def step_(context, snapshot_type, step_name):
context.adg_s3_prefix = os.path.join(context.mongo_snapshot_path,
context.test_run_name)
context.adg_export_date = datetime.now().strftime("%Y-%m-%d")
payload = {
CORRELATION_ID: context.test_run_name,
S3_PREFIX: context.adg_s3_prefix,
SNAPSHOT_TYPE: snapshot_type,
EXPORT_DATE: context.adg_export_date,
}
payload_json = json.dumps(payload)
cluster_response = invoke_lambda.invoke_adg_emr_launcher_lambda(
payload_json)
cluster_arn = cluster_response[CLUSTER_ARN]
cluster_arn_arr = cluster_arn.split(":")
cluster_identifier = cluster_arn_arr[len(cluster_arn_arr) - 1]
cluster_identifier_arr = cluster_identifier.split("/")
cluster_id = cluster_identifier_arr[len(cluster_identifier_arr) - 1]
console_printer.print_info(f"Started emr cluster : '{cluster_id}'")
step = aws_helper.get_emr_cluster_step(step_name, cluster_id)
context.adg_cluster_id = cluster_id
step_id = step["Id"]
console_printer.print_info(f"Step id for '{step_name}' : '{step_id}'")
if step is not None:
execution_state = aws_helper.poll_emr_cluster_step_status(
step_id, cluster_id, 2500)
if execution_state != COMPLETED_STATUS:
raise AssertionError(
f"'{step_name}' step failed with final status of '{execution_state}'"
)
def s3_clear_snapshot(context, timeout=30, **kwargs):
console_printer.print_info("Executing 's3_clear_snapshot' fixture")
aws_helper.clear_s3_prefix(
context.mongo_snapshot_bucket,
os.path.join(context.mongo_snapshot_path, context.test_run_name),
True,
)
aws_helper.clear_s3_prefix(
context.mongo_snapshot_bucket,
os.path.join(context.snapshot_s3_status_path, context.test_run_name),
True,
)
context.add_cleanup(print, "Executing 's3_clear_snapshot' cleanup")
context.add_cleanup(
aws_helper.clear_s3_prefix,
context.mongo_snapshot_bucket,
os.path.join(context.mongo_snapshot_path, context.test_run_name),
True,
)
context.add_cleanup(
aws_helper.clear_s3_prefix,
context.mongo_snapshot_bucket,
os.path.join(context.snapshot_s3_status_path, context.test_run_name),
True,
)
def get_metadata_for_id_and_timestamp_from_file(table_name,
file_path,
topic_name,
wrap_id=False):
"""Returns the metadata for a given id and a tuple of the id and timestamp searched for.
Arguments:
table_name -- the table name to check
file_path -- the file containing the id
topic_name -- the topic name to get metadata for
wrap_id -- True is the id format should be wrapped with an "id" object (default False)
"""
console_printer.print_info(
f"Retrieving metadata for id from file in '{file_path}' in metadata table '{table_name}' with topic name of '{topic_name}'"
)
qualified_topic_name = template_helper.get_topic_name(topic_name)
record_id = file_helper.get_id_object_from_json_file(file_path)
record_timestamp = file_helper.get_timestamp_as_long_from_json_file(
file_path)
id_string = json.dumps(record_id)
if wrap_id:
id_string = json.dumps({"id": id_string})
id_string_qualified = id_string.replace(" ", "")
results = get_metadata_for_specific_id_and_timestamp_in_topic(
table_name, id_string_qualified, record_timestamp,
qualified_topic_name)
return (id_string_qualified, record_timestamp, results)
def get_metadata_for_specific_id_and_timestamp_in_topic(
table_name, id_string, timestamp, topic_name):
"""Returns the metadata for a given id and timestamp.
