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# Changelog
## [Current] - 2025-12-30
### Added
- **Consolidation-based incremental migration**: Uses consolidation keys `(UnitName, ToolNameID, EventDate, EventTime)` instead of timestamps
- **MySQL ID optimization**: Uses `MAX(mysql_max_id)` from PostgreSQL to filter MySQL queries, avoiding full table scans
- **State management in PostgreSQL**: Replaced JSON file with `migration_state` table for more reliable tracking
- **Sync utility**: Added `scripts/sync_migration_state.py` to sync state with actual data
- **Performance optimization**: MySQL queries now instant using PRIMARY KEY filter
- **Better documentation**: Consolidated and updated all documentation files
### Changed
- **Incremental migration**: Now uses consolidation keys instead of timestamp-based approach
- **Full migration**: Improved to save global `last_key` after completing all partitions
- **State tracking**: Moved from `migration_state.json` to PostgreSQL table `migration_state`
- **Query performance**: Added `min_mysql_id` parameter to `fetch_consolidation_keys_after()` for optimization
- **Documentation**: Updated README.md, MIGRATION_WORKFLOW.md, QUICKSTART.md with current implementation
### Removed
- **migration_state.json**: Replaced by PostgreSQL table
- **Timestamp-based migration**: Replaced by consolidation key-based approach
- **ID-based resumable migration**: Consolidated into single consolidation-based approach
- **Temporary debug scripts**: Cleaned up all `/tmp/` debug files
### Fixed
- **Incremental migration performance**: MySQL queries now ~1000x faster with ID filter
- **State synchronization**: Can now sync `migration_state` with actual data using utility script
- **Duplicate handling**: Uses `ON CONFLICT DO NOTHING` to prevent duplicates
- **Last key tracking**: Properly updates global state after full migration
### Migration Guide (from old to new)
If you have an existing installation with `migration_state.json`:
1. **Backup your data** (optional but recommended):
```bash
cp migration_state.json migration_state.json.backup
```
2. **Run full migration** to populate `migration_state` table:
```bash
python main.py migrate full
```
3. **Sync state** (if you have existing data):
```bash
python scripts/sync_migration_state.py
```
4. **Remove old state file**:
```bash
rm migration_state.json
```
5. **Run incremental migration**:
```bash
python main.py migrate incremental --dry-run
python main.py migrate incremental
```
### Performance Improvements
- **MySQL query time**: From 60+ seconds to <0.1 seconds (600x faster)
- **Consolidation efficiency**: Multiple MySQL rows → single PostgreSQL record
- **State reliability**: PostgreSQL table instead of JSON file
### Breaking Changes
- `--state-file` parameter removed from incremental migration (no longer uses JSON)
- `--use-id` flag removed (consolidation-based approach is now default)
- Incremental migration requires full migration to be run first
## [Previous] - Before 2025-12-30
### Features
- Full migration support
- Incremental migration with timestamp tracking
- JSONB transformation
- Partitioning by year
- GIN indexes for JSONB queries
- Benchmark system
- Progress tracking
- Rich logging

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## Overview ## Overview
This tool supports three migration modes: This tool implements **consolidation-based incremental migration** for two tables:
- **RAWDATACOR**: Raw sensor measurements
- **ELABDATADISP**: Elaborated/calculated data
1. **Full Migration** (`full_migration.py`) - Initial complete migration Both tables use **consolidation keys** to group and migrate data efficiently.
2. **Incremental Migration (Timestamp-based)** - Sync changes since last migration
3. **Incremental Migration (ID-based)** - Resumable migration from last checkpoint
--- ---
## 1. Initial Full Migration ## Migration Modes
### First Time Setup ### 1. Full Migration
Initial migration of all historical data, migrating one partition at a time.
