Utilities¶
Logging¶
How to log¶
"""Snippet showing how the logger module of tamr_toolbox works"""
import tamr_toolbox as tbox
# Often I want to log things as my script progresses - to do this you need a logger object
# Tamr-toolbox will help you put all of your logs in the same place by giving you simple methods
# for creating and managing both your custom loggers, logging from your custom modules, logging
# from the toolbox modules themselves, and loggers from any python libraries that follow best
# practices. By default Tamr-toolbox logging will stream all log messages to console, but it is
# easy enough to have them all go to a file like so.
log = tbox.utils.logger.create("my_log", log_directory=".")
# By default, the logger will still log to the console when a directory is specified, but
# you can disable this and just log to a file by doing
log = tbox.utils.logger.create("my_log", log_to_terminal=False, log_directory=".")
# now I can log whatever I wish. The default log level is INFO so this
log.debug("a debug message")
# won't show up, but this
log.info("an info message")
# will be written to a file with today's date in the name
# note that by default the logging from Tamr-toolbox won't show up, however you can have it
# written to the same log file by simply calling
tbox.utils.logger.enable_toolbox_logging(log_directory=".")
# you can extend to any module/library you use in your code, including any custom modules in the
# same codebase as your script, via the helpful enable_package_logging method
# for example with the tamr unify client
tbox.utils.logger.enable_package_logging("tamr_unify_client")
# if you need debug for any of your logs that is easy enough, just use the set_logging_level
# and pass the name of the logger who's level you want to set
tbox.utils.logger.set_logging_level("my_log", "debug")
log.debug("a debug message that shows up")
# You can use this to set different levels for different packages, for example if you want the
# nitty-gritty of what is happening in the toolbox, but don't want to see all of your code's
# debug statements simply do
tbox.utils.logger.set_logging_level("my_log", "warning")
tbox.utils.logger.set_logging_level("tamr_toolbox", "debug")
Operation¶
How to use operation functions¶
"""Snippet for interacting with Tamr operations (or jobs)"""
import tamr_toolbox as tbox
# Make Tamr Client
tamr = tbox.utils.client.create(username="user", password="pw", host="localhost")
# Create an unresolved operation, i.e., PENDING or RUNNING
project = tamr.projects.by_name("my_project_id")
op = project.unified_dataset().refresh(asynchronous=True)
# Monitor the operation
# Returns the updated operation on any OperationState change
# Ex: Change from OperationState.PENDING -> OperationState.RUNNING
op_update = tbox.utils.operation.monitor(operation=op)
# Wait for the operation to finish
# Will return the operation when it reaches one of the final states:
# OperationState.CANCELED, OperationState.SUCCEEDED, OperationState.FAILED
op_finished = tbox.utils.operation.wait(operation=op)
# Get the most recent operation
latest_op = tbox.utils.operation.get_latest(tamr=tamr)
# Get all operations
all_ops = tbox.utils.operation.get_all(tamr=tamr)
# Get active operations
active_ops = tbox.utils.operation.get_active(tamr=tamr)
# Create an operation from a job id
op = tbox.utils.operation.from_resource_id(tamr=tamr, job_id=op.resource_id)
# Get the details of an operation
text_details = tbox.utils.operation.get_details(operation=op)
Testing¶
How to use the mock_api decorator for testing your code¶
logging_dir: $TAMR_PROJECT_LOGGING_DIR # Example: "/home/users/jane/my-project/logs"
my_tamr_instance:
host: $TAMR_HOST # Example: "1.2.3.4"
protocol: "http"
port: "9100"
username: "admin"
password: $TAMR_PASSWORD # Example: "abc123"
projects:
my_mastering_project: "1"
my_golden_records_project: "2"
my_categorization_project: "3"
my_schema_mapping_project: "4"
"""Example script for using the mock_api decorator for testing your code"""
import pytest
import tamr_toolbox as tbox
CONFIG = tbox.utils.config.from_yaml("/path/to/my/conf/project.config.yaml")
@tbox.utils.testing.mock_api()
def test_get_latest_operation():
"""
In this test, we initiate an operation in Tamr and check that the operation we initiated
is the same operation returned by the tamr_toolbox.utils.operation.get_latest() function
"""
client = tbox.utils.client.create(**CONFIG["my_tamr_instance"])
project = client.projects.by_resource_id(CONFIG["projects"]["my_schema_mapping_project"])
expected_op = project.unified_dataset().refresh(asynchronous=True)
actual_op = tbox.utils.operation.get_latest(client)
assert expected_op.resource_id == actual_op.resource_id
@tbox.utils.testing.mock_api(
# By default, mock_api will create a directory called "response_logs" alongside your
# test function and a directory within "response_logs" with the name of your test file.
# You can set a custom location using the "response_logs_dir" argument
response_logs_dir="/full/path/to/my_custom_dir",
# By default, mock_api runs an online test (making real API calls) if no response log file
# exists and runs an offline test (faking API calls based on a previous run) when a response
# log file does exist.
# You can make it so that mock_api always runs an online tests with this flag
# This is most useful during the development of your tests
enforce_online_test=True,
)
def test_operation_by_id_details():
"""
In this test, we initiate an operation in Tamr and check that the operation we initiated
can be retrieved by its id using the tamr_toolbox.utils.operation.from_resource_id() function
"""
client = tbox.utils.client.create(**CONFIG["my_tamr_instance"])
project = client.projects.by_resource_id(CONFIG["projects"]["my_schema_mapping_project"])
expected_op = project.unified_dataset().refresh(asynchronous=True)
actual_op = tbox.utils.operation.from_resource_id(client, job_id=expected_op.resource_id)
assert expected_op.resource_id == actual_op.resource_id
# This parameterize decorator allows us to run the same test multiple times
# with different arguments for each run. mock_api will generate a separate response file for
# each combination of parameters
@pytest.mark.parametrize(
"operation_id,expected_description,expected_state",
[("110", "Update Pairs", "CANCELED"), ("117", "Predict Pairs", "SUCCEEDED")],
)
@tbox.utils.testing.mock_api()
def test_operation_by_id(
operation_id: str, expected_description: str, expected_state: str
) -> None:
"""
In this test, we get operations by their ids and test that their description and status
match our expectations
Args:
operation_id: The id of the target operation
expected_description: The expected description of the target operation
expected_state: The expected status of the target operation
"""
client = tbox.utils.client.create(**CONFIG["my_tamr_instance"])
actual_op = tbox.utils.operation.from_resource_id(client, job_id=operation_id)
assert actual_op.description == expected_description
assert actual_op.state == expected_state
The test script above the following response log files when located at in my_project/tests/test_function_with_api_calls.py
response_logs_dir: my_project/tests/response_logs/test_function_with_api_calls
response_log_file: test_get_latest_operation.ndjson
