fix: enhance Filter Data with JQ Query, Column Selection and Index selection

[vasconceloscezar]

Consolidates JSON parsing, data filtering, and key extraction into a single streamlined component. Adds support for JQ queries, column selection, and index-based filtering, eliminating the need for separate Parse JSON and Extract Key components.

If this is approved we can remove the following:

src/backend/base/langflow/components/processing/extract_key.py src/backend/base/langflow/components/processing/filter_data_values.py src/backend/base/langflow/components/processing/parse_json_data.py

[vasconceloscezar]

Sure can do!

[ogabrielluiz]

Hey @vasconceloscezar

Please, check 12bac03 (#4774) and see if that makes sense.

Essentially, since we strive for returning a data object to maintain compatibility between components, I made it so if the jq query returns a list, it puts it inside the Data object.

[vasconceloscezar]

Why don't we keep it as a Data list? I rather have a list being returned than a results field.

It's counterintuitive having a list being transformed in a result. Especially if the starter JSON is a list already.

I believe we soon will have more components to handle Data Lists and so on, so this whole List handling will be even better.

Example of not ideal results:

Start Data List:

Filtering with: .[2] | {"missionId": .missionId}

This is not ideal, bc the user will need to add another component just for re-parse that list again.

Thoughts @ogabrielluiz ?

[ogabrielluiz]

It is unintuitive but it would be better to add a component that makes flow work rather than having them change the component they want to use because it does not work with a list of Data objects.

[ogabrielluiz]

@vasconceloscezar I've just added a new type: langflow.schema.data_set.DataSet (we can improve it in this PR) that can deal with a list of Data so we can display that more clearly now.

[codeflash-ai[bot]]

⚡️ Codeflash found optimizations for this PR

📄 3,866% (38.66x) speedup for FilterDataComponent._apply_column_filter in src/backend/base/langflow/components/processing/filter_data.py

⏱️ Runtime : 11.1 milliseconds → 281 microseconds (best of 148 runs)

📝 Explanation and details

Sure, here is the optimized version of the program.

Changes made.

1. Changed columns parameter type to set for faster membership checking.
2. Converted columns list to a set columns_set inside the function if it was a list, to take advantage of O(1) average-time complexity for set membership checks compared to O(n) for lists.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 29 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

from typing import Any

# imports
import pytest  # used for our unit tests
from langflow.components.processing.filter_data import FilterDataComponent
# function to test
from langflow.custom import Component

# unit tests

# Initialize the class instance
filter_data_component = FilterDataComponent()

def test_basic_functionality():
    # Basic functionality test case
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = ['a', 'c']
    expected = {'a': 1, 'c': 3}
    codeflash_output = filter_data_component._apply_column_filter(data, columns)

def test_no_matching_columns():
    # No matching columns test case
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = ['d', 'e']
    expected = {}
    codeflash_output = filter_data_component._apply_column_filter(data, columns)

def test_empty_data():
    # Empty data test case
    data = {}
    columns = ['a', 'b']
    expected = {}
    codeflash_output = filter_data_component._apply_column_filter(data, columns)

def test_empty_columns():
    # Empty columns test case
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = []
    expected = {}
    codeflash_output = filter_data_component._apply_column_filter(data, columns)

def test_non_dictionary_data():
    # Non-dictionary data test cases
    data = [1, 2, 3]
    columns = ['a', 'b']
    expected = [1, 2, 3]
    codeflash_output = filter_data_component._apply_column_filter(data, columns)

    data = "string"
    columns = ['a', 'b']
    expected = "string"
    codeflash_output = filter_data_component._apply_column_filter(data, columns)

def test_non_string_keys():
    # Non-string keys test case
    data = {1: 'one', 2: 'two'}
    columns = [1]
    expected = {1: 'one'}
    codeflash_output = filter_data_component._apply_column_filter(data, columns)

