End-of-Life (EoL)

Elasticsearch System Requirements

There are several options for Elasticsearch deployment, each with specific sizing requirements. Requirements for implementing Cortex XSOAR with Elasticsearch.
This topic provides information about the system requirements for implementing Cortex XSOAR with Elasticsearch.

Elasticsearch server

The information in the following table is per Elasticsearch node, and assumes that the node is assigned all Elasticsearch node roles (for example, which data is written to disk and when).
Component
Dev Environment Minimum
Production Minimum
CPU
8 CPU Cores
16 CPU cores
Memory
16 GB RAM
32 GB RAM
Storage
250 GB SSD
500 GB SSD with minimum 3k dedicated IOPS
Elasticsearch user permissions are available in the security guidelines.
You must ensure that between the Elasticsearch and Cortex XSOAR servers, and between Elasticsearch servers, latency should not exceed 100 MS. Latency that exceeds 100 MS can cause serious performance degradation.

Supported Elasticsearch Versions

Cortex XSOAR officially supports Elasticsearch versions 7.4x to 7.10, including minor versions. Other versions might still work, but have not been tested with Cortex XSOAR.

Elasticsearch in the Cloud

Cortex XSOAR supports using Elasticsearch with all the major cloud service providers, Amazon Web Services, Azure, and Google Cloud Platform.
Amazon Web Services offers OpenSearch as a replacement for Elasticsearch. Cortex XSOAR supports OpenSearch v1.0 (not for multi-tenant architecture)
You can use Elasticsearch as a service provided by your cloud provider, or install Elasticsearch on a server in the cloud.
The hardware requirements for Elasticsearch in the cloud similar to those posted above. To achieve this with your cloud provider, Cortex XSOAR recommends you use the machines based on your intentions. For example:
  • When the Elasticsearch server functions as a data node, we recommend you use Storage optimized machines, such as the AWS i3.2xlarge machine. Alternatively, you can use a memory optimized machine, such as the AWS r3.2xlarge machine.
  • When the Elasticsearch server is used for any other function (such as master mode), we recommend that you use a Compute optimized machine, such as the AWS c4.2xlarge machine.
You can configure your cloud environment to work with different regions provided that you can maintain the minimum latency requirements noted above.

General Configurations

It is recommended that you implement the following Elasticsearch configurations in Cortex XSOAR. The value of the shards and replica shards should match the sum total of Elasticsearch nodes that you have.
Set the number of shards for an index
This server configuration enables you to set the number of shards for a specific index upon creation, where
<common-indicator>
is the name of the index. The default is 1.
To improve the write-performance, you can increase the number of shards and decrease the number of replica shards.
elasticsearch.shards.
<common-indicator>
Set the number of replica shards for an index
This server configuration enables you to set the number of replica shards for a specific index upon creation, where
<common-indicator>
is the name of the index. To increase search performance and data redundancy, you should set the value to the number of Elasticsearch nodes that you have. The default is 1.
elasticsearch.replicas.
<common-indicator>

Maximum indicator capacity and disk usage comparison

The following table compares the maximum total indicator capacity and disk usage for BoltDB and Elasticsearch. The maximum indicator capacity value was determined when testing the system.
We recommend using Elasticsearch if you plan to exceed at least one of the following maximum capacities for BoltDB.
The Cortex XSOAR indicators used to test the sizing requirements did not contain a significant number of additional fields nor custom fields. The maximum size of the indicators we tested had 20 additional or custom fields and a random string between 1-16 characters. Therefore, the indicators size tested were approximately 0.5KB. If you plan to have additional or custom fields for indicators, the maximum numbers should be reduced.
Benchmark
BoltDB
Elasticsearch
Maximum indicator capacity (total)
5-7 million
(Requires up to 10 seconds for a complex query)
100 million
(Requires approximately 40 seconds for a complex query)
Disk usage
5 million (~ 30 GB)
100 million (~ 70 GB)
If performance is poor, or you know in advance that you will need more than the maximum number of indicators, you should consider scaling BoltDB or moving to Elasticsearch. If you are already in Elasticsearch, you can scale it as well. For both BoltDB and Elasticsearch, you can scale by either adding engines for one or more feed integrations or increasing the resources (CPU, RAM, Disk IOPS) of the Cortex XSOAR server. For Elasticsearch, you can also increase the Elasticsearch cluster size from 1 server to 2 or more servers.

Incident disk usage comparison

The following table compares the disk usage for BoltDB and Elasticsearch.
Number of Incidents
BoltDB
Elasticsearch
10,000
28GB
13GB
40,000
112GB
52GB
100,000
280GB
130GB

Single feed fetch comparison

The following table compares the number of indicators, time to ingestion, and disk usage for BoltDB and Elasticsearch.
Number of Indicators
Database
Time to Ingestion
Disk Usage
30k
BoltDB
16s
1.3 GB
Elasticsearch
11s
1.08 GB + 161 MB (Elasticsearch index)
50k
BoltDB
33s
1.45 GB
Elasticsearch
25s
1.08 GB + 26.7 MB (Elasticsearch index)
100k
BoltDB
1m8s
2.1 GB
Elasticsearch
49s
1.08 GB + 53 MB (Elasticsearch index)
1M
BoltDB
12m21s
13.5 GB
Elasticsearch
7m25s
1.08 GB + 570MB (Elasticsearch index)
2M
BoltDB
22m27s
32 GB
Elasticsearch
22m20s
1.23 GB + 1 GB (Elasticsearch index)

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