Index patterns for time-series in Elasticsearch - Mini Project: Build & Apply

Index Patterns for Time-Series Data in Elasticsearch

📖 Scenario: You are managing a system that collects temperature readings every hour from multiple sensors. These readings are stored in Elasticsearch indices that are created daily with names like temps-2024.06.01, temps-2024.06.02, and so on.To analyze this data efficiently, you need to create an index pattern in Kibana that matches all these daily indices so you can query and visualize the time-series data easily.

🎯 Goal: Create an Elasticsearch index pattern that matches all daily temperature indices starting with temps- and includes the date suffix. This pattern will help you query all time-series data in Kibana.

📋 What You'll Learn

Create an index pattern string that matches all indices starting with temps- followed by a date.

Define a time field name for the index pattern to enable time-based queries.

Use a wildcard character to match all daily indices.

Ensure the index pattern is valid for use in Kibana.

💡 Why This Matters

🌍 Real World

Index patterns are essential in Elasticsearch and Kibana to query and visualize time-series data efficiently, such as logs, metrics, or sensor readings.

💼 Career

Understanding index patterns helps in roles like data analyst, DevOps engineer, or backend developer working with Elasticsearch for monitoring and analytics.

1

Create the base index pattern string

Create a variable called index_pattern and set it to the string "temps-*" to match all daily temperature indices.

Elasticsearch

# Your code here

Need a hint?

Use a wildcard * after temps- to match all indices with any date suffix.

2

Define the time field for the index pattern

Create a variable called time_field and set it to the string "timestamp", which is the field storing the date and time of each temperature reading.

Elasticsearch

index_pattern = "temps-*"
# Your code here

Need a hint?

The time field is usually named timestamp in time-series data.

3

Create the index pattern configuration dictionary

Create a dictionary called index_pattern_config with two keys: "pattern" set to the variable index_pattern, and "timeFieldName" set to the variable time_field.

Elasticsearch

index_pattern = "temps-*"
time_field = "timestamp"
# Your code here

Need a hint?

Use a dictionary with keys exactly "pattern" and "timeFieldName".

4

Complete the index pattern setup for Kibana

Add a key "title" to the index_pattern_config dictionary and set it to the value of index_pattern. This completes the configuration for Kibana to recognize the index pattern.

Elasticsearch

index_pattern = "temps-*"
time_field = "timestamp"
index_pattern_config = {"pattern": index_pattern, "timeFieldName": time_field}
# Your code here

Need a hint?

The title key is required by Kibana to display the index pattern name.