40 Stories To Learn About Elasticsearch

Let's learn about Elasticsearch via these 40 free stories. They are ordered by most time reading created on HackerNoon. Visit the /Learn Repo to find the most read stories about any technology.

1. A Look at Using Elixir Streams, Elasticsearch & AWS S3

Utilizing Elixir Streams, Elasticsearch, and AWS S3

2. AWS ECS vs AWS Lambda Compared

Comparing cloud services? Read our Lambda vs ECS guide. Consider programming language, pricing, and the benefits.

3. Solve Several Problems At The Same Time With Kibana Tool

Are you a software tester? This is where you find out more about using different reading logs in Kibana in order to better track and understand the errors.

4. Clickhouse vs Elasticsearch vs Manticore Search Query Times With a 1.7B NYC Taxi Rides Benchmark

New York City (NYC) taxi rides are probably the most commonly used benchmark in the area of data analytics.

5. Logging Everything in JSON Format

Logging and monitoring are like Tony Stark and his Iron Man suit, the two will go together. Similarly, logging and monitoring work best together because they complement each other well.

6. Dead Letter Queue no Elastic Stack

Melhore a obsevabilidade da sua Stack Elastic, baseado em fatos reais

7. Analyzing 110 Million Comments from Hacker News

In this article, we’ll observe another test with1.1M Hacker News curated comments with numeric fields

8. Building a k-NN Similarity Search Engine using Amazon Elasticsearch and SageMaker

Amazon Elasticsearch Service recently added support for k-nearest neighbor search. It enables you to run high scale and low latency k-NN search across thousands of dimensions with the same ease as running any regular Elasticsearch query.

9. Jaeger Persistent Storage with Elasticsearch, Cassandra and Kafka

Running systems in production involve requirements for high availability, resilience and recovery from failure. When running cloud-native applications this becomes even more critical, as the base assumption in such environments is that compute nodes will suffer outages, Kubernetes nodes will go down and microservices instances are likely to fail, yet the service is expected to remain up and running.

10. How to Deploy & Monitor Honeypots on GCP with Kibana [Tutorial]

One of my favourite areas of cybersecurity is SIEM (Security Incident Event Management). In 2017 I wrote a post on how I got a role in cyber security, one of my recommendations was using the Elastic Stack as a SIEM as a start-off point for those looking to understand log analysis and how to investigate incidents. But one of the main gripes people had was, where can they get data to work on in their home environments. This post will focus on setting up a honeypot that already utilises the ELK Stack…

11. Using Jest to Mock Elasticsearch

Test your elastic queries like a pro using jest!

12. Custom TraceID in Elastic APM

Elastic APM is extensively useful in monitoring the lifecycle of a HTTP request in a system especially in µservices architecture. Wide variety of web frameworks and databases are supported which is useful in tracking the request up to DB calls. The documentation is simple and concise which makes it easy to instrument the application.

This article aims to help or at least make it easy to trace the HTTP request lifecycle after instrumentation. Golang is used in this article for code snippets but the concept can be extended to other languages as well.

13. A 101 on ElastAlert & How To Set It Up

Simple Framework for Alerting anomalies, spikes and other patterns from data in elasticsearch.

14. PGSync Introduction: Real-time Integration Tool For PostgreSQL And Elasticsearch

PGSync is a change data capture tool for moving data from Postgres to Elasticsearch. It allows you to keep Postgres as your source-of-truth and expose structured denormalized documents in Elasticsearch.

15. Manticore is a Faster Alternative to Elasticsearch in C++

Manticore Search is a faster alternative to Elasticsearch written in C++ with a 21-year history

16. Grafana Vs. Kibana Vs. Knowi: Battle Royale 2020

Intro: Grafana vs Kibana vs Knowi

17. How to Use Fuzzy Query Matches in Elasticsearch

Typo is something that often happens and can reduce user’s experience, fortunately, Elasticsearch can handle it easily with Fuzzy Query.

18. GitHub Actions CI config for MySQL, Redis, Elasticsearch in Ruby on Rails project with RSpec tests

How to run parallel tests with Github Actions jobs for Rails project with MySQL, Redis, Elasticsearch.

19. How To Create a Simple Autocomplete Field And Connect it With Elasticsearch

Autocomplete is a feature to predict the rest of a word a user is typing. It is an important feature to implement that can improve the user’s experience of your product.

20. How Percolate Queries in Elasticsearch Make Alerting a Breeze

Once upon a time, a company I worked for had a problem: We had thousands of messages flowing through our data pipeline each second, and we want to be able to send email and SMS alerts to ours users when messages matching specific criteria were seen.

21. Analyzer in Elasticsearch: An Introduction

If we want to create a good search engine with Elasticsearch, knowing how Analyzer works is a must. A good search engine is a search engine that returns relevant results. When the user queried something in our Search Engine, we need to return the documents relevant to the user query.

