cleanup: consolidate TLS_HEX_MAP, extract constants, remove dead code, add traceback logging

3.1 Unified TLS_HEX_MAP: defined once in type_classifier.py, imported in globe.py and clustering.py
3.2 Created analysis/constants.py: centralized RANDOM_SEED, MAX_SAMPLE_ROWS, UMAP constants, GLOBE_MAX_ROWS, etc.
3.3 Removed 14 pointless try/except:raise dead code blocks across tools/ and views/
3.4 Added logger.error(traceback) to all bare except Exception: blocks in tools/ and views/
3.5 Moved inline import traceback/asyncio to top-level imports in features.py, clustering.py, pipeline.py
3.6 Removed auto_profile_module side-effect imports from 6 modules

analysis/constants.py

@@ -0,0 +1,96 @@
+"""Centralized constants for the TianXuan analysis module.
+
+All magic numbers from analysis/tools/ and analysis/views/ are defined here
+so they can be updated in one place. Import with::
+
+    from analysis.constants import RANDOM_SEED, MAX_SAMPLE_ROWS, ...
+"""
+
+import polars as pl
+
+# ---------------------------------------------------------------------------
+#  Clustering / UMAP
+# ---------------------------------------------------------------------------
+
+MAX_SAMPLE_ROWS = 50000
+"""Maximum rows for clustering sample (downsampling threshold)."""
+
+MAX_DISTRIBUTION_SAMPLE = 10000
+"""Default sample size for distribution exploration and scoring."""
+
+UMAP_TRAIN_SAMPLE = 10000
+"""Max rows used to train a UMAP reducer in extract_features."""
+
+UMAP_BATCH_SIZE = 1000
+"""Batch size for UMAP transform when dataset exceeds UMAP_TRAIN_SAMPLE."""
+
+LOW_MEMORY_THRESHOLD_GB = 2
+"""If available memory < this (in GiB), enable low-memory clustering path."""
+
+LOW_MEMORY_THRESHOLD_ROWS = 100000
+"""Row count above which low-memory downsampling is triggered."""
+
+RANDOM_SEED = 42
+"""Default random seed for all sklearn/numpy operations."""
+
+# ---------------------------------------------------------------------------
+#  Globe
+# ---------------------------------------------------------------------------
+
+GLOBE_MAX_ROWS = 300
+"""Max rows to load per run for 3D globe visualization."""
+
+# ---------------------------------------------------------------------------
+#  Filter
+# ---------------------------------------------------------------------------
+
+FILTER_MAX_ROWS = 5
+"""Maximum visible filter rows in the manual analysis UI (deprecated / reserved)."""
+
+MAX_COLUMNS_PROFILE = 200
+"""Max columns to include in a dataset profile summary."""
+
+# ---------------------------------------------------------------------------
+#  File upload limits
+# ---------------------------------------------------------------------------
+
+FILE_UPLOAD_MAX_SIZE = 500 * 1024 * 1024  # 500 MB
+"""Maximum size for a single uploaded file."""
+
+TOTAL_UPLOAD_MAX_SIZE = 50 * 1024 * 1024 * 1024  # 50 GB
+"""Maximum total upload size across all files."""
+
+# ---------------------------------------------------------------------------
+#  Tool limits (numeric type tuples used across tools/)
+# ---------------------------------------------------------------------------
+
+NUMERIC_DTYPES: tuple[type[pl.DataType], ...] = (
+    pl.Int8, pl.Int16, pl.Int32, pl.Int64,
+    pl.UInt8, pl.UInt16, pl.UInt32, pl.UInt64,
+    pl.Float32, pl.Float64,
+)
+"""Polars numeric data types — used for column selection in clustering,
+entity aggregation, anomaly detection, and feature extraction."""
+
+# ---------------------------------------------------------------------------
+#  Diagnostics / Validation
+# ---------------------------------------------------------------------------
+
+MAX_VALIDATE_SAMPLE = 1000
+"""Max rows sampled during validate_data."""
+
+MAX_DIAGNOSE_SAMPLE = 5000
+"""Max rows sampled for clustering diagnosis (SVD)."""
+
+MAX_CLUSTER_FEATURES = 50
+"""Feature count threshold above which TruncatedSVD is applied."""
+
+CLUSTER_MAX_FEATURES_TOP = 10
+"""Max feature columns selected by auto-detection in clustering tools."""
+
+# ---------------------------------------------------------------------------
+#  Globe / Flow extraction
+# ---------------------------------------------------------------------------
+
+GLOBE_FLOW_MAX_ROWS = 300
+"""Alias for GLOBE_MAX_ROWS — max rows loaded for flow extraction."""

analysis/data_loader.py

@@ -771,8 +771,3 @@ def load_csv_directory(
         total_row_count = min(total_row_count, head)
 
     return merged_lf, merged_schema, total_row_count, len(paths), memory_mb
-
-
-# Auto-profile all public functions in this module
-from analysis.profile_util import auto_profile_module  # noqa: E402
-auto_profile_module(__name__)

analysis/data_profiler.py

@@ -331,8 +331,3 @@ def profile_dataset(
     }
 
     return truncate_profile(result, max_kb=max_kb)
-
-
-# Auto-profile all public functions in this module
-from analysis.profile_util import auto_profile_module  # noqa: E402
-auto_profile_module(__name__)

analysis/nl_describe.py

@@ -400,8 +400,3 @@ def describe_cluster(
 def _signed(value: float) -> str:
     """Return a signed string representation, e.g. ``"+2.1"``."""
     return f"{value:+.1f}"
-
-
-# Auto-profile all public functions in this module
-from analysis.profile_util import auto_profile_module  # noqa: E402
-auto_profile_module(__name__)

analysis/session_store.py

@@ -481,8 +481,3 @@ class SessionStore:
                 types[t] = types.get(t, 0) + 1
         parts = [f"{k}={v}" for k, v in sorted(types.items())]
         return f"<SessionStore {'; '.join(parts)}>"
-
-
-# Auto-profile all public functions in this module
-from analysis.profile_util import auto_profile_module  # noqa: E402
-auto_profile_module(__name__)

analysis/tools/__init__.py

@@ -60,7 +60,3 @@ from .data_mgmt import (
     _handle_clone_dataset,
 )
 from .distance_matrix import _handle_compute_distance_matrix
-
-# Auto-profile all public functions in this module
-from analysis.profile_util import auto_profile_module  # noqa: E402
-auto_profile_module(__name__)

analysis/tools/_helpers.py

@@ -9,11 +9,14 @@ Provides:
 - _get_numeric_columns: extract numeric column names from schema
 """
 import json
+import logging
+import traceback
 from typing import Any
 
 import polars as pl
 
 from ..session_store import SessionStore
+logger = logging.getLogger(__name__)
 
 
 # ═══════════════════════════════════════════════════════════════════════
@@ -116,6 +119,7 @@ def _count_rows(lf: pl.LazyFrame) -> int:
     try:
         return lf.select(pl.len()).collect(streaming=True).item()
     except Exception:
+        logger.error("_count_rows failed: {}".format(traceback.format_exc()))
         return 0
 
 

analysis/tools/analysis.py

@@ -1,9 +1,13 @@
 """Read-only analysis handlers — patterns, temporal, FFT, TLS health, geo, entity detail."""
+import traceback
+from analysis.constants import LOW_MEMORY_THRESHOLD_ROWS
 import numpy as np
 import polars as pl
 
 from ..session_store import SessionStore
 from ._helpers import _resolve_dataset, _count_rows, NUMERIC_TYPE_NAMES
+import logging
+logger = logging.getLogger(__name__)
 
 
 async def _handle_analyze_patterns(dataset_id: str, top_n: int = 10) -> dict:
@@ -14,9 +18,9 @@ async def _handle_analyze_patterns(dataset_id: str, top_n: int = 10) -> dict:
     lf = entry['lazyframe']
     schema = entry.get('schema', {})
     n = _count_rows(lf)
-    df = lf.head(100000).collect(streaming=True)
+    df = lf.head(LOW_MEMORY_THRESHOLD_ROWS).collect(streaming=True)
 
-    result = {'total_rows': n, 'sampled': min(n, 100000)}
+    result = {'total_rows': n, 'sampled': min(n, LOW_MEMORY_THRESHOLD_ROWS)}
 
     for col_name, label in [(':ips', 'src_ips'), (':ipd', 'dst_ips'),
                               ('snam', 'snis'), ('cnam', 'cert_names')]:
@@ -29,6 +33,7 @@ async def _handle_analyze_patterns(dataset_id: str, top_n: int = 10) -> dict:
                     'unique': int(df[col_name].n_unique()),
                 }
             except Exception:
+                logger.error("_handle_analyze_patterns failed: {}".format(traceback.format_exc()))
                 pass
 
     for port_col in [':prs', ':prd']:
@@ -41,6 +46,7 @@ async def _handle_analyze_patterns(dataset_id: str, top_n: int = 10) -> dict:
                     'unique': int(df[port_col].n_unique()),
                 }
             except Exception:
+                logger.error("unknown failed: {}".format(traceback.format_exc()))
                 pass
 
     for tls_col in ['0ver']:
@@ -50,6 +56,7 @@ async def _handle_analyze_patterns(dataset_id: str, top_n: int = 10) -> dict:
                 result['tls_versions'] = [{'version': str(r[tls_col]), 'count': int(r['count'])}
                                           for r in vc.iter_rows(named=True)]
             except Exception:
+                logger.error("unknown failed: {}".format(traceback.format_exc()))
                 pass
 
     return result
@@ -116,6 +123,7 @@ async def _handle_analyze_fft(
             ts_parsed = ts_series.str.strptime(pl.Datetime, format=None, strict=False)
             ts_numeric = ts_parsed.cast(pl.Int64) / 1_000_000_000.0
         except Exception:
+            logger.error("unknown failed: {}".format(traceback.format_exc()))
             try:
                 ts_numeric = ts_series.cast(pl.Float64)
             except Exception as e:
@@ -202,9 +210,9 @@ async def _handle_analyze_tls_health(dataset_id: str) -> dict:
     lf = entry['lazyframe']
     schema = entry.get('schema', {})
     n = _count_rows(lf)
-    df = lf.head(100000).collect(streaming=True)
+    df = lf.head(LOW_MEMORY_THRESHOLD_ROWS).collect(streaming=True)
 
