Prod 325 databricks rollovers

makefile

@@ -0,0 +1,21 @@
+FILES := $(shell git diff --name-only --diff-filter=AM $$(git merge-base origin/main HEAD) -- \*.py)
+
+
+.PHONY: test
+test:
+	pytest
+
+.PHONY: lint
+lint:
+	flake8 --filename ./$(FILES) --max-complexity=10 --ignore=E501,W503
+
+.PHONY: format
+format:
+ifneq ("$(FILES)"," ")
+	black $(FILES)
+	isort $(FILES)
+endif
+
+.PHONY: tidy
+tidy: format lint
+

parse.sh

@@ -13,13 +13,11 @@ function print_usage() {
 }
 
 results_dir="$HOME/results"
-args=()
 
 while getopts l:r:h name; do
   case $name in
     l)
       event_log="$OPTARG"
-      args=("${args[@]}" -l "$OPTARG")
       ;;
     r)
       results_dir="$OPTARG"
@@ -41,6 +39,4 @@ if [[ -z $event_log ]]; then
   exit 1
 fi
 
-args=("${args[@]}" -r "$results_dir")
-
-exec python3 -m spark_log_parser "${args[@]}"
+exec python3 -m spark_log_parser -l "$event_log" -r "$results_dir"

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "flit.buildapi"
 name = "spark_log_parser"
 authors = [{"name" = "Sync Computing"}]
 readme = "README.md"
-requires-python = ">=3.6.1"
+requires-python = ">=3.10.3"
 classifiers = [
     "Intended Audience :: Information Technology",
     "Intended Audience :: System Administrators",
@@ -16,20 +16,26 @@ classifiers = [
     "Programming Language :: Python",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3 :: Only",
-    "Programming Language :: Python :: 3.6",
-    "Programming Language :: Python :: 3.7",
-    "Programming Language :: Python :: 3.8",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
 ]
 dependencies = [
     "boto3==1.20.24",
     "numpy==1.21.4",
     "pandas==1.3.5",
     "ujson==5.3.0",
+    "pydantic==1.9.0",
+    "requests==2.26.0",
 ]
 dynamic = ["version", "description"]
 
+[project.optional-dependencies]
+test = [
+    "pytest==7.1.2",
+    "black==22.6.0",
+    "isort==5.10.1",
+    "flake8==4.0.1",
+]
+
 [project.urls]
 Home = "https://github.com/synccomputingcode/spark_log_parser"
 
@@ -38,3 +44,12 @@ line-length = 100
 
 [tool.isort]
 profile = "black"
+line_length = 100
+
+[tool.pytest.ini_options]
+pythonpath = [
+    "."
+]
+filterwarnings = [
+    "ignore::UserWarning"
+]

spark_log_parser/__main__.py

@@ -1,37 +1,42 @@
+import argparse
 import logging
+import os
+import sys
+import tempfile
+from pathlib import Path
 
 logging.captureWarnings(True)
 
-from spark_log_parser.parsing_models.application_model_v2 import sparkApplication
-
-import os
-import argparse
-import sys
+from spark_log_parser.eventlog import EventLogBuilder  # noqa: E402
+from spark_log_parser.parsing_models.application_model_v2 import sparkApplication  # noqa: E402
 
 logger = logging.getLogger("spark_log_parser")
-
-parser = argparse.ArgumentParser("spark_log_parser")
-parser.add_argument("-l", "--log-file", required=True, help="path to event log")
-parser.add_argument("-r", "--result-dir", required=True, help="path to directory in which to save parsed logs")
-args = parser.parse_args()  
-
-print("\n" + "*" * 12 + " Running the Log Parser for Spark Predictor " + "*" * 12 + "\n")
 
-log_path = os.path.abspath(args.log_file)
+parser = argparse.ArgumentParser("spark_log_parser")
+parser.add_argument("-l", "--log-file", required=True, type=Path, help="path to event log")
+parser.add_argument(
+    "-r", "--result-dir", required=True, help="path to directory in which to save parsed logs"
+)
+args = parser.parse_args()
 
 if not os.path.isdir(args.result_dir):
     logger.error("%s is not a directory", args.result_dir)
     sys.exit(1)
 
