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ThoughtSpot (via Collector method) - v3.0.0

Owned by Chichi
Last updated: Jul 14, 2023 by Sidney Chen
5 min read

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About Collectors

Collector Method

Pre-requisites

  • Python 3.6 - 3.10

  • Access to K landing directory

  • Access to ThoughtSpot

  • Local User Permissions

    • Local user must be authenticated with Username and Password not SSO

    • Access to the following v1 APIs

      • tspublic/v1/metadata/list

      • tspublic/v1/metadata/details

      • tspublic/v1/session/orgs

      • tspublic/v1/session/org

      • tspublic/v1/session/login

This collector is a metadata only collector and no usage information can be extracted at this stage. Ensure that the meta_only property value is set to true.

Step 1: Create the Source in K

Create an ThoughtSpot source in K

  • Go to Settings, Select Sources and click Add Source

  • Select “Load from File system” option

  • Give the source a Name - e.g. ThoughtSpot Production

  • Add the Host name for the ThoughtSpot Server

  • Click Finish Setup

Step 3: Getting Access to the Source Landing Directory

Collector Method

Step 4: Install the Collector

It is recommended to use a python environment such as pyenv or pipenv if you are not intending to install this package at the system level.

Some python packages also have dependencies on the OS level packages, so you may be required to install additional OS packages if the below fails to install.

You can download the latest Core Library and ThoughtSpot whl via Platform Settings → Sources → Download Collectors

Run the following command to install the collector

pip install kada_collectors_extractors_thoughtspot-3.0.0-py3-none-any.whl

You will also need to install the common library kada_collectors_lib for this collector to function properly.

pip install kada_collectors_lib-1.1.1-py3-none-any.whl

Step 5: Configure the Collector

The collector requires a set of parameters to connect to and extract metadata from ThoughtSpot

FIELD

FIELD TYPE

DESCRIPTION

EXAMPLE

FIELD

FIELD TYPE

DESCRIPTION

EXAMPLE

username

string

Username to log into the Thoughtspot account

“myuser”

password

string

Password to log into the Thoughtspot account

 

org_ids

list<integer>

A list of organisation ids you wish to extract from

If you are unable to locate the Organisation ID through the Admin console, you may use this python script instead to list the IDs

import requests import json BASE_URL = 'REPLACE BASE URL' # e.g. https://kada.thoughtspot.cloud LOGIN_URL = BASE_URL + '/callosum/v1/tspublic/v1/session/login' ORG_URL = BASE_URL + '/callosum/v1/tspublic/v1/session/orgs' data = { 'username': "REPLACE USERNAME", 'password': "REPLACE PASSWORD" } with requests.Session() as session: session.post(LOGIN_URL, verify=True, data=data) res = session.get(ORG_URL, verify=True) res_json = json.loads(res.text) for org in res_json.get('orgs', []): print("Org Name: {}, Org Id: {}".format(org['orgName'], org['orgId']))

[123, 34]

output_path

string

Absolute path to the output location where files are to be written

“/tmp/output”

mask

boolean

To enable masking or not

true

timeout

integer

Timeout in seconds allowed against the powerbi APIs, for slower connections we recommend 30, default is 20

20

mapping

JSON

Mapping file of data source names against the onboarded host and database name in K

Assuming I have a “myDSN” data source name in powerbi, I’ll map it to host “myhost” and database “mydatabase” onboarded in K, snowflake type references are handled automatically

compress

boolean

To gzip the output or not

true

meta_only

boolean

To only extract metadata or not. Only true is supported for this source

true

base_api_url

string

Should be in the form https://x.thoughtspot.cloud depending on your account

https://kada.thoughtspot.cloud

These parameters can be added directly into the run or you can use pass the parameters in via a JSON file. The following is an example you can use that is included in the example run code below.

kada_thoughtspot_extractor_config.json

Step 6: Run the Collector

The following code is an example of how to run the extractor. You may need to uplift this code to meet any code standards at your organisation.

This can be executed in any python environment where the whl has been installed. It will produce and read a high water mark file from the same directory as the execution called thoughtspot_hwm.txt and produce files according to the configuration JSON.

This is the wrapper script: kada_thoughtspot_extractor.py

 

Advance options:

If you wish to maintain your own high water mark files elsewhere you can use the above section’s script as a guide on how to call the extractor. The configuration file is simply the keyword arguments in JSON format. Refer to this document for more information https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#Storing-HWM-in-another-location

If you are handling external arguments of the runner yourself, you’ll need to consider additional items for the run method. Refer to this document for more information https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#The-run-method

username: Thoughtspot username.
password: Thoughtspot password.
org_ids: List of organisation ids that should be extracted, should be a numeric ID.
mapping: Dict of project ids to corresponding database hosts
timeout: Timeout for the API call
output_path: full or relative path to where the outputs should go
mask: To mask the META/DATABASE_LOG files or not
compress: To gzip output files or not
meta_only: To extract metadata only or not

Step 7: Check the Collector Outputs

K Extracts

A set of files (eg metadata, databaselog, linkages, events etc) will be generated. These files will appear in the output_path directory you set in the configuration details

High Water Mark File

A high water mark file is created in the same directory as the execution called thoughtspot_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.

If you want prefer file managed hwm, you can edit the location of the hwn by following these instructions https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#Storing-High-Water-Marks-(HWM)

Step 8: Push the Extracts to K

Once the files have been validated, you can push the files to the K landing directory.

You can use Azure Storage Explorer if you want to initially do this manually. You can push the files using python as well (see Airflow example below)

Example: Using Airflow to orchestrate the Extract and Push to K

Collector Method