Akamas Docs
3.1.3
3.1.3
  • 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
    • 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 telemetry instances
      • Dynatrace provider
        • Install Dynatrace provider
        • Create Dynatrace telemetry instances
      • Prometheus provider
        • Install Prometheus provider
        • Create Prometheus telemetry instances
        • CloudWatch Exporter
        • OracleDB Exporter
      • Spark History Server provider
        • Install Spark History Server provider
        • Create Spark History Server telemetry instances
      • NeoLoadWeb provider
        • Install NeoLoadWeb telemetry provider
        • Create NeoLoadWeb telemetry instances
      • LoadRunner Professional provider
        • Install LoadRunner Professional provider
        • Create LoadRunner Professional telemetry instances
      • LoadRunner Enterprise provider
        • Install LoadRunner Enterprise provider
        • Create LoadRunner Enterprise telemetry instances
      • AWS provider
        • Install AWS provider
        • Create AWS telemetry 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
    • Release Notes
  • 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 cost of a Kubernetes application while preserving SLOs in production
    • Optimizing a live full-stack deployment (K8s + JVM)
  • Akamas Free Trial
Powered by GitBook
On this page
  • Workflows
  • Telemetry Providers
  • Examples

Was this helpful?

Export as PDF
  1. Using Akamas
  2. Guidelines for defining optimization studies

Optimizing Oracle Database

Last updated 2 years ago

Was this helpful?

When optimizing a MongoDB instance, typically the goal is to maximize the throughput of an Oracle-backed application or to minimize its resource consumption, thus reducing costs.

Please refer to the for the list of component types, parameters, metrics, and constraints.

Workflows

Applying parameters

One common way to configure Oracle parameters is through the execution ALTER SYSTEM statements on the database instance: to automate the execution of this task Akamas provides the . For finer control, Akamas provides the , which allows building custom statements in a script file that can be executed by the .

Oracle Configurator

The allows the workflow to configure an on-premise instance with minimal configuration. The following snippet is an example of a configuration task, where all the connection arguments are already defined in the referenced component:

name: Update Oracle parameters
operator: OracleConfigurator
arguments:
  component: oracledb

File Configurator and Executor

Most cloud providers offer web APIs as the only way to configure database services. In this case, the can submit an API request through a custom executable using a configuration file generated by a . The following is an example workflow where a FileConfigurator task generates a configuration file (oraconf), followed by an Executor task that parses and submits the configuration to the API endpoint through a custom script (api_update_db_conf.sh):

tasks:
  - name: Generate Oracle configuration
    operator: FileConfigurator
    arguments:
      sourcePath: /home/akamas/oraconf.template
      targetPath: /home/akamas/oraconf
      component: oracledb

  - name: Update conf
    operator: Executor
    arguments:
      command: bash /home/akamas/oraconf/api_update_db_conf.sh /home/akamas/oraconf
      component: oracleML

A typical workflow

The optimization of an Oracle database usually includes the following tasks in the workflow, as implemented in the example below:

  1. Apply the Oracle configuration suggested by Akamas and restart the instance if needed (Update parameters task).

  2. Perform any additional warm-up task that may be required to bring the database up at the operating regime (Execute warmup task).

  3. Execute the workload targeting the database or the front-end in front of it (Execute performance test task).

  4. Restore the original state of the database in order to guarantee the consistency of further tests, removing any dirty data added by the workload and possibly flushing the database caches (Cleanup task).

The following is the complete YAML configuration file of the workflow described above:

name: workflow
description: Test Oracle instance configuration.
tasks:

  - name: Update parameters
    operator: OracleConfigurator
    arguments:
      component: oracledb

  - name: Execute warmup
    operator: Executor
    arguments:
      host:
        hostname: perf.mycompany.com
        key: ...
        username: perf
      command: /home/perf/warmup.sh

  - name: Execute performance test
    operator: Executor
    arguments:
      host:
        hostname: perf.mycompany.com
        key: ...
        username: perf
      command: /home/perf/start.sh

  - name: Cleanup
    operator: OracleExecutor
    arguments:
      sql:
        - TRUNCATE TABLE user_actions
      component: oracledb

Telemetry Providers

[[metric]]
context = "sessions"
labels = [ "status", "type" ]
metricsdesc = { value= "Gauge metric with count of sessions by status and type." }
request = "SELECT status, type, COUNT(*) as value FROM v$session GROUP BY status, type"

The following example shows how to configure a telemetry instance for a Prometheus provider in order to query the data points extracted from the exporter described above:

provider: Prometheus
config:
  address: akamas.mycompany.com
  port: 9090

metrics:
  - metric: sessions_active_user
    datasourceMetric: oracledb_sessions_value{instance='$INSTANCE$', type='USER', status='ACTIVE', %FILTERS%}

  - metric: sessions_inactive_user
    datasourceMetric: oracledb_sessions_value{instance='$INSTANCE$', type='USER', status='INACTIVE', %FILTERS%}

Examples

Akamas offers many telemetry providers to extract Oracle Database metrics; one of them is the , which we can use to query Oracle Database metrics collected by a Prometheus instance via the .

The snippet below shows a configuration example for the Oracle Exporter extracting metrics regarding the Oracle sessions:

See and for examples of studies leveraging the Oracle Database pack.

Oracle Database optimization pack
OracleConfigurator operator
FileConfigurator operator
Executor operator
OracleConfigurator operator
Executor operator
FileConfigurator operator
Prometheus provider
Prometheus Oracle Exporter
toml
Optimizing an Oracle Database server instance
Optimizing an Oracle Database for an e-commerce service