April 2026 Summaries

The recent release of clickhousectl, a command-line interface for ClickHouse, streamlines the management of local installations, local servers, and ClickHouse Cloud operations. Previously, comparing query performances between different ClickHouse versions required using Docker or finding binaries on GitHub, but clickhousectl simplifies this process by enabling easy installation and version management. The blog post highlights the installation process, server management, and data loading capabilities of clickhousectl, demonstrating significant performance improvements in recent ClickHouse versions, such as a 5x speedup for DISTINCT queries over low cardinality columns and an effective Parquet metadata cache that reduces repeat query times from around 9 seconds to 1-2 seconds. Users are encouraged to try clickhousectl for efficient ClickHouse version management and explore its documentation for further capabilities.

Google Cloud Next highlighted a series of significant developments from ClickHouse, underscoring its expanding integration with Google Cloud. Notable announcements included the launch of ClickHouse Cloud on Google’s custom ARM-based processor, Axion, which demonstrated enhanced performance for cloud-native workloads, achieving top rankings in benchmark tests. ClickHouse also introduced native integration with Google Cloud's Lakehouse Runtime Catalog, allowing direct queries on Iceberg tables without data movement, and announced the availability of its Bring Your Own Cloud (BYOC) service on the Google Cloud Marketplace, catering to enterprise clients with specific data residency needs. Additionally, ClickHouse revealed support for Google’s AI-native IDE, Antigravity, facilitating natural language queries and real-time analytics, and improved its GCS ClickPipes connector to support unordered data processing, ensuring better data quality. The company also teased future collaborations, such as a Dataflow template for Pub/Sub pipelines and a native Pub/Sub connector, reflecting its commitment to deepening its partnership with Google Cloud and enhancing its analytics capabilities.

ClickHouse, a leading company in real-time data platforms, has announced the launch of a new global partner program called "House Mates" to enhance collaboration with partner companies and provide optimal solutions to customers. This program is now officially available to partner companies in Japan, aiming to support the development and implementation of AI solutions and data analytics infrastructures by leveraging the high-speed and efficient data processing capabilities of ClickHouse. The program offers multiple partner types, including service partners, channel partners, and soon-to-be-added technology partners, each with specific roles such as consulting, reselling, and product development. ClickHouse provides comprehensive support to partners through initiatives like the ClickHouse Academy, joint marketing efforts, and technical and business assistance tailored for AI application development. Additionally, partnerships are structured into three tiers—Prime, Accelerate, and Ignite—allowing partners to benefit from varying levels of support based on their expertise and achievements. ClickHouse is recognized for its high-performance, open-source columnar database management system designed for real-time data processing and analysis, trusted by major companies like Sony and Tesla for its scalability and efficiency in generating insights and supporting decision-making processes.

ClickHouse, a leading company in real-time data platforms, has announced the launch of "Langfuse" in Japan, hosted on AWS Tokyo Region, to support the development and improvement of AI applications. This platform integrates ClickHouse's high-speed database capabilities with Langfuse's observability and evaluation features to provide a robust environment for Japanese companies to scale AI products without sacrificing quality or cost efficiency. Langfuse is designed to record and evaluate the internal mechanisms of AI operations, enabling developers to efficiently identify bottlenecks and optimize performance in real-time. The combination of ClickHouse's rapid data processing, Langfuse's detailed AI monitoring, and a sophisticated user interface creates a comprehensive AI-focused data stack that supports seamless integration from front-end interaction to back-end data analysis. Industry leaders such as Sony, Tesla, and Lyft trust ClickHouse's modern data platform, which offers high throughput and low latency to meet the demands of increasingly complex AI queries.

The pg_clickhouse extension has been well-received by the community for enabling queries on ClickHouse databases via Postgres, prompting continuous improvements and feedback-driven updates. Key advancements include enhanced pushdown capabilities for JSONB accessors, SQL value functions, and array functions, allowing efficient query processing by converting Postgres operations into ClickHouse-compatible syntax. This approach optimizes query execution by pushing down operations to ClickHouse, reducing the load on Postgres. Version v0.2.0 introduces support for current date and time functions, ensuring better synchronization with Postgres time zone settings. Additionally, the extension now supports query result streaming, addressing memory consumption issues associated with large data sets by efficiently managing the handling of results. Future developments are hinted at, including support for window function pushdown and regular expression compatibility, with ongoing community engagement reflected in events like PGConf.dev to share insights gained from building the Foreign Data Wrapper.

