When should I use DBAL instead of the Doctrine ORM?

Raw DBAL is recommended for simple bulk updates or inserts where you don't need entity lifecycle events or complex object mapping, as it significantly reduces CPU and memory overhead.

How does this skill prevent 'Out of Memory' errors in PHP?

It implements patterns such as 'em->clear()' and 'toIterable()' which ensure that the Doctrine Unit of Work is periodically emptied, preventing thousands of objects from being stored in memory simultaneously.

Why is ID-based pagination better than using SQL offsets?

ID-based pagination (WHERE id > lastId) is much faster on large tables because SQL databases can jump directly to an indexed ID, whereas high OFFSET values require the engine to scan through all previous rows.

Does this skill work with Symfony Console commands?

Yes, it provides specific implementation patterns for integrating batch logic with SymfonyStyle progress bars and memory monitoring tools designed for CLI environments.

Doctrine Batch Processing

Name: Doctrine Batch Processing
Author: MakFly

byMakFly

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Database Management

Optimizes large-scale Symfony data operations through memory-efficient Doctrine ORM patterns and bulk database strategies.

About

The Doctrine Batch Processing skill equips Claude with specialized knowledge to handle massive datasets within Symfony applications without triggering 'Out of Memory' errors. It provides standardized implementation patterns for memory-safe iteration, automated entity clearing, ID-based pagination for high-performance scanning, and raw DBAL bulk operations. This skill is essential for developers building data migrations, import scripts, or long-running console commands that require stable and performant interaction with large MySQL or PostgreSQL databases.

Key Features

Memory-safe iteration using toIterable() to prevent entity bloat
High-performance ID-based pagination for processing massive tables
Real-time memory monitoring and Symfony progress bar integration
21 GitHub stars
Automated batch flushing and entity manager clearing strategies
Bulk data operations using raw DBAL to bypass ORM overhead

Use Cases

Importing large external datasets from CSV or JSON into a relational database
Running complex data migrations that require entity-level logic without crashing
Updating millions of database records via Symfony Console commands

About

Key Features

Memory-safe iteration using toIterable() to prevent entity bloat
High-performance ID-based pagination for processing massive tables
Real-time memory monitoring and Symfony progress bar integration
21 GitHub stars
Automated batch flushing and entity manager clearing strategies
Bulk data operations using raw DBAL to bypass ORM overhead

Use Cases

Importing large external datasets from CSV or JSON into a relational database
Running complex data migrations that require entity-level logic without crashing
Updating millions of database records via Symfony Console commands