Riccardo,

Using your examples I was able to implement a workaround for my problem.
 Instead of specifying the total number of rows upfront and then
populating, I just call addRows method passing in 1 and then populate the
one row using setValue. I'm doing something a bit more complex so I
couldn't simply pass in an array. But this method works well for me until
you are able to implement a fix.

Thanks again for the guidance,

Yolanda

On Thu, Jan 26, 2012 at 8:33 AM, Yolanda Davis <[email protected]>wrote:

> Riccardo,
>
> Thanks for your response to this!  One thing to note is that I am not
> passing rows to the addRows method, I'm simply passing the number of rows
> and then later populate the table using the setValue method.  I get the
> error when I pass in the the number of rows to the addRows method.
>
> Thank you for sending the examples.  I will see if I can use those for a
> workaround until this is resolved.
>
> Thanks Again for your help!
>
> Yolanda
>
>
> 2012/1/26 Riccardo Govoni ☢ <[email protected]>
>
>> This has to do with the size of the array you pass to the addRows()
>> method.
>>
>> If you add the rows one at a time, or in small batches, then no problem
>> arises. For example:
>> http://jsfiddle.net/qgV4D/1/  : adding 100K rows one at a time
>> http://jsfiddle.net/qgV4D/3/ : adding 100K in batches of 10K at a time.
>>
>> Here is an example of adding all the rows at the same time where your
>> error shows up : http://jsfiddle.net/qgV4D/2/
>>
>> The cause is likely residing in the addRows() implementation. I'm looking
>> into it.
>>
>> - R.
>>
>> On 25 January 2012 19:19, Yolanda Davis <[email protected]>wrote:
>>
>>> I am attempting to create a DataTable with a large amount of rows,
>>> which eventually is grouped and displayed as a smaller set using the
>>> Table chart api.  However if the size of the rows are over 20k then
>>> the addRows method on the data table fails, producing the call stack
>>> size exceeded message.  I could not find information where this was
>>> posted as a known bug nor did I see information on there being a data
>>> size limitation on the data table.  Is there any way to fix this or is
>>> there a workaround available? If size is an issue can there be more
>>> specific messaging around that limitation and improvements made to
>>> avoid exceeding the stack?
>>>
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