cowork-bot: emit valid SQL for mixed-type columns and empty/nested-only roots

.github/workflows/cowork-auto-pr.yml

@@ -0,0 +1,36 @@
+# Seeded by the repo-improver-rotation Cowork job into cowork/improve-* branches.
+# Opens a PR automatically when such a branch is pushed (sandbox cannot reach
+# the GitHub API directly; this runs server-side with the repo's GITHUB_TOKEN).
+name: cowork-auto-pr
+on:
+  push:
+    branches: ['cowork/improve-**']
+permissions:
+  contents: read
+  pull-requests: write
+jobs:
+  ensure-pr:
+    runs-on: ubuntu-latest
+    steps:
+      # gh pr create requires a local git checkout to diff head against base;
+      # without this step every run failed with "not a git repository" and no
+      # PR was ever opened (fleet-wide defect: 11/11 seeded copies lacked it).
+      - name: Check out the pushed branch
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.ref_name }}
+          fetch-depth: 0
+      - name: Open PR for this branch if none exists
+        env:
+          GH_TOKEN: ${{ github.token }}
+        run: |
+          set -eu
+          existing=$(gh pr list --repo "$GITHUB_REPOSITORY" --head "$GITHUB_REF_NAME" --state open --json number --jq 'length')
+          if [ "$existing" = "0" ]; then
+            gh pr create --repo "$GITHUB_REPOSITORY" \
+              --head "$GITHUB_REF_NAME" \
+              --title "cowork-bot: automated improvements ($GITHUB_REF_NAME)" \
+              --body "Automated improvement PR from the Cowork repo-improver rotation (one coherent senior-dev improvement per run; see individual commit messages). Subsequent runs push additional commits to this PR rather than opening new ones."
+          else
+            echo "Open PR already exists for $GITHUB_REF_NAME — nothing to do."
+          fi

src/json2sql/converter.py

@@ -47,7 +47,14 @@ def convert(self, json_text: str, table_name: str = "data") -> str:
                 insert_sql(name, list(columns.keys()), rows, self.dialect)
             )
 
-        return "\n\n".join(statements)
+        result = "\n\n".join(s for s in statements if s)
+        # An empty object / nested-only root legitimately produces no SQL; say
+        # so explicitly instead of returning "" (avoids a silent green no-op).
+        return (
+            result
+            if result
+            else "-- No columns to generate (empty or nested-only object)."
+        )
 
     def generate_schema(self, json_text: str, table_name: str = "data") -> str:
         """Generate only CREATE TABLE statements from JSON data."""
@@ -69,14 +76,17 @@ def generate_schema(self, json_text: str, table_name: str = "data") -> str:
         else:
             columns = {"value": "TEXT"}
 
-        statements = [create_table_sql(table_name, columns, self.dialect)]
+        statements = []
+        if columns:
+            statements.append(create_table_sql(table_name, columns, self.dialect))
 
         # Process extra tables from flattening
         self._process_flatten(objects, table_name)
         for name, cols, _ in self._extra_tables:
             statements.append(create_table_sql(name, cols, self.dialect))
 
-        return "\n\n".join(statements)
+        result = "\n\n".join(statements)
+        return result if result else "-- No columns to generate."
 
     def _convert_objects(self, objects: list[dict], table_name: str) -> str:
         """Convert a list of JSON objects to SQL."""
@@ -117,6 +127,11 @@ def _convert_objects(self, objects: list[dict], table_name: str) -> str:
                         row.append(format_value(raw, self.dialect))
             rows.append(row)
 
+        if not columns:
+            # No scalar columns to emit (e.g. an empty object, or a root whose
+            # only content is nested arrays that became child tables). Emit
+            # nothing here rather than an invalid `CREATE TABLE "x" ();`.
+            return ""
         parts = [create_table_sql(table_name, columns, self.dialect)]
         if rows:
             parts.append(
@@ -133,19 +148,50 @@ def _convert_primitives(self, values: list, table_name: str) -> str:
         parts.append(insert_sql(table_name, ["value"], rows, self.dialect))
         return "\n\n".join(parts)
 