Arguments:
table_name -- the table name to check
id_string -- the json dumped id string
timestamp -- the timestamp as an int
topic_name -- the topic name to get metadata for
"""
console_printer.print_info(
f"Retrieving metadata for id of '{id_string}' in metadata table '{table_name}' with topic name of '{topic_name}' and timestamp of '{str(timestamp)}'"
)
qualified_topic_name = template_helper.get_topic_name(topic_name)
payload_dict = {
"table-name": table_name,
"hbase-id-like": id_string,
"topic-name-equals": qualified_topic_name,
"hbase-timestamp-equals": timestamp,
}
payload_json = json.dumps(payload_dict)
return invoke_lambda.invoke_ingestion_metadata_query_lambda(payload_json)
def generate_script_step(emr_cluster_id,
script_location,
step_type,
command_line_arguments=None):
"""Starts a step of type script and returns its id.
Keyword arguments:
emr_cluster_id -- the id of the cluster
script_location -- the location on the EMR instance (or an S3 URI) of the script to run
step_type -- the name of the step type being run (i.e. "major compaction")
command_line_arguments -- the arguments to pass to the script as a string, if any
"""
arguments_array = [script_location]
if command_line_arguments is not None:
arguments_array.extend(command_line_arguments.split())
console_printer.print_info(
f"Executing script step type '{step_type}' with arguments of \"{arguments_array}\""
)
bash_command = " ".join(arguments_array)
console_printer.print_info(
f"Converted arguments array to bash command of '{bash_command}'")
step_name = f"Automated Script Step - {step_type}"
return generate_local_step(emr_cluster_id, bash_command, step_name)
def s3_clear_historic_data_start(context, timeout=30, **kwargs):
console_printer.print_info("Executing 's3_clear_historic_data_start' fixture")
aws_helper.clear_s3_prefix(
context.s3_ingest_bucket,
os.path.join(context.ucfs_historic_data_prefix, context.test_run_name),
True,
)
def claimant_api_setup(context):
console_printer.print_info("Executing 'claimant_api_setup' fixture")
context.execute_steps(
f"given The claimant API 'business' region is set to 'Ireland'"
)
context.execute_steps(f"given The claimant API 'storage' region is set to 'London'")
context.execute_steps(f"given The nino salt has been retrieved")
def generate_snapshot_file_from_hbase_records(test_run_name, topic,
hbase_records_folder,
output_folder):
"""Generate raw snapshot file from hbase db object record for snapshot sender output comparisons.
Keyword arguments:
test_run_name -- unique name for this test run
topic -- singular topic
hbase_records_folder -- location for files containing a singular hbase record
output_folder -- output folder for snapshot records
"""
console_printer.print_info(
f"Generating snapshot output for topic '{topic}' and folder '{hbase_records_folder}'"
)
hbase_records_folder_for_topic = os.path.join(hbase_records_folder, topic)
snapshot_file_name = f"{topic}-snapshot-{test_run_name}.txt.gz.enc"
hbase_records = file_helper.get_contents_of_files_in_folder(
hbase_records_folder_for_topic, False)
snapshot_file = os.path.join(output_folder, snapshot_file_name)
with open(snapshot_file, "wt") as file_to_write:
file_to_write.write("\n".join(hbase_records))
return snapshot_file
def historic_data_importer_start_data_load(context, timeout=30, **kwargs):
console_printer.print_info(
"Executing 'historic_data_importer_start_data_load' fixture"
)
historic_data_importer_start_base(
context, context.mongo_data_load_prefixes_comma_delimited
)
def retrieve_assessment_periods_from_claimant_data_file(
input_data_file_name, fixture_files_root, fixture_data_folder):
"""Gets all the assessment periods from the given data file and returns them as an array of arrays
Keyword arguments:
input_data_file_name -- the input file name containing the data
fixture_files_root -- the local path to the feature file to send
fixture_data_folder -- the folder from the root of the fixture data
"""
data_file_name = os.path.join(fixture_files_root, fixture_data_folder,
input_data_file_name)
console_printer.print_info(
