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With the previous notes from the current book (Amazon Elasticsearch Service) and Elasticsearch Essentials, you should have decent understanding of Elasticsearch. Next, we will try to learn Amazon ES in deep.

Following are some of the links that will help start that journey to master Amazon ES:

1. Creating and Configuring Amazon Elasticsearch Service Domains

1. Jest Bulk soruce code

2. jest-common/src/test/java/io/searchbox/core/BulkTest.java

3. BulkableAction examples

Java programmers had two choices when communicating with Elasticsearch: they could use either the REST API over HTTP, or the internal Java API (aka transport client), which is used by Elasticsearch itself for node-to-node communication. When using the HTTP rest client, Elasticsearch parses the JSON to Java objects and then use the internal Java API, and when we use the internal Java API we use those internal Java objects directly. 

Elasticsearch provides a full Query DSL based on JSON to define queries. The behaviour of a query clause depends on whether it is used in query context or in filter context.

In Query context, besides deciding whether or not the document matches, the query clause also calculates a _score representing how well the document matches, relative to other documents. Here, a query clause is passed to a query parameter, such as the query parameter in the search API.

To show only running containers:

docker ps

or

docker container ps

To show all containers:

docker ps -a

or

docker container ps -a

To show the latest created container (includes all states):

docker ps -l

To show n last created containers (includes all states):

docker ps -n=-1

Datasets are needed for practicing and learning most big data technologies. Here are few datasets that may be useful for ElasticSearch. It may be useful for other big data technologies as well.

Elastic.co Datasets

Below datasets are provided by elastic.co website:

Datasets are needed for practicing and learning most big data technologies. Here are few datasets that may be useful for ElasticSearch. It may be useful for other big data technologies as well.

Elastic.co Datasets

Below datasets are provided by elastic.co website:

Elasticsearch aggregations group and extract summaries out of your data. 

Aggregations belong into four main families:

1. Bucketing

   1. Bucketing aggregations build buckets (groups) and put documents into relevant bucket based on some criteria. Each bucket is associated with a key and a document criterion

2. Metric

   1. Metric Aggregations compute metrics over a set of documents.

      1. Two types:

         1. Single value numeric metric aggregations (output single value)

An inverted index is a data structure that consists of a list of all unique words and list of the documents in which it appears. New documents are analyzed and then stored as inverted indexes, to allow very fast full-text searches.