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5 Commits

Author SHA1 Message Date
a1d9a19d04 Updated view queries 2025-11-12 12:47:31 +00:00
Jonas Linter
c5fa92c4ec Fixed stuff 2025-11-10 13:07:08 +01:00
Jonas Linter
a92c5b699f But no account_currency 2025-11-10 11:49:00 +01:00
Jonas Linter
5f83ecd7ee Fix field schema validation test and update database schema
- Fixed field extraction logic in test_field_schema_validation.py to properly parse methods with docstrings
  - Previous regex was too greedy and matched across multiple method definitions
  - Now uses proper parenthesis and docstring matching to isolate method bodies
  - Correctly handles both 'fields = [...]' and 'fields = common_fields + [...]' patterns

- Updated db_schema.sql to include missing columns:
  - campaign_insights: added frequency, cpp, cost_per_action_type columns
  - adset_insights: added account_currency column
  - campaign_insights_by_country: added frequency, cpp, cost_per_action_type columns

- All field schema validation tests now pass
  - Test dynamically extracts fields from scheduled_grabber.py source code
  - Compares against actual database schema from db_schema.sql
  - Properly filters metadata-only fields (campaign_id, campaign_name, etc.)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-10 11:48:17 +01:00
Jonas Linter
511f381ff2 Simplified insight grabbers 2025-11-10 11:27:54 +01:00
18 changed files with 1069 additions and 375 deletions

View File

@@ -1,2 +1,4 @@
Uv managed python project that grabs data from the meta api and saves it in a timescaledb database.
Always use uv run to execute python related stuff

View File

@@ -15,6 +15,12 @@ dependencies = [
"sqlalchemy[asyncio]>=2.0.44",
]
[project.optional-dependencies]
test = [
"pytest>=8.0.0",
"pytest-asyncio>=0.25.0",
]
[project.scripts]
meta-auth = "meta_api_grabber.auth:main"
meta-scheduled = "meta_api_grabber.scheduled_grabber:main"

View File

@@ -310,7 +310,6 @@ class TimescaleDBClient:
account_id: str,
data: Dict[str, Any],
date_preset: str = "today",
cache_metadata: bool = True,
):
"""
Insert campaign-level insights data.
@@ -321,10 +320,9 @@ class TimescaleDBClient:
account_id: Ad account ID
data: Insights data dictionary from Meta API
date_preset: Date preset used
cache_metadata: If True, automatically cache campaign metadata from insights data
"""
# Cache campaign metadata if requested and available in the insights data
if cache_metadata and data.get("campaign_name"):
# Auto-cache campaign metadata if available in the insights data
if data.get("campaign_name"):
await self.upsert_campaign(
campaign_id=campaign_id,
account_id=account_id,
@@ -389,7 +387,6 @@ class TimescaleDBClient:
account_id: str,
data: Dict[str, Any],
date_preset: str = "today",
cache_metadata: bool = True,
):
"""
Insert ad set level insights data.
@@ -401,11 +398,8 @@ class TimescaleDBClient:
account_id: Ad account ID
data: Insights data dictionary from Meta API
date_preset: Date preset used
cache_metadata: If True, automatically cache adset/campaign metadata from insights data
"""
# Cache metadata if requested and available in the insights data
if cache_metadata:
# Cache adset metadata if available
# Auto-cache adset metadata if available in the insights data
# Note: Campaign should already exist from cache_campaigns_metadata or grab_campaign_insights
# If it doesn't exist, the foreign key constraint will fail with a clear error
# This is intentional - we should never silently create campaigns with 'Unknown' names

View File

@@ -14,10 +14,17 @@ ALTER TABLE IF EXISTS account_insights ADD COLUMN IF NOT EXISTS date_stop DATE;
ALTER TABLE IF EXISTS campaign_insights ADD COLUMN IF NOT EXISTS date_start DATE;
ALTER TABLE IF EXISTS campaign_insights ADD COLUMN IF NOT EXISTS date_stop DATE;
ALTER TABLE IF EXISTS campaign_insights ADD COLUMN IF NOT EXISTS frequency NUMERIC(10, 4);
ALTER TABLE IF EXISTS campaign_insights ADD COLUMN IF NOT EXISTS cpp NUMERIC(10, 4);
ALTER TABLE IF EXISTS campaign_insights ADD COLUMN IF NOT EXISTS cost_per_action_type JSONB;
ALTER TABLE IF EXISTS adset_insights ADD COLUMN IF NOT EXISTS date_start DATE;
ALTER TABLE IF EXISTS adset_insights ADD COLUMN IF NOT EXISTS date_stop DATE;
ALTER TABLE IF EXISTS campaign_insights_by_country ADD COLUMN IF NOT EXISTS frequency NUMERIC(10, 4);
ALTER TABLE IF EXISTS campaign_insights_by_country ADD COLUMN IF NOT EXISTS cpp NUMERIC(10, 4);
ALTER TABLE IF EXISTS campaign_insights_by_country ADD COLUMN IF NOT EXISTS cost_per_action_type JSONB;
-- ============================================================================
-- METADATA TABLES (Regular PostgreSQL tables for caching)
-- ============================================================================
@@ -115,14 +122,17 @@ CREATE TABLE IF NOT EXISTS campaign_insights (
clicks BIGINT,
spend NUMERIC(12, 2),
reach BIGINT,
frequency NUMERIC(10, 4),
-- Calculated metrics
ctr NUMERIC(10, 6),
cpc NUMERIC(10, 4),
cpm NUMERIC(10, 4),
cpp NUMERIC(10, 4), -- Cost per reach
-- Actions
actions JSONB,
cost_per_action_type JSONB,
-- Metadata
date_preset VARCHAR(50),
@@ -163,6 +173,7 @@ CREATE TABLE IF NOT EXISTS adset_insights (
cpc NUMERIC(10, 4),
cpm NUMERIC(10, 4),
-- Actions
actions JSONB,
@@ -201,14 +212,17 @@ CREATE TABLE IF NOT EXISTS campaign_insights_by_country (
clicks BIGINT,
spend NUMERIC(12, 2),
reach BIGINT,
frequency NUMERIC(10, 4),
-- Calculated metrics
ctr NUMERIC(10, 6),
cpc NUMERIC(10, 4),
cpm NUMERIC(10, 4),
cpp NUMERIC(10, 4), -- Cost per reach
-- Actions
actions JSONB,
cost_per_action_type JSONB,
-- Metadata
date_preset VARCHAR(50),

