- 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: 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This documentation is no longer maintained and may be removed.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.
Fetch Phase
editFetch Phase
editThe query phase identifies which documents satisfy the search request, but we still need to retrieve the documents themselves. This is the job of the fetch phase, shown in Figure 15, “Fetch phase of distributed search”.
The distributed phase consists of the following steps:
-
The coordinating node identifies which documents need to be fetched and
issues a multi
GET
request to the relevant shards. - Each shard loads the documents and enriches them, if required, and then returns the documents to the coordinating node.
- Once all documents have been fetched, the coordinating node returns the results to the client.
The coordinating node first decides which documents actually need to be
fetched. For instance, if our query specified { "from": 90, "size": 10 }
,
the first 90 results would be discarded and only the next 10 results would
need to be retrieved. These documents may come from one, some, or all of the
shards involved in the original search request.
The coordinating node builds a multi-get request for each shard that holds a pertinent document and sends the request to the same shard copy that handled the query phase.
The shard loads the document bodies—the _source
field—and, if
requested, enriches the results with metadata and
search snippet highlighting.
Once the coordinating node receives all results, it assembles them into a
single response that it returns to the client.
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