ClickHouse has introduced a new full-text search feature that integrates with its analytical engine, making it a competitive alternative to Elasticsearch for log analytics and observability tasks. The new feature leverages inverted indexes and ClickHouse's vectorized analytical capabilities to perform fast multi-token searches alongside large-scale aggregations. A benchmark comparing ClickHouse and Elasticsearch on a realistic OpenTelemetry log workload demonstrated that ClickHouse not only runs full-text analytical tasks 2-6 times faster but also stores data more compactly, using roughly one-fifth the disk space required by Elasticsearch. The benchmark highlighted ClickHouse's efficiency in both cold and hot query scenarios, with its storage and execution advantages especially evident in workloads combining text search with analytical operations like grouping and time-based trend analysis. This development positions ClickHouse as a robust solution for teams seeking to optimize log search and analytics within a managed observability stack, such as Managed ClickStack, enhancing both the performance and cost-efficiency of handling large-scale data sets.

Eisan System Development has built a real-time ID-POS analytics platform for multiple retail clients to support large-scale basket analysis, cross-analysis, and personalized recommendations by transitioning from a QlikView-based BI stack to ClickHouse Cloud. This shift has improved reliability and performance while reducing licensing and infrastructure costs. The system currently processes hundreds of millions of records per client, with a clear path toward handling tens of billions in the future, driven by continuous optimization. Eisan System Development has a nearly 40-year history of developing specialized software across various precision-demanding industries, such as healthcare and IoT platforms. The transition to ClickHouse was prompted by increasing data volumes and the limitations of the existing QlikView system, which was costly, unreliable, and began to falter under the load. The adoption of ClickHouse has provided predictable performance even under high load and allowed for a more efficient use of resources, with ongoing testing and adjustments to optimize server specifications. The company plans to scale the system to handle 100 billion records and is exploring strategies such as shifting processing to off-peak hours and considering auto-scaling to improve resource utilization. ClickHouse serves as a foundation for future expansion, with operational challenges now focusing on smoothly running a large-scale real-time analytics system as data volumes and client expectations grow.

The migration of Google Cloud's fleet to Axion C4A instances has resulted in significant performance improvements, with ClickBench queries running 30-55% faster and a reduction of approximately 15% in compute credits across production workloads. Axion's C4A instances, powered by Google Axion processors, offer enhanced memory bandwidth and performance per watt compared to traditional x86 instances, addressing challenges such as CPU throttling and capacity shortages during peak loads. The transition, which began in staging environments and expanded to a controlled rollout, has shown consistent advantages in analytical workloads, particularly for compute-bound tasks, with C4A instances outperforming N2D instances across various configurations. These improvements are attributed to Axion's ARM-based cores maintaining consistent throughput and exhibiting less frequency variation under load. The migration process has been seamless, with a systematic approach to service migration, ensuring minimal disruption and tangible benefits for customers, who are now experiencing improved efficiency without needing to alter their data or queries. The collaboration with the Google Cloud Axion team has been crucial in facilitating this transition, making Axion the anticipated standard for ClickHouse Cloud, promising continued advancements in performance and efficiency.

ClickHouse has announced an expansion of its strategic collaboration with Google Cloud, featuring four key developments aimed at enhancing real-time analytics capabilities. These include native integration with Google Cloud Lakehouse, enabling seamless data querying without the need for complex ETL processes, and the introduction of the Bring Your Own Cloud (BYOC) model, which allows organizations to utilize ClickHouse Cloud services within their own Google Cloud VPCs while maintaining control over their data environment. Additionally, ClickHouse Cloud on Google Cloud is transitioning to Google's Arm-based Axion processors, resulting in improved query performance and cost efficiency, and a new integration between the ClickHouse MCP server and Google Antigravity, which enhances workflow for developers and data analysts. This collaboration underscores a commitment to providing data teams with faster, more flexible, and cost-effective infrastructure solutions for next-generation analytics, reinforcing ClickHouse's position as a leader in real-time data warehousing and analytics.

