"""
Behavioral tests for monitoring patterns.

These tests verify the logic of monitoring patterns like metrics collection,
health checks, alerting, and observability without making actual system calls.
"""

import time
from dataclasses import dataclass, field
from enum import Enum


class TestMetricsCollection:
    """Tests for metrics collection patterns."""

    def test_counter_increment(self):
        """Test counter metric incrementing."""

        class Counter:
            def __init__(self, name):
                self.name = name
                self.value = 0

            def inc(self, amount=1):
                self.value += amount

            def get(self):
                return self.value

        counter = Counter("requests_total")
        counter.inc()
        counter.inc(5)
        assert counter.get() == 6

    def test_gauge_set_value(self):
        """Test gauge metric setting."""

        class Gauge:
            def __init__(self, name):
                self.name = name
                self.value = 0

            def set(self, value):
                self.value = value

            def inc(self, amount=1):
                self.value += amount

            def dec(self, amount=1):
                self.value -= amount

            def get(self):
                return self.value

        gauge = Gauge("active_connections")
        gauge.set(10)
        assert gauge.get() == 10
        gauge.inc(5)
        assert gauge.get() == 15
        gauge.dec(3)
        assert gauge.get() == 12

    def test_histogram_record(self):
        """Test histogram metric recording."""

        class Histogram:
            def __init__(self, name, buckets):
                self.name = name
                self.buckets = sorted(buckets)
                self.bucket_counts = {b: 0 for b in self.buckets}
                self.bucket_counts[float("inf")] = 0
                self.sum = 0
                self.count = 0

            def observe(self, value):
                self.sum += value
                self.count += 1
                for bucket in self.buckets:
                    if value <= bucket:
                        self.bucket_counts[bucket] += 1
                self.bucket_counts[float("inf")] += 1

            def get_percentile(self, p):
                # Simplified percentile calculation
                if self.count == 0:
                    return 0
                return self.sum / self.count  # Return mean for simplicity

        histogram = Histogram("request_duration", [0.1, 0.5, 1.0, 5.0])
        histogram.observe(0.05)
        histogram.observe(0.3)
        histogram.observe(0.8)
        assert histogram.count == 3
        assert histogram.bucket_counts[0.1] == 1
        assert histogram.bucket_counts[0.5] == 2
        assert histogram.bucket_counts[1.0] == 3

    def test_summary_quantiles(self):
        """Test summary metric quantiles."""

        class Summary:
            def __init__(self, name):
                self.name = name
                self.values = []

            def observe(self, value):
                self.values.append(value)

            def quantile(self, q):
                if not self.values:
                    return 0
                sorted_values = sorted(self.values)
                index = int(q * (len(sorted_values) - 1))
                return sorted_values[index]

        summary = Summary("response_size")
        for i in range(100):
            summary.observe(i)
        assert summary.quantile(0.5) == 49  # Median
        assert summary.quantile(0.9) == 89  # 90th percentile

    def test_metric_labels(self):
        """Test metrics with labels."""

        class LabeledCounter:
            def __init__(self, name, label_names):
                self.name = name
                self.label_names = label_names
                self.values = {}

            def labels(self, **kwargs):
                key = tuple(sorted(kwargs.items()))
                if key not in self.values:
                    self.values[key] = 0
                return key

            def inc(self, label_key, amount=1):
                self.values[label_key] += amount

            def get(self, label_key):
                return self.values.get(label_key, 0)

        counter = LabeledCounter("requests", ["method", "endpoint"])
        key1 = counter.labels(method="GET", endpoint="/api/users")
        key2 = counter.labels(method="POST", endpoint="/api/users")

        counter.inc(key1, 5)
        counter.inc(key2, 3)

        assert counter.get(key1) == 5
        assert counter.get(key2) == 3


class TestHealthChecks:
    """Tests for health check patterns."""

    def test_basic_health_check(self):
        """Test basic health check."""

