syda run report

2026-06-30 08:00 UTC  ·  model: grok-4.3

18,700
total rows
604
LLM calls
195,936
input tokens
280,816
output tokens
$0.9470
est. cost
9184.5s
total time

Summary

Table Rows Mode LLM calls In tokens Out tokens Cost Time Cache
customers 500 direct 18 11,196 38,759 $0.1109 463.2s
products 200 direct 6 3,252 14,157 $0.0395 178.9s
orders 5,000 codegen 200 73,428 76,291 $0.2825 2664.1s HIT
reviews 3,000 codegen 180 51,060 83,636 $0.2729 2730.7s HIT
order_items 10,000 codegen 200 57,000 67,973 $0.2412 3147.6s HIT
TOTAL 18,700 604 195,936 280,816 $0.9470

Per-table details

customers 500 rows  ·  direct  ·  463.2s fa248a53e2f991b4

Column Strategy Calls In tok Out tok Cost Cache Function
_direct_ Direct LLM 18 11,196 38,759 $0.11089

products 200 rows  ·  direct  ·  178.9s 5c8b95de36fa0cef

Column Strategy Calls In tok Out tok Cost Cache Function
_direct_ Direct LLM 6 3,252 14,157 $0.03946

orders 5,000 rows  ·  codegen  ·  2664.1s HIT 21a14bdfa18fe8f5

Column Strategy Calls In tok Out tok Cost Cache Function
order_id Code-gen 0 0 0 — HIT
View generated function
def generate_order_id(row, col_name):
    return row.name + 1
order_date Code-gen 0 0 0 — HIT
View generated function
def generate_order_date(row, col_name):
    import random
    from datetime import datetime, timedelta
    end_date = datetime.now().date()
    start_date = end_date - timedelta(days=730)
    random_days = random.randint(0, (end_date - start_date).days)
    return (start_date + timedelta(days=random_days)).isoformat()
status Code-gen 0 0 0 — HIT
View generated function
def generate_status(row, col_name):
    import random
    statuses = ['pending', 'processing', 'shipped', 'delivered', 'cancelled']
    weights = [0.10, 0.20, 0.25, 0.40, 0.05]
    return random.choices(statuses, weights=weights, k=1)[0]
total_amount Code-gen 0 0 0 — HIT
View generated function
def generate_total_amount(row, col_name):
    import random
    return round(random.uniform(10.0, 10000.0), 2)
customer_id Semantic LLM 100 45,128 51,169 $0.18433
shipping_method Semantic LLM 100 28,300 25,122 $0.09818

reviews 3,000 rows  ·  codegen  ·  2730.7s HIT 8e10a7047151ce40

Column Strategy Calls In tok Out tok Cost Cache Function
review_id Code-gen 0 0 0 — HIT
View generated function
def generate_review_id(row, col_name):
    return row.name + 1
rating Code-gen 0 0 0 — HIT
View generated function
def generate_rating(row, col_name):
    import random
    rand = random.random()
    if rand < 0.30:
        return 5
    elif rand < 0.65:
        return 4
    elif rand < 0.85:
        return 3
    elif rand < 0.95:
        return 2
    else:
        return 1
review_date Code-gen 0 0 0 — HIT
View generated function
def generate_review_date(row, col_name):
    from datetime import datetime, timedelta
    import random
    start_date = datetime(2020, 1, 1)
    end_date = datetime(2024, 12, 31)
    days_between = (end_date - start_date).days
    random_days = random.randint(0, days_between)
    review_date = start_date + timedelta(days=random_days)
    return review_date.strftime('%Y-%m-%d')
product_id Semantic LLM 60 17,040 22,382 $0.07726
customer_id Semantic LLM 60 17,040 24,307 $0.08207
review_text Semantic LLM 60 16,980 36,947 $0.11359

order_items 10,000 rows  ·  codegen  ·  3147.6s HIT 09b5cb78f1b98db8

Column Strategy Calls In tok Out tok Cost Cache Function
item_id Code-gen 0 0 0 — HIT
View generated function
def generate_item_id(row, col_name):
    return row.name + 1
quantity Code-gen 0 0 0 — HIT
View generated function
def generate_quantity(row, col_name):
    import random
    return random.randint(1, 5)
unit_price Code-gen 0 0 0 — HIT
View generated function
def generate_unit_price(row, col_name):
    import random
    return round(random.uniform(0.01, 10000.00), 2)
discount Code-gen 0 0 0 — HIT
View generated function
def generate_discount(row, col_name):
    import random
    return round(random.uniform(0, 20), 2)
order_id FK sampler 0 0 0 —
product_id Semantic LLM 200 57,000 67,973 $0.24118