I attempt on explaining the performance implications of poorly designed pagination implementation. I have also analyzed how Google, Yahoo and Facebook handle pagination implementation. Then finally i present my suggestion which will greatly improve the performance related to pagination.
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Resolve many-to-many relations a bit different with MySQL

Сентябрь 2nd, 2010

In database modeling, a m:n relationship is usually resolved by an additional table. But what if this relation is used only for archiving and the number of links in the resulting table is not too high? In that context, I got the idea to store all referring ID's as CSV string directly into a TEXT column of one of the referring tables. I came to this idea, because otherwise I would have to build complicated foreign keys and this way I also save one additional table. Certainly, this only makes sense if the data is not frequently accessed as foreign key. Nevertheless, I would like to tackle the problem, even if the implementation is very MySQL-oriented.

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