f"Retrieving assessment periods from data file at '{data_file_name}'")
return_data = []
input_data = yaml.safe_load(open(data_file_name))
for item in input_data:
if "assessment_periods" in item:
for assessment_period in item["assessment_periods"]:
return_data.append(assessment_period)
console_printer.print_info(
f"Successfully retrieved '{len(return_data)}' assessment periods")
return return_data
def clean_up_role_and_s3_objects(context, timeout=30, **kwargs):
console_printer.print_info("Executing 'clean_up_role_and_s3_objects' fixture")
aws_helper.remove_role(
context.analytical_test_e2e_role, context.analytical_test_e2e_policies
)
aws_helper.clear_session()
aws_helper.set_details_for_role_assumption(
context.aws_role, context.aws_session_timeout_seconds
)
if context.analytical_test_data_s3_location.get("path"):
aws_helper.remove_file_from_s3_and_wait_for_consistency(
context.published_bucket,
os.path.join(
context.analytical_test_data_s3_location["path"],
context.analytical_test_data_s3_location["file_name"],
),
)
if context.analytical_test_data_s3_location.get("paths"):
for path in context.analytical_test_data_s3_location["paths"]:
aws_helper.remove_file_from_s3_and_wait_for_consistency(
context.published_bucket,
os.path.join(
path, context.analytical_test_data_s3_location["file_name"]
),
)
def ingest_ecs_cluster_start(context, timeout=30, **kwargs):
console_printer.print_info("Executing 'ingestion_ecs_cluster_start' fixture")
context.last_scaled_asg = aws_helper.scale_asg_if_desired_count_is_not_already_set(
context.asg_prefix_ingestion_ecs_cluster,
int(context.asg_max_count_ingestion_ecs_cluster),
)
def generate_test_run_topics(context):
"""Generates the topics in use for this test run.
Keyword arguments:
context -- the behave context object
"""
console_printer.print_info(f"Generating the topics for this test run")
if context.config.userdata.get("IS_SYNTHETIC_DATA_INGESTION"):
context.topics = template_helper.generate_synthetic_data_topic_names(
context.synthetic_rawdata_prefix, context.aws_datasets_bucket
)
else:
context.topics = template_helper.generate_topic_names(
context.test_run_name,
int(context.number_of_topics_to_use),
context.db_name,
False,
)
context.topics_delimited = ",".join(context.topics)
context.topics_unique = template_helper.generate_topic_names(
context.test_run_name,
int(context.number_of_topics_to_use),
context.db_name,
True,
)
context.topics_unique_delimited = ",".join(context.topics_unique)
console_printer.print_info(f"Generated the topics for this test run")
def s3_clear_pdm_start(context, timeout=30, **kwargs):
console_printer.print_info("Executing 's3_clear_pdm_start' fixture")
aws_helper.clear_s3_prefix(
context.published_bucket,
os.path.join(context.fixture_path_local, "pdm-test-data"),
True,
)
def s3_clear_published_bucket_pdm_test_output(context, timeout=30, **kwargs):
console_printer.print_info(
"Executing 's3_clear_published_bucket_pdm_test_output' fixture"
)
aws_helper.clear_s3_prefix(
context.published_bucket, context.pdm_test_output_s3_prefix, False
)
def before_scenario(context, scenario):
global current_scenario
console_printer.print_info("Executing before scenario hook")
# Create temp scenario folder
context.temp_folder = os.path.join(context.root_temp_folder,
str(uuid.uuid4()))
console_printer.print_info(
f"Creating temp scenario folder at '{context.temp_folder}'")
os.makedirs(context.temp_folder)
# Reset topics each scenario
context.topics_for_test = []
for topic in context.topics:
context.topics_for_test.append({
"topic": topic,
"key": str(uuid.uuid4())
})
context.snapshot_files_temp_folder = os.path.join(context.temp_folder,
"snapshots")
os.makedirs(context.snapshot_files_temp_folder)
context.snapshot_files_hbase_records_temp_folder = os.path.join(
context.temp_folder, "hbase-records")
os.makedirs(context.snapshot_files_hbase_records_temp_folder)
context.historic_data_generations_count_per_test = 0
# Set the scenario
current_scenario = scenario
def step_impl(context, number_of_snapshots, match_type, snapshot_type):
s3_qualified_prefix = os.path.join(
