--- title: "Clickhouse" description: "High-performance open-source columnar database management system designed for real-time analytics and data warehousing at scale" --- # Clickhouse > High-performance open-source columnar database management system designed for real-time analytics and data warehousing at scale ## Articles ### [How Socialpruf built a faster, more reliable data stack by replacing Neon with Postgres managed by ClickHouse](https://yomu.fyi/post/how-socialpruf-built-a-faster-more-reliable-data-stack-by-replacing-ne.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Author: ClickHouse - Published: Mar 17, 2026 Socialpruf, a social media performance tracking platform ingesting hundreds of posts per second, experienced analytics latency, connection dropouts, and high network transfer costs while running Postgres on Neon alongside ClickHouse Cloud. To resolve these issues, the team migrated their transactional system of record to NVMe-backed Postgres managed by ClickHouse, colocating transactional and analytical workloads. Although standard Postgres logical replication failed on their 0.5 TB dataset, they achieved continuous synchronization using PeerDB before conducting a production cutover. The migration eliminated connection dropouts, reduced network transfer fees, and improved overall Postgres query performance by approximately 30 percent. To manage thousands of concurrent connections generated by ingestion workers, Socialpruf implemented ClickHouse's bundled, horizontally scalable PgBouncer instances. ### [AI is redrawing the database market](https://yomu.fyi/post/ai-is-redrawing-the-database-market.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Author: Tanya Bragin - Published: Mar 17, 2026 Emerging artificial intelligence workloads are fundamentally transforming data platform requirements across real-time analytics, data warehousing, and observability. Conversational analytics and autonomous agents replace periodic batch queries with rapid bursts of concurrent, low-latency requests, exposing the architectural limits of traditional cloud data warehouses and standalone transactional databases. To resolve these constraints, architectures are converging on engine-level integrations between Postgres for transactions and ClickHouse for analytical scale, while unifying operational observability and business event storage. ClickHouse also incorporates turnkey agent interfaces through its acquisition of LibreChat alongside dedicated execution tracing from tools like Langfuse. These combined capabilities ensure organizations can reliably support interactive agentic workflows without incurring severe latency penalties or compounding infrastructure costs. ### [Designing the new async-native ClickHouse Python client](https://yomu.fyi/post/designing-the-new-async-native-clickhouse-python-client.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Author: Joe Spadola - Published: Mar 16, 2026 The official ClickHouse Python client, clickhouse-connect, originally addressed asynchronous execution by wrapping its synchronous HTTP client in a thread pool executor. However, this executor model experienced thread exhaustion, GIL contention, and severe tail latency variance under heavy concurrent workloads. To resolve these scalability bottlenecks, the developers redesigned the client around a half-sync/half-async pattern utilizing aiohttp for async network I/O. A custom bounded queue bridges streaming socket reads on the event loop with synchronous, CPU-intensive binary parsing running inside background threads. Benchmarks demonstrate that this architecture stabilizes tail latencies, delivering an average P95 latency of 556 milliseconds compared to 869 milliseconds for the legacy executor client. ### [ClickHouse Release 26.2](https://yomu.fyi/post/clickhouse-release-26-2.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Author: ClickHouse - Published: Mar 16, 2026 ClickHouse version 26.2 introduces twenty-five new features, forty-three performance optimizations, and one hundred eighty-three bug fixes across the database engine. This release graduates both the text-index and the QBit vector embedding data type to production-ready status for search and retrieval workloads. For streaming ingestion pipelines where row arrival rates are low, the engine adds time-based block flushing configurations to write data parts before hitting default row or byte thresholds. The update also embeds the ClickStack observability user interface directly into the standard binary distribution, accessible through the local HTTP server port. Further enhancements include automatic min-max indexing for temporal columns in MergeTree tables, interactive time-based one-time password authentication in the client, and a high-performance prime number generator function. ### [Unordered mode for GCS ClickPipes is now available](https://yomu.fyi/post/unordered-mode-for-gcs-clickpipes-is-now-available.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Author: Marta Paes - Published: Mar 16, 2026 ClickHouse Cloud has added unordered mode to its Google Cloud Storage (GCS) ClickPipes connector, enabling continuous data ingestion regardless of file naming or arrival sequence. Standard GCS ClickPipes rely on polling every 30 seconds and require files to arrive in strict lexicographical order, which causes out-of-order data and backfills to be ignored. Unordered mode replaces polling by consuming OBJECT\_FINALIZE notifications from Google Cloud Pub/Sub whenever objects land in a bucket. State tracking is managed through a metadata store, and the connector automatically reprocesses batches upon failure while maintaining exactly-once insertion guarantees. Setup requires configuring a Pub/Sub topic and bucket notifications via tools like gcloud, followed by configuring the ClickPipe via the console or Terraform provider. ### [What's new in ClickStack. February '26.](https://yomu.fyi/post/what-s-new-in-clickstack-february-26.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Author: The ClickStack Team - Published: Mar 12, 2026 ClickStack introduced several platform updates and query performance enhancements across both open-source and Cloud environments for February 2026. ClickHouse Cloud OpenAPI now exposes ClickStack resources including dashboards, alerts, sources, and webhooks to support automated configuration-as-code workflows. In addition, ClickHouse 26.2 embeds the ClickStack UI directly into the database binary with a 4.1 MB footprint for local exploration and diagnostics. Visualizations now support native SQL mode for tables, allowing users to join disparate datasets like logs and traces across injected dynamic time variables. Finally, query execution speeds improve through streaming skip indexes, index pruning on disjunctions, and an expanded 100,000-row lazy materialization limit alongside new threshold alerting on number charts. ### [Announcing chDB 4: write Pandas, run ClickHouse, now on Hex](https://yomu.fyi/post/announcing-chdb-4-write-pandas-run-clickhouse-now-on-hex.