def test_flat_schema(self):
schema = '{ "type": "SCHEMA", "stream": "simple_stream", "schema": { "properties": { "id": { "type": [ "null", "string" ] }, "name": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "integer" ] }, "ratio": { "type": [ "null", "number" ] }, "timestamp": { "type": "string", "format": "date-time" }, "date": { "type": "string", "format": "date" } }, "type": [ "null", "object" ] }, "key_properties": [ "id" ], "bookmark_properties": [ "date" ] }'
msg = singer.parse_message(schema)
schema = build_schema(msg.schema,
key_properties=msg.key_properties,
add_metadata=True)
for f in schema:
if f.name == "id":
self.assertEqual(f.field_type.upper(), "STRING")
elif f.name == "name":
self.assertEqual(f.field_type.upper(), "STRING")
elif f.name == "value":
self.assertEqual(f.field_type.upper(), "INTEGER")
elif f.name == "ratio":
self.assertEqual(f.field_type.upper(), "FLOAT")
elif f.name == "timestamp":
self.assertEqual(f.field_type.upper(), "TIMESTAMP")
elif f.name == "date":
self.assertEqual(f.field_type.upper(), "DATE")
def _load_to_bq(self, client, dataset, table_name, table_schema,
table_config, key_props, metadata_columns, truncate, rows):
logger = self.logger
partition_field = table_config.get("partition_field", None)
cluster_fields = table_config.get("cluster_fields", None)
force_fields = table_config.get("force_fields", {})
schema = build_schema(table_schema,
key_properties=key_props,
add_metadata=metadata_columns,
force_fields=force_fields)
load_config = LoadJobConfig()
load_config.ignore_unknown_values = True
load_config.schema = schema
if partition_field:
load_config.time_partitioning = bigquery.table.TimePartitioning(
type_=bigquery.table.TimePartitioningType.DAY,
field=partition_field)
if cluster_fields:
load_config.clustering_fields = cluster_fields
load_config.source_format = SourceFormat.NEWLINE_DELIMITED_JSON
if truncate:
logger.info(f"Load {table_name} by FULL_TABLE (truncate)")
load_config.write_disposition = WriteDisposition.WRITE_TRUNCATE
else:
logger.info(f"Appending to {table_name}")
load_config.write_disposition = WriteDisposition.WRITE_APPEND
logger.info("loading {} to BigQuery".format(table_name))
load_job = None
try:
load_job = client.load_table_from_file(rows,
dataset.table(table_name),
job_config=load_config,
rewind=True)
logger.info("loading job {}".format(load_job.job_id))
job = load_job.result()
logger.info(job._properties)
return job
except google_exceptions.BadRequest as err:
logger.error("failed to load table {} from file: {}".format(
table_name, str(err)))
if load_job and load_job.errors:
reason = err.errors[0]["reason"]
messages = [f"{err['message']}" for err in load_job.errors]
logger.error("reason: {reason}, errors:\n{e}".format(
reason=reason, e="\n".join(messages)))
err.message = f"reason: {reason}, errors: {';'.join(messages)}"
raise err
def test_nested_schema_v3(self):
schema = '{ "type":"SCHEMA", "stream":"orders", "schema": { "properties": { "address_id": { "type": [ "null", "string" ] }, "address_is_active": { "type": [ "null", "boolean" ] }, "billing_address": { "properties": { "address1": { "type": [ "null", "string" ] }, "address2": { "type": [ "null", "string" ] }, "city": { "type": [ "null", "string" ] }, "company": { "type": [ "null", "string" ] }, "country": { "type": [ "null", "string" ] }, "first_name": { "type": [ "null", "string" ] }, "last_name": { "type": [ "null", "string" ] }, "phone": { "type": [ "null", "string" ] }, "province": { "type": [ "null", "string" ] }, "zip": { "type": [ "null", "string" ] } }, "type": [ "null", "object" ], "additionalProperties": false }, "charge_id": { "type": [ "null", "string" ] }, "charge_status": { "type": [ "null", "string" ] }, "created_at": { "format": "date-time", "type": [ "null", "string" ] }, "customer_id": { "type": [ "null", "string" ] }, "discount_codes": { "anyOf": [ { "type": "array", "items": { "type": "object", "additionalProperties": false, "properties": { "amount": { "type": [ "null", "number" ] }, "code": { "type": [ "null", "string" ] }, "type": { "type": [ "null", "string" ] } } } }, { "type": "null" } ] }, "email": { "type": [ "null", "string" ] }, "first_name": { "type": [ "null", "string" ] }, "hash": { "type": [ "null", "string" ] }, "id": { "type": [ "null", "string" ] }, "is_prepaid": { "type": [ "null", "boolean" ] }, "last_name": { "type": [ "null", "string" ] }, "line_items": { "anyOf": [ { "type": "array", "items": { "type": "object", "additionalProperties": false, "properties": { "grams": { "type": [ "null", "integer" ] }, "images": { "type": [ "null", "object" ], "additionalProperties": false, "properties": { "large": { "type": [ "null", "string" ] }, "medium": { "type": [ "null", "string" ] }, "original": { "type": [ "null", "string" ] }, "small": { "type": [ "null", "string" ] } } }, "price": { "type": [ "null", "number" ], "multipleOf": 1e-08 }, "properties": { "anyOf": [ { "type": "array", "items": { "type": "object", "additionalProperties": false, "properties": { "name": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "string" ] } } } }, { "type": "null" } ] }, "quantity": { "type": [ "null", "integer" ] }, "shopify_product_id": { "type": [ "null", "string" ] }, "shopify_variant_id": { "type": [ "null", "string" ] }, "sku": { "type": [ "null", "string" ] }, "subscription_id": { "type": [ "null", "string" ] }, "title": { "type": [ "null", "string" ] }, "variant_title": { "type": [ "null", "string" ] }, "vendor": { "type": [ "null", "string" ] } } } }, { "type": "null" } ] }, "note": { "type": [ "null", "string" ] }, "note_attributes": { "anyOf": [ { "type": "array", "items": { "type": "object", "additionalProperties": false, "properties": { "name": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "string" ] } } } }, { "type": "null" } ] }, "payment_processor": { "type": [ "null", "string" ] }, "processed_at": { "format": "date-time", "type": [ "null", "string" ] }, "scheduled_at": { "format": "date-time", "type": [ "null", "string" ] }, "shipped_date": { "format": "date-time", "type": [ "null", "string" ] }, "shipping_address": { "properties": { "address1": { "type": [ "null", "string" ] }, "address2": { "type": [ "null", "string" ] }, "city": { "type": [ "null", "string" ] }, "company": { "type": [ "null", "string" ] }, "country": { "type": [ "null", "string" ] }, "first_name": { "type": [ "null", "string" ] }, "last_name": { "type": [ "null", "string" ] }, "phone": { "type": [ "null", "string" ] }, "province": { "type": [ "null", "string" ] }, "zip": { "type": [ "null", "string" ] } }, "type": [ "null", "object" ], "additionalProperties": false }, "shipping_date": { "format": "date-time", "type": [ "null", "string" ] }, "shipping_lines": { "anyOf": [ { "type": "array", "items": { "type": "object", "additionalProperties": false, "properties": { "code": { "type": [ "null", "string" ] }, "price": { "type": [ "null", "number" ] }, "title": { "type": [ "null", "string" ] } } } }, { "type": "null" } ] }, "shopify_cart_token": { "type": [ "null", "string" ] }, "shopify_customer_id": { "type": [ "null", "string" ] }, "shopify_id": { "type": [ "null", "string" ] }, "shopify_order_id": { "type": [ "null", "string" ] }, "shopify_order_number": { "type": [ "null", "string" ] }, "status": { "type": [ "null", "string" ] }, "subtotal_price": { "type": [ "null", "number" ] }, "tags": { "type": [ "null", "string" ] }, "tax_lines": { "anyOf": [ { "type": "array", "items": { "type": "object", "additionalProperties": false, "properties": { "code": { "type": [ "null", "string" ] }, "price": { "type": [ "null", "number" ] }, "title": { "type": [ "null", "string" ] } } } }, { "type": "null" } ] }, "total_discounts": { "multipleOf": 1e-08, "type": [ "null", "number" ] }, "total_line_items_price": { "multipleOf": 1e-08, "type": [ "null", "number" ] }, "total_price": { "type": [ "null", "number" ] }, "total_refunds": { "multipleOf": 1e-08, "type": [ "null", "number" ] }, "total_tax": { "multipleOf": 1e-08, "type": [ "null", "number" ] }, "total_weight": { "type": [ "null", "integer" ] }, "transaction_id": { "type": [ "null", "string" ] }, "type": { "type": [ "null", "string" ] }, "updated_at": { "format": "date-time", "type": [ "null", "string" ] } }, "type": "object", "additionalProperties": false }, "key_properties":[ "Id" ] }'
msg = singer.parse_message(schema)
schema = build_schema(msg.schema,
key_properties=msg.key_properties,
add_metadata=True)
self.assertTrue(True)
def test_nested_schema_v2(self):
schema = '{"type": "SCHEMA", "stream": "campaigns", "schema": {"type": ["null", "object"], "additionalProperties": false, "properties": {"AudienceAdsBidAdjustment": {"type": ["null", "integer"]}, "BiddingScheme": {"anyOf": [{"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}, "InheritedBidStrategyType": {"type": ["null", "string"]}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}, "MaxCpc": {"type": ["null", "object"], "additionalProperties": false, "properties": {"Amount": {"type": ["null", "number"]}}}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}, "MaxCpc": {"type": ["null", "object"], "additionalProperties": false, "properties": {"Amount": {"type": ["null", "number"]}}}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}, "MaxCpc": {"type": ["null", "object"], "additionalProperties": false, "properties": {"Amount": {"type": ["null", "number"]}}}, "TargetCpa": {"type": ["null", "number"]}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}, "TargetRoas": {"type": ["null", "number"]}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}, "MaxCpc": {"type": ["null", "object"], "additionalProperties": false, "properties": {"Amount": {"type": ["null", "number"]}}}, "TargetRoas": {"type": ["null", "number"]}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}, "MaxCpc": {"type": ["null", "object"], "additionalProperties": false, "properties": {"Amount": {"type": ["null", "number"]}}}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}, "MaxCpc": {"type": ["null", "object"], "additionalProperties": false, "properties": {"Amount": {"type": ["null", "number"]}}}, "TargetAdPosition": {"type": ["null", "string"]}, "TargetImpressionShare": {"type": ["null", "number"]}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}}}]}, "BudgetType": {"type": ["null", "string"]}, "DailyBudget": {"type": ["null", "number"]}, "ExperimentId": {"type": ["null", "integer"]}, "FinalUrlSuffix": {"type": ["null", "string"]}, "ForwardCompatibilityMap": {"type": ["null", "object"], "properties": {"KeyValuePairOfstringstring": {"type": ["null", "array"], "items": {"type": ["null", "object"], "additionalProperties": false, "properties": {"key": {"type": ["null", "string"]}, "value": {"type": ["null", "string"]}}}}}}, "Id": {"type": ["null", "integer"]}, "Name": {"type": ["null", "string"]}, "Status": {"type": ["null", "string"]}, "SubType": {"type": ["null", "string"]}, "TimeZone": {"type": ["null", "string"]}, "TrackingUrlTemplate": {"type": ["null", "string"]}, "UrlCustomParameters": {"type": ["null", "object"], "additionalProperties": false, "properties": {"Parameters": {"type": ["null", "object"], "properties": {"CustomParameter