v1
This commit is contained in:
admin
497
service_request_metrics/main.py
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497
service_request_metrics/main.py
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@@ -0,0 +1,497 @@
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__generated_with = "0.13.15"
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# %%
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import sys
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import time
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from pyspark.sql.utils import AnalysisException
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sys.path.append('/opt/spark/work-dir/')
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from workflow_templates.spark.udf_manager import bootstrap_udfs
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from util import (
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get_logger, observe_metrics, collect_metrics, log_info, log_error, forgiving_serializer,
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run_component, apply_data_quality, compute_dq_stats, enforce_error_threshold,
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build_dq_error_log, build_api_error_log, ERROR_LOG_SCHEMA, RetryConfig, with_retry,
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app_scoped_error_code,
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registry_error_code,
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rewrite_response_body_json_access,
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rewrite_response_body_json_access_if_json,
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)
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from exception_utils import (
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ErrorMessage,
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Severity,
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ConnectionException,
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AuthenticationException,
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SSLException,
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RateLimitException,
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ServiceUnavailableException,
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TimeoutException,
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ValidationException,
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SchemaMappingException,
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ExpressionException,
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MergeException,
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ConfigurationException,
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RetryExhaustedException,
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format_exception,
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mask_pii,
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mask_pii_dict,
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)
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from py4j.protocol import Py4JJavaError
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from component_error_handler import handle_analysis_error, handle_java_error, classify_java_error
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from util import get_logger, observe_metrics, collect_metrics, log_info, log_error, forgiving_serializer, set_correlation_id, set_workflow_context
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from pyspark.sql.functions import udf
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from pyspark.sql.functions import count, expr, lit, input_file_name
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from pyspark.sql.types import StringType, IntegerType, MapType, StructType,StructField
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from postal.parser import parse_address
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import uuid
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from pathlib import Path
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from pyspark import SparkConf, Row
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from pyspark.sql import SparkSession
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from pyspark.sql.observation import Observation
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from pyspark import StorageLevel
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import os
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import pandas as pd
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import polars as pl
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import pyarrow as pa
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from pyspark.sql.functions import approx_count_distinct, avg, collect_list, collect_set, corr, count, countDistinct, covar_pop, covar_samp, first, kurtosis, last, max, mean, min, skewness, stddev, stddev_pop, stddev_samp, sum, var_pop, var_samp, variance,expr,to_json,struct, date_format, col, lit, when, regexp_replace, ltrim, lpad, format_number
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from functools import reduce
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from handle_structs_or_arrays import preprocess_then_expand
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import requests
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from requests.adapters import HTTPAdapter
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from urllib3.util.retry import Retry
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from jinja2 import Template
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import json
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import orjson
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from ocular_ai_sdk import OcularClient
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from ocular_ai_sdk.exceptions import (
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OcularSDKException,
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AuthenticationError,
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ResourceNotFoundError
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)
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from secrets_manager import SecretsManager
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from WorkflowManager import WorkflowDSL, WorkflowManager
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from KnowledgebaseManager import KnowledgebaseManager
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from gitea_client import GiteaClient, WorkspaceVersionedContent
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from FilesystemManager import FilesystemManager, SupportedFilesystemType
