Are MySQL stored procedures slow?

 

Yes, if compared to code in Java or C#. For example, this overly simple code took 284 seconds.

CREATE PROCEDURE CountTest()
begin
    declare counter int default 0;
    select now();
    repeat
        set counter = counter + 1;
    until counter > 120000000
    end repeat;
    select counter;
    select now();
end

Ignoring my off by one error, here is equivalent code in C# (the language I’m currently learning).  It took 419 milliseconds, or MySQL took 677 times longer to execute. From my experience, Java isn’t going to be any slower.

 int counter = 0;
while (counter < 120000000)
{
    counter++;
}

Slow stored procedure performance is one of the reasons why it usually isn’t wise to implement computationally expensive business logic in the database.   With networks not being the bottleneck they once were it is often better to extract the data to the application layer and process it in the programming language of your choice, even if that takes a few extra round trips to the database.  There are exceptions where those extra round trips are too numerous and prohibitively expensive.  With something as complex as a database there are always exceptions.

This example is a bit too simple, but, based on experience, it is representative of the general performance of stored procedure logic.

How can MySQL get away with such poor performance? Well, the equivalent code in MS SQL Server took 80 seconds on the same hardware, which is also two orders of magnitude slower than C# or Java code. 

create procedure testCount
as
begin
    declare @countNo int;
    set @countNo = 0;
    while (@countNo < 120000000)
    begin
        set @countNo = @countNo + 1
    end
    select @countNo
end

MS SQL Server code runs 3.5 times faster than MySQL, but given how much older MS SQL Server is, MySQL is doing well here.  So are MySQL Stored procedures slow?  Not really, if compared to other databases I’ve used.

Just don’t use them to do computationally expensive business logic.

Implementing Sharding in the Database

Over the past few weeks (years really) there has been some discussion on sharding. Instead of discussing when sharding is required, as there are good discussions on this already, I want to discuss how I would like to have sharding implemented in the database.

I want the database to handle sharding automatically, and where it can't be automatic, I want the database to help as much as it can.  Just like I want my business logic in a language ideally suited to it, and not stored procs (generally, there are always exceptions); I want all my physical persistence to be handled by the place that already does most of it, the database.  Having the database handle some of the physical persistence and the object relational layer handle the sharding logic isn’t ideal to me, and not just because the current object relational layers don’t have all the sharding functionality I want.  So here is what I want my database to do.  

1) I want to automatically route simple queries to the one shard that has the relevant data.  Hibernate shards already does this; I just want it implemented in the database.  The MPP (massively parallel processing) version of DB2 can also do this.

2) I need the ability to reshard data when a rebalancing of the shards is required, without downtime.  This is similar in concept to an index rebuild being done while the database is still up, which the expensive databases have implemented.  This is more complex than an index rebuild as multiple tables will need to be resharded before the resharding is complete. DB2 has functionality to reshard, but it requires down time last time I checked (which was a few years ago) .

3) Finally, and this is a superset of #1, I want to be able to run one SQL statement across multiple shards at the same time.  The database will send the SQL to all the relevant shards, run as much of the SQL on that shard as possible, using something like the function shipping that DB2 MPP has, and then aggregate the data from the various shards into one result set.  All I need to do this is execute one SQL statement.  Stated differently, all the shards will look like one database.  This won’t be easy to implement, and will probably be limited in functionality at first, but this long term goal is why implementing sharding in the database makes sense. 

This might sound a bit like an advertisement for the MPP version of DB2, but if it were open source I would at strongly consider implementing it for applications that need database sharding. Given that that is not likely to happen, I hope a reliable open source database, like MySQL, or Drizzle, or PostgreSQL, starts implementing this reasonably soon.

This sharding functionality, for the types of problems I’m seeing, is more important than any other new MySQL functionality, including better scaling on multi-core systems. With all above functionality, I can reasonably easily have multiple shards on one machine. Not that I don’t want much better multi-core CPU scalability, I do, as more shards are still harder to manage than less shards, it just isn’t as important as better sharding functionality.  I absolutely need the ability to shard; I don't absolutely need the ability to effectively use every CPU on a server with one shard. 