{"method": "GET", "url": "http://ip-00001:9100/api/service/health", "status": 200, "content_type": "application/json", "body": "{\"/api/dataset\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.778Z\"},\"/api/dedup\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.861Z\"},\"/api/preview\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.863Z\"},\"/api/pubapi\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.868Z\"},\"/api/recipe\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.825Z\"},\"/api/taxonomy\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.812Z\"},\"/api/transform\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.844Z\"},\"/api/user\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.846Z\"},\"/api/versioned\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.819Z\"},\"EsRefreshDebouncer.ConsumerDebouncer\":{\"healthy\":true,\"message\":\"last invocation succeeded\",\"timestamp\":\"2020-09-23T16:19:50.417Z\"},\"UnifyElasticSearchDataCluster\":{\"healthy\":true,\"message\":\"Cluster Status: GREEN\",\"timestamp\":\"2020-09-23T16:19:50.797Z\"},\"Zookeeper\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.844Z\"},\"dataDirectory\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.812Z\"},\"deadlocks\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.866Z\"},\"hadoopNameNode\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.812Z\"},\"persistence\":{\"healthy\":true,\"timestamp\":\"2020-09-23T16:19:50.847Z\"},\"procurify-jobs-poller\":{\"healthy\":true,\"message\":\"last invocation succeeded\",\"timestamp\":\"2020-09-23T16:19:50.482Z\"}}"}
{"method": "GET", "url": "http://ip-00001:9100/api/versioned/v1/projects/4", "status": 200, "content_type": "application/json", "body": "{\"id\":\"unify://unified-data/v1/projects/4\",\"name\":\"minimal_schema_mapping\",\"description\":\"Schema Mapping Recommendations Project\",\"type\":\"SCHEMA_MAPPING_RECOMMENDATIONS\",\"unifiedDatasetName\":\"minimal_schema_mapping_unified_dataset\",\"created\":{\"username\":\"admin\",\"time\":\"2020-03-24T18:53:13.500Z\",\"version\":\"127\"},\"lastModified\":{\"username\":\"admin\",\"time\":\"2020-08-14T17:35:15.143Z\",\"version\":\"4423\"},\"relativeId\":\"projects/4\",\"externalId\":\"94e53657-62ed-4563-a2d1-e8edb49d5e3d\"}"}
{"method": "GET", "url": "http://ip-00001:9100/api/versioned/v1/projects/4/unifiedDataset", "status": 200, "content_type": "application/json", "body": "{\"id\":\"unify://unified-data/v1/datasets/65\",\"name\":\"minimal_schema_mapping_unified_dataset\",\"description\":\"\",\"version\":\"1046\",\"keyAttributeNames\":[\"tamr_id\"],\"tags\":[],\"created\":{\"username\":\"admin\",\"time\":\"2020-03-24T18:53:40.499Z\",\"version\":\"1868\"},\"lastModified\":{\"username\":\"admin\",\"time\":\"2020-09-23T14:46:43.706Z\",\"version\":\"66242\"},\"relativeId\":\"datasets/65\",\"upstreamDatasetIds\":[\"unify://unified-data/v1/datasets/64\",\"unify://unified-data/v1/datasets/3\"],\"externalId\":\"minimal_schema_mapping_unified_dataset\"}"}
{"method": "POST", "url": "http://ip-00001:9100/api/versioned/v1/projects/4/unifiedDataset:refresh", "status": 200, "content_type": "application/json", "body": "{\"id\":\"760\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"PENDING\",\"startTime\":\"\",\"endTime\":\"\",\"message\":\"Job has not yet been submitted to Spark\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-09-23T16:19:51.852Z\",\"version\":\"66272\"},\"lastModified\":{\"username\":\"admin\",\"time\":\"2020-09-23T16:19:51.852Z\",\"version\":\"66272\"},\"relativeId\":\"operations/760\"}"}
{"method": "GET", "url": "http://ip-00001:9100/api/versioned/v1/operations", "status": 200, "content_type": "application/json", "body": "[{\"id\":\"760\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"PENDING\",\"startTime\":\"\",\"endTime\":\"\",\"message\":\"Job has not yet been submitted to Spark\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-09-23T16:19:51.852Z\",\"version\":\"66272\"},\"lastModified\":{\"username\":\"admin\",\"time\":\"2020-09-23T16:19:51.876Z\",\"version\":\"66274\"},\"relativeId\":\"operations/760\"},{\"id\":\"759\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-09-23T14:47:13.668Z\",\"endTime\":\"2020-09-23T14:47:33.204Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-09-23T14:46:43.834Z\",\"version\":\"66245\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-09-23T14:47:33.924Z\",\"version\":\"66258\"},\"relativeId\":\"operations/759\"},{\"id\":\"758\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-09-23T14:38:23.285Z\",\"endTime\":\"2020-09-23T14:39:11.471Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-09-23T14:37:46.200Z\",\"version\":\"66200\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-09-23T14:39:12.610Z\",\"version\":\"66226\"},\"relativeId\":\"operations/758\"},{\"id\":\"757\",\"type\":\"SPARK\",\"description\":\"Profiling [groceries_tiny.csv] attributes.\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-09-10T20:26:11.496Z\",\"endTime\":\"2020-09-10T20:26:45.963Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-09-10T20:25:38.698Z\",\"version\":\"66125\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-09-10T20:26:50.766Z\",\"version\":\"66145\"},\"relativeId\":\"operations/757\"},{\"id\":\"756\",\"type\":\"SPARK\",\"description\":\"Predict record categorizations\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-09-03T20:26:56.886Z\",\"endTime\":\"2020-09-03T20:27:19.865Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-09-03T20:26:25.245Z\",\"version\":\"65975\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-09-03T20:27:22.720Z\",\"version\":\"65990\"},\"relativeId\":\"operations/756\"},{\"id\":\"755\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_categorization_unified_dataset] to 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minimal_categorization_unified_dataset_classifications_average_confidences\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-28T22:36:06.991Z\",\"endTime\":\"2020-08-28T22:37:13.066Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-28T22:35:33.058Z\",\"version\":\"64721\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-28T22:37:13.178Z\",\"version\":\"64763\"},\"relativeId\":\"operations/739\"},{\"id\":\"738\",\"type\":\"SPARK\",\"description\":\"Predict record categorizations\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-28T22:02:35.125Z\",\"endTime\":\"2020-08-28T22:03:54.269Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-28T22:02:05.211Z\",\"version\":\"64490\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-28T22:03:55.961Z\",\"version\":\"64542\"},\"relativeId\":\"operations/738\"},{\"id\":\"737\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_categorization_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-28T22:01:13.381Z\",\"endTime\":\"2020-08-28T22:01:49.979Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-28T22:00:44.569Z\",\"version\":\"64435\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-28T22:01:54.286Z\",\"version\":\"64454\"},\"relativeId\":\"operations/737\"},{\"id\":\"736\",\"type\":\"SPARK\",\"description\":\"Materialize