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Recommended Cluster Configuration

DataOS offers the flexibility to create clusters on-demand based on specific use cases, allowing you to customize the cluster according to your needs. The following cluster configurations are recommended for different query loads.

Scenario 1: Running ad hoc queries by a group of analysts

In this scenario, workloads are not intensive, and the cluster is shared among users.

High availability

For this use case, it is recommended to have multiple Minerva clusters to ensure high availability.

Recommended Configuration

A default Minerva cluster can be used with the properties specified in the toggle below.

Minerva Cluster Configuration
name: minervab
version: v1    
type: cluster
description: the default minerva cluster b
- cluster
- minerva
    "": "query"
toleration: query
runAsApiKey: api-key
    replicas: 2
        cpu: 2000m
        memory: 4Gi
        cpu: 2000m
        memory: 4Gi
    logLevel: INFO
    trinoLogLevel: ERROR

For more information, refer to the Multi-Cluster Setup.

Scenario 2: Data scientists/analysts running intensive data exploration

In this scenario, clusters are required for specialized use cases or teams, such as data scientists running complex data exploration and machine learning algorithms.

Recommended Configuration

For these use cases, it is recommended to use on-demand Minerva Clusters and consider the following configurations:

  1. Use a bigger cluster by increasing the maximum worker node count.
  2. Add a limit clause for all subqueries.
  3. Use a larger cluster instance.

Increasing the number of nodes in a single cluster improves throughput and resource utilization, enabling efficient processing of large queries.

Scenario 3: CPU optimized instance

In scenarios where a high number of concurrent small queries with significant CPU time is required, a CPU-optimized instance is recommended.

Recommended Configuration

For a combination of large and medium queries, where data scanned ranges from a hundred Megabytes to a Terabyte, a memory-optimized instance is more suitable.

These parameters can be tuned based on specific requirements.

For further details, please refer to the Performance Tuning section.