On another note, I’ll be giving a presentation on table partitioning at the MySQL conference.  This won’t be a basic presentation on how to implement partitioning, instead I’ll be highlighting scenarios where partitioning will degrade performance and cases where it improves performance.  I’ll show examples where, in spite of degraded performance for some tasks, it still might make sense to implement it.  An analogy here would be indexes, where adding an index can speed up some selects at the expense of slowing down inserts and some updates.

Infobright Review – Part 2

First, a retraction, it turns out that the performance problem with datatimes in the previous article wasn’t due to high cardinality (I speculated too much here), but due to a type conversion issue.  From a helpful comment from Victoria Eastwood of Infobright (a good sign for a startup), the Infobright engine considered ‘2001-01-01’ to be a date, not a datetime, and it couldn’t do a conversion to a datetime.  Instead it pushed the date filtering logic from the Infobright engine to MySQL.  Effectively, the slow queries were a table scan.   The solution is to add the 00:00:00 to the dates to make them datetimes.  

With that in mind, here are some much better numbers for Infobright.   For Infobright this query took 0.05 seconds. 

1) Select sum(unit) from Sale where purchaseDate >= '2001-04-01 00:00:00' and purchaseDate < '2001-05-01 00:00:00'

This compares very favorably to 4 seconds for InnoDB if the data is cached and 8.8 if the data needs to be read from disk. 

In doing further tests I found some interesting patterns one might not expect coming from a row database background.  This more selective #2 query took 0.27 seconds for Infobright, longer than the less selective query #1.  Still 0.27 is fast enough.  The odd part is a row based database is generally faster, or at least the same performance, the more selective the query.  For Infobright the exact opposite occurs, at least in these cases I explored.   For this more selective query, InnoDB took 0.047 seconds with a cold cache (all following queries will be using a cold cache unless otherwise noted) compared to that 0.27 for Infobright.  It seems that the that Infobright is faster than InnoDB for less selective queries, and InnoDB is faster for more selective queries. 

2) select sum(unit) from Sale where purchaseDate >= '2001-04-01 00:00:00' and purchaseDate < '2001-05-01 00:00:00' and productId = 199824

This more selective query #3 took longer still, at 0.99 seconds for Infobright, but only took 0.047 seconds for InnoDB, the same time as query #2.  

3) select sum(unit) from Sale where purchaseDate >= '2001-04-01 00:00:00' and purchaseDate < '2001-05-01 00:00:00' and productId = 199824 and customerId = 418845

The same pattern becomes more evident with double the time range for the queries, with two months of data compared to just one.   Query #4 took 0.07 seconds for Infobright whereas InnoDB took 19.9 seconds, far longer.  

4) select sum(unit)  from Sale where purchaseDate >= '2001-04-01 00:00:00' and purchaseDate < '2001-06-01 00:00:00'

At 1.25 seconds, adding another criteria noticeably slowed down the performance from query #4 for Infobright.  At 0.061 seconds, InnoDB was much faster than the less selective query #4,

5) select sum(unit) from Sale where purchaseDate >= '2001-04-01 00:00:00' and purchaseDate < '2001-06-01 00:00:00' and productId = 199824

At 1.99 seconds, this more selective query #6 was again slower than #5 for Infobright.  Following the trend, InnoDB was even faster at 0.048 seconds.  

6) select sum(unit) from Sale where purchaseDate >= '2001-04-01 00:00:00' and purchaseDate < '2001-06-01 00:00:00' and productId = 199824 and customerId = 418845

But there is another difference between InnoDB and Infobright.  The below query #7 takes 0.06 seconds for InnoDB, and takes 11.5 seconds for Infobright with a cold cache (I restarted the database in both cases).  Once the cache was warmed up Infobright took 2.2 seconds to run and retrieve one row.  As there are 120 million different orderIds in the Sale table, perhaps my speculation that Infobright does worse the higher the cardinality of a column wasn't that off.  

7) select * from Sale where orderId = 1

At least for the cases I examined, InnoDB is better for more selective queries, at times far better, and Infobright is better for less selective ones, at times far better.  This assumes defining good indexes for InnoDB, work which Infobright does not require.  Which one to use for reporting?  It really does depend on the nature of the workload.   

Having said that, Infobright behaves quite differently from InnoDB and I know there is much I'm missing as I only put in a few hours of work.   I would try your own workload and come to your own conclusions. 