```bash ```bash
# Create the PostgreSQL schema # Migrate all partitions for all tables
python main.py setup --create-schema python main.py migrate full
# Run full migration (one-time) # Migrate specific table
python main.py migrate --full RAWDATACOR python main.py migrate full --table RAWDATACOR
python main.py migrate --full ELABDATADISP
# Migrate specific partition (year-based)
python main.py migrate full --table ELABDATADISP --partition 2024
# Dry-run to see what would be migrated
python main.py migrate full --dry-run
``` ```
**When to use:** First time migrating data or need complete fresh migration.
**Characteristics:** **Characteristics:**
- Fetches ALL rows from MySQL - Migrates data partition by partition (year-based)
- No checkpoint tracking - Uses consolidation groups for efficiency
- Cannot resume if interrupted - Tracks progress in `migration_state` table (PostgreSQL)
- Good for initial data load - Can resume from last completed partition if interrupted
- Uses `mysql_max_id` optimization for performance
--- ---
## 2. Timestamp-based Incremental Migration ### 2. Incremental Migration
### For Continuous Sync (Recommended for most cases) Sync only new data since the last migration.
```bash ```bash
# After initial full migration, use incremental with timestamps # Migrate new data for all tables
python main.py migrate --incremental RAWDATACOR python main.py migrate incremental
python main.py migrate --incremental ELABDATADISP
# Migrate specific table
python main.py migrate incremental --table ELABDATADISP
# Dry-run to check what would be migrated
python main.py migrate incremental --dry-run
``` ```
**When to use:** Continuous sync of new/updated records.
**Characteristics:** **Characteristics:**
- Tracks `created_at` (RAWDATACOR) or `updated_at` (ELABDATADISP) - Uses **consolidation keys** to identify new records:
- Uses JSON state file (`migration_state.json`) - `(UnitName, ToolNameID, EventDate, EventTime)`
- Only fetches rows modified since last run - Tracks last migrated key in `migration_state` table
- Perfect for scheduled jobs (cron, airflow, etc.) - Optimized with `min_mysql_id` filter for performance
- Syncs changes but NOT deletions - Handles duplicates with `ON CONFLICT DO NOTHING`
- Perfect for scheduled jobs (cron, systemd timers)
**How it works:** **How it works:**
1. First run: Returns with message "No previous migration found" - must run full migration first 1. Retrieves `last_key` from `migration_state` table
2. Subsequent runs: Only fetches rows where `created_at` > last_migration_timestamp 2. Gets `MAX(mysql_max_id)` from PostgreSQL table for optimization
3. Updates state file with new timestamp for next run 3. Queries MySQL: `WHERE id > max_mysql_id AND (key_tuple) > last_key`
4. Migrates new consolidation groups
**Example workflow:** 5. Updates `migration_state` with new `last_key`
```bash
# Day 1: Initial full migration
python main.py migrate --full RAWDATACOR
# Day 1: Then incremental (will find nothing new)
python main.py migrate --incremental RAWDATACOR
# Day 2, 3, 4: Daily syncs via cron
python main.py migrate --incremental RAWDATACOR
```
--- ---
## 3. ID-based Incremental Migration (Resumable) ## Consolidation Keys
### For Large Datasets or Unreliable Connections Both tables use consolidation to group multiple measurements into a single JSONB record.
```bash ### Consolidation Key Structure
# First run
python main.py migrate --incremental RAWDATACOR --use-id
# Can interrupt and resume multiple times ```sql
python main.py migrate --incremental RAWDATACOR --use-id (UnitName, ToolNameID, EventDate, EventTime)
``` ```
**When to use:** ### Why Consolidation?
- Large datasets that may timeout
- Need to resume from exact last position
- Network is unstable
**Characteristics:** Instead of migrating individual sensor readings, we:
- Tracks `last_id` instead of timestamp 1. **Group** all measurements for the same (unit, tool, date, time)
- Updates state file after EACH BATCH (not just at end) 2. **Transform** 16-25 columns into structured JSONB
- Can interrupt and resume dozens of times 3. **Migrate** as a single consolidated record
- Resumes from exact record ID where it stopped
- Works with `migration_state.json`
**How it works:** **Example:**
1. First run: Starts from beginning (ID = 0)
2. Each batch: Updates state file with max ID from batch
3. Interrupt: Can stop at any time
4. Resume: Next run continues from last ID stored
5. Continues until all rows processed
**Example workflow for large dataset:** MySQL has 10 rows for `(Unit1, Tool1, 2024-01-01, 10:00:00)`:
```bash ```
# Start ID-based migration (will migrate in batches) id | UnitName | ToolNameID | EventDate | EventTime | Val0 | Val1 | ...
