Observed Signal · Sep 16, 2026 · Product Launch · Source: ClickHouse · Impact: 4/5
Introducing ClickHouse's new TimeSeries Engine: Your drop-In Prometheus replacement
On September 16, 2026, ClickHouse announced the private preview of PromQL support and the TimeSeries table engine on ClickHouse Cloud. The new engine ingests metrics via the Prometheus remote-write v1 protocol and lets users query them with unchanged PromQL queries through ClickStack, Grafana, clickhouse-client, or SQL. ClickHouse positions this as a drop-in Prometheus replacement that preserves exact PromQL semantics while storing metrics alongside logs and traces.
This announcement enables teams to consolidate Prometheus metrics with logs and traces in ClickHouse Cloud, eliminating the need to rewrite PromQL queries in SQL and reducing operational complexity. It positions ClickHouse as a serious alternative to dedicated Prometheus storage while preserving ecosystem compatibility with Grafana and OpenTelemetry.
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Key Takeaways & Evidence Grounding
- Announced on September 16, 2026: private preview of PromQL support and the TimeSeries table engine on ClickHouse Cloud.
- Supports the Prometheus remote-write v1 protocol for ingesting metrics into TimeSeries tables.
- PromQL queries can be run via ClickStack, Grafana, clickhouse-client with the promql dialect, and SQL table functions prometheusQuery / prometheusQueryRange.
- PromQL dialect coverage is currently over 85%, with the goal of reaching 100%.
- The private preview does not yet include alerting and recording rules; ClickStack's standard OTLP pipeline does not populate TimeSeries tables.
- The TimeSeries engine design specifically supports Prometheus metric types such as counters, gauges, histograms, and summaries.
Connected Companies & Entities
3 Entities mapped“ClickHouse announced the private preview of PromQL support and the TimeSeries table engine on ClickHouse Cloud, allowing Prometheus metrics ...”
“Prometheus is the metrics monitoring system whose remote-write protocol and PromQL query language are supported by the new ClickHouse TimeSe...”
“OpenTelemetry Collectors can send metrics to ClickHouse TimeSeries tables via the Prometheus remote-write exporter, while standard OTLP metr...”
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
ClickHouse vs PostgreSQL: OLAP vs OLTP Comparison
A developer-authored technical post comparing ClickHouse and PostgreSQL as part of a '100 Days of ClickHouse' series. The article explains that PostgreSQL is a row-oriented OLTP database optimized for transactional workloads with frequent inserts/updates/deletes and strong transactional guarantees, while ClickHouse is a column-oriented OLAP database designed for large-scale analytics, fast aggregations and time-series/event analytics. It outlines storage and compression differences, scaling considerations, and common deployment patterns where organizations use PostgreSQL for operational data and ClickHouse for analytical/reporting workloads. The piece aims to guide database selection based on workload requirements rather than popularity or benchmarks.
NeverBlink Adds ClickHouse Support
NeverBlink, an AI-driven database administrator, announced support for ClickHouse, extending its database resilience and optimization platform to ClickHouse clusters. The integration offers continuous health checks, automated root-cause analysis, query analytics, prioritized remediation guidance, and optional unlimited expert consulting via BigData Boutique. NeverBlink already supports Elasticsearch and OpenSearch and is used in production by companies including Akamai, Perforce, and Palo Alto Networks. The article notes ClickHouse has surpassed 4,000 customers and a reported $15 billion valuation. Leadership quotes emphasize reducing downtime, lowering costs, and accelerating database optimization in the AI era.
ClickHouse HTTP API: Guide to Queries and Ingestion
This technical tutorial explains ClickHouse's built-in HTTP API, a language‑agnostic interface that accepts SQL over HTTP (default port 8123) using GET or POST. The article covers verifying the API (ping), executing queries via GET parameters or POST bodies, authentication options (URL parameters, HTTP Basic, and recommended HTTP headers), selecting databases, supported output formats (JSON, JSONEachRow, CSV, TSV, Parquet, Arrow, TabSeparated), inserting data in multiple formats, and performing DDL/DML over HTTP. It also lists useful HTTP parameters and best practices (use POST for long queries, prefer headers for credentials, enable compression, set max_execution_time). The guide positions the HTTP API as convenient for scripting, automation, REST integrations and lightweight services.
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