28543d98b1
- Profiler computes file_size_mb from actual parquet files when sync_state.json is absent (sample data / no-sync deployments) - Catalog header falls back to profiles.json for aggregate stats (tables count, total rows) when sync_state.json is missing
1355 lines
51 KiB
Python
1355 lines
51 KiB
Python
"""
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Data Profiler - YData-inspired profiling using DuckDB.
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Reads Parquet files from the server's data directory, computes comprehensive
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statistics for each table and column, generates alerts, extracts sample rows,
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merges with metadata from existing sources, and outputs a single profiles.json file.
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Usage:
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python -m src.profiler
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"""
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import json
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import logging
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import math
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import os
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import re
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import tempfile
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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import duckdb
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import yaml
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# ---------------------------------------------------------------------------
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# Logging
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# ---------------------------------------------------------------------------
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
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)
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Profiler configuration
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# ---------------------------------------------------------------------------
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SAMPLE_THRESHOLD = 500_000 # Sample tables larger than this
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SAMPLE_SIZE = 500_000
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MAX_CATEGORICAL_DISTINCT = 50 # Treat as categorical if unique <= this
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TOP_VALUES_LIMIT = 10 # Number of top values for categorical columns
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HISTOGRAM_BINS = 15 # Number of bins for numeric histograms
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SAMPLE_ROWS_LIMIT = 5 # Number of sample rows to include
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SAMPLE_VALUES_LIMIT = 5 # Number of sample distinct values per column
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# Alert thresholds
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ALERT_HIGH_MISSING_PCT = 30.0
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ALERT_MISSING_PCT = 5.0
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ALERT_IMBALANCE_PCT = 60.0
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ALERT_ZEROS_PCT = 50.0
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ALERT_HIGH_CARDINALITY = 50
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# Paths - configurable via environment or defaults for server
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DATA_DIR = Path(os.environ.get("PROFILER_DATA_DIR", "/data/src_data"))
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DOCS_DIR = Path(os.environ.get("PROFILER_DOCS_DIR", str(Path(__file__).parent.parent / "docs")))
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PARQUET_DIR = DATA_DIR / "parquet"
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METADATA_DIR = DATA_DIR / "metadata"
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SYNC_STATE_PATH = METADATA_DIR / "sync_state.json"
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PROFILES_OUTPUT_PATH = METADATA_DIR / "profiles.json"
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METRICS_YML_PATH = DOCS_DIR / "metrics.yml"
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METRICS_DIR = DOCS_DIR / "metrics"
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DATA_DESCRIPTION_PATH = DOCS_DIR / "data_description.md"
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# Jira tables - loaded dynamically if Jira connector is enabled
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# The Jira connector stores partitioned parquet files in PARQUET_DIR/jira/
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def _load_jira_tables() -> tuple:
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"""Load Jira table definitions if the connector directory exists."""
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jira_dir = PARQUET_DIR / "jira"
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if not jira_dir.exists():
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return jira_dir, []
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return jira_dir, [
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{
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"name": "jira_issues",
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"subdir": "issues",
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"description": "Jira issues. Key fields: issue_key, summary, description, status, priority, assignee, created_at, resolved_at.",
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"primary_key": "issue_key",
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"foreign_keys": [],
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},
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{
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"name": "jira_comments",
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"subdir": "comments",
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"description": "Comments on Jira issues. Key fields: comment_id, issue_key, author_email, body, created_at.",
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"primary_key": "comment_id",
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"foreign_keys": [{"column": "issue_key", "references": "jira_issues.issue_key", "description": "Parent issue"}],
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},
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{
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"name": "jira_attachments",
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"subdir": "attachments",
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"description": "Attachment metadata with local file paths. Key fields: attachment_id, issue_key, filename, local_path, size_bytes, mime_type.",
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"primary_key": "attachment_id",
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"foreign_keys": [{"column": "issue_key", "references": "jira_issues.issue_key", "description": "Parent issue"}],
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},
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{
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"name": "jira_changelog",
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"subdir": "changelog",
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"description": "History of all field changes on issues. Key fields: change_id, issue_key, field_name, from_value, to_value, changed_at.",
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"primary_key": "change_id",
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"foreign_keys": [{"column": "issue_key", "references": "jira_issues.issue_key", "description": "Parent issue"}],
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},
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{
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"name": "jira_issuelinks",
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"subdir": "issuelinks",
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"description": "Links between Jira issues (blocks, duplicates, relates to). Key fields: issue_key, link_id, link_type, direction, linked_issue_key.",
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"primary_key": "link_id",
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"foreign_keys": [
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{"column": "issue_key", "references": "jira_issues.issue_key", "description": "Source issue"},
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{"column": "linked_issue_key", "references": "jira_issues.issue_key", "description": "Target linked issue"},
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],
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},
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{
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"name": "jira_remote_links",
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"subdir": "remote_links",
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"description": "External links attached to issues (Confluence pages, Slack threads, etc.). Key fields: issue_key, remote_link_id, url, title.",
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"primary_key": "remote_link_id",
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"foreign_keys": [{"column": "issue_key", "references": "jira_issues.issue_key", "description": "Parent issue"}],
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},
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]
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JIRA_PARQUET_DIR, JIRA_TABLES = _load_jira_tables()
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# ---------------------------------------------------------------------------
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# Dataclasses for parsed metadata
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# ---------------------------------------------------------------------------
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class ForeignKeyInfo:
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"""Foreign key definition from data_description.md."""
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def __init__(self, column: str, references: str, description: Optional[str] = None):
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self.column = column
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self.references = references
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self.description = description
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class TableInfo:
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"""Table definition parsed from data_description.md YAML."""
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def __init__(
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self,
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table_id: str,
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name: str,
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description: str,
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primary_key: str,
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sync_strategy: str,
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foreign_keys: Optional[List[ForeignKeyInfo]] = None,
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partition_by: Optional[str] = None,
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partition_granularity: Optional[str] = None,
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):
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self.id = table_id
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self.name = name
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self.description = description
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self.primary_key = primary_key
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self.sync_strategy = sync_strategy
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self.foreign_keys = foreign_keys or []
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self.partition_by = partition_by
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self.partition_granularity = partition_granularity
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def get_primary_key_columns(self) -> List[str]:
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return [col.strip() for col in self.primary_key.split(",")]
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def is_partitioned(self) -> bool:
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if self.sync_strategy == "partitioned":
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return True
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if self.sync_strategy == "incremental" and self.partition_by:
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return True
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return False
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# ---------------------------------------------------------------------------
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# DuckDB type classification
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# ---------------------------------------------------------------------------
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def classify_type(duckdb_type: str) -> str:
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"""Map a DuckDB type string to a simplified category."""