def test_mixed_data_types_in_columns():
    # Mixed data types in columns test case
    data = {'a': 1, 'b': 2, 3: 'three'}
    columns = ['a', 3]
    expected = {'a': 1, 3: 'three'}
    codeflash_output = filter_data_component._apply_column_filter(data, columns)

def test_large_scale():
    # Large scale test case
    data = {f'key_{i}': i for i in range(1000)}
    columns = [f'key_{i}' for i in range(1000)]
    expected = {f'key_{i}': i for i in range(1000)}
    codeflash_output = filter_data_component._apply_column_filter(data, columns)

def test_nested_dictionaries():
    # Nested dictionaries test case
    data = {'a': {'nested': 1}, 'b': 2}
    columns = ['a']
    expected = {'a': {'nested': 1}}
    codeflash_output = filter_data_component._apply_column_filter(data, columns)

def test_columns_with_non_existent_keys():
    # Columns with non-existent keys test case
    data = {'a': 1, 'b': 2}
    columns = ['a', 'c']
    expected = {'a': 1}
    codeflash_output = filter_data_component._apply_column_filter(data, columns)

def test_case_sensitivity():
    # Case sensitivity test case
    data = {'a': 1, 'A': 2}
    columns = ['a']
    expected = {'a': 1}
    codeflash_output = filter_data_component._apply_column_filter(data, columns)

def test_immutable_data_types():
    # Immutable data types test case
    data = (1, 2, 3)
    columns = ['a', 'b']
    expected = (1, 2, 3)
    codeflash_output = filter_data_component._apply_column_filter(data, columns)

def test_columns_with_duplicates():
    # Columns with duplicates test case
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = ['a', 'a', 'b']
    expected = {'a': 1, 'b': 2}
    codeflash_output = filter_data_component._apply_column_filter(data, columns)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from typing import Any

# imports
import pytest  # used for our unit tests
from langflow.components.processing.filter_data import FilterDataComponent
# function to test
from langflow.custom import Component

# unit tests

# Basic Functionality
def test_standard_dict_with_matching_columns():
    component = FilterDataComponent()
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = ['a', 'c']
    codeflash_output = component._apply_column_filter(data, columns)

def test_standard_dict_with_non_matching_columns():
    component = FilterDataComponent()
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = ['d', 'e']
    codeflash_output = component._apply_column_filter(data, columns)

# Edge Cases
def test_empty_data_dict():
    component = FilterDataComponent()
    data = {}
    columns = ['a', 'b']
    codeflash_output = component._apply_column_filter(data, columns)

def test_empty_columns_list():
    component = FilterDataComponent()
    data = {'a': 1, 'b': 2}
    columns = []
    codeflash_output = component._apply_column_filter(data, columns)

def test_columns_list_with_all_keys():
    component = FilterDataComponent()
    data = {'a': 1, 'b': 2}
    columns = ['a', 'b']
    codeflash_output = component._apply_column_filter(data, columns)

# Non-Dictionary Data
def test_list_input():
    component = FilterDataComponent()
    data = [1, 2, 3]
    columns = ['a', 'b']
    codeflash_output = component._apply_column_filter(data, columns)

def test_string_input():
    component = FilterDataComponent()
    data = 'string'
    columns = ['a', 'b']
    codeflash_output = component._apply_column_filter(data, columns)

def test_integer_input():
    component = FilterDataComponent()
    data = 123
    columns = ['a', 'b']
    codeflash_output = component._apply_column_filter(data, columns)

# Mixed Data Types in Dictionary
def test_dict_with_mixed_value_types():
    component = FilterDataComponent()
    data = {'a': 1, 'b': 'string', 'c': [1, 2, 3]}
    columns = ['a', 'c']
    codeflash_output = component._apply_column_filter(data, columns)

