## Announcing ClickStack 

Today we’re excited to announce [ClickStack](https://clickhouse.com/o11y), a new open-source observability solution built on ClickHouse. ClickStack delivers a complete, out-of-the-box experience for logs, metrics, traces, and session replay - powered by the performance and efficiency of ClickHouse, but designed as a full observability stack that’s open, accessible, and ready for everyone.

Start your journey with ClickStack by checking out our [Getting Started](https://clickhouse.com/docs/use-cases/observability/clickstack/getting-started) guide in the docs.

![hyperdx-landing.png](/uploads/hyperdx_landing_c7bdd6582c.png)

For years, engineering teams at scale such as Netflix and eBay have turned to ClickHouse as the database of choice for observability. Its column-oriented design, compression, and high-throughput vectorized query engine made it ideal for storing wide events- context-rich, high-cardinality records that unify logs, metrics, and traces. This modern Observability approach (some have called this "Observability 2.0"), breaks away from the traditional "three pillars" model and eliminates the complexity of stitching together siloed telemetry sources.

Until now, the full benefits of this model were mostly only realized by teams with the resources to build bespoke observability experiences on top of ClickHouse. Everyone else? They relied on general-purpose visualization tools or third-party proprietary platforms built on ClickHouse. While these tools provided basic interfaces for ClickHouse, they sometimes required long SQL queries for routine observability tasks or didn't fully utilize ClickHouse's performance capabilities and open architecture.

That changes today. With the release of ClickStack, powered by HyperDX, we’re leveling the playing field. This fully open-source stack includes an out-of-the-box OpenTelemetry collector, a UI designed for wide events, natural language querying, session replay, alerting, and more.

And it all runs on the same high-performance, high-compression ClickHouse engine trusted by the biggest names in observability.

Before this, teams were often forced to choose between expensive closed-source SaaS products or piecing together open-source alternatives. Search engines offered fast, flexible querying, but operating them at scale and achieving fast aggregation performance proved challenging. Metrics stores offered better aggregation performance but required rigid pre-aggregation and lacked deep search capabilities. Neither approach handled high-cardinality data well, and stitching them together added complexity without solving the core problem.

With ClickStack, you don’t have to choose - enjoy fast search and fast aggregations over high-cardinality, wide event data. At scale. Open source. And now, for everyone.

## The evolution of ClickHouse for Observability

![clickstack_timeline.png](/uploads/clickstack_timeline_c2f60d65eb.png)

### Just another data problem

Early adopters of ClickHouse recognized something fundamental: observability is a data problem. The database you choose defines the cost, scale, and capabilities of your observability platform - which is why selecting the right one is often the most important architectural decision when building in-house or starting an observability company.

This is exactly why ClickHouse has been at the core of observability stacks for years. From industry giants like[ Netflix](https://www.youtube.com/watch?v=HRh5setqGCU) and[ eBay](https://clickhouse.com/videos/monitorama-pdx-2024-distributed-tracing-all-the-warning-signs-were-out-there) to observability startups like Sentry and Dash0, ClickHouse powers logs, metrics, and traces at a massive scale. Its column-oriented storage, aggressive compression, and vectorized execution engine dramatically reduce costs and deliver the sub-second queries engineers need to debug live systems without waiting on slow tooling.

### All you need is wide events…and a column store

In our earlier post ["The State of SQL-Based Observability"](https://clickhouse.com/blog/the-state-of-sql-based-observability) and subsequent [follow-ups](https://clickhouse.com/blog/evolution-of-sql-based-observability-with-clickhouse), we explored this trend in depth - though we didn't name it at the time, it aligns perfectly with today's Observability 2.0 movement: a unified model[ built around wide events](https://isburmistrov.substack.com/p/all-you-need-is-wide-events-not-metrics), not pillars. For too long, teams relied on separate stores for logs, metrics, and traces, which led to fragmentation, manual correlation, and unnecessary complexity. Wide events eliminate these silos by consolidating all observability signals into a single, queryable structure.

A wide event captures the full application context in a single record - user, service, HTTP path, status code, cache result, and more. This unified structure is key to eliminating silos and enabling fast search and aggregation across high-cardinality data - provided you have a storage engine that can compress and store it efficiently! 

While No-SQL solutions, such as search engines, embraced this structure they lacked the aggregation performance to deliver on the promise - great for search and "finding needles in galaxies", less so if you want to aggregate across wide ranges. ClickHouse's secret sauce to this problem remains unchanged: columnar storage, a rich codec library for deep compression, and a massively parallel engine optimized for analytical workloads.

### Resource efficient and scalable

In ClickHouse Cloud we went further and embraced object storage to deliver separation of storage and compute, essential if you're needing to scale your observability to PB and beyond and need to scale elastically. To support even more demanding use cases, we also introduced compute-compute separation allowing users to dedicate compute to specific workloads while reading from the same data e.g. ingest and querying.

