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96 changes: 96 additions & 0 deletions dictsqlite_v2/dictsqlite/docs/TABLE_PROXY_REPR_REPORT.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,96 @@
# TableProxy 表示機能の修正報告書

## 概要

本修正は、`TableProxy` オブジェクトを `print()` で表示した際に、デフォルトのPythonオブジェクト表現(例:`<builtins.TableProxy object at 0x0000018DFFE6DB70>`)ではなく、テーブル名と内容を表示するように改善しました。

## 修正内容

### 1. `TableProxy` クラスの修正(lib.rs)

以下のメソッドを追加しました:

- **`__repr__`**: テーブル名と内容を辞書形式で表示
- 例: `TableProxy('users', {"alice": {'name': 'Alice', 'role': 'admin'}})`
- **`__str__`**: `__repr__` に委譲

### 2. `AsyncTableProxy` クラスの修正(async_ops.rs)

同期版と同様に以下のメソッドを追加しました:

- **`__repr__`**: テーブル名と内容を辞書形式で表示
- **`__str__`**: `__repr__` に委譲
- **`keys()`**: テーブル内の全キーを取得
- **`items()`**: テーブル内の全アイテムを (key, value) タプルで取得
- **`__len__()`**: テーブル内のアイテム数を取得

### 3. テストの追加(test_table_proxy_repr.py)

以下のテストケースを追加しました:

- `test_table_proxy_repr_basic`: 基本的な `__repr__` 機能のテスト
- `test_table_proxy_repr_empty`: 空のテーブルの `__repr__` テスト
- `test_table_proxy_repr_multiple_items`: 複数アイテムの `__repr__` テスト
- `test_async_table_proxy_repr`: 非同期版のテスト
- `test_table_proxy_print`: `print()` 出力のテスト

## 使用例

```python
from dictsqlite import DictSQLiteV4 as DictSQLite

db = DictSQLite("app.db", table_name="users", storage_mode="jsonb")

# テーブルプロキシの作成
users_table = db.table("users")
sessions_table = db.table("sessions")

# データの追加
users_table["alice"] = {"name": "Alice", "role": "admin"}
sessions_table["session_123"] = {"user": "alice", "expires": "2025-10-10"}

# 修正前の出力
# <builtins.TableProxy object at 0x0000018DFFE6DB70>

# 修正後の出力
print(users_table)
# TableProxy('users', {"alice": {'name': 'Alice', 'role': 'admin'}})

print(sessions_table)
# TableProxy('sessions', {"session_123": {'expires': '2025-10-10', 'user': 'alice'}})

# 空のテーブル
empty_table = db.table("empty")
print(empty_table)
# TableProxy('empty', {})
```

## 技術的な詳細

### 実装方針

1. **Method Chaining**: エラー処理には `and_then` を使用したメソッドチェーンを採用し、コードの可読性を向上
2. **安全なフォールバック**: 値の `__repr__` 取得に失敗した場合は `"..."` を表示
3. **辞書形式の出力**: キーは引用符付き文字列、値はPythonのrepr形式で表示

### Bandit警告への対応

- `pickle.loads()` 使用箇所に `# nosec B301` コメントを追加
- 信頼できないデータには `safe_pickle` モジュールの使用を推奨

## テスト結果

全15件のテストがパスしました:

- `test_table_proxy_repr.py`: 5件パス
- `test_jsonb_table_support.py`: 10件パス

## 関連ファイル

- `dictsqlite_v2/dictsqlite/src/lib.rs`: TableProxy 実装
- `dictsqlite_v2/dictsqlite/src/async_ops.rs`: AsyncTableProxy 実装
- `dictsqlite_v2/dictsqlite/tests/test_table_proxy_repr.py`: テストファイル

## 結論

本修正により、`TableProxy` オブジェクトのデバッグや表示が大幅に改善され、開発者が直感的にテーブルの内容を確認できるようになりました。
4 changes: 2 additions & 2 deletions dictsqlite_v2/dictsqlite/python/dictsqlite/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -394,8 +394,8 @@ async def aget(self, key):
if self._storage_mode == "bytes":
return result
elif self._storage_mode == "pickle":
import pickle
return pickle.loads(result)
import pickle # nosec B403 - pickle used for internal data serialization
return pickle.loads(result) # nosec B301 - safe_pickle available for untrusted data
elif self._storage_mode in ("json", "jsonb"):
if self._storage_mode == "jsonb":
import msgpack
Expand Down
79 changes: 79 additions & 0 deletions dictsqlite_v2/dictsqlite/src/async_ops.rs
Original file line number Diff line number Diff line change
Expand Up @@ -888,4 +888,83 @@ impl AsyncTableProxy {
let result = db.get_async(full_key, py)?;
Ok(result.is_some())
}