": {"type": ["null", "array"], "items": {"type": ["null", "object"], "additionalProperties": false, "properties": {"Key": {"type": ["null", "string"]}, "Value": {"type": ["null", "string"]}}}}}}}}, "CampaignType": {"type": ["null", "string"]}, "Settings": {"type": ["null", "object"], "properties": {"Setting": {"type": ["null", "array"], "items": {"anyOf": [{"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}, "Details": {"type": ["null", "object"], "properties": {"TargetSettingDetail": {"type": ["null", "array"], "items": {"type": ["null", "object"], "additionalProperties": false, "properties": {"CriterionTypeGroup": {"type": ["null", "string"]}, "TargetAndBid": {"type": ["boolean"]}}}}}}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}, "LocalInventoryAdsEnabled": {"type": ["null", "boolean"]}, "Priority": {"type": ["null", "integer"]}, "SalesCountryCode": {"type": ["null", "string"]}, "StoreId": {"type": ["null", "integer"]}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}, "BidBoostValue": {"type": ["null", "number"]}, "BidMaxValue": {"type": ["null", "number"]}, "BidOption": {"type": ["null", "string"]}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}, "DomainName": {"type": ["null", "string"]}, "Language": {"type": ["null", "string"]}, "PageFeedIds": {"type": ["null", "object"], "properties": {"long": {"type": ["null", "array"], "items": {"type": "integer"}}}}, "Source": {"type": ["null", "string"]}}}, {"type": ["null", "object"], "additionalProperties": false, "properties": {"Type": {"type": ["null", "string"]}}}]}}}}, "BudgetId": {"type": ["null", "integer"]}, "Languages": {"type": ["null", "object"], "properties": {"string": {"type": ["null", "array"], "items": {"type": "string"}}}}, "AdScheduleUseSearcherTimeZone": {"type": ["null", "boolean"]}}}, "key_properties": ["Id"]}'
msg = singer.parse_message(schema)
schema = build_schema(msg.schema,
key_properties=msg.key_properties,
add_metadata=True)
self.assertTrue(True)
def test_nested_schema(self):
schema = '{ "type": "SCHEMA", "stream": "nested_stream", "schema": { "properties": { "account_id": { "type": [ "null", "string" ] }, "account_name": { "type": [ "null", "string" ] }, "action_values": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "ad_id": { "type": [ "null", "string" ] }, "ad_name": { "type": [ "null", "string" ] }, "adset_id": { "type": [ "null", "string" ] }, "adset_name": { "type": [ "null", "string" ] }, "age": { "type": [ "null", "integer", "string" ] }, "campaign_id": { "type": [ "null", "string" ] }, "campaign_name": { "type": [ "null", "string" ] }, "canvas_avg_view_percent": { "type": [ "null", "number" ] }, "canvas_avg_view_time": { "type": [ "null", "number" ] }, "clicks": { "type": [ "null", "integer" ] }, "conversion_rate_ranking": { "type": [ "null", "string" ] }, "cost_per_action_type": { "items": { "properties": { "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "string" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "cost_per_inline_link_click": { "type": [ "null", "number" ] }, "cost_per_inline_post_engagement": { "type": [ "null", "number" ] }, "cost_per_unique_action_type": { "items": { "properties": { "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "string" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "cost_per_unique_click": { "type": [ "null", "number" ] }, "cost_per_unique_inline_link_click": { "type": [ "null", "number" ] }, "cpc": { "type": [ "null", "number" ] }, "cpm": { "type": [ "null", "number" ] }, "cpp": { "type": [ "null", "number" ] }, "ctr": { "type": [ "null", "number" ] }, "date_start": { "format": "date-time", "type": [ "null", "string" ] }, "date_stop": { "format": "date-time", "type": [ "null", "string" ] }, "engagement_rate_ranking": { "type": [ "null", "string" ] }, "frequency": { "type": [ "null", "number" ] }, "gender": { "type": [ "null", "string" ] }, "impressions": { "type": [ "null", "integer" ] }, "inline_link_click_ctr": { "type": [ "null", "number" ] }, "inline_link_clicks": { "type": [ "null", "integer" ] }, "inline_post_engagement": { "type": [ "null", "integer" ] }, "objective": { "type": [ "null", "string" ] }, "outbound_clicks": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "quality_ranking": { "type": [ "null", "string" ] }, "reach": { "type": [ "null", "integer" ] }, "social_spend": { "type": [ "null", "number" ] }, "spend": { "type": [ "null", "number" ] }, "unique_actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "unique_clicks": { "type": [ "null", "integer" ] }, "unique_ctr": { "type": [ "null", "number" ] }, "unique_inline_link_click_ctr": { "type": [ "null", "number" ] }, "unique_inline_link_clicks": { "type": [ "null", "integer" ] }, "unique_link_clicks_ctr": { "type": [ "null", "number" ] }, "video_30_sec_watched_actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "video_p100_watched_actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "video_p25_watched_actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "video_p50_watched_actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "video_p75_watched_actions": { "items": { "properties": { "1d_click": { "type": [ "null", "number" ] }, "1d_view": { "type": [ "null", "number" ] }, "28d_click": { "type": [ "null", "number" ] }, "28d_view": { "type": [ "null", "number" ] }, "7d_click": { "type": [ "null", "number" ] }, "7d_view": { "type": [ "null", "number" ] }, "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "video_play_curve_actions": { "items": { "properties": { "action_type": { "type": [ "null", "string" ] }, "value": { "items": { "type": [ "null", "integer" ] }, "type": [ "null", "array" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] }, "website_ctr": { "items": { "properties": { "action_destination": { "type": [ "null", "string" ] }, "action_target_id": { "type": [ "null", "string" ] }, "action_type": { "type": [ "null", "string" ] }, "value": { "type": [ "null", "number" ] } }, "type": [ "null", "object" ] }, "type": [ "null", "array" ] } }, "type": [ "null", "object" ] }, "key_properties": [ "campaign_id", "adset_id", "ad_id", "date_start", "age", "gender" ], "bookmark_properties": [ "date_start" ] }'
msg = singer.parse_message(schema)
schema = build_schema(msg.schema,
key_properties=msg.key_properties,
add_metadata=True)
for f in schema:
if f.name in ("date_start", "date_stop"):
self.assertEqual(f.field_type.upper(), "TIMESTAMP")
def persist_lines_job(
client,
dataset,
lines=None,
truncate=False,
forced_fulltables=[],
validate_records=True,
table_suffix=None,
):
state = None
schemas = {}
key_properties = {}
rows = {}
errors = {}
table_suffix = table_suffix or ""
for line in lines:
try:
msg = singer.parse_message(line)
except json.decoder.JSONDecodeError:
logger.error("Unable to parse:\n{}".format(line))
raise
if isinstance(msg, singer.RecordMessage):
table_name = msg.stream + table_suffix
if table_name not in schemas:
raise Exception(
"A record for stream {} was encountered before a corresponding schema"
.format(table_name))
schema = schemas[table_name]
if validate_records:
validate(msg.record, schema)
new_rec = filter(schema, msg.record)
# NEWLINE_DELIMITED_JSON expects literal JSON formatted data, with a newline character splitting each row.
data = bytes(
json.dumps(new_rec, cls=DecimalEncoder) + "\n", "UTF-8")
rows[table_name].write(data)
state = None
elif isinstance(msg, singer.StateMessage):
logger.debug("Setting state to {}".format(msg.value))
