diff --git a/udf/anomaly-detection/.codecov.yml b/udf/anomaly-detection/.codecov.yml new file mode 100644 index 00000000..26ab4f35 --- /dev/null +++ b/udf/anomaly-detection/.codecov.yml @@ -0,0 +1,10 @@ +coverage: + status: + project: + default: + target: auto + threshold: 20% + patch: + default: + target: auto + threshold: 20% diff --git a/udf/anomaly-detection/.coveragerc b/udf/anomaly-detection/.coveragerc new file mode 100644 index 00000000..b4897f28 --- /dev/null +++ b/udf/anomaly-detection/.coveragerc @@ -0,0 +1,5 @@ +[run] +branch = True +parallel = True +source = numalogic-prometheus +omit = tests/* diff --git a/udf/anomaly-detection/.dockerignore b/udf/anomaly-detection/.dockerignore new file mode 100644 index 00000000..47614e11 --- /dev/null +++ b/udf/anomaly-detection/.dockerignore @@ -0,0 +1,16 @@ +tests/ +manifests/ +docs/ +.github +CHANGELOG.md +CODE_OF_CONDUCT.md +CONTRIBUTING.md +README.md +LICENSE +.gitignore +.idea/ +.codecov.yml +.coveragerc +.flake8 +.hack/ +.venv/ \ No newline at end of file diff --git a/udf/anomaly-detection/.flake8 b/udf/anomaly-detection/.flake8 new file mode 100644 index 00000000..9fc18494 --- /dev/null +++ b/udf/anomaly-detection/.flake8 @@ -0,0 +1,5 @@ +[flake8] +ignore = E203, F821 +exclude = .git,__pycache__,docs/source/conf.py,old,build,dist +max-complexity = 10 +max-line-length = 110 \ No newline at end of file diff --git a/udf/anomaly-detection/.gitignore b/udf/anomaly-detection/.gitignore new file mode 100644 index 00000000..04f3dcdb --- /dev/null +++ b/udf/anomaly-detection/.gitignore @@ -0,0 +1,138 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST +prometheus-serde/vendor +prometheus-serde/dist +prometheus-serde/coverage.out + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains project +.idea/ + +# Mac related +.DS_Store diff --git a/udf/anomaly-detection/Dockerfile b/udf/anomaly-detection/Dockerfile new file mode 100644 index 00000000..34a73fed --- /dev/null +++ b/udf/anomaly-detection/Dockerfile @@ -0,0 +1,57 @@ +#################################################################################################### +# builder: install needed dependencies +#################################################################################################### + +FROM python:3.10-slim-bullseye AS builder + +ENV PYTHONFAULTHANDLER=1 \ + PYTHONUNBUFFERED=1 \ + PYTHONHASHSEED=random \ + PIP_NO_CACHE_DIR=on \ + PIP_DISABLE_PIP_VERSION_CHECK=on \ + PIP_DEFAULT_TIMEOUT=100 \ + POETRY_VERSION=1.4.2 \ + POETRY_HOME="/opt/poetry" \ + POETRY_VIRTUALENVS_IN_PROJECT=true \ + POETRY_NO_INTERACTION=1 \ + PYSETUP_PATH="/opt/pysetup" \ + VENV_PATH="/opt/pysetup/.venv" + +ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH" + +RUN apt-get update \ + && apt-get install --no-install-recommends -y \ + curl \ + wget \ + # deps for building python deps + build-essential \ + && apt-get install -y git \ + && apt-get clean && rm -rf /var/lib/apt/lists/* \ + \ + # install dumb-init + && wget -O /dumb-init https://github.com/Yelp/dumb-init/releases/download/v1.2.5/dumb-init_1.2.5_x86_64 \ + && chmod +x /dumb-init \ + && curl -sSL https://install.python-poetry.org | python3 - + +#################################################################################################### +# udf: used for running the udf vertices +#################################################################################################### +FROM builder AS udf + +WORKDIR $PYSETUP_PATH +COPY ./pyproject.toml ./poetry.lock ./ +COPY requirements ./requirements + +RUN poetry install --no-cache --no-root && \ + poetry run pip install --no-cache -r requirements/requirements-torch.txt && \ + rm -rf ~/.cache/pypoetry/ + +ADD . /app +WORKDIR /app + +RUN chmod +x entry.sh + +ENTRYPOINT ["/dumb-init", "--"] +CMD ["/app/entry.sh"] + +EXPOSE 5000 \ No newline at end of file diff --git a/udf/anomaly-detection/Makefile b/udf/anomaly-detection/Makefile new file mode 100644 index 00000000..f27fe380 --- /dev/null +++ b/udf/anomaly-detection/Makefile @@ -0,0 +1,40 @@ +# Check Python +PYTHON:=$(shell command -v python 2> /dev/null) +ifndef PYTHON +PYTHON:=$(shell command -v python3 2> /dev/null) +endif +ifndef PYTHON +$(error "Python is not available, please install.") +endif + +clean: + @rm -rf build dist .eggs *.egg-info + @rm -rf .benchmarks .coverage coverage.xml htmlcov report.xml .tox + @find . -type d -name '.mypy_cache' -exec rm -rf {} + + @find . -type d -name '__pycache__' -exec rm -rf {} + + @find . -type d -name '*pytest_cache*' -exec rm -rf {} + + @find . -type f -name "*.py[co]" -exec rm -rf {} + + +format: clean + poetry run black src/ tests/ + poetry run black starter.py + +lint: format + poetry run flake8 . + +# install all dependencies +setup: + poetry install --with dev --all-extras + +test: + poetry run pytest -v tests/ + +requirements: + poetry export -f requirements.txt --output requirements.txt --without-hashes + +requirements-dev: + poetry export -f requirements.txt --with dev --output requirements-dev.txt --without-hashes + +pipeline: + kustomize build manifests/prerequisites | kubectl apply -f - + kustomize build manifests/ | kubectl apply -f - \ No newline at end of file diff --git a/udf/anomaly-detection/README.md b/udf/anomaly-detection/README.md new file mode 100644 index 00000000..8d667053 --- /dev/null +++ b/udf/anomaly-detection/README.md @@ -0,0 +1,3 @@ +# Anomaly Detection + +![anomalydetection](./docs/images/anomalydetection.png) diff --git a/udf/anomaly-detection/__init__.py b/udf/anomaly-detection/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/udf/anomaly-detection/config/default-configs/config.yaml b/udf/anomaly-detection/config/default-configs/config.yaml new file mode 100644 index 00000000..608b3f5e --- /dev/null +++ b/udf/anomaly-detection/config/default-configs/config.yaml @@ -0,0 +1,28 @@ +configs: + - name: "service-mesh" + composite_keys: [ "destination_asset_id", "env" ] + metrics: [ "error_rate" , "error_count" ] + - name: "service-mesh-s2s" + composite_keys: ["source_asset_id, destination_asset_id", "env"] + metrics: [ "error_rate", "error_count" ] + - name: "default-argorollouts" + composite_keys: [ "namespace", "name", "app", "rollouts_pod_template_hash" ] + metrics: [ "namespace_app_rollouts_http_request_error_rate"] + metric_configs: + - metric: "namespace_app_rollouts_http_request_error_rate" + static_threshold: + weight: 0.7 + - name: "default-argocd" + composite_keys: [ "namespace", "name" ] + metrics: [ "namespace_app_http_server_requests_errors", + "namespace_app_http_server_requests_error_rate", + "namespace_app_http_server_requests_latency", + "namespace_app_cpu_utilization", + "namespace_app_memory_utilization" ] + metric_configs: + - metric: "namespace_app_cpu_utilization" + static_threshold: + upper_limit: 80 + - metric: "namespace_app_memory_utilization" + static_threshold: + upper_limit: 80 \ No newline at end of file diff --git a/udf/anomaly-detection/config/default-configs/numalogic_config.yaml b/udf/anomaly-detection/config/default-configs/numalogic_config.yaml new file mode 100644 index 00000000..2d101406 --- /dev/null +++ b/udf/anomaly-detection/config/default-configs/numalogic_config.yaml @@ -0,0 +1,21 @@ +model: + name: "SparseVanillaAE" + conf: + seq_len: 12 + n_features: 1 + encoder_layersizes: + - 16 + - 8 + decoder_layersizes: + - 8 + - 16 + dropout_p: 0.25 +trainer: + max_epochs: 30 +preprocess: + - name: "StandardScaler" +threshold: + name: "StdDevThreshold" +postprocess: + name: "TanhNorm" + stateful: false diff --git a/udf/anomaly-detection/config/default-configs/pipeline_config.yaml b/udf/anomaly-detection/config/default-configs/pipeline_config.yaml new file mode 100644 index 00000000..6ba55952 --- /dev/null +++ b/udf/anomaly-detection/config/default-configs/pipeline_config.yaml @@ -0,0 +1,5 @@ +redis_conf: + host: "isbsvc-redis-isbs-redis-svc.numalogic-rollouts.svc" + port: 26379 + expiry: 360 + master_name: "mymaster" \ No newline at end of file diff --git a/udf/anomaly-detection/config/user-configs/config.yaml b/udf/anomaly-detection/config/user-configs/config.yaml new file mode 100644 index 00000000..ac3ff822 --- /dev/null +++ b/udf/anomaly-detection/config/user-configs/config.yaml @@ -0,0 +1,13 @@ +configs: + - name: "dev-devx-o11yfuzzygqlfederation-usw2-qal" + composite_keys: ["namespace", "name", "hash_id"] + metrics: ["namespace_rollout_api_error_rate", "namespace_rollout_api_latency"] + - name: "dev-devx-o11yfuzzygqlfederation-usw2-pprd" + composite_keys: [ "namespace", "name", "hash_id" ] + metrics: ["namespace_rollout_api_error_rate", "namespace_rollout_api_latency"] + - name: "dev-devx-o11yfuzzygqlfederation-usw2-stg" + composite_keys: [ "namespace", "name", "hash_id" ] + metrics: ["namespace_rollout_api_error_rate", "namespace_rollout_api_latency"] + - name: "dev-devx-o11yfuzzygqlfederation-usw2-prd" + composite_keys: [ "namespace", "name", "hash_id" ] + metrics: ["namespace_rollout_api_error_rate", "namespace_rollout_api_latency"] \ No newline at end of file diff --git a/udf/anomaly-detection/docs/images/anomalydetection.png 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+[tool.poetry.group.mlflowserver] +optional = true + +[tool.poetry.group.mlflowserver.dependencies] +mlflow = "^2.2" + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +coverage = "^6.3" +black = "^23.1" +fakeredis = "^2.11" +flake8 = "^5.0" +pytest = "^7.1" +pytest-cov = "^4.0" +freezegun = "^1.2.2" + +[tool.black] +line-length = 100 +include = '\.pyi?$' +exclude = ''' +/( + \.eggs + | \.git + | \.hg + | \.mypy_cache + | \.tox + | \.venv + | \.idea + | _build + | buck-out + | build + | dist + | tests/.*/setup.py +)/ +''' + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/udf/anomaly-detection/requirements/requirements-torch.txt b/udf/anomaly-detection/requirements/requirements-torch.txt new file mode 100644 index 00000000..b2598b14 --- /dev/null +++ b/udf/anomaly-detection/requirements/requirements-torch.txt @@ -0,0 +1,3 @@ +# torch related requirements file +torch >= 2.0, <3.0; -i https://download.pytorch.org/whl/cpu +pytorch-lightning >=2.0, < 3.0 \ No newline at end of file diff --git a/udf/anomaly-detection/src/__init__.py b/udf/anomaly-detection/src/__init__.py new file mode 100644 index 00000000..2fdbf5e6 --- /dev/null +++ b/udf/anomaly-detection/src/__init__.py @@ -0,0 +1,28 @@ +import logging +import os + +from src._config import UnifiedConf, MetricConf, DataStreamConf, PipelineConf, Configs + + +def get_logger(name): + formatter = logging.Formatter("%(asctime)s-%(levelname)s-%(message)s") + logger = logging.getLogger(name) + stream_handler = logging.StreamHandler() + if os.getenv("DEBUG", False): + logger.setLevel(logging.DEBUG) + stream_handler.setLevel(logging.DEBUG) + else: + logger.setLevel(logging.INFO) + stream_handler.setLevel(logging.INFO) + + stream_handler.setFormatter(formatter) + logger.addHandler(stream_handler) + + pl_logger = logging.getLogger("pytorch_lightning") + pl_logger.propagate = False + pl_logger.setLevel(logging.ERROR) + pl_logger.addHandler(stream_handler) + return logger + + +__all__ = ["get_logger", "UnifiedConf", "MetricConf", "DataStreamConf", "Configs", "PipelineConf"] diff --git a/udf/anomaly-detection/src/_config.py b/udf/anomaly-detection/src/_config.py new file mode 100644 index 00000000..dbf04a26 --- /dev/null +++ b/udf/anomaly-detection/src/_config.py @@ -0,0 +1,67 @@ +from enum import Enum +from typing import List +from omegaconf import MISSING +from dataclasses import dataclass, field + +from numalogic.config import NumalogicConf + +from src.connectors import RedisConf, RegistryConf, PrometheusConf +from src.connectors._config import DruidConf, DruidFetcherConf + + +@dataclass +class UnifiedConf: + strategy: str = "max" + weights: List[float] = field(default_factory=list) + + +@dataclass +class ReTrainConf: + train_hours: int = 36 + min_train_size: int = 2000 + retrain_freq_hr: int = 8 + resume_training: bool = False + + +@dataclass +class StaticThreshold: + upper_limit: int = 3 + weight: float = 0.0 + + +@dataclass +class MetricConf: + metric: str + retrain_conf: ReTrainConf = field(default_factory=lambda: ReTrainConf()) + static_threshold: StaticThreshold = field(default_factory=lambda: StaticThreshold()) + numalogic_conf: NumalogicConf = MISSING + + +class DataSource(str, Enum): + PROMETHEUS = "prometheus" + DRUID = "druid" + + +@dataclass +class DataStreamConf: + name: str = "default" + source: DataSource = DataSource.PROMETHEUS + window_size: int = 12 + composite_keys: List[str] = field(default_factory=list) + metrics: List[str] = field(default_factory=list) + metric_configs: List[MetricConf] = field(default_factory=list) + unified_config: UnifiedConf = field(default_factory=lambda: UnifiedConf()) + druid_fetcher: DruidFetcherConf = MISSING + + +@dataclass +class Configs: + configs: List[DataStreamConf] + + +@dataclass +class PipelineConf: + redis_conf: RedisConf + registry_conf: RegistryConf + prometheus_conf: PrometheusConf = MISSING + druid_conf: DruidConf = MISSING diff --git a/udf/anomaly-detection/src/_constants.py b/udf/anomaly-detection/src/_constants.py new file mode 100644 index 00000000..b0406382 --- /dev/null +++ b/udf/anomaly-detection/src/_constants.py @@ -0,0 +1,16 @@ +import os + +BASE_DIR = os.path.dirname(__file__) +ROOT_DIR = os.path.split(BASE_DIR)[0] +TESTS_DIR = os.path.join(ROOT_DIR, "tests") +TESTS_RESOURCES = os.path.join(TESTS_DIR, "resources") +DATA_DIR = os.path.join(BASE_DIR, "data") +CONFIG_DIR = os.path.join(ROOT_DIR, "config") + +# UDF constants +TRAIN_VTX_KEY = "train" +INFERENCE_VTX_KEY = "inference" +THRESHOLD_VTX_KEY = "threshold" +POSTPROC_VTX_KEY = "postproc" + +CONFIG_PATHS = ["./config/user-configs", "./config/default-configs"] diff --git a/udf/anomaly-detection/src/connectors/__init__.py b/udf/anomaly-detection/src/connectors/__init__.py new file mode 100644 index 00000000..653074d4 --- /dev/null +++ b/udf/anomaly-detection/src/connectors/__init__.py @@ -0,0 +1 @@ +from src.connectors._config import RedisConf, RegistryConf, PrometheusConf diff --git a/udf/anomaly-detection/src/connectors/_config.py b/udf/anomaly-detection/src/connectors/_config.py new file mode 100644 index 00000000..56c64ace --- /dev/null +++ b/udf/anomaly-detection/src/connectors/_config.py @@ -0,0 +1,39 @@ +from dataclasses import dataclass, field + +from pydruid.utils.aggregators import doublesum + + +@dataclass +class PrometheusConf: + server: str + pushgateway: str + + +@dataclass +class RegistryConf: + tracking_uri: str + + +@dataclass +class RedisConf: + host: str + port: int + expiry: int = 300 + master_name: str = "mymaster" + + +@dataclass +class DruidConf: + url: str + endpoint: str + + +@dataclass +class DruidFetcherConf: + datasource: str + dimensions: list[str] = field(default_factory=list) + aggregations: dict = field(default_factory=lambda: {"count": doublesum("count")}) + group_by: list[str] = field(default_factory=list) + pivot: dict = field(default_factory=dict) + granularity: str = "minute" + hours: float = 24 diff --git