def test_dataset_schema_invalid(self):
with self.assertRaises(DartValidationException) as context:
columns = [
Column('c1', DataType.VARCHAR, 50),
Column('c2', DataType.BIGINT)
]
df = DataFormat(FileFormat.PARQUET, RowFormat.NONE)
location = None
ds = Dataset(data=DatasetData(name='test-dataset',
table_name='test_dataset_table',
load_type=LoadType.INSERT,
location=location,
data_format=df,
columns=columns,
tags=[]))
# should fail because location is required
default_and_validate(ds, dataset_schema())
self.assertTrue(isinstance(context.exception, DartValidationException))
def setUp(self):
dart = Dart(host='localhost', port=5000)
""" :type dart: dart.client.python.dart_client.Dart """
self.dart = dart
cs = [
Column('c1', DataType.VARCHAR, 50),
Column('c2', DataType.BIGINT)
]
df = DataFormat(FileFormat.TEXTFILE, RowFormat.DELIMITED)
dataset_data = DatasetData(
'test-dataset', 'test_dataset_table',
's3://' + os.environ['DART_TEST_BUCKET'] + '/impala', df, cs)
self.dataset = self.dart.save_dataset(Dataset(data=dataset_data))
start = 's3://' + os.environ['DART_TEST_BUCKET'] + '/impala/impala'
end = 's3://' + os.environ['DART_TEST_BUCKET'] + '/impala/install'
regex = '.*\\.rpm'
ds = Subscription(data=SubscriptionData(
'test-subscription', self.dataset.id, start, end, regex))
self.subscription = self.dart.save_subscription(ds)
dst_args = {'action_sleep_time_in_seconds': 0}
dst = Datastore(data=DatastoreData('test-datastore',
'no_op_engine',
args=dst_args,
state=DatastoreState.TEMPLATE))
self.datastore = self.dart.save_datastore(dst)
wf = Workflow(data=WorkflowData(
'test-workflow', self.datastore.id, state=WorkflowState.ACTIVE))
self.workflow = self.dart.save_workflow(wf, self.datastore.id)
a_args = {'subscription_id': self.subscription.id}
a0 = Action(data=ActionData(NoOpActionTypes.action_that_succeeds.name,
NoOpActionTypes.action_that_succeeds.name,
state=ActionState.TEMPLATE))
a1 = Action(data=ActionData(NoOpActionTypes.consume_subscription.name,
NoOpActionTypes.consume_subscription.name,
a_args,
state=ActionState.TEMPLATE))
self.action0, self.action1 = self.dart.save_actions(
[a0, a1], workflow_id=self.workflow.id)
LoadType
if __name__ == '__main__':
dart = Dart('localhost', 5000)
assert isinstance(dart, Dart)
dataset = dart.save_dataset(
Dataset(data=(DatasetData(
name='beacon_native_app_parsed_v01',
table_name='beacon_native_app',
location='s3://example-bucket/nb.retailmenot.com/parsed_logs',
load_type=LoadType.INSERT,
data_format=DataFormat(FileFormat.TEXTFILE,
RowFormat.DELIMITED,
delimited_by='\t',
quoted_by='"',
escaped_by='\\',
null_string='NULL',
num_header_rows=1),
compression=Compression.NONE,
partitions=[
Column('year', DataType.STRING),
Column('week', DataType.STRING),
],
columns=[
Column('logFileId', DataType.BIGINT),
Column('lineNumber', DataType.INT),
Column('created',
DataType.TIMESTAMP,
date_pattern="yyyy-MM-dd HH:mm:ss"),
Column('remoteip', DataType.STRING),
from dart.model.action import ActionData
from dart.model.dataset import Column, DatasetData, Dataset, DataFormat, FileFormat, RowFormat, DataType, Compression
from dart.model.datastore import Datastore, DatastoreData, DatastoreState
if __name__ == '__main__':
dart = Dart('localhost', 5000)
# dart = Dart()
assert isinstance(dart, Dart)
dataset = dart.save_dataset(Dataset(data=DatasetData(
name='weblogs_v01',
table_name='weblogs',
location='s3://example-bucket/weblogs/www.retailmenot.com/ec2/',
data_format=DataFormat(
file_format=FileFormat.TEXTFILE,
row_format=RowFormat.REGEX,
