feat(etl): Reconf type

Signed-off-by: pmikus <peter.mikus@protonmail.ch>
Change-Id: I1ce012e22d715a7371d03c086bb83e17592bd4e3

1 files changed, 171 insertions, 0 deletions

diff --git a/csit.infra.etl/trending_reconf.py b/csit.infra.etl/trending_reconf.py
new file mode 100644
index 0000000000..e30808c457
--- /dev/null
+++ b/csit.infra.etl/trending_reconf.py
@@ -0,0 +1,171 @@
+#!/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]
+
+out_sdf = process_json_to_dataframe("reconf", 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