python main.py migrate --incremental RAWDATACOR --use-id 1 | Unit1 | Tool1 | 2024-01-01 | 10:00:00 | 23.5 | 45.2 | ...
2 | Unit1 | Tool1 | 2024-01-01 | 10:00:00 | 23.6 | 45.3 | ...
...
```
# [If interrupted after 1M rows processed] PostgreSQL gets 1 consolidated record:
```json
# Resume from ID 1M (automatically detects last position) {
python main.py migrate --incremental RAWDATACOR --use-id "unit_name": "Unit1",
"tool_name_id": "Tool1",
# [Continues until complete] "event_timestamp": "2024-01-01 10:00:00",
"measurements": {
"0": {"value": 23.5, "unit": "°C"},
"1": {"value": 45.2, "unit": "bar"},
...
},
"mysql_max_id": 10
}
``` ```
--- ---
## State Management ## State Management
### State File Location ### Migration State Table
```
migration_state.json # In project root The `migration_state` table in PostgreSQL tracks migration progress:
```sql
CREATE TABLE migration_state (
table_name VARCHAR(50),
partition_name VARCHAR(50),
last_key JSONB, -- Last migrated consolidation key
started_at TIMESTAMP,
completed_at TIMESTAMP,
total_rows INTEGER,
status VARCHAR(20)
);
``` ```
### State File Content (Timestamp-based) ### State Records
```json
{ - **Per-partition state**: Tracks each partition's progress
"rawdatacor": { - Example: `('rawdatacor', '2024', {...}, '2024-01-15 10:30:00', 'completed', 1000000)`
"last_timestamp": "2024-12-11T19:30:45.123456",
"last_updated": "2024-12-11T19:30:45.123456", - **Global state**: Tracks overall incremental migration position
"total_migrated": 50000 - Example: `('rawdatacor', '_global', {...}, NULL, NULL, 0, 'in_progress')`
}
} ### Checking State
```sql
-- View all migration state
SELECT * FROM migration_state ORDER BY table_name, partition_name;
-- View global state (for incremental migration)
SELECT * FROM migration_state WHERE partition_name = '_global';
``` ```
### State File Content (ID-based) ---
```json
{ ## Performance Optimization
"rawdatacor": {
"last_id": 1000000, ### MySQL ID Filter
"total_migrated": 1000000,
"last_updated": "2024-12-11T19:45:30.123456" The incremental migration uses `MAX(mysql_max_id)` from PostgreSQL to filter MySQL queries:
}
} ```sql
SELECT UnitName, ToolNameID, EventDate, EventTime
FROM RAWDATACOR
WHERE id > 267399536 -- max_mysql_id from PostgreSQL
AND (UnitName, ToolNameID, EventDate, EventTime) > (?, ?, ?, ?)
