Elasticsearch (ES) is a JSON-based, distributed, RESTful search engine based on the Apache Lucene library.

Here is a diagram that illustrates the architectural relationship among units: index > shard > segment

es architectural units

Terms
  • document: the basic unit of data in ES
    • You say, “Index a document” when inserting data into ES.
  • index: logical namespace of collection of documents
  • shard: horizontal split of an index; a self-contained Lucene index in itself
Inverted Index
  • created by tokenizing the terms in each document
  • creates a sorted list of all unique terms (terms are normalized, stemmed, etc)
  • associated list of documents where the word can be found
Cluster
  • essentially a group of computers which can be logically seen as a unit, trying to accomplish a particular task
  • a cluster consists of 4 nodes, of primary and replica shards.
  • a copy (replica) exists for robustness and fault tolerance in case a primary shard is lost
Node Types
  • master node: cluster-wide operations, coordination
  • data node: hold data and index
  • client node: load balancer (neither data nor master nodes)
3 Ways to connect to ES Cluster
  • Kibana Console, GET _alias
  • cURL, RESTful requests, curl -XGET "http://localhost:9200/_alias?pretty"
  • Client (python, golang, java, etc)
Basic Operations in ES
  1. Write operations in ES
  • For ES, the basic unit of storage is a shard
  • For Lucene, the basic unit of storage is a segment
  • Each segment is an inverted index
  • New docs are added to new segment
  • Segments are in memory and data is later persisted to disk

  • Translog (transaction log)
    • A lucene commit is a relatively resource-expensive operation
    • Since lucene commits are expensive, each shard copy writes operations into its translog before they are acknowledged
    • request written to translog; document added to memory buffer


  1. Refresh operation
  • the _refresh operation is set to be executed every second by default
  • during this operation, the in-memory buffer contents is copied to a newly created segment in the memory
  • as a result, new data becomes available for search


  1. Flush operation
  • all the documents in the in-memory buffer are written to new Lucene segments, they are combined, then committed to the disk, which clears the translog


  1. Delete operation

    • Each segment on disk has a .del file associated with it
    • When a delete request is sent, the document is not really deleted, but marked as deleted in the .del file
    • During search, deleted docs are filtered out
    • During segment merge, they are not included so in this way they are deleted forever


  1. Update operation
  • When an update is performed, the old version is marked as deleted in the .del file and the new version is indexed in a new segment


  1. Read operation

    • a search request is sent to a coordinating node (cn)
  • cn sends requests to all shards in an index
  • each shard performs search locally and creates a priority queue, send back all the results (doc ids and relevance scores) to the cn
  • cn creates a priority queue to globally sort results returned by the shards
  • cn requests docs to be returned from individual shards
  • each shard fetches certain documents by requested doc id, enriches them and return them to the cn

References