diff --git a/docs/assets/anomalydetection.png b/docs/assets/anomalydetection.png new file mode 100644 index 00000000..f8407386 Binary files /dev/null and b/docs/assets/anomalydetection.png differ diff --git a/numalogic/config/_conn.py b/numalogic/config/_conn.py new file mode 100644 index 00000000..e8f1623f --- /dev/null +++ b/numalogic/config/_conn.py @@ -0,0 +1,45 @@ +from dataclasses import dataclass, field + + +@dataclass +class PrometheusConf: + server: str + pushgateway: str + + +@dataclass +class RedisConf: + host: str + port: int + expiry: int = 300 + master_name: str = "mymaster" + + +@dataclass +class DruidConf: + url: str + endpoint: str + + +@dataclass +class Pivot: + index: str = "timestamp" + columns: list[str] = field(default_factory=list) + value: list[str] = field(default_factory=lambda: ["count"]) + + +@dataclass +class DruidFetcherConf: + from pydruid.utils.aggregators import doublesum + + datasource: str + dimensions: list[str] = field(default_factory=list) + aggregations: dict = field(default_factory=dict) + group_by: list[str] = field(default_factory=list) + pivot: Pivot = field(default_factory=lambda: Pivot()) + granularity: str = "minute" + hours: float = 36 + + def __post_init__(self): + if not self.aggregations: + self.aggregations = {"count": self.doublesum("count")} diff --git a/numalogic/config/_metric.py b/numalogic/config/_metric.py new file mode 100644 index 00000000..063d9ce7 --- /dev/null +++ b/numalogic/config/_metric.py @@ -0,0 +1,65 @@ +from dataclasses import dataclass, field +from enum import Enum + +from omegaconf import MISSING + +from numalogic.config._conn import DruidFetcherConf, RedisConf, DruidConf, PrometheusConf +from numalogic.config._numa import NumalogicConf + + +@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 StaticThresholdConf: + upper_limit: int = 3 + weight: float = 0.0 + + +@dataclass +class MetricConf: + metric: str + retrain_conf: ReTrainConf = field(default_factory=lambda: ReTrainConf()) + static_threshold: StaticThresholdConf = field(default_factory=lambda: StaticThresholdConf()) + numalogic_conf: NumalogicConf = MISSING + + +class DataSource(str, Enum): + PROMETHEUS = "prometheus" + DRUID = "druid" + + +@dataclass +class DataStreamConf: + name: str = "default" + source: str = DataSource.PROMETHEUS.value + 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/numalogic/config/_numa.py b/numalogic/config/_numa.py new file mode 100644 index 00000000..54523bd5 --- /dev/null +++ b/numalogic/config/_numa.py @@ -0,0 +1,79 @@ +# Copyright 2022 The Numaproj Authors. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from dataclasses import dataclass, field +from typing import Optional, Any + +from omegaconf import MISSING + + +@dataclass +class ModelInfo: + """Schema for defining the model/estimator. + + Args: + ---- + name: name of the model; this should map to a supported list of models + mentioned in the factory file + conf: kwargs for instantiating the model class + stateful: flag indicating if the model is stateful or not + """ + + name: str = MISSING + conf: dict[str, Any] = field(default_factory=dict) + stateful: bool = True + + +@dataclass +class RegistryInfo: + """Registry config base class. + + Args: + ---- + name: name of the registry + conf: kwargs for instantiating the model class + """ + + name: str = MISSING + conf: dict[str, Any] = field(default_factory=dict) + + +@dataclass +class LightningTrainerConf: + """Schema for defining the Pytorch Lightning trainer behavior. + + More details on the arguments are provided here: + https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html#trainer-class-api + """ + + accelerator: str = "auto" + max_epochs: int = 100 + logger: bool = False + check_val_every_n_epoch: int = 5 + log_every_n_steps: int = 20 + enable_checkpointing: bool = False + enable_progress_bar: bool = True + enable_model_summary: bool = True + limit_val_batches: bool = 0 + callbacks: Optional[Any] = None + + +@dataclass +class NumalogicConf: + """Top level config schema for numalogic.""" + + model: ModelInfo = field(default_factory=ModelInfo) + trainer: LightningTrainerConf = field(default_factory=LightningTrainerConf) + registry: RegistryInfo = field(default_factory=RegistryInfo) + preprocess: list[ModelInfo] = field(default_factory=list) + threshold: ModelInfo = field(default_factory=ModelInfo) + postprocess: ModelInfo = field(default_factory=ModelInfo) diff --git a/numalogic/connectors/__init__.py b/numalogic/connectors/__init__.py new file mode 100644 index 00000000..3cb0e26d --- /dev/null +++ b/numalogic/connectors/__init__.py @@ -0,0 +1,4 @@ +from numalogic.connectors.druid import DruidFetcher +from numalogic.connectors.prometheus import PrometheusDataFetcher + +__all__ = ["DruidFetcher", "PrometheusDataFetcher"] diff --git a/numalogic/numaflow/entities.py b/numalogic/numaflow/entities.py new file mode 100644 index 00000000..d78dbb33 --- /dev/null +++ b/numalogic/numaflow/entities.py @@ -0,0 +1,149 @@ +from copy import copy +from dataclasses import dataclass, field +from enum import Enum +from typing import 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): + """Status is the enum that is used to identify the status of the payload.""" + + 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): + """Header is the enum that is used to identify the type of payload.""" + + STATIC_INFERENCE = "static_threshold" + MODEL_INFERENCE = "model_inference" + TRAIN_REQUEST = "request_training" + MODEL_STALE = "model_stale" + + +@dataclass +class _BasePayload: + """_BasePayload is the base data structure that is passed.""" + + uuid: str + composite_keys: list[str] + + +PayloadType = TypeVar("PayloadType", bound=_BasePayload) + + +@dataclass +class TrainerPayload(_BasePayload): + """ + TrainerPayload is the data structure that is passed + around in the system when a training request is made. + """ + + metrics: list[str] + header: Header = Header.TRAIN_REQUEST + + def to_json(self): + return orjson.dumps(self) + + +@dataclass(repr=False) +class StreamPayload(_BasePayload): + """StreamPayload is the main data structure that is passed around in the system.""" + + 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 a string representation of the object.""" + return "header: {}, status: {}, composite_keys: {}, data: {}, metadata: {}}}".format( + 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: + """Input payload.""" + + 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: + """Output payload.""" + + 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/numalogic/numaflow/server.py b/numalogic/numaflow/server.py new file mode 100644 index 00000000..e69de29b diff --git a/numalogic/numaflow/udf/__init__.py b/numalogic/numaflow/udf/__init__.py new file mode 100644 index 00000000..79ec349b --- /dev/null +++ b/numalogic/numaflow/udf/__init__.py @@ -0,0 +1,4 @@ +from numalogic.numaflow.udf.blockinfer import InferenceBlockUDF +from numalogic.numaflow.udf.block_train import TrainBlockUDF + +__all__ = ["InferenceBlockUDF", "TrainBlockUDF"] diff --git a/numalogic/numaflow/udf/block_train.py b/numalogic/numaflow/udf/block_train.py new file mode 100644 index 00000000..0bb77fea --- /dev/null +++ b/numalogic/numaflow/udf/block_train.py @@ -0,0 +1,136 @@ +import logging +import os + +import pandas as pd +from omegaconf import OmegaConf +from orjson import orjson +from pynumaflow.function import Datum, Messages, Message +from sklearn.preprocessing import StandardScaler + +from numalogic.blocks import ( + PreprocessBlock, + NNBlock, + ThresholdBlock, + PostprocessBlock, + BlockPipeline, +) +from numalogic.config import ( + NumalogicConf, + RedisConf, + DataStreamConf, + LightningTrainerConf, + DruidConf, +) +from numalogic.connectors import DruidFetcher +from numalogic.models.autoencoder.variants import SparseVanillaAE +from numalogic.models.threshold import StdDevThreshold +from numalogic.numaflow import NumalogicUDF +from numalogic.numaflow.entities import TrainerPayload +from numalogic.registry import RedisRegistry +from numalogic.registry.redis_registry import get_redis_client_from_conf +from numalogic.transforms import TanhNorm + +_LOGGER = logging.getLogger(__name__) +REQUEST_EXPIRY = int(os.getenv("REQUEST_EXPIRY") or 300) +MIN_RECORDS = 1000 + + +class TrainBlockUDF(NumalogicUDF): + """UDF to train the ML model.""" + + def __init__( + self, + numalogic_conf: NumalogicConf, + redis_conf: RedisConf, + stream_conf: DataStreamConf, + trainer_conf: LightningTrainerConf, + druid_conf: DruidConf, + ): + super().