context.mongo_snapshot_path,
context.test_run_name,
context.formatted_date,
snapshot_type,
)
topic_names = [
template_helper.get_topic_name(topic["topic"])
for topic in context.topics_for_test
]
snapshot_count = int(number_of_snapshots)
if snapshot_count == 0:
for result in aws_helper.assert_no_snapshots_in_s3_threaded(
topic_names, context.mongo_snapshot_bucket,
s3_qualified_prefix, 60):
console_printer.print_info(
f"Asserted no snapshots created in s3 with key of {result}")
else:
snapshot_string = "snapshots" if snapshot_count > 1 else "snapshot"
for result in aws_helper.assert_snapshots_in_s3_threaded(
topic_names,
context.mongo_snapshot_bucket,
s3_qualified_prefix,
snapshot_count,
context.timeout,
("exact" == match_type),
):
console_printer.print_info(
f"Asserted exactly {number_of_snapshots} {snapshot_string} created in s3 with key of {result}"
)
def dynamodb_clear_ingest_start(context, snapshot_type, topics_list):
console_printer.print_info("Executing 'dynamodb_clear_ingest_start' fixture")
updated_topics = message_helper.get_consolidated_topics_list(
topics_list,
snapshot_type,
context.default_topic_list_full_delimited,
context.default_topic_list_incremental_delimited,
[
context.generate_snapshots_topics_override,
context.send_snapshots_topics_override,
],
)
correlation_id = (
snapshots_helper.get_snapshot_run_correlation_id(
context.test_run_name, snapshot_type
)
if not context.send_snapshots_correlation_id_override
else context.send_snapshots_correlation_id_override
)
for topic in updated_topics:
topic_name = template_helper.get_topic_name(topic)
export_status_helper.delete_item_in_export_status_table(
context.dynamo_db_export_status_table_name, topic_name, correlation_id
)
def before_feature(context, feature):
global current_feature
console_printer.print_info("Executing before feature hook")
# Set the feature
current_feature = feature
def before_all(context):
# Ensure all required userdata was supplied
console_printer.set_log_level_info()
# Set variables that are simply from user data and string manipulations
environment_helper.set_test_run_common_variables(context)
environment_helper.set_manifest_variables(context)
# Assume AWS role before any calls
aws_helper.set_details_for_role_assumption(
context.aws_role, context.aws_session_timeout_seconds)
# Generate the topics for this test run
environment_helper.generate_test_run_topics(context)
# Clear and delete root temp folder if it exists
if os.path.exists(context.root_temp_folder):
console_printer.print_info(
f"Clearing out root temp folder at '{context.root_temp_folder}'")
file_helper.clear_and_delete_directory(context.root_temp_folder)
# Create the root temp folder for test run
console_printer.print_info(
f"Creating temp root folder at '{context.root_temp_folder}'")
os.makedirs(context.root_temp_folder)
def after_feature(context, feature):
global failed_feature
console_printer.print_info("Executing after feature hook")
# Set the feature
if feature.status == Status.failed:
failed_feature = feature
def ucfs_claimant_kafka_consumer_stop(context, timeout=30, **kwargs):
console_printer.print_info("Executing 'ucfs_claimant_kafka_consumer_stop' fixture")
aws_helper.scale_ecs_service_if_desired_count_is_not_already_set(
context.ucfs_claimant_api_kafka_consumer_cluster_name,
context.ucfs_claimant_api_kafka_consumer_service_name,
0,
)
def s3_clear_corporate_data_start(context, timeout=30, **kwargs):
console_printer.print_info("Executing 's3_clear_corporate_data_start' fixture")
aws_helper.clear_s3_prefix(
context.corporate_storage_s3_bucket_id,
context.cdl_data_load_s3_base_prefix_tests,
True,
)
def hbase_clear_ingest_equalities_start(context, timeout=30, **kwargs):
console_printer.print_info(
"Executing 'hbase_clear_ingest_equalities_start' fixture"
)
for topic in streaming_data_helper.generate_topics_override(
"kafka_equalities", context.topics
):
aws_helper.truncate_hbase_table(topic["topic"])
def step_impl(context, module_name):
schema_config = context.kickstart_schema_config[module_name]
if schema_config["record_layout"].lower() == "csv":