a/udf/anomaly-detection/src/connectors/druid.py b/udf/anomaly-detection/src/connectors/druid.py new file mode 100644 index 00000000..3aa79996 --- /dev/null +++ b/udf/anomaly-detection/src/connectors/druid.py @@ -0,0 +1,93 @@ +import pytz +import logging +import pandas as pd +from datetime import datetime, timedelta +from pydruid.client import PyDruid +from pydruid.query import QueryBuilder +from pydruid.utils.filters import Filter +import urllib.request +import urllib.error + +_LOGGER = logging.getLogger(__name__) + + +class DruidFetcher: + def __init__(self, url: str, endpoint: str): + self.client = PyDruid(url, endpoint) + self.query_builder = QueryBuilder() + + def fetch_data( + self, + datasource: str, + filter_keys: list[str], + filter_values: list[str], + dimensions: list[str], + granularity: str = "minute", + aggregations: dict = None, + group_by: list[str] = None, + pivot: dict = None, + hours: float = 24, + ) -> pd.DataFrame: + filter_pairs = {} + for k, v in zip(filter_keys, filter_values): + filter_pairs[k] = v + + _filter = Filter( + type="and", + fields=[Filter(type="selector", dimension=k, value=v) for k, v in filter_pairs.items()], + ) + + end_dt = datetime.now(pytz.utc) + start_dt = end_dt - timedelta(hours=hours) + intervals = f"{start_dt.isoformat()}/{end_dt.isoformat()}" + + params = { + "datasource": datasource, + "granularity": granularity, + "intervals": intervals, + "aggregations": aggregations, + "filter": _filter, + "dimensions": dimensions, + } + response = self.client.groupby(**params) + df = response.export_pandas() + + if df is None or df.shape[0] == 0: + logging.warning("No data found for keys %s", filter_pairs) + return pd.DataFrame() + + df["timestamp"] = pd.to_datetime(df["timestamp"]).astype("int64") // 10**6 + + if group_by: + df = df.groupby(by=group_by).sum().reset_index() + + if pivot: + df = df.pivot( + index=pivot["index"], + columns=pivot["columns"], + values=pivot["values"], + ) + + return df + + +# url = "https://getafix.odldruid-prd.a.intuit.com" +# endpoint = "druid/v2/" +# +# druid_fetcher = DruidFetcher(url, endpoint) +# df = druid_fetcher.fetch_data( +# filter_keys=["assetId"], +# filter_values=["1084259202722926969"], +# dimensions=["ciStatus"], +# datasource="tech-ip-customer-interaction-metrics", +# aggregations={"count": doublesum("count")}, +# group_by=["timestamp", "ciStatus"], +# hours=0.5, +# pivot={ +# "index": "timestamp", +# "columns": ["ciStatus"], +# "values": "count" +# } +# ) +# +# print(df) diff --git a/udf/anomaly-detection/src/connectors/entities.py b/udf/anomaly-detection/src/connectors/entities.py new file mode 100644 index 00000000..cb421411 --- /dev/null +++ b/udf/anomaly-detection/src/connectors/entities.py @@ -0,0 +1,56 @@ +from dataclasses import dataclass +from typing import Optional, Dict, Union, Self + +from orjson import orjson + + +@dataclass(repr=False) +class PrometheusPayload: + timestamp_ms: int + name: str + namespace: str + subsystem: Optional[str] + type: str + value: float + labels: Dict[str, str] + + def as_json(self) -> bytes: + return orjson.dumps( + { + "TimestampMs": self.timestamp_ms, + "Name": self.name, + "Namespace": self.namespace, + "Subsystem": self.subsystem, + "Type": self.type, + "Value": self.value, + "Labels": self.labels, + } + ) + + @classmethod + def from_json(cls, json_obj: Union[bytes, str]) -> Self: + obj = orjson.loads(json_obj) + return cls( + timestamp_ms=obj["TimestampMs"], + name=obj["Name"], + namespace=obj["Namespace"], + subsystem=obj["Subsystem"], + type=obj["Type"], + value=obj["Value"], + labels=obj["Labels"], + ) + + def __repr__(self) -> str: + return ( + "{timestamp_ms: %s, name: %s, namespace: %s, " + "subsystem: %s, type: %s, value: %s, labels: %s}" + % ( + self.timestamp_ms, + self.name, + self.namespace, + self.subsystem, + self.type, + self.value, + self.labels, + ) + ) diff --git a/udf/anomaly-detection/src/connectors/prometheus.py b/udf/anomaly-detection/src/connectors/prometheus.py new file mode 100644 index 00000000..f875f9bd --- /dev/null +++ b/udf/anomaly-detection/src/connectors/prometheus.py @@ -0,0 +1,180 @@ +import time +from datetime import datetime, timedelta + +import pytz +import requests +import numpy as np +import pandas as pd + +from src import get_logger + +_LOGGER = get_logger(__name__) + + +class Prometheus: + def __init__(self, prometheus_server: str): + self.PROMETHEUS_SERVER = prometheus_server + + def query_metric( + self, + metric_name: str, + start: float, + end: float, + labels_map: dict = None, + return_labels: list[str] = None, + step: int = 30, + ) -> pd.DataFrame: + query = metric_name + if labels_map: + label_list = [str(key + "=" + "'" + labels_map[key] + "'") for key in labels_map] + query = metric_name + "{" + ",".join(label_list) + "}" + + _LOGGER.debug("Prometheus Query: %s", query) + + if end < start: + raise ValueError("end_time must not be before start_time") + + results = self.query_range(query, start, end, step) + + frames = [] + for result in results: + _LOGGER.debug( + "Prometheus query has returned %s values for %s.", + len(result["values"]), + result["metric"], + ) + arr = np.array(result["values"], dtype=float) + _df = pd.DataFrame(arr, columns=["timestamp", metric_name]) + + data = result["metric"] + if return_labels: + for label in return_labels: + if label in data: + _df[label] = data[label] + frames.append(_df) + + df = pd.DataFrame() + if frames: + df = pd.concat(frames, ignore_index=True) + df.sort_values(by=["timestamp"], inplace=True) + df["timestamp"] = pd.to_datetime(df["timestamp"], unit="s") + + return df + + def query_range(self, query: str, start: float, end: float, step: int = 30) -> list | None: + results = [] + data_points = (end - start) / step + temp_start = start + while data_points > 11000: + temp_end = temp_start + 11000 * step + response = self.query_range_limit(query, temp_start, temp_end, step) + for res in response: + results.append(res) + temp_start = temp_end + data_points = (end - temp_start) / step + + if data_points > 0: + response = self.query_range_limit(query, temp_start, end) + for res in response: + results.append(res) + return results + + def query_range_limit(self, query: str, start: float, end: float, step: int = 30) -> list: + results = [] + data_points = (end - start) / step + + if data_points > 11000: + _LOGGER.info("Limit query only supports 11,000 data points") + return results + try: + response = requests.get( + self.PROMETHEUS_SERVER + "/api/v1/query_range", + params={"query": query, "start": start, "end": end, "step": f"{step}s"}, + ) + results = response.json()["data"]["result"] + _LOGGER.debug( + "Prometheus query has returned results for %s metric series.", + len(results), + ) + except Exception as ex: + _LOGGER.exception("Prometheus error: %r", ex) + return results + + def query(self, query: str) -> dict | None: + results = [] + try: + response = requests.get( + self.PROMETHEUS_SERVER + "/api/v1/query", params={"query": query} + ) + if response: + results = response.json()["data"]["result"] + else: + _LOGGER.debug("Prometheus query has returned empty results.") + except Exception as ex: + _LOGGER.exception("error: %r", ex) + + return results + + +class PrometheusDataFetcher: + def __init__(self, prometheus_server: str): + self.prometheus = Prometheus(prometheus_server) + + @classmethod + def clean_data(cls, df: pd.DataFrame, return_labels: list[str], limit=12): + df.replace([np.inf, -np.inf], np.nan, inplace=True) + df = df.fillna(method="ffill", limit=limit) + df = df.fillna(method="bfill", limit=limit) + if df.columns[df.isna().any()].tolist(): + df.dropna(inplace=True) + + if df.empty: + return pd.DataFrame() + + if "rollouts_pod_template_hash" in return_labels: + df = df.reset_index() + df = ( + pd.merge( + df, df[df.duplicated("timestamp", keep=False)], indicator=True, how="outer" + ) + .query('_merge=="left_only"') + .drop("_merge", axis=1) + ) + df.set_index("timestamp", inplace=True) + df.drop("rollouts_pod_template_hash", axis=1, inplace=True) + df = df.sort_values(by=["timestamp"], ascending=True) + return df + + def fetch_data( + self, + metric: str, + labels: dict, + hours: int = 36, + scrape_interval: int = 30, + return_labels=None, + ) -> pd.DataFrame: + _start_time = time.time() + end_dt = datetime.now(pytz.utc) + start_dt = end_dt - timedelta(hours=hours) + + df = self.prometheus.query_metric( + metric_name=metric, + labels_map=labels, + return_labels=return_labels, + start=start_dt.timestamp(), + end=end_dt.timestamp(), + step=scrape_interval, + ) + + try: + df = self.clean_data(df, return_labels) + except Exception as ex: + _LOGGER.exception("Error while cleaning the data: Metric: %s, Error: %r", metric, ex) + + _LOGGER.info( + "Time taken to fetch data: %s, for Metric: %s, for df shape: %s", + time.time() - _start_time, + metric, + df.shape, + ) + return df diff --git a/udf/anomaly-detection/src/connectors/redis.py b/udf/anomaly-detection/src/connectors/redis.py new file mode 100644 index 00000000..d3df8b10 --- /dev/null +++ b/udf/anomaly-detection/src/connectors/redis.py @@ -0,0 +1,44 @@ +import json +from typing import Optional + +from redis.cluster import RedisCluster +from redis.backoff import ExponentialBackoff +from redis.exceptions import RedisClusterException, RedisError +from redis.retry import Retry + +from src import get_logger +from src.tools import is_host_reachable + +_LOGGER = get_logger(__name__) +redis_client: Optional[RedisCluster] = None + + +def get_redis_client( + host: str, port: str, password: str = None, decode_responses: bool = True, recreate=False +) -> RedisCluster: + global redis_client + + if not recreate and redis_client: + return redis_client + + redis_params = { + "host": host, + "port": port, + "password": password, + "decode_responses": decode_responses, + "skip_full_coverage_check": True, + "dynamic_startup_nodes": False, + "cluster_error_retry_attempts": 3, + } + _LOGGER.info("Redis params: %s", json.dumps(redis_params, indent=4)) + + if not is_host_reachable(host, port): + _LOGGER.error("Redis Cluster is unreachable after retries!") + + retry = Retry( + ExponentialBackoff(cap=2, base=1), + 3, + supported_errors=(ConnectionError, TimeoutError, RedisClusterException, RedisError), + ) + redis_client = RedisCluster(**redis_params, retry=retry) + return redis_client diff --git a/udf/anomaly-detection/src/connectors/sentinel.py b/udf/anomaly-detection/src/connectors/sentinel.py new file mode 100644 index 00000000..3f628291 --- /dev/null +++ b/udf/anomaly-detection/src/connectors/sentinel.py @@ -0,0 +1,91 @@ +import os +from typing import Optional + +from numalogic.tools.types import redis_client_t +from redis.backoff import ExponentialBackoff +from redis.exceptions import RedisClusterException, RedisError +from redis.retry import Retry +from redis.sentinel import Sentinel, MasterNotFoundError + +from src import get_logger +from src._config import RedisConf +from src.watcher import ConfigManager + +_LOGGER = get_logger(__name__) +SENTINEL_MASTER_CLIENT: Optional[redis_client_t] = None + + +def get_redis_client( + host: str, + port: int, + password: str, + mastername: str, + decode_responses: bool = False, + recreate: bool = False, +) -> redis_client_t: + """ + Return a master redis client for sentinel connections, with retry. + + Args: + host: Redis host + port: Redis port + password: Redis password + mastername: Redis sentinel master name + decode_responses: Whether to decode responses + recreate: Whether to flush and recreate the client + + Returns: + Redis client instance + """ + global SENTINEL_MASTER_CLIENT + + if not recreate and SENTINEL_MASTER_CLIENT: + return SENTINEL_MASTER_CLIENT + + retry = Retry( + ExponentialBackoff(cap=2, base=1), + 3, + supported_errors=( + ConnectionError, + TimeoutError, + RedisClusterException, + RedisError, + MasterNotFoundError, + ), + ) + sentinel_args = { + "sentinels": [(host, port)], + "socket_timeout": 0.1, + "decode_responses": decode_responses, + } + + _LOGGER.info("Sentinel redis params: %s", sentinel_args) + + sentinel = Sentinel( + **sentinel_args, sentinel_kwargs=dict(password=password), password=password, retry=retry + ) + SENTINEL_MASTER_CLIENT = sentinel.master_for(mastername) + return SENTINEL_MASTER_CLIENT + + +def get_redis_client_from_conf(redis_conf: RedisConf = None, **kwargs) -> redis_client_t: + """ + Return a master redis client from config for sentinel connections, with retry. + + Args: + redis_conf: RedisConf object with host, port, master_name, etc. + **kwargs: Additional arguments to pass to get_redis_client. + + Returns: + Redis client instance + """ + if not redis_conf: + redis_conf = ConfigManager.get_pipeline_config().redis_conf + + return get_redis_client( + redis_conf.host, + redis_conf.port, + password=os.getenv("REDIS_AUTH"), + mastername=redis_conf.master_name, + **kwargs + ) diff --git a/udf/anomaly-detection/src/entities.py b/udf/anomaly-detection/src/entities.py new file mode 100644 index 00000000..db4acc16 --- /dev/null +++ b/udf/anomaly-detection/src/entities.py @@ -0,0 +1,131 @@ +from copy import copy +from dataclasses import dataclass, field +from enum import Enum +from typing import List, Dict, Any, Union, TypeVar + +import numpy as np +import numpy.typing as npt +import orjson +import pandas as pd + +Vector = List[float] +Matrix = Union[Vector, List[Vector], npt.NDArray[float]] + + +class Status(str, Enum): + RAW = "raw" + EXTRACTED = "extracted" + PRE_PROCESSED = "pre_processed" + INFERRED = "inferred" + THRESHOLD = "threshold_complete" + POST_PROCESSED = "post_processed" + ARTIFACT_NOT_FOUND = "artifact_not_found" + ARTIFACT_STALE = "artifact_is_stale" + RUNTIME_ERROR = "runtime_error" + + +class Header(str, Enum): + STATIC_INFERENCE = "static_threshold" + MODEL_INFERENCE = "model_inference" + TRAIN_REQUEST = "request_training" + MODEL_STALE = "model_stale" + + +@dataclass +class _BasePayload: + uuid: str + composite_keys: List[str] + + +PayloadType = TypeVar("PayloadType", bound=_BasePayload) + + +@dataclass +class TrainerPayload(_BasePayload): + metric: str + header: Header = Header.TRAIN_REQUEST + + def to_json(self): + return orjson.dumps(self) + + +@dataclass(repr=False) +class StreamPayload(_BasePayload): + data: Matrix + raw_data: Matrix + metrics: List[str] + timestamps: List[int] + status: Dict[str, Status] = field(default_factory=dict) + header: Dict[str, Header] = field(default_factory=dict) + metadata: Dict[str, Dict[str, Any]] = field(default_factory=dict) + + def get_df(self, original=False) -> pd.DataFrame: + return pd.DataFrame(self.get_data(original), columns=self.metrics) + + def set_data(self, arr: Matrix) -> None: + self.data = arr + + def set_metric_data(self, metric: str, arr: Matrix) -> None: + _df = self.get_df().copy() + _df[metric] = arr + self.set_data(np.asarray(_df.values.tolist())) + + def get_metric_arr(self, metric: str) -> npt.NDArray[float]: + return self.get_df()[metric].values + + def get_data(self, original=False) -> npt.NDArray[float]: + if original: + return np.asarray(self.raw_data) + return