What is Elastic Stack?

Elastic Stack is a group of products that can reliably and securely take data from any source, in any format, then search, analyze, and visualize it in real-time. Elasticsearch is a distributed, RESTful search and analytics engine that can address a huge number of use cases. Also considered as the heart of the Elastic Stack, it centrally stores user data for high-efficiency search, excellent relevancy, and powerful analytics that is highly scalable.

How Does The ELK Stack Work?

The ELK is a popular tool which used for the log management, search, and analytics. It consists of mainly three components as discussed below. All these three have their own significance and by combing these three you’ll get analysis and analytics of your data.

1. Elastic Search: Search and analytics engine.
2. Logstash: Data processing pipeline.
3. Kibana: Dashboard to visualize data. 

Elastic search can store large amount data and can processes very quickly when the operations are performed it is best analytics engine which can be used for the log management different types of data can be stored in the ELK stack as mentioned below.

1. Text documents.
2. Images.
3. Videos.

It mostly suitable for the data which index and search log data.

Elastic Search

Elastic search is the core component of an elastic stack which is widely for the Elasticsearch is a full-text search and analytics engine based on Apache Lucene. Elasticsearch makes it easier to perform data aggregation operations on data from multiple sources and to perform unstructured queries such as Fuzzy Searches on the stored data. It stores data in a document-like format, similar to how MongoDB does it. Data is serialized in JSON format. This adds a Non-relational nature to it and thus, it can also be used as a NoSQL/Non-relational database. To know more about how elastic search works refer to the Elasticsearch Search Engine | An introduction.

Logstash

Logstash is an another important core component of the ELK which is mainly used for the user to collect data from a variety of sources, transform it and then send the result to the desired location. It was developed in 2016 by Jordan Selassie. It is written in Java and Ruby language. It is one of the ELT tools. It can be used when complex pipelines are handling multiple data formats.To know more about how Logstash.

Kibana

Kibana is an open-source visualization and is a part of the ELK stack. It is used for time-series analysis, log analysis, and application monitoring. It offers a presentation tool, known as Canvas. With this tool, you can create slide decks that extract live data directly from Elasticsearch. It lets the customer visualize their Elasticsearch data and navigate the Elastic Stack. Live data can be seen through the help of Charts, tables, maps, and other tools in Kibana. To know more about kibana.

What Does The ELK Stack Do?

ELK is mostly used for the log management and for the analytics purpose in various scenarios such as mentioned below.

1. To troubleshoot the issues of application which are generated in production servers.
2. ELK can monitor the health of an application and the performances of the applications.
3. To analyze log data for the business intelligence which can be used for the gain the insights into customer behavior ,product usage, and other business metrics.

Why Is The ELK Stack Important?

ELK stack play’s an important role for log management, search, and analytics. it allows big scale organisations to collect, store, search, and analyze large volumes of log data. It will helps troubleshooting, identifying issues, and gaining insights into system performance.Following are the some reasons why it is very important.

1. Log and Data Analysis
2. Real-Time Monitoring
3. Security and Compliance
4. Data Visualization
5. Full-Text Search
6. Scalability and Performance
7. Open Source and Community Support

How can I choose the right solution for the ELK stack?

So these two most important tools for any business. You can achieve these by your Data. And with the help of these two, you can grow your business and clear business insights. Now, it’s How? Because to analyze this large data in less amount of time is not an easy task. 
Challenges and Solutions: 

• What happens in very large companies you get data from different places in different formats. It can be JSON or XML whatever. So we need one mechanism to get whole data in one place and also in one format. So for that, we use Logstash. 
• Now when we get data we need to arrange data in a systematic order, so we can evaluate the things very easily. Also, we want to analyze the data, in that case, First, go through with data very quickly. For that we have Elasticsearch. Elasticsearch is developed in Java and is released as open-source under the terms of the Apache License. 
• Now after completing this, we need a visualization platform where we can show our data analytics. There Kibana comes into the picture. That is how the whole Elastic stack worked. For better Business insights.

Which AWS offerings support your ELK stack?

The following AWS offerings support the ELK stack:

1. Amazon Elasticsearch Service (Amazon ES)
2. Amazon OpenSearch Service
3. Amazon Kinesis Data Firehose
4. Amazon S3
5. Amazon CloudWatch Logs
6. Amazon Kibana

One can utilize the AWS services mentioned to construct a comprehensive ELK stack solution in the cloud.

What ingestion tools are offered by AWS?

AWS offers wide variety of ingestion tools some of them are mentioned below.