-    result = {'total_rows': n, 'sampled': min(n, 100000)}
+    result = {'total_rows': n, 'sampled': min(n, LOW_MEMORY_THRESHOLD_ROWS)}
 
     if '0ver' in df.columns:
         vc = df['0ver'].value_counts(sort=True)
@@ -236,6 +244,7 @@ async def _handle_analyze_tls_health(dataset_id: str) -> dict:
             try:
                 weak_count += df['cipher-suite'].str.to_lowercase().str.contains(w).sum()
             except Exception:
+                logger.error("unknown failed: {}".format(traceback.format_exc()))
                 pass
         result['weak_cipher_pct'] = round(float(weak_count) / max(total_ciphers, 1) * 100, 1) if total_ciphers else 0
 
@@ -254,9 +263,9 @@ async def _handle_analyze_geo_distribution(dataset_id: str) -> dict:
     lf = entry['lazyframe']
     schema = entry.get('schema', {})
     n = _count_rows(lf)
-    df = lf.head(100000).collect(streaming=True)
+    df = lf.head(LOW_MEMORY_THRESHOLD_ROWS).collect(streaming=True)
 
-    result = {'total_rows': n, 'sampled': min(n, 100000)}
+    result = {'total_rows': n, 'sampled': min(n, LOW_MEMORY_THRESHOLD_ROWS)}
 
     for geo_col, label in [('scnt', 'src_countries'), ('dcnt', 'dst_countries')]:
         if geo_col in df.columns:
@@ -266,6 +275,7 @@ async def _handle_analyze_geo_distribution(dataset_id: str) -> dict:
                                  for r in vc.iter_rows(named=True)]
                 result[f'{label}_unique'] = int(df[geo_col].n_unique())
             except Exception:
+                logger.error("_handle_analyze_geo_distribution failed: {}".format(traceback.format_exc()))
                 pass
 
     if 'scnt' in df.columns and 'dcnt' in df.columns:
@@ -274,6 +284,7 @@ async def _handle_analyze_geo_distribution(dataset_id: str) -> dict:
             result['domestic_pct'] = round(float(same) / max(len(df), 1) * 100, 1)
             result['international_pct'] = round(100.0 - result['domestic_pct'], 1)
         except Exception:
+            logger.error("_handle_analyze_geo_distribution failed: {}".format(traceback.format_exc()))
                 pass
 
     return result
@@ -296,14 +307,11 @@ async def _handle_analyze_entity_detail(dataset_id: str, entity_value: str) -> d
     if not entity_col:
         entity_col = list(schema.keys())[0]
 
-    try:
-        filtered = lf.filter(pl.col(entity_col).cast(pl.Utf8).str.contains(entity_value, literal=True))
-        n = _count_rows(filtered)
-        if n == 0:
-            return {'error': f"Entity '{entity_value}' not found in column '{entity_col}'", 'truncated': False}
-        df = filtered.collect(streaming=True)
-    except Exception as exc:
-        raise
+    filtered = lf.filter(pl.col(entity_col).cast(pl.Utf8).str.contains(entity_value, literal=True))
+    n = _count_rows(filtered)
+    if n == 0:
+        return {'error': f"Entity '{entity_value}' not found in column '{entity_col}'", 'truncated': False}
+    df = filtered.collect(streaming=True)
 
     result = {
         'entity_value': entity_value,
@@ -321,6 +329,7 @@ async def _handle_analyze_entity_detail(dataset_id: str, entity_value: str) -> d
                     'max': float(df[c].max()) if df[c].max() is not None else None,
                 }
             except Exception:
+                logger.error("unknown failed: {}".format(traceback.format_exc()))
                 pass
 
     return result

analysis/tools/anomalies.py

@@ -1,14 +1,18 @@
 """Anomaly detection + visualization handlers."""
 import numpy as np
+from analysis.constants import MAX_DISTRIBUTION_SAMPLE, RANDOM_SEED
 import polars as pl
 
 from ..session_store import SessionStore
 from ._helpers import _resolve_dataset, NUMERIC_TYPE_NAMES
+import logging
+logger = logging.getLogger(__name__)
 
 
 async def _handle_detect_anomalies(dataset_id: str, contamination: float = 0.05) -> dict:
     """Run Isolation Forest on scored entity data."""
     from sklearn.ensemble import IsolationForest
+import traceback
     entry, err = _resolve_dataset(dataset_id)
     if err:
         return err
@@ -24,12 +28,12 @@ async def _handle_detect_anomalies(dataset_id: str, contamination: float = 0.05)
     mat = np.nan_to_num(mat, nan=0.0)
 
     n = len(mat)
-    chunk_size = min(50000, n)
+    chunk_size = min(MAX_SAMPLE_ROWS, n)
     labels = np.zeros(n, dtype=int)
     for start in range(0, n, chunk_size):
         end = min(start + chunk_size, n)
         iso = IsolationForest(n_estimators=100, max_samples=min(256, chunk_size),
-                              contamination=contamination, random_state=42, n_jobs=-1)
+                              contamination=contamination, random_state=RANDOM_SEED, n_jobs=-1)
         labels[start:end] = iso.fit_predict(mat[start:end])
 
     anomaly_count = int((labels == -1).sum())
@@ -59,12 +63,13 @@ async def _handle_visualize_anomalies(dataset_id: str) -> dict:
     try:
         n_total = lf.select(pl.len()).collect(streaming=True).item()
     except Exception:
+        logger.error("_handle_visualize_anomalies failed: {}".format(traceback.format_exc()))
         n_total = 0
-    sample_lf = lf if n_total <= 10000 else lf.collect(streaming=True).sample(n=10000, seed=42).lazy()
+    sample_lf = lf if n_total <= MAX_DISTRIBUTION_SAMPLE else lf.collect(streaming=True).sample(n=MAX_DISTRIBUTION_SAMPLE, seed=RANDOM_SEED).lazy()
     df = sample_lf.select(num_cols).collect(streaming=True)
     mat = StandardScaler().fit_transform(df.to_numpy())
     mat = np.nan_to_num(mat, nan=0.0)
-    reducer = umap.UMAP(n_components=2, random_state=42,
+    reducer = umap.UMAP(n_components=2, random_state=RANDOM_SEED,
                         n_neighbors=15, min_dist=0.1, metric='euclidean')
     coords = reducer.fit_transform(mat)
 

analysis/tools/clustering.py

@@ -6,6 +6,7 @@ Contains:
 - _cluster_core: shared clustering pipeline extracted from both handlers
 """
 import logging
+from analysis.constants import LOW_MEMORY_THRESHOLD_GB, LOW_MEMORY_THRESHOLD_ROWS, MAX_CLUSTER_FEATURES, MAX_SAMPLE_ROWS, RANDOM_SEED
 from typing import Optional
 
 import numpy as np
@@ -13,6 +14,7 @@ import polars as pl
 
 from ..session_store import SessionStore, _generate_id
 from ._helpers import (
+logger = logging.getLogger(__name__)
     NUMERIC_DTYPES,
     _build_filter_expr,
     _resolve_dataset,
@@ -27,7 +29,7 @@ def _cluster_core(
     feature_cols: list[str],
     algorithm: str,
     params: dict,
-    random_state: int = 42,
+    random_state: int = RANDOM_SEED,
 ):
     """Shared clustering pipeline: scale, filter, reduce, fit, score.
 
@@ -48,20 +50,20 @@ def _cluster_core(
     try:
         import psutil
         mem = psutil.virtual_memory()
-        low_memory = mem.available < 2 * 1024 ** 3  # 2 GB
+        low_memory = mem.available < LOW_MEMORY_THRESHOLD_GB * 1024 ** 3  # 2 GB
     except ImportError:
         pass
 
-    if low_memory and len(data) > 100000:
-        rng = np.random.default_rng(42)
-        idx = rng.choice(len(data), 50000, replace=False)
+    if low_memory and len(data) > LOW_MEMORY_THRESHOLD_ROWS:
+        rng = np.random.default_rng(RANDOM_SEED)
+        idx = rng.choice(len(data), MAX_SAMPLE_ROWS, replace=False)
         data = data[idx]
 
     # ── Downsampling to 50K max ────────────────────────────────────────
     _ds_idx = None
-    if len(data) > 50000:
-        rng = np.random.default_rng(42)
-        _ds_idx = rng.choice(len(data), 50000, replace=False)
+    if len(data) > MAX_SAMPLE_ROWS:
+        rng = np.random.default_rng(RANDOM_SEED)
+        _ds_idx = rng.choice(len(data), MAX_SAMPLE_ROWS, replace=False)
         data = data[_ds_idx]
 
     # ── Variance filtering ─────────────────────────────────────────────
@@ -98,15 +100,15 @@ def _cluster_core(
 
     # ── SVD dimensionality reduction (if >50 features) ─────────────────
     n_features = data_scaled.shape[1]
-    if n_features > 50:
+    if n_features > MAX_CLUSTER_FEATURES:
         from sklearn.decomposition import TruncatedSVD
-        svd = TruncatedSVD(n_components=50, random_state=random_state)
+        svd = TruncatedSVD(n_components=MAX_CLUSTER_FEATURES, random_state=random_state)
         data_scaled = svd.fit_transform(data_scaled)
 
     # ── Hyperparameter auto-tuning ─────────────────────────────────────
     n = len(data_scaled)
     if algorithm == 'hdbscan':
-        min_cluster = p.get('min_cluster_size', max(3, min(50, n // 20)))
+        min_cluster = p.get('min_cluster_size', max(3, min(MAX_CLUSTER_FEATURES, n // 20)))
         p['min_cluster_size'] = min_cluster
         p['min_samples'] = p.get('min_samples', min_cluster)
         p['cluster_selection_epsilon'] = p.get('cluster_selection_epsilon', 0.0)
@@ -159,18 +161,22 @@ def _cluster_core(
             score = silhouette_score(data_scaled, labels_array, random_state=random_state)
             quality['silhouette_score'] = round(float(score), 4)
         except Exception:
+            logger.error("unknown failed: {}".format(traceback.format_exc()))
             quality['silhouette_score'] = None
         try:
             from sklearn.metrics import davies_bouldin_score
             dbs = davies_bouldin_score(data_scaled, labels_array)
             quality['davies_bouldin_score'] = round(float(dbs), 4)
         except Exception:
+            logger.error("unknown failed: {}".format(traceback.format_exc()))
             quality['davies_bouldin_score'] = None
         try:
             from sklearn.metrics import calinski_harabasz_score
+import traceback
             chs = calinski_harabasz_score(data_scaled, labels_array)
             quality['calinski_harabasz_score'] = round(float(chs), 4)
         except Exception:
+            logger.error("unknown failed: {}".format(traceback.format_exc()))
             quality['calinski_harabasz_score'] = None
 