The integration between ClickHouse and Google's Lakehouse Runtime Catalog enables direct querying of Google Cloud Lakehouse Iceberg tables in ClickHouse via the Apache Iceberg REST Catalog, initially as a beta feature in ClickHouse 26.2 and soon in ClickHouse Cloud. This development addresses the challenge of data accessibility across different storage systems by allowing interoperability with various Iceberg-compatible engines like BigQuery, Apache Spark, Trino, and now ClickHouse. Utilizing the integration requires a ClickHouse instance and a Google Cloud project with Lakehouse Runtime Catalog enabled, allowing ClickHouse to connect to the Iceberg REST Catalog API for data discovery and querying without data movement or additional connectors. Authentication is facilitated through Google's Application Default Credentials (ADC), and the integration supports querying and loading data into ClickHouse's native format for fast analytics. Future enhancements include write support for Google Cloud Lakehouse Iceberg tables and an improved user interface in ClickHouse Cloud to streamline connections and data queries.

ClickHouse is advancing its vision of agentic analytics through the Agentic Data Stack and by integrating its ClickHouse MCP server with platforms like Google Antigravity. This integration allows developers to perform high-speed analytical queries using natural language, which Antigravity translates into SQL for seamless interaction with data. Users can explore datasets such as UK property transactions, and the platform supports the creation of custom visualizations called Artifacts, which can be interactively iterated upon to refine insights. This iterative process, enhanced by features like commenting on Artifacts, represents a significant shift from traditional data analysis workflows, reducing reliance on intermediary steps and fostering real-time data exploration. The collaboration between ClickHouse and Antigravity exemplifies the future of agentic analytics, offering innovative tools and workflow improvements for developers and data analysts.

Index sharding in ClickHouse is a method designed to improve the efficiency of index analysis by distributing the analysis workload across multiple replicas, thereby reducing the working memory requirement for each replica and accelerating the analysis process. This approach partitions indexes across the fleet of replicas, allowing each to handle only a portion of the index and collectively covering the entire data set. As a result, it not only reduces memory usage significantly—especially crucial at massive scales involving billions of rows and petabytes of data—but also enhances performance by leveraging increased parallelism. This is particularly beneficial for workloads with extensive secondary indexes, where index analysis constitutes a substantial part of query execution time. Furthermore, ClickHouse's architecture, which separates compute and storage, facilitates this distribution without necessitating data movement, thereby allowing new replicas to integrate swiftly and efficiently. The introduction of index sharding enables horizontal scaling of index analysis, converting the previous single-node bottleneck into a distributed task, thus improving query speed and reducing resource overhead.

Nava leveraged ClickHouse to significantly enhance ELO's real-time payments monitoring platform, transitioning from Elasticsearch to reduce storage from 12 TB to 2 TB and cut infrastructure costs by 87% to R$120,000 annually. This migration resulted in 5x faster data aggregations and improved dashboard refresh rates to every five seconds. Founded in 1995, Nava initially focused on data center infrastructure but pivoted to financial services in 2012, partnering with Itaú to develop real-time business monitoring solutions. ClickHouse's adoption, driven by data engineer Lucas Souza, was based on its superior performance, efficient storage compression, and user-friendly SQL support, which replaced the cumbersome EQL syntax. The successful deployment at ELO, processing 22 million transactions daily, highlighted the advantages of ClickHouse, leading to broader adoption across Brazil's financial sector, with Nava now exploring ClickHouse Cloud for its clients. This transition has positioned ClickHouse as Nava's preferred solution for managing high-volume analytical workloads, offering improved performance and cost savings, and reinforcing ELO's role as a prominent advocate for the platform.

ClickHouse Cloud has announced the general availability of ClickPipes resources via Terraform and OpenAPI, ensuring full connector coverage and improved usability for managing data ingestion as code. Initially introduced in beta, this update addresses previous gaps, notably adding support for CDC ClickPipes for Postgres, MySQL, and MongoDB, along with new connectors such as BigQuery and Azure Blob Storage. The integration allows users to manage ClickPipes through a programmatic interface, aligning with the infrastructure-as-code approach to streamline deployment workflows. Users can now create, manage, and import ClickPipes configurations in Terraform, enhancing automation and version control while allowing seamless integration with existing workflows. Additionally, ClickHouse plans to expand ClickPipes functionality with features like SSH tunneling and compatibility with the ClickHouse CLI, aiming to further reduce ETL complexity and enhance data movement efficiency.

