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Modern ride-hailing and food delivery apps rely heavily on location-based search. When a user opens the app, it must quickly find nearby drivers or restaurants, usually within a few kilometers.The faster the search, the better the user experience. Elasticsearch is not only for text search, it also supports geo-spatial queries that can find locations inside…
Modern search systems don’t just find exact matches, they understand partial words, typos, and phrases as you type. Elasticsearch makes this possible with a mix of analyzers and special queries like N-Gram, Reverse, Fuzzy, and Search-as-you-type. In this article, we’ll learn how these techniques work, when to use each, and how to combine them for…
When you search for text in Elasticsearch, the system doesn’t just compare exact words.It first processes your text, breaking it into tokens, lowercasing, removing stopwords, and sometimes even finding the root form of words. This process is handled by something called an analyzer. Understanding analyzers is the first step to building powerful and accurate search…
Modern systems generate millions of logs every day, from API servers, databases, applications, and containers. Managing, searching, and visualizing all of these logs in real-time is not easy. This is where the ELK Stack, Elasticsearch, Logstash, and Kibana, comes in.When combined with Beats, it becomes one of the most powerful and flexible log management systems…
When your Elasticsearch grows very large, managing all indices by hand becomes impossible.Old data takes space, slows down queries, and increases cost. Index Lifecycle Management (ILM) helps you automate this, deciding when to roll over, move, merge, freeze, or delete indices automatically. This article explains ILM in simple English, including the frozen phase and how…
Reindexing or upgrading Elasticsearch can be risky when your system is already in production.If you change mappings, update analyzers, or move to a new version, stopping the cluster is not an option.That’s where the blue-green strategy helps, it allows you to build a new index (or cluster), test it, and switch traffic smoothly without downtime.
Elasticsearch can handle millions or even billions of documents. It is fast and scalable, but only if you manage it correctly. When your data grows very large, bad shard planning or poor data balance can make the cluster slow or unstable. This article explains how to maintain very large datasets in Elasticsearch, including the trade-offs…
Elasticsearch is powerful, but it can also be confusing for new users.Many people make mistakes that slow down performance, waste memory, or even break the cluster.This article explains best practices for beginners — simple rules that help you build a stable and fast Elasticsearch setup.
In previous article, Basic Concept of ElasticSearch (Part 2): Architectural Perspective, I have present about elasticsearch from architectural perspective, starting from elasticsearch role node, indexing flow, and searching flow. But, if you are not familiar with ElasticSearch, I suggest to read Basic Concept of ElasticSearch (Part 1): Introduction first. As I mention in the previous…
In previous article Basic Concept of ElasticSearch (PART 1), I have present about definition of ElasticSearch, comparison between ElasticSearch and SQL, some terminologies in ElasticSearch, relation between ElasticSearch & Lucene, and also data types in ElasticSearch. In this article, I will tell you about a brief of architectural perspective of ElasticSearch, general indexing flow, and general…