-print("\n--Processing log file: " + log_path)
+print("\n" + "*" * 12 + " Running the Log Parser for Spark Predictor " + "*" * 12 + "\n")
+print("--Processing log file: " + str(args.log_file))
 
-log_name = os.path.basename(log_path)
-result_path = os.path.join(args.result_dir, "parsed-" + log_name)
+with tempfile.TemporaryDirectory() as work_dir:
+    event_log = EventLogBuilder(args.log_file.resolve().as_uri(), work_dir).build()
+    app = sparkApplication(eventlog=str(event_log))
 
-if os.path.exists(result_path):
-    os.remove(result_path)
+if args.log_file.suffixes:
+    result_path = os.path.join(
+        args.result_dir, "parsed-" + args.log_file.name[: -len("".join(args.log_file.suffixes))]
+    )
+else:
+    result_path = os.path.join(args.result_dir, "parsed-" + args.log_file.name)
 
-appobj = sparkApplication(eventlog=log_path)
-appobj.save(result_path)
+app.save(result_path)
 
-print(f"--Log directory saved to: {result_path}")
+print(f"--Result saved to: {result_path}.json")

spark_log_parser/eventlog.py

@@ -0,0 +1,73 @@
+import json
+import tempfile
+from pathlib import Path
+from urllib.parse import ParseResult
+
+import pandas as pd
+
+from spark_log_parser.extractor import Extractor
+
+
+class EventLogBuilder:
+    def __init__(self, source_url: ParseResult | str, work_dir: Path | str, s3_client=None):
+        self.source_url = source_url
+        self.work_dir = self._validate_work_dir(work_dir)
+        self.s3_client = s3_client
+        self.extractor = Extractor(self.source_url, self.work_dir, self.s3_client)
+
+    def _validate_work_dir(self, work_dir: Path | str) -> Path:
+        work_dir_path = work_dir if isinstance(work_dir, Path) else Path(work_dir)
+        if not work_dir_path.is_dir():
+            raise ValueError("Path is not a directory")
+
+        return work_dir_path
+
+    def build(self) -> Path:
+        event_logs = self.extractor.extract()
+
+        self.event_log = self._concat(event_logs)
+
+        return self.event_log
+
+    def _concat(self, event_logs: list[Path]) -> Path:
+        if len(event_logs) == 1:
+            return event_logs[0]
+
+        dat = []
+        for log in event_logs:
+            with open(log) as log_file:
+                try:
+                    line = json.loads(log_file.readline())
+                except ValueError:
+                    continue  # Maybe a Databricks pricing file
+                if line["Event"] == "DBCEventLoggingListenerMetadata":
+                    dat.append((line["Rollover Number"], line["SparkContext Id"], log))
+                else:
+                    raise ValueError("Expected DBC event not found")
+
+        df = pd.DataFrame(dat, columns=["rollover_index", "context_id", "path"]).sort_values(
+            "rollover_index"
+        )
+
+        self._validate_rollover_logs(df)
+
+        event_log = Path(tempfile.mkstemp(suffix="-concatenated.json", dir=str(self.work_dir))[1])
+        with open(event_log, "w") as fobj:
+            for path in df.path:
+                with open(path) as part_fobj:
+                    for line in part_fobj:
+                        fobj.write(line)
+
+        return event_log
+
+    def _validate_rollover_logs(self, df: pd.DataFrame):
+        if not len(df.context_id.unique()) == 1:
+            raise ValueError("Not all rollover files have the same Spark context ID")
+
+        diffs = df.rollover_index.diff()[1:]
+
+        if any(diffs > 1) or df.rollover_index[0] > 0:
+            raise ValueError("Rollover file appears to be missing")
+
+        if any(diffs < 1):
+            raise ValueError("Duplicate rollover file detected")