22. Digging into Postgres's Lesser Known Features

Postgres Handles More than You Think

23. Create a Full Autocomplete Search Application with Elasticsearch, Kibana, NestJS and React

In this article, I will be walking you through how to set up elasticsearch on your PC.

24. Utilizing the Elasticsearch Snapshot Module for Databackups on Azure blob Storage

While running a self managed elasticsearch cluster like any other database, it's important to make provisions for data backups. Data backups on Elasticsearch can't be done by simply copying elasticsearch data files from one disk to another, this tutorial guides you through making the best use of the Elasticsearch snapshot module for creating cluster snapshots and leverages the Azure blob storage for securely storing your backed up data. Also besides backing up data, the snapshot api also comes in handy for migrating data from one cluster to another.

25. Using KSQL Stream Processing & Real-Time Databases to Analyze Kafka Streaming Data [A How-To Guide]

Intro

26. How To Master Elasticsearch Query DSL

Photo by Evgeni Tcherkasski on Unsplash

27. How To Develop Your Custom Autocorrect Implementation with Manticore [A Step by Step Guide]

In this text I will explain what is spell correction in the area of search functionality, how it works in Google, Amazon and Pinterest and will demonstrate how to make your own implementation from the ground up using custom search engine Manticore Search.

28. Native Analytics On Elasticsearch With Knowi

Table of Contents

29. Graph Databases: Full Detailed Review

There are many ideas and considerations behind graph databases. This includes their use cases, advantages, and the trends behind this database model. There are also several real-world examples to dissect.

30. Docker Centralized Logging with ELK Stack [EXPLAINED]

As your infrastructure grows, it becomes crucial to have robots and a reliable centralized logging system. Log centralization is becoming a key aspect of a variety of IT tasks and provides you with an overview of your entire system.

31. To be Relevant or not to be: a Search Story about Precision and Recall

With the amount of data created growing exponentially each year and forecasted to reach 59 zettabytes in 2020 and more than 175 zettabytes by 2025, the importance of discovering and understanding this data will continue to be, even more than before, a decisive and competitive differentiator for many companies.

32. Elasticsearch in Java Spring Boot: Starter Pack

In this article, I want to teach you how to connect Java Spring Boot 2 with Elasticsearch. We’ll learn how to create an API that’ll call Elasticsearch to produ

33. Aggregate Logs with Elasticsearch, Kibana, Logstash & Docker

Improve logging in your microservices architecture to make tracking smoother with ELK Stack.

34. Let's Export Cloudwatch Logs to ELK

Cloudwatch is an AWS service that allows storage and monitoring of your application logs from an array of AWS services. This can be really useful for creating alerts to notify developers when a certain threshold of errors has been hit, but sometimes we might need to deeply analyse our logs, not only to spot errors but to find insights into our application and improve performance. This is where an ELK (Elasticsearch, Logstash, Kibana) stack can really outperform Cloudwatch. ELK allows us to collate data from any source, in any format, and to analyse, search and visualise the data in real time.

35. Highlighting in Search Results

In this tutorial you will learn how to highlight search results in Manticore Search. You can benefit from search results highlighting if you want to improve readability of search results in your application or a web site.

36. Doing First Steps with the Kubernetes Operator

This article demonstrates how you can use the Operator Lifecycle Manager to deploy a Kubernetes Operator to your cluster. Then, you will use the Operator to spin up an Elastic Cloud on Kubernetes (ECK) cluster.

37. 3 Years After Forking Sphinx: A Brief Report on Manticore Search

In May 2017 we made a fork of Sphinxsearch 2.3.2, which we called Manticore Search. Below you will find a brief report on Manticore Search as a fork of Sphinx and our achievements since then.

38. An Introduction to Elasticsearch: Lightning Fast Search Solutions

If you're reading this blog, chances are you really interested in Elasticsearch and the solutions that it provides. This blog will introduce you to Elasticsearch and explain how to get started with implementing a fast search for your app in less than 10 minutes. Of course, we're not going to code up a full-blown production-ready search solution here. But, the below-mentioned concepts will help you get up to speed quickly. So, without further ado, let's start!

39. To be Relevant or not to be: a Search Story about Precision and Recall

With the amount of data created growing exponentially each year and forecasted to reach 59 zettabytes in 2020 and more than 175 zettabytes by 2025, the importance of discovering and understanding this data will continue to be, even more than before, a decisive and competitive differentiator for many companies.

40. Fundamentals of Full-Text Operators and Basic Search

In this tutorial, we will explore full-text search operators available in Manticore Search.

Thank you for checking out the 40 most read stories about Elasticsearch on HackerNoon.

Visit the /Learn Repo to find the most read stories about any technology.