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t = duckdb_type.upper()
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if t in ("BOOLEAN", "BOOL"):
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return "BOOLEAN"
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if t in ("DATE",):
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return "DATE"
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if "TIMESTAMP" in t:
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return "TIMESTAMP"
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# Strip parameters for parameterized types (e.g. DECIMAL(18,3) -> DECIMAL)
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base_type = t.split("(")[0].strip()
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if base_type in (
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"FLOAT", "DOUBLE", "DECIMAL", "REAL", "FLOAT4", "FLOAT8",
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"NUMERIC", "HUGEINT", "INTEGER", "INT", "BIGINT", "SMALLINT",
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"TINYINT", "INT8", "INT4", "INT2", "INT1", "UBIGINT",
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"UINTEGER", "USMALLINT", "UTINYINT",
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):
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return "NUMERIC"
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return "STRING"
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _round(value: Any, digits: int = 2) -> Any:
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"""Round a value if it is a float, otherwise return as-is."""
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if value is None:
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return None
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if isinstance(value, float):
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if math.isnan(value) or math.isinf(value):
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return None
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return round(value, digits)
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return value
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def _format_number(n: float) -> str:
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"""Format large numbers with human-readable suffixes for histogram bin labels."""
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if n is None:
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return "?"
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abs_n = abs(n)
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if abs_n >= 1_000_000_000:
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return f"{n / 1_000_000_000:.1f}B"
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if abs_n >= 1_000_000:
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return f"{n / 1_000_000:.1f}M"
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if abs_n >= 1_000:
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return f"{n / 1_000:.1f}K"
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if isinstance(n, float) and n != int(n):
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return f"{n:.2f}"
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return str(int(n))
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def write_json_atomic(path: Path, data: Any) -> None:
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"""Write JSON to *path* atomically via tempfile + os.replace."""
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path.parent.mkdir(parents=True, exist_ok=True)
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fd, tmp_path = tempfile.mkstemp(dir=str(path.parent), suffix=".tmp")
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try:
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with os.fdopen(fd, "w") as f:
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json.dump(data, f, indent=2, default=str)
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os.chmod(tmp_path, 0o644) # readable by webapp (www-data)
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os.replace(tmp_path, str(path))
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logger.info("Wrote %s", path)
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except Exception:
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# Clean up temp file on failure
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try:
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os.unlink(tmp_path)
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except OSError:
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pass
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raise
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# ---------------------------------------------------------------------------
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# Metadata loading
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# ---------------------------------------------------------------------------
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def parse_data_description(path: Path) -> Tuple[List[TableInfo], Dict[str, str]]:
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"""Parse data_description.md and return (tables, folder_mapping)."""
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if not path.exists():
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logger.warning("data_description.md not found at %s", path)
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return [], {}
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content = path.read_text()
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yaml_pattern = r"```yaml\n(.*?)```"
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yaml_matches = re.findall(yaml_pattern, content, re.DOTALL)
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all_tables: List[TableInfo] = []
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folder_mapping: Dict[str, str] = {}
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for block in yaml_matches:
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try:
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data = yaml.safe_load(block)
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except yaml.YAMLError as exc:
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logger.error("YAML parse error: %s", exc)
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continue
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if not data:
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continue
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if "folder_mapping" in data:
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folder_mapping.update(data["folder_mapping"])
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if "tables" in data:
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for td in data["tables"]:
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fk_list = []
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for fk in td.get("foreign_keys", []) or []:
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fk_list.append(
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ForeignKeyInfo(
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column=fk["column"],
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references=fk["references"],
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description=fk.get("description"),
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)
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)
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all_tables.append(
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TableInfo(
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table_id=td["id"],
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name=td["name"],
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description=td["description"],
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primary_key=td["primary_key"],
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sync_strategy=td["sync_strategy"],
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foreign_keys=fk_list,
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partition_by=td.get("partition_by"),
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partition_granularity=td.get("partition_granularity"),
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)
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)
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return all_tables, folder_mapping
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|
|
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def load_sync_state(path: Path) -> Dict[str, Any]:
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"""Load sync_state.json and return dict keyed by table_id."""
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if not path.exists():
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logger.warning("sync_state.json not found at %s", path)
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return {}
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try:
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with open(path) as f:
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return json.load(f)
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except (json.JSONDecodeError, OSError) as exc:
|
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logger.error("Error reading sync_state.json: %s", exc)
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return {}
|
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|
|
|
|
def load_metrics(path: Path) -> Dict[str, List[str]]:
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"""Scan individual metric YAML files and return {table_name: [metric_name, ...]}.
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Scans individual category YAML files in docs/metrics/*/*.yml for table references.
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The path argument can be either docs/metrics.yml (legacy) or docs/metrics/ directory.
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"""
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table_metrics: Dict[str, List[str]] = {}
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# Resolve to the metrics directory (docs/metrics/)
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if path.is_file() or path.name == "metrics.yml":
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metrics_dir = path.parent / "metrics"
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else:
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metrics_dir = path
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if metrics_dir.is_dir():
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for yml_file in sorted(metrics_dir.glob("*/*.yml")):
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try:
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with open(yml_file) as f:
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data = yaml.safe_load(f)
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|
except (yaml.YAMLError, OSError) as exc:
|
|
logger.warning("Error reading metric file %s: %s", yml_file, exc)
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continue
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if not data:
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continue
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|
# Individual metric files contain a list with one metric definition
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metric_list = data if isinstance(data, list) else [data]
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for metric in metric_list:
|
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if not isinstance(metric, dict):
|
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continue
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metric_name = metric.get("name", "")
|
|
if not metric_name:
|
|
continue
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# Single-table metrics
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table = metric.get("table")
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if table:
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table_metrics.setdefault(table, []).append(metric_name)
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# Multi-table metrics
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for t in metric.get("tables", []):
|
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table_metrics.setdefault(t, []).append(metric_name)
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|
|
|
if not table_metrics:
|
|
logger.warning("No metric-table mappings found in %s", metrics_dir)
|
|
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|
return table_metrics
|
|
|
|
|
|
def load_metric_file_map(path: Path) -> Dict[str, str]:
|
|
"""Return {metric_name: 'category/file.yml'} for linking metrics in the UI."""
|
|
metric_files: Dict[str, str] = {}
|
|
if path.is_file() or path.name == "metrics.yml":
|
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metrics_dir = path.parent / "metrics"
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else:
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metrics_dir = path
|
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if not metrics_dir.is_dir():
|
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return metric_files
|
|
|
|
for yml_file in sorted(metrics_dir.glob("*/*.yml")):
|
|
try:
|
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with open(yml_file) as f:
|
|
data = yaml.safe_load(f)
|
|
except (yaml.YAMLError, OSError):
|
|
continue
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|
if not data:
|
|
continue
|
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metric_list = data if isinstance(data, list) else [data]
|
|
for metric in metric_list:
|
|
if isinstance(metric, dict) and metric.get("name"):
|
|
# Relative path: "sales_revenue/mrr.yml"
|
|
rel_path = f"{yml_file.parent.name}/{yml_file.name}"
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metric_files[metric["name"]] = rel_path
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return metric_files
|
|
|
|
|
|
def get_parquet_path(table: TableInfo, folder_mapping: Dict[str, str]) -> Path:
|
|
"""Compute the Parquet file/directory path for a table.
|
|
|
|
Tries subfolder path first (Keboola-style: parquet/<folder>/<table>.parquet),
|
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then falls back to flat path (parquet/<table>.parquet) for simple layouts.