1. Amazon Kinesis Data Firehose
2. AWS Snowball
3. AWS DataSync
4. AWS Transfer Family
5. Storage Gateway
6. AWS Direct Connect

AWS also offers no.of data ingestion tools such as AWS Glue, AWS Lambda, and Amazon Simple Workflow Service (Amazon SWF).Choosing the right ingestion by depending on the requirment of your work it depends on the type of stream line data that your application is going process.

Why Elastic Stack is needed? 

As per the survey, Facebook generates 4 Petabytes data every day i.e 40 million GB. The Data, Now it’s a world of data. So We need a system that analyzes our data. There are two terms to understand:

• Analysis – In the analysis part, You’ll get results from the past data or the existing data that you have.
• Analytics – When you want to predict user requirements, You want graphs based visualization for better business clarity and also want to understand Data patterns.
  Setting up Elasticsearch, Logstash, and Kibana 

At first let’s download the three open-source software from their respective links [elasticsearch], [logstash], and [kibana]. Unzip the files and put all three in the project folder. Firstly, set up Kibana and Elasticsearch on the local system. We run Kibana by the following command in the bin folder of Kibana.

• bin\kibana

Similarly, Elasticsearch is set up like this: 

bin\elasticsearch

Now, in the two separate terminals, we can see both of the modules running. In order to check that the services are running open localhost:5621 for Kibana and localhost:9600 for Elasticsearch. 

Here, we are ready with set up for elastic stack. Now go to localhost:5621 and open dev tools here in the console. It is the place where you can write Elasticsearch queries. As we will talk more on Elasticsearch this time. Now we’ll see how exactly Elasticsearch Works.

Architecture of ELK Stack

• Cluster: In Elasticsearch, we store our data in nodes, there can be n number of nodes in a machine. And each node is related to the cluster. So the Cluster is a set of nodes.
• Documents: You store your data as documents which are JSON objects. So how these data organized in the cluster? The answer is indices. In the world of relational databases, documents can be compared to a row in a table.
• Index: Elasticsearch Indices are logical partitions of documents and can be compared to a database in the world of relational databases.
• Types: Each index has one or more mapping types that are used to divide documents into a logical group. It can be compared to a table in the world of relational databases. 
  Every document is stored as an index. The index you can say is the collection of documents. That has similar characteristics for instance, the Department will have A index, and Employees have B index i.e they are logically related.
• Sharding
  a) Sharding is just a way to divided index into smaller pieces. 
  b) Each piece is known as a shard. 
  c) Sharding is done at an index level.
  Shard is just like an index. For scalability. With sharing, you can store billions of documents within the one index. There are also Replicas as well but for now, it is well enough for us to start and understand Elasticsearch. So let’s move further towards building and search engine.

Working Of Elastic Search 

Before any operation, we have to index our Data. Once indexed in Elasticsearch, users can run complex queries against their data and use aggregations to retrieve complex summaries of their data. Elasticsearch stores data as JSON documents and uses Data structure as called an inverted index, which is designed to allow very fast full-text searches. An inverted index lists every unique word that appears in any document and identifies all of the documents each word occurs in. For a better understanding, we’ll divide Elasticsearch into several topics.

• Managing Documents 
• Mappings 
• Analysis 
• Search Methodology 
• Aggregation and Filters

1. Managing Documents 

Before that, get the Elasticsearch package manager. 

npm -i elasticsearch 

Step 1: Link Your application to Elasticsearch by following. 

javascript

var elasticsearch = require('elasticsearch');
var client = new elasticsearch.Client({
  host: 'localhost:9200',
  log: 'trace',
  // use the same version
  // of your Elasticsearch instance
  apiVersion: '7.2',
});

module.exports = client;

Step 2: Create Index for an eg We create an index as gov. 

javascript

// Write Javascript code here
var client = require('./connection.js');

client.indices.create({
  index: 'gov'
},function(err,resp,status) {
  if(err) {
    console.log(err);
  }
  else {
    console.log("create",resp);
  }
});

Step 3: Now we will add documents to index gov and in index gov, there is a type called constituencies. You can relate as there is a database called gov and the table is constituencies. 

javascript

// Write Javascript code here
var client = require('./connection.js');

client.index({
  index: 'gov',
  id: '1',
  type: 'constituencies',
  body: {
    "ConstituencyName": "Ipswich",
    "ConstituencyID": "E14000761",
    "ConstituencyType": "Borough",
    "Electorate": 74499,
    "ValidVotes": 48694,
  }
},function(err,resp,status) {
    console.log(resp);
});

2. Mappings 

Mapping is the process of defining document, and its fields. Just like defining table-schema in RDBMS. 
Step 4: Now we will define mappings to index gov type constituencies. 

javascript

var client = require('./connection.js');