View File

@@ -26,6 +26,23 @@ from .view_manager import ViewManager
logger = logging.getLogger(__name__)
common_fields = [
AdsInsights.Field.impressions,
AdsInsights.Field.clicks,
AdsInsights.Field.spend,
AdsInsights.Field.cpc,
AdsInsights.Field.cpm,
AdsInsights.Field.ctr,
AdsInsights.Field.cpp,
AdsInsights.Field.reach,
AdsInsights.Field.frequency,
AdsInsights.Field.actions,
AdsInsights.Field.cost_per_action_type,
AdsInsights.Field.date_start,
AdsInsights.Field.date_stop,
]
class ScheduledInsightsGrabber:
"""
Scheduled grabber for Meta ad insights with TimescaleDB storage.
@@ -410,6 +427,131 @@ class ScheduledInsightsGrabber:
print(f" {count} ad sets cached for {account_id}")
async def _master_grab_insights(
self,
account_id: str,
fields: list,
level: str,
db_insert_func,
date_preset: Optional[str] = None,
start_date: Optional[date] = None,
end_date: Optional[date] = None,
breakdowns: Optional[list] = None,
limit: Optional[int] = None,
required_fields: Optional[dict] = None,
extra_data_processor=None,
) -> tuple[int, Optional[date]]:
"""
Master method to grab and store insights at any level.
Args:
account_id: Ad account ID
fields: List of AdsInsights fields to retrieve
level: Insights level ("account", "campaign", "adset", etc.)
db_insert_func: Database insert function to call for each insight
date_preset: Meta date preset (e.g., "today", "yesterday"). Use either this or start_date/end_date
start_date: Start date for custom date range (optional)
end_date: End date for custom date range (optional)
breakdowns: List of breakdown fields (optional)
limit: Maximum number of results (optional)
required_fields: Dict of field_name -> label for validation before insert
extra_data_processor: Optional callable to process/add extra data to insight_dict
Returns:
Tuple of (count of records stored, date_start from insights)
"""
# Build params
params = {"level": level}
if date_preset:
params["date_preset"] = date_preset
date_preset_for_db = date_preset
else:
# Use time_range for custom date ranges
params["time_range"] = {
"since": start_date.isoformat(),
"until": end_date.isoformat(),
}
params["time_increment"] = 1 # Daily breakdown
date_preset_for_db = "custom"
if breakdowns:
params["breakdowns"] = breakdowns
if limit:
params["limit"] = limit
# Fetch insights from Meta API
ad_account = AdAccount(account_id)
try:
insights = await self._rate_limited_request(
ad_account.get_insights,
fields=fields,
params=params,
)
except FacebookRequestError as e:
error_code = e.api_error_code()
if error_code in [190, 102]: # Invalid OAuth token errors
raise ValueError(f"Access token is invalid (error {error_code}): {e.api_error_message()}")
raise
# Get account timezone from database
account_timezone = await self._get_account_timezone(account_id)
# Store insights
count = 0
date_start_value = None
for insight in insights:
insight_dict = dict(insight)
# Extract date_start if available (for return value)
date_start_str = insight_dict.get("date_start")
if date_start_str and date_start_value is None:
date_start_value = date.fromisoformat(date_start_str)
# Check required fields before processing
if required_fields:
skip = False
for field_name, field_label in required_fields.items():
if not insight_dict.get(field_name):
skip = True
break
if skip:
continue
# Call extra processor if provided
if extra_data_processor:
extra_data_processor(insight_dict)
# Compute appropriate timestamp based on date_start and account timezone
timestamp = self._compute_timestamp(date_start_str, account_timezone)
# Build kwargs for insert function based on level
kwargs = {
"time": timestamp,
"account_id": account_id,
"data": insight_dict,
"date_preset": date_preset_for_db,
}
# Add level-specific parameters
if level == "campaign":
kwargs["campaign_id"] = insight_dict.get("campaign_id")
elif level == "adset":
kwargs["adset_id"] = insight_dict.get("adset_id")
kwargs["campaign_id"] = insight_dict.get("campaign_id")
# Add country for breakdown queries
if "country" in insight_dict:
kwargs["country"] = insight_dict.get("country")
# Call the appropriate database insert function with level-specific parameters
await db_insert_func(**kwargs)
count += 1
return count, date_start_value
async def grab_account_insights(self, account_id: str, date_preset: str = "today") -> Optional[date]:
"""
Grab and store account-level insights.
@@ -437,50 +579,13 @@ class ScheduledInsightsGrabber:
AdsInsights.Field.date_stop,
]
params = {
"date_preset": date_preset,
"level": "account",
}
ad_account = AdAccount(account_id)
try:
insights = await self._rate_limited_request(
ad_account.get_insights,
fields=fields,
params=params,
)
except FacebookRequestError as e:
# Check if it's a token error
error_code = e.api_error_code()
if error_code in [190, 102]: # Invalid OAuth token errors
raise ValueError(f"Access token is invalid (error {error_code}): {e.api_error_message()}")
raise
# Get account timezone from database
account_timezone = await self._get_account_timezone(account_id)
# Store insights
count = 0
date_start_value = None
for insight in insights:
insight_dict = dict(insight)
# Extract date_start if available
date_start_str = insight_dict.get("date_start")
if date_start_str and date_start_value is None:
date_start_value = date.fromisoformat(date_start_str)
# Compute appropriate timestamp based on date_start and account timezone
timestamp = self._compute_timestamp(date_start_str, account_timezone)
await self.db.insert_account_insights(
time=timestamp,
count, date_start_value = await self._master_grab_insights(
account_id=account_id,
data=insight_dict,
fields=fields,
level="account",
db_insert_func=self.db.insert_account_insights,
date_preset=date_preset,
)
count += 1
print(f" Account insights stored for {account_id} ({count} records, date: {date_start_value})")
return date_start_value
@@ -494,64 +599,22 @@ class ScheduledInsightsGrabber:
date_preset: Meta date preset
limit: Maximum number of campaigns
"""
fields = [
fields = common_fields + [
AdsInsights.Field.campaign_id,
AdsInsights.Field.campaign_name,
AdsInsights.Field.impressions,
AdsInsights.Field.clicks,
AdsInsights.Field.spend,
AdsInsights.Field.ctr,
AdsInsights.Field.cpc,
AdsInsights.Field.cpm,
AdsInsights.Field.reach,
AdsInsights.Field.actions,
AdsInsights.Field.date_start,
AdsInsights.Field.date_stop,
]
params = {
"date_preset": date_preset,
"level": "campaign",
"limit": limit,
}
ad_account = AdAccount(account_id)
try:
insights = await self._rate_limited_request(
ad_account.get_insights,
fields=fields,
params=params,
)
except FacebookRequestError as e:
error_code = e.api_error_code()
if error_code in [190, 102]:
raise ValueError(f"Access token is invalid (error {error_code}): {e.api_error_message()}")
raise
# Get account timezone from database
account_timezone = await self._get_account_timezone(account_id)
# Store insights (metadata is automatically cached from insights data)
count = 0
for insight in insights:
campaign_id = insight.get('campaign_id')
if campaign_id:
insight_dict = dict(insight)
# Compute appropriate timestamp based on date_preset and account timezone
date_start_str = insight_dict.get("date_start")
timestamp = self._compute_timestamp(date_start_str, account_timezone)
# Insert insights - metadata is automatically cached from the insights data
await self.db.insert_campaign_insights(
time=timestamp,
campaign_id=campaign_id,
count, _ = await self._master_grab_insights(
account_id=account_id,
data=insight_dict,
fields=fields,
level="campaign",
db_insert_func=self.db.insert_campaign_insights,
date_preset=date_preset,
cache_metadata=True, # Automatically cache campaign name from insights
limit=limit,
required_fields={"campaign_id": "campaign_id"},
)
count += 1
print(f" Campaign insights stored for {account_id} ({count} records)")
@@ -580,51 +643,15 @@ class ScheduledInsightsGrabber:
AdsInsights.Field.date_stop,
