Intro

• See: https://github.com/codingexplained/complete-guide-to-elasticsearch
• See: https://www.elastic.co/guide/en/elasticsearch/reference/current/elasticsearch-intro.html

• Elasticsearch is the distributed search and analytics engine ... Elasticsearch provides near real-time search and analytics for all types of data.

• Often used for Application Performance Management (APM)
  • Analayse application logs and system metrics, most often to detect errors or understand resource usage etc.
• Log stash
  • Input > Filters > Output
  • Each stage can have plugins
  • E.g. reading in log files
    • File is input and log stash treats each line as an event
    • Filter stage parses the input data to make sense of it: i.e., structure unstructured data
      • E.g. use of "grok pattern"
      • Take raw line and turn into fields, to send for eg to ElasticSearch
  • Allows seperation of concerns - the apps sending the data don't need to known about how it should be processed - logstash handles that logic.
• X-Pack
  • Add user authentication and access control to ElasticStash
  • Monitoring
  • Reporting
  • Elasticsearch SQL
    • Normally used Query DSL - this makes it easier for SQL-familiaar developers
    • Translates SQL to Query DSL.
    • Translate APIs also exist
    • Helper tool to get started - Query DSL probably best to use once familiar
• Beats
  • Collection of light weight data shippers
  • Single pupose - send data to logstash or ES.
    • e.g. FileBeat - sends log files to ES
    • e.g. MetricBeat - sends resource usage info to ES.
• Single node Docker image for learning:
  • docker pull docker.elastic.co/elasticsearch/elasticsearch:7.16.3
  • docker run -p 127.0.0.1:9200:9200 -p 127.0.0.1:9300:9300 -e "discovery.type=single-node" docker.elastic.co/elasticsearch/elasticsearch:7.16.3
    • Took quite some time to start up for me on an i7 with 16bg ram although system was doing other things.
  • curl http://127.0.0.1:9200 to figure out if it is running okay
• Run Kibana and ES:
  • See https://www.elastic.co/guide/en/kibana/current/docker.html
  • docker network create elastic docker pull docker.elastic.co/elasticsearch/elasticsearch:7.16.3 docker run --name es01-test --net elastic -p 127.0.0.1:9200:9200 -p 127.0.0.1:9300:9300 -e "discovery.type=single-node" docker.elastic.co/elasticsearch/elasticsearch:7.16.3
  • docker pull docker.elastic.co/kibana/kibana:7.16.3 docker run --name kib01-test --net elastic -p 127.0.0.1:5601:5601 -e "ELASTICSEARCH_HOSTS=http://es01-test:9200" docker.elastic.co/kibana/kibana:7.16.3
  • To access Kibana, go to http://localhost:5601
• Some ES directory structure:
  • Notable bin utils

     |-- bin
     |   |-- ...
     |   |-- elasticsearch-cli
     |   |-- elasticsearch-plugin     # Install plugins
     |   |-- elasticsearch-sql-cli    # Do SQL-like queries instead of using Query DSM
     |   
    

  • Config

    |-- config
    |   |-- elasticsearch-plugins.example.yml
    |   |-- elasticsearch.keystore
    |   |-- elasticsearch.yml                 #<<< This is the main config file
    |   |-- jvm.options                       #<<< Runs on JVM - HEAP size best thing to mod.
    |   |-- jvm.options.d
    |   |-- log4j2.file.properties
    |   |-- log4j2.properties
    |   |-- role_mapping.yml
    |   |-- roles.yml
    |   |-- users
    |   `-- users_roles
    

    * elasticsearch.yaml' * Commented out by default so defaults are used *cluster.name: Best practice to set this! *node.name`: Best practice to set this!
• Basic Architecture
  • Node: Esentially an ES instance
    • Can run many nodes. Each node can store part of data set: distributed storage = large storage.
    • Node == ES instance so can run many nodes on one machine.
    • Each node belongs to a cluster
    • Node is always part of a cluster, even if single node in cluster.
  • Cluster - collection of nodes.
    • Split is normally for logical seperation.
    • Document - A unit of data stored in a cluster.
      • JSON object.
      • Index: Every document is stored within an index.
        • Groups documents together logicially.
        • Provide scalability and availablility settings.
        • Search queries are run against indicies.
• Basic cURL queries (Kibana makes this waaay easier!)
  • Local ES
    • curl -XGET "http://localhost:9200/_cluster/health"
    • curl -XGET "http://localhost:9208/_cat/indicies?v"
    • curl -XGET "http://localhost:9208/.kibana/_search" -H 'Content-Type: applicaton/json' -d'{ "query": { "match_all": {} }}'
  • Cloud ES - needs authentication
    • `curl -XGET -u username:password "https://3aodfff....sa.eu-central.aws.cloud.es.io:9243/.kibana/_search" -H 'Content-Type: applicaton/json' -d'{ "query": { "match_all": {} }}'
• Sharding & Scalability
  • To store 1TB can use 2 nodes of 0.5TB - can aggregate node storage
    • Done using sharding.
    • Sharding is a way to divide inidicies into smaller pieces, where each piece is a shard
    • Done at the index level!
      • One shard must be on a single node and can be placed on any node. Many to one.
    • Horizontally scale the data volume.
    • Each shard in an Apache Lucene index
    • Can improve performance by running queries on multiple shards at the same time.