Discover Elasticsearch’s architecture and capabilities. Learn about indexing and storing data, conducting precise queries, including fuzzy searches, and executing real-time data analysis effectively.

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This course explores Elasticsearch, an open-source, Java-based, full-text search and analytics engine that harnesses the power of the Lucene library. You’ll discover its capabilities as a versatile search engine for business data, offering the ability to store information, conduct searches on accurate and typo-ridden text, and perform real-time data analysis on vast datasets.

Throughout this course, you will gain comprehensive insights into the architecture of Elasticsearch and master the art of indexing and storing data within the Elasticsearch framework. You will progress to learn how to execute various types of queries, including fuzzy queries, and explore the intricacies of each operation and how it is implemented within Elasticsearch.

After completing the course, you’ll have gained a comprehensive understanding of the internal mechanisms of Elasticsearch and developed the skills to effectively use it for your data management and analysis requirements.

Elasticsearch Fundamentals: Indexing and Querying Data

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# Aggregation 1: Average lifetime gross by studio

To solve this challenge, we can use the `terms` aggregation on the `studio` attribute combined with the `avg` aggregation `lifetime_gross`. 


To implement this aggregation, we will initiate a `_search` request targeting the `movie` index, and within the request body, we will define the aggregation structure. Initially, we will specify `aggs` as `terms` and designate the `field` as `studio`. Nested within this aggregation, we will include another `aggs` section labeled as `avg`, where we will specify the `field` as `lifetime_gross`.


GET /movie/_search
{
  "size": 0,
  "aggs": {
    "average_lifetime_gross_by_studio": {
      "terms": {
        "field": "studio"
      },
      "aggs": {
        "avg_lifetime_gross": {
          "avg": {
            "field": "lifetime_gross"
          }
        }
      }
    }
  }
}

# Aggregation 2: Number of movies released each year

To solve this challenge, we can employ the `terms` aggregation on the `Year` field to count the number of movies released each year.

To implement this aggregation, we will initiate a `_search` request targeting the `movie` index. Within the request body, we will define the aggregation structure by specifying `aggs` as `terms` and designating the `field` as `Year`.

GET /movie/_search
{
  "size": 0,
  "aggs": {
    "movies_per_year": {
      "terms": {
        "field": "year"
      }
    }
  }
}

# Aggregation 3: Average budget vs. average lifetime gross by year

To address this challenge, we can utilize aggregation to calculate both the average budget and average lifetime gross for each year, subsequently determining the average budget to average lifetime ratio.

To implement this aggregation, we will initiate a `_search` request targeting the `movies` index. Within the request body, we will define the aggregation structure as follows:

1. We specify `aggs` as `terms` and designate the `field` as `year` to group movies by their release year.

2. Within each year bucket, we employ the `avg` aggregations to compute the average budget and average lifetime gross.

3. To calculate the `budget_lifetime_ratio`, representing the ratio of average budget to average lifetime gross, we utilize a `bucket_script` pipeline aggregation.

Review the solutions to the aggregation challenge.

Introduction to Elasticsearch

Getting started on Elasticsearch

Text Analysis

Search on ElasticSearch

Aggregation

Conclusion

Integrate Elasticsearch in the Ruby on Rails Application

Solution: Aggregation

Aggregation 1: Average lifetime gross by studio

Aggregation 2: Number of movies released each year