        def health_check(services):
            status = {"healthy": True, "services": {}}
            for service, check_fn in services.items():
                try:
                    is_healthy = check_fn()
                    status["services"][service] = {
                        "status": "healthy" if is_healthy else "unhealthy"
                    }
                    if not is_healthy:
                        status["healthy"] = False
                except Exception as e:
                    status["services"][service] = {
                        "status": "unhealthy",
                        "error": str(e),
                    }
                    status["healthy"] = False
            return status

        services = {
            "database": lambda: True,
            "cache": lambda: True,
        }
        result = health_check(services)
        assert result["healthy"] is True

        services["cache"] = lambda: False
        result = health_check(services)
        assert result["healthy"] is False

    def test_dependency_health_check(self):
        """Test dependency health check with timeout."""

        def check_dependency(check_fn, timeout_seconds=5):
            start = time.time()
            try:
                result = check_fn()
                duration = time.time() - start
                return {
                    "healthy": result,
                    "latency_ms": int(duration * 1000),
                }
            except Exception as e:
                return {"healthy": False, "error": str(e)}

        result = check_dependency(lambda: True)
        assert result["healthy"] is True
        assert "latency_ms" in result

    def test_readiness_vs_liveness(self):
        """Test readiness vs liveness probes."""

        class HealthProbes:
            def __init__(self):
                self.ready = False
                self.alive = True

            def liveness(self):
                return {"status": "alive" if self.alive else "dead"}

            def readiness(self):
                return {"status": "ready" if self.ready else "not_ready"}

        probes = HealthProbes()
        assert probes.liveness()["status"] == "alive"
        assert probes.readiness()["status"] == "not_ready"

        probes.ready = True
        assert probes.readiness()["status"] == "ready"

    def test_graceful_degradation(self):
        """Test graceful degradation health status."""

        def aggregate_health(checks):
            all_healthy = all(c["healthy"] for c in checks.values())
            any_healthy = any(c["healthy"] for c in checks.values())

            if all_healthy:
                return "healthy"
            if any_healthy:
                return "degraded"
            return "unhealthy"

        checks = {"db": {"healthy": True}, "cache": {"healthy": True}}
        assert aggregate_health(checks) == "healthy"

        checks["cache"]["healthy"] = False
        assert aggregate_health(checks) == "degraded"

        checks["db"]["healthy"] = False
        assert aggregate_health(checks) == "unhealthy"

    def test_health_check_caching(self):
        """Test caching health check results."""

        class CachedHealthCheck:
            def __init__(self, check_fn, cache_seconds=30):
                self.check_fn = check_fn
                self.cache_seconds = cache_seconds
                self.cached_result = None
                self.cached_at = 0

            def check(self):
                now = time.time()
                if (
                    self.cached_result
                    and (now - self.cached_at) < self.cache_seconds
                ):
                    return self.cached_result
                self.cached_result = self.check_fn()
                self.cached_at = now
                return self.cached_result

        call_count = {"count": 0}

        def expensive_check():
            call_count["count"] += 1
            return True

        cached = CachedHealthCheck(expensive_check, cache_seconds=60)
        cached.check()
        cached.check()
        cached.check()
        assert call_count["count"] == 1  # Only called once due to cache


class TestAlerting:
    """Tests for alerting patterns."""

    def test_threshold_alert(self):
        """Test threshold-based alerting."""

        def check_threshold(value, warning_threshold, critical_threshold):
            if value >= critical_threshold:
                return "critical"
            if value >= warning_threshold:
                return "warning"
            return "ok"

        assert check_threshold(50, 70, 90) == "ok"
        assert check_threshold(75, 70, 90) == "warning"
        assert check_threshold(95, 70, 90) == "critical"

    def test_alert_deduplication(self):
        """Test alert deduplication."""

        class AlertDeduplicator:
            def __init__(self, window_seconds=300):
                self.active_alerts = {}
                self.window_seconds = window_seconds

            def should_send(self, alert_key):
                now = time.time()
                if alert_key in self.active_alerts:
                    last_sent = self.active_alerts[alert_key]
                    if now - last_sent < self.window_seconds:
                        return False
                self.active_alerts[alert_key] = now
                return True

        dedup = AlertDeduplicator(window_seconds=300)
        assert dedup.should_send("cpu_high") is True
        assert dedup.should_send("cpu_high") is False  # Too soon
        assert dedup.should_send("memory_high") is True  # Different alert

    def test_alert_grouping(self):
        """Test grouping related alerts."""

