⚡️ Speed up method RedisUpdateBuffer._combine_list_of_transactions by 11%

[codeflash-ai]

📄 11% (0.11x) speedup for RedisUpdateBuffer._combine_list_of_transactions in litellm/proxy/db/db_transaction_queue/redis_update_buffer.py

⏱️ Runtime : 3.27 milliseconds → 2.96 milliseconds (best of 250 runs)

📝 Explanation and details

The optimized code achieves a 10% speedup by reducing expensive attribute lookups and object creation overhead during the transaction combining process.

Key Optimizations:

1. Reduced Object Creation: Instead of creating the DBSpendUpdateTransactions object upfront and repeatedly accessing its fields, the optimization uses plain Python dictionaries (combined_data) to accumulate results and only creates the final object once at the end.

2. Eliminated Repeated Attribute Lookups: The original code performed combined_transaction[field] lookups twice per entity (once for .get() and once for assignment). The optimized version caches combined_dict = combined_data[field] and reuses it, reducing hash lookups.

3. Moved Field List Creation: The field_names list is created once outside the loop instead of being recreated for each function call.

Why This Speeds Up Python Code:

• Dictionary access in Python has overhead, especially when accessing nested attributes on custom objects
• The original code created a complex object early and performed many attribute accesses during accumulation
• Plain dict operations are faster than custom object attribute access in Python
• Reducing the number of hash table lookups per entity provides cumulative performance benefits

Test Case Performance Patterns:

• Small transactions (single entities): Slight slowdown due to initialization overhead
• Large-scale operations: Significant speedup (18-39% faster) where the reduced lookup overhead compounds
• Multiple fields/entities: Better performance as the optimization scales with data complexity
• Sparse data: Maintains performance as the optimization doesn't add overhead for empty transactions

The optimization is most effective for workloads with many transactions or entities, which appears to be the primary use case based on the large-scale test scenarios showing the biggest improvements.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 32 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

from typing import Any, Dict, List

# imports
import pytest  # used for our unit tests
from litellm.proxy.db.db_transaction_queue.redis_update_buffer import \
    RedisUpdateBuffer


# --- Minimal DBSpendUpdateTransactions definition for testing ---
class DBSpendUpdateTransactions(dict):
    """
    A dict subclass to mimic the expected behavior of DBSpendUpdateTransactions.
    It holds 7 dict fields for different transaction types.
    """
    def __init__(
        self,
        user_list_transactions=None,
        end_user_list_transactions=None,
        key_list_transactions=None,
        team_list_transactions=None,
        team_member_list_transactions=None,
        org_list_transactions=None,
        tag_list_transactions=None,
    ):
        super().__init__()
        self['user_list_transactions'] = user_list_transactions or {}
        self['end_user_list_transactions'] = end_user_list_transactions or {}
        self['key_list_transactions'] = key_list_transactions or {}
        self['team_list_transactions'] = team_list_transactions or {}
        self['team_member_list_transactions'] = team_member_list_transactions or {}
        self['org_list_transactions'] = org_list_transactions or {}
        self['tag_list_transactions'] = tag_list_transactions or {}

    def get(self, key, default=None):
        # Override get to work like a dict
        return super().get(key, default)

    def __getitem__(self, key):
        return super().__getitem__(key)

    def __setitem__(self, key, value):
        super().__setitem__(key, value)
from litellm.proxy.db.db_transaction_queue.redis_update_buffer import \
    RedisUpdateBuffer

# --- Unit Tests ---
# 1. Basic Test Cases

def test_single_transaction_basic():
    # Single transaction, single field, single entity
    tx = DBSpendUpdateTransactions(user_list_transactions={"u1": 10})
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx]); result = codeflash_output # 4.55μs -> 4.64μs (2.00% slower)
    # All other fields should be empty
    for field in [
        "end_user_list_transactions", "key_list_transactions",
        "team_list_transactions", "team_member_list_transactions",
        "org_list_transactions", "tag_list_transactions"
    ]:
        pass

def test_multiple_transactions_same_field_same_entity():
    # Two transactions, same field, same entity
    tx1 = DBSpendUpdateTransactions(user_list_transactions={"u1": 5})
    tx2 = DBSpendUpdateTransactions(user_list_transactions={"u1": 7})
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 5.99μs -> 6.05μs (1.06% slower)