The creation of otel.fyi emerged from frustration with navigating the fragmented documentation of the OpenTelemetry Collector, a vendor-neutral service crucial for collecting and routing observability data. Often deployed as an agent or gateway, the Collector's documentation is dispersed across various contrib packages, making it cumbersome for users to find relevant configuration details. otel.fyi offers a search-first interface that consolidates and normalizes this scattered information, allowing users to quickly access structured views of receivers, processors, exporters, and extensions, complete with configuration examples. Initially built for internal use, its utility for configuring the Collector efficiently inspired its public release, aiming to ease workflows for others using OpenTelemetry. Future enhancements may include AI assistance to further streamline the configuration process.

Trio, a Brazilian fintech specializing in high-volume electronic payments, utilizes ClickHouse as its single source of truth for payment analytics, which aids in reconciliation, compliance reporting, and customer dashboards. Following its acquisition of PayBrokers in 2025, Trio faced the challenge of managing a vast stream of payment data, which led them to adopt ClickHouse for its speed and efficiency, reducing storage needs by 88%. The architecture involves a custom ETL solution using Redpanda and Airflow, allowing for precise data shaping and handling schema drift before ingestion into ClickHouse. This setup supports real-time analytics and reconciliation through a sliding window approach, addressing late, duplicate, and out-of-order events without extensive recomputations. The transition to ClickHouse has significantly enhanced operational efficiency, enabling tighter feedback loops and easier data management. Trio's future plans include refining their architecture to further streamline operations and leverage new ClickHouse features to maintain a robust, scalable analytical foundation.

The April 2026 ClickHouse newsletter introduces several new features and events, such as a new CLI for ClickHouse local and cloud, agentic coding advancements, and materialized CTEs in the latest release. The newsletter highlights Nazarii Piontko, a solutions architect who contributed significant features to ClickHouse, and details the upcoming Open House 2026, a free ClickHouse user conference in San Francisco. ClickHouse 26.3 introduces materialized Common Table Expressions and WebAssembly User-defined functions, enhancing performance and flexibility. Additionally, the newsletter discusses agentic coding, which has shown productivity gains by utilizing AI agents for various tasks, and shares how Goldsky optimized blockchain data backfills through ClickHouse. There are insights into building a SIEM with ClickHouse and Clickdetect, and an exploration of high-performance full-text search for object storage. The newsletter also outlines various upcoming events, training sessions, and opportunities for ClickHouse users to engage with the community and enhance their skills.

clickhousectl is a command-line tool designed to streamline both local and cloud-based management of ClickHouse environments, providing functionalities for version management, project scaffolding, and server operations. It allows users to manage ClickHouse installations locally by installing versions, creating isolated servers, and setting up projects with standardized layouts for tables, views, and queries. For cloud operations, clickhousectl offers browser-based OAuth for read-only access and API key authentication for operations that alter state, enabling users to create, scale, and manage ClickHouse Cloud services. The tool supports usage in CI/CD pipelines by allowing for schema validation and query testing, and it integrates with AI agents to facilitate project setup and deployment. clickhousectl's open-source nature and its adaptability for both local and cloud environments make it a versatile tool for developers working with ClickHouse.

The blog post explores three index-based pruning techniques in ClickHouse to optimize query performance by minimizing data reads, focusing on a dataset of UK property sales. The primary index, which organizes data based on a table's primary key, allows for efficient pruning by skipping entire granules when the filter condition is met. Lightweight projections offer secondary indexing without duplicating full rows by storing only a sorting key and a pointer to the base table, which is useful for non-primary key columns and can significantly improve query performance. The minmax index, a type of skip index, records minimum and maximum values for each granule, enabling effective pruning if the indexed column is correlated with the primary key. The post demonstrates these techniques using real-world queries on a substantial dataset, showcasing how they can drastically reduce query times and data processing loads, ultimately enhancing the efficiency of data retrieval in ClickHouse.

