feat(etl): Add rls2210

Signed-off-by: pmikus <peter.mikus@protonmail.ch>
Change-Id: Icda348f7381255deb27b1ada69fcb9fbd4ead600

1 files changed, 0 insertions, 172 deletions

diff --git a/csit.infra.etl/trending.py b/csit.infra.etl/trending.py
deleted file mode 100644
index bc27aaa063..0000000000
--- a/csit.infra.etl/trending.py
+++ /dev/null
@@ -1,172 +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"trending_{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 "daily" in path or "weekly" in path]
-
-for schema_name in ["mrr", "ndrpdr", "soak"]:
-    out_sdf = process_json_to_dataframe(schema_name, filtered_paths)
-    out_sdf.show(truncate=False)
-    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/trending",
-            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