Introduction To ElasticSearch

Hasan Rahhal gives a good introduction to ElasticSearch:

On the top level is the total number of the docs using an empty search query, and max_score is the maximum score a document can take in a specific query. In our case it’s one, since no query was specified.

In __shards.total_ the value is the number of Lucene indexes that Elasticsearch created for that index. The default number is always 5 unless we specify otherwise on index creation time. More details about shards are explained here.

ElasticSearch is designed to store things like logs and monitoring metrics, and the interface is JSON.  This makes it very useful for certain tasks and infuriatingly difficult to do other things (like advanced aggregations).  Still, in a medium-sized or larger environment, this is probably a technology you either are using today or want to use.

Related Posts

Dropping Columns With Logstash

Mike Hillwig shows how to ignore columns with Logstash: Like I said earlier, we have some data that I know I’ll never use. This is flight performance data. The dataset contains diversion information. If a flight gets diverted more than once, it’s tracked here. I don’t care about that, so I’m dropping the diversion information […]

Read More

Using Kafka And Elasticsearch For IoT Data

Angelos Petheriotis talks about building an IoT structure which handles ten billion messages per day: We splitted the pipeline into 2 main units: The aggregator job and the persisting job. The aggregator has one and only one responsibility. To read from the input kafka topic, process the messages and finally emit them to a new […]

Read More


May 2016
« Apr Jun »