- Elasticsearch - The Definitive Guide:
- Foreword
- Preface
- Getting Started
- You Know, for Search…
- Installing and Running Elasticsearch
- Talking to Elasticsearch
- Document Oriented
- Finding Your Feet
- Indexing Employee Documents
- Retrieving a Document
- Search Lite
- Search with Query DSL
- More-Complicated Searches
- Full-Text Search
- Phrase Search
- Highlighting Our Searches
- Analytics
- Tutorial Conclusion
- Distributed Nature
- Next Steps
- Life Inside a Cluster
- Data In, Data Out
- What Is a Document?
- Document Metadata
- Indexing a Document
- Retrieving a Document
- Checking Whether a Document Exists
- Updating a Whole Document
- Creating a New Document
- Deleting a Document
- Dealing with Conflicts
- Optimistic Concurrency Control
- Partial Updates to Documents
- Retrieving Multiple Documents
- Cheaper in Bulk
- Distributed Document Store
- Searching—The Basic Tools
- Mapping and Analysis
- Full-Body Search
- Sorting and Relevance
- Distributed Search Execution
- Index Management
- Inside a Shard
- You Know, for Search…
- Search in Depth
- Structured Search
- Full-Text Search
- Multifield Search
- Proximity Matching
- Partial Matching
- Controlling Relevance
- Theory Behind Relevance Scoring
- Lucene’s Practical Scoring Function
- Query-Time Boosting
- Manipulating Relevance with Query Structure
- Not Quite Not
- Ignoring TF/IDF
- function_score Query
- Boosting by Popularity
- Boosting Filtered Subsets
- Random Scoring
- The Closer, The Better
- Understanding the price Clause
- Scoring with Scripts
- Pluggable Similarity Algorithms
- Changing Similarities
- Relevance Tuning Is the Last 10%
- Dealing with Human Language
- Aggregations
- Geolocation
- Modeling Your Data
- Administration, Monitoring, and Deployment
WARNING: This documentation covers Elasticsearch 2.x. The 2.x versions of Elasticsearch have passed their EOL dates. If you are running a 2.x version, we strongly advise you to upgrade.
This documentation is no longer maintained and may be removed. For the latest information, see the current Elasticsearch documentation.
Adding a Metric to the Mix
editAdding a Metric to the Mix
editThe previous example told us the number of documents in each bucket, which is useful. But often, our applications require more-sophisticated metrics about the documents. For example, what is the average price of cars in each bucket?
To get this information, we need to tell Elasticsearch which metrics to calculate, and on which fields. This requires nesting metrics inside the buckets. Metrics will calculate mathematical statistics based on the values of documents within a bucket.
Let’s go ahead and add an average
metric to our car example:
GET /cars/transactions/_search { "size" : 0, "aggs": { "colors": { "terms": { "field": "color.keyword" }, "aggs": { "avg_price": { "avg": { "field": "price" } } } } } }
We add a new |
|
We then give the metric a name: |
|
And finally, we define it as an |
As you can see, we took the previous example and tacked on a new aggs
level.
This new aggregation level allows us to nest the avg
metric inside the
terms
bucket. Effectively, this means we will generate an average for each
color.
Just like the colors
example, we need to name our metric (avg_price
) so we
can retrieve the values later. Finally, we specify the metric itself (avg
)
and what field we want the average to be calculated on (price
):
{ ... "aggregations": { "colors": { ... "buckets": [ { "key": "red", "doc_count": 4, "avg_price": { "value": 32500 } }, { "key": "blue", "doc_count": 2, "avg_price": { "value": 20000 } }, { "key": "green", "doc_count": 2, "avg_price": { "value": 21000 } } ] } } ... }
Although the response has changed minimally, the data we get out of it has grown substantially. Before, we knew there were four red cars. Now we know that the average price of red cars is $32,500. This is something that you can plug directly into reports or graphs.