Mastering INFINI Easysearch: A Practical Roadmap from Beginner to Expert

Introduction

INFINI Easysearch is a lightweight, secure, and highly compatible domestic distributed search engine that serves as a drop‑in replacement for Elasticsearch.

Roadmap Overview

The learning process is divided into five stages, like leveling up in a game.

Stage 1: Basic Understanding (1‑2 weeks) – grasp what Easysearch is.

Stage 2: Environment Setup (1 week) – get a runnable cluster.

Stage 3: Basic Operations (2‑3 weeks) – practice CRUD.

Stage 4: Advanced Learning (3‑4 weeks) – master core features.

Stage 5: Real‑world Application (4+ weeks) – deploy to production.

Stage 1 – Basic Understanding

What is Easysearch?

Domestic alternative to Elasticsearch.

Lightweight: installation package ~50 MB versus hundreds of MB for ES.

All‑in‑one: supports full‑text, vector, and geo search.

Built‑in security (e.g., LDAP) without extra plugins.

Core Terminology

Cluster : a group of servers working together.

Node : each server in the cluster.

Master node : manages cluster state.

Data node : stores data and executes queries.

Index : collection of documents, similar to a database table.

Shard : a slice of an index for distributed storage.

Stage 2 – Environment Setup

System Requirements

Java 15.0.1+9 or Java 17.0.6+10 recommended; minimum Java 11.

Verify version with java -version.

OS support: Chinese OS (Kylin, UnionTech), Linux, Windows.

Installation Options

Linux tar package : download, extract, configure, start.

Docker : use Docker Compose to launch a three‑node cluster.

Kubernetes : deploy with Helm chart.

Stage 3 – Basic Operations

Index Management

Mapping : define field types (keyword vs text) to avoid performance penalties.

Index Template : reuse settings for newly created indices.

CRUD

Create : single and bulk writes (production essential).

Read : use GET API.

Update : partial updates.

Delete : remove expired data.

Monitoring with Cat API

Check cluster health: _cat/health (Green or Red).

List nodes: _cat/nodes.

List indices: _cat/indices (add ?v for verbose headers).

Stage 4 – Advanced Learning

Advanced Search

Full‑text relevance tuning and Chinese tokenization.

Vector search for semantic retrieval.

Combined queries with Bool Query (must, should, filter).

Performance Tuning

Shard strategy: balance number of shards.

JVM tuning: heap size, GC settings.

Query optimization: leverage filter cache.

Security Hardening

RBAC – role‑based access control.

LDAP integration for enterprise authentication.

Stage 5 – Real‑world Application

Integration

Java and Python client usage (e.g., Spring Boot + Easy‑ES 3.0).

Data sync from MySQL to Easysearch (ETL).

Production Architecture

High availability: node redundancy.

Cold‑hot separation: SSD for hot data, HDD for cold.

Monitoring & alerting with Prometheus + Grafana.

Typical Scenarios

Log analysis – replace Elasticsearch in ELK stack.

Site search for e‑commerce or CMS content.

Time‑series storage for IoT data.

Conclusion

By following this roadmap, a learner can become an Easysearch expert within 2‑3 months, greatly enhancing career competitiveness in the domestic "Xinchuang" ecosystem.