regex_input="(?<ip>^(?:(?:unknown(?:,\\s)?|(?:\\d+\\.\\d+\\.\\d+\\.\\d+(?:,\\s)?))+)|\\S*)\\s+\\S+\\s+(?<userIdentifier>(?:[^\\[]+|\\$\\S+\\['\\S+'\\]|\\[username\\]))\\s*\\s+\\[(?<requestDate>[^\\]]+)\\]\\s+\"(?<httpMethod>(?:GET|HEAD|POST|PUT|DELETE|TRACE))\\s(?<urlPath>(?:[^ ?]+))(?:\\?(?<queryString>(?:[^ ]+)))?\\sHTTP/(?<httpVersion>(?:[\\d\\.]+))\"\\s+(?<statusCode>[0-9]+)\\s+(?<bytesSent>\\S+)\\s+\"(?<referrer>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<userAgent>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+(?<responseTime>[-0-9]*)\\s+\"(?<hostName>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<userFingerprint>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<userId>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<sessionId>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<requestId>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<visitorId>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<vegSlice>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<fruitSlice>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<cacheHitMiss>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s*\\Z",
regex_output="%1$s %2$s %3$s %4$s %5$s %6$s %7$s %8$s %9$s %10$s %11$s %12$s %13$s %14$s %15$s %16$s %17$s %18$s %19$s %20$s %21s",
),
columns=[
Column('ip', DataType.STRING),
Column('user', DataType.STRING),
Column('requestDate', DataType.TIMESTAMP, date_pattern='dd/MMM/yyyy:HH:mm:ss Z'),
Column('httpMethod', DataType.STRING),
Column('urlPath', DataType.STRING),
Column('queryString', DataType.STRING),
Column('httpVersion', DataType.STRING),
Column('statusCode', DataType.STRING),
Column('bytesSent', DataType.INT),
Column('referrer', DataType.STRING),
Column('userAgent', DataType.STRING),
Column('responseTime', DataType.BIGINT),
from dart.model.trigger import TriggerData
from dart.model.workflow import Workflow, WorkflowState
from dart.model.workflow import WorkflowData
if __name__ == '__main__':
dart = Dart('localhost', 5000)
assert isinstance(dart, Dart)
dataset = dart.save_dataset(
Dataset(data=(DatasetData(
name='owen_eu_DW-3213_v3',
table_name='owen_eu',
location='s3://example-bucket/prd/inbound/overlord/eu-all-events',
load_type=LoadType.MERGE,
data_format=DataFormat(
file_format=FileFormat.TEXTFILE,
row_format=RowFormat.JSON,
),
compression=Compression.GZIP,
partitions=[
Column('year', DataType.STRING),
Column('month', DataType.STRING),
Column('day', DataType.STRING),
],
columns=[
Column('host', DataType.STRING, path='metadata.host'),
Column('referer', DataType.STRING, path='metadata.referer'),
Column('userAgent',
DataType.STRING,
path='owen.context.userAgent'),
Column('ipAddress',
DataType.STRING,
from dart.model.event import EventData
from dart.model.trigger import Trigger, TriggerData
from dart.model.workflow import Workflow, WorkflowState
from dart.model.workflow import WorkflowData
if __name__ == '__main__':
dart = Dart('localhost', 5000)
assert isinstance(dart, Dart)
dataset = dart.save_dataset(Dataset(data=(DatasetData(
name='beacon_native_app_v02',
table_name='beacon_native_app',
location='s3://example-bucket/prd/beacon/native_app/v2/parquet/snappy',
hive_compatible_partition_folders=True,
load_type=LoadType.INSERT,
data_format=DataFormat('parquet'),
columns=[
Column('logFileId', DataType.BIGINT),
Column('lineNumber', DataType.INT),
Column('created', DataType.BIGINT),
Column('remoteip', DataType.STRING),
Column('useragent', DataType.STRING),
Column('eventType', DataType.STRING),
Column('appVersion', DataType.STRING),
Column('advertiserID', DataType.STRING),
Column('couponsOnPage', DataType.INT),
Column('coupons', DataType.STRING),
Column('channel', DataType.STRING),
Column('geoCouponCount', DataType.STRING),
Column('geofence', DataType.STRING),
Column('geofenceTimeSpent', DataType.STRING),
def prepare_load_dataset_steps(dry_run,
args_by_name,
datastore,
dataset,
action_id,
s3_path_and_file_size_gen,
target_is_dynamodb=False):
""" :type dataset: dart.model.dataset.Dataset """
def add_to(step_partials, step_num, func, *args):
# add all params except the last one, which is the total steps (known at the end)
step_partials.append(
functools.partial(func, *(list(args) + [step_num])))
