DOC: HDF access limitations and Comparison of SQL/HDF
I think that the documentation for HDFStore could be more explicit about the limitations of accessing data from an HDF file. For example, it is not at all clear from the docs that when selecting several columns of data from a saved table that the entire table needs to be read into memory. Specifically, one might be tempted to do the following to select 3 columns from a very large table:

```
df = h5.select('test', columns=['col2', 'col7', 'col128'])
```

However, that process will potentially read a very large portion (if not all) of the table into memory, which is obviously a problem if the table doesn't fit into memory. Instead, one should really do something like this:

```
columns = ['col2', 'col7', 'col128']
df = pd.concat([tmp for tmp in h5.select('test', columns=columns, chunksize=50000)])
```

I think it also might be helpful to include some sort of comparison of HDF and SQL solutions for table storage. Maybe we could solicit use-cases from the mailing list for successful applications of one or the other. I've been using HDF files for several years, though never before with tables containing many, many columns, which tripped me up in my example above. I almost turned to SQLite, because the above selection is very simple and has a reasonable memory usage footprint:

```
engine = sqlalchemy.create_engine('sqlite:///test.db')
df = pd.read_sql_query('select col2,col7,col128 from test', engine)
```

However, I profiled this and it is several times slower, so I'll probably stick with HDF because I am much more familiar with the format and prefer the access speed for large tables. 

Anyway, just a couple of thoughts. I'm happy to try to put some of this into the docs, if people think it is appropriate. 