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"""
|
|
bucket_name = ".".join(table.id.split(".")[:-1])
|
|
folder_name = folder_mapping.get(bucket_name, bucket_name)
|
|
base = PARQUET_DIR / folder_name
|
|
if table.is_partitioned():
|
|
subfolder_path = base / table.name
|
|
if subfolder_path.is_dir():
|
|
return subfolder_path
|
|
# Fallback: flat layout (partitioned dir directly under PARQUET_DIR)
|
|
flat_path = PARQUET_DIR / table.name
|
|
return flat_path if flat_path.is_dir() else subfolder_path
|
|
subfolder_path = base / f"{table.name}.parquet"
|
|
if subfolder_path.exists():
|
|
return subfolder_path
|
|
# Fallback: flat layout (file directly in PARQUET_DIR)
|
|
flat_path = PARQUET_DIR / f"{table.name}.parquet"
|
|
return flat_path if flat_path.exists() else subfolder_path
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Related tables enrichment
|
|
# ---------------------------------------------------------------------------
|
|
def compute_related_tables(
|
|
table: TableInfo, all_tables: List[TableInfo]
|
|
) -> List[Dict[str, str]]:
|
|
"""Build related_tables list from foreign key metadata (both directions)."""
|
|
related: List[Dict[str, str]] = []
|
|
|
|
# Outgoing: this table's foreign keys
|
|
for fk in table.foreign_keys:
|
|
parts = fk.references.split(".")
|
|
ref_table = parts[0]
|
|
ref_col = parts[1] if len(parts) > 1 else parts[0]
|
|
related.append(
|
|
{
|
|
"table": ref_table,
|
|
"join_column": fk.column,
|
|
"foreign_column": ref_col,
|
|
"direction": "belongs_to",
|
|
"description": fk.description or f"References {ref_table}",
|
|
}
|
|
)
|
|
|
|
# Incoming: other tables that reference this table
|
|
for other in all_tables:
|
|
if other.name == table.name:
|
|
continue
|
|
for fk in other.foreign_keys:
|
|
parts = fk.references.split(".")
|
|
ref_table = parts[0]
|
|
ref_col = parts[1] if len(parts) > 1 else parts[0]
|
|
if ref_table == table.name:
|
|
related.append(
|
|
{
|
|
"table": other.name,
|
|
"join_column": ref_col,
|
|
"foreign_column": fk.column,
|
|
"direction": "has_many",
|
|
"description": fk.description or f"Referenced by {other.name}",
|
|
}
|
|
)
|
|
|
|
return related
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Metrics referencing a table
|
|
# ---------------------------------------------------------------------------
|
|
def get_metrics_for_table(
|
|
table_name: str, metrics_map: Dict[str, List[str]]
|
|
) -> List[str]:
|
|
"""Return list of metric names that reference a given table."""
|
|
return sorted(set(metrics_map.get(table_name, [])))
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Batch statistics functions
|
|
# ---------------------------------------------------------------------------
|
|
def _batch_base_stats(
|
|
con: duckdb.DuckDBPyConnection,
|
|
view_name: str,
|
|
columns: List[str],
|
|
) -> Dict[str, Tuple[int, int]]:
|
|
"""Get non_null and unique counts for all columns in a single query.
|
|
|
|
Returns: {col_name: (non_null_count, unique_count)}
|
|
"""
|
|
if not columns:
|
|
return {}
|
|
|
|
parts = []
|
|
for col_name in columns:
|
|
safe = f'"{col_name}"'
|
|
parts.append(f"COUNT({safe})")
|
|
parts.append(f"COUNT(DISTINCT {safe})")
|
|
|
|
sql = f"SELECT {', '.join(parts)} FROM {view_name}"
|
|
row = con.execute(sql).fetchone()
|
|
|
|
result: Dict[str, Tuple[int, int]] = {}
|
|
idx = 0
|
|
for col_name in columns:
|
|
result[col_name] = (row[idx], row[idx + 1])
|
|
idx += 2
|
|
return result
|
|
|
|
|
|
def _batch_numeric_stats(
|
|
con: duckdb.DuckDBPyConnection,
|
|
view_name: str,
|
|
numeric_cols: List[str],
|
|
) -> Dict[str, Dict[str, Any]]:
|
|
"""Get aggregate statistics for all numeric columns in a single query.
|
|
|
|
DuckDB aggregations ignore NULLs, so no WHERE filter needed.
|
|
"""
|
|
if not numeric_cols:
|
|
return {}
|
|
|
|
parts = []
|
|
for col_name in numeric_cols:
|
|
safe = f'"{col_name}"'
|
|
parts.extend([
|
|
f"MIN({safe})",
|
|
f"MAX({safe})",
|
|
f"AVG({safe})",
|
|
f"MEDIAN({safe})",
|
|
f"STDDEV({safe})",
|
|
f"PERCENTILE_CONT(0.05) WITHIN GROUP (ORDER BY {safe})",
|
|
f"PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY {safe})",
|
|
f"PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY {safe})",
|
|
f"PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY {safe})",
|
|
f"SUM(CASE WHEN {safe} = 0 THEN 1 ELSE 0 END)",
|
|
f"SUM(CASE WHEN {safe} < 0 THEN 1 ELSE 0 END)",
|
|
])
|
|
|
|
sql = f"SELECT {', '.join(parts)} FROM {view_name}"
|
|
row = con.execute(sql).fetchone()
|
|
|
|
result: Dict[str, Dict[str, Any]] = {}
|
|
idx = 0
|
|
for col_name in numeric_cols:
|
|
result[col_name] = {
|
|
"min": row[idx], "max": row[idx + 1], "mean": row[idx + 2],
|
|
"median": row[idx + 3], "stddev": row[idx + 4],
|
|
"p5": row[idx + 5], "p25": row[idx + 6],
|
|
"p75": row[idx + 7], "p95": row[idx + 8],
|
|
"zeros": row[idx + 9], "negative": row[idx + 10],
|
|
}
|
|
idx += 11
|
|
return result
|
|
|
|
|
|
def _batch_string_stats(
|
|
con: duckdb.DuckDBPyConnection,
|
|
view_name: str,
|
|
string_cols: List[str],
|
|
) -> Dict[str, Dict[str, Any]]:
|
|
"""Get string length statistics for all string columns in a single query.
|
|
|
|
LENGTH(NULL) returns NULL which aggregations skip automatically.
