--- /dev/null
+#!/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-2210" in path]
+
+out_sdf = process_json_to_dataframe("mrr", 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_rls2210",
+ 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
Code Review / csit.git / blobdiff