__init__() + self.conf = numalogic_conf + self.druid_conf = druid_conf + self.stream_conf = stream_conf + self.trainer_conf = trainer_conf + self._rclient = self._get_redis_client(redis_conf) + self.model_registry = RedisRegistry(client=self._rclient) + self._blocks_args = ( + PreprocessBlock(StandardScaler()), + NNBlock( + SparseVanillaAE( + seq_len=self.stream_conf.window_size, n_features=len(self.stream_conf.metrics) + ), + self.stream_conf.window_size, + ), + ThresholdBlock(StdDevThreshold()), + PostprocessBlock(TanhNorm()), + ) + self._dkeys = ("stdscaler", "sparsevanillae", "stddevthreshold") + + @staticmethod + def _get_redis_client(redis_conf: RedisConf): + return get_redis_client_from_conf(redis_conf) + + @staticmethod + def _drop_message(): + return Messages(Message.to_drop()) + + def fetch_data(self, payload: TrainerPayload) -> pd.DataFrame: + """Fetch the data from the data source.""" + fetcher_conf = self.stream_conf.druid_fetcher + data_fetcher = DruidFetcher(url=self.druid_conf.url, endpoint=self.druid_conf.endpoint) + + return data_fetcher.fetch_data( + datasource=fetcher_conf.datasource, + filter_keys=self.stream_conf.composite_keys + fetcher_conf.dimensions, + filter_values=payload.composite_keys[1:] + payload.metrics, + dimensions=OmegaConf.to_container(fetcher_conf.dimensions), + granularity=fetcher_conf.granularity, + aggregations=OmegaConf.to_container(fetcher_conf.aggregations), + group_by=OmegaConf.to_container(fetcher_conf.group_by), + pivot=fetcher_conf.pivot, + hours=fetcher_conf.hours, + ) + + def _is_new_request(self, payload: TrainerPayload) -> bool: + _ckeys = ":".join(*payload.composite_keys, *payload.metrics) + r_key = f"train::{_ckeys}" + value = self._rclient.get(r_key) + if value: + return False + + self._rclient.setex(r_key, time=REQUEST_EXPIRY, value=1) + return True + + def exec(self, keys: list[str], datum: Datum) -> Messages: + """Execute the UDF.""" + payload = TrainerPayload(**orjson.loads(datum.value)) + is_new = self._is_new_request(payload) + + if not is_new: + return self._drop_message() + + try: + train_df = self.fetch_data(payload) + except Exception as err: + _LOGGER.exception( + "%s - Error while fetching data for keys: %s, metrics: %s, err: %r", + payload.uuid, + payload.composite_keys, + payload.metrics, + err, + ) + return self._drop_message() + + if len(train_df) < MIN_RECORDS: + _LOGGER.warning( + "%s - Insufficient data for training. Expected: %d, Actual: %d", + payload.uuid, + MIN_RECORDS, + len(train_df), + ) + return self._drop_message() + + block_pl = BlockPipeline(*self._blocks_args, registry=self.model_registry) + block_pl.fit(train_df.to_numpy()) + block_pl.save(skeys=payload.composite_keys, dkeys=self._dkeys) + return self._drop_message() diff --git a/numalogic/numaflow/udf/blockinfer.py b/numalogic/numaflow/udf/blockinfer.py new file mode 100644 index 00000000..bbe5263d --- /dev/null +++ b/numalogic/numaflow/udf/blockinfer.py @@ -0,0 +1,144 @@ +import logging +from typing import Optional +from uuid import uuid4 + +import numpy as np +import numpy.typing as npt +from orjson import orjson +from pynumaflow.function import Datum, Messages, Message +from sklearn.preprocessing import StandardScaler + +from numalogic.blocks import ( + BlockPipeline, + PreprocessBlock, + NNBlock, + ThresholdBlock, + PostprocessBlock, +) +from numalogic.config import NumalogicConf, RedisConf, DataStreamConf +from numalogic.models.autoencoder.variants import SparseVanillaAE +from numalogic.models.threshold import StdDevThreshold +from numalogic.numaflow import NumalogicUDF +from numalogic.numaflow.entities import OutputPayload, TrainerPayload +from numalogic.registry import RedisRegistry +from numalogic.registry.redis_registry import get_redis_client_from_conf +from numalogic.tools.exceptions import RedisRegistryError, ModelKeyNotFound, ConfigError +from numalogic.transforms import TanhNorm + +_LOGGER = logging.getLogger(__name__) +TRAIN_VTX_KEY = "train" + + +class InferenceBlockUDF(NumalogicUDF): + """UDF to preprocess the input data for ML inference.""" + + def __init__( + self, numalogic_conf: NumalogicConf, redis_conf: RedisConf, stream_conf: DataStreamConf + ): + super().__init__() + self.conf = numalogic_conf + self.stream_conf = stream_conf + self.model_registry = RedisRegistry(client=self._get_redis_client(redis_conf)) + self._blocks_args = ( + PreprocessBlock(StandardScaler()), + NNBlock( + SparseVanillaAE( + seq_len=self.stream_conf.window_size, n_features=len(self.stream_conf.metrics) + ), + self.stream_conf.window_size, + ), + ThresholdBlock(StdDevThreshold()), + PostprocessBlock(TanhNorm()), + ) + self._dkeys = ("stdscaler", "sparsevanillae", "stddevthreshold") + + @staticmethod + def _get_redis_client(redis_conf: RedisConf): + return get_redis_client_from_conf(redis_conf) + + @staticmethod + def read_datum(datum: Datum, uuid: str = "") -> dict: + """Read the input datum and return the data payload.""" + try: + data_payload = orjson.loads(datum.value) + except orjson.JSONDecodeError as e: + _LOGGER.exception("%s - Error while reading input json %r", uuid, e) + return {} + _LOGGER.info("%s - Data payload: %s", uuid, data_payload) + return data_payload + + def extract_data(self, input_data: dict, uuid: str = "") -> Optional[npt.NDArray]: + """Extract the data from the input payload.""" + stream_array = [] + metrics = self.stream_conf.metrics + if not metrics: + raise ConfigError("No metrics found in stream config") + for row in input_data["data"]: + stream_array.append([row[metric] for metric in self.stream_conf.metrics]) + if not stream_array: + _LOGGER.warning("%s - No data found in input payload", uuid) + return None + return np.array(stream_array) + + def _construct_output( + self, anomaly_scores: npt.NDArray, input_payload: dict, model_version: str + ) -> OutputPayload: + """Construct the output payload.""" + unified_score = np.max(anomaly_scores) + metric_data = { + metric: { + "anomaly_score": anomaly_scores[idx], + } + for idx, metric in enumerate(self.stream_conf.metrics) + } + metric_data["model_version"] = model_version + + return OutputPayload( + timestamp=input_payload["end_time"], + unified_anomaly=unified_score, + data=metric_data, + metadata=input_payload.get("metadata", {}), + ) + + def _send_training_req(self, keys: list[str], uuid: str = "") -> Messages: + """Send a training request to the training UDF.""" + train_payload = TrainerPayload( + uuid=uuid, composite_keys=keys, metrics=self.stream_conf.metrics + ) + return Messages(Message(keys=keys, value=train_payload.to_json(), tags=[TRAIN_VTX_KEY])) + + def exec(self, keys: list[str], datum: Datum) -> Messages: + """Runs the UDF.""" + _uuid = uuid4().hex + input_payload = self.read_datum(datum, uuid=_uuid) + if not input_payload: + return Messages(Message.to_drop()) + + block_pl = BlockPipeline(*self._blocks_args, registry=self.model_registry) + stream_array = self.extract_data(input_payload, uuid=_uuid) + if stream_array is None: + return Messages(Message.to_drop()) + + # Load the model from the registry + try: + artifact_data = block_pl.load(skeys=keys, dkeys=self._dkeys) + except ModelKeyNotFound: + _LOGGER.info("%s - Model not found for skeys: %s, dkeys: %s", _uuid, keys, self._dkeys) + return self._send_training_req(keys, _uuid) + except RedisRegistryError as redis_err: + _LOGGER.exception("%s - Error loading block pipeline: %r", _uuid, redis_err) + return Messages(Message.to_drop()) + + # Run inference + try: + raw_scores = block_pl(stream_array) + except Exception as err: + _LOGGER.error("%s - Error running block pipeline: %r", _uuid, err) + return Messages(Message.to_drop()) + + # Find final anomaly score + anomaly_scores = np.mean(raw_scores, axis=0) + out_payload = self._construct_output( + anomaly_scores, input_payload, artifact_data.extras.get("version") + ) + return Messages(Message(keys=keys, value=out_payload.to_json())) diff --git a/tests/numaflow/__init__.py b/tests/numaflow/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/numaflow/test_blockinfer.py b/tests/numaflow/test_blockinfer.py new file mode 100644 index 00000000..e392e936 --- /dev/null +++ b/tests/numaflow/test_blockinfer.py @@ -0,0 +1,106 @@ +import logging +import unittest +from datetime import datetime +from unittest.mock import patch, Mock + +import numpy as np +from fakeredis import FakeStrictRedis, FakeServer +from orjson import orjson +from pynumaflow.function import Datum, DatumMetadata +from sklearn.preprocessing import StandardScaler + +from numalogic.blocks import ( + BlockPipeline, + PreprocessBlock, + NNBlock, + ThresholdBlock, + PostprocessBlock, +) +from numalogic.config import NumalogicConf, RedisConf, DataStreamConf +from numalogic.models.autoencoder.variants import SparseVanillaAE +from numalogic.models.threshold import StdDevThreshold +from numalogic.numaflow.entities import TrainerPayload, OutputPayload +from numalogic.numaflow.udf import InferenceBlockUDF +from numalogic.registry import RedisRegistry +from numalogic.transforms import TanhNorm + +PAYLOAD = { + "data": [ + {"degraded": 0, "failed": 0, "success": 14, "timestamp": 1689189540000}, + {"degraded": 0, "failed": 0, "success": 8, "timestamp": 1689189600000}, + {"degraded": 0, "failed": 0, "success": 6, "timestamp": 1689189660000}, + {"degraded": 0, "failed": 4, "success": 2, "timestamp": 1689189720000}, + {"degraded": 0, "failed": 0, "success": 2, "timestamp": 1689189780000}, + {"degraded": 2, "failed": 0, "success": 4, "timestamp": 1689189840000}, + {"degraded": 0, "failed": 0, "success": 6, "timestamp": 1689190080000}, + {"degraded": 0, "failed": 0, "success": 6, "timestamp": 1689190140000}, + {"degraded": 1, "failed": 0, "success": 2, "timestamp": 1689190500000}, + {"degraded": 0, "failed": 0, "success": 2, "timestamp": 1689190560000}, + {"degraded": 0, "failed": 0, "success": 2, "timestamp": 1689190620000}, + {"degraded": 0, "failed": 0, "success": 2, "timestamp": 1689190680000}, + ], + "start_time": 1689189540000, + "end_time": 1689190740000, + "metadata": { + "current_pipeline_name": "PluginAsset", + "druid": {"source": "numalogic"}, + "wavefront": {"source": "numalogic"}, + }, +} +MOCK_REDIS_CLIENT = FakeStrictRedis(server=FakeServer()) +logging.basicConfig(level=logging.DEBUG) + + +class TestInferenceBlockUDF(unittest.TestCase): + def setUp(self) -> None: + self.skeys = ["PluginAsset", "1804835142986662399"] + self.dkeys = ["stdscaler", "sparsevanillae", "stddevthreshold"] + self.datum = Datum( + keys=self.skeys, + value=orjson.dumps(PAYLOAD), + event_time=datetime.now(), + watermark=datetime.now(), + metadata=DatumMetadata(msg_id="", num_delivered=0), + ) + + def tearDown(self) -> None: + MOCK_REDIS_CLIENT.flushall() + + def train_block(self) -> None: + block_pl = BlockPipeline( + PreprocessBlock(StandardScaler()), + NNBlock(SparseVanillaAE(seq_len=12, n_features=3), 12), + ThresholdBlock(StdDevThreshold()), + PostprocessBlock(TanhNorm()), + registry=RedisRegistry(client=MOCK_REDIS_CLIENT), + ) + rng = np.random.default_rng() + block_pl.fit(rng.random((1000, 3)), nn__max_epochs=5) + block_pl.save(skeys=self.skeys, dkeys=self.dkeys) + + @patch.object(InferenceBlockUDF, "_get_redis_client", Mock(return_value=MOCK_REDIS_CLIENT)) + def test_exec_success(self) -> None: + self.train_block() + udf = InferenceBlockUDF( + NumalogicConf(), + RedisConf(host="localhost", port=6379), + DataStreamConf(metrics=["degraded", "failed", "success"]), + ) + out_msgs = udf(self.skeys, self.datum) + self.assertEqual(1, len(out_msgs)) + self.assertIsInstance(OutputPayload(**orjson.loads(out_msgs[0].value)), OutputPayload) + + @patch.object(InferenceBlockUDF, "_get_redis_client", Mock(return_value=MOCK_REDIS_CLIENT)) + def test_exec_no_model(self) -> None: + udf = InferenceBlockUDF( + NumalogicConf(), + RedisConf(host="localhost", port=6379), + DataStreamConf(metrics=["degraded", "failed", "success"]), + ) + out_msgs = udf(self.skeys, self.datum) + self.assertEqual(1, len(out_msgs)) + self.assertIsInstance(TrainerPayload(**orjson.loads(out_msgs[0].value)), TrainerPayload) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_numaflow.py b/tests/numaflow/test_numaflow.py similarity index 100% rename from tests/test_numaflow.py rename to tests/numaflow/test_numaflow.py 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/docs/images/anomalydetection.png b/udf/anomaly-detection/docs/images/anomalydetection.png new file mode 100644 index 00000000..f8407386 Binary files /dev/null and b/udf/anomaly-detection/docs/images/anomalydetection.png differ diff --git a/udf/anomaly-detection/entry.sh b/udf/anomaly-detection/entry.sh new file mode 100644 index 00000000..e595bf43 --- /dev/null +++ b/udf/anomaly-detection/entry.sh @@ -0,0 +1,4 @@ +#!/bin/sh +set -eux + +python starter.py diff --git a/udf/anomaly-detection/poetry.lock 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(>=0.9.1)", "pytest-ruff"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.9, <3.12" +content-hash = "712fd20c0460465033143cc26b4447d5610819821a0caedeee4df1ea6b4b89ac" diff --git a/udf/anomaly-detection/pyproject.toml b/udf/anomaly-detection/pyproject.toml new file mode 100644 index 00000000..71c5f630 --- /dev/null +++ b/udf/anomaly-detection/pyproject.toml @@ -0,0 +1,71 @@ +[tool.poetry] +name = "anomaly-detection" +version = "0.3.2" +description = "Numalogic UDFs to do anomaly detection on timeseries data" +authors = ["Numalogic developers"] +packages = [{ include = "anomalydetection" }] +maintainers = [ + "Avik Basu ", + "Nandita Koppisetty ", +] +classifiers = [ + "Topic :: Software Development :: Libraries", + "License :: OSI Approved :: Apache Software License", + "Intended Audience :: Developers", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Programming Language :: Python :: 3.10" +] +repository = "https://github.com/numaproj/numalogic" + +[tool.poetry.dependencies] +python = ">=3.9, <3.12" +redis = {extras = ["hiredis"], version = "^4.5" } +pynumaflow = "~0.4.1" +numalogic = {version = "~0.5", extras = ["redis"], allow-prereleases = true} +orjson = "^3.8.4" +omegaconf = "^2.3.0" +watchdog = "^3.0.0" +backoff = "^2.2.1" +pydruid = "^0.6.5" + +[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..7404784e --- /dev/null +++ b/udf/anomaly-detection/src/__init__.py @@ -0,0 +1,28 @@ +import logging +import os + +from src._config import UnifiedConf, StreamConf, 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", "StreamConf", "Configs", "PipelineConf"] diff --git a/udf/anomaly-detection/src/_config.py b/udf/anomaly-detection/src/_config.py new file mode 100644 index 00000000..ec161c15 --- /dev/null +++ b/udf/anomaly-detection/src/_config.py @@ -0,0 +1,63 @@ +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 = 24 * 8 # 8 days worth of data + min_train_size: int = 2000 + retrain_freq_hr: int = 24 + resume_training: bool = False + model_expiry_sec: int = 86400 # 24 hrs + dedup_expiry_sec: int = 1800 # 30 days + + +@dataclass +class StaticThreshold: + upper_limit: List[int] = field(default_factory=list) + weight: List[float] = field(default_factory=list) + + +class DataSource(str, Enum): + PROMETHEUS = "prometheus" + DRUID = "druid" + + +@dataclass +class StreamConf: + config_id: str = "default" + source: str = DataSource.PROMETHEUS.value + window_size: int = 12 + composite_keys: List[str] = field(default_factory=list) + metrics: List[str] = field(default_factory=list) + retrain_conf: ReTrainConf = field(default_factory=lambda: ReTrainConf()) + static_threshold: StaticThreshold = field(default_factory=lambda: StaticThreshold()) + numalogic_conf: NumalogicConf = MISSING + unified_config: UnifiedConf = field(default_factory=lambda: UnifiedConf()) + druid_fetcher: DruidFetcherConf = MISSING + + +@dataclass +class Configs: + configs: List[StreamConf] + + +@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..c51f2d23 --- /dev/null +++ b/udf/anomaly-detection/src/_constants.py @@ -0,0 +1,17 @@ +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" +LOCAL_CACHE_TTL = int(os.getenv("LOCAL_CACHE_TTL", 3600)) + +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..b6ce7893 --- /dev/null +++ b/udf/anomaly-detection/src/connectors/_config.py @@ -0,0 +1,50 @@ +from dataclasses import dataclass, field + +from pydruid.utils.aggregators import doublesum + + +@dataclass +class PrometheusConf: + server: str + pushgateway: str + scrape_interval: int = 30 + + +@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 Pivot: + index: str = "timestamp" + columns: list[str] = field(default_factory=list) + value: list[str] = field(default_factory=lambda: ["count"]) + + +@dataclass +class DruidFetcherConf: + datasource: str + dimensions: list[str] = field(default_factory=list) + aggregations: dict = field(default_factory=dict) + group_by: list[str] = field(default_factory=list) + pivot: Pivot = field(default_factory=lambda: Pivot()) + granularity: str = "minute" + + def __post_init__(self): + if not self.aggregations: + self.aggregations = {"count": doublesum("count")} diff --git a/udf/anomaly-detection/src/connectors/druid.py b/udf/anomaly-detection/src/connectors/druid.py new file mode 100644 index 00000000..4edf970f --- /dev/null +++ b/udf/anomaly-detection/src/connectors/druid.py @@ -0,0 +1,85 @@ +import time +import pytz +import logging +import pandas as pd +from datetime import datetime, timedelta +from pydruid.client import PyDruid +from pydruid.utils.filters import Filter + +from src.connectors._config import Pivot + +_LOGGER = logging.getLogger(__name__) + + +class DruidFetcher: + def __init__(self, url: str, endpoint: str): + self.client = PyDruid(url, endpoint) + + 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: Pivot = None, + hours: float = 24, + ) -> pd.DataFrame: + _start_time = time.time() + 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, + } + + _LOGGER.info( + "Fetching data with params: %s", + params, + ) + + 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.columns: + df = df.pivot( + index=pivot.index, + columns=pivot.columns, + values=pivot.value, + ) + df.columns = df.columns.map("{0[1]}".format) + df.reset_index(inplace=True) + + _LOGGER.info( + "Time taken to fetch data: %s, for keys: %s, for df shape: %s", + time.time() - _start_time, + filter_pairs, + df.shape, + ) + return 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..eee16575 --- /dev/null +++ b/udf/anomaly-detection/src/connectors/sentinel.py @@ -0,0 +1,99 @@ +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_CLIENT: Optional[redis_client_t] = None + + +def get_redis_client( + host: str, + port: int, + password: str, + mastername: str, + recreate: bool = False, + master_node: bool = True, +) -> 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 + recreate: Whether to flush and recreate the client + master_node: Whether to use the master node or the slave nodes + + Returns: + Redis client instance + """ + global SENTINEL_CLIENT + + if not recreate and SENTINEL_CLIENT: + return SENTINEL_CLIENT + + retry = Retry( + ExponentialBackoff(), + 3, + supported_errors=( + ConnectionError, + TimeoutError, + RedisClusterException, + RedisError, + MasterNotFoundError, + ), + ) + + conn_kwargs = { + "socket_timeout": 1, + "socket_connect_timeout": 1, + "socket_keepalive": True, + "health_check_interval": 10, + } + + sentinel = Sentinel( + [(host, port)], + sentinel_kwargs=dict(password=password, **conn_kwargs), + retry=retry, + password=password, + **conn_kwargs + ) + if master_node: + SENTINEL_CLIENT = sentinel.master_for(mastername) + else: + SENTINEL_CLIENT = sentinel.slave_for(mastername) + _LOGGER.info("Sentinel redis params: %s, master_node: %s", conn_kwargs, master_node) + return SENTINEL_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..309baf42 --- /dev/null +++ b/udf/anomaly-detection/src/entities.py @@ -0,0 +1,127 @@ +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 + config_id: str + composite_keys: List[str] + + +PayloadType = TypeVar("PayloadType", bound=_BasePayload) + + +@dataclass +class TrainerPayload(_BasePayload): + metrics: List[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: Status = Status.RAW + header: Header = Header.MODEL_INFERENCE + metadata: 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_data(self, original=False) -> npt.NDArray[float]: + if original: + return np.asarray(self.raw_data) + return np.asarray(self.data) + + def set_status(self, status: Status) -> None: + self.status = status + + def set_header(self, header: Header) -> None: + self.header = header + + 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: + uuid: str + config_id: str + 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: + uuid: str + config_id: str + 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..7f3fc2ec --- /dev/null +++ b/udf/anomaly-detection/src/factory.py @@ -0,0 +1,23 @@ +from src.udf import Preprocess, Inference, Threshold, Postprocess +from src.udf.trainer import Trainer + + +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 == "trainer": + return Trainer().