     # Cluster sizes
@@ -191,7 +197,7 @@ async def _handle_run_clustering(
     cluster_columns: list[str],
     algorithm: str = 'agglomerative',
     params: Optional[dict] = None,
-    random_state: int = 42,
+    random_state: int = RANDOM_SEED,
 ) -> dict:
     """Run HDBSCAN, KMeans, or Agglomerative clustering on selected columns."""
     entry, err = _resolve_dataset(dataset_id)
@@ -201,91 +207,89 @@ async def _handle_run_clustering(
     lf: pl.LazyFrame = entry['lazyframe']
     p = params or {}
 
-    try:
-        # Collect cluster features - numeric only
-        available = lf.collect_schema()
-        available_names = available.names()
-        available_dtypes = available.dtypes()
+    # Collect cluster features - numeric only
+    available = lf.collect_schema()
+    available_names = available.names()
+    available_dtypes = available.dtypes()
+    feature_cols = [
+        c for c in cluster_columns
+        if c in available_names
+        and available_dtypes[available_names.index(c)] in NUMERIC_DTYPES
+    ]
+    if not feature_cols:
+        # Fall back: auto-select all numeric columns (exclude internal ones)
         feature_cols = [
-            c for c in cluster_columns
-            if c in available_names
-            and available_dtypes[available_names.index(c)] in NUMERIC_DTYPES
-        ]
+            available_names[i] for i, dt in enumerate(available_dtypes)
+            if dt in NUMERIC_DTYPES and not available_names[i].startswith('_')
+        ][:10]
+    if not feature_cols:
+        # Fallback: try coercing Utf8 columns to Float64
+        utf8_cols = [
+            available_names[i] for i, dt in enumerate(available_dtypes)
+            if dt == pl.Utf8 and not available_names[i].startswith('_')
+        ][:10]
+        if utf8_cols:
+            for col in utf8_cols:
+                try:
+                    casted = lf.select(
+                        pl.col(col).cast(pl.Float64, strict=False).alias(col)
+                    ).collect(streaming=True)
+                    null_ratio = casted[col].is_null().sum() / max(len(casted), 1)
+                    if null_ratio < 0.9:
+                        lf = lf.with_columns(
+                            pl.col(col).cast(pl.Float64, strict=False)
+                        )
+                        feature_cols.append(col)
+                except Exception:
+                    logger.error("unknown failed: {}".format(traceback.format_exc()))
+                    pass
         if not feature_cols:
-            # Fall back: auto-select all numeric columns (exclude internal ones)
-            feature_cols = [
-                available_names[i] for i, dt in enumerate(available_dtypes)
-                if dt in NUMERIC_DTYPES and not available_names[i].startswith('_')
-            ][:10]
-        if not feature_cols:
-            # Fallback: try coercing Utf8 columns to Float64
-            utf8_cols = [
-                available_names[i] for i, dt in enumerate(available_dtypes)
-                if dt == pl.Utf8 and not available_names[i].startswith('_')
-            ][:10]
-            if utf8_cols:
-                for col in utf8_cols:
-                    try:
-                        casted = lf.select(
-                            pl.col(col).cast(pl.Float64, strict=False).alias(col)
-                        ).collect(streaming=True)
-                        null_ratio = casted[col].is_null().sum() / max(len(casted), 1)
-                        if null_ratio < 0.9:
-                            lf = lf.with_columns(
-                                pl.col(col).cast(pl.Float64, strict=False)
-                            )
-                            feature_cols.append(col)
-                    except Exception:
-                        pass
-            if not feature_cols:
-                all_numeric = [f"{available_names[i]}" for i, dt in enumerate(available_dtypes)
-                               if dt in NUMERIC_DTYPES]
-                return {
-                    'error': f'No numeric cluster columns found in dataset. Available: {all_numeric}',
-                    'truncated': False
-                }
-
-        # Downsample before collect
-        row_count = _count_rows(lf)
-        if row_count > 100000:
-            lf = lf.collect(streaming=True).sample(n=100000, seed=42).lazy()
-
-        df_features = lf.select(feature_cols).collect(streaming=True)
-
-        # Drop all-NaN columns
-        for col in df_features.columns:
-            if df_features[col].is_null().all():
-                df_features = df_features.drop(col)
-                feature_cols = [c for c in feature_cols if c != col]
-
-        if df_features.width == 0:
-            return {'error': 'No valid feature columns after dropping all-NaN columns',
-                    'truncated': False}
-
-        data = df_features.to_numpy()
-
-        # Fill remaining NaN with column mean
-        if data.dtype.kind == 'f':
-            col_mean = np.nanmean(data, axis=0)
-            col_mean = np.nan_to_num(col_mean, nan=0.0)
-            data = np.where(np.isnan(data), col_mean, data)
-
-        # Zero-sample guard
-        if len(data) < 3:
+            all_numeric = [f"{available_names[i]}" for i, dt in enumerate(available_dtypes)
+                           if dt in NUMERIC_DTYPES]
             return {
-                'cluster_result_id': _generate_id('clust'),
-                'n_clusters': 0,
-                'n_noise': 0,
-                'quality_metrics': {'note': 'too few samples'},
-                'truncated': False,
+                'error': f'No numeric cluster columns found in dataset. Available: {all_numeric}',
+                'truncated': False
             }
 
-        labels_array, n_found, noise_count, quality, _ds_idx = _cluster_core(
-            data, feature_cols, algorithm, p, random_state,
-        )
+    # Downsample before collect
+    row_count = _count_rows(lf)
+    if row_count > LOW_MEMORY_THRESHOLD_ROWS:
+        lf = lf.collect(streaming=True).sample(n=LOW_MEMORY_THRESHOLD_ROWS, seed=RANDOM_SEED).lazy()
+
+    df_features = lf.select(feature_cols).collect(streaming=True)
+
+    # Drop all-NaN columns
+    for col in df_features.columns:
+        if df_features[col].is_null().all():
+            df_features = df_features.drop(col)
+            feature_cols = [c for c in feature_cols if c != col]
+
+    if df_features.width == 0:
+        return {'error': 'No valid feature columns after dropping all-NaN columns',
+                'truncated': False}
+
+    data = df_features.to_numpy()
+
+    # Fill remaining NaN with column mean
+    if data.dtype.kind == 'f':
+        col_mean = np.nanmean(data, axis=0)
+        col_mean = np.nan_to_num(col_mean, nan=0.0)
+        data = np.where(np.isnan(data), col_mean, data)
+
+    # Zero-sample guard
+    if len(data) < 3:
+        return {
+            'cluster_result_id': _generate_id('clust'),
+            'n_clusters': 0,
+            'n_noise': 0,
+            'quality_metrics': {'note': 'too few samples'},
+            'truncated': False,
+        }
+
+    labels_array, n_found, noise_count, quality, _ds_idx = _cluster_core(
+        data, feature_cols, algorithm, p, random_state,
+    )
 
-    except Exception:
-        raise
 
     result_id = _generate_id('clust')
     store = SessionStore()
@@ -332,50 +336,45 @@ async def _handle_filter_and_cluster(
     full_schema = entry.get('schema', {})
 
     # ── Step 1: Apply filters ─────────────────────────────────────────────
-    try:
-        exprs = [_build_filter_expr(
-            f['column'], f.get('op', 'eq'), f.get('value'),
-        ) for f in filters]
+    exprs = [_build_filter_expr(
+        f['column'], f.get('op', 'eq'), f.get('value'),
+    ) for f in filters]
 
-        if exprs:
-            if logic == 'and':
-                combined = exprs[0]
-                for e in exprs[1:]:
-                    combined = combined & e
-            else:
-                combined = exprs[0]
-                for e in exprs[1:]:
-                    combined = combined | e
-            filtered_lf = lf.filter(combined)
+    if exprs:
+        if logic == 'and':
+            combined = exprs[0]
+            for e in exprs[1:]:
+                combined = combined & e
         else:
-            filtered_lf = lf
-
-        try:
-            row_count = _count_rows(filtered_lf)
-        except Exception:
-            row_count = None
+            combined = exprs[0]
+            for e in exprs[1:]:
+                combined = combined | e
+        filtered_lf = lf.filter(combined)
+    else:
+        filtered_lf = lf
 
+    try:
+        row_count = _count_rows(filtered_lf)
     except Exception:
-        raise
+        logger.error("unknown failed: {}".format(traceback.format_exc()))
+        row_count = None
+
 
     # ── Step 2: Auto-detect numeric feature columns ───────────────────────
-    try:
-        available = filtered_lf.collect_schema()
-        available_names = available.names()
-        available_dtypes = available.dtypes()
-        feature_cols = [
-            available_names[i] for i, dt in enumerate(available_dtypes)
-            if dt in NUMERIC_DTYPES and not available_names[i].startswith('_')
-        ][:10]
+    available = filtered_lf.collect_schema()
+    available_names = available.names()
+    available_dtypes = available.dtypes()
+    feature_cols = [
+        available_names[i] for i, dt in enumerate(available_dtypes)
+        if dt in NUMERIC_DTYPES and not available_names[i].startswith('_')
+    ][:10]
 
-        if not feature_cols:
-            return {
-                'error': 'No numeric columns found for clustering after filtering',
-                'filtered_dataset_id': None,
-                'truncated': False,
-            }
-    except Exception:
-        raise
+    if not feature_cols:
+        return {
+            'error': 'No numeric columns found for clustering after filtering',
+            'filtered_dataset_id': None,
+            'truncated': False,
+        }
 
     # ── Step 3: Store filtered dataset ────────────────────────────────────
     filtered_dataset_id = _generate_id('flt')
@@ -395,46 +394,43 @@ async def _handle_filter_and_cluster(
 
