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author | 2022-09-27 14:23:42 +0200 | |
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committer | 2022-09-28 09:04:50 +0000 | |
commit | 4b9dd265f37c642e69c9642fecc1b9e008217c5d (patch) | |
tree | 897ca2156ab0b403feede5717c1d234a1bfc7b7f /csit.infra.etl/iterative_soak_rls2210.py | |
parent | 077869971779023a558cc58f722974fc5e1e17a3 (diff) |
feat(etl): Add rls2210
Signed-off-by: pmikus <peter.mikus@protonmail.ch>
Change-Id: Icda348f7381255deb27b1ada69fcb9fbd4ead600
(cherry picked from commit bff439b69ee71b654b1da92564ff62de7327fe71)
Diffstat (limited to 'csit.infra.etl/iterative_soak_rls2210.py')
-rw-r--r-- | csit.infra.etl/iterative_soak_rls2210.py | 170 |
1 files changed, 170 insertions, 0 deletions
diff --git a/csit.infra.etl/iterative_soak_rls2210.py b/csit.infra.etl/iterative_soak_rls2210.py new file mode 100644 index 0000000000..b74d7b44dc --- /dev/null +++ b/csit.infra.etl/iterative_soak_rls2210.py @@ -0,0 +1,170 @@ +#!/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"iterative_{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-iterative-2210" in path] + +out_sdf = process_json_to_dataframe("soak", 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/iterative_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 |