np.asarray(self.data) + + def set_status(self, metric: str, status: Status) -> None: + self.status[metric] = status + + def set_header(self, metric: str, header: Header) -> None: + self.header[metric] = header + + def set_metric_metadata(self, metric: str, key: str, value) -> None: + if metric in self.metadata.keys(): + self.metadata[metric][key] = value + else: + self.metadata[metric] = {key: value} + + def set_metadata(self, key: str, value) -> None: + self.metadata[key] = value + + def get_metadata(self, key: str) -> Dict[str, Any]: + return copy(self.metadata[key]) + + def __repr__(self) -> str: + return "header: %s, status: %s, composite_keys: %s, data: %s, metadata: %s}" % ( + self.header, + self.status, + self.composite_keys, + list(self.data), + self.metadata, + ) + + def to_json(self): + return orjson.dumps(self, option=orjson.OPT_SERIALIZE_NUMPY) + + +@dataclass +class InputPayload: + start_time: int + end_time: int + data: list[dict[str, Any]] + metadata: dict[str, Any] + + def to_json(self): + return orjson.dumps(self, option=orjson.OPT_SERIALIZE_NUMPY) + + +@dataclass +class OutputPayload: + timestamp: int + unified_anomaly: float + data: dict[str, Any] + metadata: dict[str, Any] + + def to_json(self): + return orjson.dumps(self, option=orjson.OPT_SERIALIZE_NUMPY) diff --git a/udf/anomaly-detection/src/factory.py b/udf/anomaly-detection/src/factory.py new file mode 100644 index 00000000..6bf0cf3a --- /dev/null +++ b/udf/anomaly-detection/src/factory.py @@ -0,0 +1,23 @@ +from src.udf import Preprocess, Inference, Threshold, Postprocess +from src.udsink import Train + + +class HandlerFactory: + @classmethod + def get_handler(cls, step: str): + if step == "preprocess": + return Preprocess().run + + if step == "inference": + return Inference().run + + if step == "threshold": + return Threshold().run + + if step == "postprocess": + return Postprocess().run + + if step == "train": + return Train().run + + raise NotImplementedError(f"Invalid step provided: {step}") diff --git a/udf/anomaly-detection/src/tools.py b/udf/anomaly-detection/src/tools.py new file mode 100644 index 00000000..609830d6 --- /dev/null +++ b/udf/anomaly-detection/src/tools.py @@ -0,0 +1,190 @@ +import time + +import numpy as np +import pytz +import socket + +import pandas as pd +from typing import List +from functools import wraps +from json import JSONDecodeError +from collections import OrderedDict +from datetime import timedelta, datetime + +from numalogic.config import PostprocessFactory, ModelInfo +from numalogic.models.threshold import SigmoidThreshold + +from src import get_logger, MetricConf +from src._config import StaticThreshold +from src.connectors.prometheus import Prometheus +from src.entities import TrainerPayload, Matrix +from src.watcher import ConfigManager + +_LOGGER = get_logger(__name__) + + +def catch_exception(func): + @wraps(func) + def inner_function(*args, **kwargs): + try: + return func(*args, **kwargs) + except JSONDecodeError as err: + _LOGGER.exception("Error in json decode for %s: %r", func.__name__, err) + except Exception as ex: + _LOGGER.exception("Error in %s: %r", func.__name__, ex) + + return inner_function + + +def create_composite_keys(msg: dict, keys: List[str]) -> OrderedDict: + labels = msg.get("labels") + result = OrderedDict() + for k in keys: + if k in msg: + result[k] = msg[k] + if k in labels: + result[k] = labels[k] + return result + + +def get_ipv4_by_hostname(hostname: str, port=0) -> list: + return list( + idx[4][0] + for idx in socket.getaddrinfo(hostname, port) + if idx[0] is socket.AddressFamily.AF_INET and idx[1] is socket.SocketKind.SOCK_RAW + ) + + +def is_host_reachable(hostname: str, port=None, max_retries=5, sleep_sec=5) -> bool: + retries = 0 + assert max_retries >= 1, "Max retries has to be at least 1" + + while retries < max_retries: + try: + get_ipv4_by_hostname(hostname, port) + except socket.gaierror as ex: + retries += 1 + _LOGGER.warning( + "Failed to resolve hostname: %s: error: %r", hostname, ex, exc_info=True + ) + time.sleep(sleep_sec) + else: + return True + _LOGGER.error("Failed to resolve hostname: %s even after retries!") + return False + + +def fetch_data( + payload: TrainerPayload, + metric_config: MetricConf, + labels: dict, + return_labels=None, + hours: int = 36, +) -> pd.DataFrame: + _start_time = time.time() + prometheus_conf = ConfigManager.get_prom_config() + datafetcher = Prometheus(prometheus_conf.server) + + end_dt = datetime.now(pytz.utc) + start_dt = end_dt - timedelta(hours=hours) + + df = datafetcher.query_metric( + metric_name=payload.composite_keys["name"], + labels_map=labels, + return_labels=return_labels, + start=start_dt.timestamp(), + end=end_dt.timestamp(), + step=metric_config.scrape_interval, + ) + _LOGGER.info( + "%s - Time taken to fetch data: %s, for df shape: %s", + payload.uuid, + time.time() - _start_time, + df.shape, + ) + return df + + +def calculate_static_thresh(x_arr: Matrix, static_threshold: StaticThreshold) -> np.ndarray: + """ + Calculates anomaly scores using static thresholding. + """ + static_clf = SigmoidThreshold(upper_limit=static_threshold.upper_limit) + static_scores = static_clf.score_samples(x_arr) + return static_scores + + +class WindowScorer: + """ + Class to calculate the final anomaly scores for the window. + + Args: + static_thresh: StaticThreshold instance + postprocess_conf: ModelInfo instance + """ + + __slots__ = ("static_thresh", "model_wt", "postproc_clf") + + def __init__(self, static_thresh: StaticThreshold, postprocess_conf: ModelInfo): + self.static_thresh = static_thresh + self.model_wt = 1.0 - self.static_thresh.weight + postproc_factory = PostprocessFactory() + self.postproc_clf = postproc_factory.get_instance(postprocess_conf) + + def get_ensemble_score(self, x_arr: Matrix) -> float: + """ + Returns the final normalized window score. + + Performs soft voting ensembling if valid static threshold + weight found in config. + + Args: + x_arr: Metric scores array + + Returns: + Final score for the window + """ + norm_score = self.get_norm_score(x_arr) + + if not self.static_thresh.weight: + return norm_score + + norm_static_score = self.get_static_score(x_arr) + ensemble_score = (self.static_thresh.weight * norm_static_score) + ( + self.model_wt * norm_score + ) + return ensemble_score + + def get_static_score(self, x_arr) -> float: + """ + Returns the normalized window score + calculated using the static threshold estimator. + + Args: + x_arr: Metric scores array + + Returns: + Score for the window + """ + static_scores = calculate_static_thresh(x_arr, self.static_thresh) + static_score = np.mean(static_scores) + return self.postproc_clf.transform(static_score) + + def get_norm_score(self, x_arr: Matrix): + """ + Returns the normalized window score + + Args: + x_arr: Metric scores array + + Returns: + Score for the window + """ + win_score = np.mean(x_arr) + return self.postproc_clf.transform(win_score) + + def adjust_weights(self): + """ + Adjust the soft voting weights depending on the streaming input. + """ + raise NotImplementedError diff --git a/udf/anomaly-detection/src/udf/__init__.py b/udf/anomaly-detection/src/udf/__init__.py new file mode 100644 index 00000000..1a047f63 --- /dev/null +++ b/udf/anomaly-detection/src/udf/__init__.py @@ -0,0 +1,6 @@ +from src.udf.preprocess import Preprocess +from src.udf.inference import Inference +from src.udf.threshold import Threshold +from src.udf.postprocess import Postprocess + +__all__ = ["Preprocess", "Inference", "Threshold", "Postprocess"] diff --git a/udf/anomaly-detection/src/udf/inference.py b/udf/anomaly-detection/src/udf/inference.py new file mode 100644 index 00000000..970f0842 --- /dev/null +++ b/udf/anomaly-detection/src/udf/inference.py @@ -0,0 +1,149 @@ +import time + +import numpy as np +from typing import List + +import orjson +from torch.utils.data import DataLoader + +from numalogic.models.autoencoder import AutoencoderTrainer +from numalogic.registry import RedisRegistry, ArtifactData +from numalogic.tools.data import StreamingDataset +from numalogic.tools.exceptions import RedisRegistryError +from pynumaflow.function import Datum, Messages, Message + +from src import get_logger +from src.connectors.sentinel import get_redis_client_from_conf +from src.entities import StreamPayload +from src.entities import Status, Header +from src.watcher import ConfigManager + +_LOGGER = get_logger(__name__) + + +class Inference: + def __init__(self): + self.model_registry = RedisRegistry(client=get_redis_client_from_conf()) + + @classmethod + def _run_inference( + cls, + keys: list[str], + metric: str, + payload: StreamPayload, + artifact_data: ArtifactData, + ) -> np.ndarray: + model = artifact_data.artifact + win_size = ConfigManager.get_ds_config(config_name=keys[0]).window_size + metric_arr = payload.get_metric_arr(metric=metric).reshape(-1, 1) + stream_loader = DataLoader(StreamingDataset(metric_arr, win_size)) + + trainer = AutoencoderTrainer() + try: + recon_err = trainer.predict(model, dataloaders=stream_loader) + except Exception as err: + _LOGGER.exception( + "%s - Runtime error while performing inference: Keys: %s, Metric: %s, Error: %r", + payload.uuid, + keys, + metric, + err, + ) + raise RuntimeError("Failed to infer") from err + _LOGGER.info("%s - Successfully inferred: Keys: %s, Metric: %s", payload.uuid, keys, metric) + return recon_err.numpy().flatten() + + def inference( + self, keys: List[str], metric: str, payload: StreamPayload + ) -> (np.ndarray, Status, Header, int): + static_response = (None, Status.ARTIFACT_NOT_FOUND, Header.STATIC_INFERENCE, -1) + # Check if metric needs static inference + if payload.header[metric] == Header.STATIC_INFERENCE: + _LOGGER.debug( + "%s - Models not found in the previous steps, forwarding for static thresholding. Keys: %s, Metric: %s", + payload.uuid, + payload.composite_keys, + metric, + ) + return static_response + + # Load config + retrain_config = ConfigManager.get_retrain_config(config_name=keys[0], metric_name=metric) + numalogic_conf = ConfigManager.get_numalogic_config(config_name=keys[0], metric_name=metric) + + # Load inference artifact + try: + artifact_data = self.model_registry.load( + skeys=keys + [metric], + dkeys=[numalogic_conf.model.name], + ) + except RedisRegistryError as err: + _LOGGER.exception( + "%s - Error while fetching inference artifact, Keys: %s, Metric: %s, Error: %r", + payload.uuid, + payload.composite_keys, + metric, + err, + ) + return static_response + + # Check if artifact is found + if not artifact_data: + _LOGGER.info( + "%s - Inference artifact not found, forwarding for static thresholding. Keys: %s, Metric: %s", + payload.uuid, + payload.composite_keys, + metric, + ) + return static_response + + # Check if artifact is stale + header = Header.MODEL_INFERENCE + if RedisRegistry.is_artifact_stale(artifact_data, int(retrain_config.retrain_freq_hr)): + _LOGGER.info( + "%s - Inference artifact found is stale, Keys: %s, Metric: %s", + payload.uuid, + payload.composite_keys, + metric, + ) + header = Header.MODEL_STALE + + # Generate predictions + try: + x_infered = self._run_inference(keys, metric, payload, artifact_data) + except RuntimeError: + _LOGGER.info( + "%s - Failed to infer, forwarding for static thresholding. Keys: %s, Metric: %s", + payload.uuid, + payload.composite_keys, + metric, + ) + return None, Status.RUNTIME_ERROR, Header.STATIC_INFERENCE, -1 + + return x_infered, Status.INFERRED, header, int(artifact_data.extras.get("version")) + + def run(self, keys: List[str], datum: Datum) -> Messages: + _start_time = time.perf_counter() + _ = datum.event_time + _ = datum.watermark + + # Construct payload object + _in_msg = datum.value.decode("utf-8") + payload = StreamPayload(**orjson.loads(_in_msg)) + + _LOGGER.info("%s - Received Msg: { Keys: %s, Payload: %r }", payload.uuid, keys, payload) + + messages = Messages() + # Perform inference for each metric + for metric in payload.metrics: + x_infered, status, header, version = self.inference(keys, metric, payload) + payload.set_status(metric=metric, status=status) + payload.set_header(metric=metric, header=header) + payload.set_metric_metadata(metric=metric, key="model_version", value=version) + + if x_infered is not None: + payload.set_metric_data(metric=metric, arr=x_infered) + + messages.append(Message(keys=keys, value=payload.to_json())) + _LOGGER.info("%s - Sending Msg: { Keys: %s, Payload: %r }", payload.uuid, keys, payload) + return messages diff --git a/udf/anomaly-detection/src/udf/postprocess.py b/udf/anomaly-detection/src/udf/postprocess.py new file mode 100644 index 00000000..57d84468 --- /dev/null +++ b/udf/anomaly-detection/src/udf/postprocess.py @@ -0,0 +1,117 @@ +import time +import numpy as np +from typing import List +from orjson import orjson + +from pynumaflow.function import Datum, Messages, Message + +from src import get_logger +from src.entities import StreamPayload, Header, OutputPayload +from src.tools import WindowScorer +from src.watcher import ConfigManager + +_LOGGER = get_logger(__name__) + + +class Postprocess: + @classmethod + def postprocess(cls, keys: list[str], metric: str, payload: StreamPayload) -> float: + static_thresh = ConfigManager.get_static_threshold_config( + config_name=keys[0], metric_name=metric + ) + postprocess_conf = ConfigManager.get_postprocess_config( + config_name=keys[0], metric_name=metric + ) + + # Compute score using static thresholding + metric_arr = payload.get_metric_arr(metric=metric) + win_scorer = WindowScorer(static_thresh, postprocess_conf) + if payload.header[metric] == Header.STATIC_INFERENCE: + final_score = win_scorer.get_norm_score(metric_arr) + _LOGGER.info( + "%s - Final static threshold score: %s, keys: %s, metric: %s", + payload.uuid, + final_score, + keys, + metric, + ) + + # Compute ensemble score otherwise + else: + final_score = win_scorer.get_ensemble_score(metric_arr) + _LOGGER.info( + "%s - Final ensemble score: %s, static thresh wt: %s, keys: %s, metric: %s", + payload.uuid, + final_score, + static_thresh.weight, + keys, + metric, + ) + return final_score + + @classmethod + def get_unified_anomaly( + cls, keys: List[str], scores: list[float], payload: StreamPayload + ) -> float: + unified_config = ConfigManager.get_ds_config(config_name=keys[0]).unified_config + unified_weights = unified_config.weights + if unified_weights: + weighted_anomalies = np.multiply(scores, unified_weights) + unified_anomaly = float(np.sum(weighted_anomalies) / np.sum(unified_weights)) + _LOGGER.info( + "%s - Generating unified anomaly, using unified weights. Unified Anomaly: %s", + payload.uuid, + unified_anomaly, + ) + else: + unified_anomaly = max(scores) + _LOGGER.info( + "%s - Generated unified anomaly, using max strategy. Unified Anomaly: %s", + payload.uuid, + unified_anomaly, + ) + + return unified_anomaly + + def run(self, keys: List[str], datum: Datum) -> Messages: + _start_time = time.perf_counter() + _ = datum.event_time + _ = datum.watermark + + # Construct payload object + _in_msg = datum.value.decode("utf-8") + payload = StreamPayload(**orjson.loads(_in_msg)) + + _LOGGER.info("%s - Received Msg: { Keys: %s, Payload: %r }", payload.uuid, keys, payload) + + messages = Messages() + + scores = [] + metric_data = {} + + # Perform postprocess for each metric + for metric in payload.metrics: + final_score = self.postprocess(keys, metric, payload) + scores.append(final_score) + metric_data[metric] = { + "anomaly_score": final_score, + "model_version": payload.get_metadata(key=metric)["model_version"], + } + + unified_anomaly = self.get_unified_anomaly(keys, scores, payload) + + out_payload = OutputPayload( + timestamp=payload.timestamps[-1], + unified_anomaly=unified_anomaly, + data=metric_data, + metadata=payload.metadata, + ) + + _LOGGER.info("%s - Sending Msg: { Keys: %s, Payload: %s }", payload.uuid, keys, out_payload) + _LOGGER.debug( + "%s - Time taken in postprocess: %.4f sec", + payload.uuid, + time.perf_counter() - _start_time, + ) + messages.append(Message(keys=keys, value=out_payload.to_json())) + return messages diff --git a/udf/anomaly-detection/src/udf/preprocess.py b/udf/anomaly-detection/src/udf/preprocess.py new file mode 100644 index 00000000..7b6f17f2 --- /dev/null +++ b/udf/anomaly-detection/src/udf/preprocess.py @@ -0,0 +1,146 @@ +import json +import time +import uuid + +import numpy as np +import pandas as pd +from typing import List + +from numalogic.registry import RedisRegistry +from numalogic.tools.exceptions import RedisRegistryError +from pynumaflow.function import Datum, Messages, Message + +from src import get_logger +from src.connectors.sentinel import get_redis_client_from_conf +from src.entities import Status, StreamPayload, Header +from src.watcher import ConfigManager + +_LOGGER = get_logger(__name__) + + +class Preprocess: + @classmethod + def get_df(cls, data_payload: dict, features: List[str]) -> (pd.DataFrame, List[int]): + timestamps = range( + int(data_payload["start_time"]), int(data_payload["end_time"]) - 1, 60 * 1000 + ) + given_timestamps = [int(data["timestamp"]) for data in data_payload["data"]] + + rows = [] + for data in data_payload["data"]: + feature_values = [float(data.get(feature)) for feature in features] + rows.append(pd.Series(feature_values + [int(data["timestamp"])])) + + for timestamp in timestamps: + if timestamp not in given_timestamps: + rows.append(pd.Series([0] * len(features) + [timestamp])) + + df = pd.concat(rows, axis=1).T + df.columns = features + ["timestamp"] + df.sort_values("timestamp", inplace=True) + df.reset_index(drop=True, inplace=True) + df = df.fillna(0) + df = df.drop("timestamp", axis=1) + return df, [*timestamps] + + def preprocess( + self, keys: List[str], metric: str, payload: StreamPayload + ) -> (np.ndarray, Status): + preprocess_cfgs = ConfigManager.get_preprocess_config( + config_name=keys[0], metric_name=metric + ) + + model_registry = RedisRegistry(client=get_redis_client_from_conf()) + # Load preproc artifact + try: + preproc_artifact = model_registry.load( + skeys=keys + [metric], + dkeys=[_conf.name for _conf in preprocess_cfgs], + ) + except RedisRegistryError as err: + _LOGGER.error( + "%s - Error while fetching preproc artifact, Keys: %s, Metric: %s, Error: %r", + payload.uuid, + keys, + metric, + err, + ) + return None, Status.RUNTIME_ERROR + + # Check if artifact is found + if not preproc_artifact: + _LOGGER.info( + "%s - Preprocess artifact not found, forwarding for static thresholding. Keys: %s, Metric: %s", + payload.uuid, + keys, + metric, + ) + return None, Status.ARTIFACT_NOT_FOUND + + # Perform preprocessing + x_raw = payload.get_metric_arr(metric).reshape(-1, 1) + preproc_clf = preproc_artifact.artifact + x_scaled = preproc_clf.transform(x_raw).flatten() + _LOGGER.info( + "%s - Successfully preprocessed, Keys: %s, Metric: %s, x_scaled: %s", + payload.uuid, + keys, + metric, + list(x_scaled), + ) + return x_scaled, Status.PRE_PROCESSED + + def run(self, keys: List[str], datum: Datum) -> Messages: + _start_time = time.perf_counter() + _ = datum.event_time + _ = datum.watermark + _uuid = uuid.uuid4().hex + _LOGGER.info("%s - Received Msg: { Keys: %s, Value: %s }", _uuid, keys, datum.value) + + messages = Messages() + try: + data_payload = json.loads(datum.value) + _LOGGER.info("%s - Data payload: %s", _uuid, data_payload) + except Exception as e: + _LOGGER.error("%s - Error while reading input json %r", e) + messages.append(Message.to_drop()) + return messages + + # Load config + stream_conf = ConfigManager.get_ds_config(config_name=keys[0]) + raw_df, timestamps = self.get_df(data_payload, stream_conf.metrics) + + # Prepare payload for forwarding + payload = StreamPayload( + uuid=_uuid, + composite_keys=keys, + data=np.asarray(raw_df.values.tolist()), + raw_data=np.asarray(raw_df.values.tolist()), + metrics=raw_df.columns.tolist(), + timestamps=timestamps, + metadata=data_payload["metadata"], + ) + + if not np.isfinite(raw_df.values).any(): + _LOGGER.warning( + "%s - Non finite values encountered: %s for keys: %s", + payload.uuid, + list(raw_df.values), + keys, + ) + + # Perform preprocessing for each metric + for metric in payload.metrics: + x_scaled, status = self.preprocess(keys, metric, payload) + payload.set_status(metric=metric, status=status) + + # If preprocess failed, forward for static thresholding + if x_scaled is None: + payload.set_header(metric=metric, header=Header.STATIC_INFERENCE) + else: + payload.set_header(metric=metric, header=Header.MODEL_INFERENCE) + payload.set_metric_data(metric=metric, arr=x_scaled) + + messages.append(Message(keys=keys, value=payload.to_json())) + _LOGGER.info("%s - Sending Msg: { Keys: %s, Payload: %r }", payload.uuid, keys, payload) + return messages diff --git a/udf/anomaly-detection/src/udf/threshold.py b/udf/anomaly-detection/src/udf/threshold.py new file mode 100644 index 00000000..66e17860 --- /dev/null +++ b/udf/anomaly-detection/src/udf/threshold.py @@ -0,0 +1,132 @@ +import time + +import numpy as np +from typing import List +from orjson import orjson + +from numalogic.registry import RedisRegistry +from numalogic.tools.exceptions import RedisRegistryError +from pynumaflow.function import Datum, Messages, Message + +from src import get_logger +from src._constants import TRAIN_VTX_KEY, POSTPROC_VTX_KEY +from src.connectors.sentinel import get_redis_client_from_conf +from src.entities import Status, TrainerPayload, Header, StreamPayload +from src.tools import calculate_static_thresh +from src.watcher import ConfigManager + +_LOGGER = get_logger(__name__) + + +class Threshold: + def __init__(self): + self.model_registry = RedisRegistry(client=get_redis_client_from_conf()) + + def threshold( + self, keys: List[str], metric: str, payload: StreamPayload + ) -> (np.ndarray, Status, Header, int): + metric_arr = payload.get_metric_arr(metric=metric) + + # Load config + static_thresh = ConfigManager.get_static_threshold_config( + config_name=keys[0], metric_name=metric + ) + thresh_cfg = ConfigManager.get_threshold_config(config_name=keys[0], metric_name=metric) + + # Check if metric needs static inference + if payload.header[metric] == Header.STATIC_INFERENCE: + _LOGGER.info( + "%s - Sending to trainer and performing static thresholding. Keys: %s, Metric: %s", + payload.uuid, + payload.composite_keys, + metric, + ) + static_scores = calculate_static_thresh(metric_arr, static_thresh) + return static_scores, Status.ARTIFACT_NOT_FOUND, Header.STATIC_INFERENCE, -1 + + # Load threshold artifact + try: + thresh_artifact = self.model_registry.load( + skeys=keys + [metric], + dkeys=[thresh_cfg.name], + ) + except RedisRegistryError as err: + _LOGGER.exception( + "%s - Error while fetching threshold artifact, Keys: %s, Metric: %s, Error: %r", + payload.uuid, + payload.composite_keys, + metric, + err, + ) + static_scores = calculate_static_thresh(metric_arr, static_thresh) + return static_scores, Status.RUNTIME_ERROR, Header.STATIC_INFERENCE, -1 + + # Check if artifact is found + if not thresh_artifact: + _LOGGER.info( + "%s - Threshold artifact not found, performing static thresholding. Keys: %s", + payload.uuid, + payload.composite_keys, + ) + static_scores = calculate_static_thresh(metric_arr, static_thresh) + return static_scores, Status.ARTIFACT_NOT_FOUND, Header.STATIC_INFERENCE, -1 + + # Calculate anomaly score + recon_err = payload.get_metric_arr(metric=metric) + thresh_clf = thresh_artifact.artifact + y_score = thresh_clf.score_samples(recon_err) + + return ( + y_score, + Status.THRESHOLD, + payload.header[metric], + payload.get_metadata(key=metric)["model_version"], + ) + + def run(self, keys: List[str], datum: Datum) -> Messages: + _start_time = time.perf_counter() + _ = datum.event_time + _ = datum.watermark + + # Construct payload object + _in_msg = datum.value.decode("utf-8") + payload = StreamPayload(**orjson.loads(_in_msg)) + + _LOGGER.info("%s - Received Msg: { Keys: %s, Payload: %r }", payload.uuid, keys, payload) + + messages = Messages() + + # Perform threshold for each metric + for metric in payload.metrics: + y_score, status, header, version = self.threshold(keys, metric, payload) + payload.set_status(metric=metric, status=status) + payload.set_header(metric=metric, header=header) + payload.set_metric_metadata(metric=metric, key="model_version", value=version) + + if y_score is not None: + payload.set_metric_data(metric=metric, arr=y_score) + + if y_score is None or header == Header.MODEL_STALE: + train_payload = TrainerPayload( + uuid=payload.uuid, composite_keys=keys, metric=metric + ) + _LOGGER.info( + "%s - Sending Msg: { Keys: %s, Tags:%s, Payload: %s }", + payload.uuid, + keys, + [TRAIN_VTX_KEY], + train_payload, + ) + messages.append( + Message(keys=keys, value=train_payload.to_json(), tags=[TRAIN_VTX_KEY]) + ) + + messages.append(Message(keys=keys, value=payload.to_json(), tags=[POSTPROC_VTX_KEY])) + _LOGGER.info( + "%s - Sending Msg: { Keys: %s, Tags:%s, Payload: %r }", + payload.uuid, + keys, + [POSTPROC_VTX_KEY], + payload, + ) + return messages diff --git a/udf/anomaly-detection/src/udsink/__init__.py b/udf/anomaly-detection/src/udsink/__init__.py new file mode 100644 index 00000000..ee5fd796 --- /dev/null +++ b/udf/anomaly-detection/src/udsink/__init__.py @@ -0,0 +1,3 @@ +from src.udsink.train import Train + +__all__ = ["Train"] diff --git a/udf/anomaly-detection/src/udsink/train.py b/udf/anomaly-detection/src/udsink/train.py new file mode 100644 index 00000000..23942781 --- /dev/null +++ b/udf/anomaly-detection/src/udsink/train.py @@ -0,0 +1,274 @@ +import os +import time +import orjson +import pandas as pd +from typing import List, Iterator +from numalogic.config import ( + NumalogicConf, + ThresholdFactory, + ModelInfo, + PreprocessFactory, + ModelFactory, +) +from numalogic.models.autoencoder import AutoencoderTrainer +from numalogic.registry import RedisRegistry +from numalogic.tools.data import StreamingDataset +from numalogic.tools.exceptions import RedisRegistryError +from numalogic.tools.types import redis_client_t +from pynumaflow.function import Datum +from pynumaflow.sink import Responses, Response +from sklearn.pipeline import make_pipeline +from torch.utils.data import DataLoader + +from src import get_logger +from src._config import DataSource +from src.connectors.druid import DruidFetcher +from src.connectors.prometheus import PrometheusDataFetcher +from src.connectors.sentinel import get_redis_client_from_conf +from src.entities import TrainerPayload +from src.watcher import ConfigManager + +_LOGGER = get_logger(__name__) + +REQUEST_EXPIRY = int(os.getenv("REQUEST_EXPIRY", 300)) + + +class Train: + @classmethod + def fetch_prometheus_data(cls, payload: TrainerPayload) -> pd.DataFrame: + prometheus_conf = ConfigManager.get_prom_config() + if prometheus_conf is None: + _LOGGER.error("Prometheus config is not available") + return pd.DataFrame() + data_fetcher = PrometheusDataFetcher(prometheus_conf.server) + return data_fetcher.fetch_data( + metric=payload.metric, + labels={"namespace": payload.composite_keys[1]}, + return_labels=["rollouts_pod_template_hash"], + ) + + @classmethod + def fetch_druid_data(cls, payload: TrainerPayload) -> pd.DataFrame: + stream_config = ConfigManager.get_ds_config(payload.composite_keys[0]) + druid_conf = ConfigManager.get_druid_config() + fetcher_conf = stream_config.druid_fetcher + if druid_conf is None: + _LOGGER.error("Druid config is not available") + return pd.DataFrame() + data_fetcher = DruidFetcher(url=druid_conf.url, endpoint=druid_conf.endpoint) + return data_fetcher.fetch_data( + filter_keys=stream_config.composite_keys, + filter_values=payload.composite_keys, + **fetcher_conf.__dict__, + ) + + @classmethod + def fetch_data(cls, payload: TrainerPayload) -> pd.DataFrame: + stream_config = ConfigManager.get_ds_config(payload.composite_keys[0]) + if stream_config.source == DataSource.PROMETHEUS: + return cls.fetch_prometheus_data(payload) + elif stream_config.source == DataSource.DRUID: + return cls.fetch_druid_data(payload) + + _LOGGER.error( + "Data source is not supported, source: %s, keys: %s", + stream_config.source, + payload.composite_keys, + ) + return pd.DataFrame() + + @classmethod + def _is_new_request(cls, redis_client: redis_client_t, payload: TrainerPayload) -> bool: + _ckeys = ":".join(payload.composite_keys + [payload.metric]) + r_key = f"train::{_ckeys}" + value = redis_client.get(r_key) + if value: + return False + + redis_client.setex(r_key, time=REQUEST_EXPIRY, value=1) + return True + + @classmethod + def _train_model(cls, uuid, x, model_cfg, trainer_cfg): + _start_train = time.perf_counter() + + model_factory = ModelFactory() + model = model_factory.get_instance(model_cfg) + dataset = StreamingDataset(x, model.seq_len) + + trainer = AutoencoderTrainer(**trainer_cfg) + trainer.fit(model, train_dataloaders=DataLoader(dataset, batch_size=64)) + + _LOGGER.debug( + "%s - Time taken to train model: %.3f sec", uuid, time.perf_counter() - _start_train + ) + + train_reconerr = trainer.predict(model, dataloaders=DataLoader(dataset, batch_size=64)) + # return the trainer to avoid Weakreference error + return train_reconerr.numpy(), model, trainer + + @classmethod + def _preprocess(cls, x_raw, preproc_cfgs: List[ModelInfo]): + preproc_factory = PreprocessFactory() + preproc_clfs = [] + for _cfg in preproc_cfgs: + _clf = preproc_factory.get_instance(_cfg) + preproc_clfs.append(_clf) + preproc_pl = make_pipeline(*preproc_clfs) + + x_scaled = preproc_pl.fit_transform(x_raw) + return x_scaled, preproc_pl + + @classmethod + def _find_threshold(cls, x_reconerr, thresh_cfg: ModelInfo): + thresh_factory = ThresholdFactory() + thresh_clf = thresh_factory.get_instance(thresh_cfg) + thresh_clf.fit(x_reconerr) + return thresh_clf + + def _train_and_save( + self, + numalogic_conf: NumalogicConf, + payload: TrainerPayload, + redis_client: redis_client_t, + train_df: pd.DataFrame, + ) -> None: + model_cfg = numalogic_conf.model + preproc_cfgs = numalogic_conf.preprocess + + x_train, preproc_clf = self._preprocess(train_df.to_numpy(), preproc_cfgs) + + trainer_cfg = numalogic_conf.trainer + x_reconerr, anomaly_model, trainer = self._train_model( + payload.uuid, x_train, model_cfg, trainer_cfg + ) + + thresh_cfg = numalogic_conf.threshold + thresh_clf = self._find_threshold(x_reconerr, thresh_cfg) + + skeys = payload.composite_keys + [payload.metric] + + # TODO if one of the models fail to save, delete the previously saved models and transition stage + # Save main model + model_registry = RedisRegistry(client=redis_client) + try: + version = model_registry.save( + skeys=skeys, + dkeys=[model_cfg.name], + artifact=anomaly_model, + uuid=payload.uuid, + train_size=train_df.shape[0], + ) + except RedisRegistryError as err: + _LOGGER.exception( + "%s - Error while saving Model with skeys: %s, err: %r", payload.uuid, skeys, err + ) + else: + _LOGGER.info( + "%s - Model saved with skeys: %s with version: %s", payload.uuid, skeys, version + ) + # Save preproc model + try: + version = model_registry.save( + skeys=skeys, + dkeys=[_conf.name for _conf in preproc_cfgs], + artifact=preproc_clf, + uuid=payload.uuid, + ) + except RedisRegistryError as err: + _LOGGER.exception( + "%s - Error while saving Preproc model with skeys: %s, err: %r", + payload.uuid, + skeys, + err, + ) + else: + _LOGGER.info( + "%s - Preproc model saved with skeys: %s with version: %s", + payload.uuid, + skeys, + version, + ) + # Save threshold model + try: + version = model_registry.save( + skeys=skeys, + dkeys=[thresh_cfg.name], + artifact=thresh_clf, + uuid=payload.uuid, + ) + except RedisRegistryError as err: + _LOGGER.error( + "%s - Error while saving Threshold model with skeys: %s, err: %r", + payload.uuid, + skeys, + err, + ) + else: + _LOGGER.info( + "%s - Threshold model saved with skeys: %s with version: %s", + payload.uuid, + skeys, + version, + ) + + def run(self, datums: Iterator[Datum]) -> Responses: + responses = Responses() + redis_client = get_redis_client_from_conf() + + for _datum in datums: + payload = TrainerPayload(**orjson.loads(_datum.value)) + + _LOGGER.debug( + "%s - Starting Training for keys: %s, metric: %s", + payload.uuid, + payload.composite_keys, + payload.metric, + ) + + is_new = self._is_new_request(redis_client, payload) + + if not is_new: + _LOGGER.debug( + "%s - Skipping train request with keys: %s, metric: %s", + payload.uuid, + payload.composite_keys, + payload.metric, + ) + responses.append(Response.as_success(payload.uuid)) + continue + + metric_config = ConfigManager.get_metric_config( + payload.composite_keys[0], payload.metric + ) + retrain_config = metric_config.retrain_conf + numalogic_config = metric_config.numalogic_conf + + try: + train_df = self.fetch_data(payload) + except Exception as err: + _LOGGER.error( + "%s - Error while fetching data for keys: %s, metric: %s, err: %r", + payload.uuid, + payload.composite_keys, + payload.metric, + err, + ) + responses.append(Response.as_success(payload.uuid)) + continue + + if len(train_df) < retrain_config.min_train_size: + _LOGGER.warning( + "%s - Skipping training, train data less than minimum required: %s, df shape: %s", + payload.uuid, + retrain_config.min_train_size, + train_df.shape, + ) + responses.append(Response.as_success(payload.uuid)) + continue + + self._train_and_save(numalogic_config, payload, redis_client, train_df) + + responses.append(Response.as_success(payload.uuid)) + + return responses diff --git a/udf/anomaly-detection/src/watcher.py b/udf/anomaly-detection/src/watcher.py new file mode 100644 index 00000000..12d26eee --- /dev/null +++ b/udf/anomaly-detection/src/watcher.py @@ -0,0 +1,189 @@ +import os +import time +from typing import Optional + +from omegaconf import OmegaConf +from watchdog.observers import Observer +from numalogic.config import NumalogicConf +from watchdog.events import FileSystemEventHandler + +from src import PipelineConf, Configs +from src.connectors import RedisConf, PrometheusConf, RegistryConf +from src._constants import CONFIG_DIR +from src import DataStreamConf, get_logger, MetricConf, UnifiedConf +from src.connectors._config import DruidConf + +_LOGGER = get_logger(__name__) + + +class ConfigManager: + config = {} + + @staticmethod + def load_configs(): + schema: Configs = OmegaConf.structured(Configs) + + conf = OmegaConf.load(os.path.join(CONFIG_DIR, "user-configs", "config.yaml")) + user_configs = OmegaConf.merge(schema, conf).configs + + conf = OmegaConf.load(os.path.join(CONFIG_DIR, "default-configs", "config.yaml")) + default_configs = OmegaConf.merge(schema, conf).configs + + conf = OmegaConf.load(os.path.join(CONFIG_DIR, "default-configs", "numalogic_config.yaml")) + schema: NumalogicConf = OmegaConf.structured(NumalogicConf) + default_numalogic = OmegaConf.merge(schema, conf) + + conf = OmegaConf.load(os.path.join(CONFIG_DIR, "default-configs", "pipeline_config.yaml")) + schema: PipelineConf = OmegaConf.structured(PipelineConf) + pipeline_config = OmegaConf.merge(schema, conf) + + return user_configs, default_configs, default_numalogic, pipeline_config + + @classmethod + def update_configs(cls): + user_configs, default_configs, default_numalogic, pipeline_config = cls.load_configs() + + cls.config["user_configs"] = dict() + for _config in user_configs: + cls.config["user_configs"][_config.name] = _config + + cls.config["default_configs"] = dict(map(lambda c: (c.name, c), default_configs)) + cls.config["default_numalogic"] = default_numalogic + cls.config["pipeline_config"] = pipeline_config + + _LOGGER.info("Successfully updated configs - %s", cls.config) + return cls.config + + @classmethod + def get_ds_config(cls, config_name: str) -> DataStreamConf: + if not cls.config: + cls.update_configs() + + ds_config = None + + # search and load from user configs + if config_name in cls.config["user_configs"]: + ds_config = cls.config["user_configs"][config_name] + + # if not search and load from default configs + if not ds_config and config_name in cls.config["default_configs"]: + ds_config = cls.config["default_configs"][config_name] + + # if not in default configs, initialize conf with default values + if not ds_config: + ds_config = OmegaConf.structured(DataStreamConf) + + # loading and setting default numalogic config + for metric_config in ds_config.metric_configs: + if OmegaConf.is_missing(metric_config, "numalogic_conf"): + metric_config.numalogic_conf = cls.config["default_numalogic"] + + return ds_config + + @classmethod + def get_metric_config(cls, config_name: str, metric_name: str) -> MetricConf: + ds_config = cls.get_ds_config(config_name) + metric_config = list( + filter(lambda conf: (conf.metric == metric_name), ds_config.metric_configs) + ) + + # if no config found for metric, initialize with default values + if not metric_config: + metric_config = OmegaConf.structured(MetricConf(metric=metric_name)) + if OmegaConf.is_missing(metric_config, "numalogic_conf"): + metric_config.numalogic_conf = cls.config["default_numalogic"] + + return metric_config + return metric_config[0] + + @classmethod + def get_unified_config(cls, config_name: str) -> UnifiedConf: + ds_config = cls.get_ds_config(config_name) + return ds_config.unified_config + + @classmethod + def get_pipeline_config(cls) -> PipelineConf: + if not cls.config: + cls.update_configs() + return cls.config["pipeline_config"] + + @classmethod + def get_prom_config(cls) -> Optional[PrometheusConf]: + if "prometheus_conf" in cls.get_pipeline_config(): + return cls.get_pipeline_config().prometheus_conf + return None + + @classmethod + def get_druid_config(cls) -> Optional[DruidConf]: + if "druid_conf" in cls.get_pipeline_config(): + return cls.get_pipeline_config().druid_conf + return None + + @classmethod + def get_numalogic_config(cls, config_name: str, metric_name: str): + return cls.get_metric_config( + config_name=config_name, metric_name=metric_name + ).numalogic_conf + + @classmethod + def get_preprocess_config(cls, config_name: str, metric_name: str): + return cls.get_numalogic_config(config_name=config_name, metric_name=metric_name).preprocess + + @classmethod + def get_retrain_config(cls, config_name: str, metric_name: str): + return cls.get_metric_config(config_name=config_name, metric_name=metric_name).retrain_conf + + @classmethod + def get_static_threshold_config(cls, config_name: str, metric_name: str): + return cls.get_metric_config( + config_name=config_name, metric_name=metric_name + ).static_threshold + + @classmethod + def get_threshold_config(cls, config_name: str, metric_name: str): + return cls.get_metric_config( + config_name=config_name, metric_name=metric_name + ).numalogic_conf.threshold + + @classmethod + def get_postprocess_config(cls, config_name: str, metric_name: str): + return cls.get_numalogic_config( + config_name=config_name, metric_name=metric_name + ).postprocess + + @classmethod + def get_trainer_config(cls, config_name: str, metric_name: str): + return cls.get_numalogic_config(config_name=config_name, metric_name=metric_name).trainer + + +class ConfigHandler(FileSystemEventHandler): + def on_any_event(self, event): + if event.event_type == "created" or event.event_type == "modified": + _file = os.path.basename(event.src_path) + _dir = os.path.basename(os.path.dirname(event.src_path)) + + _LOGGER.info("Watchdog received %s event - %s/%s", event.event_type, _dir, _file) + ConfigManager.update_configs() + + +class Watcher: + def __init__(self, directories=None, handler=FileSystemEventHandler()): + if directories is None: + directories = ["."] + self.observer = Observer() + self.handler = handler + self.directories = directories + + def run(self): + for directory in self.directories: + self.observer.schedule(self.handler, directory, recursive=True) + _LOGGER.info("\nWatcher Running in {}/\n".format(directory)) + + self.observer.start() + try: + while True: + time.sleep(1) + except KeyboardInterrupt: + self.observer.stop() + self.observer.join() + _LOGGER.info("\nWatcher Terminated\n") diff --git a/udf/anomaly-detection/starter.py b/udf/anomaly-detection/starter.py new file mode 100644 index 00000000..e126cdce --- /dev/null +++ b/udf/anomaly-detection/starter.py @@ -0,0 +1,36 @@ +import sys +import threading + +import aiorun +from pynumaflow.function import Server, AsyncServer +from pynumaflow.sink import Sink + +from src._constants import CONFIG_PATHS +from src.factory import HandlerFactory +from src.watcher import Watcher, ConfigHandler + + +def run_watcher(): + w = Watcher(CONFIG_PATHS, ConfigHandler()) + w.run() + + +if __name__ == "__main__": + background_thread = threading.Thread(target=run_watcher, args=()) + background_thread.daemon = True + background_thread.start() + + step_handler = HandlerFactory.get_handler(sys.argv[2]) + server_type = sys.argv[1] + + if server_type == "udsink": + server = Sink(sink_handler=step_handler) + server.start() + elif server_type == "udf": + server = Server(map_handler=step_handler) + server.start() + elif server_type == "async_udf": + server = AsyncServer(reduce_handler=step_handler) + aiorun.run(server.start()) + else: + raise ValueError(f"sys arg: {server_type} not understood!") diff --git a/udf/anomaly-detection/tests/__init__.py b/udf/anomaly-detection/tests/__init__.py new file mode 100644 index 00000000..4200b211 --- /dev/null +++ b/udf/anomaly-detection/tests/__init__.py @@ -0,0 +1,56 @@ +import os +from unittest.mock import patch + +import fakeredis +from numalogic.config import NumalogicConf +from omegaconf import OmegaConf + +from src._config import PipelineConf, Configs +from src._constants import TESTS_RESOURCES, CONFIG_DIR +from src.watcher import ConfigManager + +server = fakeredis.FakeServer() +redis_client = fakeredis.FakeStrictRedis(server=server, decode_responses=False) + + +def mock_configs(): + schema: Configs = OmegaConf.structured(Configs) + + conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "users-config.yaml")) + user_configs = OmegaConf.merge(schema, conf).configs + + conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "default-config.yaml")) + default_configs = OmegaConf.merge(schema, conf).configs + + conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "numalogic_config.yaml")) + schema: NumalogicConf = OmegaConf.structured(NumalogicConf) + default_numalogic = OmegaConf.merge(schema, conf) + + conf = OmegaConf.load(os.path.join(CONFIG_DIR, "default-configs", "pipeline_config.yaml")) + schema: PipelineConf = OmegaConf.structured(PipelineConf) + pipeline_config = OmegaConf.merge(schema, conf) + + return user_configs, default_configs, default_numalogic, pipeline_config + + +with patch("src.connectors.sentinel.get_redis_client") as mock_get_redis_client: + mock_get_redis_client.return_value = redis_client + with patch( + "src.connectors.sentinel.get_redis_client_from_conf" + ) as mock_get_redis_client_from_conf: + mock_get_redis_client_from_conf.return_value = redis_client + with patch.object(ConfigManager, "load_configs") as mock_confs: + mock_confs.return_value = mock_configs() + from src.udf import Preprocess, Inference, Threshold, Postprocess + from src.udsink import Train + + +__all__ = [ + "redis_client", + "Train", + "Preprocess", + "Inference", + "Threshold", + "Postprocess", + "mock_configs", +] diff --git a/udf/anomaly-detection/tests/resources/configs/default-config.yaml b/udf/anomaly-detection/tests/resources/configs/default-config.yaml new file mode 100644 index 00000000..4da03ead --- /dev/null +++ b/udf/anomaly-detection/tests/resources/configs/default-config.yaml @@ -0,0 +1,38 @@ +configs: + - name: "fciAsset" + composite_keys: [ "assetId" ] + metrics: [ "ciStatus_failed" , "ciStatus_degraded" ] + druid_fetcher: + dimensions: [ "ciStatus" ] + datasource: "tech-ip-customer-interaction-metrics" + group_by: [ "timestamp", "ciStatus" ] + pivot: + columns: + - "ciStatus" + - name: "service-mesh" + composite_keys: [ "destination_asset_id" ] + metrics: [ "error_rate" , "error_count" ] + - name: "service-mesh-s2s" + composite_keys: ["source_asset_id, destination_asset_id"] + metrics: [ "error_rate", "error_count" ] + - name: "argo-rollouts" + composite_keys: [ "namespace", "name", "app", "rollouts_pod_template_hash" ] + metrics: [ "namespace_app_rollouts_http_request_error_rate"] + metric_configs: + - metric: "namespace_app_rollouts_http_request_error_rate" + static_threshold: + weight: 0.7 + - name: "argo-cd" + composite_keys: [ "namespace", "name" ] + metrics: [ "namespace_app_http_server_requests_errors", + "namespace_app_http_server_requests_error_rate", + "namespace_app_http_server_requests_latency", + "namespace_app_cpu_utilization", + "namespace_app_memory_utilization" ] + metric_configs: + - metric: "namespace_app_cpu_utilization" + static_threshold: + upper_limit: 80 + - metric: "namespace_app_memory_utilization" + static_threshold: + upper_limit: 80 \ No newline at end of file diff --git a/udf/anomaly-detection/tests/resources/configs/numalogic_config.yaml b/udf/anomaly-detection/tests/resources/configs/numalogic_config.yaml new file mode 100644 index 00000000..d7535001 --- /dev/null +++ b/udf/anomaly-detection/tests/resources/configs/numalogic_config.yaml @@ -0,0 +1,27 @@ +model: + name: "SparseVanillaAE" + conf: + seq_len: 2 + n_features: 1 + encoder_layersizes: + - 16 + - 8 + decoder_layersizes: + - 8 + - 16 + dropout_p: 0.25 +trainer: + max_epochs: 5 +preprocess: + - name: "LogTransformer" + stateful: false + conf: + add_factor: 3 + - name: "StandardScaler" + conf: + with_mean: False +threshold: + name: "StdDevThreshold" +postprocess: + name: "TanhNorm" + stateful: false \ No newline at end of file diff --git a/udf/anomaly-detection/tests/resources/configs/users-config.yaml b/udf/anomaly-detection/tests/resources/configs/users-config.yaml new file mode 100644 index 00000000..7a51abb1 --- /dev/null +++ b/udf/anomaly-detection/tests/resources/configs/users-config.yaml @@ -0,0 +1,22 @@ +configs: + - name: "sandbox_numalogic_demo1" + composite_keys: [ "namespace", "name", "hash_id" ] + metrics: ["rollout_error_rate", "rollout_latency"] + metric_configs: + - metric: "rollout_error_rate" + static_threshold: + weight: 0.6 + - metric: "rollout_latency" + - name: "sandbox_numalogic_demo2" + composite_keys: [ "namespace", "name", "hash_id" ] + metrics: ["rollout_error_rate", "rollout_latency"] + metric_configs: + - metric: "rollout_error_rate" + static_threshold: + weight: 0.7 + - metric: "rollout_latency" + unified_config: + weights: [0.7, 0.3] + - name: "sandbox_numalogic_demo3" + metrics: ["namespace_app_rollouts_http_request_error_rate", "namespace_app_rollouts_http_request_latency"] + composite_keys: [ "namespace", "name", "app", "rollouts_pod_template_hash" ] \ No newline at end of file diff --git a/udf/anomaly-detection/tests/resources/data/argocd.csv b/udf/anomaly-detection/tests/resources/data/argocd.csv new file mode 100644 index 00000000..02393033 --- /dev/null +++ b/udf/anomaly-detection/tests/resources/data/argocd.csv @@ -0,0 +1,122 @@ +timestamp,metric_1 +2022-05-26 20:00:40,0.4000000000000001 +2022-05-26 20:01:10,0.4000000000000001 +2022-05-26 20:01:40,0.4000000000000001 +2022-05-26 20:02:10,0.4000000000000001 +2022-05-26 20:02:40,0.4000000000000001 +2022-05-26 20:03:10,0.4000000000000001 +2022-05-26 20:03:40,0.4000000000000001 +2022-05-26 20:04:10,0.4000000000000001 +2022-05-26 20:04:40,nan +2022-05-26 20:05:10,0.4000000000000001 +2022-05-26 20:05:40,0.4000000000000001 +2022-05-26 20:06:10,0.4000000000000001 +2022-05-26 20:06:40,0.4000000000000001 +2022-05-26 20:07:10,0.4000000000000001 +2022-05-26 20:07:40,0.4000000000000001 +2022-05-26 20:08:10,0.4000000000000001 +2022-05-26 20:08:40,0.4000000000000001 +2022-05-26 20:09:10,0.4000000000000001 +2022-05-26 20:09:40,0.4000000000000001 +2022-05-26 20:10:10,0.4000000000000001 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+2022-09-07 23:59:55,0.001241729568443769,888c786d9 diff --git a/udf/anomaly-detection/tests/resources/data/generate.py b/udf/anomaly-detection/tests/resources/data/generate.py new file mode 100644 index 00000000..8c1ac0c0 --- /dev/null +++ b/udf/anomaly-detection/tests/resources/data/generate.py @@ -0,0 +1,26 @@ +import json +import time +import random + + +def create_data(start_time, end_time, filename): + data = [] + current_time = start_time + while current_time < end_time: + timestamp = str(int(current_time * 1000)) + # unique_key = "service-mesh-s2s:6055107291188110321:" + str(random.randint(0, 10**19)) + unique_key = "service-mesh-s2s:6055107291188110321" + error_rate = str(round(random.uniform(0, 100), 2)) + data.append({"timestamp": timestamp, "unique_key": unique_key, "error_rate": error_rate}) + current_time += 60 # increase timestamp value by 1 min + + with open(filename, "w") as f: + json.dump(data, f, indent=4) + return data + + +# example usage: +start_time = time.time() # current UTC timestamp in seconds +end_time = start_time + 360 # 1 hour later +data = create_data(start_time, end_time, "stream2.json") +print(data) # output the generated JSON output the generated JSON diff --git a/udf/anomaly-detection/tests/resources/data/stream.json b/udf/anomaly-detection/tests/resources/data/stream.json new file mode 100644 index 00000000..4fcaa24a --- /dev/null +++ b/udf/anomaly-detection/tests/resources/data/stream.json @@ -0,0 +1,45 @@ +{ + "data": [ + { + "timestamp": "1684472580000", + "error_rate": "56.97", + "error_count": "10" + }, + { + "timestamp": "1684472700000", + "error_rate": "70.46", + "error_count": "15" + }, + { + "timestamp": "1684472820000", + "error_rate": "65.02", + "error_count": "12" + }, + { + "timestamp": "1684472940000", + "error_rate": "16.75", + "error_count": "3" + }, + { + "timestamp": "1684473060000", + "error_rate": "86.91", + "error_count": "18" + }, + { + "timestamp": "1684473180000", + "error_rate": "13.49", + "error_count": "1" + }, + { + "timestamp": "1684473240000", + "error_rate": "62.74", + "error_count": "11" + } + ], + "start_time": "1684472580000", + "end_time": "1684473300000", + "metadata": { + "source": "kafka", + "label": "value" + } +} \ No newline at end of file diff --git a/udf/anomaly-detection/tests/resources/data/stream1.json b/udf/anomaly-detection/tests/resources/data/stream1.json new file mode 100644 index 00000000..d0d680da --- /dev/null +++ b/udf/anomaly-detection/tests/resources/data/stream1.json @@ -0,0 +1,74 @@ +[ + { + "timestamp": "1684193070196", + "source_asset_id": "6055107291188110321", + "destination_asset_id": "1218463880781465009", + "error_rate": "56.97" + }, + { + "timestamp": "1684193130196", + "source_asset_id": "6055107291188110321", + "destination_asset_id": "1218463880781465009", + "error_rate": "70.46" + }, + { + "timestamp": "1684193190196", + "source_asset_id": "6055107291188110321", + "destination_asset_id": "1218463880781465009", + "error_rate": "65.02" + }, + { + "timestamp": "1684193250196", + "source_asset_id": "6055107291188110321", + "destination_asset_id": "1218463880781465009", + "error_rate": "16.75" + }, + { + "timestamp": "1684193310196", + "source_asset_id": "6055107291188110321", + "destination_asset_id": "1218463880781465009", + "error_rate": "86.91" + }, + { + "timestamp": "1684193370196", + "source_asset_id": "6055107291188110321", + "destination_asset_id": "1218463880781465009", + "error_rate": "13.49" + }, + { + "timestamp": "1684193133192", + "unique_key": "service-mesh:6055107291188110321", + "error_rate": "33.97", + "latency": "0.8" + }, + { + "timestamp": "1684193193192", + "unique_key": "service-mesh:6055107291188110321", + "error_rate": "30.51", + "latency": "0.2" + }, + { + "timestamp": "1684193253192", + "unique_key": "service-mesh:6055107291188110321", + "error_rate": "60.92", + "latency": "0.6" + }, + { + "timestamp": "1684193313192", + "unique_key": "service-mesh:6055107291188110321", + "error_rate": "26.28", + "latency": "0.1" + }, + { + "timestamp": "1684193373192", + "unique_key": "service-mesh:6055107291188110321", + "error_rate": "85.37", + "latency": "0.5" + }, + { + "timestamp": "1684193433192", + "unique_key": "service-mesh:6055107291188110321", + "error_rate": "62.74", + "latency": "0.3" + } +] \ No newline at end of file diff --git a/udf/anomaly-detection/tests/resources/data/stream_nan.json b/udf/anomaly-detection/tests/resources/data/stream_nan.json new file mode 100644 index 00000000..8031a01e --- /dev/null +++ b/udf/anomaly-detection/tests/resources/data/stream_nan.json @@ -0,0 +1,45 @@ +{ + "data": [ + { + "timestamp": "1684472580000", + "error_rate": "NaN", + "error_count": "NaN" + }, + { + "timestamp": "1684472700000", + "error_rate": "70.46", + "error_count": "15" + }, + { + "timestamp": "1684472820000", + "error_rate": "65.02", + "error_count": "12" + }, + { + "timestamp": "1684472940000", + "error_rate": "16.75", + "error_count": "3" + }, + { + "timestamp": "1684473060000", + "error_rate": "NaN", + "error_count": "NaN" + }, + { + "timestamp": "1684473180000", + "error_rate": "13.49", + "error_count": "1" + }, + { + "timestamp": "1684473240000", + "error_rate": "62.74", + "error_count": "11" + } + ], + "start_time": "1684472580000", + "end_time": "1684473300000", + "metadata": { + "source": "kafka", + "label": "value" + } +} \ No newline at end of file diff --git a/udf/anomaly-detection/tests/resources/models/model_cpu.pth b/udf/anomaly-detection/tests/resources/models/model_cpu.pth new file mode 100644 index 00000000..0a5299bc Binary files /dev/null and b/udf/anomaly-detection/tests/resources/models/model_cpu.pth differ diff --git a/udf/anomaly-detection/tests/resources/models/model_error_count.pth b/udf/anomaly-detection/tests/resources/models/model_error_count.pth new file mode 100644 index 00000000..0a5299bc Binary files /dev/null and b/udf/anomaly-detection/tests/resources/models/model_error_count.pth differ diff --git a/udf/anomaly-detection/tests/resources/models/model_error_rate.pth b/udf/anomaly-detection/tests/resources/models/model_error_rate.pth new file mode 100644 index 00000000..0a5299bc Binary files /dev/null and b/udf/anomaly-detection/tests/resources/models/model_error_rate.pth differ diff --git a/udf/anomaly-detection/tests/resources/models/model_latency.pth b/udf/anomaly-detection/tests/resources/models/model_latency.pth new file mode 100644 index 00000000..0059faff Binary files /dev/null and b/udf/anomaly-detection/tests/resources/models/model_latency.pth differ diff --git a/udf/anomaly-detection/tests/resources/models/model_memory.pth b/udf/anomaly-detection/tests/resources/models/model_memory.pth new file mode 100644 index 00000000..0a5299bc Binary files /dev/null and b/udf/anomaly-detection/tests/resources/models/model_memory.pth differ diff --git a/udf/anomaly-detection/tests/test_client.py b/udf/anomaly-detection/tests/test_client.py new file mode 100644 index 00000000..eec5ac22 --- /dev/null +++ b/udf/anomaly-detection/tests/test_client.py @@ -0,0 +1,18 @@ +import unittest +from unittest.mock import patch + +from redis.sentinel import Sentinel +import fakeredis + +from src.connectors.sentinel import get_redis_client + + +server = fakeredis.FakeServer() +fake_redis_client = fakeredis.FakeStrictRedis(server=server, decode_responses=True) + + +class TestRedisClient(unittest.TestCase): + def test_sentinel_redis_client(self): + with patch.object(Sentinel, "master_for", return_value=fake_redis_client): + r = get_redis_client("hostname", 6379, "pass", "mymaster") + self.assertTrue(r.ping()) diff --git a/udf/anomaly-detection/tests/test_druid.py b/udf/anomaly-detection/tests/test_druid.py new file mode 100644 index 00000000..0c484b77 --- /dev/null +++ b/udf/anomaly-detection/tests/test_druid.py @@ -0,0 +1,59 @@ +import json +import unittest +import datetime +from unittest.mock import patch, Mock +import pydruid.query +from pydruid.client import PyDruid +from pydruid.utils.aggregators import doublesum + +from src.connectors.druid import DruidFetcher + + +def mock_group_by(*_, **__): + result = [ + { + "version": "v1", + "timestamp": "2023-07-11T01:36:00.000Z", + "event": {"count": 5.0, "ciStatus": "success"}, + }, + { + "version": "v1", + "timestamp": "2023-07-11T01:37:00.000Z", + "event": {"count": 1.0, "ciStatus": "success"}, + }, + ] + query = pydruid.query.Query(query_dict={}, query_type="groupBy") + query.parse(json.dumps(result)) + return query + + +class TestDruid(unittest.TestCase): + start = None + end = None + prom = None + + @classmethod + def setUpClass(cls) -> None: + end = datetime.datetime.now() + start = end - datetime.timedelta(hours=36) + cls.start = start.timestamp() + cls.end = end.timestamp() + cls.druid = DruidFetcher(url="http://localhost:8888", endpoint="druid/v2/") + + @patch.object(PyDruid, "groupby", Mock(return_value=mock_group_by())) + def test_fetch_data(self): + _out = self.druid.fetch_data( + filter_keys=["assetId"], + filter_values=["1084259202722926969"], + dimensions=["ciStatus"], + datasource="tech-ip-customer-interaction-metrics", + aggregations={"count": doublesum("count")}, + group_by=["timestamp", "ciStatus"], + hours=0.1, + pivot={"index": "timestamp", "columns": ["ciStatus"], "values": "count"}, + ) + self.assertEqual(_out.shape, (2, 1)) + + +if __name__ == "__main__": + unittest.main() diff --git a/udf/anomaly-detection/tests/test_factory.py b/udf/anomaly-detection/tests/test_factory.py new file mode 100644 index 00000000..002d3507 --- /dev/null +++ b/udf/anomaly-detection/tests/test_factory.py @@ -0,0 +1,17 @@ +import unittest + +from src.factory import HandlerFactory + + +class TestFactory(unittest.TestCase): + def test_preprocess(self): + func = HandlerFactory.get_handler("preprocess") + self.assertTrue(func) + + def test_invalid(self): + with self.assertRaises(NotImplementedError): + HandlerFactory.get_handler("Lionel Messi") + + +if __name__ == "__main__": + unittest.main() diff --git a/udf/anomaly-detection/tests/test_prometheus.py b/udf/anomaly-detection/tests/test_prometheus.py new file mode 100644 index 00000000..6c3e6d25 --- /dev/null +++ b/udf/anomaly-detection/tests/test_prometheus.py @@ -0,0 +1,151 @@ +import requests +import datetime +import unittest +from unittest.mock import patch, Mock, MagicMock + +from src.connectors.prometheus import Prometheus + + +def mock_multiple_metrics(*_, **__): + result = [ + { + "metric": { + "__name__": "namespace_app_rollouts_http_request_error_rate", + "assetAlias": "sandbox.numalogic.demo", + "numalogic": "true", + "namespace": "sandbox-numalogic-demo", + "rollouts_pod_template_hash": "7b4b4f9f9d", + }, + "values": [[1656334767.73, "14.744611739611193"], [1656334797.73, "14.73040822323633"]], + }, + { + "metric": { + "__name__": "namespace_app_rollouts_http_request_error_rate", + "assetAlias": "sandbox.numalogic.demo", + "numalogic": "true", + "namespace": "sandbox-numalogic-demo", + "rollouts_pod_template_hash": "5b4b4f9f9d", + }, + "values": [[1656334767.73, "14.744611739611193"], [1656334797.73, "14.73040822323633"]], + }, + ] + + return result + + +def mock_query_range(*_, **__): + result = [ + { + "metric": { + "__name__": "namespace_asset_pod_cpu_utilization", + "assetAlias": "sandbox.numalogic.demo", + "numalogic": "true", + "namespace": "sandbox-numalogic-demo", + }, + "values": [[1656334767.73, "14.744611739611193"], [1656334797.73, "14.73040822323633"]], + } + ] + + return result + + +def mock_response(*_, **__): + response = MagicMock() + response.status_code = 200 + response.json.return_value = { + "status": "success", + "data": { + "resultType": "vector", + "result": [ + { + "metric": { + "__name__": "namespace_asset_pod_cpu_utilization", + "numalogic": "true", + "namespace": "sandbox-numalogic-demo", + }, + "values": [ + [1656334767.73, "14.744611739611193"], + [1656334797.73, "14.73040822323633"], + ], + } + ], + }, + } + return response + + +class TestPrometheus(unittest.TestCase): + start = None + end = None + prom = None + + @classmethod + def setUpClass(cls) -> None: + end = datetime.datetime.now() + start = end - datetime.timedelta(hours=36) + cls.start = start.timestamp() + cls.end = end.timestamp() + cls.prom = Prometheus(prometheus_server="http://localhost:8490") + + @patch.object(Prometheus, "query_range", Mock(return_value=mock_query_range())) + def