minimal_categorization_unified_dataset_classifications_average_confidences\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-28T21:59:14.015Z\",\"endTime\":\"2020-08-28T22:00:27.215Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-28T21:58:40.033Z\",\"version\":\"64329\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-28T22:00:29.019Z\",\"version\":\"64373\"},\"relativeId\":\"operations/736\"},{\"id\":\"735\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-28T20:09:02.023Z\",\"endTime\":\"2020-08-28T20:09:22.281Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-28T20:08:34.055Z\",\"version\":\"64272\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-28T20:09:22.936Z\",\"version\":\"64285\"},\"relativeId\":\"operations/735\"},{\"id\":\"734\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-28T20:07:02.333Z\",\"endTime\":\"2020-08-28T20:07:22.997Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-28T20:06:29.520Z\",\"version\":\"64244\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-28T20:07:27.853Z\",\"version\":\"64258\"},\"relativeId\":\"operations/734\"},{\"id\":\"733\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-28T20:05:07.117Z\",\"endTime\":\"2020-08-28T20:05:28.563Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-28T20:04:35.813Z\",\"version\":\"64217\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-28T20:05:32.377Z\",\"version\":\"64230\"},\"relativeId\":\"operations/733\"},{\"id\":\"732\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-28T19:58:15.277Z\",\"endTime\":\"2020-08-28T19:58:35.949Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-28T19:57:43.805Z\",\"version\":\"64190\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-28T19:58:40.088Z\",\"version\":\"64203\"},\"relativeId\":\"operations/732\"},{\"id\":\"731\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-28T17:50:04.845Z\",\"endTime\":\"2020-08-28T17:50:49.737Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-28T17:49:32.631Z\",\"version\":\"64146\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-28T17:50:54.548Z\",\"version\":\"64171\"},\"relativeId\":\"operations/731\"},{\"id\":\"730\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-28T16:54:30.177Z\",\"endTime\":\"2020-08-28T16:55:11.221Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-28T16:53:57.617Z\",\"version\":\"64066\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-28T16:55:11.346Z\",\"version\":\"64090\"},\"relativeId\":\"operations/730\"},{\"id\":\"729\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-28T16:51:03.288Z\",\"endTime\":\"2020-08-28T16:51:21.008Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-28T16:50:30.791Z\",\"version\":\"64032\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-28T16:51:24.270Z\",\"version\":\"64045\"},\"relativeId\":\"operations/729\"},{\"id\":\"728\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-28T16:46:42.029Z\",\"endTime\":\"2020-08-28T16:47:22.462Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-28T16:46:11.661Z\",\"version\":\"63965\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-28T16:47:23.404Z\",\"version\":\"64013\"},\"relativeId\":\"operations/728\"},{\"id\":\"727\",\"type\":\"SPARK\",\"description\":\"Predict record categorizations\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-28T16:44:32.361Z\",\"endTime\":\"2020-08-28T16:46:14.025Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-28T16:43:59.827Z\",\"version\":\"63892\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-28T16:46:17.912Z\",\"version\":\"63980\"},\"relativeId\":\"operations/727\"},{\"id\":\"726\",\"type\":\"SPARK\",\"description\":\"Predict record categorizations\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-25T20:59:10.268Z\",\"endTime\":\"2020-08-25T21:00:31.337Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-25T20:58:39.767Z\",\"version\":\"63682\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-25T21:00:31.967Z\",\"version\":\"63734\"},\"relativeId\":\"operations/726\"},{\"id\":\"725\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_categorization_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-25T20:57:51.011Z\",\"endTime\":\"2020-08-25T20:58:29.133Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-25T20:57:24.764Z\",\"version\":\"63626\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-25T20:58:31.755Z\",\"version\":\"63646\"},\"relativeId\":\"operations/725\"},{\"id\":\"724\",\"type\":\"SPARK\",\"description\":\"Materialize minimal_categorization_unified_dataset_classifications_average_confidences\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-25T20:55:46.263Z\",\"endTime\":\"2020-08-25T20:57:02.108Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-25T20:55:12.830Z\",\"version\":\"63520\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-25T20:57:06.852Z\",\"version\":\"63564\"},\"relativeId\":\"operations/724\"},{\"id\":\"723\",\"type\":\"SPARK\",\"description\":\"Predict record categorizations\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-22T01:56:01.518Z\",\"endTime\":\"2020-08-22T01:57:14.943Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-22T01:55:37.707Z\",\"version\":\"63304\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-22T01:57:19.699Z\",\"version\":\"63357\"},\"relativeId\":\"operations/723\"},{\"id\":\"722\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_categorization_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-22T01:54:48.096Z\",\"endTime\":\"2020-08-22T01:55:22.473Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-22T01:54:17.259Z\",\"version\":\"63249\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-22T01:55:25.118Z\",\"version\":\"63268\"},\"relativeId\":\"operations/722\"},{\"id\":\"721\",\"type\":\"SPARK\",\"description\":\"Materialize minimal_categorization_unified_dataset_classifications_average_confidences\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-22T01:52:53.862Z\",\"endTime\":\"2020-08-22T01:53:57.761Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-22T01:52:21.866Z\",\"version\":\"63144\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-22T01:54:01.081Z\",\"version\":\"63187\"},\"relativeId\":\"operations/721\"},{\"id\":\"720\",\"type\":\"SPARK\",\"description\":\"Predict record categorizations\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-22T01:39:57.108Z\",\"endTime\":\"2020-08-22T01:41:13.561Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-22T01:39:30.111Z\",\"version\":\"62993\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-22T01:41:14.787Z\",\"version\":\"63045\"},\"relativeId\":\"operations/720\"},{\"id\":\"719\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_categorization_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-22T01:38:32.163Z\",\"endTime\":\"2020-08-22T01:39:16.181Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-22T01:38:06.372Z\",\"version\":\"62938\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-22T01:39:20.214Z\",\"version\":\"62957\"},\"relativeId\":\"operations/719\"},{\"id\":\"718\",\"type\":\"SPARK\",\"description\":\"Materialize minimal_categorization_unified_dataset_classifications_average_confidences\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-22T01:36:43.894Z\",\"endTime\":\"2020-08-22T01:37:48.228Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-22T01:36:11.924Z\",\"version\":\"62834\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-22T01:37:50.667Z\",\"version\":\"62876\"},\"relativeId\":\"operations/718\"},{\"id\":\"717\",\"type\":\"SPARK\",\"description\":\"Predict record categorizations\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-22T01:28:01.562Z\",\"endTime\":\"2020-08-22T01:29:17.607Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-22T01:27:38.880Z\",\"version\":\"62683\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-22T01:29:19.260Z\",\"version\":\"62735\"},\"relativeId\":\"operations/717\"},{\"id\":\"716\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_categorization_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-22T01:26:53.267Z\",\"endTime\":\"2020-08-22T01:27:28.120Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-22T01:26:24.725Z\",\"version\":\"62628\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-22T01:27:30.061Z\",\"version\":\"62647\"},\"relativeId\":\"operations/716\"},{\"id\":\"715\",\"type\":\"SPARK\",\"description\":\"Materialize minimal_categorization_unified_dataset_classifications_average_confidences\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-22T01:24:58.809Z\",\"endTime\":\"2020-08-22T01:26:08.707Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-22T01:24:29.293Z\",\"version\":\"62524\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-22T01:26:10.191Z\",\"version\":\"62566\"},\"relativeId\":\"operations/715\"},{\"id\":\"714\",\"type\":\"SPARK\",\"description\":\"Publish