# Large Scale Test Cases
def test_large_dict():
    component = FilterDataComponent()
    data = {f'key{i}': i for i in range(1000)}
    columns = [f'key{i}' for i in range(5000, 6000)]
    expected_output = {f'key{i}': i for i in range(5000, 6000)}
    codeflash_output = component._apply_column_filter(data, columns)

def test_large_columns_list():
    component = FilterDataComponent()
    data = {f'key{i}': i for i in range(100)}
    columns = [f'key{i}' for i in range(1000)]
    expected_output = {f'key{i}': i for i in range(100)}
    codeflash_output = component._apply_column_filter(data, columns)

# Duplicate Columns in List
def test_duplicate_columns():
    component = FilterDataComponent()
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = ['a', 'a', 'b']
    codeflash_output = component._apply_column_filter(data, columns)

# Columns Not Strings
def test_non_string_columns():
    component = FilterDataComponent()
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = [1, 2, 3]
    codeflash_output = component._apply_column_filter(data, columns)

# Case Sensitivity
def test_case_sensitivity_in_columns():
    component = FilterDataComponent()
    data = {'a': 1, 'A': 2}
    columns = ['a']
    codeflash_output = component._apply_column_filter(data, columns)

def test_case_sensitivity_with_mixed_case_columns():
    component = FilterDataComponent()
    data = {'a': 1, 'A': 2}
    columns = ['a', 'A']
    codeflash_output = component._apply_column_filter(data, columns)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

:loudspeaker: Feedback on this optimization?

[codeflash-ai[bot]]

⚡️ Codeflash found optimizations for this PR

📄 60% (0.60x) speedup for FilterDataComponent._apply_column_filter in src/backend/base/langflow/components/processing/filter_data.py

⏱️ Runtime : 323 microseconds → 201 microseconds (best of 94 runs)

📝 Explanation and details

Here's an optimized version of your program.

Optimizations.

1. Avoiding set conversion: The original code converts the columns list to a set (columns_set), which has some overhead. Instead, we iterate directly over the columns list and check if each element exists in the data dictionary.
2. Direct dictionary access: The optimized version directly accesses the dictionary with data[k] for any key in columns, only if it exists in data, reducing the need for the additional check of set membership.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 34 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

from typing import Any

# imports
import pytest  # used for our unit tests
from langflow.components.processing.filter_data import FilterDataComponent
# function to test
from langflow.custom import Component

# unit tests

# Basic Functionality
def test_standard_dictionary_matching_columns():
    component = FilterDataComponent()
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = ['a', 'b']
    codeflash_output = component._apply_column_filter(data, columns)

def test_standard_dictionary_no_matching_columns():
    component = FilterDataComponent()
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = ['d', 'e']
    codeflash_output = component._apply_column_filter(data, columns)

# Edge Cases
def test_empty_dictionary():
    component = FilterDataComponent()
    data = {}
    columns = ['a', 'b']
    codeflash_output = component._apply_column_filter(data, columns)

def test_empty_columns_list():
    component = FilterDataComponent()
    data = {'a': 1, 'b': 2}
    columns = []
    codeflash_output = component._apply_column_filter(data, columns)

def test_columns_list_with_non_existent_keys():
    component = FilterDataComponent()
    data = {'a': 1, 'b': 2}
    columns = ['c', 'd']
    codeflash_output = component._apply_column_filter(data, columns)

# Non-Dictionary Data
def test_list_input():
    component = FilterDataComponent()
    data = [1, 2, 3]
    columns = ['a', 'b']
    codeflash_output = component._apply_column_filter(data, columns)

def test_string_input():
    component = FilterDataComponent()
    data = 'hello'
    columns = ['h', 'e']
    codeflash_output = component._apply_column_filter(data, columns)

def test_integer_input():
    component = FilterDataComponent()
    data = 123
    columns = ['1', '2']
    codeflash_output = component._apply_column_filter(data, columns)