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..a100eba7 --- /dev/null +++ b/udf/anomaly-detection/src/tools.py @@ -0,0 +1,193 @@ +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 +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, + 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[1], + labels_map=labels, + return_labels=return_labels, + start=start_dt.timestamp(), + end=end_dt.timestamp(), + step=prometheus_conf.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_scores = np.zeros(x_arr.shape) + for col in range(x_arr.shape[1]): + static_clf = SigmoidThreshold(upper_limit=static_threshold.upper_limit[col]) + static_scores[:, col] = static_clf.score_samples(x_arr[:, col]) + 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 - np.array(self.static_thresh.weight) + postproc_factory = PostprocessFactory() + self.postproc_clf = postproc_factory.get_instance(postprocess_conf) + + def get_ensemble_score(self, x_arr: Matrix) -> np.ndarray: + """ + 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) -> np.ndarray: + """ + 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, axis=0) + return self.postproc_clf.transform(static_score) + + def get_norm_score(self, x_arr: Matrix) -> np.ndarray: + """ + Returns the normalized window score + + Args: + x_arr: Metric scores array + + Returns: + Score for the window + """ + + win_score = np.mean(x_arr, axis=0) + 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..b1831986 --- /dev/null +++ b/udf/anomaly-detection/src/udf/__init__.py @@ -0,0 +1,7 @@ +from src.udf.preprocess import Preprocess +from src.udf.inference import Inference +from src.udf.threshold import Threshold +from src.udf.postprocess import Postprocess +from src.udf.trainer import Trainer + +__all__ = ["Preprocess", "Inference", "Threshold", "Postprocess", "Trainer"] diff --git a/udf/anomaly-detection/src/udf/inference.py b/udf/anomaly-detection/src/udf/inference.py new file mode 100644 index 00000000..43bbf920 --- /dev/null +++ b/udf/anomaly-detection/src/udf/inference.py @@ -0,0 +1,195 @@ +import os +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, LocalLRUCache +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__) +LOCAL_CACHE_TTL = int(os.getenv("LOCAL_CACHE_TTL", 3600)) + + +class Inference: + def __init__(self): + local_cache = LocalLRUCache(cachesize=2000, ttl=LOCAL_CACHE_TTL) + self.model_registry = RedisRegistry( + client=get_redis_client_from_conf(master_node=False), cache_registry=local_cache + ) + + @classmethod + def _run_inference( + cls, + keys: list[str], + payload: StreamPayload, + artifact_data: ArtifactData, + ) -> np.ndarray: + model = artifact_data.artifact + win_size = ConfigManager.get_stream_config(config_id=payload.config_id).window_size + data_arr = payload.get_data() + _start_time = time.perf_counter() + stream_loader = DataLoader(StreamingDataset(data_arr, win_size)) + _LOGGER.info( + "%s - Time taken for DataLoader: %.4f sec", + payload.uuid, + time.perf_counter() - _start_time, + ) + trainer = AutoencoderTrainer() + try: + _start_time = time.perf_counter() + recon_err = trainer.predict(model, dataloaders=stream_loader) + _LOGGER.info( + "%s - Time taken to predict from the model: %.4f sec", + payload.uuid, + time.perf_counter() - _start_time, + ) + except Exception as err: + _LOGGER.exception( + "%s - Runtime error while performing inference: Keys: %s, Metric: %s, Error: %r", + payload.uuid, + keys, + payload.metrics, + err, + ) + raise RuntimeError("Failed to infer") from err + _LOGGER.info( + "%s - Successfully inferred: Keys: %s, Metric: %s", payload.uuid, keys, payload.metrics + ) + return recon_err.numpy() + + def inference( + self, keys: List[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 == Header.STATIC_INFERENCE: + _LOGGER.debug( + "%s - Models not found in the previous steps, forwarding for static thresholding. Keys: %s, Metrics: %s", + payload.uuid, + payload.composite_keys, + payload.metrics, + ) + return static_response + + # Load config + retrain_config = ConfigManager.get_retrain_config(config_id=payload.config_id) + numalogic_conf = ConfigManager.get_numalogic_config(config_id=payload.config_id) + + # Load inference artifact + try: + _start_time = time.perf_counter() + artifact_data = self.model_registry.load( + skeys=keys, + dkeys=[numalogic_conf.model.name], + ) + _LOGGER.info( + "%s - Time taken to load the model from registry/cache: %.4f sec", + payload.uuid, + time.perf_counter() - _start_time, + ) + except RedisRegistryError as err: + _LOGGER.exception( + "%s - Error while fetching inference artifact, Keys: %s, Metric: %s, Error: %r", + payload.uuid, + payload.composite_keys, + payload.metrics, + err, + ) + return static_response + + except Exception as ex: + _LOGGER.exception( + "%s - Unhandled exception while fetching inference artifact, Keys: %s, Metric: %s, Error: %r", + payload.uuid, + payload.composite_keys, + payload.metrics, + ex, + ) + 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, + payload.metrics, + ) + return static_response + + # Check if artifact is stale + header = Header.MODEL_INFERENCE + + _LOGGER.info( + "%s - Loaded artifact data from %s", + payload.uuid, + artifact_data.extras.get("source"), + ) + if ( + RedisRegistry.is_artifact_stale(artifact_data, int(retrain_config.retrain_freq_hr)) + and artifact_data.extras.get("source") == "registry" + ): + _LOGGER.info( + "%s - Inference artifact found is stale, Keys: %s, Metric: %s", + payload.uuid, + payload.composite_keys, + payload.metrics, + ) + header = Header.MODEL_STALE + + # Generate predictions + try: + x_inferred = self._run_inference(keys, payload, artifact_data) + except RuntimeError: + _LOGGER.info( + "%s - Failed to infer, forwarding for static thresholding. Keys: %s, Metric: %s", + payload.uuid, + payload.composite_keys, + payload.metrics, + ) + return None, Status.RUNTIME_ERROR, Header.STATIC_INFERENCE, -1 + + return x_inferred, 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 + x_inferred, status, header, version = self.inference(keys, payload) + payload.set_status(status=status) + payload.set_header(header=header) + payload.set_metadata(key="model_version", value=version) + + if x_inferred is not None: + payload.set_data(arr=x_inferred) + + messages.append(Message(keys=keys, value=payload.to_json())) + _LOGGER.info("%s - Sending Msg: { Keys: %s, Payload: %r }", payload.uuid, keys, payload) + _LOGGER.debug( + "%s - Time taken in inference: %.4f sec", + payload.uuid, + time.perf_counter() - _start_time, + ) + 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..f5f82847 --- /dev/null +++ b/udf/anomaly-detection/src/udf/postprocess.py @@ -0,0 +1,113 @@ +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], payload: StreamPayload) -> (float, dict): + static_thresh = ConfigManager.get_static_threshold_config(config_id=payload.config_id) + postprocess_conf = ConfigManager.get_postprocess_config(config_id=payload.config_id) + + # Compute static threshold score if header is static inference + metric_arr = payload.get_data() + win_scorer = WindowScorer(static_thresh, postprocess_conf) + if payload.header == Header.STATIC_INFERENCE: + final_score = win_scorer.get_norm_score(metric_arr) + _LOGGER.info( + "%s - Final static threshold score: %s, keys: %s, metrics: %s", + payload.uuid, + final_score, + keys, + payload.metrics, + ) + + # 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, metrics: %s", + payload.uuid, + final_score, + static_thresh.weight, + keys, + payload.metrics, + ) + + # TODO: construct map + metric_scores = {} + for i in range(len(payload.metrics)): + metric_scores[payload.metrics[i]] = final_score[i] + + return cls.get_unified_anomaly(final_score.tolist(), payload), metric_scores + + @classmethod + def get_unified_anomaly(cls, scores: list[float], payload: StreamPayload) -> float: + unified_config = ConfigManager.get_stream_config(config_id=payload.config_id).