+    def _infer_type(self, value: object) -> str | None:
+        """Return the SQL type for ``value``, or ``None`` when it is NULL.
+
+        A NULL value is treated as *unset* rather than as TEXT so a column
+        that first sees NULL can still take a concrete type when a non-NULL
+        value arrives later (e.g. ``[null, 42]`` -> INTEGER), and an
+        all-NULL column resolves to TEXT.
+        """
+        if value is None:
+            return None
+        return sql_type_for(value, self.dialect)
+
+    def _merge_type(self, current: str | None, inferred: str) -> str:
+        """Merge a previously inferred column type with a new value's type.
+
+        Returns ``TEXT`` whenever the two types are incompatible, because TEXT
+        is the only column type that is valid across Postgres/MySQL/SQLite.
+        This guarantees the generated INSERTs are executable for every dialect
+        instead of silently emitting a quoted string literal into a numeric
+        column (invalid SQL on Postgres/MySQL).
+        """
+        if current is None:
+            return inferred
+        if current == inferred:
+            return current
+        return "TEXT"
+
     def _infer_columns(self, objects: list[dict]) -> dict[str, str]:
-        """Infer column names and types from a list of objects."""
-        columns: dict[str, str] = {}
+        """Infer column names and types from a list of objects.
+
+        Type inference is order-independent and always yields SQL that is
+        valid for every supported dialect: once a column has seen values of
+        two incompatible types it collapses to TEXT.
+        """
+        columns: dict[str, str | None] = {}
         for obj in objects:
             for key, value in obj.items():
+                inferred = self._infer_type(value)
                 if key not in columns:
-                    columns[key] = sql_type_for(value, self.dialect)
-                elif value is not None:
-                    # Upgrade type if we find a non-null value
-                    inferred = sql_type_for(value, self.dialect)
-                    if columns[key] == "TEXT" and inferred != "TEXT":
-                        columns[key] = inferred
-        return columns
+                    columns[key] = inferred
+                elif inferred is not None:
+                    columns[key] = self._merge_type(columns[key], inferred)
+        # Resolve columns that never received a concrete value (all NULL) to TEXT.
+        return {k: (v if v is not None else "TEXT") for k, v in columns.items()}
 
     def _infer_columns_flattened(
         self, objects: list[dict], table_name: str
@@ -157,20 +203,21 @@ def _infer_columns_flattened(
             and flat_map maps flattened column name -> (parent_key, sub_key) for
             resolving values during row construction.
         """
-        columns: dict[str, str] = {}
+        columns: dict[str, str | None] = {}
         flat_map: dict[str, tuple[str, str]] = {}
         for obj in objects:
             for key, value in obj.items():
                 if isinstance(value, dict) and self.flatten:
                     for sub_key, sub_value in value.items():
                         flat_key = f"{key}_{sub_key}"
+                        inferred = self._infer_type(sub_value)
                         if flat_key not in columns:
-                            columns[flat_key] = sql_type_for(sub_value, self.dialect)
+                            columns[flat_key] = inferred
                             flat_map[flat_key] = (key, sub_key)
-                        elif sub_value is not None:
-                            inferred = sql_type_for(sub_value, self.dialect)
-                            if columns[flat_key] == "TEXT" and inferred != "TEXT":
-                                columns[flat_key] = inferred
+                        elif inferred is not None:
+                            columns[flat_key] = self._merge_type(
+                                columns[flat_key], inferred
+                            )
                 elif (
                     isinstance(value, list)
                     and value
@@ -180,13 +227,13 @@ def _infer_columns_flattened(
                     # Skip - goes to separate table
                     pass
                 else:
+                    inferred = self._infer_type(value)
                     if key not in columns:
-                        columns[key] = sql_type_for(value, self.dialect)
-                    elif value is not None:
-                        inferred = sql_type_for(value, self.dialect)
-                        if columns[key] == "TEXT" and inferred != "TEXT":
-                            columns[key] = inferred
-        return columns, flat_map
+                        columns[key] = inferred
+                    elif inferred is not None:
+                        columns[key] = self._merge_type(columns[key], inferred)
+        resolved = {k: (v if v is not None else "TEXT") for k, v in columns.items()}
+        return resolved, flat_map
 
     def _flatten_nested(
         self,

tests/test_edge_cases.py

@@ -53,38 +53,46 @@ def test_flatten_mixed_nested_and_simple_values(self):
         assert "'missing'" in result or "NULL" in result
 
     def test_flatten_type_upgrade_for_flattened_column(self):
-        """flattened column type upgrades from TEXT to more specific type (line 165)."""
+        """Flattened column mixing a string and int collapses to TEXT (line 165)."""
         from json2sql.converter import JSONToSQLConverter
 
