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docs: add BigQuery errors table schema and update README
Add BigQuery schema: - Create errors table with partitioning and clustering - Metadata table for tracking loaded files - Example queries for debugging Update README: - Error Store (Dead Letter Queue) section - Storage structure and usage examples - BigQuery setup and query examples - Updated roadmap (error store complete) Features documented: - Two error types (validation vs processing) - 30-day retention with GCS lifecycle - Full event context for debugging - BigQuery integration for analysis
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README.md

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See `scripts/bigquery/README.md` and `specs/gcs-bigquery-storage/` for full details.
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### Error Store (Dead Letter Queue)
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All failed events are stored in a GCS-based dead letter queue for debugging and retry:
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**Two Error Types:**
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- **Validation Errors**: Missing required fields, invalid schema
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- **Processing Errors**: Storage failures, unexpected exceptions
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**Storage Structure:**
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```
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gs://bucket/errors/
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date=2026-01-15/
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error_type=validation/
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error-20260115-100000-abc123.parquet
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error_type=processing/
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error-20260115-100500-def456.parquet
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```
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**Create BigQuery Errors Table:**
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```bash
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cd scripts/bigquery
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export PROJECT_ID=my-project DATASET=events
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cat create_errors_table.sql | sed "s/{PROJECT_ID}/$PROJECT_ID/g" | sed "s/{DATASET}/$DATASET/g" | bq query --use_legacy_sql=false
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```
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**Query Errors:**
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```sql
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-- Find validation errors in last 24 hours
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SELECT
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error_message,
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stream,
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COUNT(*) as count
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FROM `project.dataset.errors`
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WHERE date >= CURRENT_DATE() - 1
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AND error_type = 'validation_error'
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GROUP BY error_message, stream
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ORDER BY count DESC;
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-- Get processing errors with stack traces
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SELECT
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timestamp,
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error_message,
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JSON_EXTRACT_SCALAR(error_details, '$.exception_type') as exception,
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JSON_EXTRACT_SCALAR(error_details, '$.stack_trace') as stack_trace
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FROM `project.dataset.errors`
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WHERE error_type = 'processing_error'
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ORDER BY timestamp DESC
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LIMIT 10;
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```
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**Key Features:**
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- Never loses events - all failures stored for debugging
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- Automatic 30-day retention (GCS lifecycle rules)
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- Full event context (payload, error, timestamp, stream)
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- Queryable via BigQuery for pattern analysis
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### Custom Storage
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Implement the `EventStore` protocol for any backend:
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- [x] Prometheus metrics
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- [x] EventSubscriptionCoordinator (dual-path architecture)
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- [x] Hash-based sequencer for consistent ordering
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- [ ] Error handling and dead letter queue (ErrorStore protocol exists, needs implementation)
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- [x] Error store with dead letter queue (GCS-based)
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- [ ] Performance benchmarks (10k+ events/sec)
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### v1.0