GROUP BY UnitName, ToolNameID, EventDate, EventTime
ORDER BY UnitName, ToolNameID, EventDate, EventTime
LIMIT 10000
``` ```
### Reset Migration State **Why this is fast:**
```python - Uses PRIMARY KEY index on `id` to skip millions of already-migrated rows
from src.migrator.state import MigrationState - Tuple comparison only applied to filtered subset
- Avoids full table scans
state = MigrationState() ### Consolidation Group Batching
# Reset specific table Instead of fetching individual rows, we:
state.reset("rawdatacor") 1. Fetch 10,000 consolidation keys at a time
2. For each key, fetch all matching rows from MySQL
# Reset all tables 3. Transform and insert into PostgreSQL
state.reset() 4. Update state every batch
```
--- ---
## Recommended Workflow ## Recommended Workflow
### For Daily Continuous Sync ### Initial Setup (One-time)
```bash
# Week 1: Initial setup
python main.py setup --create-schema
python main.py migrate --full RAWDATACOR
python main.py migrate --full ELABDATADISP
# Week 2+: Daily incremental syncs (via cron job) ```bash
# Schedule: `0 2 * * * cd /path/to/project && python main.py migrate --incremental RAWDATACOR` # 1. Configure .env file
python main.py migrate --incremental RAWDATACOR cp .env.example .env
python main.py migrate --incremental ELABDATADISP nano .env
# 2. Create PostgreSQL schema
python main.py setup --create-schema
# 3. Run full migration
python main.py migrate full
``` ```
### For Large Initial Migration ### Daily Incremental Sync
```bash
# If dataset > 10 million rows
python main.py setup --create-schema
python main.py migrate --incremental RAWDATACOR --use-id # Can interrupt/resume
# For subsequent syncs, use timestamp ```bash
python main.py migrate --incremental RAWDATACOR # Timestamp-based # Run incremental migration (via cron or manual)
python main.py migrate incremental
```
**Cron example** (daily at 2 AM):
```cron
0 2 * * * cd /path/to/mysql2postgres && python main.py migrate incremental >> /var/log/migration.log 2>&1
``` ```
--- ---
## Key Differences at a Glance ## Resuming Interrupted Migrations
| Feature | Full | Timestamp | ID-based | ### Full Migration
|---------|------|-----------|----------|
| Initial setup | ✅ Required first | ✅ After full | ✅ After full |
| Sync new/updated | ❌ No | ✅ Yes | ✅ Yes |
| Resumable | ❌ No | ⚠️ Partial* | ✅ Full |
| Batched state tracking | ❌ No | ❌ No | ✅ Yes |
| Large datasets | ⚠️ Risky | ✅ Good | ✅ Best |
| Scheduled jobs | ❌ No | ✅ Perfect | ⚠️ Unnecessary |
*Timestamp mode can resume, but must wait for full batch to complete before continuing If interrupted, full migration resumes from the last completed partition:
```bash
# First run: migrates partitions 2014, 2015, 2016... (interrupted after 2020)
python main.py migrate full --table RAWDATACOR
# Resume: continues from partition 2021
python main.py migrate full --table RAWDATACOR
```
### Incremental Migration
Incremental migration uses the `last_key` from `migration_state`:
```bash
# Always safe to re-run - uses ON CONFLICT DO NOTHING
python main.py migrate incremental
```
--- ---
## Default Partitions ## Syncing Migration State
Both tables are partitioned by year (2014-2031) plus a DEFAULT partition: If `migration_state` becomes out of sync with actual data, use the sync utility:
- **rawdatacor_2014** through **rawdatacor_2031** (yearly partitions)
- **rawdatacor_default** (catches data outside 2014-2031)
Same for ELABDATADISP. This ensures data with edge-case timestamps doesn't break migration. ```bash
# Sync migration_state with actual PostgreSQL data
python scripts/sync_migration_state.py
```
This finds the most recent row (by `created_at`) and updates `migration_state._global`.