ClickHouse and Grafana are increasingly used together for observability and real-time analytics, providing teams with powerful options for handling large volumes of data efficiently. This post outlines the ongoing enhancements in the Grafana plugin to improve user experience by simplifying SQL requirements and integrating intuitive features like instant filtering, structured attribute displays, and automatic log volume histograms. Future plans include out-of-the-box dashboards for OpenTelemetry and Kubernetes observability, a metrics exploration tool that eliminates the need for complex SQL queries, and improvements for bidirectional SQL parsing and per-user query identity to enhance access control and user context. The goal is to create a seamless, user-friendly experience that combines the flexibility of Grafana's existing ecosystem with the efficiency and scalability of ClickHouse, while continuing to gather user feedback to refine these developments.

Mintlify has transitioned from using PostHog to ClickHouse Cloud to enhance its analytics capabilities, significantly reducing dashboard load times from tens of seconds to under a second, which has contributed to a 30% improvement in their Net Promoter Score (NPS). This move was driven by the need for a more scalable solution to accommodate their growing customer base and the increasing involvement of AI agents in accessing their documentation. Mintlify, serving companies like Microsoft and Coinbase, found ClickHouse Cloud to be a cost-effective solution, running at 60% lower costs than PostHog, and providing zero ongoing maintenance while supporting real-time analytics crucial for their operations. The migration process was smooth, involving a two-week period during which events were written to both ClickHouse and PostHog, allowing for a seamless transition. The new setup not only improved customer satisfaction by eliminating rate limits and enhancing dashboard reliability but also freed up engineering resources, as ClickHouse Cloud handles maintenance automatically. Looking forward, Mintlify aims to leverage ClickHouse's capabilities to better track and analyze interactions from both human and AI agents, ensuring that their infrastructure is well-prepared for future growth.

Eisan System Development has transitioned from a QlikView-based BI system to ClickHouse Cloud to enhance the reliability and performance of its real-time ID-POS analytics platform for retail clients, allowing for basket analysis, cross-analysis, and personalized recommendations. This change was driven by the growing data volumes and demands for real-time insights that the previous system could not efficiently handle, leading to high costs and unreliable performance. ClickHouse, discovered by Eisan during an exploration of open-source alternatives, provided a solution with its speed and predictable performance under pressure, even with lower hardware specifications. Eisan's journey with ClickHouse involves ongoing optimization, with plans to scale the system to handle up to 100 billion records by making smart architectural decisions and exploring efficient resource usage. The focus is not only on improving processing times but also on operational strategies to manage large-scale, real-time analytics systems effectively as data and user expectations grow.

The March edition of What's New in ClickStack highlights a range of enhancements, including deeper event analysis with Event Deltas, expanded SQL chart support with Grafana-style template macros, and improved dashboard organization. Notable updates include the introduction of AI Notebooks for investigating logs and metrics, and important changes to Event Deltas for more effective root cause analysis, such as always-on attribute distribution and refined attribute scoring. The platform now supports raw SQL in various chart types, allowing for more complex queries, and introduces sample-aware aggregations to address statistical accuracy in sampled datasets. Additionally, ClickStack has improved dashboard management with a template gallery and listing pages for easier navigation, added the ability to mark dashboards and searches as favorites, and enhanced dashboard filters for more flexible and performant use. In local mode, dashboards and searches can now persist using browser localStorage, and an expanded range of chart display units has been introduced to better present metrics.

A Solutions Architect at ClickHouse developed a comprehensive AI-driven retail analytics platform called ClickShop in just a few days, demonstrating the effectiveness of agentic coding when supported by a robust data platform that handles complex tasks natively. The application integrates real-time analytics with transactional workflows and features 18 AI agents specialized for various business roles, enhanced by a dual-layer observability system for both infrastructure and AI performance. Utilizing open-source components like ClickHouse for analytics, PostgreSQL for transactions, and tools like LibreChat and Langfuse for AI and monitoring, the project underscores the advantages of a cohesive, open-source data stack that simplifies architecture and accelerates development. The use of an AI-powered IDE like Cursor enabled rapid iteration and deployment, highlighting the potential for streamlined application development when leveraging a well-integrated data platform.