]
params = {
"date_preset": date_preset,
"level": "adset",
"limit": limit,
}
ad_account = AdAccount(account_id)
try:
insights = await self._rate_limited_request(
ad_account.get_insights,
fields=fields,
params=params,
)
except FacebookRequestError as e:
error_code = e.api_error_code()
if error_code in [190, 102]:
raise ValueError(f"Access token is invalid (error {error_code}): {e.api_error_message()}")
raise
# Get account timezone from database
account_timezone = await self._get_account_timezone(account_id)
# Store insights (metadata is automatically cached from insights data)
count = 0
for insight in insights:
adset_id = insight.get('adset_id')
campaign_id = insight.get('campaign_id')
if adset_id and campaign_id:
insight_dict = dict(insight)
# Compute appropriate timestamp based on date_preset and account timezone
date_start_str = insight_dict.get("date_start")
timestamp = self._compute_timestamp(date_start_str, account_timezone)
# Insert insights - metadata is automatically cached from the insights data
await self.db.insert_adset_insights(
time=timestamp,
adset_id=adset_id,
campaign_id=campaign_id,
count, _ = await self._master_grab_insights(
account_id=account_id,
data=insight_dict,
fields=fields,
level="adset",
db_insert_func=self.db.insert_adset_insights,
date_preset=date_preset,
cache_metadata=True, # Automatically cache adset/campaign from insights
limit=limit,
required_fields={"adset_id": "adset_id", "campaign_id": "campaign_id"},
)
count += 1
print(f" Ad set insights stored for {account_id} ({count} records)")
@@ -637,66 +664,21 @@ class ScheduledInsightsGrabber:
date_preset: Meta date preset
limit: Maximum number of campaigns
"""
fields = [
fields = common_fields + [
AdsInsights.Field.campaign_id,
AdsInsights.Field.campaign_name,
AdsInsights.Field.impressions,
AdsInsights.Field.clicks,
AdsInsights.Field.spend,
AdsInsights.Field.ctr,
AdsInsights.Field.cpc,
AdsInsights.Field.cpm,
AdsInsights.Field.reach,
AdsInsights.Field.actions,
AdsInsights.Field.date_start,
AdsInsights.Field.date_stop,
]
params = {
"date_preset": date_preset,
"level": "campaign",
"breakdowns": [AdsInsights.Breakdowns.country],
"limit": limit,
}
ad_account = AdAccount(account_id)
try:
insights = await self._rate_limited_request(
ad_account.get_insights,
fields=fields,
params=params,
)
except FacebookRequestError as e:
error_code = e.api_error_code()
if error_code in [190, 102]:
raise ValueError(f"Access token is invalid (error {error_code}): {e.api_error_message()}")
raise
# Get account timezone from database
account_timezone = await self._get_account_timezone(account_id)
# Store insights (metadata is automatically cached from insights data)
count = 0
for insight in insights:
campaign_id = insight.get('campaign_id')
country = insight.get('country')
if campaign_id and country:
insight_dict = dict(insight)
# Compute appropriate timestamp based on date_preset and account timezone
date_start_str = insight_dict.get("date_start")
timestamp = self._compute_timestamp(date_start_str, account_timezone)
# Insert insights - metadata is automatically cached from the insights data
await self.db.insert_campaign_insights_by_country(
time=timestamp,
campaign_id=campaign_id,
count, _ = await self._master_grab_insights(
account_id=account_id,
country=country,
data=insight_dict,
fields=fields,
level="campaign",
db_insert_func=self.db.insert_campaign_insights_by_country,
date_preset=date_preset,
breakdowns=[AdsInsights.Breakdowns.country],
limit=limit,
required_fields={"campaign_id": "campaign_id", "country": "country"},
)
count += 1
print(f" Campaign insights by country stored for {account_id} ({count} records)")
@@ -733,48 +715,14 @@ class ScheduledInsightsGrabber:
AdsInsights.Field.date_stop,
]
# Use time_range instead of date_preset for custom date ranges
params = {
"time_range": {
"since": start_date.isoformat(),
"until": end_date.isoformat(),
},
"level": "account",
"time_increment": 1, # Daily breakdown
}
ad_account = AdAccount(account_id)
try:
insights = await self._rate_limited_request(
ad_account.get_insights,
fields=fields,
params=params,
)
except FacebookRequestError as e:
error_code = e.api_error_code()
if error_code in [190, 102]:
raise ValueError(f"Access token is invalid (error {error_code}): {e.api_error_message()}")
raise
# Get account timezone from database
account_timezone = await self._get_account_timezone(account_id)
# Store insights
count = 0
for insight in insights:
insight_dict = dict(insight)
# Compute appropriate timestamp based on date_start and account timezone
date_start_str = insight_dict.get("date_start")
timestamp = self._compute_timestamp(date_start_str, account_timezone)
await self.db.insert_account_insights(
time=timestamp,
count, _ = await self._master_grab_insights(
account_id=account_id,
data=insight_dict,
date_preset="custom", # Indicate this was a custom date range
fields=fields,
level="account",
db_insert_func=self.db.insert_account_insights,
start_date=start_date,
end_date=end_date,
)
count += 1
return count
@@ -812,51 +760,16 @@ class ScheduledInsightsGrabber:
AdsInsights.Field.date_stop,
]
params = {
"time_range": {
"since": start_date.isoformat(),
"until": end_date.isoformat(),
},
"level": "campaign",
"time_increment": 1, # Daily breakdown
"limit": limit,
}
ad_account = AdAccount(account_id)
try:
insights = await self._rate_limited_request(
ad_account.get_insights,
fields=fields,
params=params,
)
except FacebookRequestError as e:
error_code = e.api_error_code()
if error_code in [190, 102]:
raise ValueError(f"Access token is invalid (error {error_code}): {e.api_error_message()}")
raise
# Get account timezone from database
account_timezone = await self._get_account_timezone(account_id)
# Store insights
count = 0
for insight in insights:
campaign_id = insight.get('campaign_id')
if campaign_id:
insight_dict = dict(insight)
date_start_str = insight_dict.get("date_start")
timestamp = self._compute_timestamp(date_start_str, account_timezone)
await self.db.insert_campaign_insights(
time=timestamp,
campaign_id=campaign_id,
count, _ = await self._master_grab_insights(
account_id=account_id,
data=insight_dict,
date_preset="custom",
cache_metadata=True,
fields=fields,
level="campaign",
db_insert_func=self.db.insert_campaign_insights,
start_date=start_date,
end_date=end_date,
limit=limit,
required_fields={"campaign_id": "campaign_id"},
)
count += 1
return count
@@ -895,53 +808,16 @@ class ScheduledInsightsGrabber:
AdsInsights.Field.date_stop,
]
params = {
"time_range": {
"since": start_date.isoformat(),
"until": end_date.isoformat(),
},
"level": "adset",
"time_increment": 1, # Daily breakdown
"limit": limit,
}
ad_account = AdAccount(account_id)
try:
insights = await self._rate_limited_request(
ad_account.get_insights,
fields=fields,
params=params,
)
except FacebookRequestError as e:
error_code = e.api_error_code()
if error_code in [190, 102]:
raise ValueError(f"Access token is invalid (error {error_code}): {e.api_error_message()}")
raise
# Get account timezone from database
account_timezone = await self._get_account_timezone(account_id)
# Store insights
count = 0
for insight in insights:
adset_id = insight.get('adset_id')
campaign_id = insight.get('campaign_id')
if adset_id and campaign_id:
insight_dict = dict(insight)
date_start_str = insight_dict.get("date_start")
timestamp = self._compute_timestamp(date_start_str, account_timezone)
await self.db.insert_adset_insights(
time=timestamp,
adset_id=adset_id,
campaign_id=campaign_id,
count, _ = await self._master_grab_insights(
account_id=account_id,
data=insight_dict,
date_preset="custom",
cache_metadata=True,
fields=fields,
level="adset",
db_insert_func=self.db.insert_adset_insights,
start_date=start_date,
end_date=end_date,
limit=limit,
required_fields={"adset_id": "adset_id", "campaign_id": "campaign_id"},
)
count += 1
return count

