torch-sdk 0.0.27

AccelData Torch SDK.

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.
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.

pip install torch-sdk

Read more about torch-sdk from here

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.

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.
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.

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)
pipeline_run = pipeline_response.get_latest_pipeline_run()
# Create Job
job = CreateJob(
    uid='monthly_sales_aggregate',
    name='Monthly Sales Aggregate',
    version=pipeline_run.versionId,
    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.

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.

client = TorchDAGClient()
pipeline = client.get_pipeline('monthly.reporting.pipeline')
pipeline_run = pipeline.get_latest_pipeline_run()

This changed in version 0.0.27

Get Pipeline Run with a particular pipeline run id

Torch sdk can get a pipeline run of the pipeline with a particular pipeline run id. With use of the 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.

client = TorchDAGClient()
pipeline = client.get_pipeline('monthly.reporting.pipeline')
pipeline_run = pipeline.get_pipeline_run(pipeline_run_id)

This changed in version 0.0.27

Execute policy synchronously and asynchronously

Torch sdk provides utility function to execute rules synchronously and asynchronously. To get the result of execution we can call get_result. To get the current status we can call get_status which will get the current resultStatus of the execution. Asynchronous execution example

from torch_sdk.torch_client import TorchClient
from torch_airflow_sdk.initialiser import torch_credentials
import torch_sdk.constants as const
torch_client = TorchClient(**torch_credentials)
async_executor = torch_client.execute_rule(const.DATA_QUALITY, 46, sync=False)
# Wait for execution to get final result
execution_result = async_executor.get_result()
# Get the current status
execution_status = async_executor.get_status()

Synchronous execution example.

from torch_sdk.torch_client import TorchClient
from torch_airflow_sdk.initialiser import torch_credentials
import torch_sdk.constants as const
torch_client = TorchClient(**torch_credentials)
# This will wait for execution to get final result
sync_executor = torch_client.execute_rule(const.DATA_QUALITY, 46, sync=True)
# Wait for execution to get final result
execution_result = sync_executor.get_result(FailureStrategy = const.FailureStrategy.DoNotFail)
# Get the current status
execution_status = sync_executor.get_status()

#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 Virtual 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.

from torch_sdk.models.datasource import CreateDataSource, SourceType

# get connection types available in torch
connection_types = torch_client.get_connection_types()

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 Non-virtual Datasource

Torch has support for more 15+ datasource crawling support. In order yo create a datasource, first create connection along with available analytics pipeline available. Then, create datasource with required parameters.

# get all analysis pipelines
torch_client.list_all_analysis_pipelines()
# get pipeline by id
analysis_pipeline = torch_client.get_analysis_pipeline(analysis_pipeline_id)

# connections : list all connections
connection_list = torch_client.list_all_connections()

# create a connection. Make sure proper connection type id has been passed
create_conn_obj = CreateConnection(
    name='mysql_connection_sdk',
    description='create connection from torch sdk',
    connection_type_id=12,
    analytics_pipeline_id=analysis_pipeline.id,
    properties=[ConfigProperty(key='jdbc.url', value='jdbc:postgresql://localhost:5432/'),
                ConfigProperty(key='jdbc.user', value='admin'),
                ConfigProperty(key='jdbc.password', value='admin')]
)
created_conn = torch_client.create_connection(create_conn_obj)

# update connection
update_conn_obj = CreateConnection(
    name='mysql_connection_sdk',
    description='create connection from torch sdk',
    connection_type_id=12,
    analytics_pipeline_id=1,
    properties=[ConfigProperty(key='jdbc.url', value='jdbc:postgresql://localhost:5432/'),
                ConfigProperty(key='jdbc.user', value='admin'),
                ConfigProperty(key='jdbc.password', value='admin')]
)
updated_conn = torch_client.update_connection(update_connection=update_conn_obj)

# test connection - whether parameters are valid or not.  
check_conn_status = torch_client.check_connection(update_conn_obj)

# get asset connection
connection = torch_client.get_connection(name='mysql_connection_sdk')

# delete connection
torch_client.delete_connection(id=connection.id)

# create datasource
datasource = CreateDataSource(
    name='snowflake_ds_local',
    sourceType=SourceType(5, 'SNOWFLAKE'),
    description='snowflake schema',
    connectionId=9,
    configProperties=[ConfigProperty(key='jdbc.warehouse', value='COMPUTE_WH'),
                      ConfigProperty(key='databases.0', value='FINANCE')]
)
ds_res = torch_client.create_datasource(datasource)
# get datasource
ds_res = torch_client.get_datasource('snowflake_ds_local')

#  update datasource
datasource = CreateDataSource(
    name='snowflake_ds_local',
    sourceType=SourceType(5, 'SNOWFLAKE'),
    description='snowflake schema',
    connectionId=9,
    configProperties=[ConfigProperty(key='jdbc.warehouse', value='COMPUTE_WH'),
                      ConfigProperty(key='databases.0', value='CRAWLER_DB1')]
)
ds_res = ds_res.update_datasource(datasource)

# get root assets
ds_res.get_root_assets()

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.

# 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()

# list all snapshots for a datasource
snapshots = datasource.list_all_snapshots()

# list snapshots in torch for all datasources
snapshots = torch_client.list_all_snapshots()

Create Asset And Relations B/w Them

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.

from torch_sdk.models.create_asset import AssetMetadata

# get asset by id/uid
datasource_response = torch_client.get_datasource('Feature_bag_datasource')

# discover all the assets
discover_res = torch_client.discover()

# 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')


# get related and child asset
relations = asset.get_related_assets()
child_assets = asset.get_child_assets()

# delete an asset
datasource_response.delete_asset(id=asset.id)

Asset's tags, labels, annotations and activities

User can add tags, labels and also update annotation and activities of the asset using sdk. To give alias to an asset, add label with alias key and value as an alias name. Tags and labels can be used to filter out asset easily.

