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Foglight Evolve 7.1.0 - Performance Tuning Field Guide

Table of Contents

Overview

Characteristics of Poor Performance in Foglight Parameters that Affect Foglight Performance Critical Areas that Can Affect Foglight Performance Identifying Performance Problems

Hardware and Operating System Tuning

Running on Virtual Hardware

Performance Challenges CPU

Ready Time

Memory

Diagnostic Snapshot

Shares Disk

Management Server Tuning

Dashboard Default Timeout

Java Virtual Machine Tuning

Getting Started Foglight JVM Configuration

JVM Options

-XX:+UseConcMarkSweepGC -Xmx and -Xms -XX:NewSize, -XX:MaxNewSize, and -XX:NewRatio -XX:MaxPermSize -Xss

Common Symptoms and Tuning Resolutions

JVM Code Cache

Backend Database Tuning

Initial Database Configuration Settings

Monitoring and Managing Database Size Backup and Recovery Recommendations init.ora Configuration

Sample init.ora:

Database Maintenance Recommendations

MySQL Tuning

Case

Oracle Tuning

How the Management Server Uses the Database Index Management Memory Block Size Oracle Striping Oracle Tablespaces

Undo tablespace Custom Tablespaces

Microsoft SQL Server Tuning PostgreSQL Tuning Troubleshooting

Management Server Load Preventing Database Connections

High Availability (HA) Tuning

Tuning Connection Issues in an HA Implementation Managing Hosts with Multiple Network Interfaces JDK with IPv6 on Linux Management Server Automatically Restarted Other JGroup Related Issues and Information

Agent Tuning

Topology Changes and Topology Churn Canonical Data Transformations (CDTs) Agent Weight / Environment Complexity Large Topologies Sampling Frequency XML-HTTP Agent Adapter Dropped Agent Manager Log Messages Java EE Technologies

Store and Forward Processing

About Store and Forward Processing in Foglight Store and Forward Processing and the Foglight Management Server Store and Forward Processing and the Foglight Agent Manager

Viewing the Settings that Control the Size of Stored Data

Examples

Example 1: Reporting on Disk Usage Example 2: Calculating Service Availability Metrics

Appendix: Performance Health Check

Is the Server Getting the Right Amount of Memory?

Management Server Memory is Healthy if ... Management Server Memory Checks Possible Actions

Is the Server Getting Enough CPU?

Management Server CPU is Healthy if ... Management Server CPU Checks Extended Browser Interface Checks Possible Actions

Is the Database too Slow?

The Database is Healthy if ... Database Checks Possible Actions

Is the Database Growth Reasonable?

Database Growth is Healthy if ... Database Checks Possible Actions

Is There Too Much Data to Process?

Data Volume is Healthy if ... Data Volume Checks Possible Actions

Is the Model Stable?

The Model is Healthy if ... Model Checks Possible Actions

Are Too Many Alarms Firing?

The Number of Alarms is in a Healthy State if... Alarms Checks Possible Actions

Is the Business Logic Properly Tuned?

Business Logic is Healthy if... Business Logic Checks Possible Actions

Are There Too Many User Requests?

User Activity Is Healthy If... Possible Actions

Appendix: Analyzing a Support Bundle

Server Log

Topology Sync Topology Limits

Diagnostic Snapshot

Memory Consumption Threads, Deadlocks, and Overall CPU Usage Useful Information ---- jboss.system:type=ServerInfo ---- jboss.jca:service=ManagedConnectionPool,name=jdbc/nitrogen ---- jboss.web:type=RequestProcessor,* ---- com.quest.nitro:service=Derivation

Derivation Related Issues

---- com.quest.nitro:service=Topology

Topology Related Issues

---- com.quest.nitro:service=DataCacheEviction

Analyzing a Performance Report

Server Rule Information System-wide Topology Changes JVM Memory Usage Management Server Garbage Collectors JDBC Connection Pool Derivation Rulette

Data Volume Checks

•

Look at the data service graph to see if there are any dropped batches of data. It is fine for data to be dropped occasionally due to temporary overload. However, even a small amount of dropped data over a long period of time indicates data overload.

•

Look in the Management Server logs for dropped data messages.

•

Look at the batch processing time. If the batch processing time is excessive, then data is backing up. This is an early indicator that data will be dropped. If batch processing time is growing, this will eventually result in data overload.

Possible Actions

•

Remove agents.

•

Provide more resources to the Management Server, so that it can keep up.

•

Tune agents to collect data less frequently.

Is the Model Stable?

Foglight® requires a stable topology in order to function well. A stable topology has infrequent topology changes. If topology changes occur often, it means the model is growing or changing. Model growth leads to memory issues. Model changes cause churn in the business logic that in turn result in CPU consumption.

The Model is Healthy if ...

•

There are no significant topology changes over time.

•

If the model size (topology object count) is constant.

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Foglight Evolve 7.1.0 - Performance Tuning Field Guide

Data Volume Checks

Possible Actions

Is the Model Stable?

The Model is Healthy if ...