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from Materialization import Materialization
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init_start_time=time.time()
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LOGGER = get_logger()
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alias_str='abcdefghijklmnopqrstuvwxyz'
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workspace = os.getenv('WORKSPACE') or 'exp360uat'
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workflow = 'service_request_metrics'
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execution_environment = os.getenv('EXECUTION_ENVIRONMENT') or 'CLUSTER'
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job_id = os.getenv("EXECUTION_ID") or str(uuid.uuid4())
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retry_job_id = os.getenv("RETRY_EXECUTION_ID") or ''
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correlation_id = job_id
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set_correlation_id(correlation_id)
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set_workflow_context(workspace=workspace, workflow=workflow, job_id=job_id, retry_job_id=retry_job_id, execution_environment=execution_environment)
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log_info(LOGGER, f"Workspace: '{workspace}', Workflow: '{workflow}', Execution Environment: '{execution_environment}', Job Id: '{job_id}', Retry Job Id: '{retry_job_id}', Correlation Id: '{correlation_id}'")
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sm = SecretsManager(os.getenv('SECRET_MANAGER_URL'), os.getenv('SECRET_MANAGER_NAMESPACE'), os.getenv('SECRET_MANAGER_ENV'), os.getenv('SECRET_MANAGER_TOKEN'))
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secrets = sm.list_secrets(workspace)
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import dremio_operations
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dremio_operations.configure(secrets)
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import kb_query
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kb_query.configure(secrets)
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gitea_client=GiteaClient(os.getenv('GITEA_HOST'), os.getenv('GITEA_TOKEN'), os.getenv('GITEA_OWNER') or 'gitea_admin', os.getenv('GITEA_REPO') or 'tenant1')
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workspaceVersionedContent=WorkspaceVersionedContent(gitea_client)
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client = OcularClient(
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pat_token=secrets.get('OCULAR_AI_PAT_TOKEN')
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)
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if 'AZURE_SERVICE_PRINCIPAL' in secrets:
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_storage_options=orjson.loads(secrets['AZURE_SERVICE_PRINCIPAL'])
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else:
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_storage_options = {
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'key': secrets.get('S3_ACCESS_KEY'),
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'secret': secrets.get('S3_SECRET_KEY'),
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'region': secrets.get('S3_REGION')
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}
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filesystemManager = FilesystemManager.create(secrets.get('LAKEHOUSE_BUCKET'), storage_options=_storage_options)
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if retry_job_id:
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logs = Materialization.get_execution_history_by_job_id(filesystemManager, secrets.get('LAKEHOUSE_BUCKET'), workspace, workflow, retry_job_id, selected_components=['finalize']).to_dicts()
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if len(logs) == 1 and logs[0].get('metrics').get('execute_status') == 'SUCCESS':
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log_info(LOGGER, f"Workspace: '{workspace}', Workflow: '{workflow}', Execution Environment: '{execution_environment}', Job Id: '{job_id}' - Retry Job Id: '{retry_job_id}' was already successful. Hence exiting to forward processing to next in chain.")
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sys.exit(0)
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_conf = SparkConf()
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_params = {
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"spark.jars.ivy": "/opt/spark/.ivy2/",
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"spark.hadoop.fs.s3a.access.key": secrets.get('S3_ACCESS_KEY'),
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"spark.hadoop.fs.s3a.secret.key": secrets.get('S3_SECRET_KEY'),
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"spark.hadoop.fs.s3a.aws.region": secrets.get("S3_REGION") or "us-east-1",
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"spark.sql.catalog.dremio.warehouse" : secrets.get('LAKEHOUSE_BUCKET'),
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"spark.hadoop.fs.s3a.aws.credentials.provider": "com.amazonaws.auth.DefaultAWSCredentialsProviderChain",
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"spark.hadoop.fs.s3.aws.credentials.provider": "com.amazonaws.auth.DefaultAWSCredentialsProviderChain",
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"spark.sql.catalog.dremio" : "org.apache.iceberg.spark.SparkCatalog",
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"spark.sql.catalog.dremio.type" : "hadoop",
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"spark.hadoop.fs.s3a.impl": "org.apache.hadoop.fs.s3a.S3AFileSystem",
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"spark.hadoop.fs.s3.impl": "org.apache.hadoop.fs.s3a.S3AFileSystem",
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"spark.hadoop.fs.gs.impl": "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem",
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"spark.sql.extensions": "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions"
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}
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if filesystemManager.storage_type == SupportedFilesystemType.AZUREBLOB:
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_params[f"fs.azure.account.auth.type.{_storage_options['account_name']}.dfs.core.windows.net"] = "OAuth"
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_params[f"fs.azure.account.oauth.provider.type.{_storage_options['account_name']}.dfs.core.windows.net"] = "org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider"