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Author: Ryadh Dahimene, Auxten Wang - Published: Mar 11, 2026 chDB 4 introduces Data Store, a native component that allows users to write Pandas-like code that executes on the ClickHouse OLAP SQL engine. Standard Pandas workflows suffer from eager execution, excessive memory materialization, and lack of automatic query optimizations. To solve this, Data Store implements a four-layer architecture that lazily records operations as a chain of LazyOp objects and compiles them into optimized ClickHouse SQL plans upon evaluation triggers. When encountering operations unsupported by ClickHouse, the QueryPlanner splits the pipeline into segments, routing tasks between ClickHouse and Pandas while using Python's memoryview to minimize data copying overhead. This release also integrates natively into Hex notebooks. ### [GitTrends: A Google Trends style view of the GitHub ecosystem](https://yomu.fyi/post/gittrends-a-google-trends-style-view-of-the-github-ecosystem.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Author: Lionel Palacin - Published: Mar 10, 2026 GitHub produces billions of events across issues, pull requests, and comments, but turning this vast stream into a real-time trends analyzer presents significant technical challenges. GitTrends is an open-source demo application designed to search and compare technology keywords across nearly ten billion GitHub events. Built to showcase the full-text search capabilities recently released in ClickHouse, the application indexes raw text without pre-computed answers or intermediate data transformation. ClickHouse executes full-text searches and aggregations within a single engine in one pass, eliminating cross-system joins and data movement. The demo includes live comparisons toggling between full-text search, bloom filters, and full table scans to illustrate query performance at scale. ### [Announcing General Availability of ClickHouse Full-text Search](https://yomu.fyi/post/announcing-general-availability-of-clickhouse-full-text-search.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Author: Melvyn Peignon - Published: Mar 10, 2026 ClickHouse has reached general availability for its native Full-text Search feature, designed to accelerate token-based text filtering across large datasets. The capability relies on deterministic inverted indexes rather than probabilistic Bloom filter skip indexes, mapping individual tokens directly to row numbers. It supports plain strings, string arrays, and map keys or values while allowing configurable pre-processing and tokenization using standard SQL expressions. The system does not implement relevance scoring or store positional data for ranking, focusing instead on rapid filtering paired with large-scale analytical aggregations. Testing shows significant reductions in scanned granules alongside substantial latency improvements, though text indexes require more storage than Bloom filters. ### [How Critical Manufacturing uses ClickHouse to bring real-time intelligence to the factory floor](https://yomu.fyi/post/how-critical-manufacturing-uses-clickhouse-to-bring-real-time-intellig.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Author: ClickHouse - Published: Mar 10, 2026 Critical Manufacturing faced performance and scaling bottlenecks while processing millions of factory floor events using a legacy Microsoft SQL Server analytics stack. Increasing data volumes caused historical queries to take hours, straining real-time dashboards and escalating operational maintenance. The engineering team replaced this architecture by migrating analytics workloads to ClickHouse connected directly to streaming Kafka topics. They implemented an ELT pattern alongside ReplacingMergeTree engines to deduplicate late-arriving sensor records and utilized TTL policies for automated lifecycle management. The resulting platform delivers sub-second analytical queries across historical and real-time operational data, expands into OpenTelemetry observability storage, and maintains embeddings for retrieval-augmented generation workloads. ### [What Replo learned optimizing 100+ billion events in ClickHouse](https://yomu.fyi/post/what-replo-learned-optimizing-100-billion-events-in-clickhouse.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Author: ClickHouse - Published: Mar 9, 2026 Replo built an analytics pipeline on ClickHouse to serve more than 4,000 Shopify merchants processing 3,000 to 5,000 events per second. The initial single-table architecture recalculated session metrics on the fly, leading to poor query efficiency as volume scaled. To speed up dashboard queries, the team implemented a mark-and-unmark pattern with SummingMergeTree and refreshable materialized views, but real-time deduplication and fractional attribution caused runaway query backlog loops. Replo resolved the issue by limiting real-time recomputations to a specialized table holding only the last 40 minutes of purchase-related events. This architecture allowed processing over 100 billion records while keeping query times fast and ingestion lag to approximately one minute. ### [Introducing ClickStack embedded in ClickHouse](https://yomu.fyi/post/introducing-clickstack-embedded-in-clickhouse.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Author: The ClickStack Team - Published: Mar 5, 2026 ClickHouse 26.2 introduces an embedded distribution of the ClickStack observability UI packaged directly inside the ClickHouse binary. Designed for local exploration and instance inspection, this distribution allows developers to visualize internal query logs, traces, and system metrics without deploying external services. To preserve build integrity and keep the added binary footprint around 4.1 to 4.2 MB, the team avoided a Node.js dependency by embedding a pre-built static Next.js application using CMake-generated C++ structs. Supporting components such as MongoDB, Express, and the Pyodide WebAssembly runtime were omitted, disabling alerting and persistent dashboards in favor of simplicity. Users can access the interface directly over HTTP to monitor local databases and query performance out of the box. [Newer posts](https://yomu.fyi/company/clickhouse/page/3.md)