return step_num + 1
def stage_table_not_needed(ds, file_format, row_format, compression,
delimited_by, quoted_by, escaped_by,
null_string):
""" :type ds: dart.model.dataset.Dataset """
return file_format == ds.data.data_format.file_format\
and row_format == ds.data.data_format.row_format\
and compression == ds.data.compression\
and delimited_by == ds.data.data_format.delimited_by\
and quoted_by == ds.data.data_format.quoted_by\
and escaped_by == ds.data.data_format.escaped_by\
and null_string == ds.data.data_format.null_string
# some steps require producing a dataset specific file based on a template, so we will copy all to a tempdir
tempdir = tempfile.mkdtemp()
try:
local_step_path, s3_step_path, s3_temp_path = prepare_step_paths(
datastore, tempdir)
target_table_name = args_by_name.get(
'target_table_name') or dataset.data.table_name
target_file_format = args_by_name.get(
'target_file_format') or dataset.data.data_format.file_format
target_row_format = args_by_name.get(
'target_row_format') or dataset.data.data_format.row_format
target_compression = args_by_name.get(
'target_compression') or dataset.data.compression
target_delimited_by = args_by_name.get(
'target_delimited_by') or dataset.data.data_format.delimited_by
target_quoted_by = args_by_name.get(
'target_quoted_by') or dataset.data.data_format.quoted_by
target_escaped_by = args_by_name.get(
'target_escaped_by') or dataset.data.data_format.escaped_by
target_null_string = args_by_name.get(
'target_null_string') or dataset.data.data_format.null_string
stage_table_name = target_table_name + '_stage_for_action_' + action_id
staging_not_needed = stage_table_not_needed(
dataset, target_file_format, target_row_format, target_compression,
target_delimited_by, target_quoted_by, target_escaped_by,
target_null_string)
first_table_name = target_table_name if staging_not_needed and not target_is_dynamodb else stage_table_name
drop_table_names = []
step_funcs = []
i = 1
# ------------------------------------------------------------------------------------------------------------
# all code paths below require copying the data to HDFS, and lowercasing the table is required because of hive
# ------------------------------------------------------------------------------------------------------------
i = add_to(step_funcs, i,
s3distcp_files_step, s3_path_and_file_size_gen,
first_table_name.lower(), dataset, s3_step_path,
local_step_path, action_id)
# ------------------------------------------------------------------------------------------------------------
# not all folder structures on s3 are hive compatible... if not, rename directories after copying
# ------------------------------------------------------------------------------------------------------------
if dataset.data.partitions and not dataset.data.hive_compatible_partition_folders:
i = add_to(step_funcs, i, python_fix_partition_folder_names,
first_table_name.lower(), dataset.data.partitions,
s3_step_path, action_id)
# ------------------------------------------------------------------------------------------------------------
# special case to share functionality with the dynamodb_engine
# ------------------------------------------------------------------------------------------------------------
if target_is_dynamodb:
dyn_dataset = Dataset.from_dict(dataset.to_dict())
assert isinstance(dyn_dataset, Dataset)
dyn_dataset.data.data_format = DataFormat('DYNAMODB_TABLE',
RowFormat.NONE, 0)
dyn_dataset.data.compression = Compression.NONE
dyn_dataset.data.columns = [
Column(c.name, dynamodb_column_type(c))
for c in dataset.data.columns
]
set_hive_vars = 'SET dynamodb.retry.duration = 0;\nSET dynamodb.throughput.write.percent = %s;'
set_hive_vars = set_hive_vars % args_by_name[
'write_capacity_utilization_percent']
i = add_to(step_funcs, i, hive_table_definition_step,
