Lens for drag-and-drop analysis in Elasticsearch - Deep Dive

Overview - Lens for drag-and-drop analysis

What is it?

Lens is a visual tool in Elasticsearch that lets you explore and analyze your data by dragging and dropping fields to create charts and tables. It simplifies data analysis by turning complex queries into easy visual steps without needing to write code. You can quickly see patterns, trends, and summaries from your data using Lens. It works inside Kibana, the user interface for Elasticsearch.

Why it matters

Without Lens, analyzing Elasticsearch data requires writing complex queries or scripts, which can be hard for beginners or slow for experts. Lens makes data analysis accessible and fast, helping people make decisions based on data insights quickly. It reduces errors and speeds up understanding by showing results visually, which is easier to grasp than raw numbers or code.

Where it fits

Before using Lens, you should understand basic Elasticsearch concepts like indexes, documents, and fields. Knowing how Kibana works helps too. After learning Lens, you can explore advanced data visualization, dashboard creation, and custom query building in Elasticsearch.

Mental Model

Core Idea

Lens turns your data fields into visual stories by letting you drag and drop them to build charts and tables without writing queries.

Think of it like...

Using Lens is like arranging ingredients on a kitchen counter to make a meal: you pick what you want, combine them visually, and get a tasty dish without needing a recipe book.

┌─────────────┐       ┌───────────────┐       ┌───────────────┐
│  Data Index │──────▶│ Drag & Drop   │──────▶│ Visual Output │
│ (Documents) │       │  Fields in    │       │ (Charts/Tables)│
└─────────────┘       │   Lens Panel  │       └───────────────┘
                      └───────────────┘

Build-Up - 7 Steps

1

Foundation

Understanding Elasticsearch Data Basics

Concept: Learn what data looks like inside Elasticsearch: indexes, documents, and fields.

Elasticsearch stores data in indexes, which are like folders. Each index holds many documents, like pages in a folder. Each document has fields, which are pieces of information like name, date, or number. Knowing this helps you pick what data to analyze.

Result

You can identify which fields you want to explore in Lens.

Understanding the data structure is key to knowing what you can analyze and how to find it in Lens.

2

Foundation

Introduction to Kibana and Lens Interface

Concept: Get familiar with Kibana and how Lens fits inside it as a visual tool.

Kibana is the dashboard and visualization tool for Elasticsearch. Lens is a feature inside Kibana that lets you drag fields from your data to create charts and tables. You open Lens, see your fields on the left, and a blank canvas on the right to build visuals.

Result

You can open Lens and see your data fields ready for analysis.

Knowing the Lens interface reduces confusion and helps you start building visuals quickly.

3

Intermediate

Creating Basic Visualizations with Drag-and-Drop

🤔Before reading on: do you think dragging a numeric field creates a table or a chart? Commit to your answer.

Concept: Learn how dragging different types of fields creates different visualizations automatically.

When you drag a numeric field, Lens often creates bar or line charts showing sums or averages. Dragging a text field creates tables or lists. You can combine fields, like dragging a date field to the horizontal axis and a number field to the vertical axis to see trends over time.

Result

You get a chart or table that summarizes your data visually.

Understanding how field types affect visualization helps you choose the right fields to answer your questions.

4

Intermediate

Using Filters and Breakdowns in Lens

🤔Before reading on: do you think filters limit data before or after visualization? Commit to your answer.

Concept: Learn to refine your analysis by filtering data and breaking it down by categories.

Filters let you include or exclude data, like only showing sales from last month. Breakdowns split your chart by categories, like sales by region. You add filters or breakdowns by dragging fields to special Lens areas or using the filter bar.

Result

Your visualization focuses on specific data slices, making insights clearer.

Knowing how to filter and break down data lets you zoom in on important details without distractions.

5

Intermediate

Switching Visualization Types Easily

Concept: Lens lets you change how data is shown without rebuilding your analysis.