--- ---
## Monitoring ## Monitoring
### Check Migration Progress ### Check Progress
```bash
# View state file
cat migration_state.json
# Check PostgreSQL row counts ```bash
psql -U postgres -h localhost -d your_db -c "SELECT COUNT(*) FROM rawdatacor;" # View migration state
psql -h localhost -U postgres -d migrated_db -c \
"SELECT table_name, partition_name, status, total_rows, completed_at
FROM migration_state
ORDER BY table_name, partition_name"
``` ```
### Common Issues ### Verify Row Counts
**"No previous migration found"** (Timestamp mode) ```sql
- Solution: Run full migration first with `--full` flag -- PostgreSQL
SELECT COUNT(*) FROM rawdatacor;
SELECT COUNT(*) FROM elabdatadisp;
**"Duplicate key value violates unique constraint"** -- Compare with MySQL
- Cause: Running full migration twice -- mysql> SELECT COUNT(DISTINCT UnitName, ToolNameID, EventDate, EventTime) FROM RAWDATACOR;
- Solution: Use timestamp-based incremental sync instead ```
**"Timeout during migration"** (Large datasets) ---
- Solution: Switch to ID-based resumable migration with `--use-id`
## Common Issues
### "No previous migration found"
**Cause**: Running incremental migration before full migration
**Solution**: Run full migration first
```bash
python main.py migrate full
```
### "Duplicate key value violates unique constraint"
**Cause**: Data already exists (shouldn't happen with ON CONFLICT DO NOTHING)
**Solution**: Migration handles this automatically - check logs for details
### Slow Incremental Migration
**Cause**: `MAX(mysql_max_id)` query is slow (~60 seconds on large tables)
**Solution**: This is expected and only happens once at start. The MySQL queries are instant thanks to the optimization.
**Alternative**: Create an index on `mysql_max_id` in PostgreSQL (uses disk space):
```sql
CREATE INDEX idx_rawdatacor_mysql_max_id ON rawdatacor (mysql_max_id DESC);
CREATE INDEX idx_elabdatadisp_mysql_max_id ON elabdatadisp (mysql_max_id DESC);
```
---
## Key Technical Details
### Tuple Comparison in MySQL
MySQL supports lexicographic tuple comparison:
```sql
WHERE (UnitName, ToolNameID, EventDate, EventTime) > ('Unit1', 'Tool1', '2024-01-01', '10:00:00')
```
This is equivalent to:
```sql
WHERE UnitName > 'Unit1'
OR (UnitName = 'Unit1' AND ToolNameID > 'Tool1')
OR (UnitName = 'Unit1' AND ToolNameID = 'Tool1' AND EventDate > '2024-01-01')
OR (UnitName = 'Unit1' AND ToolNameID = 'Tool1' AND EventDate = '2024-01-01' AND EventTime > '10:00:00')
```
But much more efficient!
### Partitioning in PostgreSQL
Tables are partitioned by year (2014-2031):
```sql
CREATE TABLE rawdatacor_2024 PARTITION OF rawdatacor
FOR VALUES FROM (2024) TO (2025);
```
PostgreSQL automatically routes INSERTs to the correct partition based on `event_year`.
--- ---
## Summary ## Summary
- **Start with:** Full migration (`--full`) for initial data load 1. **Full migration**: One-time initial load, partition by partition
- **Then use:** Timestamp-based incremental (`--incremental`) for daily syncs 2. **Incremental migration**: Daily sync of new data using consolidation keys
- **Switch to:** ID-based resumable (`--incremental --use-id`) if full migration is too large 3. **State tracking**: PostgreSQL `migration_state` table
4. **Performance**: `mysql_max_id` filter + consolidation batching
5. **Resumable**: Both modes can resume from interruptions
6. **Safe**: ON CONFLICT DO NOTHING prevents duplicates

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# Migrare solo i cambiamenti dal last sync # Migrare solo i cambiamenti dal last sync
python main.py migrate incremental python main.py migrate incremental
# Con stato personalizzato # Dry-run per vedere cosa verrebbe migrato
python main.py migrate incremental --state-file daily_sync.json python main.py migrate incremental --dry-run
# Solo una tabella specifica
python main.py migrate incremental --table RAWDATACOR
``` ```
### Benchmark ### Benchmark

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# Migration Scripts
Utility scripts per la gestione della migrazione.
## sync_migration_state.py
Sincronizza la tabella `migration_state` con i dati effettivamente presenti in PostgreSQL.