# asset metadata
asset = torch_client.get_asset(id=asset_id)
metadata = [AssetMetadata('STRING', 'key', 'source', 'value'), AssetMetadata('STRING', 's3_location', 'AWS_S3', 's3://aws/path/test/logs')]
# update and get metadata of an asset
asset.update_asset_metadata(metadata)
asset.get_asset_metadata()

# get all tags
tags = torch_client.get_tags()
# update data protection for tag
torch_client.update_data_protection(tag='asset_tag', is_protected=True)
# delete tag 
torch_client.delete_tag(tag='asset_tag')

# get an asset by id
asset = datasource.get_asset(id=asset_id)

# get asset tags
tags = asset.get_asset_tags()
# add tag asset
tag_add = asset.add_asset_tag(tag='asset_tag')
# remove asset
tag_rm = asset.remove_asset_tag(tag='asset_tag')

# get asset activities
activty = asset.get_asset_activity()

# get asset comments
comments = asset.get_asset_comment()

# add asset labels
labels = asset.add_asset_labels(labels = ['teamtorch', 'comp:adinc'])
# get asset labels
labels = asset.get_asset_labels()

# update asset annotation
annotation = asset.update_asset_annotation(annotation='this asset is created and accessed from torch sdk')

# start watching asset
start_watch = asset.start_watch()
# stop watching asset
asset.stop_watch()


# classification configs : get all, create, update, delete
configs = torch_client.list_all_classification_configurations()
create_config = ClassificationConfig(
    name='CCV', protectedData=False, classification='regex',
    classificationType='card', enabled= False, description='Card CCV Checking with regex',
    defaultValue='^\\d{3}$', value='^\\d{4}$'
)

config_created = torch_client.create_classification_configuration(create_config)

config = torch_client.get_classification_configuration(name='CCV')

update_config = ClassificationConfig(
    id=config.id, name='CCV', protectedData=False, classification='regex',
    classificationType='card', enabled= False,
    description='Card CCV Checking with regex',
    defaultValue='^\\d{3}$', value='^\\d{3}$'
)

config_updated = torch_client.update_classification_configuration(update_config)

torch_client.delete_classification_configuration(name='CCV')

Crawler Operations

User can start and restart crawler as well check for running crawler status too.

# start a crawler
datasource.start_crawler()

# get running crawler status
datasource.get_crawler_status()

# restart a crawler
datasource.restart_crawler()

User And API Keys Management

Torch user and API key management support. API keys can be used for pipelines creations. Also, for API authentication too. Settings can be set up via sdk. (Settings has only an update and a read permission)

# get user by name/id
user = torch_client.get_user(user_id=id)
user = torch_client.get_user(username='admin')


# api keys: create, get, delete
keys = torch_client.create_api_key()
torch_client.get_api_keys()
torch_client.delete_api_key(keys.accessKey)

# notifications and incidents in torch
torch_client.get_notifications()
torch_client.get_incidents()

# setting groups: get all
setting_groups = torch_client.list_setting_groups()
# get setting available in each group by group name
settings = torch_client.find_settings_by_group(setting_group_name= 'data_protection')
# get setting key
setting = torch_client.get_setting(key='notification.channels.email.toEmail')
# update setting key value
updated_setting = torch_client.update_setting_value(key='data.protection.enabled', value="true")
# reset setting value
reset_setting_value = torch_client.reset_setting_value(key='data.protection.enabled')

Trigger policies, Profiling and sampling of an asset

Crawled assets can be profiled and sampled with use of spark jobs running on the livy. Furthermore, Created policies (Recon + DQ) can be triggered too.

# get all auto profile configs
torch_client.get_all_auto_profile_configurations()
# get auto profile for a datasource
ds_res.get_auto_profile_configuration()
# remove auto profile for a datasource
ds_res.remove_auto_profile_configuration()

# profile an asset, get profile req details, cancel profile, autotag profile
profile_res = asset.profile_asset(profiling_type= ProfilingType.SAMPLE, job_type=JobType.MINI_PROFILE)

profile_req_details = profile_res.get_profile_request_details()

cancel_profile_res = profile_res.cancel_profile()

profile_res = asset.get_profile_status()

autotag_res = asset.auto_tag_asset()

profile_req_details_by_req_id = asset.get_profile_request_details(req_id= profile_req_details.id)

# sample data
sample_data = asset.sample_data()



# rule execution and ops

enable_rule = torch_client.enable_rule(rule_id=rule_id)
disable_rule = torch_client.disable_rule(rule_id=rule_id)

# trigger dq rule
execute_dq_rule = torch_client.execute_dq_rule(rule_id=rule_id, incremental=False)
get_dq_execution_details = torch_client.get_dq_rule_execution_details(execution_id=rule_id)

# trigger reconciliation rule
execute_reconciliation_rule = torch_client.execute_reconciliation_rule(rule_id=rule_id, incremental=False)
get_recon_exe_details = torch_client.get_reconciliation_rule_execution_details(execution_id=get_dq_execution_details.id)

# cancel rule execution job
cancel_rule = torch_client.cancel_rule_execution(execution_id=execute_dq_rule.id)

Version Log

0.1.0 (14/06/2021)

• Torch python sdk
• Support for flow APIs and catalog APIs of the torch