        def group_alerts(alerts, group_by="service"):
            groups = {}
            for alert in alerts:
                key = alert.get(group_by, "unknown")
                if key not in groups:
                    groups[key] = []
                groups[key].append(alert)
            return groups

        alerts = [
            {"service": "api", "message": "High CPU"},
            {"service": "api", "message": "High memory"},
            {"service": "db", "message": "Slow queries"},
        ]
        groups = group_alerts(alerts, "service")
        assert len(groups["api"]) == 2
        assert len(groups["db"]) == 1

    def test_alert_severity(self):
        """Test alert severity classification."""

        class AlertSeverity(Enum):
            INFO = 1
            WARNING = 2
            ERROR = 3
            CRITICAL = 4

        def classify_alert(alert):
            if alert.get("error_rate", 0) > 50:
                return AlertSeverity.CRITICAL
            if alert.get("error_rate", 0) > 20:
                return AlertSeverity.ERROR
            if alert.get("error_rate", 0) > 5:
                return AlertSeverity.WARNING
            return AlertSeverity.INFO

        assert classify_alert({"error_rate": 60}) == AlertSeverity.CRITICAL
        assert classify_alert({"error_rate": 30}) == AlertSeverity.ERROR
        assert classify_alert({"error_rate": 10}) == AlertSeverity.WARNING
        assert classify_alert({"error_rate": 1}) == AlertSeverity.INFO

    def test_alert_routing(self):
        """Test routing alerts to different channels."""

        def route_alert(alert, routing_rules):
            channels = []
            for rule in routing_rules:
                if rule["match"](alert):
                    channels.extend(rule["channels"])
            return list(set(channels))  # Deduplicate

        rules = [
            {
                "match": lambda a: a.get("severity") == "critical",
                "channels": ["pager", "slack"],
            },
            {
                "match": lambda a: a.get("severity") == "warning",
                "channels": ["slack"],
            },
            {
                "match": lambda a: a.get("service") == "payments",
                "channels": ["pager"],
            },
        ]

        alert1 = {"severity": "critical", "service": "api"}
        assert "pager" in route_alert(alert1, rules)
        assert "slack" in route_alert(alert1, rules)

        alert2 = {"severity": "warning", "service": "payments"}
        channels = route_alert(alert2, rules)
        assert "slack" in channels
        assert "pager" in channels


class TestTracing:
    """Tests for distributed tracing patterns."""

    def test_generate_trace_id(self):
        """Test trace ID generation."""
        import secrets

        def generate_trace_id():
            return secrets.token_hex(16)

        trace_id = generate_trace_id()
        assert len(trace_id) == 32
        assert all(c in "0123456789abcdef" for c in trace_id)

    def test_span_context(self):
        """Test span context management."""

        @dataclass
        class SpanContext:
            trace_id: str
            span_id: str
            parent_span_id: str = None

        def create_child_span(parent_context):
            import secrets

            return SpanContext(
                trace_id=parent_context.trace_id,
                span_id=secrets.token_hex(8),
                parent_span_id=parent_context.span_id,
            )

        parent = SpanContext(trace_id="abc123", span_id="def456")
        child = create_child_span(parent)
        assert child.trace_id == parent.trace_id
        assert child.parent_span_id == parent.span_id
        assert child.span_id != parent.span_id

    def test_span_timing(self):
        """Test span timing."""

        @dataclass
        class Span:
            name: str
            start_time: float = field(default_factory=time.time)
            end_time: float = None

            def finish(self):
                self.end_time = time.time()

            @property
            def duration_ms(self):
                if self.end_time is None:
                    return None
                return (self.end_time - self.start_time) * 1000

        span = Span(name="db_query")
        span.finish()
        assert span.duration_ms is not None
        assert span.duration_ms >= 0

    def test_span_tags(self):
        """Test span tagging."""