clusters\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-21T17:32:53.619Z\",\"endTime\":\"2020-08-21T17:34:09.733Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-21T17:32:21.514Z\",\"version\":\"62253\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-21T17:34:10.452Z\",\"version\":\"62338\"},\"relativeId\":\"operations/714\"},{\"id\":\"713\",\"type\":\"SPARK\",\"description\":\"Clustering\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-21T17:30:32.769Z\",\"endTime\":\"2020-08-21T17:32:04.426Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-21T17:29:59.518Z\",\"version\":\"62085\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-21T17:32:05.445Z\",\"version\":\"62164\"},\"relativeId\":\"operations/713\"},{\"id\":\"712\",\"type\":\"SPARK\",\"description\":\"Generate High-impact Pairs\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-21T17:29:03.165Z\",\"endTime\":\"2020-08-21T17:29:38.094Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-21T17:28:34.169Z\",\"version\":\"61937\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-21T17:29:40.726Z\",\"version\":\"61958\"},\"relativeId\":\"operations/712\"},{\"id\":\"711\",\"type\":\"SPARK\",\"description\":\"Predict Pairs\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-21T17:27:29.416Z\",\"endTime\":\"2020-08-21T17:28:17.890Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-21T17:27:03.197Z\",\"version\":\"61827\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-21T17:28:21.570Z\",\"version\":\"61853\"},\"relativeId\":\"operations/711\"},{\"id\":\"710\",\"type\":\"SPARK\",\"description\":\"Train Mastering Model\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-21T17:26:00.607Z\",\"endTime\":\"2020-08-21T17:26:48.072Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-21T17:25:32.070Z\",\"version\":\"61716\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-21T17:26:52.352Z\",\"version\":\"61741\"},\"relativeId\":\"operations/710\"},{\"id\":\"709\",\"type\":\"SPARK\",\"description\":\"Update Pairs\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-21T17:24:35.863Z\",\"endTime\":\"2020-08-21T17:25:13.491Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-21T17:24:04.456Z\",\"version\":\"61593\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-21T17:25:17.762Z\",\"version\":\"61625\"},\"relativeId\":\"operations/709\"},{\"id\":\"708\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_mastering_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-21T17:23:31.650Z\",\"endTime\":\"2020-08-21T17:23:51.170Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-21T17:23:05.337Z\",\"version\":\"61519\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-21T17:23:53.148Z\",\"version\":\"61532\"},\"relativeId\":\"operations/708\"},{\"id\":\"707\",\"type\":\"SPARK\",\"description\":\"Train Mastering Model\",\"status\":{\"state\":\"CANCELED\",\"startTime\":\"2020-08-21T17:21:57.875Z\",\"endTime\":\"2020-08-21T17:22:38.786Z\",\"message\":\"java.lang.IllegalStateException: Cannot call methods on a stopped SparkContext.\\nThis stopped SparkContext was created at:\\n\\norg.apache.spark.SparkContext.<init>(SparkContext.scala:76)\\ncom.tamr.pipeline.driver.PipelineContext.<init>(PipelineContext.java:64)\\ncom.tamr.pipeline.driver.PipelineDriver.createPipelineContext(PipelineDriver.java:211)\\ncom.tamr.pipeline.driver.PipelineDriver.main(PipelineDriver.java:158)\\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\\njava.lang.reflect.Method.invoke(Method.java:498)\\norg.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:685)\\n\\nThe currently active SparkContext was created at:\\n\\norg.apache.spark.SparkContext.<init>(SparkContext.scala:76)\\ncom.tamr.pipeline.driver.PipelineContext.<init>(PipelineContext.java:64)\\ncom.tamr.pipeline.driver.PipelineDriver.createPipelineContext(PipelineDriver.java:211)\\ncom.tamr.pipeline.driver.PipelineDriver.main(PipelineDriver.java:158)\\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\\njava.lang.reflect.Method.invoke(Method.java:498)\\norg.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:685)\\n \\n\\tat org.apache.spark.SparkContext.assertNotStopped(SparkContext.scala:100)\\n\\tat org.apache.spark.SparkContext.defaultParallelism(SparkContext.scala:2359)\\n\\tat org.apache.spark.api.java.JavaSparkContext.parallelize(JavaSparkContext.scala:146)\\n\\tat com.tamr.workflow.spark.components.dedup.DecisionTreeTrainerComponent.train(DecisionTreeTrainerComponent.java:81)\\n\\tat com.tamr.workflow.spark.components.dedup.EnsembleTrainerComponent.train(EnsembleTrainerComponent.java:53)\\n\\tat com.tamr.workflow.spark.components.dedup.TrainerComponent.process(TrainerComponent.java:38)\\n\\tat com.tamr.workflow.spark.DagRunner.invokeComponent(DagRunner.java:93)\\n\\tat com.tamr.workflow.spark.DagRunner.lambda$accept$0(DagRunner.java:53)\\n\\tat com.tamr.collections.Dag.reduce(Dag.java:404)\\n\\tat com.tamr.workflow.spark.DagRunner.accept(DagRunner.java:56)\\n\\tat com.tamr.workflow.spark.DagRunner.accept(DagRunner.java:26)\\n\\tat com.tamr.pipeline.driver.PipelineDriver.run(PipelineDriver.java:97)\\n\\tat com.tamr.pipeline.driver.PipelineDriver.main(PipelineDriver.java:178)\\n\\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\\n\\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\\n\\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\\n\\tat java.lang.reflect.Method.invoke(Method.java:498)\\n\\tat org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:685)\\n\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-21T17:20:22.992Z\",\"version\":\"61435\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-21T17:22:43.964Z\",\"version\":\"61505\"},\"relativeId\":\"operations/707\"},{\"id\":\"706\",\"type\":\"SPARK\",\"description\":\"Publish clusters\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-08-21T17:20:24.351Z\",\"endTime\":\"2020-08-21T17:21:44.756Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-08-21T17:19:34.158Z\",\"version\":\"61265\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-08-21T17:21:48.417Z\",\"version\":\"61466\"},\"relativeId\":\"operations/706\"},{\"id\":\"705\",\"type\":\"SPARK\",\"description\":\"Update 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clusters\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T15:24:14.812Z\",\"endTime\":\"2020-04-09T15:25:33.652Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T15:24:03.098Z\",\"version\":\"12011\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T15:25:34.557Z\",\"version\":\"12097\"},\"relativeId\":\"operations/172\"},{\"id\":\"171\",\"type\":\"SPARK\",\"description\":\"Clustering\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T15:22:26.282Z\",\"endTime\":\"2020-04-09T15:23:54.776Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T15:22:13.425Z\",\"version\":\"11823\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T15:23:54.976Z\",\"version\":\"11904\"},\"relativeId\":\"operations/171\"},{\"id\":\"170\",\"type\":\"SPARK\",\"description\":\"Predict Pairs\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T15:21:42.331Z\",\"endTime\":\"2020-04-09T15:22:04.147Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T15:21:32.543Z\",\"version\":\"11665\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T15:22:05.084Z\",\"version\":\"11678\"},\"relativeId\":\"operations/170\"},{\"id\":\"169\",\"type\":\"SPARK\",\"description\":\"Update