test_query_metric1(self): + _out = self.prom.query_metric( + metric_name="namespace_app_pod_http_server_requests_errors", + start=self.start, + end=self.end, + ) + self.assertEqual(_out.shape, (2, 2)) + + @patch.object(Prometheus, "query_range", Mock(return_value=mock_query_range())) + def test_query_metric2(self): + _out = self.prom.query_metric( + metric_name="namespace_app_pod_http_server_requests_errors", + labels_map={"namespace": "sandbox-rollout-numalogic-demo"}, + start=self.start, + end=self.end, + ) + self.assertEqual(_out.shape, (2, 2)) + + @patch.object(Prometheus, "query_range", Mock(return_value=mock_query_range())) + def test_query_metric3(self): + _out = self.prom.query_metric( + metric_name="namespace_app_pod_http_server_requests_errors", + labels_map={"namespace": "sandbox-numalogic-demo"}, + return_labels=["namespace"], + start=self.start, + end=self.end, + ) + self.assertEqual(_out.shape, (2, 3)) + + @patch.object(Prometheus, "query_range", Mock(return_value=mock_multiple_metrics())) + def test_query_metric4(self): + _out = self.prom.query_metric( + metric_name="namespace_app_pod_http_server_requests_errors", + labels_map={"namespace": "sandbox-numalogic-demo"}, + return_labels=["rollouts_pod_template_hash"], + start=self.start, + end=self.end, + ) + self.assertEqual(_out.shape, (4, 3)) + self.assertEqual(_out["rollouts_pod_template_hash"].unique().shape[0], 2) + + @patch.object(requests, "get", Mock(return_value=mock_response())) + def test_query_range(self): + _out = self.prom.query_range( + query="namespace_asset_pod_cpu_utilization{" "namespace='sandbox-numalogic-demo'}", + start=self.start, + end=self.end, + ) + self.assertEqual(len(_out), 1) + self.assertEqual(len(_out[0]["values"]), 2) + + @patch.object(requests, "get", Mock(return_value=mock_response())) + def test_query(self): + _out = self.prom.query( + query="namespace_asset_pod_cpu_utilization{" "namespace='sandbox-numalogic-demo'}" + ) + self.assertEqual(len(_out), 1) + + +if __name__ == "__main__": + unittest.main() diff --git a/udf/anomaly-detection/tests/test_tools.py b/udf/anomaly-detection/tests/test_tools.py new file mode 100644 index 00000000..4433de32 --- /dev/null +++ b/udf/anomaly-detection/tests/test_tools.py @@ -0,0 +1,42 @@ +import os +import socket +import unittest +import numpy as np +from unittest.mock import patch, Mock + +from src.tools import is_host_reachable, WindowScorer +from src.watcher import ConfigManager +from tests import mock_configs + + +def mock_resolver(*_, **__): + raise socket.gaierror + + +class TestTools(unittest.TestCase): + INFER_OUT = None + + def test_is_host_reachable(self): + self.assertTrue(is_host_reachable("google.com")) + + @patch("src.tools.get_ipv4_by_hostname", mock_resolver) + def test_is_host_reachable_err(self): + self.assertFalse(is_host_reachable("google.com", max_retries=2, sleep_sec=1)) + + +@patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) +class TestWindowScorer(unittest.TestCase): + def test_get_winscore(self): + static_threshold = ConfigManager().get_static_threshold_config( + config_name="sandbox_numalogic_demo2", + metric_name="namespace_app_rollouts_http_request_error_rate", + ) + postprocess_conf = ConfigManager().get_postprocess_config( + config_name="sandbox_numalogic_demo2", + metric_name="namespace_app_rollouts_http_request_error_rate", + ) + + stream = np.random.uniform(low=1, high=2, size=(10, 1)) + winscorer = WindowScorer(static_threshold, postprocess_conf) + final_score = winscorer.get_ensemble_score(stream) + self.assertLess(final_score, 3.0) diff --git a/udf/anomaly-detection/tests/test_trainer.py b/udf/anomaly-detection/tests/test_trainer.py new file mode 100644 index 00000000..e886c581 --- /dev/null +++ b/udf/anomaly-detection/tests/test_trainer.py @@ -0,0 +1,66 @@ +import json +import os +import unittest +from datetime import datetime +from unittest.mock import patch, Mock + +from pynumaflow.sink import Datum + +from src._constants import TESTS_DIR +from src.connectors.prometheus import Prometheus +from tests.tools import ( + mock_prom_query_metric, + mock_prom_query_metric2, +) +from tests import redis_client, Train + +DATA_DIR = os.path.join(TESTS_DIR, "resources", "data") +STREAM_DATA_PATH = os.path.join(DATA_DIR, "stream.json") + + +def as_datum(data: str | bytes | dict, msg_id="1") -> Datum: + if type(data) is not bytes: + data = json.dumps(data).encode("utf-8") + elif type(data) == dict: + data = json.dumps(data) + + return Datum( + sink_msg_id=msg_id, value=data, event_time=datetime.now(), watermark=datetime.now(), keys=[] + ) + + +class TestTrainer(unittest.TestCase): + train_payload = { + "uuid": "123124543", + "composite_keys": [ + "sandbox_numalogic_demo", + "metric_1", + "123456789", + ], + "metric": "metric_1", + } + + train_payload2 = { + "uuid": "123124543", + "composite_keys": ["fciAsset", "5984175597303660107"], + "metric": "metric_1", + } + + def setUp(self) -> None: + redis_client.flushall() + + @patch.object(Prometheus, "query_metric", Mock(return_value=mock_prom_query_metric())) + def test_prometheus_01(self): + _out = Train().run(datums=iter([as_datum(self.train_payload)])) + self.assertTrue(_out[0].success) + self.assertEqual("123124543", _out[0].id) + + @patch.object(Prometheus, "query_metric", Mock(return_value=mock_prom_query_metric2())) + def test_prometheus_03(self): + _out = Train().run(datums=iter([as_datum(self.train_payload2)])) + self.assertTrue(_out[0].success) + self.assertEqual("123124543", _out[0].id) + + +if __name__ == "__main__": + unittest.main() diff --git a/udf/anomaly-detection/tests/test_watcher.py b/udf/anomaly-detection/tests/test_watcher.py new file mode 100644 index 00000000..be5b2753 --- /dev/null +++ b/udf/anomaly-detection/tests/test_watcher.py @@ -0,0 +1,116 @@ +import time +import unittest +from unittest.mock import patch, Mock + +from src.watcher import ConfigManager +from tests import mock_configs + + +@patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) +class TestConfigManager(unittest.TestCase): + @classmethod + def setUpClass(cls) -> None: + cls.cm = ConfigManager + + def test_update_configs(self): + config = self.cm.update_configs() + self.assertTrue(len(config), 3) + + def test_load_configs(self): + app_configs, default_configs, default_numalogic, pipeline_config = self.cm.load_configs() + self.assertTrue(app_configs) + self.assertTrue(default_configs) + self.assertTrue(default_numalogic) + self.assertTrue(pipeline_config) + + def test_get_datastream_config(self): + # from users config + ds_config = self.cm.get_ds_config(config_name="sandbox_numalogic_demo1") + self.assertTrue(ds_config) + self.assertEqual(ds_config.name, "sandbox_numalogic_demo1") + + # from given default config + ds_config = self.cm.get_ds_config(config_name="argo-rollouts") + self.assertTrue(ds_config) + self.assertEqual(ds_config.name, "argo-rollouts") + + # default config + ds_config = self.cm.get_ds_config(config_name="random") + self.assertTrue(ds_config) + self.assertEqual(ds_config.name, "default") + + def test_get_metric_config(self): + # from given config + metric_config = self.cm.get_metric_config( + config_name="sandbox_numalogic_demo1", metric_name="rollout_latency" + ) + self.assertTrue(metric_config) + self.assertEqual(metric_config.metric, "rollout_latency") + + # from given default config + metric_config = self.cm.get_metric_config( + config_name="argo-rollouts", + metric_name="namespace_app_rollouts_http_request_error_rate", + ) + self.assertTrue(metric_config) + self.assertEqual(metric_config.metric, "namespace_app_rollouts_http_request_error_rate") + + # from given default config + metric_config = self.cm.get_metric_config( + config_name="fciAsset", + metric_name="ciStatus_failed", + ) + self.assertTrue(metric_config) + self.assertEqual(metric_config.metric, "ciStatus_failed") + + # default config + metric_config = self.cm.get_metric_config(config_name="random", metric_name="random_metric") + self.assertTrue(metric_config) + self.assertEqual(metric_config.metric, "random_metric") + self.assertTrue(metric_config.numalogic_conf) + + def test_get_unified_config(self): + # from given config + unified_config = self.cm.get_unified_config(config_name="sandbox_numalogic_demo1") + self.assertTrue(unified_config) + + # from given default config + unified_config = self.cm.get_unified_config(config_name="default-argorollouts") + self.assertTrue(unified_config) + + # default config - will not have unified config + unified_config = self.cm.get_unified_config(config_name="random") + self.assertTrue(unified_config.strategy, "max") + + def test_get_datastream_config_time(self): + _start_time = time.perf_counter() + ConfigManager.get_ds_config(config_name="sandbox_numalogic_demo1") + time1 = time.perf_counter() - _start_time + + _start_time = time.perf_counter() + ConfigManager.get_ds_config(config_name="sandbox_numalogic_demo1") + time2 = time.perf_counter() - _start_time + _start_time = time.perf_counter() + self.assertTrue(time2 <= time1) + + def test_get_metric_config_time(self): + _start_time = time.perf_counter() + ConfigManager().get_metric_config( + config_name="sandbox_numalogic_demo1", metric_name="rollout_latency" + ) + time1 = time.perf_counter() - _start_time + _start_time = time.perf_counter() + ConfigManager().get_metric_config( + config_name="sandbox_numalogic_demo1", metric_name="rollout_latency" + ) + time2 = time.perf_counter() - _start_time + self.assertTrue(time2 < time1) + + def test_get_unified_config_time(self): + _start_time = time.perf_counter() + ConfigManager().get_unified_config(config_name="sandbox_numalogic_demo1") + time1 = time.perf_counter() - _start_time + _start_time = time.perf_counter() + ConfigManager().get_unified_config(config_name="sandbox_numalogic_demo1") + time2 = time.perf_counter() - _start_time + self.assertTrue(time2 < time1) diff --git a/udf/anomaly-detection/tests/tools.py b/udf/anomaly-detection/tests/tools.py new file mode 100644 index 00000000..86bc2c4f --- /dev/null +++ b/udf/anomaly-detection/tests/tools.py @@ -0,0 +1,220 @@ +import datetime +import json +import os +import sys +import fakeredis +import numpy as np +import pandas as pd +from typing import Union, Optional +from unittest import mock +from unittest.mock import MagicMock, patch, Mock +from sklearn.preprocessing import MinMaxScaler + +from numalogic.models.autoencoder.variants import VanillaAE, LSTMAE +from numalogic.models.threshold import StdDevThreshold +from numalogic.registry import ArtifactData, RedisRegistry +from pynumaflow.function import Datum +from pynumaflow.function._dtypes import DROP, DatumMetadata + +from src._constants import TESTS_DIR, POSTPROC_VTX_KEY +from src.udf import Preprocess, Inference, Threshold + +sys.modules["numaprom.mlflow"] = MagicMock() +MODEL_DIR = os.path.join(TESTS_DIR, "resources", "models") + + +def mockenv(**envvars): + return mock.patch.dict(os.environ, envvars, clear=True) + + +def get_datum(keys: list[str], data: str or bytes) -> Datum: + if type(data) is not bytes: + data = json.dumps(data).encode("utf-8") + + if not keys: + keys = ["random_key"] + + return Datum( + keys=keys, + value=data, + event_time=datetime.datetime.now(), + watermark=datetime.datetime.now(), + metadata=DatumMetadata(msg_id="", num_delivered=0), + ) + + +def get_stream_data(data_path: str) -> dict[str, Union[dict, str, list]]: + with open(data_path) as fp: + data = json.load(fp) + return data + + +def get_mock_redis_client(): + server = fakeredis.FakeServer() + redis_client = fakeredis.FakeStrictRedis(server=server, decode_responses=False) + return redis_client + + +def get_prepoc_input(keys: list[str], data_path: str) -> Datum: + data = get_stream_data(data_path) + return get_datum(keys, data) + + +def get_inference_input(keys: list[str], data_path: str, prev_clf_exists=True) -> Optional[Datum]: + preproc_input = get_prepoc_input(keys, data_path) + _mock_return = return_preproc_clf() if prev_clf_exists else None + with patch.object(RedisRegistry, "load", Mock(return_value=_mock_return)): + msg = Preprocess().run(keys, preproc_input)[0] + + if len(msg.tags) > 0 and msg.tags[0] == DROP: + if not msg.tags[0] == DROP: + return None + return get_datum(keys, msg.value) + + +def get_threshold_input( + keys: list[str], data_path: str, prev_clf_exists=True, prev_model_stale=False +) -> Optional[Datum]: + inference_input = get_inference_input(keys, data_path) + if prev_model_stale: + _mock_return = return_stale_model() + elif prev_clf_exists: + _mock_return = return_mock_lstmae() + else: + _mock_return = None + with patch.object(RedisRegistry, "load", Mock(return_value=_mock_return)): + msg = Inference().run(keys, inference_input)[0] + + if len(msg.tags) > 0 and msg.tags[0] == DROP: + if not msg.tags[0] == DROP: + return None + return get_datum(keys, msg.value) + + +def get_postproc_input( + keys: list[str], data_path: str, prev_clf_exists=True, prev_model_stale=False +) -> Optional[Datum]: + thresh_input = get_threshold_input(keys, data_path, prev_model_stale=prev_model_stale) + _mock_return = return_threshold_clf() if prev_clf_exists else None + with patch.object(RedisRegistry, "load", Mock(return_value=_mock_return)): + _out = Threshold().run(keys, thresh_input) + for msg in _out: + if POSTPROC_VTX_KEY in msg.tags: + return get_datum(keys, msg.value) + return None + + +def return_mock_lstmae(*_, **__): + return ArtifactData( + artifact=LSTMAE(seq_len=12, no_features=1, embedding_dim=4), + metadata={}, + extras={ + "creation_timestamp": 1653402941169, + "timestamp": 1653402941, + "current_stage": "Production", + "description": "", + "last_updated_timestamp": 1645369200000, + "name": "test::error", + "run_id": "a7c0b376530b40d7b23e6ce2081c899c", + "run_link": "", + "source": "mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts/model", + "status": "READY", + "status_message": "", + "tags": {}, + "user_id": "", + "version": "5", + }, + ) + + +def return_stale_model(*_, **__): + return ArtifactData( + artifact=VanillaAE(seq_len=12), + metadata={}, + extras={ + "creation_timestamp": 1653402941169, + "timestamp": 1653402941, + "current_stage": "Production", + "description": "", + "last_updated_timestamp": 1656615600000, + "name": "test::error", + "run_id": "a7c0b376530b40d7b23e6ce2081c899c", + "run_link": "", + "source": "mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts/model", + "status": "READY", + "status_message": "", + "tags": {}, + "user_id": "", + "version": "5", + }, + ) + + +def return_preproc_clf(n_feat=1): + x = np.random.randn(100, n_feat) + clf = MinMaxScaler() + clf.fit(x) + return ArtifactData( + artifact=clf, + metadata={}, + extras={ + "creation_timestamp": 1653402941169, + "current_stage": "Production", + "description": "", + "last_updated_timestamp": 1656615600000, + "name": "test::preproc", + "run_id": "a7c0b376530b40d7b23e6ce2081c899c", + "run_link": "", + "source": "mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts/preproc", + "status": "READY", + "status_message": "", + "tags": {}, + "user_id": "", + "version": "1", + }, + ) + + +def