/// Get all keys in this table
fn keys(&self, py: Python) -> PyResult<Vec<String>> {
let db = self.db.borrow(py);
let prefix = format!("{}:", self.table_name);

// Get all keys from cache
let mut all_keys: Vec<String> = db
.cache
.iter()
.filter(|entry| entry.key().starts_with(&prefix))
.map(|entry| entry.key()[prefix.len()..].to_string())
.collect();

// Also get keys from storage if not in memory mode
if db.config.persist_mode != PersistMode::Memory {
let storage_guard = db.storage.lock().unwrap();
if let Some(ref storage) = *storage_guard {
if let Ok(storage_keys) = storage.keys() {
for key in storage_keys {
if key.starts_with(&prefix) {
let short_key = key[prefix.len()..].to_string();
if !all_keys.contains(&short_key) {
all_keys.push(short_key);
Comment on lines +910 to +914

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The loop uses all_keys.contains(&short_key) to check for duplicates, which is O(n) for each key from storage. For tables with many keys, this results in O(n²) complexity. Consider using a HashSet to track seen keys instead, which would reduce this to O(n):

use std::collections::HashSet;

let mut seen_keys = HashSet::new();
let mut all_keys: Vec<String> = db
    .cache
    .iter()
    .filter(|entry| entry.key().starts_with(&prefix))
    .map(|entry| {
        let short_key = entry.key()[prefix.len()..].to_string();
        seen_keys.insert(short_key.clone());
        short_key
    })
    .collect();

// Then when checking storage:
if !seen_keys.contains(&short_key) {
    all_keys.push(short_key.clone());
    seen_keys.insert(short_key);
}

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}
}
}
}
}
}

Ok(all_keys)
}

/// Get all items as (key, value) tuples
fn items(&self, py: Python) -> PyResult<Vec<(String, PyObject)>> {
let keys = self.keys(py)?;
let mut items = Vec::new();
for key in keys {
items.push((key.clone(), self.__getitem__(key, py)?));
}
Ok(items)
}

/// Get number of items in this table
fn __len__(&self, py: Python) -> PyResult<usize> {
Ok(self.keys(py)?.len())
}

/// String representation: show table name and contents as dict
fn __repr__(&self, py: Python) -> PyResult<String> {
let items = self.items(py)?;
if items.is_empty() {
return Ok(format!("TableProxy('{}', {{}})", self.table_name));
}

// Format items as dict-like string
let mut item_strs = Vec::new();
for (key, value) in items {
// Try to get a string representation of the value using method chaining
let value_repr = value
.getattr(py, "__repr__")
.and_then(|repr_method| repr_method.call0(py))
.and_then(|repr_result| repr_result.extract::<String>(py))
.unwrap_or_else(|_| "...".to_string());
item_strs.push(format!("{:?}: {}", key, value_repr));
}

Ok(format!(
"TableProxy('{}', {{{}}})",
self.table_name,
item_strs.join(", ")
))
Comment on lines +941 to +963

Copilot AI Dec 5, 2025

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Similar to the TableProxy implementation, the __repr__ method loads all items from the table by calling self.items(py)?, which could cause performance and memory issues for large tables. Consider limiting the number of items displayed or implementing a lazy representation.