clickhousectl is the official command-line interface for ClickHouse, designed to manage both local installations and ClickHouse Cloud environments, and is currently in beta. It supports agentic development by providing a streamlined interface for both human users and AI agents, allowing them to interact efficiently with ClickHouse's infrastructure. The CLI functions as a version manager, facilitating the discovery and installation of different ClickHouse versions, and offers project scaffolding to maintain consistent SQL file structures. It can manage local server instances with isolated data directories, while also handling cloud infrastructure tasks such as service creation, scaling, and management, with commands supporting JSON output for easy integration with scripts. clickhousectl also enables destructive operations, although caution is advised when pairing with AI agents, particularly in production environments. It supports OAuth and API key authentication, with the latter providing more extensive permissions for modifying cloud resources. Additionally, the CLI offers agent skills that can be installed to enhance interactions with ClickHouse, and feedback from users is encouraged to improve its functionality.

FOSDEM 2026 in Brussels was a vibrant weekend for the ClickHouse community, featuring a mix of social gatherings and insightful presentations. The event began with a community dinner attended by 106 participants, including ClickHouse founder Alexey Milovidov. Key presentations included a session on building real-time visualizations with ClickHouse, a novel approach to hot-patching production systems using LLVM's XRay, and leveraging inverted database indexes for efficient text processing. Other talks covered integrating Rust with C++ in ClickHouse, optimizing byte movement in software, and contributing to open-source projects like MariaDB and Postgres. Additionally, community discussions highlighted dynamic bot blocking using web-server access-log analytics. The event was not only a chance for learning but also for networking and building connections within the open-source community.

The text discusses the challenges and limitations of modern observability practices, particularly focusing on the constraints posed by retention, sampling, and roll-ups. These practices, often seen as necessary optimizations, are argued to be workarounds for the limitations of current storage systems, which struggle with scalability, cost, and high-cardinality data. The text suggests that these constraints not only limit the effectiveness of human operators but also hinder the potential of AI-driven workflows in observability by removing critical data context. It advocates for a shift towards using columnar storage models, which can handle full-fidelity, high-cardinality data more economically, thereby reducing the need for trade-offs like sampling and roll-ups. ClickHouse is highlighted as a standout solution due to its real-time focus, efficient compression, and ability to handle large datasets without requiring storage tiers. The text envisions an observability future where data is retained in full fidelity, enabling faster, more accurate analysis and supporting the next generation of agent-driven workflows.

ClickHouse version 26.3 introduces numerous enhancements, including 27 new features, 40 performance optimizations, and 202 bug fixes, with significant contributions from a growing community of developers. This release enables asynchronous inserts by default, substantially improving throughput by batching data on the server side rather than relying on client-side operations. The MATERIALIZED clause for Common Table Expressions (CTEs) now optimizes query performance by evaluating subqueries once and storing results in temporary tables, while the naturalSortKey function enhances sorting capabilities for more intuitive data organization. Additionally, JOIN reordering has been extended to support more join types, such as ANTI, SEMI, and FULL, improving execution efficiency across complex join scenarios. Enhancements to the Map data type's storage format reduce data processing time by implementing hash-based bucketing, which accelerates single-key lookups. New settings for the EXPLAIN clause, such as pretty=1 for indented output and compact=1 for collapsing expression steps, help users better understand query execution plans. These improvements collectively aim to boost ClickHouse's performance, efficiency, and user experience, particularly in handling large datasets and complex queries.

Respan, formerly Keywords AI, leverages ClickHouse Cloud to handle high-throughput observability for large language model (LLM) applications, processing around 50 million events per day. Initially using a Postgres-based system, Respan faced challenges as their workload increased to 50-100 requests per second, prompting a migration to ClickHouse Cloud to better manage real-time data ingestion and analytics. The platform's backend, now optimized for high-speed analytics, employs incremental materialized views and compact data storage to maintain dashboard performance while minimizing query costs. This architecture supports both real-time metrics and user metadata, allowing efficient data aggregation and refreshing without the need for constant recomputation. Raymond Huang, co-founder and CTO, highlights the role of ClickHouse in simplifying operations through its indexing model, which ensures scalability and efficient querying without frequent architectural changes. Respan’s observability stack, now underpinned by ClickHouse, provides robust support for tracing LLM workflows, enabling detailed tracking of individual requests within the system.

