- 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This documentation is no longer maintained and may be removed.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.
Multi-index, Multitype
editMulti-index, Multitype
editDid you notice that the results from the preceding empty search
contained documents of different types—user and tweet—from two
different indices—us and gb?
By not limiting our search to a particular index or type, we have searched across all documents in the cluster. Elasticsearch forwarded the search request in parallel to a primary or replica of every shard in the cluster, gathered the results to select the overall top 10, and returned them to us.
Usually, however, you will want to search within one or more specific indices, and probably one or more specific types. We can do this by specifying the index and type in the URL, as follows:
-
/_search - Search all types in all indices
-
/gb/_search -
Search all types in the
gbindex -
/gb,us/_search -
Search all types in the
gbandusindices -
/g*,u*/_search -
Search all types in any indices beginning with
gor beginning withu -
/gb/user/_search -
Search type
userin thegbindex -
/gb,us/user,tweet/_search -
Search types
userandtweetin thegbandusindices -
/_all/user,tweet/_search -
Search types
userandtweetin all indices
When you search within a single index, Elasticsearch forwards the search request to a primary or replica of every shard in that index, and then gathers the results from each shard. Searching within multiple indices works in exactly the same way—there are just more shards involved.
Searching one index that has five primary shards is exactly equivalent to searching five indices that have one primary shard each.
Later, you will see how this simple fact makes it easy to scale flexibly as your requirements change.