        @dataclass
        class Span:
            name: str
            tags: dict = field(default_factory=dict)

            def set_tag(self, key, value):
                self.tags[key] = value
                return self

        span = Span(name="http_request")
        span.set_tag("http.method", "GET").set_tag("http.status", 200)
        assert span.tags["http.method"] == "GET"
        assert span.tags["http.status"] == 200

    def test_trace_context_propagation(self):
        """Test trace context propagation headers."""

        def inject_context(context, headers):
            headers["traceparent"] = (
                f"00-{context['trace_id']}-{context['span_id']}-01"
            )
            return headers

        def extract_context(headers):
            traceparent = headers.get("traceparent", "")
            if traceparent:
                parts = traceparent.split("-")
                if len(parts) == 4:
                    return {"trace_id": parts[1], "span_id": parts[2]}
            return None

        context = {"trace_id": "abc123def456", "span_id": "789abc"}
        headers = inject_context(context, {})
        extracted = extract_context(headers)
        assert extracted["trace_id"] == "abc123def456"


class TestRateCalculation:
    """Tests for rate and throughput calculation."""

    def test_calculate_rate(self):
        """Test calculating rate per second."""

        def calculate_rate(count, duration_seconds):
            if duration_seconds == 0:
                return 0
            return count / duration_seconds

        assert calculate_rate(100, 10) == 10  # 10 per second
        assert calculate_rate(0, 10) == 0
        assert calculate_rate(100, 0) == 0

    def test_moving_average(self):
        """Test moving average calculation."""

        class MovingAverage:
            def __init__(self, window_size):
                self.window_size = window_size
                self.values = []

            def add(self, value):
                self.values.append(value)
                if len(self.values) > self.window_size:
                    self.values.pop(0)

            def average(self):
                if not self.values:
                    return 0
                return sum(self.values) / len(self.values)

        ma = MovingAverage(3)
        ma.add(10)
        ma.add(20)
        ma.add(30)
        assert ma.average() == 20

        ma.add(40)
        assert ma.average() == 30  # (20 + 30 + 40) / 3

    def test_exponential_moving_average(self):
        """Test exponential moving average."""

        class ExponentialMovingAverage:
            def __init__(self, alpha=0.1):
                self.alpha = alpha
                self.value = None

            def add(self, value):
                if self.value is None:
                    self.value = value
                else:
                    self.value = (
                        self.alpha * value + (1 - self.alpha) * self.value
                    )

            def get(self):
                return self.value

        ema = ExponentialMovingAverage(alpha=0.5)
        ema.add(100)
        assert ema.get() == 100

        ema.add(200)
        assert ema.get() == 150  # 0.5 * 200 + 0.5 * 100

    def test_percentile_calculation(self):
        """Test percentile calculation."""

        def calculate_percentile(values, percentile):
            if not values:
                return 0
            sorted_values = sorted(values)
            index = int((percentile / 100) * (len(sorted_values) - 1))
            return sorted_values[index]

        values = list(range(1, 101))  # 1 to 100
        assert calculate_percentile(values, 50) == 50
        assert calculate_percentile(values, 90) == 90
        assert calculate_percentile(values, 99) == 99

    def test_error_rate_calculation(self):
        """Test error rate calculation."""

        def calculate_error_rate(error_count, total_count):
            if total_count == 0:
                return 0
            return (error_count / total_count) * 100

        assert calculate_error_rate(5, 100) == 5.0
        assert calculate_error_rate(0, 100) == 0.0
        assert calculate_error_rate(10, 0) == 0


class TestResourceMonitoring:
    """Tests for resource monitoring patterns."""

    def test_memory_usage_calculation(self):
        """Test memory usage calculation."""

        def calculate_memory_percent(used_bytes, total_bytes):
            if total_bytes == 0:
                return 0
            return (used_bytes / total_bytes) * 100

        assert calculate_memory_percent(4 * 1024**3, 16 * 1024**3) == 25.0
        assert calculate_memory_percent(8 * 1024**3, 16 * 1024**3) == 50.0

    def test_cpu_usage_average(self):
        """Test CPU usage averaging."""

        def average_cpu_usage(cpu_percents):
            if not cpu_percents:
                return 0
            return sum(cpu_percents) / len(cpu_percents)

        # 4 cores at different utilizations
        cpu_percents = [80, 60, 40, 20]
        assert average_cpu_usage(cpu_percents) == 50

    def test_disk_space_check(self):
        """Test disk space check."""

        def check_disk_space(
            used_bytes, total_bytes, warning_threshold=80, critical_threshold=90
        ):
            if total_bytes == 0:
                return "unknown"
            percent_used = (used_bytes / total_bytes) * 100
            if percent_used >= critical_threshold:
                return "critical"
            if percent_used >= warning_threshold:
                return "warning"
            return "ok"

        total = 100 * 1024**3  # 100 GB
        assert check_disk_space(50 * 1024**3, total) == "ok"
        assert check_disk_space(85 * 1024**3, total) == "warning"
        assert check_disk_space(95 * 1024**3, total) == "critical"

    def test_connection_pool_utilization(self):
        """Test connection pool utilization."""