Pairs\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T15:20:45.192Z\",\"endTime\":\"2020-04-09T15:21:27.421Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T15:20:34.147Z\",\"version\":\"11559\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T15:21:28.136Z\",\"version\":\"11594\"},\"relativeId\":\"operations/169\"},{\"id\":\"168\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_mastering_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T15:20:07.660Z\",\"endTime\":\"2020-04-09T15:20:28.145Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T15:19:57.765Z\",\"version\":\"11484\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T15:20:29.117Z\",\"version\":\"11497\"},\"relativeId\":\"operations/168\"},{\"id\":\"167\",\"type\":\"SPARK\",\"description\":\"Updating published datasets for Golden Records\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T15:19:17.233Z\",\"endTime\":\"2020-04-09T15:19:54.549Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T15:19:06.836Z\",\"version\":\"11436\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T15:19:55.004Z\",\"version\":\"11463\"},\"relativeId\":\"operations/167\"},{\"id\":\"166\",\"type\":\"SPARK\",\"description\":\"Updating Golden Records\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T15:18:12.816Z\",\"endTime\":\"2020-04-09T15:19:03.387Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T15:18:01.335Z\",\"version\":\"11368\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T15:19:03.900Z\",\"version\":\"11411\"},\"relativeId\":\"operations/166\"},{\"id\":\"165\",\"type\":\"SPARK\",\"description\":\"Predict record categorizations\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T15:16:48.329Z\",\"endTime\":\"2020-04-09T15:17:55.722Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T15:16:37.028Z\",\"version\":\"11292\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T15:17:56.566Z\",\"version\":\"11338\"},\"relativeId\":\"operations/165\"},{\"id\":\"164\",\"type\":\"SPARK\",\"description\":\"materialize minimal_categorization_unified_dataset_classification_model\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T15:15:35.524Z\",\"endTime\":\"2020-04-09T15:16:32.234Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T15:15:24.892Z\",\"version\":\"11211\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T15:16:32.979Z\",\"version\":\"11247\"},\"relativeId\":\"operations/164\"},{\"id\":\"163\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_categorization_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T15:15:00.587Z\",\"endTime\":\"2020-04-09T15:15:22.488Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T15:14:49.771Z\",\"version\":\"11158\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T15:15:22.626Z\",\"version\":\"11170\"},\"relativeId\":\"operations/163\"},{\"id\":\"162\",\"type\":\"SPARK\",\"description\":\"Predict record categorizations\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T15:14:19.767Z\",\"endTime\":\"2020-04-09T15:14:44.588Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T15:14:08.867Z\",\"version\":\"11126\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T15:14:45.496Z\",\"version\":\"11141\"},\"relativeId\":\"operations/162\"},{\"id\":\"161\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_categorization_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T15:13:41.581Z\",\"endTime\":\"2020-04-09T15:14:05.792Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T15:13:30.752Z\",\"version\":\"11085\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T15:14:06.122Z\",\"version\":\"11097\"},\"relativeId\":\"operations/161\"},{\"id\":\"160\",\"type\":\"SPARK\",\"description\":\"Publish clusters\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T14:42:21.662Z\",\"endTime\":\"2020-04-09T14:43:36.214Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T14:42:09.589Z\",\"version\":\"10914\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T14:43:37.126Z\",\"version\":\"11007\"},\"relativeId\":\"operations/160\"},{\"id\":\"159\",\"type\":\"SPARK\",\"description\":\"Clustering\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T14:40:29.820Z\",\"endTime\":\"2020-04-09T14:42:01.646Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T14:40:17.923Z\",\"version\":\"10735\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T14:42:02.408Z\",\"version\":\"10822\"},\"relativeId\":\"operations/159\"},{\"id\":\"158\",\"type\":\"SPARK\",\"description\":\"Predict Pairs\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T14:39:18.729Z\",\"endTime\":\"2020-04-09T14:40:09.116Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T14:39:08.195Z\",\"version\":\"10588\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T14:40:09.625Z\",\"version\":\"10615\"},\"relativeId\":\"operations/158\"},{\"id\":\"157\",\"type\":\"SPARK\",\"description\":\"Train Mastering Model\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T14:38:16.493Z\",\"endTime\":\"2020-04-09T14:39:02.732Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T14:38:06.590Z\",\"version\":\"10490\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T14:39:03.560Z\",\"version\":\"10514\"},\"relativeId\":\"operations/157\"},{\"id\":\"156\",\"type\":\"SPARK\",\"description\":\"Update Pairs\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T14:37:15.523Z\",\"endTime\":\"2020-04-09T14:37:59.616Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T14:37:05.138Z\",\"version\":\"10381\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T14:38:00.368Z\",\"version\":\"10417\"},\"relativeId\":\"operations/156\"},{\"id\":\"155\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_mastering_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T14:36:38.904Z\",\"endTime\":\"2020-04-09T14:37:00.630Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T14:36:28.554Z\",\"version\":\"10306\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T14:37:01.303Z\",\"version\":\"10319\"},\"relativeId\":\"operations/155\"},{\"id\":\"154\",\"type\":\"SPARK\",\"description\":\"Update Pairs\",\"status\":{\"state\":\"CANCELED\",\"startTime\":\"2020-04-09T14:35:54.436Z\",\"endTime\":\"2020-04-09T14:36:15.560Z\",\"message\":\"Running job canceled by admin at step 0 of 18\\n\\norg.apache.spark.SparkException: Job 0 cancelled because SparkContext was shut down\\n\\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:820)\\n\\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:818)\\n\\tat scala.collection.mutable.HashSet.foreach(HashSet.scala:78)\\n\\tat org.apache.spark.scheduler.DAGScheduler.cleanUpAfterSchedulerStop(DAGScheduler.scala:818)\\n\\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onStop(DAGScheduler.scala:1732)\\n\\tat org.apache.spark.util.EventLoop.stop(EventLoop.scala:83)\\n\\tat org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1651)\\n\\tat org.apache.spark.SparkContext$$anonfun$stop$8.apply$mcV$sp(SparkContext.scala:1921)\\n\\tat org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1317)\\n\\tat org.apache.spark.SparkContext.stop(SparkContext.scala:1920)\\n\\tat org.apache.spark.SparkContext$$anonfun$2.apply$mcV$sp(SparkContext.scala:581)\\n\\tat org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:216)\\n\\tat org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ShutdownHookManager.scala:188)\\n\\tat org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188)\\n\\tat org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1$$anonfun$apply$mcV$sp$1.apply(ShutdownHookManager.scala:188)\\n\\tat org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1954)\\n\\tat org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply$mcV$sp(ShutdownHookManager.scala:188)\\n\\tat org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188)\\n\\tat org.apache.spark.util.SparkShutdownHookManager$$anonfun$runAll$1.apply(ShutdownHookManager.scala:188)\\n\\tat