uber / Moirai-SOSP25-code Conditional Complexity

Intro

Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).

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Conditional Complexity Overall

There are 104 units with 3,521 lines of code in units (91.6% of code).

3 very complex units (417 lines of code)
2 complex units (258 lines of code)
19 medium complex units (1,316 lines of code)
23 simple units (850 lines of code)
57 very simple units (680 lines of code)

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

3D view of all units

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 50 most complex units

Unit	# lines	McCabe index	# params
def solve_gurobi() in optimizer.py	213	73	13
def place_query() in scheduler.py	93	62	7
def add_y_z_w_u_v() in optimizer.py	111	51	6
def draw_overall_new() in scripts/figs.py	127	40	3
def process_jobs() in scheduler.py	131	29	6
def process_yugong() in scheduler.py	116	24	4
def test_sample_k() in tests.py	100	23	6
def prepare_replication() in optimizer.py	45	23	1
def prepare_workload() in optimizer.py	96	22	1
def process_baseline() in scheduler.py	120	20	6
def draw_traffic_rate() in scripts/figs.py	97	19	1
def test_yugong() in tests.py	104	19	3
def ingress_egress() in placement.py	78	17	2
def draw_edges_cdf() in scripts/figs.py	110	16	0
def add_workload_constr() in optimizer.py	39	16	2
def draw_job_routing() in scripts/figs.py	85	14	0
def draw_reorg() in scripts/figs.py	46	13	0
def update_y_z_w_u_v() in optimizer.py	47	13	5
def abFP_to_table_group() in utility.py	18	11	4
def anonymize_job_Spark() in scripts/anonymize.py	27	11	3
def test_long_term_effect() in tests.py	75	11	2
def data_placement_by_compute_distribution() in baselines.py	42	11	2
def merge_similar_rows() in optimizer.py	31	11	2
def update_previous_placement() in optimizer.py	40	11	2
def draw_growth() in scripts/figs.py	94	10	1
def draw_PR_heuristics() in scripts/figs.py	55	10	1
def anonymize_workload() in scripts/anonymize.py	28	10	3
def merge_similar_rows() in placement.py	29	10	1
def anonymize_job_Presto() in scripts/anonymize.py	26	9	3
def __init__() in scheduler.py	32	9	6
def print() in utility.py	61	8	1
def anonymize_table() in scripts/anonymize.py	24	8	3
def prepare_df() in tests.py	58	8	4
def rep_x_month_placement() in baselines.py	29	8	1
def data_placement_random() in baselines.py	28	8	1
def extract_movement_rep_and_sample() in scheduler.py	22	8	1
def human_readable_size() in utility.py	15	7	2
def generate_Yugong_weekly_traces() in scripts/jobtrace_to_yugong.py	62	7	0
def generate_weekly_traces() in scripts/jobtrace_to_weekly_agg.py	45	7	1
def __init__() in scripts/anonymize.py	20	7	2
def test_reorganization_cost_unaware() in tests.py	57	7	2
def log_period_statistics() in scheduler.py	47	7	13
def to_seconds() in utility.py	7	6	1
def replication_effects() in scripts/figs.py	34	6	0
def verify_traffic_rate() in scripts/figs.py	33	6	1
def calculate_traffic_percentiles() in scheduler.py	21	6	4
def iterate_logs() in scheduler.py	23	6	3
def plot_weekly_traffic() in scripts/figs.py	28	5	3
def __init__() in baselines.py	35	5	4
def get_random_project_distribution() in baselines.py	20	5	1