        def pool_utilization(active_connections, max_connections):
            if max_connections == 0:
                return 0
            return (active_connections / max_connections) * 100

        assert pool_utilization(5, 20) == 25.0
        assert pool_utilization(18, 20) == 90.0

    def test_queue_depth_monitoring(self):
        """Test queue depth monitoring."""

        def analyze_queue(current_depth, max_depth, processing_rate):
            utilization = (
                (current_depth / max_depth) * 100 if max_depth > 0 else 0
            )
            estimated_drain_time = (
                current_depth / processing_rate
                if processing_rate > 0
                else float("inf")
            )
            return {
                "depth": current_depth,
                "utilization_percent": utilization,
                "estimated_drain_seconds": estimated_drain_time,
            }

        result = analyze_queue(100, 1000, 10)
        assert result["utilization_percent"] == 10.0
        assert result["estimated_drain_seconds"] == 10.0


class TestSLOCalculation:
    """Tests for SLO/SLA calculation patterns."""

    def test_availability_calculation(self):
        """Test availability calculation."""

        def calculate_availability(uptime_seconds, total_seconds):
            if total_seconds == 0:
                return 100.0
            return (uptime_seconds / total_seconds) * 100

        # 30 days in seconds
        total = 30 * 24 * 3600
        # 99.9% uptime means ~43 minutes downtime
        downtime = 43 * 60
        uptime = total - downtime
        availability = calculate_availability(uptime, total)
        assert 99.9 <= availability <= 100

    def test_error_budget_calculation(self):
        """Test error budget calculation."""

        def calculate_error_budget(
            slo_target, current_availability, time_period_seconds
        ):
            error_budget_percent = 100 - slo_target
            max_downtime_seconds = (
                error_budget_percent / 100
            ) * time_period_seconds
            current_error_percent = 100 - current_availability
            used_budget_percent = (
                current_error_percent / error_budget_percent
            ) * 100
            remaining_budget_seconds = max_downtime_seconds * (
                1 - used_budget_percent / 100
            )
            return {
                "max_downtime_seconds": max_downtime_seconds,
                "remaining_seconds": max(0, remaining_budget_seconds),
                "used_percent": used_budget_percent,
            }

        # 30 days, 99.9% SLO
        budget = calculate_error_budget(99.9, 99.95, 30 * 24 * 3600)
        assert budget["used_percent"] == 50.0  # Used half the budget

    def test_latency_slo(self):
        """Test latency SLO calculation."""

        def check_latency_slo(latencies, target_percentile, target_latency_ms):
            if not latencies:
                return {"compliant": True, "actual_percentile_ms": 0}
            sorted_latencies = sorted(latencies)
            index = int((target_percentile / 100) * (len(sorted_latencies) - 1))
            actual = sorted_latencies[index]
            return {
                "compliant": actual <= target_latency_ms,
                "actual_percentile_ms": actual,
            }

        latencies = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
        # P90 should be <= 100ms
        result = check_latency_slo(latencies, 90, 100)
        assert result["compliant"] is True

        # P90 should be <= 50ms (will fail)
        result = check_latency_slo(latencies, 90, 50)
        assert result["compliant"] is False

    def test_throughput_slo(self):
        """Test throughput SLO calculation."""

        def check_throughput_slo(requests_per_second, min_throughput):
            return {
                "compliant": requests_per_second >= min_throughput,
                "current_rps": requests_per_second,
                "min_required_rps": min_throughput,
            }

        result = check_throughput_slo(150, 100)
        assert result["compliant"] is True

        result = check_throughput_slo(80, 100)
        assert result["compliant"] is False

    def test_composite_slo(self):
        """Test composite SLO from multiple metrics."""