def test_multiple_transactions_same_field_different_entities():
    # Two transactions, same field, different entities
    tx1 = DBSpendUpdateTransactions(user_list_transactions={"u1": 5})
    tx2 = DBSpendUpdateTransactions(user_list_transactions={"u2": 7})
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 5.81μs -> 5.92μs (1.82% slower)

def test_multiple_transactions_multiple_fields():
    # Two transactions, multiple fields
    tx1 = DBSpendUpdateTransactions(
        user_list_transactions={"u1": 5},
        key_list_transactions={"k1": 3}
    )
    tx2 = DBSpendUpdateTransactions(
        user_list_transactions={"u2": 7},
        key_list_transactions={"k1": 2, "k2": 4}
    )
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 6.87μs -> 6.72μs (2.16% faster)

def test_no_transactions():
    # Empty list should return all empty dicts
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([]); result = codeflash_output # 1.58μs -> 2.12μs (25.2% slower)
    for field in [
        "user_list_transactions", "end_user_list_transactions", "key_list_transactions",
        "team_list_transactions", "team_member_list_transactions",
        "org_list_transactions", "tag_list_transactions"
    ]:
        pass

def test_transaction_with_zero_amount():
    # Transaction with zero amount should be summed properly
    tx1 = DBSpendUpdateTransactions(user_list_transactions={"u1": 0})
    tx2 = DBSpendUpdateTransactions(user_list_transactions={"u1": 5})
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 6.06μs -> 6.33μs (4.33% slower)

def test_transaction_with_negative_amount():
    # Negative values should be summed
    tx1 = DBSpendUpdateTransactions(user_list_transactions={"u1": 10})
    tx2 = DBSpendUpdateTransactions(user_list_transactions={"u1": -4})
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 6.04μs -> 6.14μs (1.61% slower)

# 2. Edge Test Cases

def test_transaction_with_missing_fields():
    # Transaction missing some fields (should be treated as empty dicts)
    tx1 = DBSpendUpdateTransactions(user_list_transactions={"u1": 10})
    tx2 = DBSpendUpdateTransactions()  # all fields empty
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 5.28μs -> 5.60μs (5.68% slower)
    # All other fields should be empty
    for field in [
        "end_user_list_transactions", "key_list_transactions",
        "team_list_transactions", "team_member_list_transactions",
        "org_list_transactions", "tag_list_transactions"
    ]:
        pass

def test_transaction_with_non_integer_amounts():
    # Transaction with float amounts
    tx1 = DBSpendUpdateTransactions(user_list_transactions={"u1": 2.5})
    tx2 = DBSpendUpdateTransactions(user_list_transactions={"u1": 1.5})
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 6.29μs -> 6.27μs (0.335% faster)

def test_transaction_with_empty_dicts():
    # Transaction with all fields as empty dicts
    tx1 = DBSpendUpdateTransactions()
    tx2 = DBSpendUpdateTransactions()
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 4.32μs -> 4.89μs (11.7% slower)
    for field in [
        "user_list_transactions", "end_user_list_transactions", "key_list_transactions",
        "team_list_transactions", "team_member_list_transactions",
        "org_list_transactions", "tag_list_transactions"
    ]:
        pass

def test_transaction_with_multiple_fields_and_entities():
    # Each transaction covers all fields and multiple entities
    tx1 = DBSpendUpdateTransactions(
        user_list_transactions={"u1": 1, "u2": 2},
        end_user_list_transactions={"eu1": 3},
        key_list_transactions={"k1": 4},
        team_list_transactions={"t1": 5},
        team_member_list_transactions={"tm1": 6},
        org_list_transactions={"o1": 7},
        tag_list_transactions={"tag1": 8}
    )
    tx2 = DBSpendUpdateTransactions(
        user_list_transactions={"u1": 4, "u3": 3},
        end_user_list_transactions={"eu1": 2, "eu2": 1},
        key_list_transactions={"k2": 2},
        team_list_transactions={"t1": 1, "t2": 2},
        team_member_list_transactions={"tm2": 1},
        org_list_transactions={"o1": 3, "o2": 2},
        tag_list_transactions={"tag2": 5}
    )
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 11.1μs -> 8.64μs (28.5% faster)