After creating a chart, you can switch between bar, line, pie, or table views with one click. Lens adjusts the visualization to fit the data you selected. This helps you find the best way to understand your data.

Result

You see the same data in different visual forms quickly.

Flexibility in visualization helps you discover patterns you might miss with one chart type.

6

Advanced

Customizing Metrics and Aggregations

🤔Before reading on: do you think Lens can calculate averages and counts automatically? Commit to your answer.

Concept: Learn how Lens calculates summaries like sums, averages, and counts behind the scenes and how to customize them.

Lens uses Elasticsearch aggregations to summarize data. By default, it sums numbers or counts documents. You can change this to average, max, min, or unique counts by selecting metric options. This controls what your chart measures.

Result

Your visualizations show exactly the summary you want.

Understanding metrics and aggregations helps you tailor analysis to your specific questions.

7

Expert

Lens Behind the Scenes: Query Generation

🤔Before reading on: do you think Lens sends one query or multiple queries to Elasticsearch? Commit to your answer.

Concept: Lens translates your drag-and-drop actions into Elasticsearch queries automatically.

When you build a visualization, Lens creates a query with aggregations and filters matching your selections. It sends this query to Elasticsearch, which returns summarized data. Lens then renders the chart. Complex visuals may generate multiple queries or use advanced features like runtime fields.

Result

You get fast, accurate visualizations without writing queries.

Knowing Lens builds queries helps you debug and optimize your analysis when needed.

Under the Hood

Lens works by mapping each drag-and-drop action to Elasticsearch aggregation queries. It builds a JSON query that groups, filters, and summarizes data. Elasticsearch executes this query efficiently using inverted indexes and returns aggregated results. Lens then renders these results as charts or tables in the browser.

Why designed this way?

Lens was designed to make Elasticsearch data accessible to non-technical users by hiding query complexity. The drag-and-drop interface lowers the barrier to entry and speeds up analysis. Alternatives like manual query writing are powerful but slow and error-prone for many users.

┌───────────────┐       ┌───────────────┐       ┌───────────────┐
│ User Drag &   │──────▶│ Lens Builds   │──────▶│ Elasticsearch │
│ Drop Fields   │       │ Query JSON    │       │ Executes Query│
└───────────────┘       └───────────────┘       └───────────────┘
                                                      │
                                                      ▼
                                             ┌─────────────────┐
                                             │ Aggregated Data  │
                                             └─────────────────┘
                                                      │
                                                      ▼
                                             ┌─────────────────┐
                                             │ Lens Renders    │
                                             │ Visualization   │
                                             └─────────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does Lens require you to write Elasticsearch queries? Commit yes or no.

Common Belief: Lens is just a shortcut that still needs you to write queries.

Reality: Lens automatically generates all queries for you based on your drag-and-drop actions, so no query writing is needed.

Why it matters: Believing you must write queries can discourage beginners from using Lens and slow down analysis.

Quick: Can Lens visualize data not stored in Elasticsearch? Commit yes or no.

Common Belief: Lens can analyze any data source, not just Elasticsearch indexes.

Reality: Lens only works with data stored in Elasticsearch indexes accessible through Kibana.

Why it matters: Trying to use Lens with unsupported data leads to confusion and wasted time.

Quick: Does changing visualization type in Lens change the underlying data? Commit yes or no.

Common Belief: Switching chart types changes the data being analyzed.

Reality: Changing visualization type only changes how data is displayed, not the data or query itself.

Why it matters: Misunderstanding this can cause users to think their analysis changed unexpectedly.

Quick: Does Lens always send a single query to Elasticsearch? Commit yes or no.

Common Belief: Lens sends only one query per visualization.

Reality: Complex Lens visuals may send multiple queries or use advanced features like runtime fields behind the scenes.

Why it matters: Assuming a single query can lead to performance surprises in large or complex analyses.