An Infobright Review

With open source software I can install reasonably complete software and try it with my data. This way I get to see how it works in a realistic setting without having to rely on benchmarks and hoping they are a good match for my environment. And I get to do this without having to deal with commercial software sales people.

So I glad to hear the Infobright had gone open source as I have been wanting test a column based database for a while. I was even happier that it was a MySQL based engine as I would already know many of the commands. I decided to run some of the same tests I had run when comparing InnoDB and MyISAM for reporting (http://dbscience.blogspot.com/2008/08/innodb-suitability-for-reporting.html ).  InnoDB performed better than MyISAM in my reporting tests so I’m going to compare Infobright to InnoDB.

The first task is to load the data with this command.

load data infile '/dbdata/info/data/sale.dat' into table Sale fields terminated by '\t' ;

Infobright loaded 120 million rows in 8 min 2.62 seconds. Loading 14 million rows a minute is fast enough for me, considering I’m running on a commodity machine with an external disk subsystem that cost about 12k USD in total.

Next I run a simple query that takes 4 seconds to run on InnoDB when the data is cached, and 8.8 when disk is involved. This type of query is common in reporting systems.

select sum(unit) from Sale where purchaseDate >= '2001-11-01' and purchaseDate < '2001-12-01'

This same query takes 151 seconds to run on Infobright. I get the message “Brighthouse: Value out of range. Query executed by MySQL engine.” I have 631,530 different purchaseDate values for 120,000,000 rows in the Sale table, so perhaps it is the high cardinality of the column that is causing the problem. I’ve heard that column based databases can have this issue. I don’t see any noticeable disk io.

But, if I run the exact query again it now takes 0.0069 seconds; obviously data is being cached at some level.

I’m curious if the various caches need to warm up before the Infobright has fast performance. To test this I run the same query on a subset of the above date. This sql still takes 153 seconds. So it appears only the result set is being cached.

select sum(unit) from Sale where purchaseDate >= '2001-11-02' and purchaseDate < '2001-12-01'

If I limit the result set to one customer the performance doesn’t improve, with this sql taking 205 seconds. I’m still getting the message “Brighthouse: Value out of range. Query executed by MySQL engine.”

select sum(unit) from Sale where purchaseDate >= '2001-11-01' and purchaseDate < '2001-12-01' and customerId = 50

If I ignore including dates in the query and just go with simple ints conditions this sql takes 1.25 seconds. I’m no longer getting the message “Brighthouse: Value out of range. Query executed by MySQL engine”, which explains the better performance.

select sum(unit) from Sale where customerId = 50 and productId = 22641

The same query in InnoDB takes 0.035 seconds if the io is required and 0.0079 if the data is cached (I'm not using result set caching, I'm just letting the db cache the underlying data).

Perhaps the problem is all my data is too high in cardinality. I have created 10,000,000 products and 1,000,000 customers, with the data being randomly generated. Is this too much for Infobright to handle? Perhaps the data is not realistic enough; perhaps it has too high a cardinality. I do have a tendency to push to extremes to find out when things get interesting (in other words, break). Yet InnoDB handled this cardinality without a problem. Plus, in my case, for the SaaS application I’m working on, the customer count is low if we are a success and 10 million customers is plausible. Storing dates to the second precision isn't an unreasonable requirement, although 10 million products is undeniably unrealistic for most organizations. 

In spite of that, next I think about truncating the seconds on the purchaseDate column to reduce the cardinality. Then I remember the community version doesn’t allow updates (or deletes, or inserts). This is a bit too crippled for my taste for I often want to update customer demographic information.  This concerns me, as the data would have to be stored elsewhere to be updated and then the updated data, in fact the entire table, dropped and recreated in Infobright.  Having to store data twice reduces the compression advantages of Infobright and adds to the complexity of the solution, when one of the stated advantages of Infobright is simplicity compared to row based databases. 

And at that I will end this preliminary assessment. I may reduce the cardinality of the data and try the tests again.

Datetimes and Ints

In my last article I found, and others confirmed (thanks), that timestamps were much slower than datetimes. I next wondered if datetimes were slower than storing dates as ints in the UNIX timestamp format (seconds since January 1, 1970). The conclusion is that ints are a bit faster than datetimes, but not enough for me to give up all the functionality that datetimes provide.