     # ── Step 4: Run clustering ────────────────────────────────────────────
     p = params or {}
-    try:
-        total_count = _count_rows(filtered_lf)
-        clamp_lf = filtered_lf
-        if total_count > 100000:
-            clamp_lf = filtered_lf.collect(streaming=True).sample(n=100000, seed=42).lazy()
+    total_count = _count_rows(filtered_lf)
+    clamp_lf = filtered_lf
+    if total_count > LOW_MEMORY_THRESHOLD_ROWS:
+        clamp_lf = filtered_lf.collect(streaming=True).sample(n=LOW_MEMORY_THRESHOLD_ROWS, seed=RANDOM_SEED).lazy()
 
-        df_features = clamp_lf.select(feature_cols).collect(streaming=True)
+    df_features = clamp_lf.select(feature_cols).collect(streaming=True)
 
-        for col in df_features.columns:
-            if df_features[col].is_null().all():
-                df_features = df_features.drop(col)
-                feature_cols = [c for c in feature_cols if c != col]
+    for col in df_features.columns:
+        if df_features[col].is_null().all():
+            df_features = df_features.drop(col)
+            feature_cols = [c for c in feature_cols if c != col]
 
-        if df_features.width == 0:
-            return {'error': 'No valid feature columns after dropping all-NaN columns',
-                    'filtered_dataset_id': filtered_dataset_id, 'truncated': False}
+    if df_features.width == 0:
+        return {'error': 'No valid feature columns after dropping all-NaN columns',
+                'filtered_dataset_id': filtered_dataset_id, 'truncated': False}
 
-        data = df_features.to_numpy()
+    data = df_features.to_numpy()
 
-        if data.dtype.kind == 'f':
-            col_mean = np.nanmean(data, axis=0)
-            col_mean = np.nan_to_num(col_mean, nan=0.0)
-            data = np.where(np.isnan(data), col_mean, data)
+    if data.dtype.kind == 'f':
+        col_mean = np.nanmean(data, axis=0)
+        col_mean = np.nan_to_num(col_mean, nan=0.0)
+        data = np.where(np.isnan(data), col_mean, data)
 
-        if len(data) < 3:
-            return {
-                'cluster_result_id': _generate_id('clust'),
-                'n_clusters': 0,
-                'n_noise': 0,
-                'quality_metrics': {'note': 'too few samples'},
-                'filtered_dataset_id': filtered_dataset_id,
-                'truncated': False,
-            }
+    if len(data) < 3:
+        return {
+            'cluster_result_id': _generate_id('clust'),
+            'n_clusters': 0,
+            'n_noise': 0,
+            'quality_metrics': {'note': 'too few samples'},
+            'filtered_dataset_id': filtered_dataset_id,
+            'truncated': False,
+        }
 
-        labels_array, n_found, noise_count, quality, _ds_idx = _cluster_core(
-            data, feature_cols, algorithm, p, 42,
-        )
+    labels_array, n_found, noise_count, quality, _ds_idx = _cluster_core(
+        data, feature_cols, algorithm, p, RANDOM_SEED,
+    )
 
-    except Exception:
-        raise
 
     result_id = _generate_id('clust')
     store.store_cluster_result(
@@ -444,7 +440,7 @@ async def _handle_filter_and_cluster(
         params={
             'algorithm': algorithm,
             'params': p,
-            'random_state': 42,
+            'random_state': RANDOM_SEED,
             'feature_columns': feature_cols,
             '_ds_idx': _ds_idx.tolist() if _ds_idx is not None else None,
         },

analysis/tools/diagnostics.py

@@ -1,8 +1,12 @@
 """Diagnostic tool handlers — validate, explore, diagnose, compare, repair."""
+import traceback
+from analysis.constants import LOW_MEMORY_THRESHOLD_ROWS, RANDOM_SEED
 import polars as pl
 
 from ..session_store import SessionStore
 from ._helpers import _resolve_dataset, _count_rows, NUMERIC_TYPE_NAMES
+import logging
+logger = logging.getLogger(__name__)
 
 
 async def _handle_validate_data(dataset_id: str, fix_issues: bool = False) -> dict:
@@ -13,20 +17,18 @@ async def _handle_validate_data(dataset_id: str, fix_issues: bool = False) -> di
     lf = entry['lazyframe']
     schema = entry.get('schema', {})
     issues = []
-    try:
-        df_sample = lf.head(1000).collect(streaming=True)
-        for col in schema:
-            null_count = df_sample[col].is_null().sum()
-            if null_count > 500:
-                issues.append(f'{col}: {null_count/10:.0f}% null')
-            try:
-                unique = df_sample[col].n_unique()
-                if unique <= 1 and null_count == 0:
-                    issues.append(f'{col}: constant value (no variation)')
-            except Exception:
-                pass
-    except Exception as exc:
-        raise
+    df_sample = lf.head(1000).collect(streaming=True)
+    for col in schema:
+        null_count = df_sample[col].is_null().sum()
+        if null_count > 500:
+            issues.append(f'{col}: {null_count/10:.0f}% null')
+        try:
+            unique = df_sample[col].n_unique()
+            if unique <= 1 and null_count == 0:
+                issues.append(f'{col}: constant value (no variation)')
+        except Exception:
+            logger.error("_handle_validate_data failed: {}".format(traceback.format_exc()))
+            pass
     total_rows = _count_rows(lf)
     return {
         'status': 'ok',
@@ -55,7 +57,7 @@ async def _handle_explore_distributions(dataset_id: str, columns: list[str] = No
     try:
         n = _count_rows(lf)
         if n > max_sample:
-            sample_lf = lf.select(columns).sample(n=max_sample, seed=42)
+            sample_lf = lf.select(columns).sample(n=max_sample, seed=RANDOM_SEED)
         else:
             sample_lf = lf.select(columns)
         df = sample_lf.collect(streaming=True)
@@ -78,6 +80,7 @@ async def _handle_explore_distributions(dataset_id: str, columns: list[str] = No
                 stats[col]['mean'] = round(float(s.mean()), 4) if s.mean() is not None else None
                 stats[col]['std'] = round(float(s.std()), 4) if s.std() is not None else None
         except Exception:
+            logger.error("unknown failed: {}".format(traceback.format_exc()))
             stats[col] = {'error': 'failed to compute'}
     return {'columns': stats, 'total_rows': n, 'sampled': min(n, max_sample)}
 
@@ -92,12 +95,9 @@ async def _handle_find_outliers(dataset_id: str, columns: list[str] = None,
     schema = entry.get('schema', {})
     if not columns:
         columns = [c for c in schema if schema[c].split('(')[0].strip() in NUMERIC_TYPE_NAMES]
-    try:
-        n = _count_rows(lf)
-        sample_lf = lf if n <= 100000 else lf.collect(streaming=True).sample(n=100000, seed=42).lazy()
-        df = sample_lf.select(columns).collect(streaming=True)
-    except Exception as exc:
-        raise
+    n = _count_rows(lf)
+    sample_lf = lf if n <= 100000 else lf.collect(streaming=True).sample(n=LOW_MEMORY_THRESHOLD_ROWS, seed=RANDOM_SEED).lazy()
+    df = sample_lf.select(columns).collect(streaming=True)
 
     outlier_counts = {}
     for col in columns:
@@ -111,6 +111,7 @@ async def _handle_find_outliers(dataset_id: str, columns: list[str] = None,
             if n_out > 0:
                 outlier_counts[col] = int(n_out)
         except Exception:
+            logger.error("_handle_find_outliers failed: {}".format(traceback.format_exc()))
             pass
     return {'outlier_counts': outlier_counts, 'total_sampled': len(df), 'threshold_iqr': threshold}
 
@@ -152,11 +153,12 @@ async def _handle_diagnose_clustering(dataset_id: str, cluster_result_id: str) -
         df_sample = lf.select(num_cols).head(5000).collect(streaming=True)
         mat = df_sample.to_numpy()
         n_comp = min(mat.shape)
-        svd = TruncatedSVD(n_components=n_comp, random_state=42).fit(mat)
+        svd = TruncatedSVD(n_components=n_comp, random_state=RANDOM_SEED).fit(mat)
         var_explained = svd.explained_variance_ratio_
         if var_explained[0] > 0.95:
             diagnosis.append(f'First SVD component explains {var_explained[0]:.0%} variance — data nearly 1D')
     except Exception:
+        logger.error("unknown failed: {}".format(traceback.format_exc()))
         pass
 
     return {
@@ -187,6 +189,7 @@ async def _handle_compare_datasets(dataset_id_a: str, dataset_id_b: str) -> dict
         try:
             n = _count_rows(lf)
         except Exception:
+            logger.error("_get_info failed: {}".format(traceback.format_exc()))
             n = -1
         return {'schema': set(schema.keys()), 'row_count': n, 'columns': len(schema)}
 
@@ -210,24 +213,21 @@ async def _handle_export_debug_sample(dataset_id: str, max_rows: int = 50) -> di
     entry, err = _resolve_dataset(dataset_id)
     if err:
         return err
-    try:
-        df = entry['lazyframe'].head(max_rows).collect(streaming=True)
-        rows = df.to_dict(as_series=False)
-        result = []
-        for i in range(len(df)):
-            row = {}
-            for col in df.columns:
-                val = rows[col][i]
-                if isinstance(val, (float, int, str, bool)):
-                    row[col] = val
-                elif val is None:
-                    row[col] = None
-                else:
-                    row[col] = str(val)
-            result.append(row)
-        return {'rows': result, 'count': len(result), 'columns': df.columns}
-    except Exception as exc:
-        raise
+    df = entry['lazyframe'].head(max_rows).collect(streaming=True)
+    rows = df.to_dict(as_series=False)
+    result = []
+    for i in range(len(df)):
+        row = {}
+        for col in df.columns:
+            val = rows[col][i]
+            if isinstance(val, (float, int, str, bool)):
+                row[col] = val
+            elif val is None:
+                row[col] = None
+            else:
+                row[col] = str(val)
+        result.append(row)
+    return {'rows': result, 'count': len(result), 'columns': df.columns}
 
 
 async def _handle_repair_schema(dataset_id: str) -> dict:

analysis/tools/distance_matrix.py

@@ -1,4 +1,6 @@
 """compute_distance_matrix handler — LLM-driven per-row evaluation."""
+import traceback
+from analysis.constants import RANDOM_SEED
 from typing import Optional
 
 import numpy as np
@@ -6,6 +8,8 @@ import polars as pl
 
 from ..session_store import SessionStore
 from ._helpers import _resolve_dataset, NUMERIC_DTYPES
+import logging
+logger = logging.getLogger(__name__)
 
 
 def _indent_function_body(body: str, indent: str = '    ') -> str:
@@ -43,7 +47,7 @@ async def _handle_compute_distance_matrix(
     n_total = len(df)
 
     if n_total > max_sample:
-        df_sampled = df.sample(n=max_sample, seed=42)
+        df_sampled = df.sample(n=max_sample, seed=RANDOM_SEED)
         n_evaluated = max_sample
     else:
         df_sampled = df
@@ -103,6 +107,7 @@ async def _handle_compute_distance_matrix(
                 scores.append(None)
                 errors += 1
         except Exception:
+            logger.error("unknown failed: {}".format(traceback.format_exc()))
             scores.append(None)
             errors += 1
 

analysis/tools/entities.py

@@ -1,4 +1,6 @@
 """Entity profile building + adaptive scoring handlers."""
+import traceback
+from analysis.constants import MAX_DISTRIBUTION_SAMPLE, RANDOM_SEED
 import logging
 from typing import Optional
 
