Top recent clickhouse news, how-tos and comparisions

The article explains how Momentic migrated its caching system from Postgres to ClickHouse to handle massive data growth. By using ClickHouse's sparse primary index and materialized views, the team reduced latency and improved scalability. This transition allowed them to process billions of cache entries daily with high performance.

ClickHouse has significantly improved its join performance over the last two years, making it 26 times faster on heavy workloads. The updates include better query planning, lazy column replication, and automatic join reordering based on statistics. These engineering improvements allow the database to handle complex SQL queries more efficiently with default settings.

This article explains why using NULL values in ClickHouse can lead to slower performance compared to using default values like zero. Because ClickHouse is a columnar database, it manages NULLs using a separate byte map that adds extra processing and overhead. The author recommends avoiding NULLs in production to maintain better speed and efficiency.

This article explains the benefits and challenges of using a table-per-tenant approach in ClickHouse. It provides practical advice on managing parts, handling server startup times, and preventing schema drift. The author also recommends using dedicated nodes and proxy routing to balance read and write workloads effectively.

The authors announced new features for OpenData Buffer that allow high-volume logs to be sent to ClickHouse using S3 as a storage layer. This setup achieves 1.1 Gbps of throughput for around $180 per month, which is significantly cheaper than using traditional systems like Kafka. The new pipeline uses parallel processing and stable batch identities to ensure fast, reliable, and cost-effective data delivery.

ClickHouse Cloud has launched a beta service for managed Postgres that uses NVMe storage for faster transactions. This platform allows users to sync data automatically into ClickHouse for real-time analytics and use a unified query layer. The service is currently free until June 2026, with a 50% discount offered during the beta period.

ClickHouse Cloud has introduced multi-stage distributed query execution to better handle large-scale data workloads. This new system allows intermediate data to be repartitioned between execution stages, removing bottlenecks in large joins and high-cardinality aggregations. Testing shows significant performance gains, with some queries running up to 7.4 times faster on multiple nodes.

ClickStack Cloud is a new fully managed observability service that uses ClickHouse to handle logs, metrics, and traces. It allows teams to send OpenTelemetry data to a managed endpoint without having to manage any underlying infrastructure or complex schemas. The platform automatically handles scaling, storage, and data tuning to provide a simple and efficient experience for users.

The ClickHouse CTO shares how using AI coding agents has significantly boosted productivity on their large C++ codebase. He explains that while agents are best for tasks like fixing tests and resolving merge conflicts, they must be used as tools to support human judgment. The article provides practical advice for engineers to successfully integrate AI into their daily development workflows.

Jaeger version 2.18.0 introduces native support for ClickHouse as a storage backend for distributed tracing. This update provides high-speed data ingestion, significant compression ratios, and efficient analytical queries for large-scale telemetry data. The new system uses a specialized schema to balance fast trace retrieval with complex multi-filter searches.

Tinybird improved its cloud storage efficiency by cleaning up petabytes of orphaned S3 objects, reducing monthly storage costs by about 45%. The cleanup process involved identifying unused metadata and ensuring that only truly unused data was deleted. They also strengthened their recovery procedures after nearly losing real data due to incomplete snapshots during the cleanup process.

ChatFeatured is a startup that helps brands improve how they appear in AI search engines by influencing the content shared about them. Originally running ClickHouse for analytics, ChatFeatured needed to integrate it with Postgres for transactional workloads without managing two systems separately. By migrating to ClickHouse managed Postgres, the platform's analytics query times dropped from 2.5 minutes to under 1 second, improving user experience and enabling new features.

At Cloudflare, a migration to a more granular partitioning scheme in ClickHouse led to unexpected performance degradation in billing aggregation jobs. Despite initial checks showing normal I/O and memory usage, analysis revealed hidden bottleneck: excessive lock contention and vector copying during query planning due to tens of thousands of parts per namespace. Three optimizations resolved the issue: acquiring a shared lock for read-only operations, deferring vector copying by creating a shared copy of relevant parts, and implementing binary search for faster part filtering based on sorted partition keys. These changes stabilized query performance and reduced durations significantly, though long-term architectural considerations remain.

ClickHouse Cloud enhances the Join table engine by implementing it as a SharedJoin table backed by MergeTree or ReplacingMergeTree tables, solving open-source drawbacks like non-distribution and lack of frequent updates. This setup enables efficient ANY LEFT joins for dimensional modeling, automatically handling deduplication and compaction, making it ideal for frequently updated dimension data in analytical workflows.

A customer needed to perform semantic search over 20 million embeddings in Tinybird but initially faced timeouts due to fragmented vector similarity indexes across multiple partitions and insufficient memory caching. By consolidating data into a single global index, increasing the vector_similarity_index_cache_size to fit the entire graph in RAM, and maintaining all data within one partition, response times improved dramatically from 15-48 seconds to under 200 milliseconds per query, regardless of the number of top-K results requested. This demonstrates that Tinybird can effectively handle large-scale semantic similarity searches without requiring a separate dedicated vector database.