# Mapper

## Benchmark summary

We conducted a series of measurements under varying combinations of

1. Number of concurrent API requests
2. Payload size

The configuration that yielded maximum throughput at 100% utilization (either CPU or Memory) in one of the two PODs ([Mapper](/products/spar/features/spar-mapper.md) and Postgresql DB) is detailed below. Each API is reported under a separate sheet.

{% embed url="<https://docs.google.com/spreadsheets/d/14aHrtX-bht44ulzwGex3eWj3WE9eVRx4lTCVWeZBy-w/edit?usp=drive_link>" fullWidth="false" %}
1 mapper POD
{% endembed %}

## Test setup

Testing was conducted on [V4 deployment architecture](/operations/deployment.md#deployment-architecture-v4) with 2 nodes. The underlying machines specifications (TBD).

### POD configurations

First round of measurements were done with a Single POD, with autoscaling suppressed. The mapper POD and the postgresql POD had the following configurations

<table><thead><tr><th width="133">POD</th><th>CPU reserve</th><th>CPU limit</th><th>Mem reserve</th><th>Mem limit</th></tr></thead><tbody><tr><td>Mapper</td><td>0.250 cpu</td><td>2 cpu</td><td>1 GB</td><td>2 GB</td></tr><tr><td>Postgresql</td><td>0.250 cpu</td><td>1 cpu</td><td>1 GB</td><td>2.5 GB</td></tr></tbody></table>

<figure><img src="/files/OQ0wGIh5mdMKbmMMFJpZ" alt=""><figcaption><p>mapper-api POD configuration</p></figcaption></figure>

<figure><img src="/files/eZo1AEJnPfbBUJdgKUYh" alt=""><figcaption><p>Postgresql POD configuration</p></figcaption></figure>

### Postgresql configuration

TBD

### Test tools

* [Locust](https://locust.io/) to generate API load.
* [Grafana charts & Prometheus](/operations/deployment/scaling/base-infrastructure/openg2p-cluster/prometheus-and-grafana.md) to monitor and measure POD resource utilizations

## APIs

* [Resolve API](/products/spar/development/testing/performance-testing/mapper/resolve-api.md)


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