return_threshold_clf(n_feat=1): + x = np.random.randn(100, n_feat) + clf = StdDevThreshold() + clf.fit(x) + return ArtifactData( + artifact=clf, + metadata={}, + extras={ + "creation_timestamp": 1653402941169, + "current_stage": "Production", + "description": "", + "last_updated_timestamp": 1656615600000, + "name": "test::thresh", + "run_id": "a7c0b376530b40d7b23e6ce2081c899c", + "run_link": "", + "source": "mlflow-artifacts:/0/a7c0b376530b40d7b23e6ce2081c899c/artifacts/thresh", + "status": "READY", + "status_message": "", + "tags": {}, + "user_id": "", + "version": "1", + }, + ) + + +def mock_prom_query_metric(*_, **__): + return pd.read_csv( + os.path.join(TESTS_DIR, "resources", "data", "argorollouts.csv"), + index_col="timestamp", + parse_dates=["timestamp"], + infer_datetime_format=True, + ) + + +def mock_prom_query_metric2(*_, **__): + df = pd.read_csv( + os.path.join(TESTS_DIR, "resources", "data", "argorollouts.csv"), + index_col="timestamp", + parse_dates=["timestamp"], + infer_datetime_format=True, + ) + df.rename(columns={"hash_id": "rollouts_pod_template_hash"}, inplace=True) + return df diff --git a/udf/anomaly-detection/tests/udf/__init__.py b/udf/anomaly-detection/tests/udf/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/udf/anomaly-detection/tests/udf/test_inference.py b/udf/anomaly-detection/tests/udf/test_inference.py new file mode 100644 index 00000000..2a6ecfda --- /dev/null +++ b/udf/anomaly-detection/tests/udf/test_inference.py @@ -0,0 +1,105 @@ +import os +import unittest + +from orjson import orjson +from unittest.mock import patch, Mock +from freezegun import freeze_time + +from numalogic.models.autoencoder import AutoencoderTrainer +from numalogic.registry import RedisRegistry +from pynumaflow.function import Datum + +from src._constants import TESTS_DIR +from src.entities import Status, StreamPayload, Header +from tests import redis_client, Inference +from tests.tools import ( + get_inference_input, + return_stale_model, + return_mock_lstmae, +) + +DATA_DIR = os.path.join(TESTS_DIR, "resources", "data") +MODEL_DIR = os.path.join(TESTS_DIR, "resources", "models") +STREAM_DATA_PATH = os.path.join(DATA_DIR, "stream.json") + + +class TestInference(unittest.TestCase): + inference_input: Datum = None + keys: list[str] = ["service-mesh", "1", "2"] + + @classmethod + def setUpClass(cls) -> None: + redis_client.flushall() + cls.inference_input = get_inference_input(cls.keys, STREAM_DATA_PATH) + + def setUp(self) -> None: + redis_client.flushall() + + @freeze_time("2022-02-20 12:00:00") + @patch.object(RedisRegistry, "load", Mock(return_value=return_mock_lstmae())) + def test_inference(self): + _out = Inference().run(self.keys, self.inference_input)[0] + payload = StreamPayload(**orjson.loads(_out.value)) + self.assertTrue(payload.data) + self.assertTrue(payload.raw_data) + self.assertIsInstance(payload, StreamPayload) + for metric in payload.metrics: + self.assertEqual(payload.status[metric], Status.INFERRED) + self.assertEqual(payload.header[metric], Header.MODEL_INFERENCE) + self.assertGreater(payload.metadata[metric]["model_version"], 0) + + @freeze_time("2022-02-20 12:00:00") + @patch.object(RedisRegistry, "load", Mock(return_value=return_mock_lstmae())) + @patch.object(AutoencoderTrainer, "predict", Mock(side_effect=RuntimeError)) + def test_inference_err(self): + _out = Inference().run(self.keys, self.inference_input)[0] + payload = StreamPayload(**orjson.loads(_out.value)) + self.assertTrue(payload.data) + self.assertTrue(payload.raw_data) + self.assertIsInstance(payload, StreamPayload) + for metric in payload.metrics: + self.assertEqual(payload.status[metric], Status.RUNTIME_ERROR) + self.assertEqual(payload.header[metric], Header.STATIC_INFERENCE) + self.assertEqual(payload.metadata[metric]["model_version"], -1) + + @patch.object(RedisRegistry, "load", Mock(return_value=None)) + def test_no_model(self): + _out = Inference().run(self.keys, self.inference_input)[0] + payload = StreamPayload(**orjson.loads(_out.value)) + self.assertTrue(payload.data) + self.assertTrue(payload.raw_data) + self.assertIsInstance(payload, StreamPayload) + for metric in payload.metrics: + self.assertEqual(payload.status[metric], Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.header[metric], Header.STATIC_INFERENCE) + self.assertEqual(payload.metadata[metric]["model_version"], -1) + + @freeze_time("2022-02-20 12:00:00") + @patch.object(RedisRegistry, "load", Mock(return_value=return_mock_lstmae())) + def test_no_prev_model(self): + inference_input = get_inference_input(self.keys, STREAM_DATA_PATH, prev_clf_exists=False) + _out = Inference().run(self.keys, inference_input)[0] + payload = StreamPayload(**orjson.loads(_out.value)) + self.assertTrue(payload.data) + self.assertTrue(payload.raw_data) + self.assertIsInstance(payload, StreamPayload) + for metric in payload.metrics: + self.assertEqual(payload.status[metric], Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.header[metric], Header.STATIC_INFERENCE) + self.assertEqual(payload.metadata[metric]["model_version"], -1) + + @patch.object(RedisRegistry, "load", Mock(return_value=return_stale_model())) + def test_stale_model(self): + _out = Inference().run(self.keys, self.inference_input)[0] + payload = StreamPayload(**orjson.loads(_out.value)) + self.assertTrue(payload.data) + self.assertTrue(payload.raw_data) + self.assertIsInstance(payload, StreamPayload) + for metric in payload.metrics: + self.assertEqual(payload.status[metric], Status.INFERRED) + self.assertEqual(payload.header[metric], Header.MODEL_STALE) + self.assertGreater(payload.metadata[metric]["model_version"], 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/udf/anomaly-detection/tests/udf/test_postprocess.py b/udf/anomaly-detection/tests/udf/test_postprocess.py new file mode 100644 index 00000000..3d291ac6 --- /dev/null +++ b/udf/anomaly-detection/tests/udf/test_postprocess.py @@ -0,0 +1,56 @@ +import os +import orjson +import unittest + +from freezegun import freeze_time + +from src._constants import TESTS_DIR +from src.entities import OutputPayload +from tests import redis_client, Postprocess +from tests.tools import get_postproc_input + +DATA_DIR = os.path.join(TESTS_DIR, "resources", "data") +MODEL_DIR = os.path.join(TESTS_DIR, "resources", "models") +STREAM_DATA_PATH = os.path.join(DATA_DIR, "stream.json") + + +class TestPostProcess(unittest.TestCase): + keys: list[str] = ["service-mesh", "1", "2"] + + def setUp(self) -> None: + redis_client.flushall() + + @freeze_time("2022-02-20 12:00:00") + def test_postprocess(self): + postproc_input = get_postproc_input(self.keys, STREAM_DATA_PATH) + msg = Postprocess().run(self.keys, postproc_input)[0] + payload = OutputPayload(**orjson.loads(msg.value.decode("utf-8"))) + self.assertIsInstance(payload, OutputPayload) + self.assertTrue(payload.unified_anomaly) + for metric, metric_data in payload.data.items(): + self.assertGreater(metric_data["model_version"], 0) + self.assertTrue(metric_data["anomaly_score"]) + + def test_preprocess_prev_stale_model(self): + postproc_input = get_postproc_input(self.keys, STREAM_DATA_PATH, prev_model_stale=True) + msg = Postprocess().run(self.keys, postproc_input)[0] + payload = OutputPayload(**orjson.loads(msg.value.decode("utf-8"))) + self.assertIsInstance(payload, OutputPayload) + self.assertTrue(payload.unified_anomaly) + for metric, metric_data in payload.data.items(): + self.assertGreater(metric_data["model_version"], 0) + self.assertTrue(metric_data["anomaly_score"]) + + def test_preprocess_no_prev_clf(self): + postproc_input = get_postproc_input(self.keys, STREAM_DATA_PATH, prev_clf_exists=False) + msg = Postprocess().run(self.keys, postproc_input)[0] + payload = OutputPayload(**orjson.loads(msg.value.decode("utf-8"))) + self.assertIsInstance(payload, OutputPayload) + self.assertTrue(payload.unified_anomaly) + for metric, metric_data in payload.data.items(): + self.assertEqual(metric_data["model_version"], -1) + self.assertTrue(metric_data["anomaly_score"]) + + +if __name__ == "__main__": + unittest.main() diff --git a/udf/anomaly-detection/tests/udf/test_preprocess.py b/udf/anomaly-detection/tests/udf/test_preprocess.py new file mode 100644 index 00000000..d824743c --- /dev/null +++ b/udf/anomaly-detection/tests/udf/test_preprocess.py @@ -0,0 +1,70 @@ +import os +import unittest +from unittest.mock import patch, Mock + +import numpy as np +from numalogic.registry import RedisRegistry +from orjson import orjson +from pynumaflow.function import Datum + +from src._constants import TESTS_DIR +from src.entities import Status, StreamPayload, Header + +# Make sure to import this in the end +from tests import redis_client, Preprocess +from tests.tools import get_prepoc_input, return_preproc_clf + +DATA_DIR = os.path.join(TESTS_DIR, "resources", "data") +STREAM_DATA_PATH = os.path.join(DATA_DIR, "stream.json") +STREAM_NAN_DATA_PATH = os.path.join(DATA_DIR, "stream_nan.json") + + +class TestPreprocess(unittest.TestCase): + preproc_input: Datum = None + keys: list[str] = ["service-mesh", "1", "2"] + + @classmethod + def setUpClass(cls) -> None: + redis_client.flushall() + cls.preproc_input = get_prepoc_input(cls.keys, STREAM_DATA_PATH) + + def setUp(self) -> None: + redis_client.flushall() + + @patch.object(RedisRegistry, "load", Mock(return_value=return_preproc_clf())) + def test_preprocess(self): + _out = Preprocess().run(self.keys, self.preproc_input)[0] + payload = StreamPayload(**orjson.loads(_out.value)) + self.assertTrue(payload.data) + self.assertTrue(payload.raw_data) + self.assertIsInstance(payload, StreamPayload) + for metric in payload.metrics: + self.assertEqual(payload.status[metric], Status.PRE_PROCESSED) + self.assertEqual(payload.header[metric], Header.MODEL_INFERENCE) + + @patch.object(RedisRegistry, "load", Mock(return_value=None)) + def test_preprocess_no_clf(self): + _out = Preprocess().run(self.keys, self.preproc_input)[0] + payload = StreamPayload(**orjson.loads(_out.value)) + self.assertIsInstance(payload, StreamPayload) + for metric in payload.metrics: + self.assertEqual(payload.status[metric], Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.header[metric], Header.STATIC_INFERENCE) + + @patch.object(RedisRegistry, "load", Mock(return_value=return_preproc_clf())) + def test_preprocess_with_nan(self): + preproc_input = get_prepoc_input(self.keys, STREAM_NAN_DATA_PATH) + _out = Preprocess().run(self.keys, preproc_input)[0] + payload = StreamPayload(**orjson.loads(_out.value)) + + df = payload.get_df() + self.assertTrue(np.isfinite(df.values).all()) + self.assertTrue(payload.data) + self.assertIsInstance(payload, StreamPayload) + for metric in payload.metrics: + self.assertEqual(payload.status[metric], Status.PRE_PROCESSED) + self.assertEqual(payload.header[metric], Header.MODEL_INFERENCE) + + +if __name__ == "__main__": + unittest.main() diff --git a/udf/anomaly-detection/tests/udf/test_threshold.py b/udf/anomaly-detection/tests/udf/test_threshold.py new file mode 100644 index 00000000..1d89b618 --- /dev/null +++ b/udf/anomaly-detection/tests/udf/test_threshold.py @@ -0,0 +1,95 @@ +import os +import unittest + +from orjson import orjson +from freezegun import freeze_time +from unittest.mock import patch, Mock + +from numalogic.registry import RedisRegistry + +from src._constants import TESTS_DIR, TRAIN_VTX_KEY +from src.entities import Status, StreamPayload, TrainerPayload, Header +from tests import redis_client, Threshold +from tests.tools import get_threshold_input, return_threshold_clf + + +DATA_DIR = os.path.join(TESTS_DIR, "resources", "data") +STREAM_DATA_PATH = os.path.join(DATA_DIR, "stream.json") + + +class TestThreshold(unittest.TestCase): + keys: list[str] = ["service-mesh", "1", "2"] + + @classmethod + def setUpClass(cls) -> None: + redis_client.flushall() + + def setUp(self) -> None: + redis_client.flushall() + + @freeze_time("2022-02-20 12:00:00") + @patch.object(RedisRegistry, "load", Mock(return_value=return_threshold_clf())) + def test_threshold(self): + threshold_input = get_threshold_input(self.keys, STREAM_DATA_PATH) + _out = Threshold().run(self.keys, threshold_input)[0] + payload = StreamPayload(**orjson.loads(_out.value)) + self.assertTrue(payload.data) + self.assertTrue(payload.raw_data) + self.assertIsInstance(payload, StreamPayload) + for metric in payload.metrics: + self.assertEqual(payload.status[metric], Status.THRESHOLD) + self.assertEqual(payload.header[metric], Header.MODEL_INFERENCE) + self.assertGreater(payload.metadata[metric]["model_version"], 0) + + @patch.object(RedisRegistry, "load", Mock(return_value=return_threshold_clf())) + def test_threshold_prev_stale_model(self): + threshold_input = get_threshold_input(self.keys, STREAM_DATA_PATH, prev_model_stale=True) + _out = Threshold().run(self.keys, threshold_input) + for msg in _out: + if TRAIN_VTX_KEY in msg.tags: + train_payload = TrainerPayload(**orjson.loads(msg.value.decode("utf-8"))) + self.assertIsInstance(train_payload, TrainerPayload) + else: + payload = StreamPayload(**orjson.loads(msg.value.decode("utf-8"))) + self.assertIsInstance(payload, StreamPayload) + for metric in payload.metrics: + self.assertEqual(payload.header[metric], Header.MODEL_STALE) + self.assertEqual(payload.status[metric], Status.THRESHOLD) + self.assertGreater(payload.metadata[metric]["model_version"], 0) + + @patch.object(RedisRegistry, "load", Mock(return_value=None)) + def test_threshold_no_prev_clf(self): + threshold_input = get_threshold_input(self.keys, STREAM_DATA_PATH, prev_clf_exists=False) + _out = Threshold().run(self.keys, threshold_input) + for msg in _out: + if TRAIN_VTX_KEY in msg.tags: + train_payload = TrainerPayload(**orjson.loads(msg.value.decode("utf-8"))) + self.assertIsInstance(train_payload, TrainerPayload) + else: + payload = StreamPayload(**orjson.loads(msg.value.decode("utf-8"))) + self.assertIsInstance(payload, StreamPayload) + for metric in payload.metrics: + self.assertEqual(payload.header[metric], Header.STATIC_INFERENCE) + self.assertEqual(payload.status[metric], Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.metadata[metric]["model_version"], -1) + + @freeze_time("2022-02-20 12:00:00") + @patch.object(RedisRegistry, "load", Mock(return_value=None)) + def test_threshold_no_clf(self): + threshold_input = get_threshold_input(self.keys, STREAM_DATA_PATH) + _out = Threshold().run(self.keys, threshold_input) + for msg in _out: + if TRAIN_VTX_KEY in msg.tags: + train_payload = TrainerPayload(**orjson.loads(msg.value.decode("utf-8"))) + self.assertIsInstance(train_payload, TrainerPayload) + else: + payload = StreamPayload(**orjson.loads(msg.value.decode("utf-8"))) + self.assertIsInstance(payload, StreamPayload) + for metric in payload.metrics: + self.assertEqual(payload.header[metric], Header.STATIC_INFERENCE) + self.assertEqual(payload.status[metric], Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.metadata[metric]["model_version"], -1) + + +if __name__ == "__main__": + unittest.main()