Best Table Engine for massively updated MySQL table. MyISAM or HEAP?

I am creating an application which will store a (semi) real-time feed of a few different scales around a certain 开发者_开发技巧location. The weights of each scale will be put in a table with only as many rows as scales. The scale app feeds the MySQL database a new weight every second, which a PHP web app reads every 3 seconds. It doesn't seem like very much traffic that would page the hard drive very much, or if the difference would be negligible, but I'm wondering if it would be more efficient or make more sense to use a Memory/HEAP table vs a normal MyISAM table.

---

With anything from 100's to 1000's of concurrent read/write requests (think typical OLTP usage) innodb will out perform myisam hands down.

It's not about other people's observations, it's not about transactional/acid support, it's about the architecture of innodb which is far superior to that of the legacy myisam engine.

For example, innodb supports clustered primary key indexes http://dev.mysql.com/doc/refman/5.0/en/innodb-index-types.html.

Additionally, innodb has row level locking which is far more performant under concurrent load than myisam table level locking.

I could keep going but somone's already provided a really good summary of why innodb is a better choice for OLTP: http://tag1consulting.com/MySQL_Engines_MyISAM_vs_InnoDB

---

Well, if you're expecting a large amount of data, I think you almost have to go MyISAM. You'll likely run out of memory if you store it all in a memory table. Not to mention that you'll lose all of your data upon power loss with a HEAP engine (Keep in mind, you may want that depending on your use case)...

---

I know that this question is getting dated and you've probably made a very good solution by now but I just wanted to point out to anyone who may be reading this that perhaps a relational database isn't the best way to solve this problem. To me this clearly looks like a case where a flat file database is the ideal solution. You could have saved yourself a ton of overhead by just writing these values out to a binary file and then use simple mathematical operations to select rows and fields.