stage_table_name, dataset, s3_step_path,
local_step_path, action_id, False)
i = add_to(step_funcs, i, hive_table_definition_step,
target_table_name, dyn_dataset, s3_step_path,
local_step_path, action_id, True)
i = add_to(step_funcs, i, hive_msck_repair_table_step,
stage_table_name, s3_step_path, action_id)
i = add_to(step_funcs, i, hive_copy_to_table, dataset,
stage_table_name, dyn_dataset, target_table_name,
s3_step_path, local_step_path, action_id, set_hive_vars)
# ------------------------------------------------------------------------------------------------------------
# if no stage tables are needed, much complexity can be skipped
# ------------------------------------------------------------------------------------------------------------
elif staging_not_needed:
i = add_to(step_funcs, i, hive_table_definition_step,
target_table_name, dataset, s3_step_path,
local_step_path, action_id, False)
i = add_to(step_funcs, i, hive_msck_repair_table_step,
target_table_name, s3_step_path, action_id)
# ------------------------------------------------------------------------------------------------------------
# one or more staging tables are needed
# ------------------------------------------------------------------------------------------------------------
else:
stage_dataset = dataset
target_dataset = Dataset.from_dict(dataset.to_dict())
target_dataset.data.data_format = DataFormat(
target_file_format, target_row_format, 0, target_delimited_by,
target_quoted_by, target_escaped_by, target_null_string)
target_dataset.data.compression = target_compression
drop_table_names.append(stage_table_name)
# --------------------------------------------------------------------------------------------------------
# define string types for JSON/REGEX based datasets (safe), and we will cast appropriately during insert
# --------------------------------------------------------------------------------------------------------
if stage_dataset.data.data_format.row_format in [
RowFormat.JSON, RowFormat.REGEX
]:
# make a copy since we are modifying the columns
stage_dataset = Dataset.from_dict(dataset.to_dict())
assert isinstance(stage_dataset, Dataset)
for c in stage_dataset.data.columns:
c.data_type = DataType.STRING
i = add_to(step_funcs, i, hive_table_definition_step,
stage_table_name, stage_dataset, s3_step_path,
local_step_path, action_id, False)
i = add_to(step_funcs, i, hive_table_definition_step,
target_table_name, target_dataset, s3_step_path,
local_step_path, action_id, False)
i = add_to(step_funcs, i, hive_msck_repair_table_step,
stage_table_name, s3_step_path, action_id)
# --------------------------------------------------------------------------------------------------------
# hive has issues creating parquet files
# --------------------------------------------------------------------------------------------------------
if target_file_format != FileFormat.PARQUET:
i = add_to(step_funcs, i, hive_copy_to_table, stage_dataset,
stage_table_name, target_dataset, target_table_name,
s3_step_path, local_step_path, action_id, None)
# --------------------------------------------------------------------------------------------------------
# impala is better for creating parquet files
# --------------------------------------------------------------------------------------------------------
else:
# ----------------------------------------------------------------------------------------------------
# no additional staging tables needed if the source dataset file format is RCFILE (impala friendly)
# ----------------------------------------------------------------------------------------------------
if dataset.data.data_format.file_format == FileFormat.RCFILE:
i = add_to(step_funcs, i, hive_copy_to_table,
stage_dataset, stage_table_name, target_dataset,
target_table_name, s3_step_path,
local_step_path, action_id, None)
