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Model Description and Cache Settings

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Model Description and Cache Settings

A new field of Description is added in the Model, where users can provide the information about the current model, which helps other users to know the basic info regarding the model.

In Model Cache Configurations you can set the cache limit and also select the columns that are needed to be cached

Settings ---> Advanced Settings ---> Model Cache Configurations

The following fields are in the Model Cache Configurations.

 Dimensions to skip:- Users can select multiple dimensions here. The dimensions selected will be skipped in the model cache and filter value won’t be available for the same.

For ex:-  If the user selected dimensions like seller_city, buyer_city in the Dimensions to Skip field, then the filter values will not be present for the seller_city and buyer_city dimensions. It means that the selected dimensions will be skipped and filter values will note be shown.

Dim Cache Size:- At model level you can set the cache limit which is applicable for all columns. It is the distinct count size up to which data will be stored in the cache.The column won't be cached and it's filter values won't be available if the distinct count is more than the configured value.

For ex:- If the distinct count size of a particular column is 99, and Dim Cache Size is given as 99 or less than 99, then the filter value for that particular column will not be shown.

If the distinct count size of a particular column is 99, and Dim Cache Size is set as 100 or greater than 100, then values are cached and filters are shown for that column.

Hierarchy Cache Size:- This is similar to the dim cache size above. The value is used for caching columns in hierarchy.

Columns Cache Size:- At column level also you can set cache limit which is applicable for that column.It is the distinct count size of that particular column upto which the data will be stored in the cache. Users can add a max. of 5 Column Cache. It is independent of the Dim Cache Size field.

For ex:- If the distinct count size of a particular column is 9999, and Dim Cache Size is given as 100, then the filter value for that particular column will not be shown. But if the Column Cache Size of that particular column is selected as 10000 or more, then it will override the Dim Cache Size i.e 100 and filter value for that particular column will be shown.


import requests
from requests.auth import HTTPBasicAuth

auth =  HTTPBasicAuth(<API_KEY>, <API_SECRET>)
response = requests.get("https://<hostname>/api/v0.4/explore/streamresult/<EXPLORE_ID>", auth)

print(response.content)

library('httr')

username = '<API KEY>'
password = '<API SECRET>'

temp = GET("https://<hostname>/api/v0.4/explore/streamresult/<EXPLORE ID>",
           authenticate(username,password, type = "basic"))

temp = content(temp, 'text')
temp = textConnection(temp)
temp = read.csv(temp)

/*Download the Data*/

filename resp temp;
proc http
url="https://<hostname>/api/v0.4/explore/streamresult/<EXPLORE ID>"
   method= "GET"  
   WEBUSERNAME = "<API KEY>"
   WEBPASSWORD = "<API SECRET>"
   out=resp;
run;

/*Import the data in to csv dataset*/
proc import
   file=resp
   out=csvresp
   dbms=csv;
run;

/*Print the data */
PROC PRINT DATA=csvresp;
RUN;

import requests
import json

url='http://hostname/api/v0.4/createCSV'

username='API_KEY'
password='API_SECRET'

files={'file':open('FILE_PATH.csv','rb')}
values={'projectname':PROJECT_NAME','name':'CSV_DATASOURCE_NAME'}

r=requests.post(url, files=files, data=values, auth=(username,password))

res_json=json.loads(r.text)

print(res_json['success'])

import requests
import json

url='http://hostname/api/v0.4/updateCSV'

username='API_KEY'
password='API_SECRET'

files={'file':open('FILE_PATH.csv','rb')}
values={'projectname':PROJECT_NAME','name':'CSV_DATASOURCE_NAME'}

r=requests.post(url, files=files, data=values, auth=(username,password))

res_json=json.loads(r.text)

print(res_json['success'])

import requests

url='https://<hostname>/api/v0.4/explore/streamresult/<EXPLORE ID>'

username='API_KEY'
password='API_SECRET'

r=requests.get(url,auth=(username,password))
print(r)
print(r.text)

import requests
import pandas as pd

url='https://<hostname>/api/v0.4/explores/infoByFolder/<SPACE_ID>'

username='API_KEY'
password='API_SECRET'

r=requests.get(url,auth=(username,password)).json()
df = pd.DataFrame(r)
print(df)

import requests
import pandas as pd

url='https://<hostname>/api/v0.4/folders/byOrgName/<ORG_NAME>'

username='API_KEY'
password='API_SECRET'

r=requests.get(url,auth=(username,password)).json()
df = pd.DataFrame(r)
print(df.loc[:,['name','id']])

import requests

import pandas as pd

import io

url='https://<hostname>/api/v0.4/explore/streamresult/<EXPLORE ID>'

secret='API_SECRET'

r=requests.get(url,headers = {'Authorization': 'SprinkleUserKeys ' +secret } )

df = pd.read_csv(io.StringIO(r.text),sep=',')

import requests

import pandas as pd

import io

url='https://<hostname>/api/v0.4/segment/streamresult/<SEGMENT ID>'

secret='API_SECRET'

r=requests.get(url,headers = {'Authorization': 'SprinkleUserKeys ' +secret } )

df = pd.read_csv(io.StringIO(r.text),sep=',')

import requests

import json

url='http://hostname/api/v.o4/createCSV'

files={'file':open('path/file.csv’')}

values={'projectname':PROJECT_NAME,'name':'csv_datasource_name/table_name'}

secret='API_SECRET'

r=requests.post(url, files=files, data=values, headers = {'Authorization': 'SprinkleUserKeys ' +secret } )

res_json=json.loads(r.text)

import requests

import json

url='http://hostname/api/v.o4/updateCSV'

files={'file':open('path/file.csv’')}

values={'projectname':PROJECT_NAME,'name':'csv_datasource_name/table_name'}

secret='API_SECRET'

r=requests.post(url, files=files, data=values,headers = {'Authorization': 'SprinkleUserKeys ' +secret } )

res_json=json.loads(r.text)