I'm going to simplify things a bit here, and work with the assumption that 
you have two DataTables named "server1" and "server2" tracking CPU usage 
over time.  The join would be like this:

var joined = google.visualization.data.join(server1, server2, 'full', [[0, 0
]], [1], [1]);
joined.setColumnLabel(1, 'Server 1 CPU');
joined.setColumnLabel(1, 'Server 2 CPU'); 

You would have two lines, each representing the CPU usage from one server.

On Thursday, June 7, 2012 12:35:12 PM UTC-4, Sabha wrote:
>
> Thanks but I dont know how you will be able to get that joint data to 
> display in the timeline graph and distinguish line1 is for the CPU usage 
> from Server1 while line2 is from CPU usage of Server2.
>
> Server1 Dataset:
> Date, CPU %, Memory %, Disk IO% (Headers)
> ,...,     5,   10,  15
> ....,     1,  19,   9
> ....,     25, 30,  5
>
> Server2 Dataset:
> Date, CPU %, Memory %, Disk IO% (Headers)
> ,...,    15,   5,  12
> ....,     11,  39,   19
> ....,     5, 3,  35
>
> Right now, the datasets dont carry any info that they are from specific 
> servers. Lets say I add that as additional column. But even if I do a join 
> (mostly left outer join as each dataset is specific to a server although 
> they are reporting on the same set of attrbutes), how you would report in 
> the graph which trendline for CPU belongs to which server instance? I 
> already use the join to show that in table format but cannot do the same to 
> display as an annotated timeline.
>
> Sample of join table with data -> Here the Heaps, Requests are specific to 
> server and I can draw the timeline graph per server but cannot do for the 
> join table directly.
>
> *Detailed Snapshots (by chronology)* 
> Dateā–²Server Health HeapFree(%) OpenSockets HoggingRequests PendingRequests 
> MaxHeapSize(MB) CurrentHeapUsed(MB) HeapFree(MB) CompletedRequests Jun 
> 29, 2011 2:34:37 PMTF701_0101OK4190066083898.722709.2818003Jun 29, 2011 
> 2:34:38 PMTF701_0102OK3270066084493.442114.5618276Jun 29, 2011 2:35:07 PM
> TF701_0101OK3390066084427.362180.6418044Jun 29, 2011 2:35:08 PMTF701_0102
> OK2770066084823.841784.16
>
> Not sure if the above copy-paste appears correctly formatted in email.
>
> I was thinking of one option, just keep adding more columns while forming 
> the join table so the values would appear null for non-associated datasets 
> but then it becomes messy as I add more servers and want to compare them 
> all at the same time.
>
> thanks very much,
> Sabha
>
>
> On Thursday, June 7, 2012 6:46:49 AM UTC-7, asgallant wrote:
>>
>> It sounds to me like you want to do a join of the two data sets, which is 
>> supported by the 
>> google.visualization.data.join()<https://developers.google.com/chart/interactive/docs/reference#google_visualization_data_join>method.
>>
>> On Wednesday, June 6, 2012 3:25:33 PM UTC-4, Sabha wrote:
>>>
>>> Hi, 
>>>
>>>   I have datasets from multiple parties that have the same set of 
>>> fields/columns. I can create annotated timeline chart for each 
>>> individual party separately in same page but I dont know if there is 
>>> an option to merge/present the timelines all in single chart. 
>>>
>>> Example: For each stock, lets say we have an annotated timeline 
>>> plotting the buy/sell, bid/ask graph. We can draw the separate 
>>> annotated timeline for each stock plotting the buys and sells as 
>>> separate lines in the same chart. But I would like to plot the trend 
>>> lines of all buy/sells for both Google and Apple stock in the same 
>>> chart to see the comparison across time instead of viewing them in 
>>> separate charts. Similarly, plotting the buy and sell of NYSE and 
>>> Nasdaq (each has same set of columns). 
>>>
>>> In my case, I have something like runtime attributes of machine or 
>>> node with the columns as cpu usage, iostats, vmstats, swap, network, 
>>> idleness etc as columns. Each of these columns will form their own 
>>> lines spanning the timelines for each machine. But how to plot the 
>>> comparison chart of multiple machines and showing the individual 
>>> columns so one cay view that MachineA CPU is higher compared to 
>>> MachineB while the the network and iostats are way down in comparison 
>>> in a given time frame? 
>>>
>>>
>>>
>>> thanks, 
>>> Sabha
>>
>>

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