state = msg.value
elif isinstance(msg, singer.SchemaMessage):
table_name = msg.stream + table_suffix
if table_name in rows:
continue
schemas[table_name] = msg.schema
key_properties[table_name] = msg.key_properties
rows[table_name] = TemporaryFile(mode="w+b")
errors[table_name] = None
elif isinstance(msg, singer.ActivateVersionMessage):
# This is experimental and won't be used yet
pass
else:
raise Exception("Unrecognized message {}".format(msg))
for table in rows.keys():
key_props = key_properties[table]
SCHEMA = build_schema(schemas[table], key_properties=key_props)
load_config = LoadJobConfig()
load_config.schema = SCHEMA
load_config.source_format = SourceFormat.NEWLINE_DELIMITED_JSON
if truncate or (table in forced_fulltables):
logger.info(f"Load {table} by FULL_TABLE")
load_config.write_disposition = WriteDisposition.WRITE_TRUNCATE
logger.info("loading {} to Bigquery.\n".format(table))
try:
load_job = client.load_table_from_file(rows[table],
dataset.table(table),
job_config=load_config,
rewind=True)
logger.info("loading job {}".format(load_job.job_id))
logger.info(load_job.result())
except google_exceptions.BadRequest as err:
logger.error("failed to load table {} from file: {}".format(
table, str(err)))
if load_job.errors:
messages = [
f"reason: {err['reason']}, message: {err['message']}"
for err in load_job.errors
]
logger.error("errors:\n{}".format("\n".join(messages)))
raise
yield state
def persist_lines_stream(project_id,
dataset_id,
lines=None,
validate_records=True):
state = None
schemas = {}
key_properties = {}
tables = {}
rows = {}
errors = {}
bigquery_client = bigquery.Client(project=project_id)
dataset_ref = bigquery_client.dataset(dataset_id)
dataset = Dataset(dataset_ref)
try:
dataset = bigquery_client.create_dataset(
Dataset(dataset_ref)) or Dataset(dataset_ref)
except exceptions.Conflict:
pass
for line in lines:
try:
msg = singer.parse_message(line)
except json.decoder.JSONDecodeError:
logger.error("Unable to parse:\n{}".format(line))
raise
if isinstance(msg, singer.RecordMessage):
if msg.stream not in schemas:
raise Exception(
"A record for stream {} was encountered before a corresponding schema"
.format(msg.stream))
schema = schemas[msg.stream]
if validate_records:
validate(msg.record, schema)
err = None
try:
err = bigquery_client.insert_rows_json(tables[msg.stream],
[msg.record])
except Exception as exc:
logger.error(
f"failed to insert rows for {tables[msg.stream]}: {str(exc)}\n{msg.record}"
)
raise
errors[msg.stream] = err
rows[msg.stream] += 1
state = None
elif isinstance(msg, singer.StateMessage):
logger.debug("Setting state to {}".format(msg.value))
state = msg.value
elif isinstance(msg, singer.SchemaMessage):
table = msg.stream
schemas[table] = msg.schema
key_properties[table] = msg.key_properties
tables[table] = bigquery.Table(dataset.table(table),
schema=build_schema(schemas[table]))
rows[table] = 0
errors[table] = None
try:
tables[table] = bigquery_client.create_table(tables[table])
except exceptions.Conflict:
pass
elif isinstance(msg, singer.ActivateVersionMessage):
# This is experimental and won't be used yet
pass
else:
raise Exception("Unrecognized message {}".format(msg))
for table in errors.keys():
if not errors[table]:
logging.info("Loaded {} row(s) from {} into {}:{}".format(
rows[table], dataset_id, table, tables[table].path))
emit_state(state)
else:
logging.error("Errors: %s", errors[table])
return state
def persist_lines_stream( # noqa: 211
client: Client,
project_id,
dataset: Dataset,
lines: TextIO,
truncate: bool,
forced_fulltables: list,
validate_records: bool = True,
table_suffix: Optional[str] = None,
table_prefix: Optional[str] = None,
) -> Iterator[Optional[str]]:
"""Stream data into BigQuery.
