Top recent clickhouse news, how-tos and comparisions

ClickHouse 25.2 introduces several new features including improved support for HTTP event streaming with JSON formats that stream events as they appear, WithProgress variants of output formats, and enhanced Delta Lake integration. It also includes improvements like automatic partition pruning, faster query performance on large datasets, and additional optimizations. The release notes highlight the addition of a Backup table engine, experimental Delta Lake support, and real-time progress bar enhancements in ClickHouse's embedded Web UI.

Replicating analytical data from PostgreSQL to ClickHouse with Postgres CDC is an efficient way to scale your business, enabling real-time analysis of large datasets. By offloading analytical queries to ClickHouse, you can reduce the load on PostgreSQL while leveraging ClickHouse's high-performance capabilities.

The ClickHouse & Vantage teams announced the Billing API for ClickHouse Cloud, which provides programmatic access to billing and usage data. This enables users to integrate cost data into observability tools, automate financial reporting, and implement usage-based billing. A guest post from Vantage showcases how their integration with ClickHouse Cloud's Billing API improves cost visibility, optimizing analytics spend and enabling smarter financial operations. The integration allows for cost alerts, anomaly detection, budget notifications, and detailed cost reports, helping teams manage and optimize cloud spending.

This document provides a guide for setting up log monitoring and analysis using Vector, ClickHouse, and Grafana. It details the installation of these tools and configuration steps to stream logs from Caddy into ClickHouse for real-time analysis.

This article provides an introduction to PeerDB and demonstrates its deployment as part of both open source and fully managed cloud hosted solutions. It explains how PostgreSQL and ClickHouse can be used together for transactional and analytical workloads with PeerDB facilitating data migration between the two databases.

The 2025 Enterprise Clickhouse Support Report analyzes data from over 100 posts in the Clickhouse Slack #help channels to identify key trends and areas for improvement. The report highlights a need for increased official support engagement, better community strategies, and expanded regional coverage.

A detailed overview and comparison of ClickHouse Cloud with its alternatives in the open-source data ecosystem.

This article discusses how to efficiently perform bulk inserts into Clickhouse, focusing on avoiding performance degradation due to excessive parts. It recommends batching data into larger chunks, pacing inserts to allow background merges, and monitoring system tables. The article also introduces tools like Jitsu Bulker, Clickhouse Bulk, PeerDB, DLT, and Dispatch for streamlined data ingestion.

Apache Impala 3.1 release brings improvements to vectorized query execution, enhanced performance optimization techniques, new functions like generateSerialID for auto-increment, and more. Highlights include vectorized filter pushdown, support for the Hive MERGE command, improved merge table functionality, and the addition of several new functions.

DeepSeek, a Chinese AI chatbot provider, experienced an infrastructure vulnerability that exposed sensitive data from its AI database. Researchers from Wiz found the issue and disclosed it to DeepSeek, which promptly secured the exposure. The exposed ClickHouse database contained chat histories, API keys, backend operational details, and more. This incident highlights ongoing concerns about data privacy with AI models, as highlighted by ongoing investigations from regulatory bodies in Western countries.

ClickHouse consistently outperformed all other tested data stores in both storage efficiency and query performance for analytical queries. The benchmarks demonstrated that ClickHouse is thousands of times faster than leading JSON data stores like MongoDB, DuckDB, and PostgreSQL, while maintaining optimal compression on disk. This makes it an unparalleled general-purpose JSON data store for use cases where cost efficiency and analytical query performance are critical.

In this blog post, Zane Mayberry discusses the implementation of data isolation in a multitenant environment using ClickHouse row policies. The solution involves creating custom settings to enforce tenant-specific SQL queries without requiring numerous roles and policies for each tenant.

The following FAQ summarizes common questions with respect to new tiers introduced in ClickHouse Cloud starting in January 2025.

This technical overview details the migration from AMD64 to ARM64 architecture on AWS Graviton instances for a data-intensive workload, specifically ClickHouse. The document provides insights into compatibility testing, performance benchmarks, and the overall process of transitioning to an ARM-based infrastructure.

BuzzHouse was developed to address critical gaps in the fuzzing landscape used for ClickHouse. By focusing on generating complex yet correct queries and identifying issues beyond simple crashes, it complements the current suite of tools used to test databases.