# ----------------------------------------------------------------------------------------------------
# impala cannot read all hive formats, so we will introduce another staging table
# ----------------------------------------------------------------------------------------------------
else:
rc_table_name = target_table_name + '_rcfile_stage_for_action_' + action_id
rc_dataset = Dataset.from_dict(target_dataset.to_dict())
rc_dataset.data.data_format = DataFormat(
FileFormat.RCFILE, RowFormat.NONE, 0)
rc_dataset.data.compression = Compression.NONE
drop_table_names.append(rc_table_name)
i = add_to(step_funcs, i, hive_table_definition_step,
rc_table_name, rc_dataset, s3_step_path,
local_step_path, action_id, False)
i = add_to(step_funcs, i, hive_copy_to_table,
stage_dataset, stage_table_name, rc_dataset,
rc_table_name, s3_step_path, local_step_path,
action_id, None)
i = add_to(step_funcs, i, impala_copy_to_table, rc_dataset,
rc_table_name, target_dataset,
target_table_name, s3_step_path,
local_step_path, action_id)
# ------------------------------------------------------------------------------------------------------------
# at this point, the load should be considered complete even if something goes wrong in the steps below,
# so we will indicate this in the step wrapper
# ------------------------------------------------------------------------------------------------------------
considered_successful_at_this_index = i
# ------------------------------------------------------------------------------------------------------------
# drop any staging tables created
# ------------------------------------------------------------------------------------------------------------
if drop_table_names:
script = '\n'.join(
['DROP TABLE %s;' % name for name in drop_table_names])
i = add_to(step_funcs, i, hive_run_script_contents_step, script,
s3_step_path, local_step_path, action_id)
# ------------------------------------------------------------------------------------------------------------
# inform impala about changes
# ------------------------------------------------------------------------------------------------------------
if not target_is_dynamodb:
i = add_to(step_funcs, i, impala_invalidate_metadata_step,
s3_step_path, action_id)
total_steps = i - 1
steps = []
for index, f in enumerate(step_funcs, 1):
step_wrapper = f(total_steps)
assert isinstance(step_wrapper, StepWrapper)
if index >= considered_successful_at_this_index:
step_wrapper.action_considered_successful = True
steps.append(step_wrapper)
if not dry_run:
s3_copy_recursive(local_step_path, s3_step_path)
return steps
finally:
shutil.rmtree(tempdir)
def setUp(self):
dart = Dart(host='localhost', port=5000)
""" :type dart: dart.client.python.dart_client.Dart """
self.dart = dart
cs = [Column('c1', DataType.VARCHAR, 50), Column('c2', DataType.BIGINT)]
df = DataFormat(FileFormat.PARQUET, RowFormat.NONE)
dataset_data = DatasetData('test-dataset0', 'test_dataset_table0', 's3://test/dataset/0/%s' + random_id(), df, cs)
self.dataset0 = self.dart.save_dataset(Dataset(data=dataset_data))
cs = [Column('c1', DataType.VARCHAR, 50), Column('c2', DataType.BIGINT)]
df = DataFormat(FileFormat.PARQUET, RowFormat.NONE)
dataset1_location = 's3://test/dataset/1/%s' + random_id()
dataset_data = DatasetData('test-dataset1', 'test_dataset_table1', dataset1_location, df, cs)
self.dataset1 = self.dart.save_dataset(Dataset(data=dataset_data))
cs = [Column('c1', DataType.VARCHAR, 50), Column('c2', DataType.BIGINT)]
df = DataFormat(FileFormat.PARQUET, RowFormat.NONE)
dataset_data = DatasetData('test-dataset2-no-show', 'test_dataset_table2', 's3://test/dataset/2/%s' + random_id(), df, cs)
self.dataset2 = self.dart.save_dataset(Dataset(data=dataset_data))
s = Subscription(data=SubscriptionData('test-subscription0', self.dataset0.id))
self.subscription0 = self.dart.save_subscription(s)
