Enlightn

# Telescope Memory Intensive Request Analyzer PRO

Category Severity Time To Fix
🚀 Performance Major 30 minutes

Class: Enlightn\EnlightnPro\Analyzers\Performance\TelescopeMemoryIntensiveRequestAnalyzer

# Introduction

This analyzer scans your Telescope records to detect routes that are using too much memory.

By default, all routes that consume more than 50 MB of memory are flagged.

# How To Reduce Memory Usage

There can be a number of reasons why your memory usage is high. Some good practices to reduce memory usage include:

  1. Make sure that you are not hydrating too many models. Try to use lazy collections (opens new window) and query chunking (opens new window) where possible to reduce memory usage.
  2. If you are storing files, check out automatic streaming (opens new window) to reduce memory usage.
  3. If you are reading files, consider using the Filesystem::lines method to lazily read file contents.
  4. Make sure that you are not running dev packages in production that consume too much memory.

If you are unable to debug why the memory usage is high, consider using the Xdebug Profiler (opens new window).

# Configuration Options

You can customize the memory limit (default: 50 MB) using the request_memory_limit configuration option (in MB):

'request_memory_limit' => 50, // MB

# Requirements

This Telescope analyzer requires you to enable the Telescope RequestWatcher.

# How To Reset Findings

This analyzer does all its calculations based on the Telescope records in the database. So, if you happen to fix a memory usage issue, you may want to prune your Telescope records so that the next time Enlightn is run, it does not pick up the routes that have already been fixed.

Generally, it would be a good practice to prune your Telescope records each time you deploy fresh code or push a new release. If your application is relatively stable, consider pruning Telescope records at a frequency (say every week or daily).

# Query Performance of Telescope Analyzers

All Telescope analyzers work by querying your database for Telescope records. For enhanced query performance, we recommend that you convert the content column of your telescope_entries table to a json data type. Note that for PostgresQL this change is mandatory, since PostgresQL does not allow json queries on text data types.

A migration code snippet for changing the column data type is provided here.

Telescope analyzer queries can take considerable time if you have a large number of Telescope records stored in your database. To further enhance performance, we recommend regularly pruning your Telescope entries.

Note that this query is only executed when you run the enlightn Artisan command and does not impact the performance of your application.

# Skip Condition

This analyzer is skipped if your application does not have Laravel Telescope (opens new window) installed.

# References

← Telescope Model Hydration Analyzer →