Akamas Docs
3.1.2
3.1.2
  • How to use this documentation
  • Getting started with Akamas
    • Introduction to Akamas
    • Licensing
    • Deployment
      • Cloud Hosting
    • Security
    • Maintenance & Support (M&S) Services
      • Customer Support Services
      • Support levels for Customer Support Services
      • Support levels for software versions
      • Support levels with Akamas 3.1
  • Installing Akamas
    • Akamas Architecture
    • Prerequisites
      • Hardware Requirements
      • Software Requirements
      • Network requirements
    • Install Akamas dependencies
    • Install the Akamas Server
      • Online installation mode
        • Online installation behind a Proxy server
      • Offline installation mode
      • Changing UI Ports
      • Setup HTTPS configuration
    • Install the Akamas CLI
      • Setup the Akamas CLI
      • Verify the Akamas CLI
      • Initialize Akamas CLI
      • Change CLI configuration
    • Verify the Akamas Server
    • Install the Akamas license
    • Manage anonymous data collection
    • Install an Akamas Workstation
    • Troubleshoot install issues
    • Manage the Akamas Server
      • Akamas logs
      • Audit logs
      • Install upgrades and patches
      • Monitor the Akamas Server
      • Backup & Recover of the Akamas Server
  • Using Akamas
    • General optimization process and methodology
    • Preparing optimization studies
      • Modeling systems
      • Modeling components
        • Creating custom optimization packs
        • Managing optimization packs
      • Creating telemetry instances
      • Creating automation workflows
        • Creating workflows for offline studies
        • Performing load testing to support optimization activities
        • Creating workflows for live optimizations
      • Creating optimization studies
        • Defining optimization goal & constraints
        • Defining windowing policies
        • Defining KPIs
        • Defining parameters & metrics
        • Defining workloads
        • Defining optimization steps
        • Setting safety policies
    • Running optimization studies
      • Before running optimization studies
      • Analyzing results of offline optimization studies
        • Optimization Insights
      • Analyzing results of live optimization studies
      • Before applying optimization results
    • Guidelines for choosing optimization parameters
      • Guidelines for JVM (OpenJ9)
      • Guidelines for JVM layer (OpenJDK)
      • Guidelines for Oracle Database
      • Guidelines for PostgreSQL
    • Guidelines for defining optimization studies
      • Optimizing Linux
      • Optimizing Java OpenJDK
      • Optimizing OpenJ9
      • Optimizing Web Applications
      • Optimizing Kubernetes
      • Optimizing Spark
      • Optimizing Oracle Database
      • Optimizing MongoDB
      • Optimizing MySQL Database
      • Optimizing PostgreSQL
  • Integrating Akamas
    • Integrating Telemetry Providers
      • CSV provider
        • Install CSV provider
        • Create CSV provider instances
      • Dynatrace provider
        • Install Dynatrace provider
        • Create Dynatrace provider instances
      • Prometheus provider
        • Install Prometheus provider
        • Create Prometheus provider instances
        • CloudWatch Exporter
        • OracleDB Exporter
      • Spark History Server provider
        • Install Spark History Server provider
        • Create Spark History Server provider instances
      • NeoLoadWeb provider
        • Setup NeoLoadWeb telemetry provider
        • Create NeoLoadWeb provider instances
      • LoadRunner Professional provider
        • Install LoadRunner Professional provider
        • Create LoadRunner Professional provider instances
      • LoadRunner Enterprise provider
        • Install LoadRunner Enterprise provider
        • Create LoadRunner Enterprise provider instances
      • AWS provider
        • Install AWS provider
        • Create AWS provider instances
    • Integrating Configuration Management
    • Integrating Value Stream Delivery
    • Integrating Load Testing
      • Integrating NeoLoad
      • Integrating Load Runner Professional
      • Integrating LoadRunner Enterprise
  • Akamas Reference
    • Glossary
      • System
      • Component
      • Metric
      • Parameter
      • Component Type
      • Workflow
      • Telemetry Provider
      • Telemetry Instance
      • Optimization Pack
      • Goals & Constraints
      • KPI
      • Optimization Study
      • Offline Optimization Study
      • Live Optimization Study
      • Workspace
    • Construct templates
      • System template
      • Component template
      • Parameter template
      • Metric template
      • Component Types template
      • Telemetry Provider template
      • Telemetry Instance template
      • Workflows template
      • Study template
        • Goal & Constraints
        • Windowing policy
          • Trim windowing
          • Stability windowing
        • Parameter selection
        • Metric selection
        • Workload selection
        • KPIs
        • Steps
          • Baseline step
          • Bootstrap step
          • Preset step
          • Optimize step
        • Parameter rendering
    • Workflow Operators
      • General operator arguments
      • Executor Operator
      • FileConfigurator Operator
      • LinuxConfigurator Operator
      • WindowsExecutor Operator
      • WindowsFileConfigurator Operator
      • Sleep Operator
      • OracleExecutor Operator
      • OracleConfigurator Operator
      • SparkSSHSubmit Operator
      • SparkSubmit Operator
      • SparkLivy Operator
      • NeoLoadWeb Operator
      • LoadRunner Operator
      • LoadRunnerEnteprise Operator
    • Telemetry metric mapping
      • Dynatrace metrics mapping
      • Prometheus metrics mapping
      • NeoLoadWeb metrics mapping
      • Spark History Server metrics mapping
      • LoadRunner metrics mapping
    • Optimization Packs
      • Linux optimization pack
        • Amazon Linux
        • Amazon Linux 2
        • Amazon Linux 2022
        • CentOS 7
        • CentOS 8
        • RHEL 7
        • RHEL 8
        • Ubuntu 16.04
        • Ubuntu 18.04
        • Ubuntu 20.04
      • DotNet optimization pack
        • DotNet Core 3.1
      • Java-OpenJDK optimization pack
        • Java OpenJDK 8
        • Java OpenJDK 11
      • OpenJ9 optimization pack
        • IBM J9 VM 6
        • IBM J9 VM 8
        • Eclipse Open J9 11
      • NodeJS optimization pack
        • NodeJS
      • GO optimization pack
        • GO 1
      • Web Application optimization pack
        • Web Application
      • Docker optimization pack
        • Container
      • Kubernetes optimization pack
        • Kubernetes Pod
        • Kubernetes Container
        • Kubernetes Workload
        • Kubernetes Namespace
        • Kubernetes Cluster
      • WebSphere optimization pack
        • WebSphere 8.5
        • WebSphere Liberty ND
      • AWS optimization pack
        • EC2
        • Lambda
      • PostgreSQL optimization pack
        • PostgreSQL 11
        • PostgreSQL 12
      • Cassandra optimization pack
        • Cassandra
      • MySQL Database optimization pack
        • MySQL 8.0
      • Oracle Database optimization pack
        • Oracle Database 12c
        • Oracle Database 18c
        • Oracle Database 19c
        • RDS Oracle Database 11g
        • RDS Oracle Database 12c
      • MongoDB optimization pack
        • MongoDB 4
        • MongoDB 5
      • Elasticsearch optimization pack
        • Elasticsearch 6
      • Spark optimization pack
        • Spark Application 2.2.0
        • Spark Application 2.3.0
        • Spark Application 2.4.0
    • Command Line commands
      • Administration commands
      • User and Workspace management commands
      • Authentication commands
      • Resource management commands
      • Optimizer options commands
  • Knowledge Base
    • Setting up a Konakart environment for testing Akamas
    • Modeling a sample Java-based e-commerce application (Konakart)
    • Optimizing a web application
    • Optimizing a sample Java OpenJ9 application
    • Optimizing a sample Java OpenJDK application
    • Optimizing a sample Linux system
    • Optimizing a MongoDB server instance
    • Optimizing a Kubernetes application
    • Leveraging Ansible to automate AWS instance management
    • Guidelines for optimizing AWS EC2 instances
    • Optimizing a sample application running on AWS
    • Optimizing a Spark application
    • Optimizing an Oracle Database server instance
    • Optimizing an Oracle Database for an e-commerce service
    • Guidelines for optimizing Oracle RDS
    • Optimizing a MySQL server database running Sysbench
    • Optimizing a MySQL server database running OLTPBench
    • Optimizing a live K8s deployment
    • Optimizing a live full-stack deployment (K8s + JVM)
  • Akamas Free Trial
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  • Operator arguments
  • Parameters applied from Experiments
  • Examples