scala.util.Try$.apply(Try.scala:192)\\n\\tat org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)\\n\\tat org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178)\\n\\tat org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:54)\\n\\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)\\n\\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)\\n\\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)\\n\\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)\\n\\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2087)\\n\\tat org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:926)\\n\\tat org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:924)\\n\\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\\n\\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\\n\\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:362)\\n\\tat org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:924)\\n\\tat org.apache.spark.api.java.JavaRDDLike$class.foreachPartition(JavaRDDLike.scala:219)\\n\\tat org.apache.spark.api.java.AbstractJavaRDDLike.foreachPartition(JavaRDDLike.scala:45)\\n\\tat com.tamr.pipeline.driver.LogUtils.initExecutors(LogUtils.java:60)\\n\\tat com.tamr.pipeline.driver.PipelineDriver.main(PipelineDriver.java:173)\\n\\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\\n\\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\\n\\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\\n\\tat java.lang.reflect.Method.invoke(Method.java:498)\\n\\tat org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:635)\\n\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T14:35:44.550Z\",\"version\":\"10255\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T14:36:15.336Z\",\"version\":\"10284\"},\"relativeId\":\"operations/154\"},{\"id\":\"153\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_mastering_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T14:35:17.648Z\",\"endTime\":\"2020-04-09T14:35:39.712Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T14:35:04.888Z\",\"version\":\"10180\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T14:35:40.372Z\",\"version\":\"10193\"},\"relativeId\":\"operations/153\"},{\"id\":\"152\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T01:55:19.320Z\",\"endTime\":\"2020-04-09T01:55:41.140Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T01:55:08.062Z\",\"version\":\"10127\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T01:55:41.348Z\",\"version\":\"10139\"},\"relativeId\":\"operations/152\"},{\"id\":\"151\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_mastering_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T01:54:41.574Z\",\"endTime\":\"2020-04-09T01:55:03.464Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T01:54:32.350Z\",\"version\":\"10099\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T01:55:04.407Z\",\"version\":\"10112\"},\"relativeId\":\"operations/151\"},{\"id\":\"150\",\"type\":\"SPARK\",\"description\":\"Publish clusters\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T01:53:11.548Z\",\"endTime\":\"2020-04-09T01:54:28.780Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T01:53:00.108Z\",\"version\":\"9971\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T01:54:29.681Z\",\"version\":\"10060\"},\"relativeId\":\"operations/150\"},{\"id\":\"149\",\"type\":\"SPARK\",\"description\":\"Clustering\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T01:51:22.794Z\",\"endTime\":\"2020-04-09T01:52:52.275Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T01:51:10.705Z\",\"version\":\"9779\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T01:52:53.202Z\",\"version\":\"9864\"},\"relativeId\":\"operations/149\"},{\"id\":\"148\",\"type\":\"SPARK\",\"description\":\"Predict Pairs\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T01:50:40.268Z\",\"endTime\":\"2020-04-09T01:51:01.877Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T01:50:29.829Z\",\"version\":\"9622\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T01:51:02.361Z\",\"version\":\"9634\"},\"relativeId\":\"operations/148\"},{\"id\":\"147\",\"type\":\"SPARK\",\"description\":\"Update Pairs\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T01:49:42.446Z\",\"endTime\":\"2020-04-09T01:50:24.453Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T01:49:30.666Z\",\"version\":\"9516\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T01:50:25.497Z\",\"version\":\"9551\"},\"relativeId\":\"operations/147\"},{\"id\":\"146\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_mastering_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T01:49:02.631Z\",\"endTime\":\"2020-04-09T01:49:24.426Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T01:48:52.720Z\",\"version\":\"9441\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T01:49:25.380Z\",\"version\":\"9454\"},\"relativeId\":\"operations/146\"},{\"id\":\"145\",\"type\":\"SPARK\",\"description\":\"Updating published datasets for Golden Records\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T01:48:11.617Z\",\"endTime\":\"2020-04-09T01:48:48.894Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T01:48:01.825Z\",\"version\":\"9394\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T01:48:49.539Z\",\"version\":\"9420\"},\"relativeId\":\"operations/145\"},{\"id\":\"144\",\"type\":\"SPARK\",\"description\":\"Updating Golden Records\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T01:47:13.114Z\",\"endTime\":\"2020-04-09T01:47:57.686Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T01:47:02.097Z\",\"version\":\"9340\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T01:47:58.526Z\",\"version\":\"9373\"},\"relativeId\":\"operations/144\"},{\"id\":\"143\",\"type\":\"SPARK\",\"description\":\"Predict record categorizations\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-04-09T01:45:45.426Z\",\"endTime\":\"2020-04-09T01:46:58.258Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-04-09T01:45:34.302Z\",\"version\":\"9269\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-04-09T01:46:58.402Z\",\"version\":\"9314\"},\"relativeId\":\"operations/143\"},{\"id\":\"142\",\"type\":\"SPARK\",\"description\":\"materialize 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response_logs_dir: my_project/tests/response_logs/test_function_with_api_calls
response_log_file: test_operation_by_id_details__operation_id=110_expected_description=Update Pairs_expected_state=CANCELED.ndjson
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response_logs_dir: my_project/tests/response_logs/test_function_with_api_calls
response_log_file: test_operation_by_id_details__operation_id=117_expected_description=Predict Pairs_expected_state=SUCCEEDED.ndjson
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response_logs_dir: my_project/tests/my_custom_dir
response_log_file: test_get_latest_operation.ndjson
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{"method": "GET", "url": "http://ip-00001:9100/api/versioned/v1/projects/4", "status": 200, "content_type": "application/json", "body": "{\"id\":\"unify://unified-data/v1/projects/4\",\"name\":\"minimal_schema_mapping\",\"description\":\"Schema Mapping Recommendations Project\",\"type\":\"SCHEMA_MAPPING_RECOMMENDATIONS\",\"unifiedDatasetName\":\"minimal_schema_mapping_unified_dataset\",\"created\":{\"username\":\"admin\",\"time\":\"2020-03-24T18:53:13.500Z\",\"version\":\"127\"},\"lastModified\":{\"username\":\"admin\",\"time\":\"2020-08-14T17:35:15.143Z\",\"version\":\"4423\"},\"relativeId\":\"projects/4\",\"externalId\":\"94e53657-62ed-4563-a2d1-e8edb49d5e3d\"}"}
