This article was published as a part of the Data Science Blogathon.
Amazon Elasticsearch Service is a powerful tool that allows you to perform a number of functions. Let us examine how this powerful tool works behind the scenes. Elasticsearch acts a lot like a database and a distributed system that works like a freely available power tool to join in with other AWS services for application development. This allows you to develop on the go with time-saving techniques. Here, we will discuss a few key points about the AWS Elasticsearch service that will help you to develop faster and more effectively.
Some of the key tools and services that help developers to load a large amount of data and build solutions for users are as follows:
Kibana dashboard helps devs to visualize and build smoothly.
Elasticsearch service acts as the ad hoc search engine
Logstash helps to migrate data into the Elasticsearch search engine
These are inbuilt features of AWS Elasticsearch which make developing easier and more efficient. With these tools at your fingertips, you can easily create an application from scratch in no time. As we know, distributed systems require a number of maintenance type steps such as deploying new nodes, restarting clusters and maintaining the health of clusters by restarting failed nodes. However, AWS Elasticsearch is different as it provides us with the luxury of easily maintained, deployable and scalable clusters. So we no longer need to do all of the maintenance work that is required with most other distributed systems.
Data Nodes: To store data that needs to be provided to the search engine.
Master Nodes: To administer the elastic search cluster.
Ultra-warm nodes: These are highly available nodes that store data for long periods of time. Such nodes are distributed across availability zones or AZs so that they remain accessible under all circumstances.
ES Documents refers to the final products that we can retrieve from Elasticsearch and save it. A document is generally a JSON object which is searchable. Under Elasticsearch, these documents are stored under indexes created by REST APIs. These documents can then be searched using their respective indexes through field matching, boolean queries, sorting and analysis. These tasks are again performed by another set of powerful REST APIs.
AWS Elasticsearch service supports the highest level of security features to support development. Here are four main points that constitute ES Security.
AWS Elasticsearch service provides data encryption.
Node to node communication encryption
IAM secure authentication
It provides an open distro needed for the Elasticsearch security plugin for smooth and intricate access control
If you want to integrate it with the various other services of AWS, there are multiple inputs and outputs which can be used for AWS ES.
Amazon Kinesis Data Firehose
AWS Database Migration Service
Amazon CloudWatch Logs and output integrations with:
Amazon CloudWatch
AWS CloudTrail
The 5 input integrations mentioned above are the most important and the in-built ones which are supported as default by the AWS ES Service. We can also integrate a custom one for the AWS Services by using the IAM roles and Lambda Functions.
AWS ES service does support various types of workloads and these workloads can be used to solve many of our problems which come under various scenarios. Thus this is how Elasticsearch service can be used to solve real-world problems.
Elasticsearch service supports various different workloads. Workloads help to create abstracts of the solutions which will then help you to create the solution you want. Some common workload categories are as follows:
Search workloads:
Searching from large data files and loading data.
Perform queries, adjust rankings and select from various language features.
Analytics workloads:
Near real-time availability of log data
Perform visualizations, create dashboards, set up alerting and monitoring systems
Cluster configurations are extremely important as it should be tailor-made for the purposes of the project. As you configure your cluster, you need to focus on the current data load and also the data load you may receive in the future when your project is operational. Depending on the purpose of your project, you may need to splice up your data for it to be meaningful for your purposes. Now the data chunks will be ready to be represented in Elasticsearch clusters. Now there are a few factors on which the cluster config will depend. Those are as follows:
The count of Instances — To scale your cluster, you need to increase or decrease the number of instances in your cluster
The Type of Instance — Depending upon the type of instance, the cluster capacity will differ.
Adding Storage— You need to keep adding storage to expand and scale your ES cluster with time
The count of the shard — The perfect balance between your index and storage amount related to every index is required and you need to identify it.
The AWS Elasticsearch service is powerfully efficient but it also has its own set of cons and downsides. Amongst the number of useful features, ES cons also constitute a considerable premium charge to avail of all of the tools and services. The reason why the price is a con is because of its relatively expensive nature as compared to other tools and services provided by AWS. Another con which needs to be listed here is the restrictive clusters provided by AWS Elasticsearch. The paired APIs provided by AWS ES is also restrictive and less efficient when compared to other open-source models and tools. Cluster configuration and other setup tasks also constitute a huge part of the process and there are no guides or tools to help out developers who are new to the environment.
ES is a service provided and managed by AWS so it reduces the amount of effort that goes into the maintenance and care of the ES clusters. There are also a number of APIs paired with the service to help you work on different aspects. Being a service provided by AWS, it has key features that help you to integrate the Elasticsearch projects with other AWS services which is a very useful feature for developers building complex applications and projects.
The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.
Lorem ipsum dolor sit amet, consectetur adipiscing elit,