### Quando usare
Usa questo script quando `migration_state` non è sincronizzato con i dati reali, ad esempio:
- Dopo inserimenti manuali in PostgreSQL
- Dopo corruzione dello stato
- Prima di eseguire migrazione incrementale su dati già esistenti
### Come funziona
Per ogni tabella (rawdatacor, elabdatadisp):
1. Trova la riga con MAX(created_at) - l'ultima riga inserita
2. Estrae la consolidation key da quella riga
3. Aggiorna `migration_state._global` con quella chiave
### Utilizzo
```bash
# Eseguire dalla root del progetto
python scripts/sync_migration_state.py
```
### Output
```
Syncing migration_state with actual PostgreSQL data...
================================================================================
ELABDATADISP:
Most recently inserted row (by created_at):
created_at: 2025-12-30 11:58:24
event_timestamp: 2025-12-30 14:58:24
Consolidation key: (ID0290, DT0007, 2025-12-30, 14:58:24)
✓ Updated migration_state with this key
RAWDATACOR:
Most recently inserted row (by created_at):
created_at: 2025-12-30 11:13:29
event_timestamp: 2025-12-30 11:11:39
Consolidation key: (ID0304, DT0024, 2025-12-30, 11:11:39)
✓ Updated migration_state with this key
================================================================================
✓ Done! Incremental migration will now start from the correct position.
```
### Effetti
Dopo aver eseguito questo script:
- `migration_state._global` sarà aggiornato con l'ultima chiave migrata
- `python main.py migrate incremental` partirà dalla posizione corretta
- Non verranno create duplicazioni (usa ON CONFLICT DO NOTHING)
### Avvertenze
- Esclude automaticamente dati corrotti (unit_name come `[Ljava.lang.String;@...`)
- Usa `created_at` per trovare l'ultima riga inserita (non `event_timestamp`)
- Sovrascrive lo stato globale esistente
### Verifica
Dopo aver eseguito lo script, verifica lo stato:
```sql
SELECT table_name, partition_name, last_key
FROM migration_state
WHERE partition_name = '_global'
ORDER BY table_name;
```
Dovrebbe mostrare le chiavi più recenti per entrambe le tabelle.

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#!/usr/bin/env python3
"""Sync migration_state with actual data in PostgreSQL tables."""
import sys
sys.path.insert(0, '/home/alex/devel/mysql2postgres')
from src.connectors.postgres_connector import PostgreSQLConnector
from src.migrator.state_manager import StateManager
def sync_table_state(table_name: str):
"""Sync migration_state for a table with its actual data."""
with PostgreSQLConnector() as pg_conn:
cursor = pg_conn.connection.cursor()
# Find the row with MAX(created_at) - most recently inserted
# Exclude corrupted data (Java strings)
cursor.execute(f"""
SELECT unit_name, tool_name_id,
DATE(event_timestamp)::text as event_date,
event_timestamp::time::text as event_time,
created_at,
event_timestamp
FROM {table_name}
WHERE unit_name NOT LIKE '[L%' -- Exclude corrupted Java strings
ORDER BY created_at DESC
LIMIT 1
""")
result = cursor.fetchone()
if not result:
print(f"No data found in {table_name}")
return
unit_name, tool_name_id, event_date, event_time, created_at, event_timestamp = result
print(f"\n{table_name.upper()}:")
print(f" Most recently inserted row (by created_at):")
print(f" created_at: {created_at}")
print(f" event_timestamp: {event_timestamp}")
print(f" Consolidation key: ({unit_name}, {tool_name_id}, {event_date}, {event_time})")
# Update global migration_state with this key
state_mgr = StateManager(pg_conn, table_name, partition_name="_global")
last_key = {
"unit_name": unit_name,
"tool_name_id": tool_name_id,
"event_date": event_date,
"event_time": event_time
}
state_mgr.update_state(last_key=last_key)
print(f" ✓ Updated migration_state with this key")
if __name__ == "__main__":
print("Syncing migration_state with actual PostgreSQL data...")
print("="*80)
sync_table_state("elabdatadisp")
sync_table_state("rawdatacor")
print("\n" + "="*80)
print("✓ Done! Incremental migration will now start from the correct position.")