{"method": "GET", "url": "http://ip-00001:9100/api/versioned/v1/projects/4/unifiedDataset", "status": 200, "content_type": "application/json", "body": "{\"id\":\"unify://unified-data/v1/datasets/65\",\"name\":\"minimal_schema_mapping_unified_dataset\",\"description\":\"\",\"version\":\"1046\",\"keyAttributeNames\":[\"tamr_id\"],\"tags\":[],\"created\":{\"username\":\"admin\",\"time\":\"2020-03-24T18:53:40.499Z\",\"version\":\"1868\"},\"lastModified\":{\"username\":\"admin\",\"time\":\"2020-09-23T16:29:41.743Z\",\"version\":\"66378\"},\"relativeId\":\"datasets/65\",\"upstreamDatasetIds\":[\"unify://unified-data/v1/datasets/64\",\"unify://unified-data/v1/datasets/3\"],\"externalId\":\"minimal_schema_mapping_unified_dataset\"}"}
{"method": "POST", "url": "http://ip-00001:9100/api/versioned/v1/projects/4/unifiedDataset:refresh", "status": 200, "content_type": "application/json", "body": "{\"id\":\"765\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"PENDING\",\"startTime\":\"\",\"endTime\":\"\",\"message\":\"Job has not yet been submitted to Spark\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-09-23T16:31:18.157Z\",\"version\":\"66408\"},\"lastModified\":{\"username\":\"admin\",\"time\":\"2020-09-23T16:31:18.157Z\",\"version\":\"66408\"},\"relativeId\":\"operations/765\"}"}
{"method": "GET", "url": "http://ip-00001:9100/api/versioned/v1/operations/765", "status": 200, "content_type": "application/json", "body": "{\"id\":\"765\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"PENDING\",\"startTime\":\"\",\"endTime\":\"\",\"message\":\"Job has not yet been submitted to Spark\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-09-23T16:31:18.157Z\",\"version\":\"66408\"},\"lastModified\":{\"username\":\"admin\",\"time\":\"2020-09-23T16:31:18.181Z\",\"version\":\"66410\"},\"relativeId\":\"operations/765\"}"}
{"method": "GET", "url": "http://ip-00001:9100/api/versioned/v1/operations/765", "status": 200, "content_type": "application/json", "body": "{\"id\":\"765\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"PENDING\",\"startTime\":\"\",\"endTime\":\"\",\"message\":\"Job has not yet been submitted to Spark\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-09-23T16:31:18.157Z\",\"version\":\"66408\"},\"lastModified\":{\"username\":\"admin\",\"time\":\"2020-09-23T16:31:18.181Z\",\"version\":\"66410\"},\"relativeId\":\"operations/765\"}"}
{"method": "GET", "url": "http://ip-00001:9100/api/versioned/v1/operations/765", "status": 200, "content_type": "application/json", "body": "{\"id\":\"765\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"RUNNING\",\"startTime\":\"2020-09-23T16:31:48.304Z\",\"endTime\":\"\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-09-23T16:31:18.157Z\",\"version\":\"66408\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-09-23T16:32:01.401Z\",\"version\":\"66419\"},\"relativeId\":\"operations/765\"}"}
{"method": "GET", "url": "http://ip-00001:9100/api/versioned/v1/operations/765", "status": 200, "content_type": "application/json", "body": "{\"id\":\"765\",\"type\":\"SPARK\",\"description\":\"Materialize views [minimal_schema_mapping_unified_dataset] to Elastic\",\"status\":{\"state\":\"SUCCEEDED\",\"startTime\":\"2020-09-23T16:31:48.304Z\",\"endTime\":\"2020-09-23T16:32:08.976Z\",\"message\":\"\"},\"created\":{\"username\":\"admin\",\"time\":\"2020-09-23T16:31:18.157Z\",\"version\":\"66408\"},\"lastModified\":{\"username\":\"system\",\"time\":\"2020-09-23T16:32:11.828Z\",\"version\":\"66421\"},\"relativeId\":\"operations/765\"}"}
Upstream¶
How to get a list of projects upstream from a specified project¶
logging_dir: $TAMR_PROJECT_LOGGING_DIR # Example: "/home/users/jane/my-project/logs"
my_tamr_instance:
host: $TAMR_HOST # Example: "1.2.3.4"
protocol: "http"
port: "9100"
username: "admin"
password: $TAMR_PASSWORD # Example: "abc123"
projects:
my_mastering_project: "1"
my_golden_records_project: "2"
my_categorization_project: "3"
my_schema_mapping_project: "4"
"""Retrieve upstream projects from a specified project and update the entire chain."""
import argparse
from typing import List, Dict
import tamr_toolbox as tbox
from tamr_unify_client.operation import Operation
def main(*, instance_connection_info: Dict[str, str], project_id: str) -> List[Operation]:
"""Retrieve upstream projects from a specified project and update the entire chain.
Args:
instance_connection_info: Information for connecting to Tamr (host, port, username etc)
project_id: the project_id of the project for which upstream
projects are being retrieved
Returns:
List of operations run
"""
# Create the tamr client
tamr_client = tbox.utils.client.create(**instance_connection_info)
project = tamr_client.projects.by_resource_id(project_id)
# Retrieve upstream projects
LOGGER.info(f"Retrieving upstream projects for project: {project}")
upstream_projects = tbox.utils.upstream.projects(project)
operations = []
if upstream_projects:
LOGGER.info(
f"The following upstream projects were retrieved successfully {upstream_projects}"
)
# Update all projects in a chained workflow and return the operations that were carried out
operations = tbox.workflow.jobs.run(upstream_projects)
else:
LOGGER.info(f"No upstream projects found for project {project}")
# Now that all upstream project have been run, run the target project
# and add it's operations to the list
operations.extend(tbox.workflow.jobs.run([project]))
return operations
if __name__ == "__main__":
# Set up command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--config", help="path to a YAML configuration file", required=False)
args = parser.parse_args()
# Load the configuration from the file path provided or the default file path specified
CONFIG = tbox.utils.config.from_yaml(
path_to_file=args.config, default_path_to_file="/path/to/my/conf/project.config.yaml"
)
# Use the configuration to create a global logger
LOGGER = tbox.utils.logger.create(__name__, log_directory=CONFIG["logging_dir"])
# Run the main function
main(
instance_connection_info=CONFIG["my_tamr_instance"],
project_id=CONFIG["projects"]["my_golden_records_project"],
)
How to get a list of datasets upstream from a specified dataset¶
# dataset.config.yaml
logging_dir: $TAMR_PROJECT_LOGGING_DIR # Example: "/home/users/jane/my-project/logs"
my_tamr_instance:
host: $TAMR_HOST # Example: "1.2.3.4"
protocol: "http"
port: "9100"
username: "admin"
password: $TAMR_PASSWORD # Example: "abc123"
datasets:
my_mastering_project_dataset:
name: "my_mastering_project_unified_dataset_published_clusters_with_data"
id: "5"
export_file_path: "/path/to/data/output/my_mastering_project_unified_dataset_published_clusters_with_data.csv"
my_categorization_project_dataset:
name: "my_categorization_project_unified_dataset_classifications_with_data"
id: "12"
export_file_path: "/path/to/data/output/my_categorization_project_unified_dataset_classifications_with_data.csv"
"""Retrieve upstream datasets from a specified dataset."""
import argparse
from typing import List, Dict
import tamr_toolbox as tbox
from tamr_unify_client.dataset.resource import Dataset
def main(*, instance_connection_info: Dict[str, str], dataset_id: str) -> List[Dataset]:
"""Retrieve upstream datasets from a specified dataset.
Args:
instance_connection_info: Information for connecting to Tamr (host, port, username etc)
dataset_id: the dataset_id of the dataset for which upstream
datasets are being retrieved
Returns:
List of upstream datasets
"""
# Create the tamr client
tamr_client = tbox.utils.client.create(**instance_connection_info)
dataset = tamr_client.dataset.by_resource_id(dataset_id)
# Retrieve upstream projects
LOGGER.info(f"Retrieving upstream datasets for dataset: {dataset}")
upstream_datasets = tbox.utils.upstream.projects(dataset)
if upstream_datasets:
LOGGER.info(
f"The following upstream datasets were retrieved successfully {upstream_datasets}."