pyproject.toml

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"ruff>=0.1.0",
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"mypy>=1.7.0",
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"httpx>=0.25.0", # For testing FastAPI
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"locust>=2.20.0", # Load testing
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"psutil>=5.9.0", # Resource monitoring
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# Type stubs for mypy
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"types-python-dateutil>=2.8.0",
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"pandas-stubs>=2.1.0",
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]
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clickhouse = [
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"clickhouse-driver>=0.2.6",
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warn_return_any = true
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warn_unused_configs = true
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disallow_untyped_defs = true
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# Disable import-untyped for packages without stubs
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disable_error_code = ["import-untyped", "attr-defined"]
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[[tool.mypy.overrides]]
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module = "google.cloud"
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ignore_missing_imports = true
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[[tool.mypy.overrides]]
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module = "google.cloud.*"
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ignore_missing_imports = true
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[[tool.mypy.overrides]]
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module = "prometheus_client.*"
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ignore_missing_imports = true
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[[tool.mypy.overrides]]
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module = "pyarrow.*"
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ignore_missing_imports = true
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[dependency-groups]
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dev = [
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"types-python-dateutil>=2.9.0.20251115",
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-- Create errors table for dead letter queue
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--
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-- Usage:
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-- export PROJECT_ID=your-project
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-- export DATASET=events
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-- cat create_errors_table.sql | sed "s/{PROJECT_ID}/$PROJECT_ID/g" | sed "s/{DATASET}/$DATASET/g" | bq query --use_legacy_sql=false
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CREATE TABLE IF NOT EXISTS `{PROJECT_ID}.{DATASET}.errors` (
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error_id STRING NOT NULL,
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timestamp TIMESTAMP NOT NULL,
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error_type STRING NOT NULL, -- validation_error | processing_error
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error_message STRING NOT NULL,
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error_details JSON,
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stream STRING NOT NULL,
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original_payload JSON NOT NULL,
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retry_count INT64 DEFAULT 0,
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retry_after TIMESTAMP,
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date DATE NOT NULL
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)
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PARTITION BY date
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CLUSTER BY error_type, stream
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OPTIONS(
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description="Dead letter queue for failed events. All events that fail validation or processing are stored here with full context for debugging.",
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partition_expiration_days=30
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);
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-- Create metadata table for tracking loaded error files
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CREATE TABLE IF NOT EXISTS `{PROJECT_ID}.{DATASET}._loaded_error_files` (
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file_path STRING NOT NULL,
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loaded_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP(),
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row_count INT64,
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error_type STRING
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)
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PARTITION BY DATE(loaded_at)
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OPTIONS(
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description="Metadata tracking which error files have been loaded to prevent duplicates"
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);
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-- Example queries for debugging
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-- Find validation errors in last 24 hours
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-- SELECT
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-- error_type,
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-- error_message,
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-- stream,
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-- COUNT(*) as count
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-- FROM `{PROJECT_ID}.{DATASET}.errors`
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-- WHERE date >= CURRENT_DATE() - 1
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-- AND error_type = 'validation_error'
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-- GROUP BY error_type, error_message, stream
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-- ORDER BY count DESC;
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-- Find processing errors with stack traces
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-- SELECT
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-- timestamp,
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-- error_message,
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-- JSON_EXTRACT_SCALAR(error_details, '$.exception_type') as exception,
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-- JSON_EXTRACT_SCALAR(error_details, '$.stack_trace') as stack_trace,
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-- original_payload
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-- FROM `{PROJECT_ID}.{DATASET}.errors`
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-- WHERE error_type = 'processing_error'
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-- ORDER BY timestamp DESC
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-- LIMIT 10;
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-- Error rate by stream
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-- SELECT
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-- stream,
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-- error_type,
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-- COUNT(*) as error_count,
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-- ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER (PARTITION BY stream), 2) as pct
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-- FROM `{PROJECT_ID}.{DATASET}.errors`
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-- WHERE date >= CURRENT_DATE() - 7
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-- GROUP BY stream, error_type
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-- ORDER BY stream, error_count DESC;

src/eventkit/api/dependencies.py

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from eventkit.processing.sequencer import HashSequencer
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from eventkit.queues import EventQueue, create_queue
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from eventkit.stores.error_store import ErrorStore
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from eventkit.stores.gcs_error import GCSErrorStore
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from eventkit.stores.event_store import EventStore
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from eventkit.stores.gcs import GCSEventStore
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from eventkit.stores.gcs_error import GCSErrorStore
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@lru_cache

src/eventkit/loaders/bigquery_loader.py

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import time
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import structlog
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from google.cloud import bigquery, storage # type: ignore[attr-defined]
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from google.cloud import bigquery, storage
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logger = structlog.get_logger(__name__)
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src/eventkit/processing/processor.py

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- Lifecycle management (start/stop for graceful shutdown)
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"""
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from datetime import UTC, datetime
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import structlog
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from eventkit.adapters.base import EventAdapter

src/eventkit/queues/pubsub.py

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import logging
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from typing import TYPE_CHECKING
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from google.cloud import pubsub_v1 # type: ignore[attr-defined]
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from google.cloud import pubsub_v1
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from google.cloud.pubsub_v1.subscriber.message import Message
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from eventkit.config import Settings

src/eventkit/stores/error_store.py

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"""Error storage protocol and implementations."""
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from datetime import datetime
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from typing import Any, Literal, Protocol
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import structlog

src/eventkit/stores/gcs.py

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from uuid import uuid4
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import pandas as pd
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import pyarrow as pa # type: ignore[import-untyped]
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import pyarrow.parquet as pq # type: ignore[import-untyped]
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import pyarrow as pa
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import pyarrow.parquet as pq
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import structlog
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from google.cloud import storage # type: ignore[attr-defined]
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from google.cloud import storage
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from tenacity import retry, stop_after_attempt, wait_exponential
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from eventkit.schema.events import IdentifyEvent, PageEvent, TrackEvent, TypedEvent

src/eventkit/stores/gcs_error.py

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from typing import Any, Literal
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import pandas as pd
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import pyarrow as pa # type: ignore[import-not-found]
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import pyarrow.parquet as pq # type: ignore[import-not-found]
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import pyarrow as pa
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import pyarrow.parquet as pq
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from google.cloud import storage
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from tenacity import retry, stop_after_attempt, wait_exponential
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