![]() ![]() env file in the project root directory and set up the environment variables like this: POSTGRES_USER=letterpressĮLASTICSEARCH_URL=" Open the post. models - Models are Golang structs that can be marshaled into the appropriate JSON objects.logstash - This is where we keep code related to logstash such as pipeline configurations and the accompanying Dockerfile.handler - The handler package includes the API route handlers powered by the gin framework.env - Contains a “key-value” mapping of our environment variables (e.g., the database credentials) We will also use it later on to store the database migration files The current API top Our health check will now check Elasticsearch and verify if it. db - The db package acts as a bridge between our application and the database. Here is an example: Here are the options that well use when making.We’ve also added an inner api sub-folder to allow for multiple binaries which won’t be possible otherwise cmd - This is where the application binaries (i.e.Next, create the needed folders and files in the project directory to match the structure below: ├── cmd Install the dependencies by running the following command in your terminal: $ go get /lib/pq /elastic/go-elasticsearch /gin-gonic/gin /rs/zerolog gin-gonic/gin - The HTTP framework we will use for our application’s REST API.elastic/go-elasticsearch - The official Elasticsearch client for Golang.lib/pq - A PostgreSQL driver for Go that is compatible with the database/sql package in the Go standard library.The application dependencies comprise of: ![]() Some familiarity with Docker and the Go programming languageĬreate a new directory in your preferred location to house the project (I’m naming mine letterpress) and initialize a new Go module with the commands below: $ mkdir letterpress & cd letterpress.Go (version >= 1.14) installed on your machine.To follow along with the sample project in this tutorial, you will need: We will build a basic content management system with the ability to create, read, update, and delete posts, as well as the ability to search the posts through Elasticsearch. In this tutorial, we will explore how to combine the powers of Elasticsearch and Golang. ![]() It also integrates with Logstash (a data processing pipeline that can take in data from multiple sources like logs and databases) and Kibana (for data visualization) and together, they make up the ELK stack. Unlike typical SQL and NoSQL databases whose primary purpose is to store data, Elasticsearch stores, and indexes data so that it can be quickly searched through and analyzed. Michael Okoko Follow Linux and Sci-Fi ➕ = ❤️ Using Elasticsearch, Logstash, and Kibana with Go applicationsĮlasticsearch is an open-source, distributed search and analytics engine based on Apache Lucene. ![]()
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