feat: add export_schemas tool for bq

tools/export_schemas/README.md

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+# BigQuery Schema Export Tool
+
+This tool exports the DDL (Data Definition Language) for all tables in a BigQuery project's region to local SQL files, organized by dataset.
+
+
+## Use Cases
+
+- **AI Context Provisioning**: Provide a snapshot of your current DDLs to AI models for high-fidelity schema context.
+- **Version Control**: Quickly dump all table schemas to Git for version tracking.
+- **Migration**: Export schemas to specific folders to assist in migrating datasets between projects or regions.
+- **Backup**: Create a snapshot of your current DDLs for disaster recovery or audit purposes.
+- **Local Development**: Analyze table structures offline without querying BigQuery repeatedly.
+- **CI/CD**: Use as a step in a CI/CD pipeline to export schemas for testing or validation.
+
+## Prerequisites
+
+- Google Cloud SDK: The bq command-line tool must be installed and authenticated.
+- Python 3.x: Installed on your local machine or Cloud Shell.
+- IAM Permissions:
+    - bigquery.jobs.create (to run the export query).
+    - bigquery.tables.get and access to INFORMATION_SCHEMA views.
+
+## Usage
+
+```bash
+python3 export_schemas.py --project_id <YOUR_PROJECT_ID> [--region <REGION>] [--output_dir <OUTPUT_DIR>]
+```
+
+### Arguments
+
+- `--project_id`: (Required) The Google Cloud Project ID.
+- `--region`: (Optional) The BigQuery region to query. Defaults to `us-central1`.
+- `--output_dir`: (Optional) The directory to save the exported schemas. Defaults to `bq_schemas`.
+
+### Example
+
+```bash
+python3 export_schemas.py --project_id my-data-project --region us-east1
+```
+
+### Output
+
+The tool will create a folder structure like this:
+
+```
+bq_schemas/
+  ├── dataset_a/
+  │   ├── table1.sql
+  │   └── table2.sql
+  └── dataset_b/
+      └── table3.sql
+```
+
+It will also generate a zip file `bq_schema_export.zip` containing all the exported schemas.

tools/export_schemas/export_schemas.py

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+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import subprocess
+import json
+import os
+import shutil
+import argparse
+import sys
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Export BigQuery table schemas and DDLs to local files.")
+    parser.add_argument("--project_id", required=True, help="GCP Project ID")
+    parser.add_argument("--region", default="us-central1", help="BigQuery Region (default: us-central1)")
+    parser.add_argument("--output_dir", default="bq_schemas", help="Output directory for exported schemas (default: bq_schemas)")
+    return parser.parse_args()
+
+def main():
+    args = parse_args()
+
+    project_id = args.project_id
+    region = args.region
+    output_dir = args.output_dir
+
+    # Construct region scope for INFORMATION_SCHEMA
+    if region.lower().startswith("region-"):
+          region_scope = region
+    else:
+          region_scope = f"region-{region}"
+
+    # 1. Setup clean directory
+    if os.path.exists(output_dir):
+        print(f"Cleaning existing directory: {output_dir}")
+        shutil.rmtree(output_dir)
+    os.makedirs(output_dir)
+
+    print(f"--- Starting Bulk Export for {project_id} ({region_scope}) ---")
+
+    # 2. Run the query using BQ CLI and get JSON output (Safe for DDL parsing)
+    query = f"""
+    SELECT table_schema, table_name, ddl
+    FROM `{project_id}.{region_scope}.INFORMATION_SCHEMA.TABLES`
+    WHERE table_type = 'BASE TABLE'
+    """
+
+    print("Querying BigQuery metadata...")
+
+    cmd = [
+        "bq", "query", 
+        "--use_legacy_sql=false", 
+        "--format=json", 
+        "--max_rows=10000", 
+        f"--project_id={project_id}",
+        query
+    ]
+
+    try:
+        # Run command and capture output
+        result = subprocess.run(cmd, capture_output=True, text=True, check=True)
+        tables = json.loads(result.stdout)
+    except subprocess.CalledProcessError as e:
+        print("\nError running BigQuery command:")
+        print(e.stderr)
+        return
+    except json.JSONDecodeError:
+        print("\nError: Query returned no data or invalid JSON.")
+        print("Check if your region is correct or if you have permissions.")
+        return
+
+    if not tables:
+        print("No tables found. Check your project ID and region.")
+        return
+
+    print(f"Found {len(tables)} tables. Writing .sql files...")
+
+    # 3. Write files
+    for row in tables:
+        dataset = row['table_schema']
+        table = row['table_name']
+        ddl = row['ddl']
+
+        # Create dataset folder if it doesn't exist
+        ds_path = os.path.join(output_dir, dataset)
+        os.makedirs(ds_path, exist_ok=True)
+
+        # Write the DDL
+        with open(os.path.join(ds_path, f"{table}.sql"), "w") as f:
+            f.write(ddl)
+
+    # 4. Create Zip
+    print("Zipping files...")
+    shutil.make_archive("bq_schema_export", 'zip', output_dir)
+
+    print("\n----SUCCESS----!")
+    print(f"File created: {os.path.abspath('bq_schema_export.zip')}")
+    print(f"Schemas exported to: {os.path.abspath(output_dir)}")
+
+if __name__ == "__main__":
+    main()