# Getting the error in executing the tasks in parallel

**URL:** https://dask.discourse.group/t/getting-the-error-in-executing-the-tasks-in-parallel/276
**Category:** Distributed
**Tags:** delayed
**Created:** [January 24, 2022, 5:03pm UTC](https://dask.discourse.group/t/getting-the-error-in-executing-the-tasks-in-parallel/276 "2022-01-24T17:03:28Z")
**Posts on this page:** 1
**Showing post:** 3

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### Author: ![GosainRohit](https://avatars.discourse-cdn.com/v4/letter/g/94ad74/32.png) [@GosainRohit](https://dask.discourse.group/u/GosainRohit)
#### Post date: [January 25, 2022, 5:23am UTC](https://dask.discourse.group/t/getting-the-error-in-executing-the-tasks-in-parallel/276/3 "2022-01-25T05:23:11Z")

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Hi,  
Let me know what you need from my side, I will share it with you. I have shared the DAG with Gus over an email. Let me know how can I share this with you as I can’t see the option of attaching a file. Also here is the snipped of the code that we are calling.

-------Code-------

df\_corp\_cldr\_days = read\_dell\_fiscal\_calendar\_for\_days(engine,last\_saturday)

df\_final\_forecast = dask.delayed(get\_forecast\_for\_days)(engine,part,site,df\_part\_site,last\_saturday,df\_corp\_cldr\_days,cfg)  
df\_final\_forecast = dask.delayed(add\_missing\_days)(df\_final\_forecast,‘Forecast’,‘fisc\_wk\_strt\_dt’,df\_part\_site,df\_corp\_cldr\_days)  
df\_final\_forecast = dask.delayed(pivot\_table\_measure)(df\_final\_forecast,sort\_col,cfg,week)

df\_final\_backlog = dask.delayed(get\_backlog\_for\_days)(engine,part,site,df\_part\_site,current\_date,last\_saturday,df\_corp\_cldr\_days,cfg)  
df\_final\_backlog = dask.delayed(add\_missing\_days)(df\_final\_backlog,‘Backlog’,‘cldr\_dt’,df\_part\_site,df\_corp\_cldr\_days)  
df\_final\_backlog = dask.delayed(pivot\_table\_measure)(df\_final\_backlog,sort\_col,cfg,week)

df\_future\_backlog = dask.delayed(get\_future\_backlog\_for\_days)(engine,part,site,df\_part\_site,df\_corp\_cldr\_days,cfg)  
df\_future\_backlog = dask.delayed(add\_missing\_days)(df\_future\_backlog,‘Future Backlog’,‘cldr\_dt’,df\_part\_site,df\_corp\_cldr\_days)  
df\_future\_backlog = dask.delayed(pivot\_table\_measure)(df\_future\_backlog,sort\_col,cfg,week)

df\_final\_inventory = dask.delayed(get\_inventory\_for\_days)(engine,part,site,df\_part\_site,current\_date,last\_saturday,df\_corp\_cldr\_days,cfg)  
df\_final\_inventory = dask.delayed(add\_missing\_days\_inventory)(df\_final\_inventory,df\_part\_site,df\_corp\_cldr\_days)  
df\_final\_inventory = dask.delayed(pivot\_table\_measure)(df\_final\_inventory,sort\_col,cfg,week)

df\_net\_supply = dask.delayed(get\_net\_supply\_for\_days)(engine,part,site,df\_part\_site,last\_saturday,df\_corp\_cldr\_days,cfg)  
df\_net\_supply = dask.delayed(add\_missing\_days)(df\_net\_supply,‘Net Supply’,‘cldr\_dt’,df\_part\_site,df\_corp\_cldr\_days)  
df\_net\_supply = dask.delayed(pivot\_table\_measure)(df\_net\_supply,sort\_col,cfg,week)

df\_target\_dsi = dask.delayed(get\_target\_dsi)(engine,week,part,site,df\_part\_site,days,df\_corp\_cldr\_days,last\_saturday,cfg)  
df\_target\_dsi = dask.delayed(add\_missing\_days)(df\_target\_dsi,‘Target DSI’,‘fisc\_wk\_strt\_dt’,df\_part\_site,df\_corp\_cldr\_days)  
df\_target\_dsi = dask.delayed(pivot\_table\_measure)(df\_target\_dsi,sort\_col,cfg,week)

print(‘Called all 5 functions’)

# df\_list = [df\_final\_forecast,df\_final\_backlog,df\_future\_backlog,df\_net\_supply,df\_target\_dsi]

df\_list = [df\_final\_forecast,df\_final\_backlog,df\_future\_backlog,df\_final\_inventory,df\_net\_supply,df\_target\_dsi]

#img1 = dask.visualize(df\_list)  
#display(img1)  
#df\_list = dask.compute(\*df\_list)

df\_gds = dask.delayed(merge\_data)(df\_list)  
df\_final\_gds = dask.delayed(calculate\_measures\_group)(df\_gds,week)  
df\_final\_gds = dask.delayed(format\_gds)(df\_final\_gds,compressed,cfg,week)

img = dask.visualize(df\_final\_gds)  
display(img)

json = dask.compute(df\_final\_gds)

json = df\_final\_gds.groupby([‘Part’,‘Site’,‘Supplier’]).apply(lambda x: x.iloc[0:,3:].to\_dict(‘records’)).reset\_index().rename(columns={0:‘Measures’}).to\_dict(orient=‘records’)

print(‘Get Total GDS Ended’)

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