- elasticsearch-definitive-guide-en
- Introduction
- 1. Getting started
- 2. Distributed Cluster
- 3. Data In Data Out
- 4. Distributed CRUD
- 5. Search
- 6. Mapping Analysis
- 7. Query DSL
- 8. 056_Sorting
- 9. Distributed Search
-
10.
070_Index_Mgmt
- 10.1. 05_Create_Delete
- 10.2. 10_Settings
- 10.3. 15_Configure_Analyzer
- 10.4. 20_Custom_Analyzers
- 10.5. 25_Mappings
- 10.6. 30_Root_Object
- 10.7. 31_Metadata_source
- 10.8. 32_Metadata_all
- 10.9. 33_Metadata_ID
- 10.10. 35_Dynamic_Mapping
- 10.11. 40_Custom_Dynamic_Mapping
- 10.12. 45_Default_Mapping
- 10.13. 50_Reindexing
- 10.14. 55_Aliases
-
11.
075_Inside_a_shard
- 11.1. 20_Making_text_searchable
- 11.2. 30_dynamic_indices
- 11.3. 40_Near_real_time
- 11.4. 50_Persistent_changes
- 11.5. 60_Segment_merging
-
12.
080_Structured_Search
- 12.1. 05_term
- 12.2. 10_compoundfilters
- 12.3. 15_terms
- 12.4. 20_contains
- 12.5. 25_ranges
- 12.6. 30_existsmissing
- 12.7. 40_bitsets
- 12.8. 45_filter_order
Scale Horizontally
What about scaling as the demand for our application grows? If we start a third node, our cluster reorganizes itself to look like cluster-three-nodes.
A three-node cluster--shards have been reallocated to spread the load
Figure 2.5.1. A three-node cluster--shards have been reallocated to spread the load
One shard each from Node 1 and Node 2 have moved to the new
Node 3, and we have two shards per node, instead of three.
This means that the hardware resources (CPU, RAM, I/O) of each node
are being shared among fewer shards, allowing each shard to perform
better.
A shard is a fully fledged search engine in its own right, and is capable of using all of the resources of a single node. With our total of six shards (three primaries and three replicas), our index is capable of scaling out to a maximum of six nodes, with one shard on each node and each shard having access to 100% of its node's resources.