# Complex Data Structures
def test_nested_dictionary():
    component = FilterDataComponent()
    data = {'a': {'b': 2}, 'c': 3}
    columns = ['a', 'c']
    codeflash_output = component._apply_column_filter(data, columns)

def test_dictionary_with_list_values():
    component = FilterDataComponent()
    data = {'a': [1, 2], 'b': [3, 4]}
    columns = ['a']
    codeflash_output = component._apply_column_filter(data, columns)

# Large Scale Test Cases
def test_large_dictionary():
    component = FilterDataComponent()
    data = {f'key{i}': i for i in range(1000)}
    columns = [f'key{i}' for i in range(500)]
    expected_output = {f'key{i}': i for i in range(500)}
    codeflash_output = component._apply_column_filter(data, columns)

def test_large_columns_list():
    component = FilterDataComponent()
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = [f'key{i}' for i in range(1000)]
    codeflash_output = component._apply_column_filter(data, columns)

# Special Characters in Keys
def test_special_characters_in_keys():
    component = FilterDataComponent()
    data = {'a!': 1, 'b@': 2, 'c#': 3}
    columns = ['a!', 'b@']
    codeflash_output = component._apply_column_filter(data, columns)

# Mixed Data Types in Columns List
def test_mixed_data_types_in_columns_list():
    component = FilterDataComponent()
    data = {'a': 1, 'b': 2}
    columns = ['a', 2]
    codeflash_output = component._apply_column_filter(data, columns)

# Unicode Characters in Keys
def test_unicode_characters_in_keys():
    component = FilterDataComponent()
    data = {'你好': 1, '世界': 2}
    columns = ['你好']
    codeflash_output = component._apply_column_filter(data, columns)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from typing import Any

# imports
import pytest  # used for our unit tests
from langflow.components.processing.filter_data import FilterDataComponent
# function to test
from langflow.custom import Component

# unit tests

@pytest.fixture
def filter_component():
    return FilterDataComponent()

# Basic Functionality
def test_standard_dict_with_matching_columns(filter_component):
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = ['a', 'b']
    expected = {'a': 1, 'b': 2}
    codeflash_output = filter_component._apply_column_filter(data, columns)

def test_standard_dict_with_non_matching_columns(filter_component):
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = ['d', 'e']
    expected = {}
    codeflash_output = filter_component._apply_column_filter(data, columns)

# Edge Cases
def test_empty_dict(filter_component):
    data = {}
    columns = ['a', 'b']
    expected = {}
    codeflash_output = filter_component._apply_column_filter(data, columns)

def test_empty_columns_list(filter_component):
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = []
    expected = {}
    codeflash_output = filter_component._apply_column_filter(data, columns)

def test_empty_dict_and_empty_columns_list(filter_component):
    data = {}
    columns = []
    expected = {}
    codeflash_output = filter_component._apply_column_filter(data, columns)

def test_columns_list_with_keys_not_in_dict(filter_component):
    data = {'a': 1, 'b': 2}
    columns = ['c', 'd']
    expected = {}
    codeflash_output = filter_component._apply_column_filter(data, columns)

# Data Type Variations
def test_non_dict_data_list(filter_component):
    data = [1, 2, 3]
    columns = ['a', 'b']
    expected = [1, 2, 3]
    codeflash_output = filter_component._apply_column_filter(data, columns)

def test_non_dict_data_string(filter_component):
    data = 'string'
    columns = ['a', 'b']
    expected = 'string'
    codeflash_output = filter_component._apply_column_filter(data, columns)

def test_non_dict_data_int(filter_component):
    data = 123
    columns = ['a', 'b']
    expected = 123
    codeflash_output = filter_component._apply_column_filter(data, columns)

def test_dict_with_non_string_keys(filter_component):
    data = {1: 'a', 2: 'b'}
    columns = ['1', '2']
    expected = {}
    codeflash_output = filter_component._apply_column_filter(data, columns)

