OpenSearch for Beginners: The Visual Guide That Official Docs Don’t Give You
Why this search engine exists, what problems it actually solves, and when you should (and shouldn’t) use it.
I remember the first time I tried to learn OpenSearch.
I went to the official documentation, and within 30 seconds, I was drowning in terms I didn’t understand terms like shards, replicas, inverted indexes, analyzers, nodes, and clusters.
The docs told me how to install it. They told me how to write queries. But they never told me why I should care in the first place.
If you are in that same boat, curious about OpenSearch but confused by the documentation, this guide is for you.
By the end, you will understand:
The specific problem OpenSearch was built to solve
How it’s fundamentally different from a traditional database
When to use it (and when to absolutely avoid it)
The one concept that makes everything else click
Let’s start with the problem.
The Problem: Why Traditional Databases Fail at Search
Imagine you’re building an e-commerce site. You have a million products. A user searches for “iphone.”
With a traditional database like PostgreSQL, you’d write:
SELECT * FROM products
WHERE name LIKE '%iphone%'
This works. But here is the problem, the database has to scan every single row to find matches.
1,000 products? Fast.
100,000 products? Noticeable lag.
10,000,000 products? Your users are waiting 5+ seconds.
This is called a full table scan. It’s O(n) complexity—meaning the more data you have, the slower it gets. Linearly.
But it gets worse.
The Other Problems with Database Search
Beyond raw speed, traditional databases have other search limitations:
No relevance ranking. If a user searches “blue running shoes,” the database returns every product that matches—but in no particular order. The perfect match shows up on page 47.
No typo tolerance. Search for “iphon” (missing the ‘e’)? Zero results. Your user thinks you don’t sell iPhones.
No fuzzy matching. “iPhone,” “iphone,” “I-Phone,” and “i phone” are all treated as completely different searches.
Expensive analytics. Want to count how many products are in each category? Or show price distributions? These aggregations are computationally expensive on traditional databases.
Search engines like OpenSearch were explicitly built to solve these problems.
The Solution: How OpenSearch Works Differently
OpenSearch uses a data structure called an inverted index.
Instead of storing documents and then scanning through them to find words, OpenSearch flips the relationship: it stores words and points them to documents.
Here is a simple example:
Traditional approach (Document → Words):
Document 1: "The quick brown fox"
Document 2: "The lazy dog"
Document 3: "Quick brown fox jumps"
To find “fox,” you scan all three documents.
Inverted index approach (Words → Documents):
"quick" → [Document 1, Document 3]
"brown" → [Document 1, Document 3]
"fox" → [Document 1, Document 3]
"lazy" → [Document 2]
"dog" → [Document 2]
Now, to find “fox,” you do one dictionary lookup: O(1).
It doesn’t matter if you have 10 documents or 10 billion. The lookup time is essentially the same.
The Trade-Off
Here is the key insight that took me too long to understand:
OpenSearch trades write speed for read speed.
When you add a document to OpenSearch, it does extra work—analyzing the text, breaking it into tokens, and updating the inverted index. This is slower than a simple database insert.
But when you search? Instant. Because all that work was front-loaded.
This trade-off defines when OpenSearch is the right tool and when it’s the wrong one.
What OpenSearch Actually Is
Let me give you the complete picture.
OpenSearch is a distributed search and analytics engine built on Apache Lucene.
Let’s break that down:
Distributed: It runs on multiple servers (called nodes) that work together as a cluster. This lets it scale horizontally.
Search engine: Its primary job is finding relevant documents fast. It has built-in support for relevance scoring, fuzzy matching, and typo tolerance.
Analytics engine: Beyond search, it excels at real-time aggregations—counting, summing, grouping data across millions of records.
Apache Lucene: The low-level indexing library that powers the inverted index. OpenSearch (and Elasticsearch before it) are essentially Lucene with a distributed layer and REST API on top.
The Origin Story
OpenSearch is a fork of Elasticsearch.
In 2021, Elastic (the company behind Elasticsearch) changed its licensing from Apache 2.0 to a more restrictive model. AWS, along with the community, responded by forking the last open-source version (7.10.2) and continuing development under the Apache 2.0 license.
That fork became OpenSearch.
Practical implication: OpenSearch is API-compatible with Elasticsearch 7.10. If you have existing Elasticsearch code, it probably works with OpenSearch with minimal changes.
The Two Components
OpenSearch actually has two main parts:
1. OpenSearch (the engine)
Stores and indexes your data
Processes search queries
Runs aggregations and analytics
Exposes a REST API (default port: 9200)
2. OpenSearch Dashboards
Web interface for visualization
Build dashboards and charts
Dev Tools console for running queries
Alerting and monitoring
Think of it like PostgreSQL (the database) and pgAdmin (the GUI). You can use the engine without the dashboards, but the dashboards make it much easier to explore your data.
