Learnitweb

Author: Editorial Team

  • Data Manipulation Language (DML) in ClickHouse

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    Data Manipulation Language, commonly abbreviated as DML, refers to the family of SQL commands that are responsible for inserting, modifying, and removing data stored inside database tables, and in ClickHouse this topic requires special attention because ClickHouse is a columnar analytical database that is optimized primarily for high-volume reads and aggregations rather than frequent row-level…

  • Data Query Language (DQL) in ClickHouse

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    Data Query Language, commonly abbreviated as DQL, represents the part of SQL that is focused entirely on reading, retrieving, and analyzing data from the database, and in a system like ClickHouse—whose primary goal is fast analytical querying—DQL is not just a feature but the central purpose of the database itself. When you work with ClickHouse…

  • Data Definition Language (DDL) in ClickHouse SQL

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    Data Definition Language, commonly abbreviated as DDL, represents the family of SQL commands that define, shape, and control the structural objects inside a database system, and in the context of ClickHouse these commands play a foundational role because they determine how data is organized, where it lives, and how it can be accessed efficiently for…

  • Introduction to ClickHouse SQL

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    ClickHouse SQL is the primary language through which you communicate with a ClickHouse database, and therefore a serious learner or practitioner of ClickHouse must treat SQL not as a side topic but as the central interface through which data is defined, queried, transformed, and analyzed. In practical terms, no matter how advanced the surrounding ecosystem…

  • ClickHouse in Docker Environment

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    1. Introduction Running ClickHouse locally is one of the most practical ways to learn, experiment, and build analytical applications, and Docker makes this process significantly simpler by removing the need for manual installation and dependency management. In this chapter-style tutorial, you will learn how to run ClickHouse using Docker, understand why each step is performed,…

  • Why Column Storage Is Faster?

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    1. Introduction Column-oriented storage is faster for analytical workloads because it organizes data in a way that closely matches how analytical queries access, filter, and aggregate information, which leads to dramatic reductions in disk I/O, significantly better CPU cache utilization, and much stronger compression opportunities that compound into large performance gains at scale. When people…

  • Introduction to ClickHouse

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    1. What is ClickHouse? ClickHouse is a high-performance, open-source, column-oriented database management system (DBMS) designed specifically for Online Analytical Processing (OLAP) workloads where very large volumes of data must be queried and aggregated extremely fast. ClickHouse was originally developed by Yandex to power real-time analytics for web-scale applications, and it is now widely used in…

  • gRPC Channel and Stub Lifecycle: How to Use Them Correctly in Real Applications

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    When developers first learn gRPC, they usually focus on getting their first remote call working. They create a channel, create a stub, send a request, receive a response, and feel satisfied that the system works. While this is a great starting point, real-world applications require a deeper understanding of how these components should be managed…

  • gRPC Client–Server Interaction

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    In earlier steps of a typical gRPC project, you usually focus on the server side first. You define your .proto file, generate source code using the Protocol Buffer compiler, implement the service, and start a gRPC server that exposes your API. Once that is done, you might test the API using tools or simple test…

  • gRPC Unary API Tutorial – Building a Simple Bank Service

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    1. Understanding Unary Communication in gRPC A unary RPC is the simplest communication pattern in gRPC. In a unary call: This is similar to a typical REST request–response interaction, but the internal implementation and protocol are very different because gRPC uses HTTP/2 and Protocol Buffers. To understand this clearly, we will create: The client will…