Elasticsearch is a free and open source full-text search engine that is built on top of Apache Lucene. Out of the box, Elasticsearch supports horizontal scaling and data redundancy. Released in 2010, Elasticsearch quickly gained recognition in the full-text search space. Its scalability features helped the tool gain market share against similar technologies such as Apache Solr.

Elasticsearch is a persistent document store and retrieval system, and it is similar to a database. However, it is different from relational databases such as MySQL, PostgreSQL, and Oracle in many ways:

Figures 1.1 through 1.4 explain how Elasticsearch distributes data across multiple nodes and how it automatically recovers from node failures:

Figure 1.1: Elasticsearch Data Distribution

In this figure, we have an Elasticsearch cluster made up of three nodes: elasticsearch-node-01, elasticsearch-node-02, and elasticsearch-node-03. Our data index, is broken into three pieces, called shards. These shards are labeled 0, 1, and 2. Each shard is replicated once; this means that there is a redundant copy of all shards. The cluster is colored green because the cluster is in good health; all data shards and replicas are available.

Let's say that the elasticsearch-node-03 host experiences a hardware failure and shuts down. The following figures show what happens to the cluster in this scenario:

Figure 1.2: Node failure

Figure 1.2 shows elasticsearch-node-03 experiencing a failure, and the cluster entering a yellow state. This state means that there is at least one copy of each shard active in the cluster, but not all shard replicas are active. In our case, a copy of the 1 and 2 shards were on the node that failed, elasticsearch-node-03. A yellow state also warns us that if there's another hardware failure, it's possible that not all data shards will be available.

When elasticsearch-node-03 goes down, Elasticsearch will automatically start rebuilding redundant copies of the 1 and 2 shards on the remaining nodes; in our case, this is elasticsearch-node-01 and elasticsearch-node-02. This is shown in the following figure:

Figure 1.3: Cluster recovering

Once Elasticsearch finishes rebuilding the data replicas, the cluster enters a green state once again. Now, all data and shards are available to query.

Figure 1.4: Cluster recovered

The cluster recovery process demonstrated in Figures 1.3 and 1.4 happens automatically in Elasticsearch. No extra configuration or user action is required.

Full-text search refers to running keyword queries against natural-language text documents. A document can be something, such as a newspaper article, a blog post, a forum post, or a tweet. In fact, many popular newspapers, forums, and social media websites, such as The New York Times, Stack Overflow, and Foursquare, use Elasticsearch.

Assume that we were to store the following text string in Elasticsearch:

We demand rigidly defined areas of doubt and uncertainty!

A user can find this document by searching Elasticsearch using keywords, such as demand or doubt. Elasticsearch also supports word stemming. This means that if we searched for the word define, Elasticsearch would still find this document because the root word of defined is define.

This piece of text, along with some additional metadata, may be stored as follows in Elasticsearch in the JSON format:

{
    "text" : "We demand rigidly defined areas of doubt and uncertainty!",
    "author" : "Douglas Adams",
    "published" : "1979-10-12",
    "likes" : 583,
    "source" : "The Hitchhiker's Guide to the Galaxy",
    "tags" : ["science fiction", "satire"]
}

If we let Elasticsearch dynamically assign a mapping (think schema) to this document, it would look like this:

{
    "quote" : {
        "properties" : {
            "author" : {
                "type" : "string"
            },
            "likes" : {
                "type" : "long"
            },
            "published" : {
                "type" : "date",
                "format" : "strict_date_optional_time||epoch_millis"
            },
            "source" : {
                "type" : "string"
            },
            "tags" : {
                "type" : "string"
            },
            "text" : {
                "type" : "string"
            }
        }
    }
}

Note that Elasticsearch was able to pick up that the published field looked like a date.

An Elasticsearch query that searches for this document looks like this:

{
    "query" : {
        "query_string" : {
            "query" : "demand rigidly"
        }
    },
    "size" : 10
}

Specifics about Elasticsearch mappings and the Search API are beyond the scope of this book, but you can learn more about them through the official Elasticsearch documentation at the following links:

This section explains a few of the many open source full-text search engines available, and discusses how they match up to Elasticsearch.