Metadata-Version: 2.1
Name: torch-sdk
Version: 0.0.13
Summary: Accel-data Torch SDK.   Acceldata Torch is a complete solution to observe the quality of the data present in your data lake and warehouse. Using Torch, you can ensure that high-quality data backs your business decisions.
Home-page: UNKNOWN
Author: acceldata
Author-email: support@acceldata.io
License: Apache Software License
Description: # Pipeline APIs
        Acceldata Torch is a complete solution to observe the quality of the data present in your data lake and warehouse. Using Torch, you can ensure that high-quality data backs your business decisions. Torch provides you with tools to measure the quality of data in a data catalog and to never miss significant data sources. All users including analysts, data scientists, and developers, can rely on Torch to observe the data flowing in the warehouse or data lake and can rest assured that there is no loss of data. 
        <br />
        Torch SDK is used to trigger torch catalog and pipeline APIs. By creating a Torch client, all the torch apis can be accessed. 
        
        Install `torch-sdk` pypi package in a python environment.
        ```bash
        pip install torch-sdk
        ```
        
        Read more about torch-sdk from [here](https://pypi.org/project/torch-airflow-sdk/)
        
        ## Create Torch Client
        Torch client is used to send data to the torch servers. It consists of various methods to communicate with the torch server. Torch client have access to catalog and pipeline APIs. To create a torch client, torch url and API keys are required. To create torch API keys, go to torch ui’s settings and generate keys for the client.
        ```python
        from torch_sdk.torch_client import TorchClient
        
        torch_client = TorchClient(url='https://acceldata.host.dev:9999', access_key='******',
                                 secret_key='*****************')
        
        ```
        
        ## Pipeline API 
        There are various pipeline APIs are supported through torch sdk. Pipeline APIs like create pipeline, add jobs and spans, initiate pipeline run et cetera. Torch sdk is able to send various event during span life cycle. Hence, torch sdk has full control over the pipelines.
        ##### Create Pipeline And Job
        Pipeline represents the ETL pipeline in its entirety and will contain Asset nodes and Jobs associated. The complete pipeline definition forms the Lineage graph for all the data assets.
        </br>
        Job Node or Process Node represents an entity that does some job in the ETL workflow. From this representation, Job’s input is some assets, and output is few other assets.
        Torch will use the set of Jobs definition in the workflow to create the Lineage, and the will also track version changes for the Pipeline.
        
        To create pipeline and jobs, first create creation object with required parameter. And with use of supported methods by sdk, can do corresponding operation on torch server side. 
        ```python
        from torch_sdk.models.job import CreateJob, JobMetadata, Dataset
        from torch_sdk.models.pipeline import CreatePipeline, PipelineMetadata, PipelineRunResult, PipelineRunStatus
        
        # Create pipeline
        pipeline = CreatePipeline(
            uid='monthly_reporting_pipeline',
            name='Monthly reporting Pipeline',
            description='Pipeline to create monthly reporting tables',
            meta=PipelineMetadata('Vaishvik', 'torch_sdk_code', '...'),
            context={'key1': 'value1'}
        )
        pipeline_response = torch_client.create_pipeline(pipeline=pipeline)
        
        # Create Job
        job = CreateJob(
            uid='monthly_sales_aggregate',
            name='Monthly Sales Aggregate',
            description='Generates the monthly sales aggregate tables for the complete year',
            inputs=[Dataset('datasource-name', 'database.schema.table_1')],
            outputs=[Dataset('datasource-name', 'database.schema.table_2')],
            meta=JobMetadata('vaishvik', 'backend', 'https://github.com/'),
            context={'key21': 'value21'}
        )
        job_response = pipeline_response.create_job(job)
        ```
        ##### Create Pipeline Run And Generate Spans And Send Span Events
        
        Pipeline run indicates the execution of the pipeline. The same pipeline can be executed multiple times and each execution (run) has new snapshot version. Each pipeline run has hierarchical span's group. A Span is a way to group a bunch of metrics, and they are hierarchical. It can be as granular as possible. The APIs will support creating a span object from a pipeline object, and then hierarchical spans are started from parent spans. A Span typically encompasses a process or a task and can be granular. This hierarchical system is powerful enough to model extremely complex pipeline observability flows. Optionally, a span can also be associated with a Job. This way, we can track starting and completion of Job, including the failure tracking. Start and stop are implicitly tracked for a span.
        
