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)
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  1. Installing Akamas
  2. Manage the Akamas Server

Install upgrades and patches

Akamas patches and upgrades need to be installed by following the specific instructions specified in the package provided. In case of new releases, it is recommended to read the related Release Notes. Under normal circumstances, this usually requires the user to update the docker-compose configuration, as described in the next section.

Docker-compose Configuration

When using docker-compose to install Akamas, there’s a folder usually named akamas in the user home folder that contains a docker-compose.yml file. This is a YAML text file that contains a list of docker services with the URLs/version pointing to the ECR repo hosting all docker images needed to launch Akamas.

Here’s an excerpt of such a docker-compose.yml file (this example contains 3 services only):

services:
  #####################
  # Database Service #
  #####################
  database:
    image: 485790562880.dkr.ecr.us-east-2.amazonaws.com/akamas/master-db:1.7.0
    container_name: database2
    restart: always
    command: postgres -c max_connections=200

  #####################
  # Optimizer Service #
  #####################
  optimizer:
    image: 485790562880.dkr.ecr.us-east-2.amazonaws.com/akamas/optimizer_service:2.3.0
    container_name: optimizer
    restart: always
    networks:
      - akamas2
    depends_on:
      - database
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
      - /tmp/build/engine_input:/tmp/build/engine_input

  ####################
  # Campaign Service #
  ####################
  campaign:
    image: 485790562880.dkr.ecr.us-east-2.amazonaws.com/akamas/campaign_service:2.3.0
    container_name: campaign
    restart: always
    volumes:
      - config:/config
    networks:
      - akamas2
    depends_on:
      - database
      - optimizer
      - analyzer

The relevant lines that usually have to be patched during an upgrade are the lines with key "image" like:

image: 485790562880.dkr.ecr.us-east-2.amazonaws.com/akamas/master-db:1.7.0

In order to update to a new version you should replace the versions (1.7.0 or 2.3.0) after the colon with the new versions (ask your Akamas support for the correct service versions for a specific Akamas release) then you should restart Akamas with the following console commands: First login to Akamas CLI with:

akamas login

and type username and password as in the example below

ubuntu@ak_machine:~/akamas/ $ akamas login
User: akamas
Password:
User akamas logged in. Welcome.

Now make sure you have the following AWS variables with the proper value in your Linux user environment:

AWS_DEFAULT_REGION
AWS_SECRET_ACCESS_KEY
AWS_ACCESS_KEY_ID

Then log in to AWS with the following command:

aws ecr get-login-password --region us-east-2 | docker login --username AWS --password-stdin 485790562880.dkr.ecr.us-east-2.amazonaws.com
Login Succeeded

Then pull all new ECR images for the new service versions you just changed (this should be done from when inside the same folder where file docker-compose.yml resides, usually $HOME/akamas/) with the following command:

docker-compose pull

It should return an output like the following:

Pulling database                ... done
Pulling optimizer               ... done
Pulling elasticsearch           ... done
Pulling log                     ... done
Pulling metrics                 ... done
Pulling telemetry               ... done
Pulling analyzer                ... done
Pulling campaign                ... done
Pulling system                  ... done
Pulling license                 ... done
Pulling store                   ... done
Pulling airflow-db              ... done
Pulling benchmark               ... done
Pulling kong-database           ... done
Pulling kong                    ... done
Pulling user-service            ... done
Pulling keycloak                ... done
Pulling logstash                ... done
Pulling kibana                  ... done
Pulling kong-consumer-init      ... done
Pulling kong-migration          ... done
Pulling keycloak-initializer    ... done
Pulling telemetry-init          ... done
Pulling curator-only-pull-image ... done
Pulling airflow                 ... done
Pulling orchestrator            ... done
Pulling akamas-init             ... done
Pulling akamas-ui               ... done
Pulling pg-admin                ... done
Pulling grafana                 ... done
Pulling prometheus              ... done
Pulling node-exporter           ... done
Pulling cadvisor                ... done
Pulling konga                   ... done

Finally, relaunch all services with:

docker-compose up -d

(usage example below)

ubuntu@ak_machine:~/akamas/ $ docker compose up -d
pgadmin4 is up-to-date
prometheus is up-to-date
benchmark is up-to-date
kibana is up-to-date
node-exporter is up-to-date
store is up-to-date
grafana is up-to-date
cadvisor is up-to-date
Starting telemetry-init ...
Starting curator-only-pull-image ...
Recreating database2             ...
Recreating airflow-db            ...
Starting kong-initializer        ...
akamas-ui is up-to-date
elasticsearch is up-to-date
Recreating kong-db               ...
Recreating metrics               ...
logstash is up-to-date
Recreating log                   ...
...(some logging follows)

Wait for a few minutes and check the Akamas services are back up by running the command:

akamas status -d

The expected output should be like the following (repeat the command after a minute or two if the last line is not "OK" as expected):

Checking Akamas services on http://localhost:8000 service status
analyzer UP
campaign UP
metrics UP
optimizer UP
orchestrator UP
system UP
telemetry UP
license UP
log UP
users UP
OK

Last updated 1 year ago

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