|
|
"""
|
|
if not string_cols:
|
|
return {}
|
|
|
|
parts = []
|
|
for col_name in string_cols:
|
|
safe = f'"{col_name}"'
|
|
parts.extend([
|
|
f"MIN(LENGTH({safe}))",
|
|
f"MAX(LENGTH({safe}))",
|
|
f"AVG(LENGTH({safe}))",
|
|
])
|
|
|
|
sql = f"SELECT {', '.join(parts)} FROM {view_name}"
|
|
row = con.execute(sql).fetchone()
|
|
|
|
result: Dict[str, Dict[str, Any]] = {}
|
|
idx = 0
|
|
for col_name in string_cols:
|
|
result[col_name] = {
|
|
"min_length": row[idx] if row[idx] is not None else 0,
|
|
"max_length": row[idx + 1] if row[idx + 1] is not None else 0,
|
|
"avg_length": _round(row[idx + 2]) if row[idx + 2] is not None else 0.0,
|
|
}
|
|
idx += 3
|
|
return result
|
|
|
|
|
|
def _batch_date_stats(
|
|
con: duckdb.DuckDBPyConnection,
|
|
view_name: str,
|
|
date_cols: List[str],
|
|
category_map: Dict[str, str],
|
|
) -> Dict[str, Dict[str, Any]]:
|
|
"""Get date range statistics for all date/timestamp columns in a single query."""
|
|
if not date_cols:
|
|
return {}
|
|
|
|
parts = []
|
|
for col_name in date_cols:
|
|
safe = f'"{col_name}"'
|
|
cast_expr = f"CAST({safe} AS DATE)" if category_map[col_name] == "TIMESTAMP" else safe
|
|
parts.extend([
|
|
f"MIN({cast_expr})",
|
|
f"MAX({cast_expr})",
|
|
])
|
|
|
|
sql = f"SELECT {', '.join(parts)} FROM {view_name}"
|
|
row = con.execute(sql).fetchone()
|
|
|
|
result: Dict[str, Dict[str, Any]] = {}
|
|
idx = 0
|
|
for col_name in date_cols:
|
|
earliest = row[idx]
|
|
latest = row[idx + 1]
|
|
span_days = None
|
|
if earliest is not None and latest is not None:
|
|
try:
|
|
delta = latest - earliest
|
|
span_days = delta.days if hasattr(delta, "days") else int(delta)
|
|
except (TypeError, ValueError):
|
|
span_days = None
|
|
result[col_name] = {
|
|
"earliest": str(earliest) if earliest is not None else None,
|
|
"latest": str(latest) if latest is not None else None,
|
|
"span_days": span_days,
|
|
}
|
|
idx += 2
|
|
return result
|
|
|
|
|
|
def _batch_boolean_stats(
|
|
con: duckdb.DuckDBPyConnection,
|
|
view_name: str,
|
|
bool_cols: List[str],
|
|
) -> Dict[str, Dict[str, Any]]:
|
|
"""Get boolean true/false counts for all boolean columns in a single query."""
|
|
if not bool_cols:
|
|
return {}
|
|
|
|
parts = []
|
|
for col_name in bool_cols:
|
|
safe = f'"{col_name}"'
|
|
parts.extend([
|
|
f"SUM(CASE WHEN {safe} = TRUE THEN 1 ELSE 0 END)",
|
|
f"SUM(CASE WHEN {safe} = FALSE THEN 1 ELSE 0 END)",
|
|
])
|
|
|
|
sql = f"SELECT {', '.join(parts)} FROM {view_name}"
|
|
row = con.execute(sql).fetchone()
|
|
|
|
result: Dict[str, Dict[str, Any]] = {}
|
|
idx = 0
|
|
for col_name in bool_cols:
|
|
true_count = int(row[idx]) if row[idx] is not None else 0
|
|
false_count = int(row[idx + 1]) if row[idx + 1] is not None else 0
|
|
total = true_count + false_count
|
|
result[col_name] = {
|
|
"true_count": true_count,
|
|
"false_count": false_count,
|
|
"true_pct": _round(100.0 * true_count / total) if total > 0 else 0.0,
|
|
}
|
|
idx += 2
|
|
return result
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Table profiling
|
|
# ---------------------------------------------------------------------------
|
|
def profile_table(
|
|
table: TableInfo,
|
|
parquet_path: Path,
|
|
all_tables: List[TableInfo],
|
|
sync_state: Dict[str, Any],
|
|
metrics_map: Dict[str, List[str]],
|
|
metric_file_map: Optional[Dict[str, str]] = None,
|
|
) -> Dict[str, Any]:
|
|
"""Profile a single table and return its complete profile dict.
|
|
|
|
Optimized flow:
|
|
1. Materialize parquet into TEMP TABLE (read files once)
|
|
2. Batch base stats (COUNT, COUNT DISTINCT) for all columns
|
|
3. Batch type-specific aggregates (numeric, string, date, boolean)
|
|
4. Per-column: sample values, histograms, top values (can't batch)
|
|
5. Assemble profiles
|
|
"""
|
|
con = duckdb.connect()
|
|
|
|
# Determine read expression
|
|
if parquet_path.is_dir():
|
|
read_expr = f"read_parquet('{parquet_path}/*.parquet')"
|
|
else:
|
|
read_expr = f"read_parquet('{parquet_path}')"
|
|
|
|
# Get row count to decide on sampling
|
|
total_rows = con.execute(f"SELECT COUNT(*) FROM {read_expr}").fetchone()[0]
|
|
|
|
# Materialize into temp table — reads parquet files once instead of per-query
|
|
view_name = "tbl"
|
|
sampled = total_rows > SAMPLE_THRESHOLD
|
|
if sampled:
|
|
con.execute(
|
|
f"CREATE TEMP TABLE {view_name} AS SELECT * FROM {read_expr} USING SAMPLE {SAMPLE_SIZE} ROWS"
|
|
)
|
|
working_rows = con.execute(f"SELECT COUNT(*) FROM {view_name}").fetchone()[0]
|
|
else:
|
|
con.execute(f"CREATE TEMP TABLE {view_name} AS SELECT * FROM {read_expr}")
|
|
working_rows = total_rows
|
|
|
|
# Column metadata
|
|
col_info = con.execute(f"DESCRIBE {view_name}").fetchall()
|
|
pk_columns = table.get_primary_key_columns()
|
|
|
|
# Classify columns by type
|
|
all_col_names: List[str] = []
|
|
type_map: Dict[str, str] = {}
|
|
category_map: Dict[str, str] = {}
|
|
numeric_cols: List[str] = []
|
|
string_cols: List[str] = []
|
|
date_cols: List[str] = []
|
|
bool_cols: List[str] = []
|
|
|
|
for col_row in col_info:
|
|
col_name = col_row[0]
|
|
col_type = col_row[1]
|
|
all_col_names.append(col_name)
|
|
type_map[col_name] = col_type
|
|
category = classify_type(col_type)
|
|
category_map[col_name] = category
|
|
if category == "NUMERIC":
|
|
numeric_cols.append(col_name)
|
|
elif category == "STRING":
|
|
string_cols.append(col_name)
|
|
elif category in ("DATE", "TIMESTAMP"):
|