<code>client.indices.putMapping({
  index: 'gov',
  type: 'constituencies',
  body: {
    properties: {
      'constituencyname': {
        // type is a required
        // attribute if index
        // is specified
        'type': 'string',
        'index': false
      },
      'electorate': {
        'type': 'integer'
      },
      'validvotes': {
        'type': 'integer'
      }
    }
  }
},function(err,resp,status){
    if (err) {
      console.log(err);
    }
    else {
      console.log(resp);
    }
});

3. Analysis 

Text analysis is the process of converting unstructured text, like the body of an email or a product description, into a structured format that’s optimized for search. Elasticsearch performs text analysis when indexing or searching text fields. That we have defined in mappings. This is the key factor for the Search-engine. By default, Elasticsearch uses the standard analyzer for all text analysis. The standard analyzer gives you out-of-the-box support for most natural languages and use cases. If you choose to use the standard analyzer as-is, no further configuration is needed. You can also create your own custom analyzer.

4. Search Methodology 

There are different types of queries that you can apply to Elasticsearch. By that, you will get results accordingly. Here I’ll give a basic example of a query. Simplest query, which matches all documents. 

javascript

// Write Javascript code here
var client = require('./connection.js');

client.search({
  index: 'gov',
  type: 'constituencies',
  body: {
    query: {
      match: { "constituencyname": "Harwich" }
    },
  }
},function (error, response,status) {
    if (error){
      console.log("search error: "+error)
    }
    else {
      console.log("--- Response ---");
      console.log(response);
      console.log("--- Hits ---");
      response.hits.hits.forEach(function(hit){
        console.log(hit);
      })
    }
});


client.search({
    index: 'gov',
    type: 'petitions',
    body: {
      query: {
        match: { 'action': 'Ipswich' }
      },
    },function (error, response,status) {
      if (error){
        console.log("search error: "+error)
      }
      else {
        console.log("--- Response ---");
        console.log(response);
        console.log("--- Hits ---");
        response.hits.hits.forEach(function(hit){
          console.log(hit);
        })
      }
  });

5. Queries

• Compound queries: Compound queries wrap other compound or leaf queries, either to combine their results and scores, to change their behaviour, or to switch from query to filter context. 
  The default query for combining multiple leaf or compound query clauses, as must, should, must_not, or filter clauses. The must and should clauses have their scores combined.
  
• Full-text queries: The full-text queries enable you to search analyzed text fields such as the body of an email. The query string is processed using the same analyzer that was applied to the field during indexing. It will analyze your input. If the given input is not exact but still, you’ll get a result.
  
• Joining queries:  Performing full SQL-style joins in a distributed system like Elasticsearch is prohibitively expensive. Instead, Elasticsearch offers two forms of join which are designed to scale horizontally. 
  a) nested query 
  b) has_child and has_parent queries
  
• Specialized queries: This group contains queries which do not fit into the other groups, It’s found that documents which are similar in nature, pinned queries also there are many more please check out its documentation.
  
• Term-level queries: You can use term-level queries to find documents based on precise values in structured data. Examples of structured data include date ranges, IP addresses, prices, or product IDs.
  Unlike full-text queries, term-level queries do not analyze search terms. Instead, term-level queries match the exact terms stored in a field. It will find the exact match of input whereas in full-text first it will be analyzed then search so that is a big difference between Term-level and Full-text query.

6. Aggregation and Filters 

In a filter context, a query clause answers the question “Does this document match this query clause?” The answer is a simple Yes or No – no scores are calculated. Filter context is mostly used for filtering structured data, e.g.

• Does this timestamp fall into the range of 2015 to 2016? 
• Is the status field set to “published”?

Frequently used filters will be cached automatically by Elasticsearch, to speed up performance. Filter context is in effect whenever a query clause is passed to a filter parameter, such as the filter or must_not parameters in the bool query, the filter parameter in the constant_score query, or the filter aggregation. With aggregation is more like as it is in RDBMS you will find Avg, Sum, and much data insights using complex queries.

Elastic Stack is a very important Tech to learn. You will apply this in any of your projects and the ELK Stack is most commonly used as a log analytics tool. Its popularity lies in the fact that it provides a reliable and relatively scalable way to aggregate data from multiple sources, there are still Many things remain but after this, you can start with Elasticsearch.

FAQs on Elastic Stack

1. What is The Full Form of ELK Stack?

The full form of ELK stack is Elasticsearch, Logstash, and Kibana.

2. What is the ELK Stack Now Called?

The ELK stack is comprised as Elasticsearch, Kibana, Beats, and Logstash (also known as the ELK Stack). It is mostly used for log management and analytics purpose.

3. Is Elk Stack Still Free?

Yes, the ELK Stack is still free. The core components of the ELK Stack, Elasticsearch, Logstash, and Kibana, are all open source software. This means that they are free to use and modify.