for collection in schema_config["schema"].keys():
s3_result_key = os.path.join(context.kickstart_hive_result_path,
f"e2e_{collection}.csv")
console_printer.print_info(f"S3 Request Location: {s3_result_key}")
file_content = aws_helper.get_s3_object(
None, context.published_bucket, s3_result_key).decode("utf-8")
actual_content = (file_content.replace("\t", ",").replace(
"NULL", "None").strip().splitlines())
expected_file_name = [
file for file in context.kickstart_current_run_input_files
if collection in file
][0]
console_printer.print_info(
f"Expected File Name: {expected_file_name}")
expected_content = file_helper.get_contents_of_file(
expected_file_name, False).splitlines()[1:]
for input_line, output_line in zip(actual_content,
expected_content):
assert (
input_line.lower() == output_line.lower()
), f"Expected result of '{input_line}', does not match '{output_line}' for collection {collection}"
elif schema_config["record_layout"].lower() == "json":
for collection in schema_config["schema"].keys():
s3_result_key = os.path.join(context.kickstart_hive_result_path,
f"e2e_{collection}.csv")
console_printer.print_info(f"S3 Request Location: {s3_result_key}")
file_content = aws_helper.get_s3_object(
None, context.published_bucket, s3_result_key).decode("utf-8")
actual_content = file_content.replace("NULL",
"None").strip().splitlines()
console_printer.print_info(
f"This the local file name in the list: {context.kickstart_current_run_input_files}"
)
expected_file_name = [
file for file in context.kickstart_current_run_input_files
if f"{module_name}-{collection}" in file
][0]
console_printer.print_info(
f"Expected File Name: {expected_file_name}")
expected_json = json.loads(
file_helper.get_contents_of_file(expected_file_name,
False))["data"]
expected_content = "\n".join([
"\t".join([str(record[field]) for field in record])
for record in expected_json
]).splitlines()
for input_line, output_line in zip(actual_content,
expected_content):
assert (
input_line.lower() == output_line.lower()
), f"Expected result of '{input_line}', does not match '{output_line}' for collection {collection}"
def generate_historic_load_data(
s3_bucket,
s3_prefix,
method,
file_count,
record_count,
topic,
input_template,
encrypted_key,
plaintext_key,
master_key,
input_folder,
max_worker_count,
static_key=None,
):
"""Generates required historic data files from the files in the given folder.
Keyword arguments:
s3_bucket -- the s3 bucket to send input files to
s3_prefix -- the s3 prefix to send input files to
method -- 'single', 'different', 'file' or 'record' for the key method to use for this data
file_count -- the number of files to create for the collection
record_count -- the number of records per file
topic -- the topic (contains db.collection)
input_template -- the name and location for the input template json file
encrypted_key -- the encrypted version of the plaintext key
plaintext_key -- the plaintext data key for encrypting the data file
master_key -- the master key used to encrypt the data key
input_folder -- the folder to store the generated input files in
static_key -- the key to use for the messages or None
max_worker_count -- max thread number
"""
global key_method
key_method = method
_generate_keys(file_count, record_count, static_key)
console_printer.print_info(
f"Generating {str(file_count)} load files with {str(record_count)} records in each using key method {key_method} for topic {topic}"
)
short_topic = template_helper.get_short_topic_name(topic)
for result_input in _generate_input_data_files_threaded(
s3_bucket,
s3_prefix,
file_count,
record_count,
short_topic,
input_template,
encrypted_key,
plaintext_key,
master_key,
input_folder,
max_worker_count,
):
console_printer.print_info(f"Generated input file {result_input}")
def generate_input_file(
file_number,
input_base_content,
input_folder,
input_file,
record_count,
file_count,
encrypted_key,
plaintext_key,
master_key,
initialisation_vector,
):
"""Creates a local input file for historic data and returns the location and file name.
