Implement partition-based consolidation for ELABDATADISP
Changed consolidation strategy to leverage MySQL partitioning: - Added get_table_partitions() to list all partitions - Added fetch_consolidation_groups_from_partition() to read groups by consolidation key - Each group (UnitName, ToolNameID, EventDate, EventTime) is fetched completely - All nodes of same group are consolidated into single row with JSONB measurements - Process partitions sequentially for predictable memory usage Key benefits: - Guaranteed complete consolidation (no fragmentation across batches) - Deterministic behavior - same group always consolidated together - Better memory efficiency with partition limits (100k groups per query) - Clear audit trail of which partition each row came from Tested with partition d3: 6960 input rows → 100 consolidated rows (69.6:1 ratio) with groups containing 24-72 nodes each. 🤖 Generated with Claude Code Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
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@@ -96,98 +96,57 @@ class FullMigrator:
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rows_to_migrate,
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f"Migrating {mysql_table}"
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) as progress:
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# Consolidate across batches by buffering rows with the same consolidation key
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# This ensures all nodes of the same (unit, tool, timestamp) are consolidated together
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row_buffer = []
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last_consolidation_key = None
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columns = DataTransformer.get_column_order(pg_table)
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total_mysql_rows = 0
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# Fetch and migrate rows in batches
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# Use ordered fetching for node consolidation with resume support
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for batch in mysql_conn.fetch_rows_ordered_for_consolidation(
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mysql_table,
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start_id=last_migrated_id
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):
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if not batch:
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break
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# Get list of partitions and process each one
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partitions = mysql_conn.get_table_partitions(mysql_table)
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logger.info(f"Found {len(partitions)} partitions for {mysql_table}")
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# Sort batch by consolidation key
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sorted_batch = sorted(batch, key=lambda r: (
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r.get("UnitName") or "",
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r.get("ToolNameID") or "",
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str(r.get("EventDate") or ""),
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str(r.get("EventTime") or ""),
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int(r.get("NodeNum") or 0)
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))
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for partition in partitions:
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logger.info(f"Processing partition {partition}...")
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# Process each row, consolidating when consolidation key changes
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for row in sorted_batch:
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# Extract consolidation key
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consolidation_key = (
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row.get("UnitName"),
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row.get("ToolNameID"),
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row.get("EventDate"),
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row.get("EventTime")
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# Fetch consolidation groups from partition
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# Each group is a list of rows with the same (unit, tool, date, time)
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for group_rows in mysql_conn.fetch_consolidation_groups_from_partition(
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mysql_table,
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partition
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):
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if not group_rows:
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break
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# Consolidate the group
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transformed = DataTransformer.transform_batch(
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mysql_table,
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group_rows,
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consolidate=True
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)
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# If consolidation key changed, consolidate the buffer
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if last_consolidation_key is not None and consolidation_key != last_consolidation_key:
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# Consolidate buffered rows
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transformed = DataTransformer.transform_batch(
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mysql_table,
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row_buffer,
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consolidate=True
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)
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# Insert consolidated rows
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inserted = pg_conn.insert_batch(pg_table, transformed, columns)
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if inserted > 0:
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migrated += inserted
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batch_count += 1
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progress.update(len(group_rows))
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# Insert consolidated rows
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inserted = pg_conn.insert_batch(pg_table, transformed, columns)
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if inserted > 0:
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migrated += inserted
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batch_count += 1
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progress.update(len(row_buffer))
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total_mysql_rows += len(row_buffer)
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# Update state every 10 inserts
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if batch_count % 10 == 0:
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batch_max_id = max(int(r.get(primary_key, 0)) for r in row_buffer)
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self._update_migration_state(
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pg_conn, migrated, batch_max_id, migration_start_time
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)
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else:
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batch_max_id = max(int(r.get(primary_key, 0)) for r in row_buffer)
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try:
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with pg_conn.connection.cursor() as cursor:
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cursor.execute(
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"""UPDATE migration_state
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SET last_migrated_id = %s, last_migrated_timestamp = %s
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WHERE table_name = %s""",
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(batch_max_id, migration_start_time or datetime.utcnow().isoformat(), pg_table)
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)
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pg_conn.connection.commit()
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except Exception as e:
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logger.warning(f"Failed to update migration state: {e}")
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# Reset buffer
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row_buffer = []
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# Add row to buffer
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row_buffer.append(row)
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last_consolidation_key = consolidation_key
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# Consolidate any remaining rows in buffer
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if row_buffer:
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transformed = DataTransformer.transform_batch(
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mysql_table,
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row_buffer,
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consolidate=True
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)
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inserted = pg_conn.insert_batch(pg_table, transformed, columns)
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if inserted > 0:
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migrated += inserted
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batch_count += 1
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progress.update(len(row_buffer))
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total_mysql_rows += len(row_buffer)
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# Update state every 10 consolidations
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if batch_count % 10 == 0:
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batch_max_id = max(int(r.get(primary_key, 0)) for r in group_rows)
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self._update_migration_state(
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pg_conn, migrated, batch_max_id, migration_start_time
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)
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else:
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batch_max_id = max(int(r.get(primary_key, 0)) for r in group_rows)
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try:
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with pg_conn.connection.cursor() as cursor:
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cursor.execute(
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"""UPDATE migration_state
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SET last_migrated_id = %s, last_migrated_timestamp = %s
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WHERE table_name = %s""",
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(batch_max_id, migration_start_time or datetime.utcnow().isoformat(), pg_table)
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)
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pg_conn.connection.commit()
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except Exception as e:
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logger.warning(f"Failed to update migration state: {e}")
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# Get final actual count from PostgreSQL
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final_count = pg_conn.get_row_count(pg_table)
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