Arguments:
client {Client} -- BigQuery client
dataset {Dataset} -- BigQuery dataset
lines {TextIO} -- Tap stream
Keyword Arguments:
truncate {bool} -- Whether to truncunate the table
forced_fulltables {list} -- List of tables to truncunate
validate_records {bool} -- Whether to alidate records (default: {True})
table_suffix {Optional[str]} -- Suffix for tables (default: {None})
table_prefix {Optional[str]} -- Prefix for tables (default: {None})
Raises:
SchemaNotFoundException: If the schema message was not received yet
InvalidSingerMessage: Invalid Sinnger message
Yields:
Iterator[Optional[str]] -- State
"""
# Create variable in which we save data in the upcomming loop
state: Optional[str] = None
schemas: dict = {}
key_properties: dict = {}
tables: dict = {}
rows: dict = {}
errors: dict = {}
table_suffix = table_suffix or ''
table_prefix = table_prefix or ''
# For every Singer input message
for line in lines:
# Parse the message
try:
msg: Union[SchemaMessage, StateMessage,
RecordMessage] = (parse_message(line))
except json.decoder.JSONDecodeError:
LOGGER.error(f'Unable to parse Singer Message:\n{line}')
raise
# There can be several kind of messages. When inserting data, the
# schema message comes first
if isinstance(msg, SchemaMessage):
# Schema message, create the table
table_name: str = table_prefix + msg.stream + table_suffix
# Save the schema, key_properties and message to use in the
# record messages that are following
schemas[table_name] = msg.schema
key_properties[table_name] = msg.key_properties
tables[table_name] = bigquery.Table(
dataset.table(table_name),
schema=build_schema(schemas[table_name]),
)
rows[table_name] = 0
errors[table_name] = None
dataset_id: str = dataset.dataset_id
if not table_exists(client, project_id, dataset_id, table_name):
# Create the table
client.create_table(tables[table_name])
elif truncate or table_name in forced_fulltables:
LOGGER.info(f'Load {table_name} by FULL_TABLE')
# When truncating is enabled and the table exists, the table
# has to be recreated. Because of this, we have to wait
# otherwise data can be lost, see:
# https://stackoverflow.com/questions/36846571/
# bigquery-table-truncation-before-streaming-not-working
LOGGER.info(f'Deleting table {table_name} because it exists')
client.delete_table(tables[table_name])
LOGGER.info(f'Recreating table {table_name}')
client.create_table(tables[table_name])
LOGGER.info(
'Sleeping for 5 minutes before streaming data, '
f'to avoid streaming data loss in {table_name}', )
time.sleep(FIVE_MINUTES)
# Delete table
elif isinstance(msg, RecordMessage):
# Record message
table_name = table_prefix + msg.stream + table_suffix
if table_name not in schemas:
raise SchemaNotFoundException(
f'A record for stream {table_name} was encountered before '
'a corresponding schema', )
# Retrieve schema
schema: dict = schemas[table_name]
# Retrieve table
table_ref: TableReference = tables[table_name]
# Validate the record
if validate_records:
# Raises ValidationError if the record has invalid schema
validate(msg.record, schema)
# Filter the record
record_input: Optional[Union[dict, str, list]] = filter_schema(
schema,
msg.record,
)
# Somewhere in the process, the input record can have decimal
# values e.g. "value": Decimal('10.25'). These are not JSON
# erializable. Therefore, we dump the JSON here, which converts
# them to string. Thereafter, we load the dumped JSON so we get a
# dictionary again, which we can insert to BigQuery
record_json: str = json.dumps(record_input, cls=DecimalEncoder)
record: dict = json.loads(record_json)
# Save the error
err: Optional[list] = None
try:
# Insert record
err = client.insert_rows(table_ref, [record])
except Exception as exc:
LOGGER.error(
f'Failed to insert rows for {table_name}: {exc}\n'
f'{record}\n{err}', )
raise
# Save errors of the stream and increate the insert rows
errors[msg.stream] = err
rows[msg.stream] += 1
state = None
elif isinstance(msg, StateMessage):
# State messages
LOGGER.debug(f'Setting state to {msg.value}')
state = msg.value
else:
raise InvalidSingerMessage(f'Unrecognized Singer Message:\n {msg}')
for table in errors.keys():
if errors[table]:
logging.error(f'Errors: {errors[table]}')
else:
logging.info(
'Loaded {rows} row(s) from {source} into {tab}:{path}'.format(
rows=rows[table],
source=dataset.dataset_id,
tab=table,
path=tables[table].path,
), )
yield state
def persist_lines_job( # noqa: WPS210, WPS211, WPS213, WPS231, WPS238
client: Client,
dataset: Dataset,
lines: TextIO,
truncate: bool,
forced_fulltables: list,
validate_records: bool = True,
table_suffix: Optional[str] = None,
table_prefix: Optional[str] = None,
) -> Iterator[Optional[str]]:
"""Perform a load job into BigQuery.