View File

@@ -92,6 +92,9 @@ class ViewManager:
"adset_insights_flattened",
"account_insights_flattened",
"campaign_insights_flattened",
"campaign_insights_by_country_flattened",
#"campaign_insights_by_device_flattened",
#"campaign_insights_by_gender_flattened",
]
async with self.pool.acquire() as conn:

View File

@@ -0,0 +1,20 @@
--- account insights by gender
DROP VIEW IF EXISTS account_insights_by_device CASCADE;
CREATE VIEW account_insights_by_device AS
SELECT
time,
account_id,
device_platform,
SUM(impressions) AS impressions,
SUM(clicks) AS clicks,
SUM(spend) AS spend,
SUM(link_click) AS link_click,
SUM(landing_page_view) AS landing_page_view,
SUM(lead) AS lead
FROM campaign_insights_by_device_flattened
GROUP BY time, account_id, device_platform;

View File

@@ -0,0 +1,53 @@
DROP VIEW IF EXISTS account_insights_by_gender CASCADE;
CREATE VIEW account_insights_by_gender AS
SELECT
time,
account_id,
gender,
SUM(impressions) AS impressions,
SUM(clicks) AS clicks,
SUM(spend) AS spend,
SUM(link_click) AS link_click,
SUM(landing_page_view) AS landing_page_view,
SUM(lead) AS lead
FROM campaign_insights_by_gender
GROUP BY time, account_id, gender;
DROP VIEW IF EXISTS account_insights_by_age CASCADE;
CREATE VIEW account_insights_by_age AS
SELECT
time,
account_id,
age,
SUM(impressions) AS impressions,
SUM(clicks) AS clicks,
SUM(spend) AS spend,
SUM(link_click) AS link_click,
SUM(landing_page_view) AS landing_page_view,
SUM(lead) AS lead
FROM campaign_insights_by_age
GROUP BY time, account_id, age;
DROP VIEW IF EXISTS account_insights_by_gender_and_age CASCADE;
CREATE VIEW account_insights_by_gender_and_age AS
SELECT
time,
account_id,
gender,
age,
SUM(impressions) AS impressions,
SUM(clicks) AS clicks,
SUM(spend) AS spend,
SUM(link_click) AS link_click,
SUM(landing_page_view) AS landing_page_view,
SUM(lead) AS lead
FROM campaign_insights_by_gender_and_age
GROUP BY time, account_id, age, gender;

