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Elasticsearchquery~10 mins

Discover for data exploration in Elasticsearch - Interactive Code Practice

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Test this pattern10 questions across easy, medium, and hard to know if this pattern is strong
Practice - 5 Tasks
Answer the questions below
1fill in blank
easy

Complete the code to search all documents in the index.

Elasticsearch
GET /my-index/_search
{
  "[1]": {
    "match_all": {}
  }
}
Drag options to blanks, or click blank then click option'
Asort
Bfilter
Cquery
Daggs
Attempts:
3 left
💡 Hint
Common Mistakes
Using filter instead of query causes no results.
Using sort or aggs in place of query causes errors.
2fill in blank
medium

Complete the code to return only the first 5 documents.

Elasticsearch
GET /my-index/_search
{
  "size": [1],
  "query": { "match_all": {} }
}
Drag options to blanks, or click blank then click option'
A10
B5
C0
D50
Attempts:
3 left
💡 Hint
Common Mistakes
Setting size to 0 returns no documents.
Using a large number like 50 returns more than needed.
3fill in blank
hard

Fix the error in the query to filter documents where the field 'status' is 'active'.

Elasticsearch
GET /my-index/_search
{
  "query": {
    "term": { "[1]": "active" }
  }
}
Drag options to blanks, or click blank then click option'
Astatus
Bterm
Cactive
Dstate
Attempts:
3 left
💡 Hint
Common Mistakes
Using the value 'active' as the key causes errors.
Using a wrong field name like 'state' returns no results.
4fill in blank
hard

Fill both blanks to sort documents by 'timestamp' in descending order.

Elasticsearch
GET /my-index/_search
{
  "sort": [
    { "[1]": { "order": "[2]" } }
  ]
}
Drag options to blanks, or click blank then click option'
Atimestamp
Bdesc
Casc
Ddate
Attempts:
3 left
💡 Hint
Common Mistakes
Using date instead of timestamp if the field is named differently.
Using asc instead of desc to get newest first.
5fill in blank
hard

Fill all three blanks to aggregate the average 'price' of products where 'category' is 'books'.

Elasticsearch
GET /my-index/_search
{
  "query": {
    "term": { "[1]": "books" }
  },
  "aggs": {
    "average_price": { "avg": { "field": "[2]" } }
  },
  "size": [3]
}
Drag options to blanks, or click blank then click option'
Acategory
Bprice
C0
D5
Attempts:
3 left
💡 Hint
Common Mistakes
Setting size to a positive number shows documents instead of only aggregation.
Using wrong field names for filtering or aggregation.

Practice

(1/5)
1. What is the main purpose of the Discover feature in Elasticsearch?
easy
A. To explore and filter raw data in indexes
B. To create visual dashboards
C. To manage Elasticsearch cluster settings
D. To write complex aggregation queries

Solution

  1. Step 1: Understand Discover's role

    Discover is designed to let users explore raw data quickly and easily.
  2. Step 2: Compare with other features

    Dashboard creation and cluster management are separate features, not Discover's focus.
  3. Final Answer:

    To explore and filter raw data in indexes -> Option A
  4. Quick Check:

    Discover = Data exploration [OK]
Hint: Discover = explore raw data quickly [OK]
Common Mistakes:
  • Confusing Discover with Dashboard
  • Thinking Discover manages cluster settings
  • Assuming Discover creates complex queries
2. Which of the following is the correct syntax to filter data in Discover using a simple query?
easy
A. filter(status=200, extension=jpg)
B. WHERE status=200 AND extension=jpg
C. status:200 AND extension:jpg
D. SELECT * FROM index WHERE status=200

Solution

  1. Step 1: Identify Discover query syntax

    Discover uses Lucene query syntax like field:value and logical operators like AND.
  2. Step 2: Eliminate SQL and function syntax

    Options A, C, and D use SQL or function style, which is not valid in Discover queries.
  3. Final Answer:

    status:200 AND extension:jpg -> Option C
  4. Quick Check:

    Lucene syntax = status:200 AND extension:jpg [OK]
Hint: Use field:value with AND/OR in Discover queries [OK]
Common Mistakes:
  • Using SQL syntax instead of Lucene
  • Using function calls for filtering
  • Mixing query languages
3. Given the following Discover query: response:404 OR response:500, what data will be shown?
medium
A. All documents except those with response 404 or 500
B. Only documents with response code 404
C. Documents with response code 404 and 500 at the same time
D. Documents with response code 404 or 500

Solution

  1. Step 1: Understand OR operator in query

    The OR operator returns documents matching either condition, not both simultaneously.
  2. Step 2: Apply to response codes

    Documents with response 404 or response 500 will be included in results.
  3. Final Answer:

    Documents with response code 404 or 500 -> Option D
  4. Quick Check:

    OR means either condition matches [OK]
Hint: OR returns either condition matches [OK]
Common Mistakes:
  • Thinking OR means both conditions together
  • Confusing OR with AND
  • Assuming exclusion of matching documents
4. You wrote this Discover query: status:200 AND extension=jpg. Why does it cause an error?
medium
A. Because '=' is not valid; use ':' for field-value pairs
B. Because AND cannot be used between conditions
C. Because 'status' is not a valid field name
D. Because 'jpg' should be in quotes

Solution

  1. Step 1: Check field-value syntax

    Discover uses field:value syntax, not field=value.
  2. Step 2: Validate operators and values

    AND is valid, 'status' is a common field, and quotes are optional for simple values.
  3. Final Answer:

    Because '=' is not valid; use ':' for field-value pairs -> Option A
  4. Quick Check:

    Use ':' not '=' in queries [OK]
Hint: Use colon ':' for field-value, not equals '=' [OK]
Common Mistakes:
  • Using '=' instead of ':'
  • Misunderstanding AND operator usage
  • Adding unnecessary quotes
5. You want to explore documents where the field user exists and the bytes field is greater than 1000. Which Discover query achieves this?
hard
A. _exists_:user AND bytes >1000
B. _exists_:user AND bytes:{1000 TO *}
C. _exists_:user AND bytes:>=1000
D. user:* AND bytes:>1000

Solution

  1. Step 1: Check existence syntax

    Use _exists_:user to find documents where 'user' field exists.
  2. Step 2: Use range query for bytes > 1000

    Range syntax bytes:{1000 TO *} means bytes greater than 1000 (exclusive).
  3. Step 3: Verify other options

    _exists_:user AND bytes:>1000 and C have invalid range syntax; user:* AND bytes:>1000 uses wildcard incorrectly for existence.
  4. Final Answer:

    _exists_:user AND bytes:{1000 TO *} -> Option B
  5. Quick Check:

    Existence + range query = _exists_:user AND bytes:{1000 TO *} [OK]
Hint: Use _exists_ for field and range syntax for > value [OK]
Common Mistakes:
  • Using wildcard * for existence check
  • Incorrect range syntax for greater than
  • Confusing inclusive and exclusive ranges