|
date_cols.append(col_name)
|
|
elif category == "BOOLEAN":
|
|
bool_cols.append(col_name)
|
|
|
|
# ---- Batch queries (one scan per type category) ----
|
|
base_stats = _batch_base_stats(con, view_name, all_col_names)
|
|
|
|
numeric_batch: Dict[str, Dict[str, Any]] = {}
|
|
try:
|
|
numeric_batch = _batch_numeric_stats(con, view_name, numeric_cols)
|
|
except Exception as exc:
|
|
logger.warning("Batch numeric stats failed: %s", exc)
|
|
|
|
string_batch: Dict[str, Dict[str, Any]] = {}
|
|
try:
|
|
string_batch = _batch_string_stats(con, view_name, string_cols)
|
|
except Exception as exc:
|
|
logger.warning("Batch string stats failed: %s", exc)
|
|
|
|
date_batch: Dict[str, Dict[str, Any]] = {}
|
|
try:
|
|
date_batch = _batch_date_stats(con, view_name, date_cols, category_map)
|
|
except Exception as exc:
|
|
logger.warning("Batch date stats failed: %s", exc)
|
|
|
|
boolean_batch: Dict[str, Dict[str, Any]] = {}
|
|
try:
|
|
boolean_batch = _batch_boolean_stats(con, view_name, bool_cols)
|
|
except Exception as exc:
|
|
logger.warning("Batch boolean stats failed: %s", exc)
|
|
|
|
# ---- Build column profiles ----
|
|
columns: List[Dict[str, Any]] = []
|
|
variable_types: Dict[str, int] = {}
|
|
total_null_count = 0
|
|
total_cells = working_rows * len(col_info) if col_info else 0
|
|
first_date_col: Optional[Dict[str, Any]] = None
|
|
|
|
for col_name in all_col_names:
|
|
col_type = type_map[col_name]
|
|
category = category_map[col_name]
|
|
safe_col = f'"{col_name}"'
|
|
variable_types[category] = variable_types.get(category, 0) + 1
|
|
|
|
non_null, unique_count = base_stats.get(col_name, (0, 0))
|
|
null_count = working_rows - non_null
|
|
|
|
completeness_pct = _round(100.0 * non_null / working_rows) if working_rows > 0 else 0.0
|
|
unique_pct = _round(100.0 * unique_count / non_null) if non_null > 0 else 0.0
|
|
missing_pct = _round(100.0 * null_count / working_rows) if working_rows > 0 else 0.0
|
|
is_pk = col_name in pk_columns
|
|
|
|
# Sample values (per-column, fast on materialized table)
|
|
sample_values: List[str] = []
|
|
try:
|
|
rows = con.execute(
|
|
f"""
|
|
SELECT DISTINCT CAST({safe_col} AS VARCHAR) AS v
|
|
FROM {view_name}
|
|
WHERE {safe_col} IS NOT NULL
|
|
LIMIT {SAMPLE_VALUES_LIMIT}
|
|
"""
|
|
).fetchall()
|
|
sample_values = [r[0] for r in rows if r[0] is not None]
|
|
except Exception:
|
|
pass
|
|
|
|
# Alerts
|
|
alerts: List[str] = []
|
|
if unique_count == 1 and null_count == 0:
|
|
alerts.append("constant")
|
|
if unique_pct == 100.0 and null_count == 0 and non_null > 0:
|
|
alerts.append("unique")
|
|
if missing_pct > ALERT_HIGH_MISSING_PCT:
|
|
alerts.append("high_missing")
|
|
elif missing_pct > ALERT_MISSING_PCT:
|
|
alerts.append("missing")
|
|
|
|
col_profile: Dict[str, Any] = {
|
|
"name": col_name,
|
|
"type": col_type,
|
|
"type_category": category,
|
|
"completeness_pct": completeness_pct,
|
|
"null_count": null_count,
|
|
"unique_count": unique_count,
|
|
"unique_pct": unique_pct,
|
|
"sample_values": sample_values,
|
|
"is_primary_key": is_pk,
|
|
"alerts": alerts,
|
|
}
|
|
|
|
# Type-specific stats (batch results + per-column histograms/top_values)
|
|
try:
|
|
if category == "NUMERIC" and col_name in numeric_batch:
|
|
raw = numeric_batch[col_name]
|
|
min_val = _round(raw["min"])
|
|
max_val = _round(raw["max"])
|
|
zeros = int(raw["zeros"]) if raw["zeros"] is not None else 0
|
|
negative = int(raw["negative"]) if raw["negative"] is not None else 0
|
|
zeros_pct = _round(100.0 * zeros / non_null) if non_null > 0 else 0.0
|
|
negative_pct = _round(100.0 * negative / non_null) if non_null > 0 else 0.0
|
|
|
|
if zeros_pct > ALERT_ZEROS_PCT and "zeros" not in alerts:
|
|
alerts.append("zeros")
|
|
|
|
# Histogram (per-column — each has different width_bucket ranges)
|
|
histogram: Dict[str, Any] = {"bins": [], "counts": []}
|
|
if min_val is not None and max_val is not None and min_val != max_val:
|
|
try:
|
|
bucket_rows = con.execute(
|
|
f"""
|
|
SELECT
|
|
width_bucket(
|
|
CAST({safe_col} AS DOUBLE),
|
|
CAST({min_val} AS DOUBLE),
|
|
CAST({max_val} AS DOUBLE) + 0.001,
|
|
{HISTOGRAM_BINS}
|
|
) AS bucket,
|
|
COUNT(*) AS cnt
|
|
FROM {view_name}
|
|
WHERE {safe_col} IS NOT NULL
|
|
GROUP BY bucket
|
|
ORDER BY bucket
|
|
"""
|
|
).fetchall()
|
|
|
|
bin_width = (float(max_val) - float(min_val)) / HISTOGRAM_BINS
|
|
bin_labels: List[str] = []
|
|
bin_counts: List[int] = []
|
|
bucket_dict = {int(r[0]): int(r[1]) for r in bucket_rows if r[0] is not None}
|
|
for i in range(1, HISTOGRAM_BINS + 1):
|
|
lo = float(min_val) + (i - 1) * bin_width
|
|
hi = float(min_val) + i * bin_width
|
|
bin_labels.append(f"{_format_number(lo)}-{_format_number(hi)}")
|
|
bin_counts.append(bucket_dict.get(i, 0))
|
|
histogram = {"bins": bin_labels, "counts": bin_counts}
|
|
except Exception as exc:
|
|
logger.debug("Histogram failed for column %s: %s", col_name, exc)
|
|
|
|
col_profile["numeric_stats"] = {
|
|
"min": min_val,
|
|
"max": max_val,
|
|
"mean": _round(raw["mean"]),
|
|
"median": _round(raw["median"]),
|
|
"stddev": _round(raw["stddev"]),
|
|
"p5": _round(raw["p5"]),
|
|
"p25": _round(raw["p25"]),
|
|
"p75": _round(raw["p75"]),
|
|
"p95": _round(raw["p95"]),
|
|
"zeros": zeros,
|
|
"zeros_pct": zeros_pct,
|
|
"negative": negative,
|
|
"negative_pct": negative_pct,
|
|
"histogram": histogram,
|
|
}
|
|
|
|
elif category == "STRING" and col_name in string_batch:
|
|
sl = string_batch[col_name]
|
|
is_categorical = unique_count <= MAX_CATEGORICAL_DISTINCT
|
|
|
|
top_values: List[Dict[str, Any]] = []
|
|
if is_categorical and non_null > 0:
|
|
rows = con.execute(
|
|
f"""
|
|
SELECT {safe_col} AS val, COUNT(*) AS cnt
|
|
FROM {view_name}
|
|
WHERE {safe_col} IS NOT NULL
|
|
GROUP BY {safe_col}
|
|
ORDER BY cnt DESC
|
|
LIMIT {TOP_VALUES_LIMIT}
|
|
"""
|
|
).fetchall()
|
|
for row in rows:
|
|
pct = _round(100.0 * row[1] / non_null) if non_null > 0 else 0.0
|
|
top_values.append({"value": str(row[0]), "count": row[1], "pct": pct})
|
|
|
|
if top_values and top_values[0]["pct"] > ALERT_IMBALANCE_PCT:
|
|
if "imbalance" not in alerts:
|
|
alerts.append("imbalance")
|
|
else:
|
|
if unique_count > ALERT_HIGH_CARDINALITY and "high_cardinality" not in alerts:
|
|
alerts.append("high_cardinality")
|
|
|
|
col_profile["string_stats"] = {
|
|
"min_length": sl["min_length"],
|
|
"max_length": sl["max_length"],
|
|
"avg_length": sl["avg_length"],
|
|
"top_values": top_values,
|
|
}
|
|
|
|
elif category in ("DATE", "TIMESTAMP") and col_name in date_batch:
|
|
dr = date_batch[col_name]
|
|
cast_expr = f"CAST({safe_col} AS DATE)" if category == "TIMESTAMP" else safe_col
|
|
|
|
# Date histogram (per-column — YEAR/QUARTER grouping)
|
|
histogram = {"bins": [], "counts": []}
|
|
try:
|
|
rows = con.execute(
|
|
f"""
|
|
SELECT
|
|
YEAR({cast_expr}) AS yr,
|
|
QUARTER({cast_expr}) AS qtr,
|
|
COUNT(*) AS cnt
|
|
FROM {view_name}
|
|
WHERE {safe_col} IS NOT NULL
|
|
GROUP BY yr, qtr
|
|
ORDER BY yr, qtr
|
|
"""
|
|
).fetchall()
|
|
histogram["bins"] = [f"{int(r[0])}-Q{int(r[1])}" for r in rows]
|
|
histogram["counts"] = [int(r[2]) for r in rows]
|
|
except Exception as exc:
|
|
logger.debug("Date histogram failed for %s: %s", col_name, exc)
|
|
|
|
col_profile["date_stats"] = {
|
|
"earliest": dr["earliest"],
|
|
"latest": dr["latest"],
|
|
"span_days": dr["span_days"],
|
|
"histogram": histogram,
|
|
}
|
|
|
|
if first_date_col is None and dr["earliest"]:
|
|
first_date_col = col_profile["date_stats"]
|
|
|
|
elif category == "BOOLEAN" and col_name in boolean_batch:
|
|
col_profile["boolean_stats"] = boolean_batch[col_name]
|
|
|
|
except Exception as exc:
|
|
logger.warning("Type-specific stats failed for %s: %s", col_name, exc)
|
|
|
|
columns.append(col_profile)
|
|
total_null_count += null_count
|
|
|
|
# Table-level completeness
|
|
avg_completeness = 0.0
|
|
if columns:
|
|
avg_completeness = _round(
|
|
sum(c["completeness_pct"] for c in columns) / len(columns)
|
|
)
|
|
missing_cells_pct = _round(100.0 * total_null_count / total_cells) if total_cells > 0 else 0.0
|
|
|
|
# Duplicate rows (by primary key)
|
|
duplicate_rows = 0
|
|
if pk_columns and working_rows > 0:
|
|
try:
|
|
pk_expr = ", ".join(f'"{c}"' for c in pk_columns)
|
|
distinct_pk = con.execute(
|
|
f"SELECT COUNT(DISTINCT ({pk_expr})) FROM {view_name}"
|
|
).fetchone()[0]
|
|
duplicate_rows = working_rows - distinct_pk
|
|
except Exception as exc:
|
|
logger.debug("Duplicate check failed for %s: %s", table.name, exc)
|
|
|
|
# Sample rows
|
|
sample_rows: List[Dict[str, Any]] = []
|
|
try:
|
|
sample_result = con.execute(f"SELECT * FROM {view_name} LIMIT {SAMPLE_ROWS_LIMIT}")
|
|
sample_col_names = [desc[0] for desc in sample_result.description]
|
|
for row in sample_result.fetchall():
|
|
sample_rows.append(
|
|
{sample_col_names[i]: str(v) if v is not None else None for i, v in enumerate(row)}
|
|
)
|
|
except Exception as exc:
|
|
logger.debug("Sample rows failed for %s: %s", table.name, exc)
|
|
|
|
# Aggregate column alerts to table level (detailed objects for UI)
|
|
table_alerts: List[Dict[str, str]] = []
|
|
alert_messages = {
|
|
"constant": "{col} is constant (single value)",
|
|
"unique": "{col} has all unique values",
|
|
"high_missing": "{col} has {pct}% missing values",
|
|
"missing": "{col} has {pct}% missing values",
|
|
"imbalance": "{col} is highly imbalanced (top value {pct}%)",
|
|
"zeros": "{col} has {pct}% zero values",
|
|
"high_cardinality": "{col} has high cardinality ({n} distinct)",
|
|
}
|
|
for col in columns:
|
|
col_alert_name = col.get("name", "")
|
|
missing_pct_val = _round(100.0 - col.get("completeness_pct", 100.0))
|
|
for a in col.get("alerts", []):
|
|
if a in ("high_missing", "missing"):
|
|
msg = alert_messages[a].format(col=col_alert_name, pct=missing_pct_val)
|
|
elif a == "imbalance":
|
|
top_pct = 0.0
|
|
ss = col.get("string_stats", {})
|
|
tv = ss.get("top_values", [])
|
|
if tv:
|
|
top_pct = tv[0].get("pct", 0.0)
|
|
msg = alert_messages[a].format(col=col_alert_name, pct=top_pct)
|
|
elif a == "zeros":
|
|
ns = col.get("numeric_stats", {})
|
|
msg = alert_messages[a].format(col=col_alert_name, pct=ns.get("zeros_pct", 0.0))
|
|
elif a == "high_cardinality":
|
|
msg = alert_messages[a].format(col=col_alert_name, n=col.get("unique_count", 0))
|
|
else:
|
|
msg = alert_messages.get(a, f"{col_alert_name}: {a}").format(col=col_alert_name)
|
|
table_alerts.append({"column": col_alert_name, "type": a, "message": msg})
|
|
|
|
# Sync state enrichment
|
|
table_sync = sync_state.get(table.id, {})
|
|
file_size_mb = table_sync.get("file_size_mb")
|
|
last_sync = table_sync.get("last_sync")
|
|
sync_strategy_state = table_sync.get("strategy", table.sync_strategy)
|
|
|
|
# Compute file size from parquet if not in sync state
|
|
if file_size_mb is None:
|
|
try:
|
|