View File

@@ -0,0 +1,55 @@
DROP VIEW IF EXISTS g_account_insights CASCADE;
CREATE VIEW g_account_insights AS
SELECT
time,
account_id,
clicks,
impressions,
interactions,
cost_micros,
cost_micros / 1000000.0 as cost,
leads,
engagements,
customer_currency_code,
account_name,
-- CTR (Click-Through Rate)
(clicks::numeric / impressions_nz) * 100 as ctr,
-- CPM (Cost Per Mille) in micros and standard units
(cost_micros::numeric / impressions_nz) * 1000 as cpm_micros,
(cost_micros::numeric / impressions_nz) * 1000 / 1000000.0 as cpm,
-- CPC (Cost Per Click) in micros and standard units
cost_micros::numeric / clicks_nz as cpc_micros,
cost_micros::numeric / clicks_nz / 1000000.0 as cpc,
-- CPL (Cost Per Lead) in micros and standard units
cost_micros::numeric / leads_nz as cpl_micros,
cost_micros::numeric / leads_nz / 1000000.0 as cpl,
-- Conversion Rate
(leads::numeric / clicks_nz) * 100 as conversion_rate,
-- Engagement Rate
(engagements::numeric / impressions_nz) * 100 as engagement_rate
FROM (
SELECT
segments_date as time,
customer_id as account_id,
sum(metrics_clicks) as clicks,
sum(metrics_impressions) as impressions,
sum(metrics_interactions) as interactions,
sum(metrics_cost_micros) as cost_micros,
sum(metrics_conversions) as leads,
sum(metrics_engagements) as engagements,
customer_currency_code,
customer_descriptive_name as account_name,
-- Null-safe denominators
NULLIF(sum(metrics_clicks), 0) as clicks_nz,
NULLIF(sum(metrics_impressions), 0) as impressions_nz,
NULLIF(sum(metrics_conversions), 0) as leads_nz
FROM google.account_performance_report
GROUP BY account_id, time, customer_currency_code, account_name
) base;

View File

@@ -3,9 +3,8 @@
DROP MATERIALIZED VIEW IF EXISTS campaign_insights_flattened CASCADE;
CREATE MATERIALIZED VIEW campaign_insights_flattened AS
SELECT
time,
account_id,
SELECT date_start AS "time",
concat('act_', account_id) AS account_id,
campaign_id,
impressions,
clicks,
@@ -14,22 +13,18 @@ SELECT
ctr,
cpc,
cpm,
date_preset,
date_start,
date_stop,
fetched_at,
(SELECT (value->>'value')::numeric
FROM jsonb_array_elements(actions)
WHERE value->>'action_type' = 'link_click') AS link_click,
(SELECT (value->>'value')::numeric
FROM jsonb_array_elements(actions)
WHERE value->>'action_type' = 'landing_page_view') AS landing_page_view,
(SELECT (value->>'value')::numeric
FROM jsonb_array_elements(actions)
WHERE value->>'action_type' = 'lead') AS lead
FROM campaign_insights;
( SELECT (jsonb_array_elements.value ->> 'value'::text)::numeric AS "numeric"
FROM jsonb_array_elements(customcampaign_insights.actions) jsonb_array_elements(value)
WHERE (jsonb_array_elements.value ->> 'action_type'::text) = 'link_click'::text) AS link_click,
( SELECT (jsonb_array_elements.value ->> 'value'::text)::numeric AS "numeric"
FROM jsonb_array_elements(customcampaign_insights.actions) jsonb_array_elements(value)
WHERE (jsonb_array_elements.value ->> 'action_type'::text) = 'landing_page_view'::text) AS landing_page_view,
( SELECT (jsonb_array_elements.value ->> 'value'::text)::numeric AS "numeric"
FROM jsonb_array_elements(customcampaign_insights.actions) jsonb_array_elements(value)
WHERE (jsonb_array_elements.value ->> 'action_type'::text) = 'lead'::text) AS lead
FROM meta.customcampaign_insights;
CREATE INDEX idx_campaign_insights_flat_date ON campaign_insights_flattened(date_start, date_stop);

View File

@@ -3,22 +3,20 @@
DROP MATERIALIZED VIEW IF EXISTS campaign_insights_by_country_flattened CASCADE;
CREATE MATERIALIZED VIEW campaign_insights_by_country_flattened AS
SELECT
time,
account_id,
SELECT date_start AS "time",
concat('act_', account_id) AS account_id,
campaign_id,
country,
impressions,
clicks,
spend,
reach,
frequency,
ctr,
cpc,
cpm,
date_preset,
date_start,
date_stop,
fetched_at,
(SELECT (value->>'value')::numeric
FROM jsonb_array_elements(actions)
WHERE value->>'action_type' = 'link_click') AS link_click,
@@ -29,7 +27,7 @@ SELECT
FROM jsonb_array_elements(actions)
WHERE value->>'action_type' = 'lead') AS lead
FROM campaign_insights_by_country;
FROM meta.custom_campaign_country;
CREATE INDEX idx_campaign_insights_by_country_flat_date ON campaign_insights_by_country_flattened(date_start, date_stop);

View File

@@ -0,0 +1,32 @@
--- campaign insights by device
DROP MATERIALIZED VIEW IF EXISTS campaign_insights_by_device_flattened CASCADE;
CREATE MATERIALIZED VIEW campaign_insights_by_device_flattened AS
SELECT date_start AS "time",
concat('act_', account_id) AS account_id,
campaign_id,
device_platform,
impressions,
clicks,
spend,
reach,
frequency,
date_start,
date_stop,
(SELECT (value->>'value')::numeric
FROM jsonb_array_elements(actions)
WHERE value->>'action_type' = 'link_click') AS link_click,
(SELECT (value->>'value')::numeric
FROM jsonb_array_elements(actions)
WHERE value->>'action_type' = 'landing_page_view') AS landing_page_view,
(SELECT (value->>'value')::numeric
FROM jsonb_array_elements(actions)
WHERE value->>'action_type' = 'lead') AS lead
FROM meta.custom_campaign_device;
CREATE INDEX idx_campaign_insights_by_device_flat_date ON campaign_insights_by_device_flattened(date_start, date_stop);
CREATE UNIQUE INDEX idx_campaign_insights_by_device_flat_unique ON campaign_insights_by_device_flattened(time, campaign_id, device_platform);
REFRESH MATERIALIZED VIEW CONCURRENTLY campaign_insights_by_device_flattened;