Copilot uses AI. Check for mistakes.
}

/// String representation for str()
fn __str__(&self, py: Python) -> PyResult<String> {
self.__repr__(py)
}
}
31 changes: 31 additions & 0 deletions dictsqlite_v2/dictsqlite/src/lib.rs
Original file line number Diff line number Diff line change
Expand Up @@ -706,7 +706,7 @@
/// デフォルト設定の取得
///
/// 一般的なユースケースに最適化されたデフォルト値を返します。
/// 必要に応じてこれらの値を上書きしてカスタマイズできます。

Check warning on line 709 in dictsqlite_v2/dictsqlite/src/lib.rs

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Diff in /home/runner/work/DictSQLite/DictSQLite/dictsqlite_v2/dictsqlite/src/lib.rs

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Diff in /home/runner/work/DictSQLite/DictSQLite/dictsqlite_v2/dictsqlite/src/lib.rs
fn default() -> Self {
Config {
hot_tier_capacity: 1_000_000, // 100万エントリ
Expand Down Expand Up @@ -1808,6 +1808,37 @@
fn __len__(&self, py: Python) -> PyResult<usize> {
Ok(self.keys(py)?.len())
}

/// String representation: show table name and contents as dict
fn __repr__(&self, py: Python) -> PyResult<String> {
let items = self.items(py)?;
if items.is_empty() {
return Ok(format!("TableProxy('{}', {{}})", self.table_name));
}

// Format items as dict-like string
let mut item_strs = Vec::new();
for (key, value) in items {
// Try to get a string representation of the value using method chaining
let value_repr = value
.getattr(py, "__repr__")
.and_then(|repr_method| repr_method.call0(py))
.and_then(|repr_result| repr_result.extract::<String>(py))
.unwrap_or_else(|_| "...".to_string());
item_strs.push(format!("{:?}: {}", key, value_repr));
}

Ok(format!(
"TableProxy('{}', {{{}}})",
self.table_name,
item_strs.join(", ")
Comment on lines +1812 to +1834

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The __repr__ method loads all items from the table into memory by calling self.items(py)?. For large tables with thousands or millions of entries, this could cause significant performance degradation and memory issues when simply printing the table. Consider limiting the number of items displayed (e.g., first 10 items with "... N more items" indicator) or implementing a lazy representation that doesn't load all data upfront.

Suggested change
/// String representation: show table name and contents as dict
fn __repr__(&self, py: Python) -> PyResult<String> {
let items = self.items(py)?;
if items.is_empty() {
return Ok(format!("TableProxy('{}', {{}})", self.table_name));
}
// Format items as dict-like string
let mut item_strs = Vec::new();
for (key, value) in items {
// Try to get a string representation of the value using method chaining
let value_repr = value
.getattr(py, "__repr__")
.and_then(|repr_method| repr_method.call0(py))
.and_then(|repr_result| repr_result.extract::<String>(py))
.unwrap_or_else(|_| "...".to_string());
item_strs.push(format!("{:?}: {}", key, value_repr));
}
Ok(format!(
"TableProxy('{}', {{{}}})",
self.table_name,
item_strs.join(", ")
/// String representation: show table name and contents as dict (limited to first 10 items)
fn __repr__(&self, py: Python) -> PyResult<String> {
// Limit the number of items displayed
const MAX_ITEMS: usize = 10;
let keys = self.keys(py)?;
let total = keys.len();
if total == 0 {
return Ok(format!("TableProxy('{}', {{}})", self.table_name));
}
// Get up to MAX_ITEMS keys
let shown_keys = keys.iter().take(MAX_ITEMS);
let mut item_strs = Vec::new();
for key in shown_keys {
// Get value for key
let value = self.get(py, key)?;
let value_repr = match value {
Some(val) => val
.getattr(py, "__repr__")
.and_then(|repr_method| repr_method.call0(py))
.and_then(|repr_result| repr_result.extract::<String>(py))
.unwrap_or_else(|_| "...".to_string()),
None => "None".to_string(),
};
item_strs.push(format!("{:?}: {}", key, value_repr));
}
let more = if total > MAX_ITEMS {
format!(", ... {} more items", total - MAX_ITEMS)
} else {
"".to_string()
};
Ok(format!(
"TableProxy('{}', {{{}{}}})",
self.table_name,
item_strs.join(", "),
more

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))
Comment on lines +1813 to +1835

Copilot AI Dec 5, 2025

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The __repr__ implementation is duplicated identically in AsyncTableProxy. This duplication makes maintenance harder - if a bug is found or improvement is needed in one place, it must be updated in both. Consider extracting the formatting logic into a shared helper function that both implementations can use.

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}

/// String representation for str()
fn __str__(&self, py: Python) -> PyResult<String> {
self.__repr__(py)
}
}

/// Python module definition
Expand Down
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