Keyword arguments:
file_number -- the number of the current file per topic
input_base_content -- the content of the input template file
input_folder -- the location to create input files in
input_file -- the filename to create
record_count -- the number of records per file
file_count -- the number of desired files
encrypted_key -- the encrypted version of the plaintext key
plaintext_key -- the plaintext data key for encrypting the data file
master_key -- the master key used to encrypt the data key
initialisation_vector -- the initialisation vector to use for the encryption
"""
global keys
global key_method
console_printer.print_info(
f"Generating input file number {str(file_number)}")
local_keys = keys
file_contents = ""
file_full_path = os.path.join(input_folder, input_file)
for record_number in range(1, int(record_count) + 1):
current_key_index = get_current_key_index(key_method, file_number,
record_number, record_count)
key_for_record = local_keys[current_key_index][0]
(timestamp,
timestamp_string) = date_helper.add_milliseconds_to_timestamp(
_base_datetime_timestamp, file_number + record_number, True)
db_object = generate_uncrypted_record(timestamp_string,
input_base_content,
key_for_record, plaintext_key)
file_contents += json.dumps(json.loads(db_object)) + "\n"
encrypted_contents = generate_encrypted_record(initialisation_vector,
file_contents,
plaintext_key, True)
with open(file_full_path, "wb") as data:
data.write(encrypted_contents)
def step_impl(context):
for step in context.kickstart_step_ids:
console_printer.print_info(
f"check if the step with {step} is complete or not")
execution_state = aws_helper.poll_emr_cluster_step_status(
step, context.kickstart_adg_cluster_id, 1200)
if execution_state != COMPLETED_STATUS:
raise AssertionError(
f"The step Id {step} failed with final status of '{execution_state}'"
)
def step_impl(context, setting, skip_existing):
run_import_value = (
True if setting == "import" or setting == "import and manifest" else False
)
run_manifest_value = (
True if setting == "manifest" or setting == "import and manifest" else False
)
skip_existing_value = (
context.historic_data_ingestion_skip_existing_records_override is not None
and context.historic_data_ingestion_skip_existing_records_override.lower()
== "true"
)
skip_earlier_than = (
None
if not context.historic_data_ingestion_skip_earlier_than_override
else context.historic_data_ingestion_skip_earlier_than_override
)
skip_later_than = (
None
if not context.historic_data_ingestion_skip_later_than_override
else context.historic_data_ingestion_skip_later_than_override
)
console_printer.print_info(
"UC historic data import prefixes: "
+ f"{context.mongo_data_load_prefixes_comma_delimited}"
)
all_prefixes = template_helper.get_historic_data_importer_prefixes(
context.mongo_data_load_prefixes_comma_delimited,
context.historic_importer_use_one_message_per_path,
)
correlation_id = (
context.test_run_name
if not context.historic_importer_correlation_id_override
else context.historic_importer_correlation_id_override
)
for prefix in all_prefixes:
console_printer.print_info(
f"Sending work to historic importer for {prefix} in {context.s3_ingest_bucket}"
)
message_helper.send_start_import_message(
context.aws_sqs_queue_historic_data_importer,
prefix,
skip_earlier_than,
skip_later_than,
context.test_run_name,
run_import=run_import_value,
generate_manifest=run_manifest_value,
skip_existing_records=skip_existing_value,
correlation_id=correlation_id,
)
def s3_clear_k2hb_manifests_main(context, timeout=30, **kwargs):
console_printer.print_info("Executing 's3_clear_k2hb_manifests_main' fixture")
console_printer.print_info(
f"Clearing manifests from '{context.k2hb_manifest_write_s3_bucket}/{context.k2hb_main_manifest_write_s3_prefix}'"
)
aws_helper.clear_s3_prefix(
context.k2hb_manifest_write_s3_bucket,
context.k2hb_main_manifest_write_s3_prefix,
True,
False,
)