View File

@@ -0,0 +1,71 @@
--- campaign insights by country
DROP MATERIALIZED VIEW IF EXISTS campaign_insights_by_gender_and_age CASCADE;
CREATE MATERIALIZED VIEW campaign_insights_by_gender_and_age AS
SELECT date_start AS "time",
concat('act_', account_id) AS account_id,
campaign_id,
gender,
age,
impressions,
clicks,
spend,
reach,
frequency,
ctr,
cpc,
cpm,
date_start,
date_stop,
(SELECT (value->>'value')::numeric
FROM jsonb_array_elements(actions)
WHERE value->>'action_type' = 'link_click') AS link_click,
(SELECT (value->>'value')::numeric
FROM jsonb_array_elements(actions)
WHERE value->>'action_type' = 'landing_page_view') AS landing_page_view,
(SELECT (value->>'value')::numeric
FROM jsonb_array_elements(actions)
WHERE value->>'action_type' = 'lead') AS lead
FROM meta.custom_campaign_gender;
CREATE INDEX idx_campaign_insights_by_gender_and_age_date ON campaign_insights_by_gender_and_age(date_start, date_stop);
CREATE UNIQUE INDEX idx_campaign_insights_by_gender_and_age_unique ON campaign_insights_by_gender_and_age(time, campaign_id, gender, age);
REFRESH MATERIALIZED VIEW CONCURRENTLY campaign_insights_by_gender_and_age;
DROP VIEW IF EXISTS campaign_insights_by_gender CASCADE;
create view campaign_insights_by_gender as
Select time,
sum(clicks) as clicks,
sum(link_click) as link_click,
sum(lead) as lead,
sum(landing_page_view) as landing_page_view,
sum(spend) as spend,
sum(reach) as reach,
sum(impressions) as impressions,
gender,
campaign_id,
account_id
from campaign_insights_by_gender_and_age
group by time, gender, account_id, campaign_id, date_start, date_stop;
DROP VIEW IF EXISTS campaign_insights_by_age CASCADE;
create view campaign_insights_by_age as
Select time,
sum(clicks) as clicks,
sum(link_click) as link_click,
sum(lead) as lead,
sum(landing_page_view) as landing_page_view,
sum(spend) as spend,
sum(reach) as reach,
sum(impressions) as impressions,
age,
campaign_id,
account_id
from campaign_insights_by_gender_and_age
group by time, age, account_id, campaign_id, date_start, date_stop;

115
tests/README.md Normal file
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@@ -0,0 +1,115 @@
# Tests
This directory contains tests for the meta_api_grabber project.
## Running Tests
Install test dependencies:
```bash
uv sync --extra test
```
Run all tests:
```bash
uv run pytest
```
Run specific test file:
```bash
uv run pytest tests/test_field_schema_validation.py -v
```
Run with verbose output:
```bash
uv run pytest tests/test_field_schema_validation.py -v -s
```
## Test Files
### `test_field_schema_validation.py` (Integration Test)
This is a critical integration test that validates all fields requested by the grab_* methods in `scheduled_grabber.py` exist in the actual database schema.
**What it does:**
1. Parses `db_schema.sql` to extract actual table columns
2. Checks fields requested by each grab method:
- `grab_account_insights()``account_insights` table
- `grab_campaign_insights()``campaign_insights` table
- `grab_adset_insights()``adset_insights` table
- `grab_campaign_insights_by_country()``campaign_insights_by_country` table
3. Verifies all requested fields exist in the corresponding database table
**Why this test is important:** When new fields are added to the Meta API field lists, this test quickly alerts you if the corresponding database columns need to be added. Since fields are only added (never removed), the test helps catch schema mismatches early.
**Test methods:**
- `test_account_insights_fields()` - Validates account-level insight fields
- `test_campaign_insights_fields()` - Validates campaign-level insight fields
- `test_adset_insights_fields()` - Validates ad set-level insight fields
- `test_campaign_insights_by_country_fields()` - Validates country breakdown fields
- `test_all_tables_exist()` - Ensures all required insight tables exist
- `test_schema_documentation()` - Prints out the parsed schema for reference
**Output example:**
```
Table: account_insights
Columns (17): account_id, actions, clicks, cost_per_action_type, cpc, cpm, cpp, ctr, ...
Table: campaign_insights
Columns (15): account_id, actions, campaign_id, clicks, cpc, cpm, ctr, ...
```
## Writing Tests
Use markers to categorize tests:
```python
@pytest.mark.unit
def test_something():
pass
@pytest.mark.integration
async def test_database_connection():
pass
```
Run only unit tests:
```bash
uv run pytest -m unit
```
Run everything except integration tests:
```bash
uv run pytest -m "not integration"
```
## Schema Validation Workflow
When you add new fields to a grab method:
1. **Add fields to `scheduled_grabber.py`:**
```python
fields = [
...
AdsInsights.Field.new_field, # New field added
]
```
2. **Run tests to see what's missing:**
```bash
uv run pytest tests/test_field_schema_validation.py -v -s
```
3. **Test output will show:**
```
adset_insights table missing columns: {'new_field'}
Available: [account_id, actions, adset_id, ...]
```
4. **Update `db_schema.sql` with the new column:**
```sql
ALTER TABLE adset_insights ADD COLUMN IF NOT EXISTS new_field TYPE;
```
5. **Run tests again to verify:**
```bash
uv run pytest tests/test_field_schema_validation.py -v
```

1
tests/__init__.py Normal file
View File

@@ -0,0 +1 @@
"""Tests for meta_api_grabber package."""