        Torch sdk has also support for create new pipeline run, add spans in it. During the span life cycle, sdk is able to send some customs and standard span events to collect pipeline run metrics for observability.  
        ```python
        
        from torch_sdk.events.generic_event import GenericEvent
        from datetime import datetime
        
        # create a pipeline run of the pipeline
        pipeline_run = pipeline_response.create_pipeline_run()
        
        # create span in the pipeline run
        span_context = pipeline_run.create_span(uid='monthly.generate.data.span')
        
        # check current span is root or not
        span_context.is_root()
        
        # end the span 
        span_context.end()
        
        # check if the current span has children or not
        span_context.has_children()
        
        # create a child span
        child_span_context = span_context.create_child_span('monthly.generate.customer.span')
        
        # send custom event
        child_span_context.send_event(
            GenericEvent(context_data={'client_time': str(datetime.now()), 'row_count': 100}, 
                         event_uid="order.customer.join.result")
        )
        
        
        # abort span
        child_span_context.abort()
        
        # failed span
        child_span_context.failed()
        
        # update a pipeline run of the pipeline
        updatePipelineRunRes = pipeline_run.update_pipeline_run(context_data={'key1': 'value2', 'name': 'backend'},
                                                                       result=PipelineRunResult.SUCCESS,
                                                                       status=PipelineRunStatus.COMPLETED)
        
        ```
        
        ##### Get Latest Pipeline Run
        Torch sdk can get the latest pipeline run of the pipeline. with use of the latest pipeline run instance, user can continue ETL pipeline and add spans, jobs, events too. Hence, torch sdk has complete access on the torch pipeline service.
        ```python
        pipeline = torch_client.get_pipeline('monthly.reporting.pipeline')
        
        ```
        
        
        #Datasource APIs
        
        Torch SDK has full access on catalog APIs as well. Using torch sdk, one can create datasource and version it with associated assets, relations.
        ##### Create Datasource
        Torch sdk has access to create or update existing datasource. Torch has support for virtual datasource as well for ML purpose and some non-virtual/real as well for example relational databases, file based databases et cetera. To create datasource, source type details are required. To get all source types supported in torch, use `get_all_source_types()` method.
        ```python
        from torch_sdk.models.datasource import CreateDataSource, SourceType
        
        datasource = CreateDataSource(
            name='Feature_bag_datasource_sdk',
            sourceType=SourceType(21, 'FEATURE_BAG'),
            description='feature bag assembly creation using python sdk',
            isVirtual=True
        )
        datasource_response = torch_client.create_datasource(datasource)
        ```
        
        ##### Create New Version Of Datasource
        Torch sdk can version the datasource as well. Torch sdk can initiate new version the datasource and return latest instance of it. It has also method to get current latest snapshot version.
        ```python
        # get data source
        datasource_response = torch_client.get_datasource('Feature_bag_datasource')
        
        # create new version of the datasource
        new_snapshot_version = datasource_response.initialise_snapshot(uid='Habcfc38-9daa-4842-b008-f7fb3dd8439a')
        
        # get current snapshot data
        current_snapshot_version = datasource_response.get_current_snapshot()
        ```
        ##### Create Asset And Relations B/w Them
        You can create/update assets and relations between them.
        With use of the torch sdk, user can create assets in datasource and can also define relations between assets. To get asset types supported in torch, use `get_asset_types()` method. Torch sdk has methods to get existing relations and assets in the given datasource.
        ```python
        from torch_sdk.models.create_asset import AssetMetadata
        
        # get asset by id/uid
        datasource_response = torch_client.get_datasource('Feature_bag_datasource')
        
        # create assets
        asset_1 = datasource_response.create_asset(uid='Feature_bag_datasource.feature_1',
                                                    metadata=[AssetMetadata('STRING', 'abcd', 'pqr', 'sds')],
                                                    asset_type_id=22,
                                                    description='feature 1 asset.',
                                                    name='car feature'
                                                        )
        asset_2 = datasource_response.create_asset(uid='Feature_bag_datasource.feature_2',
                                                    metadata=[AssetMetadata('STRING', 'abcd', 'pqr', 'sds')],
                                                    asset_type_id=22,
                                                    description='feature asset 2',
                                                    name='bike feature'
                                                        )
        
        # create asset relation
        toAssetUUID = 'postgres-assembly-5450.ad_catalog.ad_catalog.qrtz_simple_triggers'
        relationType = RelationType.SIBLING
        asset_relation_1_to_2 = asset_1.create_asset_relation(relation_type=relationType, to_asset_uuid=toAssetUUID)
        
        # get asset by id/uid
        asset = datasource_response.get_asset(id=1)
        asset = datasource_response.get_asset(uid='Feature_bag_datasource.feature_1')
        
        ```
        
        Version Log
        ==========
        
        0.0.1 (14/06/2021)
        -------------------
        - Torch python sdk
        - Support for flow APIs and catalog APIs of the torch
        
Keywords: acceldata-torch
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
