Why indexes organize data in Elasticsearch - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an index named 'products'.

Elasticsearch

PUT /[1]

Drag options to blanks, or click blank then click option'

Aproducts

Busers

Corders

Dcustomers

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong index name that does not match the data type.

Forgetting to specify the index name after PUT.

The index name 'products' is used to organize product data in Elasticsearch.

2fill in blank

medium

Complete the code to add a document with ID '1' to the 'products' index.

Elasticsearch

PUT /products/_doc/[1]
{
  "name": "Laptop",
  "price": 1200
}

Drag options to blanks, or click blank then click option'

A10

B100

C1

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using an ID that does not match the document intended.

Omitting the document ID in the URL.

The document ID '1' uniquely identifies this product in the 'products' index.

3fill in blank

hard

Fix the error in the query to search for products with price less than 1500.

Elasticsearch

{
  "query": {
    "range": {
      "price": { "[1]": 1500 }
    }
  }
}

Drag options to blanks, or click blank then click option'

Agt

Bgte

Clte

Dlt

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'gt' or 'gte' which would find prices greater than or equal to 1500.

Using 'lte' which includes 1500, not strictly less.

The 'lt' operator means 'less than', which matches products priced below 1500.

4fill in blank

hard

Fill the blank to create an index with a mapping that sets 'price' as a double type.

Elasticsearch

PUT /products
{
  "mappings": {
    "properties": {
      "price": { "type": "[1]" }
    }
  }
}

Drag options to blanks, or click blank then click option'

Atext

Bdouble

Ckeyword

Dinteger

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'integer' which does not support decimals.

Using 'text' or 'keyword' which are for strings.

The 'double' type is used for decimal numbers like prices.

5fill in blank

hard

Fill all three blanks to write a query that finds products with name 'Laptop' and price greater than 1000.

Elasticsearch

{
  "query": {
    "bool": {
      "must": [
        { "match": { "name": "[1]" } },
        { "range": { "price": { "[2]": [3] } } }
      ]
    }
  }
}

Drag options to blanks, or click blank then click option'

ALaptop

Bgt

C1000

Dlt

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'lt' instead of 'gt' for price comparison.

Putting the wrong value for the name or price.

This query matches documents where the name is 'Laptop' and the price is greater than 1000.

Practice

1. Why do Elasticsearch indexes organize data?

easy

A. To slow down data retrieval

B. To store data in random order

C. To make searching fast and efficient

D. To delete data automatically

Solution

1. Step 1: Understand the purpose of indexes

   Indexes are designed to arrange data so that searching can be done quickly and easily.

2. Step 2: Compare options with the purpose

   Only To make searching fast and efficient matches the purpose of indexes; others describe incorrect behaviors.

3. Final Answer:

   To make searching fast and efficient -> Option C

4. Quick Check:

   Indexes speed up search = A [OK]

Hint: Indexes speed up search by organizing data [OK]

Common Mistakes:

• Thinking indexes slow down search
• Believing indexes store data randomly
• Assuming indexes delete data automatically

2. Which of the following is the correct way to create an index in Elasticsearch?

easy

A. PUT /my_index

B. GET /my_index

C. DELETE /my_index

D. POST /my_index/_search

Solution

1. Step 1: Identify the HTTP method for creating an index

   Creating an index uses the PUT method with the index name.

2. Step 2: Match the correct syntax

   PUT /my_index is the correct syntax; others perform different actions.

3. Final Answer:

   PUT /my_index -> Option A

4. Quick Check:

   PUT creates index = D [OK]

Hint: Use PUT to create an index in Elasticsearch [OK]

Common Mistakes:

• Using GET to create an index
• Confusing DELETE with creation
• Using POST for index creation instead of search

3. Given this Elasticsearch query on an index, what does it do?

{"query": {"match": {"title": "Elasticsearch"}}}

medium

A. Updates documents with title 'Elasticsearch'

B. Finds documents where the title contains 'Elasticsearch'

C. Deletes documents with title 'Elasticsearch'

D. Creates a new document with title 'Elasticsearch'

Solution

1. Step 1: Understand the query type

   The query uses 'match' which searches for documents matching the given text in the specified field.

2. Step 2: Interpret the query action

   This query finds documents where the 'title' field contains the word 'Elasticsearch'.

3. Final Answer:

   Finds documents where the title contains 'Elasticsearch' -> Option B

4. Quick Check:

   Match query searches text = C [OK]

Hint: Match queries find documents containing the text [OK]

Common Mistakes:

• Thinking match deletes or updates documents
• Confusing match with document creation
• Assuming match returns all documents

4. You tried to create an index with this command:

POST /my_index

Why might this cause an error?

medium

A. The command should be GET instead

B. The index name is missing

C. POST is only for searching

D. POST is not used to create indexes; PUT is required

Solution

1. Step 1: Recall correct HTTP method for index creation

   Creating an index requires the PUT method, not POST.

2. Step 2: Analyze the command used

   Using POST /my_index tries to create but is invalid syntax, causing an error.

3. Final Answer:

   POST is not used to create indexes; PUT is required -> Option D

4. Quick Check:

   PUT creates index, POST does not = A [OK]

Hint: Use PUT, not POST, to create indexes [OK]

Common Mistakes:

• Using POST instead of PUT for index creation
• Thinking POST is for creating indexes
• Confusing GET with creation commands

5. You want to speed up searches on a text field by controlling how data is saved. Which Elasticsearch feature helps organize data for this purpose?

hard

A. Mappings to define field types and analyzers

B. Deleting old indexes regularly

C. Using GET requests to fetch data

D. Adding more shards without mappings

Solution

1. Step 1: Understand how data organization affects search speed

   Settings and mappings control how data is stored and indexed, affecting search performance.

2. Step 2: Identify the feature that controls field types and analyzers

   Mappings define field types and analyzers, organizing data for efficient searching.

3. Final Answer:

   Mappings to define field types and analyzers -> Option A

4. Quick Check:

   Mappings organize data for fast search = B [OK]

Hint: Use mappings to organize data for faster search [OK]

Common Mistakes:

• Thinking deleting indexes speeds up search
• Confusing GET requests with data organization
• Believing shards alone improve search without mappings