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Optimal Performance Hardware setup

Find below the recommended hardware configuration to set up ReportPortal and have good performance using our centralized test automation tool.

1. Disk I/O

To speed up PostgreSQL database performance, on instance strongly recommended use SSD disk hardware.

2. CPU utilization

Consider choosing the CPU optimized instances to reduce high CPU utilization of the ReportPortal service-API and speed up ReportPortal overall.

For example:

  • Azure: Fsv2-series instances,

  • AWS: c5 instances.

    The instance capacity(4 CPUs/8Gb RAM or 8 CPUs/16Gb RAM etc.) should be selected regarding average reporting test-cases/day and average CPU/RAM utilization. If the CPU/RAM utilization of the ReportPortal instance up to 100% a long time daily, consider scale up the VM x2.

System hardware requirements

Simple Docker installation from the box:

Server typeCPU'sRAM size, GbDisk space, GbDisk typeAWS Shape



In Report Portal version 5.7.3, the log double-entry saving approach to DB and Elasticsearch (to data streams) has been implemented. This saving approach reduces the DB footprint almost x10 times, improves the speed of reporting logs, minimizes computation power to clean up data, and brings full-text search capabilities. Our general recommendation is to roll out Elasticsearch on the separate cluster nodes if you have a Kubernetes deployment (consider creating the node group for Elasticsearch). But if you have or expect a large amount of the logs reporting, separate cluster nodes for Elasticsearch are required. The proposed shapes for Elasticsearch nodes have been verified during performance testing and perform stable without degradation on all server configurations (from Small server type to Large). However, if you report many logs, you can tune the node's Disk size or scale up the shapes according to your needs. In addition, please pay attention to the number of Elasticsearch active shards (primary and total). In case you have a large count of projects, additional tuning for Elasticsearch might be needed(due to limitations that Elasticsearch has on the number of active shards). Please see Part 7 below for the tips.

Additional nodes for Elasticsearch:

**io2 = 1 GB per month x 0.149 USD x 1 instances = 0.149 USD (EBS Storage Cost) / iops = 1 Provisioned IOPS x 0.119 USD x 1 instances = 0.119 USD (EBS IOPS Cost)*

The approximate server's cost is relevant for the current cost of infrastructure on AWS. The estimated server cost is the current cost of the AWS infrastructure. When changing any cost of resources, costs need to be recalculated.

3. Which scale I need? Configuration testing results and saturation points

What does Saturation and vUser means? How to transform it into test cases?

RPS means Requests Per Second. Any request to server to upload, create, read the data. In ReportPortal terms it can be request to create Launch (execution), Test Suite, Test case, log line. Read data or update data.

Saturation (in RPS) can be assumed as maximum capacity of your configuration (app deployed on specifics instance type) which can procced requests without significant response time degradation. Let's say upper limit

vUser means Virtual user which describes regular model of behaviour of user (API client) which interacts with your system. Under vUser we assume test framework integration which will generate:

◾️ 3 Launches with

◾️◾️ 3 suites inside, with

◾️◾️◾️ 3 test classes inside, with

◾️◾️◾️◾️ 3 test cases inside, with

◾️◾️◾️◾️◾️ 5 steps inside, with

◾️◾️◾️◾️◾️◾️ 10 logs inside for each step.

Which practically will generate:

  • 10 * 5 * 3 * 3 * 3 * 3 = (4050) createLog requests.
  • 5 * 3 * 3 * 3 * 3 = (405) startStep requests and equal amount of finishStep requests. Total 910
  • 3 * 3 * 3 * 3 = (81) startTest requests and equal amount of finishTest requests (inclluding all precondition methods, as @After and @Before in Java). Total 162
  • 3 * 3 * 3 = (27) startTestClass requests and equal amount of finishTestClass requests. Total 54
  • 3 * 3 = (9) startSuite requests and equal amount of finishSuite requests. Total 18
  • 3 = (3) startLaunch requests and equal amount of finishLaunch requests. Total 6

Default configuration of integration sends logs in batches (rp.batch.size.logs = 20) which combines 20 requests at once into 1 batch request for createLog. And minimize count of createLog requests from 4050 to 203. In grand total such a structure creates 3 Launches with 243 test case inside and produces 1253 requests.

Now we can divide their number by the duration in seconds, and get the RPS result. if it runs for 6 minutes (2 minutes each lunch), then: 60s * 6 = 360 and 1253 / 360 ~=4.5 requests per second.

If launches will be executed in parallel, 3 at the time, then RPS will be equal to 4.5*3 = 13.5 rps.


Having information regarding number of test cases in your framework, average number of logs, number of parallel threads and durations, you can calculate system capacity according to the tables below.

Configuration testing results

The purpose of the configuration performance testing is to determine saturation points and overall system capacity for different instance sizes and specifications. Testing was conducted on the С5 instances which are optimized for compute-intensive workloads and deliver cost-effective high performance at a low price per compute ratio(Compute Optimized Instances) with Up to 3.4GHz, and single core turbo frequency of up to 3.5 GHz 2nd generation Intel Xeon Scalable Processors (Cascade Lake) or 1st generation Intel Xeon Platinum 8000 series (Skylake-SP) processor with a sustained all core Turbo frequency.