s = Subscription(data=SubscriptionData('test-subscription2-no-show', self.dataset2.id))
self.subscription2 = self.dart.save_subscription(s)
dst_args = {'action_sleep_time_in_seconds': 0}
dst = Datastore(data=DatastoreData('test-datastore0', 'no_op_engine', args=dst_args, state=DatastoreState.TEMPLATE))
self.datastore0 = self.dart.save_datastore(dst)
dst = Datastore(data=DatastoreData('test-datastore1', 'no_op_engine', args=dst_args, state=DatastoreState.TEMPLATE))
self.datastore1 = self.dart.save_datastore(dst)
dst = Datastore(data=DatastoreData('test-datastore2-no-show', 'no_op_engine', args=dst_args, state=DatastoreState.ACTIVE))
self.datastore2 = self.dart.save_datastore(dst)
wf0 = Workflow(data=WorkflowData('test-workflow0', self.datastore0.id, state=WorkflowState.ACTIVE))
self.workflow0 = self.dart.save_workflow(wf0, self.datastore0.id)
wf1 = Workflow(data=WorkflowData('test-workflow1', self.datastore1.id, state=WorkflowState.ACTIVE))
self.workflow1 = self.dart.save_workflow(wf1, self.datastore1.id)
wf2 = Workflow(data=WorkflowData('test-workflow2-no-show', self.datastore2.id, state=WorkflowState.ACTIVE))
self.workflow2 = self.dart.save_workflow(wf2, self.datastore2.id)
a_args = {'source_hdfs_path': 'hdfs:///user/hive/warehouse/test', 'destination_s3_path': dataset1_location}
a00 = Action(data=ActionData(NoOpActionTypes.action_that_succeeds.name, NoOpActionTypes.action_that_succeeds.name, state=ActionState.TEMPLATE))
a01 = Action(data=ActionData(NoOpActionTypes.consume_subscription.name, NoOpActionTypes.consume_subscription.name, {'subscription_id': self.subscription0.id}, state=ActionState.TEMPLATE))
a02 = Action(data=ActionData(NoOpActionTypes.action_that_succeeds.name, NoOpActionTypes.action_that_succeeds.name, state=ActionState.TEMPLATE))
a03 = Action(data=ActionData(NoOpActionTypes.copy_hdfs_to_s3_action.name, NoOpActionTypes.copy_hdfs_to_s3_action.name, a_args, state=ActionState.TEMPLATE))
a04 = Action(data=ActionData(NoOpActionTypes.action_that_succeeds.name, NoOpActionTypes.action_that_succeeds.name, state=ActionState.TEMPLATE))
self.action00, self.action01, self.action02, self.action03, self.action04 = \
self.dart.save_actions([a00, a01, a02, a03, a04], workflow_id=self.workflow0.id)
a10 = Action(data=ActionData(NoOpActionTypes.load_dataset.name, NoOpActionTypes.load_dataset.name, {'dataset_id': self.dataset1.id}, state=ActionState.TEMPLATE))
self.action10 = self.dart.save_actions([a10], workflow_id=self.workflow1.id)
a20 = Action(data=ActionData(NoOpActionTypes.action_that_succeeds.name, NoOpActionTypes.action_that_succeeds.name, state=ActionState.HAS_NEVER_RUN))
a21 = Action(data=ActionData(NoOpActionTypes.load_dataset.name, NoOpActionTypes.load_dataset.name, {'dataset_id': self.dataset2.id}, state=ActionState.TEMPLATE))
self.action20 = self.dart.save_actions([a20], datastore_id=self.datastore2.id)
self.action21 = self.dart.save_actions([a21], workflow_id=self.workflow2.id)
self.event1 = self.dart.save_event(Event(data=EventData('test-event1', state=EventState.ACTIVE)))
self.event2 = self.dart.save_event(Event(data=EventData('test-event2-no-show', state=EventState.ACTIVE)))
tr_args = {'event_id': self.event1.id}
tr = Trigger(data=TriggerData('test-event-trigger1', 'event', [self.workflow1.id], tr_args, TriggerState.ACTIVE))
self.event_trigger1 = self.dart.save_trigger(tr)
tr_args = {'event_id': self.event2.id}
tr = Trigger(data=TriggerData('test-event-trigger2-no-show', 'event', [self.workflow2.id], tr_args, TriggerState.ACTIVE))
self.event_trigger2 = self.dart.save_trigger(tr)
st_args = {'fire_after': 'ALL', 'completed_trigger_ids': [self.event_trigger1.id]}
st = Trigger(data=TriggerData('test-super-trigger1', 'super', None, st_args, TriggerState.ACTIVE))
self.super_trigger1 = self.dart.save_trigger(st)
st_args = {'fire_after': 'ANY', 'completed_trigger_ids': [self.super_trigger1.id]}
