You should think about partitioning the table for a big reason.

All indexes you have on a giant table, even just one index, can generated a lot of CPU load and disk I/O just to perform index maintenance when executing INSERTs, UPDATEs, and DELETEs.

I wrote an earlier post back on October 7, 2011 on why Table Partitioning would be a big help. Here is one excerpt from my past post:

Partitioning of data should serve to group data that are logically and cohesively in the same class. Performance of searching each partition need not be the main consideration as long as the data is correctly grouped. Once you have achieved the logical partitioning, then concentrate on search time. If you are just separating data by id only, it is possible that many rows of data may never be accessed for reads or writes. Now, that should be a major consideration: Locate all ids most frequently accessed and partition by that. All less frequently accessed ids should reside in one big archive table that is still accessible by index lookup for that 'once in a blue moon' query.

You can read my entire post later on this.

To cut right to the chase, you need to research and find out what data is rarely used in your 10GB table. That data should be placed in an archive table that is readily accessible should you need adhoc queries for a historical nature. Migrating that archival from the 10GB, followed by OPTIMIZE TABLE on the 10GB table, can result in a Working Set that is faster to run SELECTs, INSERTs, UPDATEs, and DELETEs. Even DDL would go faster on a 2GB Working Set than a 10GB table.

UPDATE 2012-02-24 16:19 EDT

Two points to consider

1. From your comment, it sounds like normalization is what you may need.
2. You may need to migrate out everything over 90 days old into an archive table but still access archive and working set at the same time. If your data is all MyISAM, I recommend using the MERGE storage engine. First, you create the MERGE table map once that unites a working set MyISAM table and an archive MyISAM table. You would keep data less than 91 days in one MyISAM table and rollover any data over 90 days old into the archive. You would query the MERGE table map only.

Here are two posts I made on how to use it:

• get column from too many tables in mysql
• Separate tables or partition one huge table?

Here is an additional post I made on tables with a lot of columns

Too many columns in MySQL

This looks oddly familiar.

I have seen this occur with one of my web hosting client's DB servers. There was a particular table that crashed mysqld every single time you accessed it, even with SHOW CREATE TABLE.

The problem stems from a corrupt data dictionary. There is really no way to correct it. You could attempt to alter the tablespace_id within the .ibd file but the headache stems from locating the tablespace_id list internal to ibdata1.

Even if you create a MyISAM table with the same name in the same database as the original InnoDB table, you cannot convert it to InnoDB because the tablespace_id is already associated with the table name. This, of course, is a corrupted state. It's like having a pidgeon hole in ibdata1 that you cannot patch up without some exploratory surgery.

You may have to mysqldump everything except the database that houses the corrupt table. You would then have to mysqldump every table in that database except the corrupt table. Remember, it is the data dictionary's view of the table that is screwed up, not necessarily the table's data.

The only sure way to clean everything up is to perform the mysqldumps as I just specified, shutdown mysql, rm -rf all DB folders except /var/lib/mytsql/mysql, delete ibdata1, delete ib_logfile0, delete ib_logfile1, startup mysql, reload all mysqldumps. See my StackOverflow post about cleaning up your InnoDB infrastructure.

Since you are not using innodb_file_per_table, any tables with this corrupt state of things within ibdata1 are lost as casualities of war. My condolences.

For future reference, click here to see an artistic conception of InnoDB and its Internals.