I ran this sql 100 times against tables of about 20 gig, with the innodb_buffer_pool_size=2G and innodb_flush_method=O_DIRECT, to get physical io and make the tests reasonably realistic.

Running this datetime sql 100 times took 21.2 minutes.  The month randomly varied for each of the 100 executions to avoid caching issues. 

select sum(unit) from Sale where purchaseDate >= '2001-02-01' and purchaseDate < '2001-03-01'

Running the equavlent int sql 100 times took 20.7 minutes, a difference of about 2.4%.

select sum(unit) from SaleTI where purchaseDate >= 981014400 and purchaseDate < 983433600

So even though a datetime takes 8 bytes and an int 4 bytes, the performance is roughly comparable.  Since I find datetimes much easier to work with than ints I'll be using datetimes. 

For the curious, here is the datetime version of Sale table:

 

CREATE TABLE `test`.`Sale` (

`orderId` int(11) NOT NULL,

`customerId` int(11) NOT NULL,

`productId` int(11) NOT NULL,

`productBigId` int(11) NOT NULL,

`unit` int(11) NOT NULL,

`purchaseAmount` decimal(16,2) NOT NULL,

`purchaseCost` decimal(16,2) NOT NULL,

`purchaseDate` datetime NOT NULL,

PRIMARY KEY (`purchaseDate`,`orderId`),

UNIQUE KEY `idx_sale_order` (`orderId`),

KEY `pf_sale_product` (`productId`),

KEY `pf_sale_customer` (`customerId`),

CONSTRAINT `pf_sale_customer` FOREIGN KEY (`customerId`) REFERENCES `Customer` (`customerId`),

CONSTRAINT `pf_sale_product` FOREIGN KEY (`productId`) REFERENCES `Product` (`productId`)

) ENGINE=InnoDB

 

And here is the int version of SaleTI table.

 

CREATE TABLE `test`.`SaleTI` (

`orderId` int(11) NOT NULL,

`customerId` int(11) NOT NULL,

`productId` int(11) NOT NULL,

`productBigId` int(11) NOT NULL,

`unit` int(11) NOT NULL,

`purchaseAmount` decimal(16,2) NOT NULL,

`purchaseCost` decimal(16,2) NOT NULL,

`purchaseDate` int(11) NOT NULL,

PRIMARY KEY (`purchaseDate`,`orderId`),

UNIQUE KEY `idx_saleTI_order` (`orderId`),

KEY `idx_saleTI_product` (`productId`),

KEY `idx_saleTI_customer` (`customerId`),

CONSTRAINT `fk_saleTI_customer` FOREIGN KEY (`customerId`) REFERENCES `Customer` (`customerId`),

CONSTRAINT `fk_saleTI_product` FOREIGN KEY (`productId`) REFERENCES `Product` (`productId`)

) ENGINE=InnoDB

Update:  While it doesn't represent the environments I work in, I just did a ran with same sql with innodb_buffer_pool_size=28G.  The tables were now able to fit into memory.  In this case datetimes were about 1% faster than ints, which I can't explain.  I suspect the difference is just random fluctuation.  The conclusion remains the same, that datetimes performance is about the same as Unix ints and datetimes have far more functionality. 

Can a timestamp be slower than a datetime?

One should use timestamps rather than datetimes as timestamps are four bytes and datetimes are eight bytes, right?  This makes sense as smaller indexes and tables are faster than larger ones. However, when I was trying to quantify the improvement (I’m strange that way), I found the exact opposite - when used as a criteria, a timestamp is slower than a datetime. Often much slower.

For example, for this simple sql, when purchaseDate was a timestamp, the query took about 9.4 seconds and the datetime version took about 4.3 seconds. In this case the data was in memory.

    select sum(unit) from Sale where purchaseDate >= '2001-07-01' and purchaseDate < '2001-08-01'

I did other tests, such as when data wasn’t cached and there was io, and I found cases where datetimes were much faster than timestamps, differences so extreme I’m hesitant to report them until I have a better explanation of what is going on. I really can’t believe the performance differences I'm measuring and I have this suspicion that something else is occurring as these numbers seem too extreme, but what I can’t say.

Was there a bug for this I don’t know about? It is hard to find information on timestamps as it is such a common word, but I did scan the MySQL bugs for timestamps and didn’t find anything. Is it possible the extra functionality of timestamps, like timezone conversions, makes timestamps slower than datetimes?