if parquet_path.is_dir():
|
|
total_bytes = sum(f.stat().st_size for f in parquet_path.glob("*.parquet"))
|
|
elif parquet_path.exists():
|
|
total_bytes = parquet_path.stat().st_size
|
|
else:
|
|
total_bytes = 0
|
|
file_size_mb = total_bytes / (1024 * 1024)
|
|
except OSError:
|
|
file_size_mb = None
|
|
|
|
# Date range from first date column
|
|
date_range = None
|
|
if first_date_col:
|
|
date_range = {
|
|
"earliest": first_date_col.get("earliest"),
|
|
"latest": first_date_col.get("latest"),
|
|
"span_days": first_date_col.get("span_days"),
|
|
}
|
|
|
|
# Related tables
|
|
related_tables = compute_related_tables(table, all_tables)
|
|
|
|
# Metrics - include file path for UI linking
|
|
metric_names = get_metrics_for_table(table.name, metrics_map)
|
|
_file_map = metric_file_map or {}
|
|
used_by_metrics = [
|
|
{"name": m, "file": _file_map.get(m, "")} for m in metric_names
|
|
]
|
|
|
|
con.close()
|
|
|
|
return {
|
|
"table_id": table.id,
|
|
"description": table.description,
|
|
"primary_key": table.primary_key,
|
|
"sync_strategy": sync_strategy_state,
|
|
"row_count": total_rows,
|
|
"column_count": len(col_info),
|
|
"file_size_mb": _round(file_size_mb) if file_size_mb is not None else None,
|
|
"avg_completeness": avg_completeness,
|
|
"missing_cells": total_null_count,
|
|
"missing_cells_pct": missing_cells_pct,
|
|
"duplicate_rows": duplicate_rows,
|
|
"variable_types": variable_types,
|
|
"date_range": date_range,
|
|
"alerts": table_alerts,
|
|
"sampled": sampled,
|
|
"last_sync": last_sync,
|
|
"related_tables": related_tables,
|
|
"used_by_metrics": used_by_metrics,
|
|
"columns": columns,
|
|
"sample_rows": sample_rows,
|
|
}
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Auto-profiling API
|
|
# ---------------------------------------------------------------------------
|
|
def profile_changed_tables(table_names: list[str]) -> dict:
|
|
"""Profile only specified tables, preserve existing profiles for others.
|
|
|
|
Public API for auto-profiling after sync.
|
|
|
|
1. Load existing profiles.json
|
|
2. For each table_name in table_names: find parquet, call profile_table()
|
|
3. Merge new profiles into existing (preserve untouched tables)
|
|
4. Write atomically
|
|
Returns: {"success": int, "errors": int, "skipped": int}
|
|
"""
|
|
success = 0
|
|
errors = 0
|
|
skipped = 0
|
|
|
|
# Parse data_description.md for table definitions and folder mapping
|
|
tables, folder_mapping = parse_data_description(DATA_DESCRIPTION_PATH)
|
|
if not tables:
|
|
logger.warning("profile_changed_tables: no tables in data_description.md")
|
|
return {"success": 0, "errors": 0, "skipped": len(table_names)}
|
|
|
|
# Build lookup by table name
|
|
table_by_name: Dict[str, TableInfo] = {t.name: t for t in tables}
|
|
|
|
# Load sync state and metrics
|
|
sync_state = load_sync_state(SYNC_STATE_PATH)
|
|
metrics_map = load_metrics(METRICS_YML_PATH)
|
|
metric_file_map = load_metric_file_map(METRICS_YML_PATH)
|
|
|
|
# Load existing profiles.json to preserve untouched tables
|
|
existing_profiles: Dict[str, Any] = {}
|
|
try:
|
|
if PROFILES_OUTPUT_PATH.exists():
|
|
with open(PROFILES_OUTPUT_PATH) as f:
|
|
existing_data = json.load(f)
|
|
existing_profiles = existing_data.get("tables", {})
|
|
except Exception as exc:
|
|
logger.warning("profile_changed_tables: could not load existing profiles: %s", exc)
|
|
|
|
# Profile each requested table
|
|
new_profiles: Dict[str, Any] = {}
|
|
for name in table_names:
|
|
table = table_by_name.get(name)
|
|
if table is None:
|
|
logger.warning("profile_changed_tables: table %r not found in data_description.md", name)
|
|
skipped += 1
|
|
continue
|
|
|
|
parquet_path = get_parquet_path(table, folder_mapping)
|
|
|
|
# Check parquet existence
|
|
if parquet_path.is_dir():
|
|
parquet_files = list(parquet_path.glob("*.parquet"))
|
|
if not parquet_files:
|
|
logger.warning("profile_changed_tables: no parquet files for %s in %s", name, parquet_path)
|
|
skipped += 1
|
|
continue
|
|
elif not parquet_path.exists():
|
|
logger.warning("profile_changed_tables: parquet not found for %s at %s", name, parquet_path)
|
|
skipped += 1
|
|
continue
|
|
|
|
try:
|
|
logger.info("Auto-profiling %s ...", name)
|
|
profile = profile_table(
|
|
table, parquet_path, tables, sync_state, metrics_map, metric_file_map
|
|
)
|
|
new_profiles[name] = profile
|
|
success += 1
|
|
logger.info(
|
|
" %s: %d rows, %d cols, %d alerts",
|
|
name,
|
|
profile["row_count"],
|
|
profile["column_count"],
|
|
len(profile["alerts"]),
|
|
)
|
|
except Exception as exc:
|
|
logger.error("Auto-profiling failed for %s: %s", name, exc)
|
|
errors += 1
|
|
|
|
# Merge: existing profiles + newly profiled (new overwrite old)
|
|
merged = {**existing_profiles, **new_profiles}
|
|
|
|
# Write atomically
|
|
output = {
|
|
"generated_at": datetime.utcnow().isoformat() + "Z",
|
|
"version": "1.0",
|
|
"tables": merged,
|
|
}
|
|
METADATA_DIR.mkdir(parents=True, exist_ok=True)
|
|
write_json_atomic(PROFILES_OUTPUT_PATH, output)
|
|
|
|
logger.info(
|
|
"Auto-profiling complete: %d profiled, %d skipped, %d errors",
|
|
success, skipped, errors,
|
|
)
|
|
return {"success": success, "errors": errors, "skipped": skipped}
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Main
|
|
# ---------------------------------------------------------------------------
|
|
def main() -> None:
|
|
"""Run profiler on all tables."""