13
tests/conftest.py Normal file
View File

@@ -0,0 +1,13 @@
"""Pytest configuration and fixtures."""
import pytest
def pytest_configure(config):
"""Configure pytest."""
config.addinivalue_line(
"markers", "integration: marks tests as integration tests (deselect with '-m \"not integration\"')"
)
config.addinivalue_line(
"markers", "unit: marks tests as unit tests"
)

View File

@@ -0,0 +1,360 @@
"""
Integration test that validates all fields requested by grab_* methods exist in the database schema.
This test:
1. Parses the SQL schema file (db_schema.sql) to extract actual table columns
2. Reads scheduled_grabber.py to find which methods call which tables
3. Verifies that all requested fields exist in the actual database schema
"""
import re
import pathlib
from typing import Dict, Set, List
import pytest
def parse_sql_schema() -> Dict[str, Set[str]]:
"""
Parse db_schema.sql to extract table columns.
Returns:
Dictionary mapping table names to sets of column names
"""
schema_file = pathlib.Path(__file__).parent.parent / "src" / "meta_api_grabber" / "db_schema.sql"
if not schema_file.exists():
raise FileNotFoundError(f"Schema file not found: {schema_file}")
with open(schema_file, 'r') as f:
content = f.read()
tables = {}
# Parse CREATE TABLE statements
# Pattern: CREATE TABLE IF NOT EXISTS table_name (...)
create_table_pattern = r'CREATE TABLE IF NOT EXISTS (\w+)\s*\((.*?)\);'
for match in re.finditer(create_table_pattern, content, re.DOTALL):
table_name = match.group(1)
table_body = match.group(2)
# Extract column names (first word before space/comma)
# Pattern: column_name TYPE ...
column_pattern = r'^\s*(\w+)\s+\w+'
columns = set()
for line in table_body.split('\n'):
line = line.strip()
if not line or line.startswith('--') or line.startswith('PRIMARY') or line.startswith('FOREIGN') or line.startswith('CONSTRAINT'):
continue
col_match = re.match(column_pattern, line)
if col_match:
columns.add(col_match.group(1))
if columns:
tables[table_name] = columns
return tables
def get_field_name(field_str: str) -> str:
"""
Extract field name from AdsInsights.Field.xxx notation.
Example: 'impressions' from 'AdsInsights.Field.impressions'
"""
if '.' in field_str:
return field_str.split('.')[-1]
return field_str
def extract_fields_from_grabber_source() -> Dict[str, List[str]]:
"""
Extract field lists from grab_* methods by reading scheduled_grabber.py source.
Returns:
Dictionary mapping method names to lists of field names
"""
grabber_file = pathlib.Path(__file__).parent.parent / "src" / "meta_api_grabber" / "scheduled_grabber.py"
if not grabber_file.exists():
raise FileNotFoundError(f"scheduled_grabber.py not found: {grabber_file}")
with open(grabber_file, 'r') as f:
source = f.read()
methods_to_table = {
'grab_account_insights': 'account_insights',
'grab_campaign_insights': 'campaign_insights',
'grab_adset_insights': 'adset_insights',
'grab_campaign_insights_by_country': 'campaign_insights_by_country',
}
result = {}
for method_name in methods_to_table.keys():
# Find the method definition by looking for: async def method_name(...)
method_pattern = rf'async def {method_name}\s*\('
method_match = re.search(method_pattern, source)
if not method_match:
continue
# Get the position after the method name pattern
start_pos = method_match.end()
# Now find where the method body actually starts (after the closing paren and docstring)
# Skip to the opening paren
open_paren_pos = start_pos - 1
# Count parentheses to find the closing paren of the function signature
paren_count = 1
pos = open_paren_pos + 1
while pos < len(source) and paren_count > 0:
if source[pos] == '(':
paren_count += 1
elif source[pos] == ')':
paren_count -= 1
pos += 1
# Now pos is after the closing paren. Find the colon
colon_pos = source.find(':', pos)
# Skip past any docstring if present
after_colon = source[colon_pos + 1:colon_pos + 10].lstrip()
if after_colon.startswith('"""') or after_colon.startswith("'''"):
quote_type = '"""' if after_colon.startswith('"""') else "'''"
docstring_start = source.find(quote_type, colon_pos)
docstring_end = source.find(quote_type, docstring_start + 3) + 3
method_body_start = docstring_end
else:
method_body_start = colon_pos + 1
# Find the next method definition to know where this method ends
next_method_pattern = r'async def \w+\s*\('
next_match = re.search(next_method_pattern, source[method_body_start:])
if next_match:
method_body_end = method_body_start + next_match.start()
else:
# Last method - use rest of file
method_body_end = len(source)
method_body = source[method_body_start:method_body_end]
# Extract fields from the method body
# Look for: fields = [...] or fields = common_fields + [...]
# First check if this method uses common_fields
uses_common_fields = 'common_fields' in method_body[:500]
if uses_common_fields:
# Pattern: fields = common_fields + [...]
fields_pattern = r'fields\s*=\s*common_fields\s*\+\s*\[(.*?)\]'
fields_match = re.search(fields_pattern, method_body, re.DOTALL)
if fields_match:
fields_str = fields_match.group(1)
# Extract individual field names
field_pattern = r'AdsInsights\.Field\.(\w+)'
fields = re.findall(field_pattern, fields_str)
# Also get common_fields from the module level
common_pattern = r'common_fields\s*=\s*\[(.*?)\]'
common_match = re.search(common_pattern, source, re.DOTALL)
if common_match:
common_str = common_match.group(1)
common_fields_list = re.findall(field_pattern, common_str)
fields = common_fields_list + fields
result[method_name] = fields
else:
# Pattern: fields = [...]
# Use bracket matching to find the correct field list
fields_keyword_pos = method_body.find('fields =')
if fields_keyword_pos != -1:
# Find the opening bracket after fields =
bracket_pos = method_body.find('[', fields_keyword_pos)
if bracket_pos != -1:
# Count brackets to find the matching closing bracket
bracket_count = 0
end_pos = bracket_pos
for i, char in enumerate(method_body[bracket_pos:]):
if char == '[':
bracket_count += 1
elif char == ']':
bracket_count -= 1
if bracket_count == 0:
end_pos = bracket_pos + i
break
fields_str = method_body[bracket_pos + 1:end_pos]