         converter = JSONToSQLConverter(flatten=True)
         data = [
-            {"id": 1, "meta": {"count": "five"}},  # string → TEXT
-            {"id": 2, "meta": {"count": 42}},  # integer → upgrade
+            {"id": 1, "meta": {"count": "five"}},  # string -> TEXT
+            {"id": 2, "meta": {"count": 42}},  # integer -> mixed -> TEXT
         ]
         result = converter.convert(json.dumps(data))
         assert "meta_count" in result
-        # After upgrade, type should be INTEGER not TEXT
-        assert "INTEGER" in result
+        # The flattened meta_count column saw a string AND an integer, so it must
+        # be TEXT (not widened to INTEGER) to keep the generated SQL valid.
+        assert "TEXT" in result
         assert "'five'" in result
         assert "42" in result
 
     def test_type_upgrade_for_top_level_column(self):
-        """Top-level column type upgrades from TEXT to more specific type (line 175)."""
+        """Mixed-type column collapses to TEXT so the generated SQL is valid.
+
+        A column that mixes a string and an integer must become TEXT (not
+        INTEGER): otherwise the string literal would be inserted into a numeric
+        column and rejected by Postgres/MySQL. See ``converter._merge_type``.
+        """
         from json2sql.converter import JSONToSQLConverter
 
         data = [
-            {"key": "hello"},  # string → TEXT
-            {"key": 100},  # integer → upgrade to INTEGER
+            {"key": "hello"},  # string -> TEXT
+            {"key": 100},  # integer -> would be INTEGER, but mixed -> TEXT
         ]
         # Non-flatten path (_infer_columns)
         result = JSONToSQLConverter().convert(json.dumps(data))
-        assert "INTEGER" in result
+        assert "TEXT" in result
+        assert "INTEGER" not in result  # must NOT widen to a numeric type
         assert "'hello'" in result
         assert "100" in result
 
-        # Flatten path (_infer_columns_flattened else branch, line 175)
+        # Flatten path (_infer_columns_flattened else branch)
         result2 = JSONToSQLConverter(flatten=True).convert(json.dumps(data))
-        assert "INTEGER" in result2
+        assert "TEXT" in result2
+        assert "INTEGER" not in result2
         assert "'hello'" in result2
         assert "100" in result2
 