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() + + # Perform postprocess + final_score, metric_scores = self.postprocess(keys, payload) + + stream_conf = ConfigManager.get_stream_config(config_id=payload.config_id) + metadata = payload.metadata + metadata["composite_keys"] = list(stream_conf.composite_keys) + + # Construct output payload + out_payload = OutputPayload( + uuid=payload.uuid, + config_id=payload.config_id, + timestamp=payload.timestamps[-1], + unified_anomaly=final_score, + data=metric_scores, + metadata=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..fcc4c913 --- /dev/null +++ b/udf/anomaly-detection/src/udf/preprocess.py @@ -0,0 +1,162 @@ +import json +import os +import time + +import numpy as np +import pandas as pd +from typing import List + +from numalogic.registry import RedisRegistry, LocalLRUCache +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__) +LOCAL_CACHE_TTL = int(os.getenv("LOCAL_CACHE_TTL", 3600)) + + +class Preprocess: + @classmethod + def get_df( + cls, data_payload: dict, features: List[str], win_size: int + ) -> (pd.DataFrame, List[int]): + df = ( + pd.DataFrame(data_payload["data"], columns=["timestamp", *features]) + .astype(float) + .fillna(0) + ) + df.index = df.timestamp.astype(int) + timestamps = np.arange( + int(data_payload["start_time"]), int(data_payload["end_time"]) + 6e4, 6e4, dtype="int" + )[-win_size:] + df = df.reindex(timestamps, fill_value=0) + return df[features], timestamps + + def preprocess(self, keys: List[str], payload: StreamPayload) -> (np.ndarray, Status): + preprocess_cfgs = ConfigManager.get_preprocess_config(config_id=keys[0]) + + local_cache = LocalLRUCache(ttl=LOCAL_CACHE_TTL) + model_registry = RedisRegistry( + client=get_redis_client_from_conf(master_node=False), cache_registry=local_cache + ) + # Load preproc artifact + try: + preproc_artifact = model_registry.load( + skeys=keys, + dkeys=[_conf.name for _conf in preprocess_cfgs], + ) + except RedisRegistryError as err: + _LOGGER.error( + "%s - Error while fetching preproc artifact, Keys: %s, Metrics: %s, Error: %r", + payload.uuid, + keys, + payload.metrics, + err, + ) + return None, Status.RUNTIME_ERROR + + except Exception as ex: + _LOGGER.exception( + "%s - Unhandled exception while fetching preproc artifact, Keys: %s, Metric: %s, Error: %r", + payload.uuid, + payload.composite_keys, + payload.metrics, + ex, + ) + 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, Metrics: %s", + payload.uuid, + keys, + payload.metrics, + ) + return None, Status.ARTIFACT_NOT_FOUND + + # Perform preprocessing + x_raw = payload.get_data() + preproc_clf = preproc_artifact.artifact + x_scaled = preproc_clf.transform(x_raw) + _LOGGER.info( + "%s - Successfully preprocessed, Keys: %s, Metrics: %s, x_scaled: %s", + payload.uuid, + keys, + payload.metrics, + 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 + _LOGGER.info("Received Msg: { Keys: %s, Value: %s }", keys, datum.value) + + messages = Messages() + try: + data_payload = json.loads(datum.value) + _LOGGER.info("%s - Data payload: %s", data_payload["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_stream_config(config_id=data_payload["config_id"]) + raw_df, timestamps = self.get_df(data_payload, stream_conf.metrics, stream_conf.window_size) + + if raw_df.shape[0] < stream_conf.window_size or raw_df.shape[1] != len(stream_conf.metrics): + _LOGGER.error( + "Dataframe shape: (%f, %f) less than window_size %f ", + raw_df.shape[0], + raw_df.shape[1], + stream_conf.window_size, + ) + messages.append(Message.to_drop()) + return messages + + # Prepare payload for forwarding + payload = StreamPayload( + uuid=data_payload["uuid"], + config_id=data_payload["config_id"], + 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 + x_scaled, status = self.preprocess(keys, payload) + payload.set_status(status=status) + + # If preprocess failed, forward for static thresholding + if x_scaled is None: + payload.set_header(header=Header.STATIC_INFERENCE) + else: + payload.set_header(header=Header.MODEL_INFERENCE) + payload.set_data(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) + _LOGGER.debug( + "%s - Time taken in preprocess: %.4f sec", + payload.uuid, + time.perf_counter() - _start_time, + ) + return messages diff --git a/udf/anomaly-detection/src/udf/stream.json b/udf/anomaly-detection/src/udf/stream.json new file mode 100644 index 00000000..3cf41d9d --- /dev/null +++ b/udf/anomaly-detection/src/udf/stream.json @@ -0,0 +1 @@ +{"uuid":"dd7dfb43-532b-49a3-906e-f78f82ad9c4b","config_id":"druid-config","data":[{"degraded":14,"degraded_rate":0.003626943005181347,"error_rate":0.009067357512953367,"failed":21,"failed_rate":0.005440414507772021,"success":3825,"timestamp":1691622660000},{"degraded":10,"degraded_rate":0.002484472049689441,"error_rate":0.009937888198757764,"failed":30,"failed_rate":0.007453416149068323,"success":3985,"timestamp":1691622720000},{"degraded":9,"degraded_rate":0.0025906735751295338,"error_rate":0.005469199769717904,"failed":10,"failed_rate":0.0028785261945883708,"success":3455,"timestamp":1691622780000},{"degraded":13,"degraded_rate":0.003798947983635301,"error_rate":0.007597895967270602,"failed":13,"failed_rate":0.003798947983635301,"success":3396,"timestamp":1691622840000},{"degraded":11,"degraded_rate":0.0030328094844223876,"error_rate":0.005789909015715467,"failed":10,"failed_rate":0.0027570995312930797,"success":3606,"timestamp":1691622900000},{"degraded":17,"degraded_rate":0.004964953271028037,"error_rate":0.009345794392523364,"failed":15,"failed_rate":0.004380841121495327,"success":3392,"timestamp":1691622960000},{"degraded":10,"degraded_rate":0.002774694783573807,"error_rate":0.006104328523862375,"failed":12,"failed_rate":0.003329633740288568,"success":3582,"timestamp":1691623020000},{"degraded":12,"degraded_rate":0.003770028275212064,"error_rate":0.00942507068803016,"failed":18,"failed_rate":0.005655042412818096,"success":3153,"timestamp":1691623080000},{"degraded":14,"degraded_rate":0.003469640644361834,"error_rate":0.006195786864931847,"failed":11,"failed_rate":0.0027261462205700124,"success":4010,"timestamp":1691623140000},{"degraded":4,"degraded_rate":0.0010887316276537834,"error_rate":0.005715841045182362,"failed":17,"failed_rate":0.004627109417528579,"success":3653,"timestamp":1691623200000},{"degraded":13,"degraded_rate":0.0035278154681139757,"error_rate":0.009497964721845319,"failed":22,"failed_rate":0.005970149253731343,"success":3650,"timestamp":1691623260000},{"degraded":7,"degraded_rate":0.0019358407079646017,"error_rate":0.00663716814159292,"failed":17,"failed_rate":0.004701327433628319,"success":3592,"timestamp":1691623320000},{"degraded":18,"degraded_rate":0.004288777698355968,"error_rate":0.00976888253514415,"failed":23,"failed_rate":0.005480104836788182,"success":4156,"timestamp":1691623380000},{"degraded":15,"degraded_rate":0.00487012987012987,"error_rate":0.00974025974025974,"failed":15,"failed_rate":0.00487012987012987,"success":3050,"timestamp":1691623440000},{"degraded":9,"degraded_rate":0.0024563318777292577,"error_rate":0.006823144104803494,"failed":16,"failed_rate":0.004366812227074236,"success":3639,"timestamp":1691623500000},{"degraded":10,"degraded_rate":0.0031959092361776927,"error_rate":0.006391818472355385,"failed":10,"failed_rate":0.0031959092361776927,"success":3109,"timestamp":1691623560000},{"degraded":12,"degraded_rate":0.003827751196172249,"error_rate":0.004784688995215311,"failed":3,"failed_rate":0.0009569377990430622,"success":3120,"timestamp":1691623620000},{"degraded":21,"degraded_rate":0.007123473541383989,"error_rate":0.009158751696065129,"failed":6,"failed_rate":0.0020352781546811396,"success":2921,"timestamp":1691623680000},{"degraded":7,"degraded_rate":0.002027222704894295,"error_rate":0.0034752389226759338,"failed":5,"failed_rate":0.0014480162177816392,"success":3441,"timestamp":1691623740000},{"degraded":8,"degraded_rate":0.0022179096201829776,"error_rate":0.0049902966454117,"failed":10,"failed_rate":0.0027723870252287217,"success":3589,"timestamp":1691623800000}],"start_time":1691622660000,"end_time":1691623860000,"metadata":{"tags":{"asset_alias":"Intuit.smallbusiness.quickbooksmobile.qbmios","asset_id":"362557362191815079","env":"prd"}}} \ No newline at end of file diff --git a/udf/anomaly-detection/src/udf/threshold.py b/udf/anomaly-detection/src/udf/threshold.py new file mode 100644 index 00000000..eebcde0d --- /dev/null +++ b/udf/anomaly-detection/src/udf/threshold.py @@ -0,0 +1,150 @@ +import os +import time + +import numpy as np +from typing import List +from orjson import orjson + +from numalogic.registry import RedisRegistry, LocalLRUCache +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__) +LOCAL_CACHE_TTL = int(os.getenv("LOCAL_CACHE_TTL", 3600)) + + +class Threshold: + def __init__(self): + local_cache = LocalLRUCache(ttl=LOCAL_CACHE_TTL) + self.model_registry = RedisRegistry( + client=get_redis_client_from_conf(master_node=False), cache_registry=local_cache + ) + + def threshold( + self, keys: List[str], payload: StreamPayload + ) -> (np.ndarray, Status, Header, int): + metric_arr = payload.get_data() + + # Load config + static_thresh = ConfigManager.get_static_threshold_config(config_id=payload.config_id) + thresh_cfg = ConfigManager.get_threshold_config(config_id=payload.config_id) + + # Check if metric needs static inference + if payload.header == Header.STATIC_INFERENCE: + _LOGGER.info( + "%s - Sending to trainer and performing static thresholding. Keys: %s, Metric: %s", + payload.uuid, + payload.composite_keys, + payload.metrics, + ) + 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, + 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, + payload.metrics, + err, + ) + static_scores = calculate_static_thresh(metric_arr, static_thresh) + return static_scores, Status.RUNTIME_ERROR, Header.STATIC_INFERENCE, -1 + + except Exception as ex: + _LOGGER.exception( + "%s - Unhandled exception while fetching threshold artifact, Keys: %s, Metric: %s, Error: %r", + payload.uuid, + payload.composite_keys, + payload.metrics, + ex, + ) + 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_data() + thresh_clf = thresh_artifact.artifact + y_score = thresh_clf.score_samples(recon_err) + + return ( + y_score, + Status.THRESHOLD, + payload.header, + payload.get_metadata(key="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() + + y_score, status, header, version = self.threshold(keys, payload) + payload.set_status(status=status) + payload.set_header(header=header) + payload.set_metadata(key="model_version", value=version) + + if y_score is not None: + payload.set_data(arr=y_score) + + if y_score is None or header == Header.MODEL_STALE or status == Status.ARTIFACT_NOT_FOUND: + train_payload = TrainerPayload( + uuid=payload.uuid, + composite_keys=keys, + metrics=payload.metrics, + config_id=payload.config_id, + ) + _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, + ) + _LOGGER.debug( + "%s - Time taken in threshold: %.4f sec", + payload.uuid, + time.perf_counter() - _start_time, + ) + return messages diff --git a/udf/anomaly-detection/src/udf/trainer.py b/udf/anomaly-detection/src/udf/trainer.py new file mode 100644 index 00000000..478f23d9 --- /dev/null +++ b/udf/anomaly-detection/src/udf/trainer.py @@ -0,0 +1,300 @@ +import os +import time +import orjson +import pandas as pd +from typing import List +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 omegaconf import OmegaConf +from pynumaflow.function import Datum, Messages, Message +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)) + + +def get_feature_df(data: pd.DataFrame, metrics: list): + for col in metrics: + if col not in data: + data.loc[:, col] = 0 + data.fillna(0, inplace=True) + return data[metrics] + + +class Trainer: + @classmethod + def fetch_prometheus_data(cls, payload: TrainerPayload, hours: int) -> pd.DataFrame: + prometheus_conf = ConfigManager.get_prom_config() + if prometheus_conf is None: + _LOGGER.error("%s - Prometheus config is not available", payload.uuid) + return pd.DataFrame() + data_fetcher = PrometheusDataFetcher(prometheus_conf.server) + return data_fetcher.fetch_data( + metric=payload.metrics[0], + labels={"namespace": payload.composite_keys[1]}, + return_labels=["rollouts_pod_template_hash"], + hours=hours, + ) + + @classmethod + def fetch_druid_data(cls, payload: TrainerPayload, hours: int) -> pd.DataFrame: + stream_config = ConfigManager.get_stream_config(payload.config_id) + druid_conf = ConfigManager.get_druid_config() + fetcher_conf = stream_config.druid_fetcher + if druid_conf is None: + _LOGGER.error("%s - Druid config is not available", payload.uuid) + return pd.DataFrame() + data_fetcher = DruidFetcher(url=druid_conf.url, endpoint=druid_conf.endpoint) + + return data_fetcher.fetch_data( + datasource=fetcher_conf.datasource, + filter_keys=stream_config.composite_keys, + filter_values=payload.composite_keys, + dimensions=OmegaConf.to_container(fetcher_conf.dimensions), + granularity=fetcher_conf.granularity, + aggregations=OmegaConf.to_container(fetcher_conf.aggregations), + group_by=OmegaConf.to_container(fetcher_conf.group_by), + pivot=fetcher_conf.pivot, + hours=hours, + ) + + @classmethod + def fetch_data(cls, payload: TrainerPayload, hours: int) -> pd.DataFrame: + _start_train = time.perf_counter() + stream_config = ConfigManager.get_stream_config(payload.config_id) + + _df = pd.DataFrame() + if stream_config.source == DataSource.PROMETHEUS: + _df = cls.fetch_prometheus_data(payload, hours) + elif stream_config.source == DataSource.DRUID: + _df = cls.fetch_druid_data(payload, hours) + else: + _LOGGER.error( + "%s - Data source is not supported, source: %s, keys: %s", + payload.uuid, + stream_config.source, + payload.composite_keys, + ) + return _df + + _LOGGER.debug( + "%s - Time taken to fetch data from %s: %.3f sec, df shape: %s", + payload.uuid, + stream_config.source, + time.perf_counter() - _start_train, + _df.shape, + ) + return _df + + @classmethod + def _is_new_request( + cls, redis_client: redis_client_t, dedup_expiry: int, payload: TrainerPayload + ) -> bool: + _ckeys = ":".join(payload.composite_keys) + r_key = f"train::{_ckeys}" + value = redis_client.get(r_key) + if value: + return False + + redis_client.setex(r_key, time=dedup_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: + _LOGGER.debug( + "%s - Starting Training for keys: %s, metric: %s", + payload.uuid, + payload.composite_keys, + payload.metrics, + ) + + model_cfg = numalogic_conf.model + preproc_cfgs = numalogic_conf.preprocess + retrain_cfg = ConfigManager.get_retrain_config(payload.config_id) + + # TODO: filter the metrics here + + 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 + + # 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, ttl=retrain_cfg.model_expiry_sec) + 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, keys: List[str], datum: Datum) -> Messages: + _start_time = time.perf_counter() + messages = Messages() + redis_client = get_redis_client_from_conf() + payload = TrainerPayload(**orjson.loads(datum.value)) + + retrain_config = ConfigManager.get_retrain_config(payload.config_id) + numalogic_config = ConfigManager.get_numalogic_config(payload.config_id) + + is_new = self._is_new_request(redis_client, retrain_config.dedup_expiry_sec, payload) + + if not is_new: + messages.append(Message.to_drop()) + return messages + + try: + df = self.fetch_data(payload, retrain_config.train_hours) + except Exception as err: + _LOGGER.error( + "%s - Error while fetching data for keys: %s, metrics: %s, err: %r", + payload.uuid, + payload.composite_keys, + payload.metrics, + err, + ) + messages.append(Message.to_drop()) + return messages + + if len(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, + df.shape, + ) + messages.append(Message.to_drop()) + return messages + + train_df = get_feature_df(df, payload.metrics) + self._train_and_save(numalogic_config, payload, redis_client, train_df) + _LOGGER.debug( + "%s - Time taken in training: %.4f sec", payload.uuid, time.perf_counter() - _start_time + ) + messages.append(Message(keys=keys, value=payload.to_json())) + + return messages diff --git a/udf/anomaly-detection/src/watcher.py b/udf/anomaly-detection/src/watcher.py new file mode 100644 index 00000000..ccfd407d --- /dev/null +++ b/udf/anomaly-detection/src/watcher.py @@ -0,0 +1,170 @@ +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 PrometheusConf +from src._constants import CONFIG_DIR +from src import StreamConf, get_logger, UnifiedConf +from src.connectors._config import DruidConf + +_LOGGER = get_logger(__name__) + + +class ConfigManager: + config = {} + + @staticmethod + def load_configs(): + schema: Configs = OmegaConf.structured(Configs) + + user_configs = {} + if os.path.exists(os.path.join(CONFIG_DIR, "user-configs", "config.yaml")): + 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.config_id] = _config + + cls.config["default_configs"] = dict(map(lambda c: (c.config_id, 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_stream_config(cls, config_id: str) -> StreamConf: + if not cls.config: + cls.update_configs() + + stream_conf = None + + # search and load from user configs + if config_id in cls.config["user_configs"]: + stream_conf = cls.config["user_configs"][config_id] + + # if not search and load from default configs + if not stream_conf and config_id in cls.config["default_configs"]: + stream_conf = cls.config["default_configs"][config_id] + + # if not in default configs, initialize conf with default values + if not stream_conf: + stream_conf = OmegaConf.structured(StreamConf) + + # loading and setting default numalogic config + if OmegaConf.is_missing(stream_conf, "numalogic_conf"): + stream_conf.numalogic_conf = cls.config["default_numalogic"] + + return stream_conf + + @classmethod + def get_unified_config(cls, config_id: str) -> UnifiedConf: + stream_conf = cls.get_stream_config(config_id) + return stream_conf.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_id: str): + return cls.get_stream_config(config_id=config_id).numalogic_conf + + @classmethod + def get_preprocess_config(cls, config_id: str): + return cls.get_numalogic_config(config_id=config_id).preprocess + + @classmethod + def get_retrain_config( + cls, + config_id: str, + ): + return cls.get_stream_config(config_id=config_id).retrain_conf + + @classmethod + def get_static_threshold_config(cls, config_id: str): + return cls.get_stream_config(config_id=config_id).static_threshold + + @classmethod + def get_threshold_config(cls, config_id: str): + return cls.get_stream_config(config_id=config_id).numalogic_conf.threshold + + @classmethod + def get_postprocess_config(cls, config_id: str): + return cls.get_numalogic_config(config_id=config_id).postprocess + + @classmethod + def get_trainer_config(cls, config_id: str): + return cls.get_numalogic_config(config_id=config_id).