# Nested Dictionaries
def test_nested_dict_with_matching_columns(filter_component):
    data = {'a': {'nested': 1}, 'b': 2}
    columns = ['a']
    expected = {'a': {'nested': 1}}
    codeflash_output = filter_component._apply_column_filter(data, columns)

def test_nested_dict_with_non_matching_columns(filter_component):
    data = {'a': {'nested': 1}, 'b': 2}
    columns = ['c']
    expected = {}
    codeflash_output = filter_component._apply_column_filter(data, columns)

# Large Scale Test Cases
def test_large_dict_with_partial_column_match(filter_component):
    data = {f'key{i}': i for i in range(1000)}
    columns = [f'key{i}' for i in range(500)]
    expected = {f'key{i}': i for i in range(500)}
    codeflash_output = filter_component._apply_column_filter(data, columns)

def test_large_dict_with_no_column_match(filter_component):
    data = {f'key{i}': i for i in range(1000)}
    columns = [f'key{i}' for i in range(1000, 2000)]
    expected = {}
    codeflash_output = filter_component._apply_column_filter(data, columns)

# Special Characters in Keys
def test_dict_with_special_character_keys(filter_component):
    data = {'a!@#': 1, 'b$%^': 2}
    columns = ['a!@#']
    expected = {'a!@#': 1}
    codeflash_output = filter_component._apply_column_filter(data, columns)

def test_dict_with_whitespace_in_keys(filter_component):
    data = {'a key': 1, 'another key': 2}
    columns = ['a key']
    expected = {'a key': 1}
    codeflash_output = filter_component._apply_column_filter(data, columns)

# Case Sensitivity
def test_dict_with_mixed_case_keys(filter_component):
    data = {'Key': 1, 'key': 2}
    columns = ['Key']
    expected = {'Key': 1}
    codeflash_output = filter_component._apply_column_filter(data, columns)

def test_columns_list_with_different_cases(filter_component):
    data = {'Key': 1, 'key': 2}
    columns = ['key']
    expected = {'key': 2}
    codeflash_output = filter_component._apply_column_filter(data, columns)

# Duplicate Columns in List
def test_columns_list_with_duplicates(filter_component):
    data = {'a': 1, 'b': 2, 'c': 3}
    columns = ['a', 'a', 'b']
    expected = {'a': 1, 'b': 2}
    codeflash_output = filter_component._apply_column_filter(data, columns)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

:loudspeaker: Feedback on this optimization?

[codeflash-ai[bot]]

⚡️ Codeflash found optimizations for this PR

📄 36% (0.36x) speedup for FilterDataComponent._filter_by_index in src/backend/base/langflow/components/processing/filter_data.py

⏱️ Runtime : 859 microseconds → 630 microseconds (best of 5 runs)

📝 Explanation and details

Here are some optimizations to improve the performance of the provided code.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 10 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

from __future__ import annotations

from collections.abc import Callable
from typing import Any

import pandas as pd
# imports
import pytest  # used for our unit tests
from cachetools import TTLCache
from langflow.components.processing.filter_data import FilterDataComponent
from langflow.custom import Component
from langflow.custom.custom_component.base_component import BaseComponent
from langflow.graph.vertex.base import Vertex
from langflow.schema import Data
from langflow.schema.schema import OutputValue
from langflow.services.tracing.schema import Log
from langflow.services.tracing.service import TracingService


# unit tests
class TestFilterDataComponent:
    @pytest.fixture
    def component(self):
        # Create an instance of FilterDataComponent for testing
        component = FilterDataComponent()
        return component

    

from __future__ import annotations

import sys
import time
import tracemalloc
from collections.abc import Callable
from typing import Any

import pandas as pd
# imports
import pytest  # used for our unit tests
from cachetools import TTLCache
from langflow.components.processing.filter_data import FilterDataComponent
from langflow.custom import Component
from langflow.custom.custom_component.base_component import BaseComponent
from langflow.graph.vertex.base import Vertex
from langflow.schema import Data
from langflow.schema.schema import OutputValue
from langflow.services.tracing.schema import Log
from langflow.services.tracing.service import TracingService