In a few other cases, I found them about the same speed; only in one case were timestamps faster than datetimes, and that was 94 seconds for timestamps and 97 seconds for datetimes, which is an insignificant difference.

I tested this on 5.1.26 and 5.0.22, and found the performance difference between timestamps and datetimes on both versions.

I'm not sure what to conclude from this. If you aren’t frequently using the date oriented columns as criteria in queries, and dates are something like a rarely used record of when an event occurred, the smaller timestamp is the better choice. If, however, if many queries are run against the date in question, and assuming these results I’m getting are accurate, then a datetime column should at least be under consideration. In other words, benchmark datetimes and timestamps in your environment with representative queries. The results are worse for timestamps when they are used as a selection criteria.

If you get the crazy performance differences I’m getting, or if you don’t, let me know.

 

For the curious, the plans for the timestamp queries looked like this:

mysql> explain select sum(unit) from SaleT where purchaseDate >= '2001-07-01'  and purchaseDate < '2001-08-01' \G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: SaleT
         type: range
possible_keys: PRIMARY
          key: PRIMARY
      key_len: 4
          ref: NULL
         rows: 20267520
        Extra: Using where
1 row in set (0.04 sec)

And the datetime queries looked like this:

mysql> explain select sum(unit) from Sale where purchaseDate >= '2001-07-01'  and purchaseDate < '2001-08-01' \G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: Sale
         type: range
possible_keys: PRIMARY
          key: PRIMARY
      key_len: 8
          ref: NULL
         rows: 20548080
        Extra: Using where
1 row in set (0.06 sec)

And the table looks like this (with the other version having a purchaseDate timestamp column instead of a datetime).

 

CREATE TABLE  `test`.`Sale` (
  `orderId` int(11) NOT NULL,
  `customerId` int(11) NOT NULL,
  `productId` int(11) NOT NULL,
  `productBigId` int(11) NOT NULL,
  `unit` int(11) NOT NULL,
  `purchaseAmount` decimal(16,2) NOT NULL,
  `purchaseCost` decimal(16,2) NOT NULL,
  `purchaseDate` datetime NOT NULL,
  PRIMARY KEY  (`purchaseDate`,`orderId`),
  UNIQUE KEY `idx_sale_order` (`orderId`),
  KEY `pf_sale_product` (`productId`),
  KEY `pf_sale_customer` (`customerId`),
  CONSTRAINT `pf_sale_customer` FOREIGN KEY (`customerId`) REFERENCES `Customer` (`customerId`),
  CONSTRAINT `pf_sale_product` FOREIGN KEY (`productId`) REFERENCES `Product` (`productId`)
) ENGINE=InnoDB

 

On a somewhat related note, I also got this repeatable bug when trying to insert data into a timestamp column on 5.1.26:

    Incorrect datetime value: '2001-03-11 02:00:39' for column 'purchaseDate' at row 226

InnoDB's Suitability for Reporting

I started using Oracle, a MVCC database, to develop reporting (data warehousing, BI, take your pick) systems years ago.  I’ve come to appreciate the scalability improvements that MVCC provides, particularly for pseudo real-time reporting applications, the ones where loads are occurring at the same time as report generation.  So when people say InnoDB, partly due to MVCC, isn’t as good as MyISAM for reporting I had to look into this in more detail.

What I found is InnoDB is a good engine for reporting.  In some ways, such as performance, it is at times better than MyISAM, and one of the downsides, such as a larger disk requirement, can be mitigated.  The trick is to for the primary key to be the one predominant access path.  In this example, the InnoDB clustered index, is purchaseDate and another column, such as orderId is added to make it unique.  This has a number of advantages.  In my experience, most reporting systems queries are for a date range, so date provides a great all round clustered index.   Also, one of the problems with indexes is they can fragment, which both slows down queries and takes up more disk.  But all the new date oriented data is added to the end of the clustered index, meaning most of the index doesn’t change and the old data, if defragmented, remains so.   Finally, as most queries are for the recent data, clustering by date tends to keep the most frequently used recent data in memory, a benefit that is hard to quantify but can be substantial. 

