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Diffstat (limited to 'csit.infra.etl/coverage_rls2206.py')
| -rw-r--r-- | csit.infra.etl/coverage_rls2206.py | 171 |
1 files changed, 0 insertions, 171 deletions
diff --git a/csit.infra.etl/coverage_rls2206.py b/csit.infra.etl/coverage_rls2206.py deleted file mode 100644 index 4e2619d924..0000000000 --- a/csit.infra.etl/coverage_rls2206.py +++ /dev/null @@ -1,171 +0,0 @@ -#!/usr/bin/env python3 - -# Copyright (c) 2022 Cisco and/or its affiliates. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at: -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""ETL script running on top of the s3://""" - -from datetime import datetime, timedelta -from json import load -from os import environ -from pytz import utc - -import awswrangler as wr -from awswrangler.exceptions import EmptyDataFrame -from awsglue.context import GlueContext -from boto3 import session -from pyspark.context import SparkContext -from pyspark.sql.functions import col, lit, regexp_replace -from pyspark.sql.types import StructType - - -S3_LOGS_BUCKET="fdio-logs-s3-cloudfront-index" -S3_DOCS_BUCKET="fdio-docs-s3-cloudfront-index" -PATH=f"s3://{S3_LOGS_BUCKET}/vex-yul-rot-jenkins-1/csit-*-perf-*" -SUFFIX="info.json.gz" -IGNORE_SUFFIX=[ - "suite.info.json.gz", - "setup.info.json.gz", - "teardown.info.json.gz", - "suite.output.info.json.gz", - "setup.output.info.json.gz", - "teardown.output.info.json.gz" -] -LAST_MODIFIED_END=utc.localize( - datetime.strptime( - f"{datetime.now().year}-{datetime.now().month}-{datetime.now().day}", - "%Y-%m-%d" - ) -) -LAST_MODIFIED_BEGIN=LAST_MODIFIED_END - timedelta(1) - - -def flatten_frame(nested_sdf): - """Unnest Spark DataFrame in case there nested structered columns. - - :param nested_sdf: Spark DataFrame. - :type nested_sdf: DataFrame - :returns: Unnest DataFrame. - :rtype: DataFrame - """ - stack = [((), nested_sdf)] - columns = [] - while len(stack) > 0: - parents, sdf = stack.pop() - for column_name, column_type in sdf.dtypes: - if column_type[:6] == "struct": - projected_sdf = sdf.select(column_name + ".*") - stack.append((parents + (column_name,), projected_sdf)) - else: - columns.append( - col(".".join(parents + (column_name,))) \ - .alias("_".join(parents + (column_name,))) - ) - return nested_sdf.select(columns) - - -def process_json_to_dataframe(schema_name, paths): - """Processes JSON to Spark DataFrame. - - :param schema_name: Schema name. - :type schema_name: string - :param paths: S3 paths to process. - :type paths: list - :returns: Spark DataFrame. - :rtype: DataFrame - """ - drop_subset = [ - "dut_type", "dut_version", - "passed", - "test_name_long", "test_name_short", - "test_type", - "version" - ] - - # load schemas - with open(f"coverage_{schema_name}.json", "r", encoding="UTF-8") as f_schema: - schema = StructType.fromJson(load(f_schema)) - - # create empty DF out of schemas - sdf = spark.createDataFrame([], schema) - - # filter list - filtered = [path for path in paths if schema_name in path] - - # select - for path in filtered: - print(path) - - sdf_loaded = spark \ - .read \ - .option("multiline", "true") \ - .schema(schema) \ - .json(path) \ - .withColumn("job", lit(path.split("/")[4])) \ - .withColumn("build", lit(path.split("/")[5])) - sdf = sdf.unionByName(sdf_loaded, allowMissingColumns=True) - - # drop rows with all nulls and drop rows with null in critical frames - sdf = sdf.na.drop(how="all") - sdf = sdf.na.drop(how="any", thresh=None, subset=drop_subset) - - # flatten frame - sdf = flatten_frame(sdf) - - return sdf - - -# create SparkContext and GlueContext -spark_context = SparkContext.getOrCreate() -spark_context.setLogLevel("WARN") -glue_context = GlueContext(spark_context) -spark = glue_context.spark_session - -# files of interest -paths = wr.s3.list_objects( - path=PATH, - suffix=SUFFIX, - last_modified_begin=LAST_MODIFIED_BEGIN, - last_modified_end=LAST_MODIFIED_END, - ignore_suffix=IGNORE_SUFFIX, - ignore_empty=True -) - -filtered_paths = [path for path in paths if "report-coverage-2206" in path] - -for schema_name in ["mrr", "ndrpdr", "soak", "device"]: - out_sdf = process_json_to_dataframe(schema_name, filtered_paths) - out_sdf.printSchema() - out_sdf = out_sdf \ - .withColumn("year", lit(datetime.now().year)) \ - .withColumn("month", lit(datetime.now().month)) \ - .withColumn("day", lit(datetime.now().day)) \ - .repartition(1) - - try: - wr.s3.to_parquet( - df=out_sdf.toPandas(), - path=f"s3://{S3_DOCS_BUCKET}/csit/parquet/coverage_rls2206", - dataset=True, - partition_cols=["test_type", "year", "month", "day"], - compression="snappy", - use_threads=True, - mode="overwrite_partitions", - boto3_session=session.Session( - aws_access_key_id=environ["OUT_AWS_ACCESS_KEY_ID"], - aws_secret_access_key=environ["OUT_AWS_SECRET_ACCESS_KEY"], - region_name=environ["OUT_AWS_DEFAULT_REGION"] - ) - ) - except EmptyDataFrame: - pass |