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  1. Akamas Reference
  2. Workflow Operators

SparkLivy Operator

The SparkLivy operator uses Livy to run Spark applications on a Spark instance.

Operator arguments

Name
type
Value restrictions
Is required
Default
Description

file

String

It should be a path to a valid java or python spark application file

Yes

Spark application to submit (jar or python file)

args

List of Strings, Numbers or Booleans

Yes

Additional application arguments

className

String

No. Required for java applications.

The entry point of the java application.

name

String

No

Name of the task. When submitted the id of the study, experiment and trial will be appended.

queue

String

No

The name of the YARN queue to which submit a Spark application

pyFiles

List of Strings

Each item of the list should be a path that matches an existing python file

No

A list of python scripts to be added to the PYTHONPATH

proxyUser

String

No

The user to be used to launch Spark applications

pollingInterval

Number

pollingInterval > 0

No

10

The number of seconds to wait before checking if a launched Spark application has finished

component

String

It should match the name of an existing Component of the System under test

Yes

The name of the component whose properties can be used as arguments of the operator

Parameters applied from Experiments

The operator fetches the following parameters from the current Experiment to apply them to the System under test.

Name
Description
Restrictions

spark_driver_memory

Memory for the driver

spark_executor_memory

Memory per executor

spark_total_executor_cores

Total cores used by the application

Spark standalone and Mesos only

spark_executor_cores

Cores per executor

Spark standalone and YARN only

spark_num_executors

The number of executors

YARN only

Examples

Execute with Livy

- name: "Run spark application"
  operator: "SparkLivy"
  arguments:
    component: "sparkemr"
    file: "/spark-examples.jar"

Last updated 2 years ago

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