Here is the InnoDB version of the table.  It holds 120 million rows, about 10 million for each month in 2001.  As the server has 28 gig of memory, in all cases I ensured that all these queries ran in memory and there was no physical io.  This isn’t completely fair as MyISAM does compact data and takes up less disk, but I’ll show later how to minimize the amount of disk that InnoDB consumes.  Plus, if there is enough (any? - comments would be great) contention on this point, I’ll run some tests with physical io for both engines.

 

CREATE TABLE  `test`.`Sale` (

  `orderId` int(11) NOT NULL,

  `customerId` int(11) NOT NULL,

  `productId` int(11) NOT NULL,

  `productBigId` int(11) NOT NULL,

  `unit` int(11) NOT NULL,

  `purchaseAmount` decimal(16,2) NOT NULL,

  `purchaseCost` decimal(16,2) NOT NULL,

  `purchaseDate` datetime NOT NULL,

  PRIMARY KEY  (`purchaseDate`,`orderId`),

  UNIQUE KEY `idx_sale_order` (`orderId`),

  KEY `pf_sale_product` (`productId`),

  KEY `pf_sale_customer` (`customerId`),

  CONSTRAINT `fk_sale_customer` FOREIGN KEY (`customerId`) REFERENCES `Customer` (`customerId`),

  CONSTRAINT `fk_sale_product` FOREIGN KEY (`productId`) REFERENCES `Product` (`productId`)

) ENGINE=InnoDB

 

The InnoDB version of this query takes seconds 4 seconds and sensibly uses the primary key to limit the amount of data it needs to query.   This means only about 1/12 of the table needs to be queried, and when it is read the access if sequential, which is fast.  

 

explain select sum(unit) from Sale where purchaseDate >= '2001-11-01' and purchaseDate < '2001-12-01'  \G

*************************** 1. row ***************************

           id: 1

  select_type: SIMPLE

        table: Sale

         type: range

possible_keys: PRIMARY

          key: PRIMARY

      key_len: 8

          ref: NULL

         rows: 19447290

        Extra: Using where

 

This is the equivalent MyISAM table with a more standard primary key of orderId. 

 

CREATE TABLE  `test`.`SaleI` (

  `orderId` int(11) NOT NULL,

  `customerId` int(11) NOT NULL,

  `productId` int(11) NOT NULL,

  `productBigId` int(11) NOT NULL,

  `unit` int(11) NOT NULL,

  `purchaseAmount` decimal(16,2) NOT NULL,

  `purchaseCost` decimal(16,2) NOT NULL,

  `purchaseDate` datetime NOT NULL,

  PRIMARY KEY  (`orderId`),

  KEY `pk_salei_product` (`productId`),

  KEY `pk_salei_customer` (`customerId`),

  KEY `idx_saleI_purchaseDate` (`purchaseDate`)

) ENGINE=MyISAM

 

As 12 seconds, the same query for MyISAM is much slower as it must visit both the index and the page which contains the data.  

 

mysql> explain select sum(unit) from SaleI where purchaseDate >= '2001-11-01' and purchaseDate < '2001-12-01' \G;

*************************** 1. row ***************************

           id: 1

  select_type: SIMPLE

        table: SaleI

         type: range

possible_keys: idx_saleI_purchaseDate

          key: idx_saleI_purchaseDate

      key_len: 8

          ref: NULL

         rows: 9079951

        Extra: Using where

 

However, this isn’t completely fair to MyISAM as a better covering index for MyISAM can be written that replicates some of performance of a clustered index.   So I change the table definition to add the idx_saleI_covering index. 

 

CREATE TABLE  `test`.`SaleI` (

  `orderId` int(11) NOT NULL,

  `customerId` int(11) NOT NULL,

  `productId` int(11) NOT NULL,

  `productBigId` int(11) NOT NULL,

  `unit` int(11) NOT NULL,

  `purchaseAmount` decimal(16,2) NOT NULL,

  `purchaseCost` decimal(16,2) NOT NULL,

  `purchaseDate` datetime NOT NULL,

  PRIMARY KEY  (`orderId`),

  KEY `pk_salei_product` (`productId`),

  KEY `pk_salei_customer` (`customerId`),

  KEY `idx_saleI_covering` (`purchaseDate`,`productId`,`customerId`,`unit`,`purchaseAmount`,`purchaseCost`)

) ENGINE=MyISAM

 

With the better idx_saleI_covering index the query now takes 8 seconds, still about twice a long as InnoDB.  

