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Sorting by a Metricedit
Often, you’ll find yourself wanting to sort based on a metric’s calculated value. For our car sales analytics dashboard, we may want to build a bar chart of sales by car color, but order the bars by the average price, ascending.
We can do this by adding a metric to our bucket, and then referencing that
metric from the order
parameter:
GET /cars/transactions/_search { "size" : 0, "aggs" : { "colors" : { "terms" : { "field" : "color", "order": { "avg_price" : "asc" } }, "aggs": { "avg_price": { "avg": {"field": "price"} } } } } }
The average price is calculated for each bucket. |
|
Then the buckets are ordered by the calculated average in ascending order. |
This lets you override the sort order with any metric, simply by referencing
the name of the metric. Some metrics, however, emit multiple values. The
extended_stats
metric is a good example: it provides half a dozen individual
metrics.
If you want to sort on a multivalue metric, you just need to use the dot-path to the metric of interest:
GET /cars/transactions/_search { "size" : 0, "aggs" : { "colors" : { "terms" : { "field" : "color", "order": { "stats.variance" : "asc" } }, "aggs": { "stats": { "extended_stats": {"field": "price"} } } } } }
In this example we are sorting on the variance of each bucket, so that colors with the least variance in price will appear before those that have more variance.
- 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