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_params[f"fs.azure.account.oauth2.client.id.{_storage_options['account_name']}.dfs.core.windows.net"] = _storage_options['client_id']
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_params[f"fs.azure.account.oauth2.client.secret.{_storage_options['account_name']}.dfs.core.windows.net"] = _storage_options['client_secret']
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_params[f"fs.azure.account.oauth2.client.endpoint.{_storage_options['account_name']}.dfs.core.windows.net"] = f"https://login.microsoftonline.com/{_storage_options['tenant_id']}/oauth2/v2.0/token"
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_conf.setAll(list(_params.items()))
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spark = SparkSession.builder.appName(workspace).config(conf=_conf).getOrCreate()
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bootstrap_udfs(spark)
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materialization = Materialization(spark, secrets.get('LAKEHOUSE_BUCKET'), workspace, workflow, job_id, retry_job_id, execution_environment, LOGGER)
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init_dependency_key="init"
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init_end_time=time.time()
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# %%
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ActionsAuditData_start_time=time.time()
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ActionsAuditData_fail_on_error=""
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try:
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_ActionsAuditData_options = {
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'jdbc':{
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'dbtable': """actionsaudit""",
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'url':secrets.get(''),
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'driver':''
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},
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'kafka' : {
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'kafka.bootstrap.servers' : secrets.get('OCULAR_KAFKA_BOOTSTRAP_SERVERS'),
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'subscribe' : '',
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'startingOffsets' : 'earliest'
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},
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'cobol' : {
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'copybook' : '',
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'encoding' : '',
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'is_text': False,
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'schema_retention_policy' : 'collapse_root'
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}
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}
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_reader = spark.read.format('iceberg')
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_ActionsAuditData_load_path = 'dremio.actionsaudit'
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_ActionsAuditData_input_data = {
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"component": "ActionsAuditData",
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"format": "iceberg",
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"iceberg_catalog": """dremio""",
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"table_name": """actionsaudit""",
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}
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try:
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ActionsAuditData_df = _reader.load(_ActionsAuditData_load_path)
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ActionsAuditData_df = ActionsAuditData_df.withColumn("ActionsAuditData_input_file", input_file_name())
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# Force partition evaluation to surface lazy errors (e.g. glob matches 0 files)
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ActionsAuditData_df.rdd.getNumPartitions()
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except AnalysisException as e:
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handle_analysis_error(
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e,
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component_name="ActionsAuditData",
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message=f"Failed to load source 'ActionsAuditData' ({_ActionsAuditData_load_path}): {e!s}",
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job_id=job_id, workspace=workspace, workflow=workflow, execution_environment=execution_environment,
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extra_details={"format": "iceberg", "load_path": _ActionsAuditData_load_path},
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input_data=_ActionsAuditData_input_data,
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)
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except Py4JJavaError as e:
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handle_java_error(
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e,
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component_name="ActionsAuditData",
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operation="load",
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format_name="iceberg",
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path=_ActionsAuditData_load_path,
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job_id=job_id, workspace=workspace, workflow=workflow, execution_environment=execution_environment,
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input_data=_ActionsAuditData_input_data,
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)
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ActionsAuditData_df, ActionsAuditData_observer = observe_metrics("ActionsAuditData_df", ActionsAuditData_df)
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ActionsAuditData_df.createOrReplaceTempView('ActionsAuditData_df')
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ActionsAuditData_dependency_key="ActionsAuditData"
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ActionsAuditData_execute_status="SUCCESS"
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except Exception as e:
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ActionsAuditData_error = e
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log_error(LOGGER, f"Component ActionsAuditData Failed", e, component_name="ActionsAuditData")
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ActionsAuditData_execute_status="ERROR"
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raise e
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finally:
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ActionsAuditData_end_time=time.time()
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# %%
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data_mapper__1_start_time=time.time()
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data_mapper__1_fail_on_error=""
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try:
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_data_mapper__1_select_clause=[]
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_data_mapper__1_expr = """DATE(action_date)""".replace("input_file_name()", "input_file")
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_data_mapper__1_expr = _data_mapper__1_expr.replace("_dq_source_file", "input_file")
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if "." in _data_mapper__1_expr:
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_data_mapper__1_expr = rewrite_response_body_json_access(_data_mapper__1_expr)
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_data_mapper__1_select_clause.append(f"{_data_mapper__1_expr} AS action_date")
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_data_mapper__1_expr = """sub_category""".replace("input_file_name()", "input_file")
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_data_mapper__1_expr = _data_mapper__1_expr.replace("_dq_source_file", "input_file")
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if "." in _data_mapper__1_expr:
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_data_mapper__1_expr = rewrite_response_body_json_access(_data_mapper__1_expr)
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_data_mapper__1_select_clause.append(f"{_data_mapper__1_expr} AS service_type")
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_data_mapper__1_expr = """action_count""".replace("input_file_name()", "input_file")
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_data_mapper__1_expr = _data_mapper__1_expr.replace("_dq_source_file", "input_file")
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if "." in _data_mapper__1_expr:
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_data_mapper__1_expr = rewrite_response_body_json_access(_data_mapper__1_expr)
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_data_mapper__1_select_clause.append(f"{_data_mapper__1_expr} AS action_count")
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_data_mapper__1_mapping_sql = ("SELECT " + ', '.join(_data_mapper__1_select_clause) + " FROM ActionsAuditData_df").replace("{job_id}", f"'{job_id}'")
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_data_mapper__1_input_data = {
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"component": "data_mapper__1",
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"datasource": "ActionsAuditData",
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"include_existing_columns": False,
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"to_schema_field_count": 3,
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}
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try:
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data_mapper__1_df = spark.sql(_data_mapper__1_mapping_sql)
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except AnalysisException as e:
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handle_analysis_error(
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e,
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component_name="data_mapper__1",
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error_code="TRF-MAP-002",
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exception_class=SchemaMappingException,
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message=f"Spark analysis error during data_mapper__1 mapping: {e!s}",
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job_id=job_id, workspace=workspace, workflow=workflow, execution_environment=execution_environment,
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extra_details={"retry_job_id": retry_job_id or None, "sql_preview": _data_mapper__1_mapping_sql[:2000]},
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input_data=_data_mapper__1_input_data,
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)
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except Py4JJavaError as e:
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handle_java_error(
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e,
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component_name="data_mapper__1",
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operation="mapping SQL",
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format_name="sql",
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job_id=job_id, workspace=workspace, workflow=workflow, execution_environment=execution_environment,
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override_class=ExpressionException, override_code="TRF-EXP-001",
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extra_details={"retry_job_id": retry_job_id or None, "sql_preview": _data_mapper__1_mapping_sql[:2000]},
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input_data=_data_mapper__1_input_data,
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)
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data_mapper__1_df, data_mapper__1_observer = observe_metrics("data_mapper__1_df", data_mapper__1_df)
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data_mapper__1_df.createOrReplaceTempView("data_mapper__1_df")
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data_mapper__1_dependency_key="data_mapper__1"
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print(ActionsAuditData_dependency_key)
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data_mapper__1_execute_status="SUCCESS"
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except Exception as e:
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data_mapper__1_error = e
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log_error(LOGGER, f"Component data_mapper__1 Failed", e, component_name="data_mapper__1")
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data_mapper__1_execute_status="ERROR"
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raise e
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finally:
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data_mapper__1_end_time=time.time()
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# %%
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LatestServiceRequests_start_time=time.time()
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print(data_mapper__1_df.columns)
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LatestServiceRequests_fail_on_error=""