# unit tests

# Basic Functionality

def test_multiple_rows_dataframe():
    df = pd.DataFrame({'A': [1, 2, 3]})
    component = FilterDataComponent()
    component.index = 1
    codeflash_output = component._filter_by_index(df)

# Edge Cases

def test_index_out_of_range():
    df = pd.DataFrame({'A': [1, 2, 3]})
    component = FilterDataComponent()
    component.index = 5
    with pytest.raises(IndexError):
        component._filter_by_index(df)


def test_index_none():
    df = pd.DataFrame({'A': [1, 2, 3]})
    component = FilterDataComponent()
    component.index = None
    codeflash_output = component._filter_by_index(df)

# DataFrame with Various Data Types
def test_different_data_types():
    df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c'], 'C': [1.1, 2.2, 3.3]})
    component = FilterDataComponent()
    component.index = 1
    codeflash_output = component._filter_by_index(df)

# Large Scale Test Cases

def test_index_max_integer():
    df = pd.DataFrame({'A': range(100)})
    component = FilterDataComponent()
    component.index = sys.maxsize
    with pytest.raises(IndexError):
        component._filter_by_index(df)

def test_index_min_integer():
    df = pd.DataFrame({'A': range(100)})
    component = FilterDataComponent()
    component.index = -sys.maxsize - 1
    with pytest.raises(IndexError):
        component._filter_by_index(df)

# Performance and Scalability

:loudspeaker: Feedback on this optimization?

[codeflash-ai[bot]]

⚡️ Codeflash found optimizations for this PR

📄 269% (2.69x) speedup for FilterDataComponent._is_safe_jq_query in src/backend/base/langflow/components/processing/filter_data.py

⏱️ Runtime : 614 microseconds → 166 microseconds (best of 97 runs)

📝 Explanation and details

Here is an optimized version of the given program.

Changes Made.

1. Short-Circuit Check on Length.

   • Added a check for the length of the query string at the beginning of the method before performing the character validation. This avoids the unnecessary computation if the query is too long.

2. Avoiding set Lookup for Characters.

   • Instead of using set for safe_chars, which incurs the overhead of constructing the set and checking membership against a set, I used a str. The in operator is more efficient for short strings as compared to sets in Python.

By implementing these changes, the program avoids unnecessary checks and constructs, potentially improving the overall performance of the function.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 84 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	undefined

🌀 Generated Regression Tests Details

import pytest  # used for our unit tests
from langflow.components.processing.filter_data import FilterDataComponent
# function to test
from langflow.custom import Component


# unit tests
@pytest.fixture
def component():
    return FilterDataComponent(max_query_length=100)

def test_valid_queries_with_safe_characters(component):
    codeflash_output = component._is_safe_jq_query(".")
    codeflash_output = component._is_safe_jq_query("foo.bar")
    codeflash_output = component._is_safe_jq_query("foo[0].bar")
    codeflash_output = component._is_safe_jq_query("foo + bar - baz")
    codeflash_output = component._is_safe_jq_query("foo * bar / baz")

def test_invalid_queries_with_unsafe_characters(component):
    codeflash_output = not component._is_safe_jq_query("foo$bar")
    codeflash_output = not component._is_safe_jq_query("foo@bar")
    codeflash_output = not component._is_safe_jq_query("foo#bar")
    codeflash_output = not component._is_safe_jq_query("foo!bar")
    codeflash_output = not component._is_safe_jq_query("foo:bar")

def test_queries_exceeding_maximum_length(component):
    codeflash_output = not component._is_safe_jq_query("a" * 101)
    codeflash_output = not component._is_safe_jq_query("foo.bar.baz" * 11)

def test_empty_query(component):
    codeflash_output = component._is_safe_jq_query("")

