Why indexes organize data in Elasticsearch - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this Elasticsearch query?

Given an Elasticsearch index named products with documents containing fields name and price, what will this query return?

{
  "query": {
    "range": {
      "price": {
        "gte": 100,
        "lte": 200
      }
    }
  }
}

A All documents regardless of price

B Documents with price less than 100

C Documents with price greater than 200

D Documents with price between 100 and 200 inclusive

💡 Hint

Look at the range query and the meaning of gte and lte.

The range query filters documents where the price field is greater than or equal to 100 and less than or equal to 200.

🧠 Conceptual

intermediate

Why does Elasticsearch use indexes to organize data?

Choose the best explanation for why Elasticsearch organizes data into indexes.

A To prevent any data from being deleted accidentally

B To store data in a single large file for easy backup

C To speed up search by allowing quick lookups on specific fields

D To make data unreadable for security reasons

💡 Hint

Think about how indexes help find data fast.

Indexes in Elasticsearch organize data so searches can quickly find matching documents without scanning everything.

🔧 Debug

advanced

What error does this Elasticsearch mapping cause?

Given this mapping snippet, what error will Elasticsearch raise?

{
  "mappings": {
    "properties": {
      "price": {
        "type": "text"
      }
    }
  }
}

Assuming price should be a number.

A Search on price range queries will fail because price is text, not numeric

B Elasticsearch will accept the mapping without error

C Elasticsearch will throw a syntax error on mapping creation

D Documents with price field will be rejected on indexing

💡 Hint

Consider the data type mismatch impact on queries.

Mapping price as text means numeric range queries on price will fail or behave incorrectly.

📝 Syntax

advanced

Which option correctly creates an index with a keyword field?

Choose the correct JSON to create an Elasticsearch index with a category field of type keyword.

A { "mappings": { "properties": { "category": { "type": "keyword" } } } }

B { "mappings": { "properties": { "category": "keyword" } } }

C { "mappings": { "fields": { "category": { "type": "keyword" } } } }

D { "mapping": { "category": { "type": "keyword" } } }

💡 Hint

Check the correct key names for mappings and properties.

The correct structure uses mappings with properties containing the field name and its type.

🚀 Application

expert

How many shards will an index have after this creation command?

You create an Elasticsearch index with this command:

PUT /my_index
{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 2
  }
}

How many primary shards and total shards will this index have?

A 5 primary shards and 10 total shards

B 3 primary shards and 9 total shards

C 3 primary shards and 6 total shards

D 1 primary shard and 3 total shards

💡 Hint

Total shards = primary shards + (primary shards × replicas).

With 3 primary shards and 2 replicas each, total shards = 3 + (3×2) = 9 shards. But replicas are copies, so total shards = 3 primary + 6 replicas = 9 total shards.