When to Use OpenSearch
OpenSearch shines in four main scenarios:
1. Full-Text Search
The problem: Users need to find content fast—products, articles, documentation, people.
Why OpenSearch: Instant results, relevance ranking, typo tolerance, autocomplete, faceted search.
Examples: E-commerce product search, internal documentation search, Wikipedia-style search boxes.
2. Log Analytics
The problem: You have millions of log lines from servers, applications, and services. Finding a specific error is like finding a needle in a haystack.
Why OpenSearch: Ingest logs in real-time, search across all sources instantly, visualize patterns and anomalies.
Examples: Debugging errors across 100 servers, tracking API latencies, security log analysis.
3. Observability & Metrics
The problem: You need visibility into system health—CPU usage, request counts, error rates—across your entire infrastructure.
Why OpenSearch: Time-series aggregations, real-time dashboards, alerting on thresholds.
Examples: Infrastructure monitoring, APM dashboards, SLA tracking.
4. AI & Vector Search
The problem: Traditional keyword search doesn’t understand meaning. “automobile” and “car” are treated as different concepts.
Why OpenSearch: Native support for vector embeddings, k-NN (nearest neighbor) search, hybrid search combining keywords and vectors.
Examples: Semantic search, RAG (Retrieval-Augmented Generation) pipelines, image similarity, recommendation systems.
When NOT to Use OpenSearch
This is the part most tutorials skip. But understanding when not to use a tool is just as important as knowing when to use it.
❌ Don’t Use It As Your Primary Database
Why not: OpenSearch is not ACID-compliant. It prioritizes speed over data consistency guarantees.
The problem: If a node fails mid-write, you might lose data or end up with inconsistent state. There’s no rollback mechanism like a traditional database transaction.
What to do instead: Use PostgreSQL, MySQL, or another relational database as your source of truth. Sync data to OpenSearch for search capabilities. This is called the “dual-write” or “CQRS” pattern.
❌ Don’t Use It for Frequently Updated Records
Why not: Documents in OpenSearch are immutable. When you “update” a document, OpenSearch actually deletes the old version and reindexes a completely new one.
The problem: If you’re updating a user’s last-login timestamp on every request, or incrementing a view counter frequently, you’re triggering delete+reindex operations constantly. This is very expensive.
What to do instead: Keep frequently-changing data in a database optimized for updates. Only sync relatively stable data to OpenSearch.
❌ Don’t Use It for Multi-Document Transactions
Why not: OpenSearch has no concept of transactions spanning multiple documents. Each document write is independent.
The problem: “Transfer $100 from Account A to Account B” requires two operations: debit A, credit B. In a database, this is one atomic transaction. In OpenSearch, A might be debited but B might fail to credit. Now your data is inconsistent.
What to do instead: Handle any operation requiring transactional guarantees in your database first, then update OpenSearch.
The Right Mental Model
Think of it this way:
Database = Source of truth, handles transactions, your data’s home
OpenSearch = Specialized search layer that syncs from your database
They work together. OpenSearch doesn’t replace your database; it complements it.
The One Concept That Makes Everything Click
If you remember nothing else from this article, remember this:
OpenSearch builds an index at write time so it can search at O(1) at read time.
That’s the fundamental insight.
Every other concept—shards, replicas, analyzers, mappings—they all exist to make that indexing process more efficient, more distributed, or more customizable.
Once you understand that core trade-off, everything else starts to make sense:
Why does OpenSearch need so much RAM? To keep indexes in memory for fast lookup.
What’s a shard? A way to split a large index across multiple machines.
What’s an analyzer? A way to customize how text is broken into tokens for the index.
Why is updating slow? Because it’s rebuilding the index, not just changing a value.
Try It Yourself
Want to see this in action? Here’s the fastest way to spin up OpenSearch locally:
docker run -p 9200:9200 -p 9600:9600 \
-e "discovery.type=single-node" \
-e "OPENSEARCH_INITIAL_ADMIN_PASSWORD=MyPassword123!" \
opensearchproject/opensearch:latest
Wait about 30 seconds, then visit https://localhost:9200 (accept the self-signed certificate warning). You should see JSON confirming OpenSearch is running.
Now you have a search engine to experiment with.
What’s Next
This was the “why” and “what” of OpenSearch. In the next article, I’ll go deeper into the “how”:
How text analysis actually works (tokenization, stemming, filters)
How relevance scoring determines which results come first
How to write your first search queries
If you found this helpful, subscribe so you don’t miss it.
And if you have questions, drop them in the comments—I read everything.
Resources:
Interactive Visual Guide for Day 1: opensearch.9cld.com/day/01-fundamentals
Complete Guide (all days): opensearch.9cld.com
This is part of my series breaking down OpenSearch from first principles. No assumed knowledge. No skipped steps. Just clear explanations of how search actually works.