Step 1: Extract Multiple Tables

Extract data from multiple database tables in a single pipeline using the sequence input.

The Goal

• Extract orders (incremental - last 24 hours)
• Extract inventory (full table)
• Extract order_items (incremental - last 24 hours)
• Tag each record with its source table

Why Sequence Input?

Instead of running 3 separate pipelines:

Before: 3 pipelines, 3 schedules, 3 configs to maintain

After: 1 pipeline processes all tables sequentially

Implementation

step-1-extract.yaml

input:
  sequence:
    inputs:
      # Orders - incremental (last 24h)
      - sql_select:
          driver: postgres
          dsn: 'postgres://${DB_USER}:${DB_PASSWORD}@${DB_HOST}:5432/${DB_NAME}?sslmode=require'
          table: orders
          columns: ['*']
          where: "updated_at >= CURRENT_DATE - INTERVAL '1 day'"
        processors:
          - mapping: |
              root = this
              root._table = "orders"
              root._backup_type = "incremental"

      # Inventory - full backup (smaller table)
      - sql_select:
          driver: postgres
          dsn: 'postgres://${DB_USER}:${DB_PASSWORD}@${DB_HOST}:5432/${DB_NAME}?sslmode=require'
          table: inventory
          columns: ['*']
        processors:
          - mapping: |
              root = this
              root._table = "inventory"
              root._backup_type = "full"

      # Order items - incremental
      - sql_select:
          driver: postgres
          dsn: 'postgres://${DB_USER}:${DB_PASSWORD}@${DB_HOST}:5432/${DB_NAME}?sslmode=require'
          table: order_items
          columns: ['*']
          where: "created_at >= CURRENT_DATE - INTERVAL '1 day'"
        processors:
          - mapping: |
              root = this
              root._table = "order_items"
              root._backup_type = "incremental"

pipeline:
  processors:
    - mapping: root = this # Pass through for now

output:
  stdout:
    codec: json_pretty

Understanding the Code

Component	Purpose
sequence.inputs	Process each input in order
sql_select.where	Incremental filter (last 24h)
processors (per-input)	Tag records with source table
root._table	Enables routing in output step
root._backup_type	Documents backup strategy

Incremental vs Full Backup

Strategy	Use When	Example
Incremental	Large tables, frequent changes	orders, transactions
Full	Small tables, need complete state	inventory, config

Incremental Filter Patterns

# Last 24 hours by updated_at
where: "updated_at >= CURRENT_DATE - INTERVAL '1 day'"

# Last 24 hours by created_at
where: "created_at >= CURRENT_DATE - INTERVAL '1 day'"

# Since last backup (requires state tracking)
where: "updated_at > '${LAST_BACKUP_TIMESTAMP}'"

Expected Output

{
  "order_id": 12345,
  "customer_id": "CUST-001",
  "total": 99.99,
  "created_at": "2024-01-15T10:30:00Z",
  "updated_at": "2024-01-15T14:22:00Z",
  "_table": "orders",
  "_backup_type": "incremental"
}

Production Considerations

Database Connection Pooling

For large extracts, configure connection limits:

sql_select:
  driver: postgres
  dsn: '...?pool_max_conns=5'

Handling Large Tables

For very large tables, add pagination:

sql_select:
  table: orders
  where: "updated_at >= CURRENT_DATE - INTERVAL '1 day'"
  # Process in batches of 10000
  batch_size: 10000

Multiple Databases

Extract from different databases in one pipeline:

sequence:
  inputs:
    - sql_select:
        dsn: 'postgres://...@db1/ecommerce'
        table: orders
    - sql_select:
        dsn: 'postgres://...@db2/inventory'
        table: stock_levels

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Next Step

📖 Step 2: Add Metadata →