ClickHouse resources for data engineers

ClickHouse Cloud introduced a TimeSeries table engine to serve as a Prometheus replacement. Data engineers can ingest metrics via Prometheus remote write and run native PromQL directly in ClickHouse, Grafana, or ClickStack without rewriting queries into SQL.

ClickHouse Cloud introduced replica-aware routing to direct requests to the same replica. This ensures access to temporary tables and enables read-after-write consistency. Clients route traffic by setting an HTTP header or overriding the TLS SNI value to target the identical host.

ClickHouse and Langfuse explore the agentic data stack, focusing on how real-time analytics powers autonomous

Build full data infrastructure to capture, store, transform, and serve analytical data directly in apps without external dependencies.

ClickHouse introduced native AI SQL functions, enabling direct LLM and embedding calls within the analytical engine. By eliminating external data movement for tasks like classification and extraction, engineers can run vector and RAG pipelines directly inside their database queries.

ClickHouse can bridge the gap when loading Parquet files into MySQL without native tooling. Using clickhouse-local alongside S3 url functions and named MySQL collections, engineers can easily inspect remote Parquet schemas and stream data directly into MySQL tables for ad hoc workflows.

Bruin orchestrates near-real-time fraud monitoring using an append-only ClickHouse MergeTree log fed by PostgreSQL change capture. It aggregates events into minute, hour, and daily rollups, applying interval modifiers to cleanly restate late-arriving payment statuses without full recomputations.

WalShadow enables sub-second Postgres replication to ClickHouse by directly consuming physical WAL streams instead of using logical replication slots. It decodes WAL off-host and writes native blocks to ClickHouse, hitting 200 ms latency and 289K rows/sec in benchmarks.

ClickHouse launched an integration for the Data agent in ChatGPT Work via a Remote MCP server. Authenticated with OAuth, the managed server exposes tools that allow agents to inspect schemas, execute SELECT queries, and construct dashboards directly from ClickHouse Cloud data.

ClickHouse Cloud launched On-Demand Compute to execute queries on shared worker pools outside a cluster. Powered by a cost-based optimizer and multistage query execution, it isolates heavy ad hoc queries from production workloads without waiting for cluster autoscaling.

ClickHouse 26.8 LTS introduces background queries that execute independently of client connections, benefiting long-running ingest and export pipelines. The release also adds pipelined SQL, expanded data lake integrations, and query optimizations for Parquet, joins, and GROUP BY operations.

ClickHouse Managed Postgres is now in Private Preview on GCP. The service pairs an NVMe-backed OLTP engine with ClickHouse OLAP, featuring native CDC replication and a unified query layer via pg_clickhouse to prevent cross-cloud egress and simplify pipelines.

ClickHouse Cloud delivered 412x better performance per dollar than Snowflake under continuous real-time ingestion. Under a 1M rows/sec stream, Snowflake aggregate queries hit bottlenecks that warehouse scaling could not fix, as pre-aggregation and sorting failed to keep data query-ready.

To curb S3 tail latency, ObsessionDB uses a distributed NVMe cache over S3, transforming high tail times into a 0.5 ms node hop. Standard clusters can optimize performance using the query condition cache and proper storage policy caching, avoiding S3 round trips on selective analytics filters.

Narwhal is an agent-native terminal database client providing schema diffing, streaming cancellable queries, and an embedded MCP server. It supports six engines including DuckDB, ClickHouse, and Postgres via a single binary.

The chdb Postgres extension embeds an in-process ClickHouse engine to accelerate imports and exports with cloud storage. It supports native ClickHouse data formats and loads CSV, JSON, and Parquet from S3 up to 2-3x faster than DuckDB-backed tools like pgduckdb and pglake.

CostBench benchmarked real-time performance per dollar across ClickHouse Cloud, Snowflake, BigQuery, and Redshift Serverless under sustained loads. Streaming 100 billion rows at 1M rows/s, ClickHouse Cloud achieved the lowest ingestion and query serving costs, outperforming alternatives by

Google MCP Toolbox bridges AI agents and ClickHouse by translating agent text queries directly into vector search. Declaring an embedding model in YAML automates generation and cosine distance ranking in SQL, abstracting bespoke embedding pipelines for real-time vector analytics.

ClickHouse 26.8 enables native streaming HTTP APIs using named HTTP handlers, result modification, and framing formats. This architecture removes intermediate API proxy layers by directly exposing controlled queries.

ClickHouse 26.8 introduces pipelined SQL via the |> pipe operator. This feature allows engineers to chain analytical transformations sequentially, simplifying execution order and replacing nested subqueries or CTEs.

Sub-second lookups across 56 TB and 203 billion rows rely on three design patterns: 3.05x compression to fit 18.4 TB on local NVMe, sorting keys matching query filters to eliminate scans, and an NVMe cache mesh using rendezvous hashing to keep object storage off the read path. P99 hit 703 ms.

ClickHouse Managed Postgres now lets you configure a scheduled two-hour maintenance window for OS patches, minor engine updates, and CDC tooling. User-driven changes apply immediately, and critical capacity tasks like auto-scaling storage above 90% disk usage bypass the window.

Teams are migrating production databases from Neon to ClickHouse Managed Postgres to solve reliability and latency issues under load. Using ClickPipes, teams completed migrations in hours, achieving consistent query performance and a unified stack for transactional and analytics workloads.

ClickHouse acquired RunReveal to enhance its platform for security workloads. The move targets continuous high-throughput ingest, years-long retention, and fast analytics across cloud audit logs and telemetry, providing better guidance on ClickHouse schema and retention design.

ClickHouse released data-agent-mnist, an open benchmark harness evaluating LLM analytics agents against data warehouses using 201 real-world questions. Claude Fable 5.1 led correctness at 76.6%, while DeepSeek V4 Flash ran the suite for $1 versus $52 for Fable 5.1 with an 11pp drop.

Uken Games cut observability costs by 87% by replacing Datadog with ClickHouse as its trace store. A single ClickHouse node holds all traces in 170 GB, ingesting telemetry through OpenTelemetry collectors integrated with SigNoz, Prometheus, and Grafana for backend monitoring.

Optimize ClickHouse by cost: tune the sorting key first. Skip indexes cost metadata, lightweight projections store sorting pointers, and full projections duplicate storage. Lightweight pruning fails when data scatters across granules, making full projections or materialized views necessary.

AI agents querying databases at machine speed shift workloads from human-scale to parallel execution. To prevent runaway costs and protect production state, architectures require decoupled storage and provisioned compute, bounding query resource consumption and isolating access without data

ClickHouse Managed Postgres pairs NVMe-backed Postgres for OLTP with ClickHouse for real-time analytics. Its onboarding streamlines loading data, configuring CDC ClickPipes for managed replication, and querying synchronized tables across both engines.