thoughtspot-rest-api-v1 1.1.0

Library implementing the ThoughtSpot V1 REST API

A simple Python implementation of ThoughtSpot's V1 REST API.

thoughtspot_rest_api_v1 implements the ThoughtSpot V1 REST API as directly as possible. Each API endpoint is represented by a single method within the TSRestApiV1 class. Method and argument names closely match to the documented API endpoints, with a few minor changes are taken to make certain parameters more obvious for an end user.

-> Learning from the Source Code
-> Getting Started
-> Using thoughtspot_rest_api_v1
-> Additional Libraries

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Learning from the source code

If you want to use the library as a reference for how a REST API endpoint is called correctly, look at the /src/thoughtspot_rest_api_v1/tsrestapiv1.py file. It contains the definition of all the ENUMs and the TSRestApiV1 class.

The TSRestApiV1 class uses the requests library to create an internal requests.Session object when the REST API sign-in command is run. This fulfils the ThoughtSpot REST API V1 requirement for session cookie details to be passed in every request.

Each method is implemented to be as self-contained as possible (other than using the shared session object), so you can use the library as an additional reference along with the V1 Playground (Swagger) to see exactly how any given call is implemented.

The library is designed to work with the latest version of ThoughtSpot Cloud. It should also work with Software versions 7.1.1 and later, but the library is not versioned, so please check your documentation for available endpoints within the release you use if on a Software release.

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Getting Started

To install ThoughtSpot Rest API V1, simply run this simple command in your terminal of choice:

$ python -m pip install thoughtspot_rest_api_v1_python

Getting the source code

$ git clone https://github.com/thoughtspot/thoughtspot_rest_api_v1_python.git

Once you have a copy of the source, you can embed it in your own Python package, or install it into your site-packages easily:

$ cd thoughtspot_rest_api_v1_python
$ python -m pip install .

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Importing the library

from thoughtspot_rest_api_v1 import *

This will bring the TSRestApiV1 class, as well as the following enumerations: MetadataNames, MetadataSorts, MetadataSubtypes, MetadataCategories, ShareModes, Privileges.

Modifying the TSRestApiV1 requests.Session object (SSL errors, etc.)

The REST API commands are all handled via the requests module, using a requests.Session object.

The session object used by all methods is accessible via:

TSRestApiV1.session

A common issue within organizations is SSL certificates not being available / included within the certificate store used by Python. One way around this issue is to use the verify=False argument in requests (this is not recommended, but may be the only easy path forward. Check with your security team and never use with ThoughtSpot Cloud or from outside your protected network).

This will set the Session object to verify=False for all calls:

ts: TSRestApiV1 = TSRestApiV1(server_url=server)
ts.session.verify = False

If you find there are other options you need to set on the Session object for your particular situation, you can use the same technique to apply other changes.

Logging into the REST API

You create a TSRestApiV1 object with the server_url argument, then use the session_login() method with username and password to log in. After login succeeds, the TSRestApiV1 object has an open requests. Session object which maintains the necessary cookies to use the REST API continuously .

username = os.getenv('username')  # or type in yourself
password = os.getenv('password')  # or type in yourself
server = os.getenv('server')      # or type in yourself

ts: TSRestApiV1 = TSRestApiV1(server_url=server)
try:
    ts.session_login(username=username, password=password)
except requests.exceptions.HTTPError as e:
    print(e)
    print(e.response.content)

In all further examples, the ts variable represents a TSRestApiV1 object after the session_login() method has been called successfully.

Logging out of the REST API

The session_logout() method of the ThoughtSpot class will send the API request to end your TS session. After you do this, you may want to del the ThoughtSpot object if you are doing a lot of other work to end the request.Session object:

ts.session_logout()
del ts

ENUM data structures

The ThoughtSpot API has internal namings for many features, which require looking up in the reference guide. To help out, the tsrestapiv1.py file defines several ENUM style classes:

• MetadataNames: The namings used in the 'type' parameter of the /metadata/ endpoint calls, with simplified names that matches the names in the UI and standard ThoughtSpot documentation. For example, MetadataNames.GROUP = 'USER_GROUP'
• MetadataSubtypes: The available options for the 'subtype' argument used for certain metadata calls
• MetadataCategories: Contains the options for the argument called 'category' in metadata calls
• MetadataSorts: Contains the available sort options for the argument called 'sort' in metadata calls
• ShareModes: The modes used in the JSON of the /security/share endpoint
• Privileges: The name of the Privileges that Groups can have (and users can inherit) in ThoughtSpot

TML operations

One primary use case of the REST APIs is to import and export ThoughtSpot Modeling Language (TML) files.

