AWS Elasticsearch

What is AWS Elasticsearch?

Aws elasticsearch is an open-source service which is a restful and distributed search and analytics engine built on Apache Lucene. In short time it became popular and the common one for log analytics, and also for full text search and security intelligence, business analytics and operational intelligence. We can send data by using ingestion tools like log stash, amazon kinesis firehose in the form of JSON documents to elasticsearch. It automatically stores the original documents and adds search-abel references for the documents using elasticsearch. We can use kibana which is an open-source visualization tool with elasticsearch for our data visualization and to build interactive dashboards. 

It is a free open-source software that can run elasticsearch on premises on amazon EC2 or on its services, by using amazon EC2 deployments we are responsible for installation of elasticsearch and other necessary software and provisioning infrastructure. Elasticsearch services are fully managed so we don't have to worry about time consuming management tasks like hardware provisioning,backups, failure recovery, monitoring, etc. Managing and scaling can be difficult and it requires expertise in setup and configuration, to make it easy for customers that helps to run elasticsearch.

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Why AWS Elasticsearch used

It is a fully managed service which makes it easy to deploy, secure and run cost effectively at scale, we can monitor and troubleshoot our applications by using these tools at scale we require. It is very useful by having many benefits, they are.

• Easy to manage and deploy
• Highly available and scalable
• Highly secure 
• Cost effective
• Application monitoring 
• SIEM
• Searching

Architecture of AWS Elasticsearch

When we observe the architecture of aws elasticsearch, we can easily get an idea regarding different services which are provided. Its cloud formation template deploys the Amazon es domain, whether it may be hardware, software or the data exposed to elasticsearch endpoints. Those templates can easily launch instances of amazon EC2, which are separately available zones of amazon VPC network. For the main purpose of distributing traffic to proxy servers and to enable automatic recovery to maintain instant availability, highly available designs are used.

Architecture overview:  This solution of deploying  builds the above environment in the AWS Cloud. This figure analyzes the Text with aws Elasticsearch Service and its Comprehend architecture on AWS. Its CloudFormation template used to deploy an aws API Gateway, which invokes the proxy microservice AWS Lambda function. microservices of aws elasticsearch allows the business logic that is used to manage the configuration of preprocessing, other native search capabilities, native indexing, etc.

The microservice of aws elasticsearch used to interact with aws Comprehend, that is for text analysis, aws CloudWatch Logs for logging, metrics, etc. Amazon Elasticsearch Service, which is for documentation indexing. While the aws API receives an authorized request, then the proxy of aws microservice sends the request for aws Comprehend for text analysis. For Amazon ES a call is provided, that is used for  indexing the data, and publishes logs and metrics for the CloudWatch. We can visualize the indexed data on the solution’s pre-configured dashboard of kibana.                                                                                    

Main features of AWS Elasticsearch

High performance: elasticsearch distributive nature enables it to process large volumes of the data in parallel and in quick findings, which match our queries.

It provides plugins and complementary tooling facility: It comes integrated with kibana which is a popular visualization and reporting tool, which also offers integrations along with beats and logstash, while enabling us to easily transform the source data to load it into the cluster. You may also use different types of open-source plugins, which are like language analyzers and suggesters which add rich functionality to our applications. 

Real-time operations: Its operations such as reading or writing data, which is usually taking less time to complete. Which lets you use elasticsearch which is used for near real-time operations, and also use cases like application monitoring and anomaly detection. 

Easy development of applications: It provides support for various languages, which includes java, python, ruby, php, etc. 

Fast time to value: Aws elasticsearch offers APIs, which are simple and rest based, a simple interface of HTTP, JSON documents which are used as schema free. It was used for us to make it easy to get started and built applications for a variety of use cases.

Advantages of aws elasticsearch

• There is no chance for major downtime, as it manages everything in a good manner when there is a failure. It provides real time document orientation, which stores entities of the real-world complex as they are structured JSON documents and indexes for all fields that are by default, which have a higher performance result. 
• It is able to execute the complex queries that are extremely fast, and also catches almost all the structured queries, which are commonly used as filters for the results to set and execute them only once. Which is used for every other request which contains a cached filter that checks the result from the cache.
• By nature it is a distributive system and can easily scale horizontally that provide the ability to extend resources and balance between the nodes in the cluster, which are used to minimize the chance of losing data. Its indices can be divided into charades, that each schrade is able to have any number of replicas. That is used for routing and re-balancing operations, which are done automatically when new documents are added. 

Disadvantages of aws elasticsearch

• It has more straight forward configuration, because we have to select a lot of things. It doesn't have a valid number of master nudes, it allows us to request nodes of a dedicated master for our cluster. It doesn't have a valid number of master nudes. It allows us to request nodes of a dedicated master for our cluster. 
• During this drop down the process for picking the number of master nodes which provision us as we will also find the completely invalid options of the master nodes. It provides multi support but not enabled by default, probably used to save that on regional data transfer costs, and also any real world cluster, which is used for this affinity feature. 
• There is no place for in place upgrades, which seemed like the biggest show stopper. When you provision the elasticsearch on its services, which you get in a cluster that runs a specific elasticsearch version, which is the easiest and also recommended way which is used for the upgrade. When we will find yourself to run a version, which has a bug or when it needs a feature from the latest version, for that we need to go through a lengthy process of launching the latest cluster. 
• It provides backup only to be executed once a day, they are very cheap to executive and it recommends a backup for twice an hour or more than for some critical systems, and this backup once a day is like a terrible default for a production system.

Conclusion

This service is integrated with cloud watch our domain monitoring and cloud trail, which helps us for adding access for our domain. We can easily scale our cluster through a single API call or a few clicks in the management console. It gave us direct access to the elasticsearch API, and the applications which are already using our existing elasticsearch environment, which may work seamlessly. It also provides built in support for kibana and we can easily analyze and visualize our data. The continuous and automatic data loading into our domain are taken care of by its integrations.

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