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: + if os.path.exists(directory): + 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..3367da1b --- /dev/null +++ b/udf/anomaly-detection/starter.py @@ -0,0 +1,39 @@ +import sys +import threading + +import aiorun +from pynumaflow.function import Server, AsyncServer, MultiProcServer +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 == "multiproc_udf": + server = MultiProcServer(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..8b85c34e --- /dev/null +++ b/udf/anomaly-detection/tests/__init__.py @@ -0,0 +1,55 @@ +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 +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(TESTS_RESOURCES, "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, Trainer + + +__all__ = [ + "redis_client", + "Preprocess", + "Inference", + "Threshold", + "Postprocess", + "Trainer", + "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..f1f45e47 --- /dev/null +++ b/udf/anomaly-detection/tests/resources/configs/default_config.yaml @@ -0,0 +1,22 @@ +configs: + - config_id: "druid-config" + source: "druid" + composite_keys: [ "assetId" ] + metrics: [ "failed" , "degraded" ] + static_threshold: + weight: [ 0.7,0.3 ] + upper_limit: [ 3, 3 ] + druid_fetcher: + dimensions: [ "ciStatus" ] + datasource: "druid-metrics-table-name" + group_by: [ "timestamp", "ciStatus" ] + pivot: + columns: + - "ciStatus" + - config_id: "prometheus-config" + source: "prometheus" + composite_keys: [ "namespace", "name", "rollout_pod_template_hash" ] + metrics: [ "error_rate" , "error_count" ] + static_threshold: + weight: [ 0.7,0.3 ] + upper_limit: [ 3, 3 ] \ 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..7327204e --- /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: 2 + 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/pipeline_config.yaml b/udf/anomaly-detection/tests/resources/configs/pipeline_config.yaml new file mode 100644 index 00000000..5b9a4590 --- /dev/null +++ b/udf/anomaly-detection/tests/resources/configs/pipeline_config.yaml @@ -0,0 +1,8 @@ +redis_conf: + host: "isbsvc-redis-isbs-redis-svc.numalogic.svc" + port: 26379 + expiry: 360 + master_name: "mymaster" +druid_conf: + url: "http://localhost:8888" + endpoint: "druid/v2/" \ 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..73436933 --- /dev/null +++ b/udf/anomaly-detection/tests/resources/configs/users_config.yaml @@ -0,0 +1,24 @@ +configs: + - config_id: "app1-config" + source: "prometheus" + composite_keys: [ "namespace", "name", "rollout_pod_template_hash" ] + metrics: ["error_rate"] + static_threshold: + weight: [0.6] + upper_limit: [3] + - config_id: "app2-config" + source: "prometheus" + composite_keys: [ "namespace", "name", "rollout_pod_template_hash" ] + metrics: ["error_rate", "error_count"] + static_threshold: + weight: [0.7, 0.3] + upper_limit: [3, 3] + unified_config: + weights: [0.7, 0.3] + - config_id: "app2-config" + source: "prometheus" + metrics: ["namespace_app_rollouts_http_request_error_rate", "namespace_app_rollouts_http_request_latency"] + composite_keys: [ "namespace", "name", "app", "rollouts_pod_template_hash" ] + static_threshold: + weight: [0.6, 0.3] + upper_limit: [3,3 ] \ 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 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+1689636300000,113.0,339.0,490878.0 +1689636360000,86.0,246.0,421626.0 +1689636420000,,3.0,6846.0 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..3cf41d9d --- /dev/null +++ b/udf/anomaly-detection/tests/resources/data/stream.json @@ -0,0 +1 @@ +{"uuid":"dd7dfb43-532b-49a3-906e-f78f82ad9c4b","config_id":"druid-config","data":[{"degraded":14,"degraded_rate":0.003626943005181347,"error_rate":0.009067357512953367,"failed":21,"failed_rate":0.005440414507772021,"success":3825,"timestamp":1691622660000},{"degraded":10,"degraded_rate":0.002484472049689441,"error_rate":0.009937888198757764,"failed":30,"failed_rate":0.007453416149068323,"success":3985,"timestamp":1691622720000},{"degraded":9,"degraded_rate":0.0025906735751295338,"error_rate":0.005469199769717904,"failed":10,"failed_rate":0.0028785261945883708,"success":3455,"timestamp":1691622780000},{"degraded":13,"degraded_rate":0.003798947983635301,"error_rate":0.007597895967270602,"failed":13,"failed_rate":0.003798947983635301,"success":3396,"timestamp":1691622840000},{"degraded":11,"degraded_rate":0.0030328094844223876,"error_rate":0.005789909015715467,"failed":10,"failed_rate":0.0027570995312930797,"success":3606,"timestamp":1691622900000},{"degraded":17,"degraded_rate":0.004964953271028037,"error_rate":0.009345794392523364,"failed":15,"failed_rate":0.004380841121495327,"success":3392,"timestamp":1691622960000},{"degraded":10,"degraded_rate":0.002774694783573807,"error_rate":0.006104328523862375,"failed":12,"failed_rate":0.003329633740288568,"success":3582,"timestamp":1691623020000},{"degraded":12,"degraded_rate":0.003770028275212064,"error_rate":0.00942507068803016,"failed":18,"failed_rate":0.005655042412818096,"success":3153,"timestamp":1691623080000},{"degraded":14,"degraded_rate":0.003469640644361834,"error_rate":0.006195786864931847,"failed":11,"failed_rate":0.0027261462205700124,"success":4010,"timestamp":1691623140000},{"degraded":4,"degraded_rate":0.0010887316276537834,"error_rate":0.005715841045182362,"failed":17,"failed_rate":0.004627109417528579,"success":3653,"timestamp":1691623200000},{"degraded":13,"degraded_rate":0.0035278154681139757,"error_rate":0.009497964721845319,"failed":22,"failed_rate":0.005970149253731343,"success":3650,"timestamp":1691623260000},{"degraded":7,"degraded_rate":0.0019358407079646017,"error_rate":0.00663716814159292,"failed":17,"failed_rate":0.004701327433628319,"success":3592,"timestamp":1691623320000},{"degraded":18,"degraded_rate":0.004288777698355968,"error_rate":0.00976888253514415,"failed":23,"failed_rate":0.005480104836788182,"success":4156,"timestamp":1691623380000},{"degraded":15,"degraded_rate":0.00487012987012987,"error_rate":0.00974025974025974,"failed":15,"failed_rate":0.00487012987012987,"success":3050,"timestamp":1691623440000},{"degraded":9,"degraded_rate":0.0024563318777292577,"error_rate":0.006823144104803494,"failed":16,"failed_rate":0.004366812227074236,"success":3639,"timestamp":1691623500000},{"degraded":10,"degraded_rate":0.0031959092361776927,"error_rate":0.006391818472355385,"failed":10,"failed_rate":0.0031959092361776927,"success":3109,"timestamp":1691623560000},{"degraded":12,"degraded_rate":0.003827751196172249,"error_rate":0.004784688995215311,"failed":3,"failed_rate":0.0009569377990430622,"success":3120,"timestamp":1691623620000},{"degraded":21,"degraded_rate":0.007123473541383989,"error_rate":0.009158751696065129,"failed":6,"failed_rate":0.0020352781546811396,"success":2921,"timestamp":1691623680000},{"degraded":7,"degraded_rate":0.002027222704894295,"error_rate":0.0034752389226759338,"failed":5,"failed_rate":0.0014480162177816392,"success":3441,"timestamp":1691623740000},{"degraded":8,"degraded_rate":0.0022179096201829776,"error_rate":0.0049902966454117,"failed":10,"failed_rate":0.0027723870252287217,"success":3589,"timestamp":1691623800000}],"start_time":1691622660000,"end_time":1691623860000,"metadata":{"tags":{"asset_alias":"Intuit.smallbusiness.quickbooksmobile.qbmios","asset_id":"362557362191815079","env":"prd"}}} \ 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..9dfece74 --- /dev/null +++ b/udf/anomaly-detection/tests/resources/data/stream_nan.json @@ -0,0 +1,61 @@ +{ + "uuid": "0987654321", + "config_id": "druid-config", + "start_time": "1684472580000", + "end_time": "1684473300000", + "data": [ + { + "timestamp": "1684472580000", + "error_rate": "NaN", + "error_count": "NaN", + "failed": "NaN", + "degraded": "NaN" + }, + { + "timestamp": "1684472700000", + "error_rate": "70.46", + "error_count": "15", + "failed": "15", + "degraded": "NaN" + }, + { + "timestamp": "1684472820000", + "error_rate": "65.02", + "error_count": "12", + "failed": "NaN", + "degraded": "12" + }, + { + "timestamp": "1684472940000", + "error_rate": "16.75", + "error_count": "3", + "failed": "NaN", + "degraded": "3" + }, + { + "timestamp": "1684473060000", + "error_rate": "NaN", + "error_count": "NaN", + "failed": "NaN", + "degraded": "NaN" + }, + { + "timestamp": "1684473180000", + "error_rate": "13.49", + "error_count": "1", + "failed": "1", + "degraded": "NaN" + }, + { + "timestamp": "1684473240000", + "error_rate": "62.74", + "error_count": "11", + "failed": "5", + "degraded": "6" + } + ], + "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..bba9a0b4 --- /dev/null +++ b/udf/anomaly-detection/tests/test_druid.py @@ -0,0 +1,64 @@ +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._config import Pivot +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=["5984175597303660107"], + dimensions=["ciStatus"], + datasource="tech-ip-customer-interaction-metrics", + aggregations={"count": doublesum("count")}, + group_by=["timestamp", "ciStatus"], + hours=36, + pivot=Pivot( + index="timestamp", + columns=["ciStatus"], + value=["count"], + ), + ) + self.assertEqual(_out.shape, (2, 2)) + + +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..20d0d1e9 --- /dev/null +++ b/udf/anomaly-detection/tests/test_tools.py @@ -0,0 +1,37 @@ +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_id="druid-config") + postprocess_conf = ConfigManager().get_postprocess_config(config_id="druid-config") + + stream = np.random.uniform(low=1, high=2, size=(10, 1)) + winscorer = WindowScorer(static_threshold, postprocess_conf) + final_scores = winscorer.get_ensemble_score(stream) + for score in final_scores: + self.assertLess(score, 3.0) diff --git a/udf/anomaly-detection/tests/test_watcher.py b/udf/anomaly-detection/tests/test_watcher.py new file mode 100644 index 00000000..0a6a800f --- /dev/null +++ b/udf/anomaly-detection/tests/test_watcher.py @@ -0,0 +1,73 @@ +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 + stream_conf = self.cm.get_stream_config(config_id="app1-config") + self.assertTrue(stream_conf) + self.assertEqual(stream_conf.config_id, "app1-config") + + # from