def test_transaction_with_large_integer_values():
    # Transaction with very large integer values
    big_num = 10**12
    tx1 = DBSpendUpdateTransactions(user_list_transactions={"u1": big_num})
    tx2 = DBSpendUpdateTransactions(user_list_transactions={"u1": big_num})
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 6.11μs -> 6.30μs (3.10% slower)

def test_transaction_with_overlapping_and_non_overlapping_entities():
    # Some entities overlap, some do not
    tx1 = DBSpendUpdateTransactions(user_list_transactions={"u1": 1, "u2": 2})
    tx2 = DBSpendUpdateTransactions(user_list_transactions={"u2": 3, "u3": 4})
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 6.19μs -> 6.24μs (0.817% slower)

def test_transaction_with_negative_and_zero_and_positive_amounts():
    # Mix of negative, zero, and positive amounts
    tx1 = DBSpendUpdateTransactions(user_list_transactions={"u1": -2, "u2": 0})
    tx2 = DBSpendUpdateTransactions(user_list_transactions={"u1": 5, "u2": 3})
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 6.29μs -> 6.28μs (0.159% faster)

# 3. Large Scale Test Cases

def test_large_number_of_transactions():
    # Combine 500 transactions, each with one entity, same field
    num_tx = 500
    txs = [DBSpendUpdateTransactions(user_list_transactions={f"u{i}": i}) for i in range(num_tx)]
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions(txs); result = codeflash_output # 679μs -> 622μs (9.10% faster)
    # Each entity should have its value from its own transaction
    for i in range(num_tx):
        pass

def test_large_number_of_entities_in_one_transaction():
    # One transaction with 1000 entities in one field
    num_entities = 1000
    tx = DBSpendUpdateTransactions(user_list_transactions={f"u{i}": i for i in range(num_entities)})
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx]); result = codeflash_output # 100μs -> 84.7μs (18.1% faster)
    for i in range(num_entities):
        pass

def test_large_scale_multiple_fields_and_transactions():
    # 100 transactions, each with 10 entities per field, all fields populated
    num_tx = 100
    num_entities = 10
    txs = []
    for t in range(num_tx):
        txs.append(DBSpendUpdateTransactions(
            user_list_transactions={f"u{e}": 1 for e in range(num_entities)},
            end_user_list_transactions={f"eu{e}": 2 for e in range(num_entities)},
            key_list_transactions={f"k{e}": 3 for e in range(num_entities)},
            team_list_transactions={f"t{e}": 4 for e in range(num_entities)},
            team_member_list_transactions={f"tm{e}": 5 for e in range(num_entities)},
            org_list_transactions={f"o{e}": 6 for e in range(num_entities)},
            tag_list_transactions={f"tag{e}": 7 for e in range(num_entities)},
        ))
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions(txs); result = codeflash_output # 898μs -> 647μs (38.8% faster)
    # Each entity in each field should be summed over all transactions
    for e in range(num_entities):
        pass

def test_large_scale_sparse_transactions():
    # 1000 transactions, only some have data, some are empty
    num_tx = 1000
    txs = []
    for i in range(num_tx):
        if i % 100 == 0:
            txs.append(DBSpendUpdateTransactions(user_list_transactions={f"u{i}": i}))
        else:
            txs.append(DBSpendUpdateTransactions())
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions(txs); result = codeflash_output # 950μs -> 987μs (3.74% slower)
    # Only every 100th entity should be present
    for i in range(0, num_tx, 100):
        pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import pytest
from litellm.proxy.db.db_transaction_queue.redis_update_buffer import \
    RedisUpdateBuffer


# Function to test (copied from above)
class DBSpendUpdateTransactions(dict):
    """
    Simple dict-based mock for DBSpendUpdateTransactions.
    Each field is a dict mapping entity_id to an amount.
    """
    def __init__(
        self,
        user_list_transactions=None,
        end_user_list_transactions=None,
        key_list_transactions=None,
        team_list_transactions=None,
        team_member_list_transactions=None,
        org_list_transactions=None,
        tag_list_transactions=None,
    ):
        super().__init__()
        self["user_list_transactions"] = user_list_transactions or {}
        self["end_user_list_transactions"] = end_user_list_transactions or {}
        self["key_list_transactions"] = key_list_transactions or {}
        self["team_list_transactions"] = team_list_transactions or {}
        self["team_member_list_transactions"] = team_member_list_transactions or {}
        self["org_list_transactions"] = org_list_transactions or {}
        self["tag_list_transactions"] = tag_list_transactions or {}

    def get(self, key, default=None):
        # Allow attribute-style access for compatibility
        return super().get(key, default)
from litellm.proxy.db.db_transaction_queue.redis_update_buffer import \
    RedisUpdateBuffer