Expert Zone

1

Lens supports runtime fields that let you create temporary fields on the fly without changing the index mapping.

2

Lens can combine multiple layers of data in one visualization, like showing breakdowns and filters simultaneously.

3

Lens integrates with Kibana's data views, allowing dynamic switching between different index patterns without rebuilding visuals.

When NOT to use

Lens is not ideal when you need highly customized queries, complex scripted fields, or very large datasets requiring optimized query tuning. In such cases, using raw Elasticsearch queries or Vega visualizations is better.

Production Patterns

In production, Lens is used for quick exploratory analysis, building dashboards for business users, and monitoring data trends. Experts often start with Lens visuals and then export or refine queries for advanced use.

Connections

Business Intelligence Tools

Lens shares the drag-and-drop visual analysis pattern common in BI tools like Tableau or Power BI.

Understanding Lens helps grasp how visual data exploration works across many platforms, not just Elasticsearch.

SQL Query Builders

Lens automates query building like SQL query builders do for relational databases.

Knowing Lens's query generation clarifies how visual tools translate user actions into database queries.

User Interface Design

Lens exemplifies how UI design can simplify complex tasks by hiding technical details behind intuitive controls.

Studying Lens shows how good UI design empowers users to perform advanced tasks without deep technical knowledge.

Common Pitfalls

#1 Trying to drag fields that are not in the selected index pattern.

Wrong approach:

Dragging a field from a different index pattern or a field that does not exist in the current data view.

Correct approach:

Ensure you select the correct index pattern in Lens before dragging fields that exist in that pattern.

Root cause:

Confusing index patterns or not understanding that Lens works only with the selected data view.

#2 Applying filters incorrectly by typing invalid syntax.

Wrong approach:

Using filter syntax like 'status == "active"' instead of the correct Kibana filter format.

Correct approach:

Use the filter bar UI to add filters or write filters in Kibana's supported syntax, e.g., 'status:active'.

Root cause:

Misunderstanding how Kibana and Lens expect filter expressions.

#3 Expecting Lens to update visualizations instantly with very large datasets.

Wrong approach:

Building complex Lens visuals on huge indexes without considering performance.

Correct approach:

Use filters to limit data size or pre-aggregate data before visualizing in Lens.

Root cause:

Not realizing that Lens queries Elasticsearch live and large data can slow response.

Key Takeaways

Lens is a drag-and-drop visual tool in Kibana that makes Elasticsearch data analysis easy and code-free.

It automatically builds queries based on your field selections and shows results as charts or tables.

Understanding your data structure and field types helps you create meaningful visualizations.

Filters and breakdowns let you focus on specific data slices and categories for deeper insights.

Lens is powerful for quick exploration but has limits when complex queries or large datasets require tuning.

Practice

1.

What is the main purpose of Lens in Elasticsearch?

easy

A. To write complex Elasticsearch queries manually

B. To manage Elasticsearch cluster settings

C. To analyze data visually by dragging and dropping fields

D. To monitor server hardware performance

Solution

1. Step 1: Understand Lens functionality

   Lens provides a visual interface to analyze data without coding.

2. Step 2: Compare options with Lens features

   Only To analyze data visually by dragging and dropping fields describes visual drag-and-drop analysis, matching Lens's purpose.

3. Final Answer:

   To analyze data visually by dragging and dropping fields -> Option C

4. Quick Check:

   Lens = Visual drag-and-drop analysis [OK]

Hint: Lens is for visual data analysis, not manual coding [OK]

Common Mistakes:

• Thinking Lens requires writing queries
• Confusing Lens with cluster management tools
• Assuming Lens monitors hardware

2.

Which of the following is the correct way to add a field to a Lens visualization?

Drag the field from the left panel and _______

easy

A. drop it onto the visualization workspace

B. double-click the field name in the index pattern

C. type the field name in the search bar

D. right-click and select 'Add to Lens'

Solution

1. Step 1: Recall Lens drag-and-drop method

   Lens uses drag-and-drop to add fields to the visualization workspace.