 

select sum(unit) from SaleI where purchaseDate >= '2001-11-01' and purchaseDate < '2001-12-01' \G

*************************** 1. row ***************************

           id: 1

  select_type: SIMPLE

        table: SaleI

         type: range

possible_keys: idx_saleI_covering

          key: idx_saleI_covering

      key_len: 8

          ref: NULL

         rows: 25370368

        Extra: Using where; Using index

 

How does a more selective query do, one which picks a specific date range and a another criteria, in this case customer?  For InnoDB, almost zero seconds, 0.0079 seconds to be exact. 

 

select sum(unit) from Sale where purchaseDate >= '2001-11-01'  and purchaseDate < '2001-12-01' and customerId = 50 \G

*************************** 1. row ***************************

           id: 1

  select_type: SIMPLE

        table: Sale

         type: index_merge

possible_keys: PRIMARY,pf_sale_customer

          key: pf_sale_customer,PRIMARY

      key_len: 4,8

          ref: NULL

         rows: 12

        Extra: Using intersect(pf_sale_customer,PRIMARY); Using where

1 row in set (0.00 sec)

 

The query plan for MyISAM is different, but the results are the same, with the query taking 0.0079 seconds to run. 

 

mysql> explain select sum(unit)   from SaleI  where purchaseDate >= '2001- -01'    and purchaseDate < '2001-12-01'    and customerId = 50 \G

*************************** 1. row ***************************

           id: 1

  select_type: SIMPLE

        table: SaleI

         type: ref

possible_keys: pk_salei_customer,idx_saleI_covering

          key: pk_salei_customer

      key_len: 4

          ref: const

         rows: 102

        Extra: Using where

 

So, not an extensive series of tests, but two of the more common types of reporting queries and InnoDB is either as fast or faster than MyISAM.  At this point, all other things being equal, I would much prefer the higher reliability of InnoDB over MyISAM. 

But what about the size of the of data on disk?  MyISAM, for the version of SaleI with the covering index, at 13.6 gig, is 63.3% of InnoDB’s 21.5 gigs.   A significant advantage for MyISAM, yes, but not enough of itself to choose MyISAM over InnoDB.  Part of the reason is all the indexes are on numbers and dates, which MyISAM doesn’t compress by default.  Generally, for performance reasons, indexes should be non-character in a reporting system, so non-character indexes are a reasonable assumption.   But still, this comparison isn’t entirely fair as the InnoDB table has been optimized and hasn’t had a chance to get fragmented, right?.   What happens if another month of data into the InnoDB Sale table without it being optimized?  I use this SQL to populate a new month of data.

 

insert into Sale (orderId, customerId, productId, productBigId, unit,

                  purchaseAmount, purchaseCost, purchaseDate)

select orderId + 120000000, customerId, productId, productBigId, unit,

                  purchaseAmount, purchaseCost, date_add(purchaseDate, interval 1 year)

  from Sale

where purchaseDate  >= '2001-01-01'

   and purchaseDate <  '2001-02-01'

And the end result of adding one more month of data to twelve existing months is InnoDB now consumes 23.4 gigs.  In other words, adding 8.3% more data results in 8.1% more disk consumed.  The lesson from this is, if you can, load your data in primary key order. 

However, the data will still get fragmented over time, in particular the other non-clustered indexes.  Plus, it isn’t always possible to load data in primary key order.   A table optimize always optimizes the entire table, so this takes a while. 

At least is does for 5.0.  There is a new way with 5.1 to reduce the table maintenance of InnoDB - one can partition by date.  The advantage this provides is instead of optimizing an entire InnoDB table, one can do an ALTER TABLE REBUILD PARTITION on only the most recent data.  This means that the table defrag should take much less time.  I’ve never done this yet, so I’m not sure if it really works and I can’t recommend it, but if/when it does work, it is another mitigating factor against the larger database sizes of InnoDB.  However, is could also be used to make date selective MyISAM queries much faster.  But see my http://dbscience.blogspot.com/2008/01/divide-and-be-conquered.html article on a potential performance downside of partitioning.

This issue is more complex than this one article can cover, but for those reporting applications where is one predominant access path, such as date, the clustered indexing advantages of the InnoDB engine makes it worth consideration for reporting applications.