given default config + stream_conf = self.cm.get_stream_config(config_id="druid-config") + self.assertTrue(stream_conf) + self.assertEqual(stream_conf.config_id, "druid-config") + + # default config + stream_conf = self.cm.get_stream_config(config_id="random") + self.assertTrue(stream_conf) + self.assertEqual(stream_conf.config_id, "default") + + def test_get_unified_config(self): + # from given user config + unified_config = self.cm.get_unified_config(config_id="app1-config") + self.assertTrue(unified_config) + + # from given default config + unified_config = self.cm.get_unified_config(config_id="prometheus-config") + self.assertTrue(unified_config) + + # default config - will not have unified config + unified_config = self.cm.get_unified_config(config_id="random") + self.assertTrue(unified_config.strategy, "max") + + def test_get_datastream_config_time(self): + _start_time = time.perf_counter() + ConfigManager.get_stream_config(config_id="druid-config") + time1 = time.perf_counter() - _start_time + + _start_time = time.perf_counter() + ConfigManager.get_stream_config(config_id="druid-config") + time2 = time.perf_counter() - _start_time + _start_time = time.perf_counter() + self.assertTrue(time2 <= time1) + + def test_get_unified_config_time(self): + _start_time = time.perf_counter() + ConfigManager().get_unified_config(config_id="druid-config") + time1 = time.perf_counter() - _start_time + _start_time = time.perf_counter() + ConfigManager().get_unified_config(config_id="druid-config") + 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..bbc851f7 --- /dev/null +++ b/udf/anomaly-detection/tests/tools.py @@ -0,0 +1,233 @@ +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 +from src.watcher import ConfigManager +from tests import mock_configs + +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) + + +@patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) +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(2) 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=2, 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, n_features=2), + metadata={}, + extras={ + "creation_timestamp": 1653402941169, + "timestamp": 1653402941, + "current_stage": "Production", + "description": "", + "last_updated_timestamp": 1656615600000, + "name": "test::error", + "run_id": "a7c0b376530b40d7b23e6ce2081c899c", + "run_link": "", + "source": "registry", + "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 + + +def mock_druid_fetch_data(*_, **__): + df = pd.read_csv( + os.path.join(TESTS_DIR, "resources", "data", "druid.csv"), + index_col="timestamp", + parse_dates=["timestamp"], + infer_datetime_format=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..df6b9d28 --- /dev/null +++ b/udf/anomaly-detection/tests/udf/test_inference.py @@ -0,0 +1,106 @@ +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 src.watcher import ConfigManager +from tests import redis_client, Inference, mock_configs +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())) + @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) + 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) + self.assertEqual(payload.status, Status.INFERRED) + self.assertEqual(payload.header, Header.MODEL_INFERENCE) + self.assertGreater(payload.metadata["model_version"], 0) + + @freeze_time("2022-02-20 12:00:00") + @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) + @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) + self.assertEqual(payload.status, Status.RUNTIME_ERROR) + self.assertEqual(payload.header, Header.STATIC_INFERENCE) + self.assertEqual(payload.metadata["model_version"], -1) + + @patch.object(RedisRegistry, "load", Mock(return_value=None)) + @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) + 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) + self.assertEqual(payload.status, Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.header, Header.STATIC_INFERENCE) + self.assertEqual(payload.metadata["model_version"], -1) + + @freeze_time("2022-02-20 12:00:00") + @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) + @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) + self.assertEqual(payload.status, Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.header, Header.STATIC_INFERENCE) + self.assertEqual(payload.metadata["model_version"], -1) + + @patch.object(RedisRegistry, "load", Mock(return_value=return_stale_model())) + @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) + 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) + self.assertEqual(payload.status, Status.INFERRED) + self.assertEqual(payload.header, Header.MODEL_STALE) + self.assertGreater(payload.metadata["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..6ee5b43f --- /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] = ["1"] + + 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) + self.assertGreater(payload.metadata["model_version"], 0) + for metric, metric_data in payload.data.items(): + self.assertTrue(metric_data) + + 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) + self.assertGreater(payload.metadata["model_version"], 0) + for metric, metric_data in payload.data.items(): + self.assertTrue(metric_data) + + 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) + self.assertEqual(payload.metadata["model_version"], -1) + for metric, metric_data in payload.data.items(): + self.assertTrue(metric_data) + + +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..dde61c68 --- /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 +from src.watcher import ConfigManager + +# Make sure to import this in the end +from tests import redis_client, Preprocess, mock_configs +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] = ["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(2))) + @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) + 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) + self.assertEqual(payload.status, Status.PRE_PROCESSED) + self.assertEqual(payload.header, Header.MODEL_INFERENCE) + + @patch.object(RedisRegistry, "load", Mock(return_value=None)) + @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) + 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) + self.assertEqual(payload.status, Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.header, Header.STATIC_INFERENCE) + + @patch.object(RedisRegistry, "load", Mock(return_value=return_preproc_clf(2))) + @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) + 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) + self.assertEqual(payload.status, Status.PRE_PROCESSED) + self.assertEqual(payload.header, 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..21c2c903 --- /dev/null +++ b/udf/anomaly-detection/tests/udf/test_threshold.py @@ -0,0 +1,90 @@ +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) + self.assertEqual(payload.status, Status.THRESHOLD) + self.assertEqual(payload.header, Header.MODEL_INFERENCE) + self.assertGreater(payload.metadata["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) + self.assertEqual(payload.header, Header.MODEL_STALE) + self.assertEqual(payload.status, Status.THRESHOLD) + self.assertGreater(payload.metadata["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) + self.assertEqual(payload.header, Header.STATIC_INFERENCE) + self.assertEqual(payload.status, Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.metadata["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) + self.assertEqual(payload.header, Header.STATIC_INFERENCE) + self.assertEqual(payload.status, Status.ARTIFACT_NOT_FOUND) + self.assertEqual(payload.metadata["model_version"], -1) + + +if __name__ == "__main__": + unittest.main() diff --git a/udf/anomaly-detection/tests/udf/test_trainer.py b/udf/anomaly-detection/tests/udf/test_trainer.py new file mode 100644 index 00000000..a5b6dc33 --- /dev/null +++ b/udf/anomaly-detection/tests/udf/test_trainer.py @@ -0,0 +1,78 @@ +import json +import os +import unittest +from datetime import datetime +from unittest.mock import patch, Mock +from src.watcher import ConfigManager + +from pynumaflow.sink import Datum + +from src._constants import TESTS_DIR +from src.connectors.druid import DruidFetcher +from src.connectors.prometheus import Prometheus +from tests.tools import ( + mock_prom_query_metric, + mock_druid_fetch_data, +) +from tests import redis_client, Trainer, mock_configs + +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": "1", + "config_id": "prometheus-config", + "composite_keys": [ + "sandbox_numalogic_demo", + "metric_1", + "123456789", + ], + "metrics": ["metric_1"], + } + + train_payload2 = { + "uuid": "2", + "config_id": "druid-config", + "composite_keys": ["5984175597303660107"], + "metrics": ["failed", "degraded"], + } + + def setUp(self) -> None: + redis_client.flushall() + + @patch.object(Prometheus, "query_metric", Mock(return_value=mock_prom_query_metric())) + @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) + def test_prometheus_01(self): + _out = Trainer().run( + keys=[ + "sandbox_numalogic_demo", + "metric_1", + "123456789", + ], + datum=as_datum(self.train_payload), + ) + self.assertTrue(_out[0]) + + @patch.object(DruidFetcher, "fetch_data", Mock(return_value=mock_druid_fetch_data())) + @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) + def test_druid_01(self): + _out = Trainer().run(keys=["5984175597303660107"], datum=as_datum(self.train_payload2)) + print(_out) + self.assertTrue(_out[0]) + + +if __name__ == "__main__": + unittest.main()