Elastic search mind mapping
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The document outlines the requirements and configuration for using Elasticsearch, detailing software and hardware prerequisites, including Linux/FreeBSD and the JVM. It also discusses data indexing, sharding, replication strategies, and the types of search queries available, emphasizing scalability and speed. Key use cases include analytics, fuzzy searching, and multi-tenancy, highlighting the system's efficiency in handling large volumes of data.
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Elastic search mind mapping
- 1. Elastic Search III.Requirements IV.Preparation & Configration I.Software II.Hardware The Linux (or FreeBSD) kernel CPU Memory Network Storage (JVM) The Java Virtual Machine The file system EXT4 XFS Generic Get Index Percolac Bulk Indices Sharding Replication Mapping Less 3M documents: 1 shard Between 3M and 5M documents with an expected frowth over 5m:2 More than 5M: int (number of expected documents / 5M +1) int( (primary + number_of_replicas) / 2 ) + 1 ClusterNode I.Why ? V.Search Types Basic Match Query Boosting Bool Query Fuzzy Queries Wildcard Query Regexp Query Match Phrase Query Match Phrase Prefix Query String Simple Query String Term/Terms Query Range Query Filtered Bool Query Scalability Speed Query Fine Tuning Data Types Plugins Text Numbers Dates string long integer short byte double date float II. Use case Analytics Fuzzy Searching Autocompletion and Instant Search Multi-Tenancy Schema Free / User-Defined Schemas User-Defined Searches Crawling and Document Processing Broadly Distributable and Highly Scalable Denormalized Document Storage: Fast, Direct access to your Data Fast, Incisive Search against Large Volumes of Data