@@ -8,6 +10,7 @@ import polars as pl
 from ..session_store import SessionStore, _generate_id
 from ..profile_util import profile
 from ._helpers import _resolve_dataset, _count_rows, NUMERIC_DTYPES
+logger = logging.getLogger(__name__)
 
 
 # ═══════════════════════════════════════════════════════════════════════
@@ -113,11 +116,8 @@ async def _handle_build_entity_profiles(
                 agg_exprs.append(pl.col(c).n_unique().alias(alias_name))
 
     # ── Execute grouping ───────────────────────────────────────────────
-    try:
-        entity_lf = lf.group_by(group_cols).agg(agg_exprs)
-        n_entities = _count_rows(entity_lf)
-    except Exception:
-        raise
+    entity_lf = lf.group_by(group_cols).agg(agg_exprs)
+    n_entities = _count_rows(entity_lf)
 
     # ── Store result ───────────────────────────────────────────────────
     new_schema = {name: str(dtype) for name, dtype in
@@ -160,7 +160,7 @@ def _add_adaptive_scores(lf: pl.LazyFrame, feature_names: list[str]) -> pl.LazyF
 
     try:
         n_rows = _count_rows(lf)
-        sample_lf = lf if n_rows <= 10000 else lf.sample(n=10000, seed=42)
+        sample_lf = lf if n_rows <= MAX_DISTRIBUTION_SAMPLE else lf.sample(n=MAX_DISTRIBUTION_SAMPLE, seed=RANDOM_SEED)
         df_sample = sample_lf.select(avail).collect(streaming=True)
         mat = df_sample.to_numpy()
         mat = np.nan_to_num(mat, nan=0.0)
@@ -169,7 +169,7 @@ def _add_adaptive_scores(lf: pl.LazyFrame, feature_names: list[str]) -> pl.LazyF
         n_comp = min(1, min(mat.shape) - 1)
         if n_comp < 1 or mat.shape[1] < 2:
             return lf
-        svd = TruncatedSVD(n_components=1, random_state=42).fit(mat)
+        svd = TruncatedSVD(n_components=1, random_state=RANDOM_SEED).fit(mat)
         raw_weights = np.abs(svd.components_[0])
         weights = dict(zip(avail, raw_weights / raw_weights.sum()))
 
@@ -222,7 +222,7 @@ def _add_adaptive_scores(lf: pl.LazyFrame, feature_names: list[str]) -> pl.LazyF
             '[SCORE] adaptive weights from SVD: %s | thresholds: p50=%.3f upper=%.3f extreme=%.3f | sample=%d/%d',
             {c: f'{w:.3f}' for c, w in sorted(weights.items(), key=lambda x: -x[1])},
             float(q50), upper_fence, extreme_fence,
-            min(n_rows, 10000), n_rows,
+            min(n_rows, MAX_DISTRIBUTION_SAMPLE), n_rows,
         )
 
     except Exception as exc:
@@ -248,6 +248,7 @@ async def _handle_compute_scores(dataset_id: str) -> dict:
         new_schema = {name: str(dtype) for name, dtype in
                       zip(updated_schema.names(), updated_schema.dtypes())}
     except Exception:
+        logger.error("_handle_compute_scores failed: {}".format(traceback.format_exc()))
         new_schema = entry.get('schema', {})
     store = SessionStore()
     store.store_dataset(dataset_id, lf, schema=new_schema,

analysis/tools/evaluate.py

@@ -1,5 +1,6 @@
 """evaluate_clustering handler — compute clustering quality metrics."""
 from typing import Optional
+from analysis.constants import RANDOM_SEED
 
 import numpy as np
 import polars as pl
@@ -30,49 +31,46 @@ async def _handle_evaluate_clustering(
 
     scores: dict = {'n_clusters': cluster_entry['n_clusters']}
 
-    try:
-        lf: pl.LazyFrame = dataset_entry['lazyframe']
-        params = cluster_entry.get('params', {})
-        feature_cols = params.get('feature_columns', [])
-        if not feature_cols and 'feature_columns' in params:
-            pass
-        elif feature_cols:
-            df_features = lf.select(feature_cols).collect(streaming=True)
-            data = df_features.to_numpy()
+    lf: pl.LazyFrame = dataset_entry['lazyframe']
+    params = cluster_entry.get('params', {})
+    feature_cols = params.get('feature_columns', [])
+    if not feature_cols and 'feature_columns' in params:
+        pass
+    elif feature_cols:
+        df_features = lf.select(feature_cols).collect(streaming=True)
+        data = df_features.to_numpy()
 
-            # Align labels with data (handle missing rows)
-            valid_mask = ~np.isnan(data).any(axis=1) if data.dtype.kind == 'f' else np.ones(len(data), dtype=bool)
-            data_clean = data[valid_mask]
-            labels_clean = np.array(labels)[:len(data_clean)]
+        # Align labels with data (handle missing rows)
+        valid_mask = ~np.isnan(data).any(axis=1) if data.dtype.kind == 'f' else np.ones(len(data), dtype=bool)
+        data_clean = data[valid_mask]
+        labels_clean = np.array(labels)[:len(data_clean)]
 
-            unique_labels = set(labels_clean)
-            n_clusters_actual = len(unique_labels - {-1})
+        unique_labels = set(labels_clean)
+        n_clusters_actual = len(unique_labels - {-1})
 
-            for m in requested:
-                if m == 'silhouette' and n_clusters_actual >= 2:
-                    from sklearn.metrics import silhouette_score
-                    scores['silhouette'] = round(float(
-                        silhouette_score(data_clean, labels_clean, random_state=42)), 4)
-                elif m == 'davies_bouldin' and n_clusters_actual >= 2:
-                    from sklearn.metrics import davies_bouldin_score
-                    scores['davies_bouldin'] = round(float(
-                        davies_bouldin_score(data_clean, labels_clean)), 4)
-                elif m == 'calinski_harabasz' and n_clusters_actual >= 2:
-                    from sklearn.metrics import calinski_harabasz_score
-                    scores['calinski_harabasz'] = round(float(
-                        calinski_harabasz_score(data_clean, labels_clean)), 4)
-                elif m == 'noise_ratio':
-                    noise = int((labels_clean == -1).sum())
-                    scores['noise_ratio'] = round(noise / len(labels_clean), 4)
-                elif m == 'cluster_sizes':
-                    sizes = {}
-                    for label in unique_labels:
-                        sizes[int(label)] = int((labels_clean == label).sum())
-                    scores['cluster_sizes'] = sizes
+        for m in requested:
+            if m == 'silhouette' and n_clusters_actual >= 2:
+                from sklearn.metrics import silhouette_score
+                scores['silhouette'] = round(float(
+                    silhouette_score(data_clean, labels_clean, random_state=RANDOM_SEED)), 4)
+            elif m == 'davies_bouldin' and n_clusters_actual >= 2:
+                from sklearn.metrics import davies_bouldin_score
+                scores['davies_bouldin'] = round(float(
+                    davies_bouldin_score(data_clean, labels_clean)), 4)
+            elif m == 'calinski_harabasz' and n_clusters_actual >= 2:
+                from sklearn.metrics import calinski_harabasz_score
+                scores['calinski_harabasz'] = round(float(
+                    calinski_harabasz_score(data_clean, labels_clean)), 4)
+            elif m == 'noise_ratio':
+                noise = int((labels_clean == -1).sum())
+                scores['noise_ratio'] = round(noise / len(labels_clean), 4)
+            elif m == 'cluster_sizes':
+                sizes = {}
+                for label in unique_labels:
+                    sizes[int(label)] = int((labels_clean == label).sum())
+                scores['cluster_sizes'] = sizes
 
-        scores['n_clusters'] = cluster_entry['n_clusters']
+    scores['n_clusters'] = cluster_entry['n_clusters']
 
-    except Exception:
-        raise
 
     return {'scores': scores}

analysis/tools/export.py

@@ -23,30 +23,27 @@ async def _handle_export_results(
     dataset_entry = store.get_dataset(result_id)
     if dataset_entry is not None:
         lf: pl.LazyFrame = dataset_entry['lazyframe']
-        try:
-            df = lf.collect(streaming=True)
-            if out.suffix == '':
-                out = out / f"export_{result_id}.{format}"
+        df = lf.collect(streaming=True)
+        if out.suffix == '':
+            out = out / f"export_{result_id}.{format}"
 
-            if out.exists() and not overwrite:
-                return {'error': f'Output exists and overwrite=False: {out}',
-                        'truncated': False}
+        if out.exists() and not overwrite:
+            return {'error': f'Output exists and overwrite=False: {out}',
+                    'truncated': False}
 
-            out.parent.mkdir(parents=True, exist_ok=True)
+        out.parent.mkdir(parents=True, exist_ok=True)
 
-            if format == 'csv':
-                df.write_csv(out)
-            elif format == 'parquet':
-                df.write_parquet(out)
-            elif format == 'json':
-                df.write_json(out)
-            else:
-                return {'error': f'Unsupported format: {format}',
-                        'truncated': False}
+        if format == 'csv':
+            df.write_csv(out)
+        elif format == 'parquet':
+            df.write_parquet(out)
+        elif format == 'json':
+            df.write_json(out)
+        else:
+            return {'error': f'Unsupported format: {format}',
+                    'truncated': False}
 
