Application performance monitoring in Elasticsearch - Step-by-Step Execution

Concept Flow - Application performance monitoring

Start Application

↓

Collect Metrics & Traces

↓

Send Data to Elasticsearch

↓

Store & Index Data

↓

Analyze & Visualize in Kibana

↓

Detect Issues & Alert

↓

Optimize Application

↓

Repeat Monitoring Cycle

This flow shows how application data is collected, sent to Elasticsearch, stored, analyzed, and used to improve performance continuously.

Execution Sample

Elasticsearch

POST /apm-7.17.0/_doc
{
  "transaction": {
    "name": "GET /api/user",
    "duration": 120
  },
  "@timestamp": "2024-06-01T12:00:00Z"
}

This example sends a transaction document to Elasticsearch APM index representing a user API call with its duration.

Execution Table

Step	Action	Data Sent	Elasticsearch Response	System State Change
1	Start Application	N/A	N/A	Application running, ready to collect data
2	Collect Metrics & Traces	Transaction data: {name: 'GET /api/user', duration: 120}	N/A	Metrics collected in memory
3	Send Data to Elasticsearch	POST /apm-7.17.0/_doc with transaction data	201 Created	Data indexed in Elasticsearch
4	Store & Index Data	N/A	N/A	Data stored and searchable in APM index
5	Analyze & Visualize in Kibana	Query APM index	Query results with transaction metrics	Dashboard updated with latest data
6	Detect Issues & Alert	N/A	N/A	Alerts triggered if thresholds exceeded
7	Optimize Application	N/A	N/A	Developers improve code based on insights
8	Repeat Monitoring Cycle	N/A	N/A	Continuous monitoring ongoing

💡 Monitoring continues indefinitely to track application performance and detect issues

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 5	Final
transaction_data	None	{"name": "GET /api/user", "duration": 120}	Sent to Elasticsearch	Stored and queried in Kibana	Used for alerts and optimization
application_state	Stopped	Running	Running	Running	Running

Key Moments - 3 Insights

Why do we send transaction data to Elasticsearch instead of storing it locally?

Because Elasticsearch indexes and stores data efficiently, making it easy to search, analyze, and visualize large volumes of performance data as shown in execution_table step 3 and 4.

How does Kibana help in application performance monitoring?

Kibana queries the indexed data in Elasticsearch and creates visual dashboards, helping to spot trends and issues quickly, as seen in execution_table step 5.

What triggers alerts in this monitoring flow?

Alerts are triggered when performance metrics exceed predefined thresholds, indicated in execution_table step 6, helping teams react fast to problems.

Visual Quiz - 3 Questions

Look at the execution table, what response does Elasticsearch give after sending transaction data?

A 404 Not Found

B 500 Internal Server Error

C 201 Created

D 200 OK

💡 Hint

Check the 'Elasticsearch Response' column at Step 3 in the execution_table

At which step does Kibana update the dashboard with the latest data?

A Step 5

B Step 3

C Step 6

D Step 2

💡 Hint

Look at the 'Action' column in execution_table for where visualization happens

If the application stops collecting metrics, which variable in variable_tracker would remain 'None'?

A application_state

B transaction_data

C Elasticsearch Response

D Alerts

💡 Hint

Refer to variable_tracker row for 'transaction_data' and its values after Step 2

Concept Snapshot

Application Performance Monitoring (APM) with Elasticsearch:
- Collects app metrics and traces
- Sends data to Elasticsearch APM index
- Stores and indexes for fast search
- Visualizes data in Kibana dashboards
- Detects issues and triggers alerts
- Enables continuous app optimization

Full Transcript

Application Performance Monitoring (APM) with Elasticsearch involves collecting performance data like transaction durations from your running application. This data is sent to Elasticsearch where it is stored and indexed for fast searching. Kibana then queries this data to create visual dashboards that help you understand how your app performs. Alerts can be set up to notify you when performance degrades. This cycle repeats continuously to help developers optimize the application based on real-time insights.