syda run report

2026-06-08 02:39 UTC  ·  model: claude-haiku-4-5-20251001

3,900
total rows
38
LLM calls
42,180
input tokens
44,550
output tokens
$0.2649
est. cost
381.2s
total time

Summary

Table Rows Mode LLM calls In tokens Out tokens Cost Time Cache
customers 200 direct 4 4,388 17,887 $0.0938 92.9s
products 100 direct 2 14,976 10,940 $0.0697 63.0s
orders 1,000 codegen 20 14,260 6,838 $0.0485 97.8s HIT
reviews 600 codegen 12 8,556 8,885 $0.0530 127.5s HIT
order_items 2,000 codegen 0 0 0 — 0.0s HIT
TOTAL 3,900 38 42,180 44,550 $0.2649

Per-table details

customers 200 rows  ·  direct  ·  92.9s fa248a53e2f991b4

Column Strategy Calls In tok Out tok Cost Cache Function
_direct_ Direct LLM 4 4,388 17,887 $0.09382

products 100 rows  ·  direct  ·  63.0s 5c8b95de36fa0cef

Column Strategy Calls In tok Out tok Cost Cache Function
_direct_ Direct LLM 2 14,976 10,940 $0.06968

orders 1,000 rows  ·  codegen  ·  97.8s 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 FK sampler 0 0 0 —
shipping_method Semantic LLM 20 14,260 6,838 $0.04845

reviews 600 rows  ·  codegen  ·  127.5s 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')
customer_id FK sampler 0 0 0 —
product_id FK sampler 0 0 0 —
review_text Semantic LLM 12 8,556 8,885 $0.05298

order_items 2,000 rows  ·  codegen  ·  0.0s 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)
product_id FK sampler 0 0 0 —
order_id FK sampler 0 0 0 —