2. Step 2: Evaluate options for adding fields

   Only drop it onto the visualization workspace describes dragging and dropping onto the workspace, matching Lens usage.

3. Final Answer:

   drop it onto the visualization workspace -> Option A

4. Quick Check:

   Drag field + drop on workspace = Add field [OK]

Hint: Drag fields directly onto the workspace to add [OK]

Common Mistakes:

• Trying to add fields by typing names
• Using double-click instead of drag-and-drop
• Looking for right-click menu options

3.

Given a Lens visualization with a date histogram on the x-axis and a count metric, what will happen if you drag a status.keyword field to the 'Break down by' area?

medium

A. The chart will show only the total count without breakdown

B. The chart will split counts by each unique status value

C. The chart will display an error and not render

D. The date histogram will be removed automatically

Solution

1. Step 1: Understand 'Break down by' in Lens

   Dragging a field to 'Break down by' splits the chart by unique values of that field.

2. Step 2: Apply to status.keyword field

   The chart will show counts split by each unique status value over time.

3. Final Answer:

   The chart will split counts by each unique status value -> Option B

4. Quick Check:

   Break down by field = split chart by field values [OK]

Hint: 'Break down by' splits chart by unique field values [OK]

Common Mistakes:

• Expecting no change in chart
• Thinking the date histogram is removed
• Assuming an error occurs

4.

In Lens, you try to drag a numeric field to the 'Y-axis' but the chart does not update. What is the most likely cause?

medium

A. The field is not mapped as a numeric type in the index pattern

B. You need to refresh the browser to see changes

C. Lens only supports string fields on the Y-axis

D. The drag-and-drop feature is disabled in settings

Solution

1. Step 1: Check field type requirements for Y-axis

   Y-axis requires numeric fields to aggregate values like count or sum.

2. Step 2: Identify cause of no update

   If the field is not numeric in the index pattern, Lens cannot use it on Y-axis, so chart won't update.

3. Final Answer:

   The field is not mapped as a numeric type in the index pattern -> Option A

4. Quick Check:

   Y-axis needs numeric field type [OK]

Hint: Y-axis fields must be numeric type in index pattern [OK]

Common Mistakes:

• Assuming browser refresh fixes it
• Thinking Lens supports strings on Y-axis
• Believing drag-and-drop can be disabled

5.

You want to create a Lens visualization showing average response time per user, but only for users with more than 10 requests. How can you achieve this using Lens drag-and-drop features?

hard

A. Use Lens to write a custom query filtering users with more than 10 requests, then drag fields normally

B. Drag 'user.keyword' to 'X-axis', 'requests' to 'Y-axis' with 'Sum' aggregation, then filter 'response_time < 10'

C. Drag 'response_time' to 'Break down by', 'user.keyword' to 'Y-axis' with 'Count' aggregation, no filters needed

D. Drag 'user.keyword' to 'Break down by', 'response_time' to 'Y-axis' with 'Average' aggregation, then add a filter 'requests > 10' to the visualization

Solution

1. Step 1: Set up breakdown and metric

   Drag 'user.keyword' to 'Break down by' to split by user, and 'response_time' to 'Y-axis' with 'Average' aggregation to get average response time.

2. Step 2: Apply filter for requests count

   Add a filter 'requests > 10' to include only users with more than 10 requests.

3. Final Answer:

   Drag 'user.keyword' to 'Break down by', 'response_time' to 'Y-axis' with 'Average' aggregation, then add a filter 'requests > 10' to the visualization -> Option D

4. Quick Check:

   Breakdown + average metric + filter = correct Lens setup [OK]

Hint: Use filters plus breakdown and aggregation to refine Lens charts [OK]

Common Mistakes:

• Filtering wrong field or with wrong condition
• Mixing up X-axis and Break down by roles
• Trying to write queries instead of using filters