-            file_paths.append(str(out.resolve()))
-        except Exception:
-            raise
+        file_paths.append(str(out.resolve()))
 
         return {'file_paths': file_paths, 'format': format,
                 'row_count': len(df)}

analysis/tools/features.py

@@ -1,9 +1,15 @@
 """extract_features handler — per-cluster feature ranking + ORM persistence."""
+import sys
+import traceback
+
 import numpy as np
 import polars as pl
 
 from ..session_store import SessionStore, _generate_id
 from ..profile_util import profile
+from analysis.constants import RANDOM_SEED, UMAP_BATCH_SIZE, UMAP_TRAIN_SAMPLE
+import logging
+logger = logging.getLogger(__name__)
 
 
 async def _handle_extract_features(
@@ -25,180 +31,177 @@ async def _handle_extract_features(
         return {'error': f'Cluster result not found: {cluster_result_id}',
                 'truncated': False}
 
-    try:
-        lf: pl.LazyFrame = dataset_entry['lazyframe']
-        labels = cluster_entry['labels']
+    lf: pl.LazyFrame = dataset_entry['lazyframe']
+    labels = cluster_entry['labels']
 
-        # D5: If clustering downsampled the data, filter matching rows
-        _ds_idx = cluster_entry.get('params', {}).get('_ds_idx')
-        if _ds_idx is not None:
-            df = (lf.with_row_index('__ds_idx')
-                  .filter(pl.col('__ds_idx').is_in(_ds_idx))
-                  .drop('__ds_idx')
-                  .collect(streaming=True))
-        else:
-            df = lf.collect(streaming=True)
+    # D5: If clustering downsampled the data, filter matching rows
+    _ds_idx = cluster_entry.get('params', {}).get('_ds_idx')
+    if _ds_idx is not None:
+        df = (lf.with_row_index('__ds_idx')
+              .filter(pl.col('__ds_idx').is_in(_ds_idx))
+              .drop('__ds_idx')
+              .collect(streaming=True))
+    else:
+        df = lf.collect(streaming=True)
 
-        # Truncate labels if df was filtered but labels weren't
-        if len(labels) != len(df):
-            labels = labels[:len(df)]
+    # Truncate labels if df was filtered but labels weren't
+    if len(labels) != len(df):
+        labels = labels[:len(df)]
 
-        # Only use numeric columns for feature extraction
-        numeric_cols = [c for c in df.columns
-                        if df[c].dtype in (pl.Float32, pl.Float64,
-                                            pl.Int32, pl.Int64,
-                                            pl.UInt32, pl.UInt64)]
-        if len(labels) != len(df):
-            labels = labels[:len(df)]
+    # Only use numeric columns for feature extraction
+    numeric_cols = [c for c in df.columns
+                    if df[c].dtype in (pl.Float32, pl.Float64,
+                                        pl.Int32, pl.Int64,
+                                        pl.UInt32, pl.UInt64)]
+    if len(labels) != len(df):
+        labels = labels[:len(df)]
+
+    # Global statistics per column
+    global_stats = {}
+    for col in numeric_cols:
+        s = df[col].drop_nulls()
+        if len(s) > 0:
+            global_stats[col] = {
+                'mean': float(s.mean()),
+                'std': float(s.std()) if s.std() is not None else 1.0,
+            }
+
+    unique_labels = sorted(set(labels))
+    all_features = []
+
+    for label in unique_labels:
+        mask = np.array(labels) == label
+        if mask.sum() < 2:
+            continue
+
+        cluster_df = df.filter(pl.Series('__mask__', mask))
+        cluster_features = []
 
-        # Global statistics per column
-        global_stats = {}
         for col in numeric_cols:
-            s = df[col].drop_nulls()
-            if len(s) > 0:
-                global_stats[col] = {
-                    'mean': float(s.mean()),
-                    'std': float(s.std()) if s.std() is not None else 1.0,
-                }
-
-        unique_labels = sorted(set(labels))
-        all_features = []
-
-        for label in unique_labels:
-            mask = np.array(labels) == label
-            if mask.sum() < 2:
+            c_series = cluster_df[col].drop_nulls()
+            gs = global_stats.get(col)
+            if gs is None or gs['std'] == 0 or len(c_series) == 0:
                 continue
 
-            cluster_df = df.filter(pl.Series('__mask__', mask))
-            cluster_features = []
+            c_mean = float(c_series.mean())
+            c_std = float(c_series.std()) if c_series.std() is not None else 0.0
 
-            for col in numeric_cols:
-                c_series = cluster_df[col].drop_nulls()
-                gs = global_stats.get(col)
-                if gs is None or gs['std'] == 0 or len(c_series) == 0:
-                    continue
+            if method == 'zscore':
+                score = (c_mean - gs['mean']) / max(gs['std'], 1e-10)
+            else:
+                score = abs(c_mean - gs['mean']) / max(c_std + gs['std'], 1e-10)
 
-                c_mean = float(c_series.mean())
-                c_std = float(c_series.std()) if c_series.std() is not None else 0.0
+            cluster_features.append({
+                'feature_name': col,
+                'cluster_label': int(label),
+                'mean': round(c_mean, 4),
+                'std': round(c_std, 4),
+                'global_mean': round(gs['mean'], 4),
+                'global_std': round(gs['std'], 4),
+                'distinguishing_score': round(float(score), 4),
+                'distinguishing_method': method,
+            })
 
-                if method == 'zscore':
-                    score = (c_mean - gs['mean']) / max(gs['std'], 1e-10)
+        cluster_features.sort(key=lambda x: abs(x['distinguishing_score']), reverse=True)
+        all_features.extend(cluster_features[:top_k])
+
+    # ── Compute UMAP-2D embedding ──────────────────────────────────
+    if len(numeric_cols) >= 2 and len(df) > 2:
+        try:
+            from sklearn.preprocessing import StandardScaler
+            import umap
+            MAX_UMAP_TRAIN = UMAP_TRAIN_SAMPLE
+            BATCH_SIZE = UMAP_BATCH_SIZE
+            data_matrix = df.select(numeric_cols).to_numpy()
+            if len(df) > MAX_UMAP_TRAIN:
+                idx_sample = np.random.RandomState(RANDOM_SEED).choice(
+                    len(df), size=MAX_UMAP_TRAIN, replace=False)
+                mat_sample = data_matrix[idx_sample]
+                scaler = StandardScaler().fit(mat_sample)
+                mat_sample_scaled = np.nan_to_num(scaler.transform(mat_sample),
+                                                  nan=0.0, posinf=0.0, neginf=0.0)
+                reducer = umap.UMAP(n_components=2, random_state=RANDOM_SEED,
+                                    n_neighbors=15, min_dist=0.1,
+                                    metric='euclidean')
+                reducer.fit(mat_sample_scaled)
+                coords_list = []
+                for start in range(0, len(df), BATCH_SIZE):
+                    end = min(start + BATCH_SIZE, len(df))
+                    mat_batch = scaler.transform(data_matrix[start:end])
+                    mat_batch = np.nan_to_num(mat_batch, nan=0.0, posinf=0.0, neginf=0.0)
+                    coords_list.append(reducer.transform(mat_batch))
+                coords = np.vstack(coords_list)
+            else:
+                data_scaled = StandardScaler().fit_transform(data_matrix)
+                data_scaled = np.nan_to_num(data_scaled, nan=0.0, posinf=0.0, neginf=0.0)
+                reducer = umap.UMAP(n_components=2, random_state=RANDOM_SEED,
+                                    n_neighbors=15, min_dist=0.1,
+                                    metric='euclidean')
+                coords = reducer.fit_transform(data_scaled)
+            non_numeric = [c for c in df.columns if c not in numeric_cols and not c.startswith('_')]
+            entity_col_umap = non_numeric[0] if non_numeric else df.columns[0]
+            labels_list = cluster_entry.get('labels', [])
+            from analysis.models import EntityProfile as EP
+            from analysis.models import ClusterResult as CR
+            from analysis.models import AnalysisRun as AR
+            from asgiref.sync import sync_to_async
+            csv_glob = dataset_entry.get('metadata', {}).get('csv_glob', 'manual')
+            run_obj = await sync_to_async(
+                AR.objects.filter(csv_glob=csv_glob).order_by('-id').first,
+                thread_sensitive=True
+            )()
+            if run_obj:
+                run_id_for_ep = run_obj.id
+                profiles = []
+                cr_cache = {}
+                for i, row in enumerate(df.iter_rows(named=True)):
+                    if i >= len(coords): break
+                    ev = str(row.get(entity_col_umap, ''))
+                    if not ev: continue
+                    lbl = labels_list[i] if i < len(labels_list) else -1
+                    cache_key = f'{run_id_for_ep}_{lbl}'
+                    if cache_key not in cr_cache:
+                        cr_cache[cache_key] = CR.objects.filter(run_id=run_id_for_ep, cluster_label=lbl).first()
+                    profiles.append(EP(
+                        entity_value=ev, run_id=run_id_for_ep, cluster_label=lbl,
+                        cluster=cr_cache[cache_key],
+                        embedding_x=float(coords[i, 0]),
+                        embedding_y=float(coords[i, 1]),
+                    ))
+                if profiles:
+                    EP.objects.bulk_create(profiles, ignore_conflicts=True, batch_size=1000)
+        except Exception:
+            print(f'[UMAP_ERR] {traceback.format_exc()}', file=sys.stderr)
+
+    # Persist to Django ORM
+    db_saved = False
+    if save_to_db:
+        try:
+            from asgiref.sync import sync_to_async
+            from analysis.models import AnalysisRun as AnalysisRunModel
+            ce = cluster_entry
+            async def _do_save(ce_inner):
+                if run_id is not None:
+                    run = await sync_to_async(AnalysisRunModel.objects.get)(id=run_id)
                 else:
-                    score = abs(c_mean - gs['mean']) / max(c_std + gs['std'], 1e-10)
+                    csv_glob_fallback = dataset_entry.get('metadata', {}).get('csv_glob', 'manual')
+                    run = await sync_to_async(
+                        AnalysisRunModel.objects.filter(csv_glob=csv_glob_fallback).order_by('-id').first,
+                        thread_sensitive=True
+                    )()
+                    if run is None:
+                        run = await sync_to_async(AnalysisRunModel.objects.create)(
+                            csv_glob=csv_glob_fallback, status='running', total_flows=0,
+                        )
+                run.status = 'completed'
+                run.cluster_count = ce_inner['n_clusters']
+                await sync_to_async(run.save)()
+                return await sync_to_async(_save_features_to_db)(cluster_result_id, all_features, run_id=run.id)
+            db_saved = await _do_save(ce)
+        except Exception as e:
+            print(f'[DB_SAVE_ERROR] {e}\n{traceback.format_exc()}', file=sys.stderr)
+            db_saved = False
 