st = Trigger(data=TriggerData('test-super-trigger2', 'super', [self.workflow1.id], st_args, TriggerState.ACTIVE))
self.super_trigger2 = self.dart.save_trigger(st)
def test_hive_table_definition_step(self):
ds = Dataset(data=DatasetData(
name='weblogs_v01',
table_name='weblogs',
location='s3://wsm-log-servers/weblogs/www.retailmenot.com/ec2/',
data_format=DataFormat(
file_format=FileFormat.TEXTFILE,
row_format=RowFormat.REGEX,
regex_input=
"(?<ip>^(?:(?:unknown(?:,\\s)?|(?:\\d+\\.\\d+\\.\\d+\\.\\d+(?:,\\s)?))+)|\\S*)\\s+\\S+\\s+(?<userIdentifier>(?:[^\\[]+|\\$\\S+\\['\\S+'\\]|\\[username\\]))\\s*\\s+\\[(?<requestDate>[^\\]]+)\\]\\s+\"(?<httpMethod>(?:GET|HEAD|POST|PUT|DELETE|TRACE))\\s(?<urlPath>(?:[^ ?]+))(?:\\?(?<queryString>(?:[^ ]+)))?\\sHTTP/(?<httpVersion>(?:[\\d\\.]+))\"\\s+(?<statusCode>[0-9]+)\\s+(?<bytesSent>\\S+)\\s+\"(?<referrer>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<userAgent>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+(?<responseTime>[-0-9]*)\\s+\"(?<hostName>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<userFingerprint>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<userId>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<sessionId>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<requestId>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<visitorId>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<vegSlice>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<fruitSlice>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s+\"(?<cacheHitMiss>(?:[^\"\\\\]*(?:\\\\.[^\"\\\\]*)*))\"\\s*\\Z",
regex_output=
"%1$s %2$s %3$s %4$s %5$s %6$s %7$s %8$s %9$s %10$s %11$s %12$s %13$s %14$s %15$s %16$s %17$s %18$s %19$s %20$s %21s",
),
columns=[
Column('ip', 'STRING'),
Column('user', 'STRING'),
Column('requestDate',
'TIMESTAMP',
date_pattern='dd/MMM/yyyy:HH:mm:ss Z'),
Column('httpMethod', 'STRING'),
Column('urlPath', 'STRING'),
Column('queryString', 'STRING'),
Column('httpVersion', 'STRING'),
Column('statusCode', 'STRING'),
Column('bytesSent', 'INT'),
Column('referrer', 'STRING'),
Column('userAgent', 'STRING'),
Column('responseTime', 'BIGINT'),
Column('hostname', 'STRING'),
Column('userFingerprint', 'STRING'),
Column('userId', 'STRING'),
Column('sessionId', 'STRING'),
Column('requestId', 'STRING'),
Column('visitorId', 'STRING'),
Column('vegSlice', 'STRING'),
Column('fruitSlice', 'STRING'),
Column('cacheHitMiss', 'STRING'),
],
compression='GZIP',
partitions=[
Column('year', 'STRING'),
Column('week', 'STRING'),
Column('day', 'STRING'),
],
))
call('mkdir -p /tmp/dart-emr-test/hive/')
this_path = os.path.dirname(os.path.abspath(__file__))
shutil.copyfile(
this_path + '/../../../engine/emr/steps/hive/copy_to_table.hql',
'/tmp/dart-emr-test/hive/copy_to_table.hql')
hive_copy_to_table(ds, 'weblogs_stage', ds, 'weblogs', 's3://test',
'/tmp/dart-emr-test/', 'actionid123', None, 1, 1)
with open(os.path.join(this_path, 'copy_to_table_weblogs.hql')) as f:
expected_contents = f.read()
with open('/tmp/dart-emr-test/hive/copy_to_table_weblogs.hql') as f:
actual_contents = f.read()
self.assertEqual(expected_contents, actual_contents)
def test_hive_table_definition_step(self):
ds = Dataset(data=DatasetData(
name='owen_eu_v01',
table_name='owen_eu',
location='s3://s3-rpt-uss-dat-warehouse/prd/inbound/overlord/eu-all-events',
data_format=DataFormat(
file_format=FileFormat.TEXTFILE,
row_format=RowFormat.JSON,
),
columns=[
Column('host', 'STRING', path='metadata.host'),
Column('pageName', 'STRING', path='owen.context.pageName'),
Column('viewInstanceUuid', 'STRING', path='owen.context.viewInstanceUuid'),
Column('previousPageName', 'STRING', path='owen.context.previousPageName'),
Column('previousViewInstanceUuid', 'STRING', path='owen.context.previousViewInstanceUuid'),
Column('session', 'STRING', path='owen.context.session'),