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try:
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_LatestServiceRequests_condition = rewrite_response_body_json_access_if_json(data_mapper__1_df, """action_date >= COALESCE((SELECT MAX(DATE(action_date)) FROM dremio.servicemetrics), (SELECT MIN(action_date) FROM data_mapper__1_df))""")
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LatestServiceRequests_df = spark.sql(f"select * from data_mapper__1_df where {_LatestServiceRequests_condition}")
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LatestServiceRequests_df, LatestServiceRequests_observer = observe_metrics("LatestServiceRequests_df", LatestServiceRequests_df)
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LatestServiceRequests_df.createOrReplaceTempView('LatestServiceRequests_df')
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LatestServiceRequests_dependency_key="LatestServiceRequests"
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LatestServiceRequests_execute_status="SUCCESS"
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except Exception as e:
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LatestServiceRequests_error = e
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log_error(LOGGER, f"Component LatestServiceRequests Failed", e, component_name="LatestServiceRequests")
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LatestServiceRequests_execute_status="ERROR"
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raise e
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finally:
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LatestServiceRequests_end_time=time.time()
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# %%
|
||||
|
||||
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||||
aggregate__3_start_time=time.time()
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||||
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||||
aggregate__3_fail_on_error="True"
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try:
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||||
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||||
|
||||
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||||
|
||||
|
||||
|
||||
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||||
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||||
|
||||
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_aggregate__3_group_cols = []
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_aggregate__3_group_cols.append(expr(rewrite_response_body_json_access_if_json(LatestServiceRequests_df, """action_date""")))
|
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|
||||
_aggregate__3_group_cols.append(expr(rewrite_response_body_json_access_if_json(LatestServiceRequests_df, """service_type""")))
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||||
|
||||
|
||||
aggregate__3_df = LatestServiceRequests_df.groupBy(*_aggregate__3_group_cols).agg(
|
||||
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sum('action_count').alias("service_count")
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||||
|
||||
)
|
||||
aggregate__3_df, aggregate__3_observer = observe_metrics("aggregate__3_df", aggregate__3_df)
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||||
|
||||
|
||||
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||||
aggregate__3_df.createOrReplaceTempView('aggregate__3_df')
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||||
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aggregate__3_dependency_key="aggregate__3"
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||||
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print(LatestServiceRequests_dependency_key)
|
||||
|
||||
aggregate__3_execute_status="SUCCESS"
|
||||
except Exception as e:
|
||||
aggregate__3_error = e
|
||||
log_error(LOGGER, f"Component aggregate__3 Failed", e, component_name="aggregate__3")
|
||||
aggregate__3_execute_status="ERROR"
|
||||
|
||||
raise e
|
||||
|
||||
finally:
|
||||
aggregate__3_end_time=time.time()
|
||||
|
||||
# %%
|
||||
|
||||
CheckpointOutput_start_time=time.time()
|
||||
|
||||
CheckpointOutput_df = aggregate__3_df.localCheckpoint()
|
||||
aggregate__3_df.persist()
|
||||
CheckpointOutput_df.createOrReplaceTempView("CheckpointOutput_df")
|
||||
|
||||
CheckpointOutput_end_time=time.time()
|
||||
|
||||
CheckpointOutput_dependency_key="CheckpointOutput"
|
||||
|
||||
print(aggregate__3_dependency_key)
|
||||
|
||||
|
||||
# %%
|
||||
|
||||
|
||||
ServiceRequestMetricsWriter_start_time=time.time()
|
||||
|
||||
ServiceRequestMetricsWriter_fail_on_error=""
|
||||
try:
|
||||
|
||||
_ServiceRequestMetricsWriter_fields_to_update = CheckpointOutput_df.columns
|
||||
_ServiceRequestMetricsWriter_set_clause=[]
|
||||
_ServiceRequestMetricsWriter_unique_key_clause= []
|
||||
|
||||
for _key in ['action_date', 'service_type']:
|
||||
_ServiceRequestMetricsWriter_unique_key_clause.append(f't.{_key} = s.{_key}')
|
||||
|
||||
for _field in _ServiceRequestMetricsWriter_fields_to_update:
|
||||
if(_field not in _ServiceRequestMetricsWriter_unique_key_clause):
|
||||
_ServiceRequestMetricsWriter_set_clause.append(f't.{_field} = s.{_field}')
|
||||
|
||||
_merge_query = '''
|
||||
MERGE INTO dremio.servicemetrics t
|
||||
USING CheckpointOutput_df s
|
||||
ON ''' + ' AND '.join(_ServiceRequestMetricsWriter_unique_key_clause) + ''' WHEN MATCHED THEN
|
||||
UPDATE SET ''' + ', '.join(_ServiceRequestMetricsWriter_set_clause) + ' WHEN NOT MATCHED THEN INSERT *'
|
||||
|
||||
spark.sql(_merge_query)
|
||||
|
||||
|
||||
|
||||
ServiceRequestMetricsWriter_dependency_key="ServiceRequestMetricsWriter"
|
||||
|
||||
print(CheckpointOutput_dependency_key)
|
||||
|
||||
ServiceRequestMetricsWriter_execute_status="SUCCESS"
|
||||
except Exception as e:
|
||||
ServiceRequestMetricsWriter_error = e
|
||||
log_error(LOGGER, f"Component ServiceRequestMetricsWriter Failed", e, component_name="ServiceRequestMetricsWriter")
|
||||
ServiceRequestMetricsWriter_execute_status="ERROR"
|
||||
|
||||
raise e
|
||||
|
||||
finally:
|
||||
ServiceRequestMetricsWriter_end_time=time.time()
|
||||
|
||||
# %%
|
||||
|
||||
finalize_start_time=time.time()
|
||||
|
||||
metrics = {
|
||||
'data': collect_metrics(locals()),
|
||||
}
|
||||
materialization.materialized_execution_history({'finalize': {'execute_status': 'SUCCESS', 'fail_on_error': 'False', 'execution_order': os.environ.get('EXECUTION_ORDER')}, **metrics['data']})
|
||||
log_info(LOGGER, f"Workflow Data metrics (correlation_id={metrics['data'].get('correlation_id')}): {metrics['data']}")
|
||||
|
||||
finalize_end_time=time.time()
|
||||
|
||||
if os.getenv('EXECUTION_ENVIRONMENT'):
|
||||
spark.stop()
|
||||
Reference in New Issue
Block a user