def test_queries_with_only_whitespace(component):
    codeflash_output = component._is_safe_jq_query(" ")
    codeflash_output = component._is_safe_jq_query("\t")
    codeflash_output = component._is_safe_jq_query("\n")
    codeflash_output = component._is_safe_jq_query("    ")

def test_queries_with_mixed_safe_and_unsafe_characters(component):
    codeflash_output = not component._is_safe_jq_query("foo.bar$baz")
    codeflash_output = not component._is_safe_jq_query("[email protected]")
    codeflash_output = not component._is_safe_jq_query("foo#bar.baz")

def test_queries_with_edge_case_characters(component):
    codeflash_output = component._is_safe_jq_query("aZ0.[]() +-*/<>=|,")
    codeflash_output = component._is_safe_jq_query("A1.[]() +-*/<>=|,")

def test_queries_with_nested_structures(component):
    codeflash_output = component._is_safe_jq_query("foo[0][1]")
    codeflash_output = component._is_safe_jq_query("foo[bar[baz]]")
    codeflash_output = component._is_safe_jq_query("foo(bar(baz))")

def test_queries_with_complex_expressions(component):
    codeflash_output = component._is_safe_jq_query("foo + bar - baz * qux / quux")
    codeflash_output = component._is_safe_jq_query("foo > bar && baz < qux || quux == corge")

def test_queries_with_commas_and_pipes(component):
    codeflash_output = component._is_safe_jq_query("foo, bar, baz")
    codeflash_output = component._is_safe_jq_query("foo | bar | baz")

def test_queries_with_maximum_length(component):
    codeflash_output = component._is_safe_jq_query("a" * 100)
    codeflash_output = component._is_safe_jq_query("foo.bar" * 14 + "foo")

def test_queries_with_special_characters_at_start_or_end(component):
    codeflash_output = component._is_safe_jq_query(".foo")
    codeflash_output = component._is_safe_jq_query("foo.")
    codeflash_output = component._is_safe_jq_query("[foo]")
    codeflash_output = component._is_safe_jq_query("foo[]")
    codeflash_output = component._is_safe_jq_query("foo()")

def test_queries_with_repeated_characters(component):
    codeflash_output = component._is_safe_jq_query("foo...bar")
    codeflash_output = component._is_safe_jq_query("foo+++bar")
    codeflash_output = component._is_safe_jq_query("foo***bar")

def test_queries_with_mixed_case_sensitivity(component):
    codeflash_output = component._is_safe_jq_query("Foo.Bar")
    codeflash_output = component._is_safe_jq_query("foo.BAR")
    codeflash_output = component._is_safe_jq_query("FOO.bar")

def test_queries_with_numeric_values(component):
    codeflash_output = component._is_safe_jq_query("foo123")
    codeflash_output = component._is_safe_jq_query("foo[123]")
    codeflash_output = component._is_safe_jq_query("foo + 123")

def test_queries_with_edge_case_lengths(component):
    codeflash_output = component._is_safe_jq_query("a" * 99)
    codeflash_output = not component._is_safe_jq_query("a" * 101)

def test_large_scale_queries(component):
    codeflash_output = component._is_safe_jq_query("a" * 50 + "b" * 50)
    codeflash_output = not component._is_safe_jq_query("foo.bar" * 15)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import pytest  # used for our unit tests
from langflow.components.processing.filter_data import FilterDataComponent
# function to test
from langflow.custom import Component

# unit tests

# Fixture to create an instance of FilterDataComponent with a specific max_query_length
@pytest.fixture
def filter_data_component():
    return FilterDataComponent(max_query_length=50)

def test_basic_valid_input(filter_data_component):
    # Basic valid inputs
    codeflash_output = filter_data_component._is_safe_jq_query("a.b.c")
    codeflash_output = filter_data_component._is_safe_jq_query("123[4](5)")
    codeflash_output = filter_data_component._is_safe_jq_query("a + b - c * d / e")
    codeflash_output = filter_data_component._is_safe_jq_query("x < y >= z")
    codeflash_output = filter_data_component._is_safe_jq_query("foo|bar, baz")