Application and Database are deployed on separate VMs

Instance TypeSaturation point, rpsvUsers countDisk IOPSJava Options
с5.xlarge64060up to 3000-Xmx1g
c5.2xlarge1374115up to 4000-Xmx2g
c5.4xlarge3104356up to 8000-Xmx3g
с5.9xlarge5700489up to 10000-Xmx4g

Application and Database are deployed on single VM

Instance TypeSaturation point, rpsvUsers countDisk IOPSJava Options
с5.xlarge52150up to 3000-Xmx1g
c5.2xlarge107883up to 4000-Xmx2g
c5.4xlarge2940305up to 8000-Xmx3g
с5.9xlarge5227440up to 10000-Xmx4g

4. The database separately from other services

Consider deploying the database separately from other RP services. It allows increasing throughput of the server and performance of the ReportPortal overall. This can be, for example, AWS RDS PostgreSQL Database or a separate VM only for the PostgreSQL database.

The separate database instance should be the same by CPU's and RAM, but started from middle+ server type, the database instance may need to have doubled CPU's and RAM size in comparison with the application instance. This is explained by the fact that with an increase in the size of the database and the number of concurrent users, the load is distributed more on the database server: increased volume of resources(CPU, memory, IOPS number, etc.) required to performing each DB query since it handles / can handle more data volume and/or can handle of a greater number of concurrent users.

Example for the middle+ server:

Instance typeCPU'sRAM size, GbDisk space, GbAWS Shape
Application instance1632200c5.4xlarge
Database instance16321000c5.4xlarge

5. PostgreSQL Performance Tuning

Since PostgreSQL Database is used, it needs some set of special configs for the best performance. These set contains two categories:

  • general and universal for any capacity of the instance hardware:

  • based on CPU/RAM size at the instance(example for 4 CPUs/8GB RAM; the database is deployed on the separate VM):


    Recommendations regarding PostgreSQL server tuning for the instance with 8 CPUs/16GB RAM(where all RP services are deployed):


    If the PostgreSQL database and services are deployed on the separate VM’s, the "effective_cache_size" parameter should be changed to "12GB"(total RAM size - shared_buffers size).

    **Simple ways to set these parameters:**

    CPU’s count related:

    max_worker_processes = <DB instance CPU’s count>
    max_parallel_workers_per_gather = <DB instance CPU’s count / 2>
    max_parallel_workers = <DB instance CPU’s count>
    max_parallel_maintenance_workers = <DB instance CPU’s count / 2>

    RAM size related:

    shared_buffers = <DB instance RAM size in GB / 4>
    effective_cache_size = <DB instance RAM size in GB - shared_buffers>
    maintenance_work_mem = < if total RAM size > 16 Gb – 2Gb; under 16Gb – 1Gb and less>

    For the docker-compose: add the following lines to the command section by the “-c” option to “postgres” service.


    image: postgres:12-alpine
    shm_size: '512m'
    -c effective_io_concurrency=200
    -c random_page_cost=0.1
    -c seq_page_cost=1
    # Some another configs

    For the AWS RDS: create new parameter group(Services -> RDS -> Parameter groups -> Create parameter group), search config by the name and set up the following values, then apply created parameter group to the RDS DB.

    The following parameters can be scaled and depends on CPU’s number and RAM size of the instance. For example, for the instance 16 CPUs/32GB RAM(the database is deployed on the separate VM) this parameters should be:

    shared_buffers = '8GB'
    effective_cache_size = '24GB'
    max_worker_processes = '16'
    max_parallel_workers = '16'

6. Application connections pool tuning

By default, ReportPortal has 27 connections on service-api and 27 connections in pool on service-authorization. In general these values are valid for the small and middle servers. But from the middle+ server type, the connection pool may be increased if it's not enough for your server load.

It can be detected as periodic freezes and the "Loading" message when opening any page, and/or slowing down the work with RP after a certain period of time during active reporting and use with UI. Restarts of API and UAT services can also be observed.

To increasing the connection pool on both services, need to add next environment variables to the service-api and to the service-authorization:


After increasing the connection pool from the application side, do not forget increase the max_connections from the Database side, using following DB configuration paramether:


The values of these parameters are given for example only, but in general, can be valid for all types of loads for servers middle+ and large.

Please note, that the max_connections paramether must be more than the sum of the RP_DATASOURCE_MAXIMUMPOOLSIZE for the API and the UAT services + several connections for connecting to the database from outside.

7. Elasticsearch Performance Tuning

As mentioned above, in some cases may be necessary to increase the limits of shards. The general rule is 2 active shards per 1 data stream(1 data stream equals 1 project in the ReportPortal on the default configuration). If the data stream reaches the default 50 GB storage limit, it will be rollovered and a new generation will be created (please see the diagram below). Please consider that the number of Elasticsearch shards on the default configuration is insufficient for ReportPortal installations with a large number of projects. As a result, after storing close to 3000 indices without any tuning the logs-saving behavior may be incorrect.

To retrieve statistics from a cluster (to check the value of shard):

GET /_cluster/stats

"_nodes": {
"total": 3,
"successful": 3,
"failed": 0
"cluster_name": "elasticsearch",
"cluster_uuid": "Oq5UUZUg3RGE0Pa_Trfds",
"timestamp": 172365897412,
"status": "green",
"indices": {
"count": 470,
"shards": {
"total": 940,
"primaries": 470,
"replication": 1.0,
"index": {
"shards": {
"min": 2,
"max": 2,
"avg": 2.0

The API returns basic index metrics (shard numbers, store size, memory usage) and information about the current nodes that form the cluster (number, roles, os, jvm versions, memory usage, cpu and installed plugins).

To increase the limits of the total number of primary and replica shards for the cluster:

PUT /_cluster/settings

"persistent" : {
"cluster.max_shards_per_node": 3000

Keep in mind, that the more shards you allow per node the more resources each node will need and the worse the performance can get.

We also recommend to check the next guides in the official Elasticsearch documentation:

Data streams

Size your shards