field_pattern = r'AdsInsights\.Field\.(\w+)'
fields = re.findall(field_pattern, fields_str)
result[method_name] = fields
return result
@pytest.fixture(scope="module")
def schema_columns():
"""Parse and cache the schema columns."""
return parse_sql_schema()
@pytest.fixture(scope="module")
def extracted_fields_by_method():
"""Extract and cache the fields from each grab_* method."""
return extract_fields_from_grabber_source()
# Mapping of method names to their insight table names
METHOD_TO_TABLE = {
'grab_account_insights': 'account_insights',
'grab_campaign_insights': 'campaign_insights',
'grab_adset_insights': 'adset_insights',
'grab_campaign_insights_by_country': 'campaign_insights_by_country',
}
# Fields that are IDs/names stored in metadata tables, not in the insights table
METADATA_ONLY_FIELDS = {
'campaign_id', 'campaign_name',
'adset_id', 'adset_name',
}
class TestFieldSchemaValidation:
"""Validate that all API field requests have corresponding database columns."""
def test_grab_account_insights_fields(self, schema_columns, extracted_fields_by_method):
"""Test that grab_account_insights fields exist in schema."""
method_name = 'grab_account_insights'
table_name = METHOD_TO_TABLE[method_name]
assert method_name in extracted_fields_by_method, f"Could not extract fields from {method_name}"
extracted_fields = set(extracted_fields_by_method[method_name])
table_cols = schema_columns.get(table_name, set())
assert table_cols, f"Table {table_name} not found in schema"
missing = extracted_fields - table_cols
assert not missing, \
f"{table_name} table missing columns: {missing}\n" \
f"Method requests: {sorted(extracted_fields)}\n" \
f"Available: {sorted(table_cols)}"
print(f"{method_name}{table_name}: {len(extracted_fields)} fields validated")
def test_grab_campaign_insights_fields(self, schema_columns, extracted_fields_by_method):
"""Test that grab_campaign_insights fields exist in schema."""
method_name = 'grab_campaign_insights'
table_name = METHOD_TO_TABLE[method_name]
assert method_name in extracted_fields_by_method, f"Could not extract fields from {method_name}"
extracted_fields = set(extracted_fields_by_method[method_name])
table_cols = schema_columns.get(table_name, set())
assert table_cols, f"Table {table_name} not found in schema"
# Remove ID/name fields (stored in metadata tables, not insights table)
insight_only_fields = extracted_fields - METADATA_ONLY_FIELDS
missing = insight_only_fields - table_cols
assert not missing, \
f"{table_name} table missing columns: {missing}\n" \
f"Method requests: {sorted(extracted_fields)}\n" \
f"Available: {sorted(table_cols)}"
print(f"{method_name}{table_name}: {len(extracted_fields)} fields validated")
def test_grab_adset_insights_fields(self, schema_columns, extracted_fields_by_method):
"""Test that grab_adset_insights fields exist in schema."""
method_name = 'grab_adset_insights'
table_name = METHOD_TO_TABLE[method_name]
assert method_name in extracted_fields_by_method, f"Could not extract fields from {method_name}"
extracted_fields = set(extracted_fields_by_method[method_name])
table_cols = schema_columns.get(table_name, set())
assert table_cols, f"Table {table_name} not found in schema"
# Remove ID/name fields (stored in metadata tables, not insights table)
insight_only_fields = extracted_fields - METADATA_ONLY_FIELDS
missing = insight_only_fields - table_cols
assert not missing, \
f"{table_name} table missing columns: {missing}\n" \
f"Method requests: {sorted(extracted_fields)}\n" \
f"Available: {sorted(table_cols)}"
print(f"{method_name}{table_name}: {len(extracted_fields)} fields validated")
def test_grab_campaign_insights_by_country_fields(self, schema_columns, extracted_fields_by_method):
"""Test that grab_campaign_insights_by_country fields exist in schema."""
method_name = 'grab_campaign_insights_by_country'
table_name = METHOD_TO_TABLE[method_name]
assert method_name in extracted_fields_by_method, f"Could not extract fields from {method_name}"
extracted_fields = set(extracted_fields_by_method[method_name])
table_cols = schema_columns.get(table_name, set())
assert table_cols, f"Table {table_name} not found in schema"
# Remove ID/name fields (stored in metadata tables, not insights table)
insight_only_fields = extracted_fields - METADATA_ONLY_FIELDS
# Country is special - it's part of the breakdown
assert "country" in table_cols, \
f"country field missing in {table_name} table\n" \
f"Available: {sorted(table_cols)}"
missing = insight_only_fields - table_cols
assert not missing, \
f"{table_name} table missing columns: {missing}\n" \
f"Method requests: {sorted(extracted_fields)}\n" \
f"Available: {sorted(table_cols)}"
print(f"{method_name}{table_name}: {len(extracted_fields)} fields validated")
def test_all_tables_exist(self, schema_columns):
"""Test that all required insight tables exist in schema."""
required_tables = {
"account_insights",
"campaign_insights",
"adset_insights",
"campaign_insights_by_country",
}
existing_tables = set(schema_columns.keys())
missing = required_tables - existing_tables
assert not missing, \
f"Missing tables: {missing}\n" \
f"Found: {sorted(existing_tables)}"
def test_schema_documentation(self, schema_columns):
"""Print out the parsed schema for verification."""
print("\n" + "="*80)
print("PARSED DATABASE SCHEMA")
print("="*80)
for table_name in sorted(schema_columns.keys()):
columns = sorted(schema_columns[table_name])
print(f"\nTable: {table_name}")
print(f"Columns ({len(columns)}): {', '.join(columns)}")
def test_extracted_fields_documentation(self, extracted_fields_by_method):
"""Print out extracted fields from each method."""
print("\n" + "="*80)
print("EXTRACTED FIELDS FROM GRAB METHODS")
print("="*80)
for method_name, fields in sorted(extracted_fields_by_method.items()):
print(f"\n{method_name}:")
print(f" Fields ({len(fields)}): {', '.join(sorted(set(fields)))}")
if __name__ == "__main__":
pytest.main([__file__, "-v"])

86
uv.lock generated
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@@ -193,6 +193,15 @@ wheels = [
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