Percolate queries (reverse search) in Elasticsearch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a percolator field in the mapping.

Elasticsearch

{
  "mappings": {
    "properties": {
      "query": {
        "type": "[1]"
      }
    }
  }
}

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

Apercolator

Btext

Ckeyword

Dnested

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'text' or 'keyword' instead of 'percolator' for the query field type.

The percolator field type is used to store queries for reverse search in Elasticsearch.

2fill in blank

medium

Complete the code to register a percolate query document.

Elasticsearch

{
  "query": {
    "match": {
      "message": "[1]"
    }
  }
}

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

Aerror

Bdebug

Cinfo

Dwarning

Attempts:

3 left

💡 Hint

Common Mistakes

Using a log level not matching the example or the intended query.

The example query matches documents where the 'message' field contains the word 'warning'.

3fill in blank

hard

Fix the error in the percolate query to match documents with 'error' in the 'message' field.

Elasticsearch

{
  "query": {
    "percolate": {
      "field": "query",
      "document": {
        "message": "[1]"
      }
    }
  }
}

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

Awarn

Berror

Cinfo

Ddebug

Attempts:

3 left

💡 Hint

Common Mistakes

Using a different log level word that does not match registered queries.

The document field 'message' should contain 'error' to match the percolate queries registered for that term.

4fill in blank

hard

Fill both blanks to create a percolate query that matches documents with 'timeout' in the 'message' field and uses the correct percolator field.

Elasticsearch

{
  "query": {
    "percolate": {
      "field": "[1]",
      "document": {
        "message": "[2]"
      }
    }
  }
}

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

Aquery

Bmessage

Ctimeout

Derror

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'message' as the percolator field name instead of 'query'.

Using the wrong keyword in the document message.

The percolate field is named 'query', and the document message contains 'timeout' to match relevant queries.

5fill in blank

hard

Fill all three blanks to create a percolate query that matches documents with 'failure' in the 'message' field, using the correct percolator field and query type.

Elasticsearch

{
  "query": {
    "[1]": {
      "field": "[2]",
      "document": {
        "message": "[3]"
      }
    }
  }
}

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

Apercolate

Bquery

Cfailure

Dmatch

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'match' instead of 'percolate' as the query type.

Using the wrong field name for the percolator.

Using a different keyword than 'failure' in the document message.

The query type is 'percolate', the field is 'query', and the document message contains 'failure' to match registered queries.

Practice

1.

What is the main purpose of a percolate query in Elasticsearch?

easy

A. To find stored queries that match a new document

B. To update documents in an index

C. To delete documents based on a condition

D. To aggregate data by terms

Solution

1. Step 1: Understand percolate query concept

   A percolate query is used to find stored queries that match a new document, reversing the usual search direction.

2. Step 2: Compare options with concept

   The other options describe other Elasticsearch operations, not percolate queries.

3. Final Answer:

   To find stored queries that match a new document -> Option A

4. Quick Check:

   Percolate query = find matching stored queries [OK]

Hint: Percolate queries match queries to documents, not documents to queries [OK]

Common Mistakes:

• Confusing percolate query with regular search
• Thinking it updates or deletes documents
• Mixing it with aggregation queries

2.

Which mapping type must be included in an Elasticsearch index to use percolate queries?

{
  "mappings": {
    "properties": {
      "query": {
        "type": "???"
      }
    }
  }
}

easy

A. "percolator"

B. "text"

C. "keyword"

D. "nested"

Solution

1. Step 1: Identify required field type for percolate queries

   Elasticsearch requires a special field type called "percolator" to store queries for percolate queries.

2. Step 2: Match options with required type

   Only "percolator" uses "percolator" type; others are for different purposes.

3. Final Answer:

   "percolator" -> Option A

4. Quick Check:

   Percolate field type = "percolator" [OK]

Hint: Use "percolator" type for storing queries in mapping [OK]

Common Mistakes:

• Using "text" or "keyword" instead of "percolator"
• Confusing nested type with percolator
• Omitting the percolator field in mapping

3.

Given the following percolate query, what will it return?

{
  "query": {
    "percolate": {
      "field": "query",
      "document": {
        "message": "Elasticsearch alerting"
      }
    }
  }
}

Assuming the index has stored queries matching documents containing "alerting".

medium

A. Documents containing the word "alerting"

B. An error because "document" is missing an ID

C. All documents in the index

D. Stored queries that match the document with message "Elasticsearch alerting"

Solution

1. Step 1: Understand percolate query behavior

   The percolate query matches stored queries against the provided document, returning matching stored queries.

2. Step 2: Analyze the given query

   The query uses "document" with a message field; it will find stored queries matching this document's content.

3. Final Answer:

   Stored queries that match the document with message "Elasticsearch alerting" -> Option D

4. Quick Check:

   Percolate query returns matching stored queries [OK]

Hint: Percolate queries return stored queries matching the input document [OK]

Common Mistakes:

• Thinking it returns documents instead of queries
• Assuming document ID is required for percolate query
• Confusing percolate with regular search

4.

Identify the error in this percolate query:

{
  "query": {
    "percolate": {
      "field": "query"
      "document": {
        "content": "Test document"
      }
    }
  }
}

medium

A. "field" should be "query_field"

B. Missing comma between "field" and "document" fields

C. "document" must include an "id" field

D. Percolate query cannot use 'content' field in document

Solution

1. Step 1: Check JSON syntax in query

   Between "field" and "document" keys, a comma is missing, causing invalid JSON.

2. Step 2: Validate other parts

   "field" name is correct, "document" can omit "id", and "content" is valid as document content.

3. Final Answer:

   Missing comma between "field" and "document" fields -> Option B

4. Quick Check:

   JSON syntax error = missing comma [OK]

Hint: Check commas between JSON fields carefully [OK]

Common Mistakes:

• Forgetting commas between JSON keys
• Assuming document must have an ID
• Changing field names unnecessarily

5.

You want to build an alert system that triggers when new documents match any stored queries. Which steps are necessary to implement this using percolate queries?

hard

A. Use aggregation queries on documents to find alerts

B. Store documents in a normal index, then run a regular search for alerts

C. Create an index with a percolator field, store queries, then percolate new documents against stored queries

D. Create a nested field for queries and filter documents manually

Solution

1. Step 1: Setup index with percolator field

   Define an index mapping with a "percolator" type field to store queries for reverse matching.

2. Step 2: Store queries and percolate new documents

   Index the alert queries into the percolator field, then use percolate queries to check if new documents match any stored queries.

3. Final Answer:

   Create an index with a percolator field, store queries, then percolate new documents against stored queries -> Option C

4. Quick Check:

   Percolate queries enable alerting by matching docs to stored queries [OK]

Hint: Store queries in percolator field, then percolate new docs [OK]

Common Mistakes:

• Using regular search instead of percolate queries
• Not defining percolator field in mapping
• Trying to use aggregations for alerting