nodejs scripting & async operations
hi,

we’re trying to create a custom service using nodejs in our dreamfactory 2.2.0 server, but we have a problem with asynchronous operations. it seems that the execution of the script finishes without waiting for asynchronous operations to finish.

for example, when using the following code which …
[@rbarriuso](/u/rbarriuso), to set script response from an async operation use the method below…

event.setresponse(content, statuscode, contenttype)

so in your case it will be like …

myapi.authentication

.login("mylogin", "mypassword")

.then(function(result) {

console.log("auth correct: ", result);

var c…
hi,

do you have joy package installed with all necessary packages/dependencies ?

try to search for joy\_node or run joy package examples to find out what is going wrong. hi james,

as you already figured out, custom transactions does not support automatic instrumentations (xhr, fetch, resources) which means the user would have to start span and attach it to the transaction manually.

and yes by setting { managed: true } in the starttransaction option the transaction… May be the function is executed before the document is ready.
Try to use document.ready function like this:



```
$( document ).ready(function() {
    $('#machines').change(function() {
        var $selected = $(this).find(':selected');
        $('#description').html($selected.data('description'));
    }).trigger('change');
});

``` nvm, i think it might be because i was force running it. i reopened the script and it appears to work. so if you want to be able to run them as separate runs, you’ll still need some way of communicating between them. one mechanism we provide for that is by serializing daml values as json through the --input-file and --output-file options to daml script. so in your case, you should make sure that the … hey [@fastwayjim](/forum/u/fastwayjim)

i could reproduce the behavior. of course this should not be the case. when you start a job first the data needed for the computation is processed before being transferred to a computation instance. if there is no instance available a new one has to be started and your job gets into… The problem is in your document.ready script:



```
$("#datepicker").datepicker();
```

This is returning an error that it is not a function. I'm not sure how the actual call should be, but check the documentation on that. A couple of notes to get started:



> 
> .apply(literal\_eval)
> 
> 
> 


Wouldn't this be better as `map`?



> 
> I then re-create the Dask DF:
> 
> 
> ddf = dd.from\_pandas(ddf\_out, npartitions=nCores)
> 
> 
> 


`ddf_out` was already a dask dataframe, I don't know why you had to do this.



> 
> The columns in result seem to match those in metadir.
> 
> 
> 


The value of `result.columns` is taken from the meta you provided, no computing happens until you ask for it (dask is lazy in most operations). Does the ValueError exception not provide further information?


Here is a full example



```
x = ({'F9_07_PZ_COMP_DIRECT': '0',
  'F9_07_PZ_DIRTRSTKEY_NAME': 'DEBRA MEALY',
  'F9_07_PZ_COMP_OTHER': '0',
  'F9_07_PZ_COMP_RELATED': '0',
  'F9_07_PZ_TITLE': 'CHAIR PERSON',
  'F9_07_PZ_AVE_HOURS_WEEK': '1.00',
  'F9_07_PC_TRUSTEE_INDIVIDUAL': 'X'},
 {'F9_07_PZ_COMP_DIRECT': '0',
  'F9_07_PZ_DIRTRSTKEY_NAME': 'HELEN GORDON',
  'F9_07_PZ_COMP_OTHER': '0',
  'F9_07_PZ_COMP_RELATED': '0',
  'F9_07_PZ_TITLE': 'VICE CHAIR',
  'F9_07_PZ_AVE_HOURS_WEEK': '1.00',
  'F9_07_PC_TRUSTEE_INDIVIDUAL': 'X'})
df = pd.DataFrame({'a': x})
d = dd.from_pandas(df, 1)
meta = pd.DataFrame(columns=['F9_07_PZ_COMP_DIRECT', 
       'F9_07_PZ_DIRTRSTKEY_NAME',
       'F9_07_PZ_COMP_OTHER', 'F9_07_PZ_COMP_RELATED', 'F9_07_PZ_TITLE',
       'F9_07_PZ_AVE_HOURS_WEEK', 'F9_07_PC_TRUSTEE_INDIVIDUAL'], dtype="O")
d.map_partitions(lambda df: df.a.apply(pd.Series), meta=meta).compute()

```

How did I know what `meta` to use? I applied the function to the pandas dataframe - you might use a small piece of the dataframe to do this.


Some additional notes:


* it is an anti-pattern to load the data with pandas, pass to dask workers and then gather the whole result back to a pandas (in memory) dataframe, you are very unlikely to see a speedup this way and may incur a lot of overhead. You are better off loading with something like `dd.read_csv`, and aggregating or writing with dask functions too. Only `compute()` on something that will be small or not return anything (because it involves writing the output). The official examples do not use from\_pandas.
* string and dict processing are python methods, and so hold the interpreter lock (GIL) of any python function: threads will not actually run in parallel. To get parallelism, you need to run in processes, which is most easily achieved using <https://docs.dask.org/en/latest/setup/single-distributed.html>
* the distributed scheduler also gives you access to the dashboard, which has a lot of useful information to diagnose how your system is running. You can also configure a lot about its behaviour, in case you have firewall rules you need to obey. Hello Dmytro,


You will need to bind to the Custom Command, create arguments for the command execution, specify parameter values that will be used to execute the command in the Scheduled Task and then pass the arguments to the Arguments property of the action. Here is the code that will do the trick:



```
# Define actions and conditions for the task

$commandID = "{a7264a61-b77d-4386-a138-ec2557c5b3ba}" # Command ID
$commandDN = "CN=MyCommand,CN=Custom Commands,CN=Configuration Objects,CN=Adaxes Configuration,CN=Adaxes" # Command DN

# Create action set
$actionsAndConditions = $task.ConditionedActions.Create()
$actionsAndConditions.ConditionsLogicalOperation = "ADM_LOGICALOPERATION_AND"
$actionsAndConditions.SetInfo()

# Create execute Custom Command action
$action = $actionsAndConditions.Actions.CreateEx("adm-CustomCommandAction")
$action.ExecutionOptions = "ADM_ACTIONEXECUTIONOPTIONS_SYNC"

# Get action
$removeAction = $action.GetAction()

# Bind to Custom Command
$customCommand = $Context.BindToObjectByDN($commandDN)

# Specify command arguments
$arguments = $customCommand.CreateArguments()
$arguments.SetParameterValue("MyParameter", "MyValue") # Parameter value

# Configure the action
$removeAction.CustomCommandId = $commandID
$removeAction.Arguments = $arguments

# Save action
$action.SetAction($removeAction)
$action.SetInfo()
```

In the script, the $commandDN variable specifies the distinguished name (DN) of the Custom Command. For information on how to get the DN of a directory object, see <http://adaxes.com/sdk/?HowDoI.GetDnOfObject.html>.


For details, have a look at the following SDK article: <http://adaxes.com/sdk/?IAdmCustomCommandAction2.html>. it looks like you are hitting lambda limit: 
<https://docs.aws.amazon.com/lambda/latest/dg/gettingstarted-limits.html> 
invocation payload (request and response) 
6 mb (synchronous) 
256 kb (asynchronous) 
can you please try to make chunks smaller and try again