Arguments:
client {Client} -- BigQuery client
dataset {Dataset} -- BigQuery dataset
lines {TextIO} -- Tap stream
Keyword Arguments:
truncate {bool} -- Whether to truncunate the table
forced_fulltables {list} -- List of tables to truncunate
validate_records {bool} -- Whether to alidate records (default: {True})
table_suffix {Optional[str]} -- Suffix for tables (default: {None})
table_prefix {Optional[str]} -- Prefix for tables (default: {None})
Raises:
SchemaNotFoundException: If the schema message was not received yet
InvalidSingerMessage: Invalid Sinnger message
Yields:
Iterator[Optional[str]] -- State
"""
# Create variable in which we save data in the upcomming loop
state: Optional[str] = None
schemas: dict = {}
key_properties: dict = {}
rows: dict = {}
errors: dict = {}
table_suffix = table_suffix or ''
table_prefix = table_prefix or ''
# For every Singer input message
for line in lines:
# Parse the message
try:
msg: Union[SchemaMessage, StateMessage, RecordMessage] = (
parse_message(line)
)
except json.decoder.JSONDecodeError:
LOGGER.error(f'Unable to parse Singer Message:\n{line}')
raise
# There can be several kind of messages. When inserting data, the
# schema message comes first
if isinstance(msg, SchemaMessage):
# Schema message, save schema
table_name: str = table_prefix + msg.stream + table_suffix
# Skip schema if already created
if table_name in rows:
continue
# Save schema and setup a temp file for data storage
schemas[table_name] = msg.schema
key_properties[table_name] = msg.key_properties
rows[table_name] = TemporaryFile(mode='w+b')
errors[table_name] = None
elif isinstance(msg, RecordMessage):
# Record message
table_name = table_prefix + msg.stream + table_suffix
if table_name not in schemas:
raise SchemaNotFoundException(
f'A record for stream {table_name} was encountered before '
'a corresponding schema',
)
# Retrieve schema
schema: dict = schemas[table_name]
# Validate the record
if validate_records:
# Raises ValidationError if the record has invalid schema
validate(msg.record, schema)
record_input: Optional[Union[dict, str, list]] = filter_schema(
schema,
msg.record,
)
# Somewhere in the process, the input record can have decimal
# values e.g. "value": Decimal('10.25'). These are not JSON
# erializable. Therefore, we dump the JSON here, which converts
# them to string. Thereafter, we load the dumped JSON so we get a
# dictionary again, which we can insert to BigQuery
record_str: str = '{rec}\n'.format(
rec=json.dumps(record_input, cls=DecimalEncoder),
)
record: bytes = bytes(record_str, 'UTF-8')
# Save data to load later
rows[table_name].write(record)
state = None
elif isinstance(msg, StateMessage):
# State messages
LOGGER.debug(f'Setting state to {msg.value}')
state = msg.value
else:
raise InvalidSingerMessage(
f'Unrecognized Singer Message:\n {msg}',
)
# After all recordsa are received, setup a load job per stream
for table in rows.keys():
# Prepare load job
key_props: str = key_properties[table]
load_config: LoadJobConfig = LoadJobConfig()
load_config.schema = build_schema(
schemas[table],
key_properties=key_props,
)
load_config.source_format = SourceFormat.NEWLINE_DELIMITED_JSON
# Overwrite the table if truncate is enabled
if truncate or table in forced_fulltables:
LOGGER.info(f'Load {table} by FULL_TABLE')
load_config.write_disposition = WriteDisposition.WRITE_TRUNCATE
LOGGER.info(f'loading {table} to Bigquery.')
# Setup load job
load_job: LoadJob = client.load_table_from_file(
rows[table],
dataset.table(table),
job_config=load_config,
rewind=True,
)
LOGGER.info(f'loading job {load_job.job_id}')
# Run load job
try:
load_job.result()
except google_exceptions.GoogleAPICallError as err:
# Parse errors
LOGGER.error(f'failed to load table {table} from file: {err}')
if load_job.errors:
messages: list = [
f"reason: {err['reason']}, message: {err['message']}"
for err in load_job.errors
]
messages_str: str = '\n'.join(messages)
LOGGER.error(f'errors:\n{messages_str}')
raise
LOGGER.info(
f'Loaded {load_job.output_rows} row(s) in '
f'{load_job.destination}',
)
yield state