Column('pageType', 'STRING', path='owen.context.pageType'),
Column('propertyName', 'STRING', path='owen.context.propertyName'),
Column('enviroment', 'STRING', path='owen.context.environment'),
Column('appForegroundFlag', 'BOOLEAN', path='owen.context.appForegroundFlag'),
Column('bluetoothEnabledFlag', 'BOOLEAN', path='owen.context.bluetoothEnabledFlag'),
Column('favoriteFlag', 'BOOLEAN', path='owen.context.favoriteFlag'),
Column('locationEnabledFlag', 'BOOLEAN', path='owen.context.locationEnabledFlag'),
Column('loggedInFlag', 'BOOLEAN', path='owen.context.loggedInFlag'),
Column('notificationEnabledFlag', 'BOOLEAN', path='owen.context.notificationEnabledFlag'),
Column('personalizationFlag', 'BOOLEAN', path='owen.context.personalizationFlag'),
Column('advertiserUuid', 'STRING', path='owen.context.advertiserUuid'),
Column('udid', 'STRING', path='owen.context.udid'),
Column('userQualifier', 'STRING', path='owen.context.userQualifier'),
Column('userId', 'STRING', path='owen.context.custom.legacy.userId'),
Column('userUuid', 'STRING', path='owen.context.userUuid'),
Column('macAddress', 'STRING', path='owen.context.macAddress'),
Column('ipAddress', 'STRING', path='owen.context.ipAddress'),
Column('osVersion', 'STRING', path='owen.context.osVersion'),
Column('osFamily', 'STRING', path='owen.context.osFamily'),
Column('osName', 'STRING', path='owen.context.osName'),
Column('browserFamily', 'STRING', path='owen.context.browserFamily'),
Column('deviceCategory', 'STRING', path='owen.context.deviceCategory'),
Column('deviceMake', 'STRING', path='owen.context.mobileDeviceMake'),
Column('deviceModel', 'STRING', path='owen.context.mobileDeviceModel'),
Column('connectionType', 'STRING', path='owen.context.connectionType'),
Column('userAgent', 'STRING', path='owen.context.userAgent'),
Column('geofenceId', 'STRING', path='owen.context.custom.legacy.geofenceId'),
Column('eventTimestamp', 'TIMESTAMP', path='owen.event.eventTimestamp', date_pattern="yyyy-MM-dd'T'HH:mm:ssZ"),
Column('eventInstanceUuid', 'STRING', path='owen.event.eventInstanceUuid'),
Column('eventPlatformVersion', 'STRING', path='owen.event.eventPlatformVersion'),
Column('eventVersion', 'STRING', path='owen.event.eventVersion'),
Column('eventCategory', 'STRING', path='owen.event.eventCategory'),
Column('eventName', 'STRING', path='owen.event.eventName'),
Column('eventAction', 'STRING', path='owen.event.eventAction'),
Column('eventPlatform', 'STRING', path='owen.event.eventPlatform'),
Column('testUnixTimestampSecondsPattern', 'TIMESTAMP', path='some.fake.path.testUnixTimestampSecondsPattern', date_pattern='UNIX_TIMESTAMP_SECONDS'),
Column('testUnixTimestampMillisPattern', 'TIMESTAMP', path='some.fake.path.testUnixTimestampMillisPattern', date_pattern='UNIX_TIMESTAMP_MILLIS'),
],
compression='GZIP',
partitions=[
Column('year', 'STRING'),
Column('week', 'STRING'),
Column('day', 'STRING'),
],
))
call('mkdir -p /tmp/dart-emr-test/hive/')
this_path = os.path.dirname(os.path.abspath(__file__))
shutil.copyfile(this_path + '/../../../engine/emr/steps/hive/copy_to_table.hql', '/tmp/dart-emr-test/hive/copy_to_table.hql')
action_id = 'actionid123'
target_dataset = Dataset.from_dict(ds.to_dict())
target_dataset.data.data_format.num_header_rows = 0
target_dataset.data.data_format = DataFormat(FileFormat.RCFILE, RowFormat.NONE)
stage_dataset = Dataset.from_dict(ds.to_dict())
assert isinstance(stage_dataset, Dataset)
for c in stage_dataset.data.columns:
c.data_type = DataType.STRING
hive_copy_to_table(stage_dataset, 'owen_eu_stage', target_dataset, 'owen_eu', 's3://test', '/tmp/dart-emr-test/', action_id, None, 1, 1)
with open(os.path.join(this_path, 'copy_to_table_owen_eu.hql')) as f:
expected_contents = f.read()
with open('/tmp/dart-emr-test/hive/copy_to_table_owen_eu.hql') as f:
actual_contents = f.read()
self.assertEqual(expected_contents, actual_contents)