Hey Hemanth,

There remain, after your post, some questions as to how you've implemented 
your system. The rate of inserts according to 47,000 / hour is 
approximately 13 / second. That seems very low - the maximum rate of 
inserts for streaming inserts is 100,000 rows per second, per table and 
1,000,000 rows per second, per project. 
<https://cloud.google.com/bigquery/streaming-data-into-bigquery> 

Correct me if I'm wrong, but this starts to look like this number (47,000 
in 1 hour) represents the overall time of the entire pipeline from API call 
to sending the BigQuery insert(), not simply the rate at which inserts 
could theoretically take place. 

Ultimately, the best way to insert BigQuery rows is not with isolated HTTP 
requests, which have a lot of overhead costs, but with batch inserts. It 
might be worth looking into ways that you could aggregate the records in a 
layer after the task pipeline but before BigQuery which would allow you to 
send big batched inserts.

Let me know your thoughts on this, and best wishes,

Nick
Cloud Platform Community Support 

On Friday, March 18, 2016 at 8:46:59 AM UTC-4, Hemanth Kumar wrote:
>
>
> HI ALL
>
>    Urgently required some help.
>
> I am facing a slowness problem while writing the data to the big query.
>
> My problem is I have 5000 task , and each task is interacting with the 
> Rest Api. The rest api is giving me the JSON response. I am parsing the 
> JSON response, and the same response I have to write to big query. 
>
> Every JSON response will give me around 3000 List of JSON Array.
> If I calculate the total data which will get inserted in the google big 
> query is around 15000000. 
> To insert 47000 record its taking 1 hour. If I have to insert lakhs of 
> record of data , my performance is getting hit.
>
> Please give a good suggestion to improve the time complexity for this 
> problem. How I will resolve this using Google App Engine,
>

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