|
|
logger.info("Starting profiler")
|
|
logger.info(" PARQUET_DIR: %s", PARQUET_DIR)
|
|
logger.info(" METADATA_DIR: %s", METADATA_DIR)
|
|
logger.info(" DOCS_DIR: %s", DOCS_DIR)
|
|
|
|
# Parse data_description.md
|
|
tables, folder_mapping = parse_data_description(DATA_DESCRIPTION_PATH)
|
|
if not tables:
|
|
logger.error("No tables found in data_description.md - aborting")
|
|
return
|
|
logger.info("Parsed %d tables from data_description.md", len(tables))
|
|
|
|
# Load sync state
|
|
sync_state = load_sync_state(SYNC_STATE_PATH)
|
|
|
|
# Load metrics
|
|
metrics_map = load_metrics(METRICS_YML_PATH)
|
|
metric_file_map = load_metric_file_map(METRICS_YML_PATH)
|
|
|
|
# Load existing profiles for fallback (preserve data for tables that fail)
|
|
existing_profiles: Dict[str, Any] = {}
|
|
try:
|
|
if PROFILES_OUTPUT_PATH.exists():
|
|
with open(PROFILES_OUTPUT_PATH) as f:
|
|
existing_data = json.load(f)
|
|
existing_profiles = existing_data.get("tables", {})
|
|
logger.info("Loaded %d existing profiles for fallback", len(existing_profiles))
|
|
except Exception as exc:
|
|
logger.warning("Could not load existing profiles: %s", exc)
|
|
|
|
# Build Jira TableInfo objects for relationship computation
|
|
jira_table_infos: List[TableInfo] = []
|
|
if JIRA_PARQUET_DIR.is_dir():
|
|
for jt in JIRA_TABLES:
|
|
fk_list = []
|
|
for fk in jt.get("foreign_keys", []):
|
|
fk_list.append(
|
|
ForeignKeyInfo(
|
|
column=fk["column"],
|
|
references=fk["references"],
|
|
description=fk.get("description"),
|
|
)
|
|
)
|
|
jira_table_infos.append(
|
|
TableInfo(
|
|
table_id=f"jira.{jt['name']}",
|
|
name=jt["name"],
|
|
description=jt["description"],
|
|
primary_key=jt["primary_key"],
|
|
sync_strategy="partitioned",
|
|
foreign_keys=fk_list,
|
|
partition_by="created_at",
|
|
partition_granularity="month",
|
|
)
|
|
)
|
|
|
|
# Combined table list for relationship computation (data_description + Jira)
|
|
all_tables_combined = list(tables) + jira_table_infos
|
|
|
|
# Profile each table
|
|
profiles: Dict[str, Any] = {}
|
|
success_count = 0
|
|
skip_count = 0
|
|
error_count = 0
|
|
|
|
for table in tables:
|
|
parquet_path = get_parquet_path(table, folder_mapping)
|
|
|
|
# For partitioned tables (directories), check if any .parquet files exist
|
|
if parquet_path.is_dir():
|
|
parquet_files = list(parquet_path.glob("*.parquet"))
|
|
if not parquet_files:
|
|
logger.warning("Skipping %s: no parquet files in %s", table.name, parquet_path)
|
|
skip_count += 1
|
|
continue
|
|
elif not parquet_path.exists():
|
|
logger.warning("Skipping %s: parquet not found at %s", table.name, parquet_path)
|
|
skip_count += 1
|
|
continue
|
|
|
|
try:
|
|
logger.info("Profiling %s ...", table.name)
|
|
profile = profile_table(table, parquet_path, all_tables_combined, sync_state, metrics_map, metric_file_map)
|
|
profiles[table.name] = profile
|
|
success_count += 1
|
|
logger.info(
|
|
" %s: %d rows, %d cols, %d alerts",
|
|
table.name,
|
|
profile["row_count"],
|
|
profile["column_count"],
|
|
len(profile["alerts"]),
|
|
)
|
|
except Exception as exc:
|
|
logger.error("Failed to profile %s: %s", table.name, exc)
|
|
error_count += 1
|
|
# Preserve old profile if available
|
|
if table.name in existing_profiles:
|
|
profiles[table.name] = existing_profiles[table.name]
|
|
profiles[table.name]["_stale"] = True
|
|
logger.info(" Using cached profile for %s", table.name)
|
|
|
|
# Profile Jira / Support tables (partitioned parquet, not in data_description.md)
|
|
if JIRA_PARQUET_DIR.is_dir():
|
|
logger.info("Profiling Jira/Support tables from %s", JIRA_PARQUET_DIR)
|
|
for jira_table in jira_table_infos:
|
|
# Find the matching JIRA_TABLES config for subdir
|
|
jt_config = next((jt for jt in JIRA_TABLES if jt["name"] == jira_table.name), None)
|
|
if not jt_config:
|
|
continue
|
|
jira_path = JIRA_PARQUET_DIR / jt_config["subdir"]
|
|
if not jira_path.is_dir():
|
|
logger.warning("Skipping %s: directory %s not found", jira_table.name, jira_path)
|
|
skip_count += 1
|
|
continue
|
|
parquet_files = list(jira_path.glob("*.parquet"))
|
|
if not parquet_files:
|
|
logger.warning("Skipping %s: no parquet files in %s", jira_table.name, jira_path)
|
|
skip_count += 1
|
|
continue
|
|
|
|
try:
|
|
logger.info("Profiling %s ...", jira_table.name)
|
|
profile = profile_table(
|
|
jira_table, jira_path, all_tables_combined, sync_state, metrics_map, metric_file_map
|
|
)
|
|
profiles[jira_table.name] = profile
|
|
success_count += 1
|
|
logger.info(
|
|
" %s: %d rows, %d cols, %d alerts",
|
|
jira_table.name,
|
|
profile["row_count"],
|
|
profile["column_count"],
|
|
len(profile["alerts"]),
|
|
)
|
|
except Exception as exc:
|
|
logger.error("Failed to profile %s: %s", jira_table.name, exc)
|
|
error_count += 1
|
|
# Preserve old profile if available
|
|
if jira_table.name in existing_profiles:
|
|
profiles[jira_table.name] = existing_profiles[jira_table.name]
|
|
profiles[jira_table.name]["_stale"] = True
|
|
logger.info(" Using cached profile for %s", jira_table.name)
|
|
else:
|
|
logger.info("Jira parquet dir %s not found - skipping Jira tables", JIRA_PARQUET_DIR)
|
|
|
|
# Build output
|
|
output = {
|
|
"generated_at": datetime.utcnow().isoformat() + "Z",
|
|
"version": "1.0",
|
|
"tables": profiles,
|
|
}
|
|
|
|
# Write output
|
|
METADATA_DIR.mkdir(parents=True, exist_ok=True)
|
|
write_json_atomic(PROFILES_OUTPUT_PATH, output)
|
|
|
|
logger.info(
|
|
"Profiling complete: %d profiled, %d skipped, %d errors",
|
|
success_count,
|
|
skip_count,
|
|
error_count,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|