def test_empty_query(filter_data_component):
    # Empty query should be valid
    codeflash_output = filter_data_component._is_safe_jq_query("")

def test_maximum_length_query(filter_data_component):
    # Query exactly at the maximum length limit
    codeflash_output = filter_data_component._is_safe_jq_query("a" * 50)

def test_exceeding_maximum_length_query(filter_data_component):
    # Query exceeding the maximum length limit
    codeflash_output = filter_data_component._is_safe_jq_query("a" * 51)

def test_invalid_characters(filter_data_component):
    # Queries containing invalid characters
    codeflash_output = filter_data_component._is_safe_jq_query("a$b")
    codeflash_output = filter_data_component._is_safe_jq_query("query_with_@_symbol")
    codeflash_output = filter_data_component._is_safe_jq_query("query_with_#_symbol")
    codeflash_output = filter_data_component._is_safe_jq_query("query_with_!_symbol")

def test_edge_cases_for_special_characters(filter_data_component):
    # Queries with special characters at the boundaries
    codeflash_output = filter_data_component._is_safe_jq_query(".")
    codeflash_output = filter_data_component._is_safe_jq_query("[]")

def test_mixed_valid_and_invalid_characters(filter_data_component):
    # Queries containing a mix of valid and invalid characters
    codeflash_output = filter_data_component._is_safe_jq_query("a.b$c")
    codeflash_output = filter_data_component._is_safe_jq_query("x+y@z")

def test_whitespace_handling(filter_data_component):
    # Queries with leading, trailing, or multiple consecutive spaces
    codeflash_output = filter_data_component._is_safe_jq_query("  a.b  ")
    codeflash_output = filter_data_component._is_safe_jq_query("a  +  b")

def test_single_character_queries(filter_data_component):
    # Queries with a single character, both valid and invalid
    codeflash_output = filter_data_component._is_safe_jq_query("a")
    codeflash_output = filter_data_component._is_safe_jq_query("1")
    codeflash_output = filter_data_component._is_safe_jq_query("+")
    codeflash_output = filter_data_component._is_safe_jq_query("@")
    codeflash_output = filter_data_component._is_safe_jq_query("$")

def test_boundary_conditions(filter_data_component):
    # Queries just below and just above the maximum length
    codeflash_output = filter_data_component._is_safe_jq_query("a" * 49)
    codeflash_output = filter_data_component._is_safe_jq_query("a" * 51)

def test_complex_valid_queries(filter_data_component):
    # Complex valid queries
    codeflash_output = filter_data_component._is_safe_jq_query("a.b[1](2) + c.d - e.f / g.h * i.j")
    codeflash_output = filter_data_component._is_safe_jq_query("foo | bar, baz < qux >= quux")

def test_performance_and_scalability():
    # Large queries to assess performance
    large_component = FilterDataComponent(max_query_length=1000)
    codeflash_output = large_component._is_safe_jq_query("a" * 1000)
    codeflash_output = large_component._is_safe_jq_query("a" * 999)

def test_unicode_and_non_ascii_characters(filter_data_component):
    # Queries containing Unicode or non-ASCII characters
    codeflash_output = filter_data_component._is_safe_jq_query("你好")
    codeflash_output = filter_data_component._is_safe_jq_query("こんにちは")
    codeflash_output = filter_data_component._is_safe_jq_query("Привет")

def test_numeric_edge_cases(filter_data_component):
    # Queries containing only numeric characters
    codeflash_output = filter_data_component._is_safe_jq_query("1234567890")

def test_special_cases_with_operators(filter_data_component):
    # Queries with only operators or a mix of operators and valid characters
    codeflash_output = filter_data_component._is_safe_jq_query("+")
    codeflash_output = filter_data_component._is_safe_jq_query("a+b-c*d/e")
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

:loudspeaker: Feedback on this optimization?