# ---------------------- UNIT TESTS ----------------------------

# 1. Basic Test Cases

def test_combine_empty_list_returns_empty():
    # Test combining an empty list returns all empty dicts
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([]); result = codeflash_output # 2.01μs -> 2.74μs (26.7% slower)
    for field in result:
        pass

def test_combine_single_transaction():
    # Test combining a single transaction returns that transaction
    tx = DBSpendUpdateTransactions(
        user_list_transactions={"u1": 10},
        end_user_list_transactions={"eu1": 5},
        key_list_transactions={},
        team_list_transactions={},
        team_member_list_transactions={},
        org_list_transactions={},
        tag_list_transactions={}
    )
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx]); result = codeflash_output # 4.82μs -> 5.30μs (8.93% slower)
    for field in ["key_list_transactions", "team_list_transactions", "team_member_list_transactions", "org_list_transactions", "tag_list_transactions"]:
        pass

def test_combine_two_transactions_different_fields():
    # Test combining two transactions with different fields
    tx1 = DBSpendUpdateTransactions(
        user_list_transactions={"u1": 10},
        end_user_list_transactions={},
        key_list_transactions={},
        team_list_transactions={},
        team_member_list_transactions={},
        org_list_transactions={},
        tag_list_transactions={}
    )
    tx2 = DBSpendUpdateTransactions(
        user_list_transactions={},
        end_user_list_transactions={"eu1": 7},
        key_list_transactions={"k1": 3},
        team_list_transactions={},
        team_member_list_transactions={},
        org_list_transactions={},
        tag_list_transactions={}
    )
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 6.03μs -> 6.45μs (6.42% slower)
    for field in ["team_list_transactions", "team_member_list_transactions", "org_list_transactions", "tag_list_transactions"]:
        pass

def test_combine_two_transactions_same_entity():
    # Test combining two transactions with the same entity in a field
    tx1 = DBSpendUpdateTransactions(
        user_list_transactions={"u1": 10},
        end_user_list_transactions={},
        key_list_transactions={},
        team_list_transactions={},
        team_member_list_transactions={},
        org_list_transactions={},
        tag_list_transactions={}
    )
    tx2 = DBSpendUpdateTransactions(
        user_list_transactions={"u1": 5},
        end_user_list_transactions={},
        key_list_transactions={},
        team_list_transactions={},
        team_member_list_transactions={},
        org_list_transactions={},
        tag_list_transactions={}
    )
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 5.72μs -> 6.27μs (8.71% slower)
    for field in ["end_user_list_transactions", "key_list_transactions", "team_list_transactions", "team_member_list_transactions", "org_list_transactions", "tag_list_transactions"]:
        pass

def test_combine_multiple_fields_and_entities():
    # Test combining transactions with multiple fields and entities
    tx1 = DBSpendUpdateTransactions(
        user_list_transactions={"u1": 10, "u2": 20},
        end_user_list_transactions={"eu1": 5},
        key_list_transactions={"k1": 3},
        team_list_transactions={},
        team_member_list_transactions={},
        org_list_transactions={},
        tag_list_transactions={}
    )
    tx2 = DBSpendUpdateTransactions(
        user_list_transactions={"u2": 5, "u3": 7},
        end_user_list_transactions={"eu1": 2, "eu2": 8},
        key_list_transactions={"k2": 4},
        team_list_transactions={},
        team_member_list_transactions={},
        org_list_transactions={},
        tag_list_transactions={}
    )
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 6.99μs -> 7.49μs (6.74% slower)