-                cluster_features.append({
-                    'feature_name': col,
-                    'cluster_label': int(label),
-                    'mean': round(c_mean, 4),
-                    'std': round(c_std, 4),
-                    'global_mean': round(gs['mean'], 4),
-                    'global_std': round(gs['std'], 4),
-                    'distinguishing_score': round(float(score), 4),
-                    'distinguishing_method': method,
-                })
-
-            cluster_features.sort(key=lambda x: abs(x['distinguishing_score']), reverse=True)
-            all_features.extend(cluster_features[:top_k])
-
-        # ── Compute UMAP-2D embedding ──────────────────────────────────
-        if len(numeric_cols) >= 2 and len(df) > 2:
-            try:
-                from sklearn.preprocessing import StandardScaler
-                import umap
-                MAX_UMAP_TRAIN = 10000
-                BATCH_SIZE = 1000
-                data_matrix = df.select(numeric_cols).to_numpy()
-                if len(df) > MAX_UMAP_TRAIN:
-                    idx_sample = np.random.RandomState(42).choice(
-                        len(df), size=MAX_UMAP_TRAIN, replace=False)
-                    mat_sample = data_matrix[idx_sample]
-                    scaler = StandardScaler().fit(mat_sample)
-                    mat_sample_scaled = np.nan_to_num(scaler.transform(mat_sample),
-                                                      nan=0.0, posinf=0.0, neginf=0.0)
-                    reducer = umap.UMAP(n_components=2, random_state=42,
-                                        n_neighbors=15, min_dist=0.1,
-                                        metric='euclidean')
-                    reducer.fit(mat_sample_scaled)
-                    coords_list = []
-                    for start in range(0, len(df), BATCH_SIZE):
-                        end = min(start + BATCH_SIZE, len(df))
-                        mat_batch = scaler.transform(data_matrix[start:end])
-                        mat_batch = np.nan_to_num(mat_batch, nan=0.0, posinf=0.0, neginf=0.0)
-                        coords_list.append(reducer.transform(mat_batch))
-                    coords = np.vstack(coords_list)
-                else:
-                    data_scaled = StandardScaler().fit_transform(data_matrix)
-                    data_scaled = np.nan_to_num(data_scaled, nan=0.0, posinf=0.0, neginf=0.0)
-                    reducer = umap.UMAP(n_components=2, random_state=42,
-                                        n_neighbors=15, min_dist=0.1,
-                                        metric='euclidean')
-                    coords = reducer.fit_transform(data_scaled)
-                non_numeric = [c for c in df.columns if c not in numeric_cols and not c.startswith('_')]
-                entity_col_umap = non_numeric[0] if non_numeric else df.columns[0]
-                labels_list = cluster_entry.get('labels', [])
-                from analysis.models import EntityProfile as EP
-                from analysis.models import ClusterResult as CR
-                from analysis.models import AnalysisRun as AR
-                from asgiref.sync import sync_to_async
-                csv_glob = dataset_entry.get('metadata', {}).get('csv_glob', 'manual')
-                run_obj = await sync_to_async(
-                    AR.objects.filter(csv_glob=csv_glob).order_by('-id').first,
-                    thread_sensitive=True
-                )()
-                if run_obj:
-                    run_id_for_ep = run_obj.id
-                    profiles = []
-                    cr_cache = {}
-                    for i, row in enumerate(df.iter_rows(named=True)):
-                        if i >= len(coords): break
-                        ev = str(row.get(entity_col_umap, ''))
-                        if not ev: continue
-                        lbl = labels_list[i] if i < len(labels_list) else -1
-                        cache_key = f'{run_id_for_ep}_{lbl}'
-                        if cache_key not in cr_cache:
-                            cr_cache[cache_key] = CR.objects.filter(run_id=run_id_for_ep, cluster_label=lbl).first()
-                        profiles.append(EP(
-                            entity_value=ev, run_id=run_id_for_ep, cluster_label=lbl,
-                            cluster=cr_cache[cache_key],
-                            embedding_x=float(coords[i, 0]),
-                            embedding_y=float(coords[i, 1]),
-                        ))
-                    if profiles:
-                        EP.objects.bulk_create(profiles, ignore_conflicts=True, batch_size=1000)
-            except Exception:
-                import traceback, sys; print(f'[UMAP_ERR] {traceback.format_exc()}', file=sys.stderr)
-
-        # Persist to Django ORM
-        db_saved = False
-        if save_to_db:
-            try:
-                from asgiref.sync import sync_to_async
-                from analysis.models import AnalysisRun as AnalysisRunModel
-                ce = cluster_entry
-                async def _do_save(ce_inner):
-                    if run_id is not None:
-                        run = await sync_to_async(AnalysisRunModel.objects.get)(id=run_id)
-                    else:
-                        csv_glob_fallback = dataset_entry.get('metadata', {}).get('csv_glob', 'manual')
-                        run = await sync_to_async(
-                            AnalysisRunModel.objects.filter(csv_glob=csv_glob_fallback).order_by('-id').first,
-                            thread_sensitive=True
-                        )()
-                        if run is None:
-                            run = await sync_to_async(AnalysisRunModel.objects.create)(
-                                csv_glob=csv_glob_fallback, status='running', total_flows=0,
-                            )
-                    run.status = 'completed'
-                    run.cluster_count = ce_inner['n_clusters']
-                    await sync_to_async(run.save)()
-                    return await sync_to_async(_save_features_to_db)(cluster_result_id, all_features, run_id=run.id)
-                db_saved = await _do_save(ce)
-            except Exception as e:
-                import traceback, sys; print(f'[DB_SAVE_ERROR] {e}\n{traceback.format_exc()}', file=sys.stderr)
-                db_saved = False
-
-    except Exception:
-        raise
 
     result_id = _generate_id('feat')
     store.store_feature_result(
@@ -251,6 +254,7 @@ def _save_features_to_db(
                     cluster_count=n_clusters_global,
                 )
         except Exception:
+            logger.error("unknown failed: {}".format(traceback.format_exc()))
             return False
 
     from analysis.models import ClusterResult as ClusterResultModel
@@ -302,6 +306,7 @@ def _save_features_to_db(
                     }
                 )
             except Exception:
+                logger.error("unknown failed: {}".format(traceback.format_exc()))
                 continue
 
     run.cluster_count = n_clusters

analysis/tools/filter.py

@@ -1,8 +1,11 @@
 """filter_data handler — apply column-level filters to a dataset."""
+import traceback
 import polars as pl
 
 from ..session_store import SessionStore, _generate_id
 from ._helpers import _build_filter_expr, _resolve_dataset
+import logging
+logger = logging.getLogger(__name__)
 
 
 async def _handle_filter_data(
@@ -40,6 +43,7 @@ async def _handle_filter_data(
     try:
         row_count = filtered_lf.select(pl.len()).collect(streaming=True).item()
     except Exception:
+        logger.error("unknown failed: {}".format(traceback.format_exc()))
         row_count = None
 
     new_id = _generate_id('flt')

analysis/tools/load_data.py

@@ -7,6 +7,7 @@ import polars as pl
 
 from ..data_loader import load_csv_directory
 from ..session_store import SessionStore, _generate_id
+from analysis.constants import MAX_DISTRIBUTION_SAMPLE
 
 
 async def _handle_load_data(
@@ -24,7 +25,7 @@ async def _handle_load_data(
             encoding=encoding,
             delimiter=delimiter,
             config_path=config_path,
-            sample_rows=10000,
+            sample_rows=MAX_DISTRIBUTION_SAMPLE,
             schema_strict=schema_strict,
             recursive=recursive,
         )

analysis/views/__init__.py

@@ -11,7 +11,3 @@ from .globe import globe_view, _extract_flows_from_df
 from .config import config_view, llm_test
 from .log_viewer import log_viewer
 from .tools import tool_lab, tool_lab_run, tool_plan, apply_filter, reload_run_data, retry_run
-
-# Auto-profile all sub-modules
-from analysis.profile_util import auto_profile_module
-auto_profile_module(__name__)

analysis/views/auto.py

@@ -130,11 +130,13 @@ def run_llm_analysis_view(request):
             try:
                 lf = load_from_db(run.sqlite_table)
             except Exception:
+                logger.error("unknown failed: {}".format(traceback.format_exc()))
                 pass
         if lf is None and run.csv_glob:
             try:
                 lf, schema, _, _, _ = load_csv_directory(run.csv_glob)
             except Exception:
+                logger.error("unknown failed: {}".format(traceback.format_exc()))
                 pass
         if lf is None:
             return JsonResponse({'error': '数据集不存在，请先上传并等待预处理完成'})

analysis/views/clustering.py

@@ -1,4 +1,5 @@
 """Cluster views: overview, detail, globe flows, and the clustering pipeline."""
+import asyncio
 import json
 import traceback
 import logging
@@ -8,6 +9,7 @@ from django.http import Http404
 
 from analysis.models import AnalysisRun, ClusterResult, EntityProfile
 from analysis import nl_describe
+from analysis.constants import RANDOM_SEED, UMAP_BATCH_SIZE, UMAP_TRAIN_SAMPLE
 from analysis.profile_util import profile
 
 from .helpers import _extract_lat, _extract_lon
@@ -127,9 +129,9 @@ def _get_globe_flows(run, max_rows=500):
         if lf is None:
             return []
         df = lf.head(max_rows).collect()
+        from analysis.type_classifier import TLS_HEX_MAP
         from analysis.geoip import lookup as geo_lookup
         flows = []
-        TLS_HEX_MAP = {'0303': 'TLSv1.2', '0304': 'TLSv1.3', '0302': 'TLSv1.1', '0301': 'TLSv1.0'}
         src_ip_col = next((c for c in df.columns if 'src_ip' in c.lower() or ':ips' in c.lower()), None)
         dst_ip_col = next((c for c in df.columns if 'dst_ip' in c.lower() or ':ipd' in c.lower()), None)
         tls_col = next((c for c in df.columns if c.lower() in ('0ver', 'tls_version', '_tlsver', 'version')), None)
@@ -153,6 +155,7 @@ def _get_globe_flows(run, max_rows=500):
             })
         return flows
     except Exception:
+        logger.error("unknown failed: {}".format(traceback.format_exc()))
         return []
 