HyperDX has introduced a new feature, AI Summarize, which transforms complex log entries, traces, and patterns into narrative summaries to bridge the gap between raw telemetry and human understanding, making them more comprehensible and engaging. This feature uses hand-written phrase pools and data extraction to create themed narratives that adapt to the context, such as Detective Noir for errors and Shakespearean Drama for performance issues, without relying on external large language models (LLMs), ensuring zero cost, zero data sharing, and zero latency. The humorous and creative narratives, like portraying a swift Redis call as a peregrine falcon or a persistent log pattern as a forgotten chorus, aim to make technical data more relatable and entertaining while maintaining accuracy with real OpenTelemetry attributes. This innovation is designed to remain client-side, ensuring privacy and transparency, and although it currently does not use AI, the infrastructure for potential LLM integration is in place, with feedback invited for future development.

ClickHouse, known for its high-performance systems, has developed a managed Postgres service focusing on speed from the outset, offering one of the fastest managed Postgres services available. This service complements ClickHouse's analytical capabilities with Postgres handling transactional workloads, forming a robust data stack for SaaS and AI applications. To evaluate this service, ClickHouse created PostgresBench, a benchmark modeled after ClickBench, to compare managed Postgres services using a transparent, reproducible methodology. Built on pgbench, it simulates common transactional patterns and tests various configurations to reflect real-world usage, capturing metrics like TPS and latency. The benchmark highlights Postgres managed by ClickHouse's superior performance due to its NVMe-backed storage, which minimizes I/O latency, a critical factor in sustaining high-performance levels in OLTP workloads. The results are publicly available, allowing for community validation and contributions to enhance the benchmark's comprehensiveness and fairness.

Role-Based Access Control (RBAC) has been introduced to Managed ClickStack, enabling teams to define permissions based on roles for accessing resources such as dashboards, saved searches, and notebooks. This feature replaces the previous model where all users shared the same permissions, which led to duplicated setups and high operational overhead. RBAC allows for roles to be assigned with varying levels of access—no access, read, or manage—across different resource types, and even supports fine-grained access rules for more precise control. Integration with ClickHouse Cloud ensures consistent user management and permissions, with ClickStack roles determining what users can do within observability workflows. Although this release establishes a robust foundation for RBAC, future updates aim to enhance data-level access controls, particularly by introducing row-level access requirements, to streamline management across both ClickStack and ClickHouse environments.

Modern businesses are experiencing a shift toward real-time analytics, driven by granular data tracking across various operations, such as cloud usage, financial transactions, and AI system metrics. Mike Driscoll, co-founder of Rill, highlights the importance of integrating disparate data sources to gain comprehensive business insights, beyond what individual dashboards offer. Rill, a BI-as-code tool, leverages ClickHouse for efficient data aggregation and aims to redefine business intelligence by allowing teams to define data sources, transformations, and business logic as code, facilitating a unified analytical workflow. This approach supports rapid data ingestion and real-time querying, empowering organizations to transition from traditional retrospective reporting to proactive operational decision-making. The system utilizes declarative configurations to streamline data flow into ClickHouse, where real-time performance enables interactive analysis and AI-assisted conversational analytics, ensuring trust through traceable insights. By maintaining a metrics-first design and eliminating manual dashboard creation, Rill offers a scalable solution for businesses to adapt to a fully metered world, where analytics becomes integral to business infrastructure.

In the evolving landscape of AI-assisted coding, ClickHouse has embraced the use of coding agents to enhance productivity and streamline various tasks, from writing boilerplate code and finishing stale pull requests to investigating complex bugs and optimizing performance. The article discusses the nuanced perspectives on AI usage, highlighting both the skepticism and the transformative potential of coding agents, particularly with advancements seen in tools like Claude Opus 4.5. ClickHouse has integrated these agents not only for mundane tasks but also for complex problem-solving, demonstrating significant improvements in efficiency and code quality. The company’s approach emphasizes using AI as a complementary tool rather than a replacement for human engineers, advocating for cautious and thoughtful integration to avoid pitfalls such as AI psychosis and maintaining a focus on core product strategies. With AI's potential to reshape engineering practices, ClickHouse encourages experimentation while maintaining rigorous standards to ensure that AI augments rather than detracts from human expertise.