# 2. Edge Test Cases

def test_combine_transactions_with_zero_and_negative_amounts():
    # Test handling zero and negative amounts
    tx1 = DBSpendUpdateTransactions(
        user_list_transactions={"u1": 0, "u2": -5},
        end_user_list_transactions={},
        key_list_transactions={},
        team_list_transactions={},
        team_member_list_transactions={},
        org_list_transactions={},
        tag_list_transactions={}
    )
    tx2 = DBSpendUpdateTransactions(
        user_list_transactions={"u1": 10, "u2": 5},
        end_user_list_transactions={},
        key_list_transactions={},
        team_list_transactions={},
        team_member_list_transactions={},
        org_list_transactions={},
        tag_list_transactions={}
    )
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 6.04μs -> 6.58μs (8.09% slower)

def test_combine_transactions_with_missing_fields():
    # Test combining transactions with some fields missing (simulate by omitting keys)
    tx1 = DBSpendUpdateTransactions(
        user_list_transactions={"u1": 1},
    )
    tx2 = DBSpendUpdateTransactions(
        end_user_list_transactions={"eu1": 2},
    )
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 5.55μs -> 6.17μs (10.1% slower)
    for field in ["key_list_transactions", "team_list_transactions", "team_member_list_transactions", "org_list_transactions", "tag_list_transactions"]:
        pass

def test_combine_transactions_with_non_integer_amounts():
    # Test combining transactions with float amounts
    tx1 = DBSpendUpdateTransactions(
        user_list_transactions={"u1": 1.5},
    )
    tx2 = DBSpendUpdateTransactions(
        user_list_transactions={"u1": 2.25},
    )
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 5.82μs -> 6.49μs (10.3% slower)

def test_combine_transactions_with_empty_dicts():
    # Test transactions with all fields empty dicts
    tx1 = DBSpendUpdateTransactions()
    tx2 = DBSpendUpdateTransactions()
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 4.34μs -> 4.91μs (11.6% slower)
    for field in result:
        pass

def test_combine_transactions_with_large_entity_ids():
    # Test combining transactions with very large entity IDs
    tx1 = DBSpendUpdateTransactions(
        user_list_transactions={str(2**62): 1},
    )
    tx2 = DBSpendUpdateTransactions(
        user_list_transactions={str(2**62): 2},
    )
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 5.67μs -> 6.14μs (7.70% slower)

# 3. Large Scale Test Cases

def test_combine_large_number_of_transactions():
    # Test combining 100 transactions with increasing amounts
    transactions = []
    for i in range(100):
        tx = DBSpendUpdateTransactions(
            user_list_transactions={f"user{i}": i},
            end_user_list_transactions={f"enduser{i}": i*2},
            key_list_transactions={},
            team_list_transactions={},
            team_member_list_transactions={},
            org_list_transactions={},
            tag_list_transactions={}
        )
        transactions.append(tx)
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions(transactions); result = codeflash_output # 150μs -> 148μs (0.915% faster)
    # Each user should have their respective amount
    for i in range(100):
        pass

def test_combine_large_number_of_entities_per_transaction():
    # Test combining a single transaction with 1000 entities in one field
    tx1 = DBSpendUpdateTransactions(
        user_list_transactions={f"user{i}": i for i in range(1000)},
    )
    tx2 = DBSpendUpdateTransactions(
        user_list_transactions={f"user{i}": 1 for i in range(1000)},
    )
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions([tx1, tx2]); result = codeflash_output # 191μs -> 163μs (17.4% faster)
    # Each user should have their respective amount + 1
    for i in range(1000):
        pass

def test_combine_large_scale_multiple_fields():
    # Test combining transactions with many entities in multiple fields
    txs = []
    for i in range(10):
        txs.append(DBSpendUpdateTransactions(
            user_list_transactions={f"user{i}": i},
            end_user_list_transactions={f"enduser{i}": i*2},
            key_list_transactions={f"key{i}": i*3},
            team_list_transactions={f"team{i}": i*4},
            team_member_list_transactions={f"member{i}": i*5},
            org_list_transactions={f"org{i}": i*6},
            tag_list_transactions={f"tag{i}": i*7},
        ))
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions(txs); result = codeflash_output # 29.1μs -> 26.7μs (9.06% faster)
    for i in range(10):
        pass

def test_combine_large_scale_sum_overlapping_entities():
    # Test combining many transactions, some with overlapping entities
    txs = []
    for i in range(100):
        txs.append(DBSpendUpdateTransactions(
            user_list_transactions={"u1": 1, f"user{i}": i},
        ))
    codeflash_output = RedisUpdateBuffer._combine_list_of_transactions(txs); result = codeflash_output # 135μs -> 134μs (0.795% faster)
    # Each "user{i}" should have i
    for i in range(100):
        pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-RedisUpdateBuffer._combine_list_of_transactions-mhwsx3g6 and push.