)
else:
LOGGER.info(f"No upstream datasets found for dataset {dataset}.")
return upstream_datasets
if __name__ == "__main__":
# Set up command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--config", help="path to a YAML configuration file", required=False)
args = parser.parse_args()
# Load the configuration from the file path provided or the default file path specified
CONFIG = tbox.utils.config.from_yaml(
path_to_file=args.config, default_path_to_file="/path/to/my/conf/dataset.config.yaml"
)
# Use the configuration to create a global logger
LOGGER = tbox.utils.logger.create(__name__, log_directory=CONFIG["logging_dir"])
# Run the main function
main(
instance_connection_info=CONFIG["my_tamr_instance"],
dataset_id=CONFIG["datasets"]["my_categorization_project_dataset"]["id"],
)
Downstream¶
How to get a list of datasets and projects downstream from a specified dataset¶
logging_dir: $TAMR_PROJECT_LOGGING_DIR # Example: "/home/users/jane/my-project/logs"
my_tamr_instance:
host: $TAMR_HOST # Example: "1.2.3.4"
protocol: "http"
port: "9100"
username: "admin"
password: $TAMR_PASSWORD # Example: "abc123"
projects:
my_mastering_project: "1"
my_golden_records_project: "2"
my_categorization_project: "3"
my_schema_mapping_project: "4"
"""
Example script for checking and cleaning up downstream datasets.
NOTE: USE AT YOUR OWN RISK. Deleting datasets can sometimes break the system
and should only be done when necessary. Always take backup before you perform the cleanup.
"""
import tamr_toolbox as tbox
from requests.exceptions import HTTPError
from typing import Dict, Any
import argparse
def main(*, instance_connection_info: Dict[str, Any], dataset_name_to_cleanup: str) -> bool:
"""Check and clean up a Tamr dataset and its downstream datasets if possible.
Args:
instance_connection_info: Information for connecting to Tamr (host, port, username etc)
dataset_name_to_cleanup: name of target dataset to clean up
Returns:
Whether the cleanup of the dataset and its downstream datasets has been done successfully.
"""
# Create Tamr Client
tamr = tbox.utils.client.create(**instance_connection_info)
my_dataset = tamr.datasets.by_name(dataset_name_to_cleanup)
# Get the list of downstream datasets by pipeline dependency
# Note: If you want to include dependencies suggested by name matching,
# (some datasets are part of a project but not registered in the pipeline)
# set `include_dependencies_by_name` to True but check
# all downstream datasets before deleting to make sure you do not delete
# any unexpected datasets
downstream_datasets = tbox.utils.downstream.datasets(my_dataset)
downstream_projects = tbox.utils.downstream.projects(my_dataset)
# Clean up if there is no downstream projects
if len(downstream_projects) == 0:
# Delete all downstream datasets
for dataset in downstream_datasets:
try:
dataset.delete(cascade=True)
LOGGER.info(f"Deleted dataset {dataset.name}")
except HTTPError as e:
if e.response.status_code == 404:
LOGGER.info(f"Dataset {dataset.name} has already been deleted.")
else:
LOGGER.error(f"Error occurred when trying to delete {dataset.name}: {e}")
raise Exception(e)
# Delete the target dataset
my_dataset.delete()
LOGGER.info(f"Deleted datset {my_dataset.name}")
return True
else:
LOGGER.error(
f"The dataset '{my_dataset.name}' cannot be deleted because the"
f"following downstream projects depend on it or its dependencies: "
f"{[p.name for p in downstream_projects]}"
)
return False
if __name__ == "__main__":
# Set up command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--config", help="path to a YAML configuration file", required=False)
args = parser.parse_args()
# Load the configuration from the file path provided or the default file path specified
CONFIG = tbox.utils.config.from_yaml(
path_to_file=args.config, default_path_to_file="/path/to/my/conf/project.config.yaml"
)
# Use the configuration to create a global logger
LOGGER = tbox.utils.logger.create(__name__, log_directory=CONFIG["logging_dir"])
# Run the main function
main(
instance_connection_info=CONFIG["my_tamr_instance"],
dataset_name_to_cleanup=CONFIG["datasets"]["dataset_to_clean_up"],
)
Manual Auth¶
How to manually pass credentials when creating a tamr client object during runtime¶
"""
Snippet for creating a tamr client object with manual passing of credentials during runtime.
Note: This approach cannot be coupled with fully automated workflows (e.g., crontab jobs)
"""
from getpass import getpass
from tamr_toolbox import utils as tbu
# load configuration variables that do not include AUTH credentials
config_path = "."
my_config = tbu.config.from_yaml(config_path)
# prompting user for AUTH info from the terminal
username = input("Enter username: ")
# the password will not be visible while being typed in the terminal
password = getpass("Enter password: ")
# creating the unify client object
tamr_client = tbu.client.create(username=username, password=password, **my_config)
Custom UI Buttons¶
How to create and register custom UI button yaml files.¶
IMPORTANT: Custom buttons are only available in v2022.008 and later.
"""
Example script for creating, saving & registering custom button yaml files.
NB: This script needs to be run on the server on which Tamr is installed, not remotely.
"""
import tamr_toolbox as tbox
import argparse
import logging
LOGGER = logging.getLogger(__name__)
def main(output_dir):
LOGGER.info("Creating button yaml files...")
# Button link to Google browser
button_1 = tbox.utils.custom_button.create_redirect_button(
extension_name="extension_1",
button_id="redirect_button_1",
button_text="Google",
page_names=["Schema Mapping:Dashboard", "Mastering:Dashboard"],
redirect_url="https://www.google.com",
open_in_new_tab=True,
output_dir=output_dir,
button_name="redirect_button_1",
)
# Button link to existing mastering dashboard
button_2 = tbox.utils.custom_button.create_redirect_button(
extension_name="extension_2",
button_id="redirect_button_2",
button_text="Mastering Dashboard",
page_names=["Mastering:Dashboard"],
redirect_url="https://example-customer.tamrfield.com/dashboard/",
open_in_new_tab=True,
output_dir=output_dir,
button_name="redirect_button_2",
)
# Example POST button
button_3 = tbox.utils.custom_button.create_post_button(
extension_name="extension_3",
button_id="post_button_1",
button_text="Export Project",
page_names=["Mastering:Clusters"],
post_url="https://example-location.tamr.com/python/api/export",
post_body_keys=["projectId"],
success_message="Project export sucessful",
fail_message="Project export failed",
display_response=True,
output_dir=output_dir,
button_name="post_button_1",
)
# Register a button individually
LOGGER.info(f"Registering individual button")
tbox.utils.custom_button.register_buttons(buttons=button_1, tamr_install_dir="/home/ubuntu")
# Group multiple buttons into a new extension yaml file & register it
button_list = [button_2, button_3]
LOGGER.info("Registering extension")
extension_1 = tbox.utils.custom_button.create_button_extension(
extension_name="extension_1", buttons=button_list, output_dir=output_dir
)
tbox.utils.custom_button.register_buttons(buttons=extension_1, tamr_install_dir="/home/ubuntu")
if __name__ == "__main__":
# Set up command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--config", help="path to a YAML configuration file", required=False)
parser.add_argument("--output_dir", help="path to output directory", required=True)
args = parser.parse_args()
# Load the configuration from the file path provided or the default file path specified
CONFIG = tbox.utils.config.from_yaml(
path_to_file=args.config, default_path_to_file="/path/to/my/conf/project.config.yaml"
)
# Use the configuration to create a global logger
LOGGER = tbox.utils.logger.create(__name__, log_directory=CONFIG["logging_dir"])
# Run the main function
main(args.output_dir)