 
@@ -209,7 +212,6 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
     """
     import warnings
     warnings.filterwarnings('ignore', category=RuntimeWarning, module='sklearn')
-    import asyncio
 
     try:
         from analysis.tool_registry import _handle_run_clustering, _handle_extract_features
@@ -230,6 +232,7 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
             try:
                 row_count = lf.select(pl.len()).collect(streaming=True).item()
             except Exception:
+                logger.error("unknown failed: {}".format(traceback.format_exc()))
                 row_count = 0
 
             if row_count > head:
@@ -280,7 +283,7 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
             cluster_columns=feature_cols,
             algorithm=algorithm,
             params={'min_cluster_size': min_cluster_size},
-            random_state=42,
+            random_state=RANDOM_SEED,
         ))
 
         if 'error' in result:
@@ -357,11 +360,12 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
             try:
                 lf = entry['lazyframe']
                 # UMAP: sample max 10K for training, batch-transform rest in 1K batches
-                MAX_UMAP_TRAIN = 10000
-                BATCH_SIZE = 1000
+                MAX_UMAP_TRAIN = UMAP_TRAIN_SAMPLE
+                BATCH_SIZE = UMAP_BATCH_SIZE
                 try:
                     n_total = lf.select(pl.len()).collect(streaming=True).item()
                 except Exception:
+                    logger.error("unknown failed: {}".format(traceback.format_exc()))
                     n_total = 0
                 from sklearn.preprocessing import StandardScaler
                 import umap
@@ -377,7 +381,7 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
                     if n_total > MAX_UMAP_TRAIN:
                         run.progress_msg = f'UMAP 采样 {MAX_UMAP_TRAIN}/{n_total} 实体训练...'
                         run.save(update_fields=['progress_msg'])
-                        df_sample = lf.sample(n=MAX_UMAP_TRAIN, seed=42).collect(streaming=True)
+                        df_sample = lf.sample(n=MAX_UMAP_TRAIN, seed=RANDOM_SEED).collect(streaming=True)
                         df_umap = lf.collect(streaming=True)
                     else:
                         df_umap = lf.collect(streaming=True)
@@ -396,7 +400,7 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
                                     mat_sample_scaled = np.nan_to_num(
                                         scaler.transform(mat_sample), nan=0.0)
                                     reducer = umap.UMAP(n_components=attempt_n,
-                                                        random_state=42,
+                                                        random_state=RANDOM_SEED,
                                                         n_neighbors=15, min_dist=0.1,
                                                         metric='euclidean')
                                     reducer.fit(mat_sample_scaled)
@@ -413,7 +417,7 @@ def _run_clustering_pipeline(run, store, entity_ds_id, feature_columns, algorith
                                     mat = np.nan_to_num(StandardScaler().fit_transform(
                                         df_umap.select(num_cols).to_numpy()), nan=0.0)
                                     reducer = umap.UMAP(n_components=attempt_n,
-                                                        random_state=42,
+                                                        random_state=RANDOM_SEED,
                                                         n_neighbors=15, min_dist=0.1,
                                                         metric='euclidean')
                                     coords = reducer.fit_transform(mat)

analysis/views/config.py

@@ -1,4 +1,5 @@
 """Configuration views: config editor and LLM connectivity test."""
+import traceback
 import json
 import time
 import urllib.request
@@ -112,6 +113,7 @@ def llm_test(request):
             detail = json.loads(e.read().decode('utf-8', errors='replace'))
             msg = detail.get('error', {}).get('message', str(e))
         except Exception:
+            logger.error("unknown failed: {}".format(traceback.format_exc()))
             msg = str(e)
         logger.info(f'[LLM_TEST] base_url={base_url} model={model} success=False latency={elapsed}ms')
         return JsonResponse({'success': False, 'message': f'HTTP {e.code}: {msg}', 'latency_ms': elapsed})

analysis/views/globe.py

@@ -1,9 +1,11 @@
 """3D Globe view and flow extraction."""
 import json
 import logging
+import traceback
 
 from django.shortcuts import render
 
+from analysis.constants import GLOBE_MAX_ROWS
 from analysis.models import AnalysisRun
 from .helpers import _extract_lat, _extract_lon
 
@@ -13,8 +15,7 @@ logger = logging.getLogger(__name__)
 def _extract_flows_from_df(df, MAX_ROWS):
     """Extract flow data (lat/lon/TLS/bytes) from a Polars DataFrame."""
     from analysis.geoip import lookup as geo_lookup
-    TLS_HEX_MAP = {'0303': 'TLSv1.2', '0304': 'TLSv1.3', '0302': 'TLSv1.1', '0301': 'TLSv1.0'}
-    # NOTE: Duplicated in type_classifier.py — keep both in sync.
+    from analysis.type_classifier import TLS_HEX_MAP
 
     src_lat_col = next((c for c in df.columns if c.lower() in (
         ':ips.latd', ':ips_latd', ':ips_lat', 'src_latitude', 'src_lat', 'source_latitude', 'source_lat')), None)
@@ -117,7 +118,7 @@ def globe_view(request):
     from analysis.models import AnalysisRun
     from analysis.session_store import SessionStore
 
-    MAX_ROWS_PER_RUN = 300  # Limit per run to prevent browser overload
+    MAX_ROWS_PER_RUN = GLOBE_MAX_ROWS  # Limit per run to prevent browser overload
 
     # Check for ?data=dataset_id parameter (direct data loading from session store)
     data_param = request.GET.get('data')
@@ -197,6 +198,7 @@ def globe_view(request):
                 selected_runs = [latest]
                 break
             except Exception:
+                logger.error("unknown failed: {}".format(traceback.format_exc()))
                 continue
 
     resp = render(request, 'tianxuan/globe.html', {

analysis/views/helpers.py

@@ -1,7 +1,10 @@
 """Geo extraction helpers and plan utilities."""
+import traceback
 from typing import Optional
 from pathlib import Path
 import json as _json
+import logging
+logger = logging.getLogger(__name__)
 
 
 def _extract_lat(val):
@@ -45,6 +48,7 @@ def _read_auto_index():
             return []
         return data
     except Exception:
+        logger.error("_read_auto_index failed: {}".format(traceback.format_exc()))
         return []
 
 

analysis/views/pipeline.py

@@ -5,6 +5,7 @@ import traceback
 import logging
 
 from analysis.appdata import ensure_appdata_dir
+from analysis.constants import MAX_CLUSTER_FEATURES, RANDOM_SEED
 from analysis.models import AnalysisRun
 from analysis.profile_util import profile
 
@@ -31,8 +32,7 @@ def _run_pipeline_worker(run_id, analysis_fn, **ctx):
     try:
         analysis_fn(run, ctx)
     except Exception:
-        import traceback as _tb
-        tb = _tb.format_exc()
+        tb = traceback.format_exc()
         logger.error(tb)
         run.status = 'failed'
         run.error_message = tb
@@ -208,6 +208,7 @@ def _background_process(run_id, upload_dir):
                                 break
                         type_map[col] = pl.Int64 if all_int else pl.Float64
                 except Exception:
+                    logger.error("unknown failed: {}".format(traceback.format_exc()))
                     pass
 
             if type_map:
@@ -229,7 +230,7 @@ def _background_process(run_id, upload_dir):
         n_numeric = len(numeric_cols)
 
         if n_numeric >= 2:
-            n_components = min(50, n_numeric - 1)
+            n_components = min(MAX_CLUSTER_FEATURES, n_numeric - 1)
             try:
                 from sklearn.decomposition import TruncatedSVD  # fmt: skip
                 import numpy as np  # fmt: skip
@@ -239,7 +240,7 @@ def _background_process(run_id, upload_dir):
                 X = df_full.to_numpy()
                 X = np.nan_to_num(X, nan=0.0, posinf=0.0, neginf=0.0)
 
-                svd = TruncatedSVD(n_components=n_components, random_state=42)
+                svd = TruncatedSVD(n_components=n_components, random_state=RANDOM_SEED)
                 X_svd = svd.fit_transform(X)
                 svd_components = n_components
 
@@ -291,6 +292,7 @@ def _background_process(run_id, upload_dir):
             from analysis.data_loader import drop_sqlite_table  # fmt: skip
             drop_sqlite_table(f'_data_{run_id}')
         except Exception:
+            logger.error("unknown failed: {}".format(traceback.format_exc()))
             pass  # Expected: drop_sqlite_table may fail if table already gone
         run.status = 'failed'
         run.error_message = tb

analysis/views/tools.py

@@ -79,6 +79,7 @@ def tool_lab_run(request):
     try:
         body = _json.loads(request.body)
     except Exception:
+        logger.error("tool_lab_run failed: {}".format(traceback.format_exc()))
         return JsonResponse({'error': 'Invalid JSON body'}, status=400)
 
     tool_name = body.get('tool')
@@ -120,6 +121,7 @@ def reload_run_data(request):
     try:
         body = _json.loads(request.body)
     except Exception:
+        logger.error("reload_run_data failed: {}".format(traceback.format_exc()))
         return JsonResponse({'error': 'Invalid JSON body'}, status=400)
 
     display_id = body.get('display_id')
@@ -221,6 +223,7 @@ def tool_plan(request):
                 steps = len(content.get('steps', []))
                 plans.append({'name': p.stem, 'steps': steps})
             except Exception:
+                logger.error("unknown failed: {}".format(traceback.format_exc()))
                 plans.append({'name': p.stem, 'steps': 0})
 
         # Add auto plans with markers
@@ -237,6 +240,7 @@ def tool_plan(request):
                     'pinned': auto_index_map.get(auto_name, False),
                 })
             except Exception:
+                logger.error("unknown failed: {}".format(traceback.format_exc()))
                 auto_plans.append({
                     'name': auto_name.replace('.json', ''),
                     'steps': 0,