Retrieving the TML as a Python OrderedDict from REST API

If you want to use the TML classes to programmatically adjust the returned TML, there is a export_tml(guid) method which retrieves the TML from the API in JSON format and then returns it as a Python OrderedDict.

This method is designed to be the input of the thoughtspot_tml library

lb_tml = ts.metadata_tml_export(guid=lb_guid, export_associated=False)

Downloading the TML directly as string

If you want the TML as you would see it within the TML editor, use

ts.metadata_tml_export_string(guid, formattype, export_associated=False)

formattype defaults to 'YAML' but can be set to 'JSON'.

This method returns a Python str object, which can then be editing in memory or saved directly to disk.

Download TML with Name-GUID map from export_associated=true

TML files don't include the GUIDs of associated objects, but they are available in the headers of the response when export_associated is set to true.

There are two functions that export both the TML and a Dict of structure {'object_name' : 'guid' }, which can be used to add the FQN property to the TML.

tml_od, name_guid_map = ts.metadata_tml_export_with_associations_map(guid=lb_guid)
tml_str, name_guid_map = ts.metadata_tml_export_string_with_associations_map(guid=lb_guid)

To add the FQN properties from the dict mapping, you'll need to modify the TML as an object, so the export_string method is less useful than the version that exports the OrderedDict.

Importing/Publishing TML back to ThoughtSpot Server

Similar to metadata_tml_export(), metadata_tml_import() uses a Python OrderedDict as input (or a List containing OrderedDict objects for multiple import). The method converts the OrderedDict to JSON format internally for use in the REST API request body.

ts.metadata_tml_import(tml_ordereddict, create_new_on_server=False, validate_only=False))

You can also import multiple using a List:

ts.metadata_tml_import([tml_od_1, tml_od_2], create_new_on_server=True)

There are a few optional arguments:

• create_new_on_server - you must set this to True, otherwise it will update the existing object with the same GUID.
• validate_only - If set to True, this only runs through validation and returns the response with any errors listed'

There is a static method for pulling the GUIDs from the import command response ts.guids_from_imported_tml(), which returns the GUIDs in a list in the order they were sent to the import.

The following example uses the thoughtspot_tml library, where each object has a .tml property containing the OrderedDict:

# Get create Worksheet object
ws_obj = Worksheet(ts.metaddata_export_tml(guid=lb_guid))
# Send the .tml property, not the whole object
import_response = ts.metadata_import_tml(ws_obj.tml, create_new_on_server=False)
new_guids = ts.guids_from_imported_tml(import_response)
new_guid = new_guids[0]  # when only a single TML imported

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Metadata operations

Doing any actions with the REST APIs requires knowing the GUIDs of the objects. The /metadata endpoints are incredibly flexible, allowing you to retrieve details about almost any object type from the same endpoints. This flexibility means you must set a number of arguments with each call, including using the internal names of the object types.

MetadataNames and MetadataSubtypes

Because the internal API names of the object types often do not reflect the names used in the current versions of ThoughtSpot, there are two ENUMs provided to map the 'product name' to the REST API string value: MetadataNames and MetadataSubtypes

The REST API has a single object_type for Tables, Views, and Worksheets, and uses an additional 'sub_type' property if you need to distinguish between those objects. You will need to pass both the object_type and the sub_type to metadata calls to limit down:

objs = ts.tsrest.metadata_listobjectheaders(object_type=MetadataNames.WORKSHEET, subtypes=[MetadataSubtypes.WORKSHEET],
                                            sort='MODIFIED', sort_ascending=False, category=category_filter, fetchids=object_guid_list)

metadata_listobjectheaders and metadata_list

The metadata/listobjectheaders and metadata/list REST API endpoints do relatively the same thing, although there are slight differences in the responses from each call.

The implementation is exactly as described in the documentation (https://developers.thoughtspot.com/docs/?pageid=metadata-api#object-header) with these changes for ease of use and clarity:

• 'object_type' argument will accept the sub_types for the data object types and automatically structure the REST API request appropriately
• 'filter' argument is the name given to the 'pattern' argument in the REST API command, as the name pattern seemed unclear to users

Additional libraries

thoughtspot_tml is a library for processing the ThoughtSpot Modeling Language (TML) files. You can use thoughtspot_tml to manipulate TML files from disk or exported via the REST API.

ts_rest_api_and_tml_tools is a convenience library that imports both thoughtspot_rest_api_v1 and thoughtspot_tml and wraps them in more convenient and obvious packaging. It also contains many example scripts to do common workflows. In particular, there are many examples of SDLC use cases that involve REST API commands and TML manipulation.

cs_tools is a package of command-line tools built by the ThoughtSpot Professional Services team, aimed at ThoughtSpot administrators.