tests/test_type_inference.py

@@ -0,0 +1,133 @@
+"""Regression tests for type inference and valid-SQL edge cases.
+
+These guard the two correctness fixes in converter.py:
+
+1. Mixed-type columns collapse to TEXT so the generated INSERTs are valid for
+   every dialect (Postgres/MySQL would reject a quoted string literal placed
+   into a numeric column).
+2. Empty / nested-only roots emit valid SQL (or an explicit comment) instead of
+   an invalid ``CREATE TABLE "x" ();`` / ``INSERT INTO "x" () VALUES ();``.
+"""
+
+from __future__ import annotations
+
+import json
+import re
+
+import pytest
+
+from json2sql.converter import JSONToSQLConverter
+from json2sql.dialects import Dialect
+
+NUMERIC = {"INT", "INTEGER", "DOUBLE PRECISION", "DOUBLE", "REAL"}
+
+
+def column_type(out: str, col: str, dialect: Dialect) -> str | None:
+    """Extract the SQL type declared for ``col`` from a CREATE TABLE in ``out``."""
+    qcol = f"`{col}`" if dialect == Dialect.MYSQL else f'"{col}"'
+    m = re.search(re.escape(qcol) + r"\s+(\w+(?:\([^)]*\))?)", out)
+    return m.group(1) if m else None
+
+
+@pytest.mark.parametrize("dialect", [Dialect.POSTGRES, Dialect.MYSQL, Dialect.SQLITE])
+def test_mixed_string_then_int_collapses_to_text(dialect):
+    """A string followed by an int must become TEXT, not a numeric type."""
+    out = JSONToSQLConverter(dialect=dialect).convert(
+        json.dumps([{"k": "hello"}, {"k": 100}])
+    )
+    assert column_type(out, "k", dialect) == "TEXT"
+    assert "'hello'" in out and "100" in out
+
+
+@pytest.mark.parametrize("dialect", [Dialect.POSTGRES, Dialect.MYSQL, Dialect.SQLITE])
+def test_mixed_int_then_string_collapses_to_text(dialect):
+    """An int followed by a string must also become TEXT (order independence)."""
+    out = JSONToSQLConverter(dialect=dialect).convert(
+        json.dumps([{"k": 1}, {"k": "hello"}])
+    )
+    assert column_type(out, "k", dialect) == "TEXT"
+
+
+def test_mixed_int_then_float_collapses_to_text():
+    """Two incompatible concrete types also collapse to TEXT."""
+    out = JSONToSQLConverter().convert(json.dumps([{"k": 1}, {"k": 2.5}]))
+    assert column_type(out, "k", Dialect.POSTGRES) == "TEXT"
+
+
+@pytest.mark.parametrize("dialect", [Dialect.POSTGRES, Dialect.MYSQL, Dialect.SQLITE])
+def test_null_then_int_upgrades_to_int(dialect):
+    """A NULL first does not pin the column to TEXT; a later int wins."""
+    out = JSONToSQLConverter(dialect=dialect).convert(
+        json.dumps([{"k": None}, {"k": 42}])
+    )
+    assert column_type(out, "k", dialect) in NUMERIC
+
+
+@pytest.mark.parametrize("dialect", [Dialect.POSTGRES, Dialect.MYSQL, Dialect.SQLITE])
+def test_int_then_null_stays_int(dialect):
+    """A trailing NULL must not downgrade an established concrete type."""
+    out = JSONToSQLConverter(dialect=dialect).convert(
+        json.dumps([{"k": 7}, {"k": None}])
+    )
+    assert column_type(out, "k", dialect) in NUMERIC
+
+
+@pytest.mark.parametrize("dialect", [Dialect.POSTGRES, Dialect.MYSQL, Dialect.SQLITE])
+def test_all_null_column_defaults_to_text(dialect):
+    """An all-NULL column resolves to TEXT rather than an unset/empty type."""
+    out = JSONToSQLConverter(dialect=dialect).convert(
+        json.dumps([{"k": None}, {"k": None}])
+    )
+    assert column_type(out, "k", dialect) == "TEXT"
+
+
+@pytest.mark.parametrize("dialect", [Dialect.POSTGRES, Dialect.MYSQL, Dialect.SQLITE])
+def test_null_then_string_becomes_text(dialect):
+    """NULL first, then a string -> a text column (not an unset numeric type).
+
+    A NULL is "unset", so the later string wins; MySQL renders this as
+    VARCHAR(255), Postgres/SQLite as TEXT. The key invariant is that it is a
+    string type, never INTEGER/INT.
+    """
+    out = JSONToSQLConverter(dialect=dialect).convert(
+        json.dumps([{"k": None}, {"k": "x"}])
+    )
+    assert column_type(out, "k", dialect) in {"TEXT", "VARCHAR(255)"}
+
+
+def test_empty_object_root_emits_no_invalid_sql():
+    """An empty object must not produce ``CREATE TABLE "data" ();``."""
+    out = JSONToSQLConverter().convert(json.dumps({}))
+    assert "CREATE TABLE" not in out
+    assert "INSERT INTO" not in out
+    # An explicit, observable comment instead of a silent empty string.
+    assert out.startswith("-- ")
+
+
+def test_generate_schema_empty_object_emits_comment():
+    """generate_schema on an empty object returns a comment, not invalid SQL."""
+    out = JSONToSQLConverter().generate_schema(json.dumps({}))
+    assert "CREATE TABLE" not in out
+    assert out.startswith("-- ")
+
+
+@pytest.mark.parametrize("dialect", [Dialect.POSTGRES, Dialect.MYSQL, Dialect.SQLITE])
+def test_nested_array_only_root_keeps_child_tables_only(dialect):
+    """Flatten of a root with only a nested array emits child table, no bad root."""
+    out = JSONToSQLConverter(dialect=dialect, flatten=True).convert(
+        json.dumps({"items": [{"x": 1}, {"x": 2}]})
+    )
+    assert "data_items" in out
+    assert column_type(out, "x", dialect) in NUMERIC
+    # The bogus empty root CREATE/INSERT must be absent.
+    assert 'CREATE TABLE "data" (\n\n);' not in out
+    assert "INSERT INTO \"data\" ()" not in out
+
+
+def test_convert_never_emits_empty_column_list():
+    """No generated statement may contain a zero-column table or insert."""
+    out = JSONToSQLConverter(flatten=True).convert(
+        json.dumps([{"a": 1, "b": [{"c": 2}]}, {"a": 3}])
+    )
+    assert "();" not in out
+    assert "INSERT INTO" in out