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Modern systems often deal with continuous data streams: logs, metrics, transactions, or sensor readings. Over time, these tables grow huge and slow down queries and backups. To keep performance high and storage clean, we need automation both in how data is stored (partitioning) and maintained (scheduling). That’s where pg_partman and pg_cron come in. Together, they…
In a previous article, PostgreSQL Write-Ahead Log (WAL): Durability, Performance Tuning, and Recovery Explained, we explored how PostgreSQL ensures data durability and crash recovery through its Write-Ahead Log mechanism. That discussion focused on how every change in the database is first written to WAL before being applied to the data files, the foundation of PostgreSQL’s…
PostgreSQL is known for its reliability, it keeps your data safe even during crashes or power failures.The secret behind this reliability is a core feature called WAL (Write-Ahead Log). In this article, we’ll explore how WAL works, how to tune it for performance, how it supports recovery, and how to monitor it effectively.
PostgreSQL is one of the most reliable open-source databases. It provides strong consistency, good performance, and flexible replication features that support high availability (HA) systems. In this article, we will explore how PostgreSQL replication works, from simple master–replica setups to multi-master clusters and replication lag monitoring.
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.