---
title: How to adjust Hyperglance's memory allocation (Legacy Docker Guide)
description: Adjust the memory allocation afforded to Hyperglance if the application is slow or you are seeing our of memory errors.
---

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# How to adjust Hyperglance's memory allocation (Legacy Docker Guide)

## Adjust the memory allocation afforded to Hyperglance if the application is slow or you are seeing out of memory errors.

THIS GUIDE IS DEPRECATED AND PERTAINS TO THE LEGACY DOCKER BASED ARCHITECTURE.  [SEE LATEST VERSION.](https://support.hyperglance.com/knowledge/memory-usage?hsLang=en)

### How Hyperglance uses memory

Hyperglance stores inventory and billing data in-memory. Increasing RAM is the usually the best way to scale your Hyperglance deployment, not by increasing CPU, Disk or Network.

By default 75% of the machine's RAM is allocated as "heap space" to the Hyperglance application.  This default works well for smaller deployments but on larger-scale deployments it can be necessary to adjust this to best utilize available memory.

If you are experiencing OutOfMemoryError in the logs or severe slowdowns in the UI then increasing the memory allocation should help with that.

### Instance/VM RAM Guidelines

Many factors affect the memory use:

- Number of accounts
- Number of resources
- Amount of billing data

The following table is a guideline based on the number of qualified resources in the license, just note this is approximate since many factors affect the memory use.

For larger deployments we recommend memory-optimised instance/VM types intended for large memory applications such as Hyperglance. AWS offer the R5-series of instances. Azure offer E series VMs.  We advise finding the cheapest type that offers the RAM needed.

 

| **Hyperglance Variant** | **Recommended RAM** | **Optimal Heap Size** | **Example Instance/VM Size** |
| --- | --- | --- | --- |
| 250 Resources | 4GB | Default 75% | AWS: t3.medium Azure: Standard\_B2s (or equivalent) |
| 500 Resources | 4GB | Default 75% | AWS: t3.medium Azure: Standard\_B2s (or equivalent) |
| 1000 Resources | 8GB | Default 75% | AWS: t3.large Azure: Standard\_B2ms (or equivalent) |
| 2000 Resources | 8GB - 16GB | Default 75% | AWS: t3.xlarge Azure: Standard\_B4ms (or equivalent) |
| 5000 Resources | 32GB | 28GB | AWS: r5.xlarge Azure: Standard\_E4as\_v5 (or equivalent) |
| 10000 Resources | 64GB - 128GB | 60GB - 120GB | AWS: r5.4xlarge Azure: Standard\_E16-4as\_v5 (or equivalent) |
| 20000 Resources | 256GB | 250GB | AWS: r5.8xlarge Azure: Standard\_E32-8as\_v5 (or equivalent) |

 

### Adjusting memory allocation

You can configure a custom allocation by editing config.env:

Edit file:

```
/var/lib/data/hyperglance/config.env
```

Set memory allocation to reflect a [recommendation:](https://support.hyperglance.com/knowledge/memory-usage-legacydocker#sizing)

```
MAX_HEAPSIZE=4g
```

Re-start the services:

```
sudo docker-compose -f /etc/docker-compose.yml up -d
```

 

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