        def check_composite_slo(metrics, slo_definitions):
            results = {}
            all_compliant = True
            for name, definition in slo_definitions.items():
                value = metrics.get(name, 0)
                target = definition["target"]
                operator = definition.get("operator", ">=")
                if operator == ">=":
                    compliant = value >= target
                elif operator == "<=":
                    compliant = value <= target
                else:
                    compliant = value == target
                results[name] = {
                    "compliant": compliant,
                    "value": value,
                    "target": target,
                }
                if not compliant:
                    all_compliant = False
            return {"overall_compliant": all_compliant, "details": results}

        metrics = {
            "availability": 99.95,
            "p99_latency_ms": 150,
            "error_rate": 0.5,
        }
        slos = {
            "availability": {"target": 99.9, "operator": ">="},
            "p99_latency_ms": {"target": 200, "operator": "<="},
            "error_rate": {"target": 1.0, "operator": "<="},
        }
        result = check_composite_slo(metrics, slos)
        assert result["overall_compliant"] is True


class TestAnomalyDetection:
    """Tests for anomaly detection patterns."""

    def test_z_score_anomaly(self):
        """Test Z-score based anomaly detection."""

        def calculate_z_score(value, mean, std_dev):
            if std_dev == 0:
                return 0
            return (value - mean) / std_dev

        def is_anomaly(value, mean, std_dev, threshold=3):
            z_score = calculate_z_score(value, mean, std_dev)
            return abs(z_score) > threshold

        # Normal values
        assert is_anomaly(100, 100, 10) is False  # Z=0
        assert is_anomaly(120, 100, 10) is False  # Z=2
        # Anomaly
        assert is_anomaly(150, 100, 10) is True  # Z=5

    def test_threshold_anomaly(self):
        """Test threshold-based anomaly detection."""

        def detect_threshold_anomaly(value, upper_bound=None, lower_bound=None):
            if upper_bound is not None and value > upper_bound:
                return {"is_anomaly": True, "type": "above_upper_bound"}
            if lower_bound is not None and value < lower_bound:
                return {"is_anomaly": True, "type": "below_lower_bound"}
            return {"is_anomaly": False}

        assert (
            detect_threshold_anomaly(150, upper_bound=100)["is_anomaly"] is True
        )
        assert (
            detect_threshold_anomaly(50, lower_bound=100)["is_anomaly"] is True
        )
        assert (
            detect_threshold_anomaly(75, upper_bound=100, lower_bound=50)[
                "is_anomaly"
            ]
            is False
        )

    def test_rate_of_change_anomaly(self):
        """Test rate of change anomaly detection."""

        def detect_rate_anomaly(current, previous, max_change_percent=50):
            if previous == 0:
                return current > 0
            change_percent = abs((current - previous) / previous) * 100
            return change_percent > max_change_percent

        assert (
            detect_rate_anomaly(150, 100) is False
        )  # 50% change - at threshold
        assert detect_rate_anomaly(200, 100) is True  # 100% change - anomaly
        assert detect_rate_anomaly(100, 100) is False  # No change

    def test_trend_detection(self):
        """Test trend detection."""

        def detect_trend(values, min_consecutive=3):
            if len(values) < min_consecutive:
                return "insufficient_data"
            increasing = 0
            decreasing = 0
            for i in range(1, len(values)):
                if values[i] > values[i - 1]:
                    increasing += 1
                elif values[i] < values[i - 1]:
                    decreasing += 1
            if increasing >= min_consecutive:
                return "increasing"
            if decreasing >= min_consecutive:
                return "decreasing"
            return "stable"

        assert detect_trend([1, 2, 3, 4, 5]) == "increasing"
        assert detect_trend([5, 4, 3, 2, 1]) == "decreasing"
        assert detect_trend([1, 2, 1, 2, 1]) == "stable"

    def test_seasonal_anomaly(self):
        """Test seasonal pattern anomaly detection."""

        def detect_seasonal_anomaly(
            value, expected_range, tolerance_percent=20
        ):
            expected_min, expected_max = expected_range
            tolerance = (expected_max - expected_min) * (
                tolerance_percent / 100
            )
            adjusted_min = expected_min - tolerance
            adjusted_max = expected_max + tolerance
            return value < adjusted_min or value > adjusted_max

        # Expected traffic between 1000-2000 for this hour
        expected = (1000, 2000)
        assert detect_seasonal_anomaly(1500, expected) is False  # Normal
        assert detect_seasonal_anomaly(500, expected) is True  # Too low
        assert detect_seasonal_anomaly(3000, expected) is True  # Too high