As observability needs became more complex, we recognized that native JSON support for semi-structured events was table stakes. ClickHouse evolved to meet this need, adding first-class support for semi-structured data while preserving the benefits of column-oriented processing. Columns are auto-created as data arrives, and ClickHouse manages type promotion and column growth automatically. It's the schema-on-write you need for observability with the performance, compression, and flexibility expected from a modern analytical engine.

### The rise of OpenTelemetry

This evolution coincided with the rise of OpenTelemetry (OTel), now the de facto standard for collecting telemetry across logs, metrics, and traces. We began officially supporting and contributing to the **OpenTelemetry Exporter for ClickHouse.**

OpenTelemetry has been a major unlock for our ecosystem. It offers a standardized, vendor-neutral way to collect and export observability data, and the Collector can be configured to send data directly into ClickHouse using the exporter we now help maintain. We've worked closely with the community to ensure the exporter is robust, scalable, and aligned with the core principles of ClickHouse.

One of the hardest problems we tackled early was schema design. There's no[ one-size-fits-all schema for observability](https://clickhouse.com/blog/clickhouse-and-open-telemtry); every team has different query patterns, retention needs, and service architectures. So, the exporter ships with default schemas for logs, metrics, and traces that work well for most users, but we encourage teams to customize based on their own workloads.

### Missing pieces

But as we quickly learned, just having a great database, good schema, and robust means of collecting and ingesting isn’t enough. Engineers need turnkey ingestion, visualization, alerting, and a UI that’s tailored to their workflow. Until now, that meant relying on OpenTelemetry for collection and Grafana for dashboards. 

This worked well enough - even our[ own observability team replaced Datadog](https://clickhouse.com/blog/building-a-logging-platform-with-clickhouse-and-saving-millions-over-datadog) with a ClickHouse-based stack, saving millions and achieving over 200x cost reduction. Today, our internal logging system stores more than 43 petabytes of OpenTelemetry data, with schemas and primary keys tuned specifically for that scale. It proved the performance and cost-efficiency of the approach - but we knew the experience could be simpler.

We wanted something more opinionated. An easier way for users to get started. But most importantly, a UI built for ClickHouse. And not just any UI, but one that understands how to construct efficient queries, surface patterns in wide events, and deliver an exceptional user experience without hiding the power of the database underneath.

Finally, while we believe SQL-based observability has played an important role in reinforcing the wide event model, we also knew we had to meet users where they were. Search engines like the ELK stack succeeded because they offered something intuitive: a natural language for querying logs. We wanted that experience for our users, but powered by ClickHouse.

## Welcome HyperDX

That’s when we found **HyperDX**, an open-source observability layer purpose-built on ClickHouse. When HyperDX open-sourced their v2 UI in late 2024, we tested it internally and quickly realized this was the missing piece. The setup was seamless, the developer experience was excellent, and we knew our users deserved the same.

HyperDX brought everything we were looking for:

* **Standards-based data collection**: HyperDX embraced OpenTelemetry from day one, aligning perfectly with our investment in the ClickHouse OpenTelemetry exporter.
* **Open-source first**: We believe robust observability tooling should be available to everyone, and HyperDX shares that philosophy. Its cloud-native design makes for a simple, cost-effective operational experience.

Beyond standards compliance, HyperDX is built with ClickHouse in mind. The team takes query optimization seriously, so you don’t have to. The UI is tightly coupled to the engine, ensuring fast, reliable performance where milliseconds matter, especially during incident investigations. 

Combined with a built-in OpenTelemetry collector ingress and a schema optimized for the HyperDX UI, ClickStack brings together ingestion, storage, and visualization in a unified experience. The default schema is designed to just work out of the box, so users don’t need to think about it - unless they want to customize it for their specific needs.

![clickstack_simple.png](/uploads/clickstack_simple_6ae8ee85d0.png)

The simplicity of ClickStack means that each layer scales independently. Need higher ingestion throughput? Just add more OpenTelemetry collector gateways. Need more query or storage capacity? Scale ClickHouse directly. This modular design makes it easy to grow with your data and your team - without overhauling the stack.

Since acquiring HyperDX, we’ve focused on simplifying the product and expanding its flexibility. You can use the default schema for a seamless out-of-the-box setup or bring your own schema tailored to your needs. Just like with ClickHouse itself, we recognize that one size never fits all, and flexibility is key to scale.

<img preview="/uploads/clickstack_simple_v2_0c8e38c417.gif"  src="/uploads/simple_clickstack_3546eb282e.gif" alt="catalogue_lakehouse.png" class="h-auto w-auto max-w-full"  style="width: 100%;">

At the same time, we’ve stayed true to our SQL roots. SQL remains the lingua franca of data, and for many ClickHouse power users, it’s still the most expressive and efficient way to explore data. That’s why the HyperDX UI includes support for native SQL queries, giving advanced users direct access to the engine without compromise.

<img preview="/uploads/clickstack_sql_small_a55c2027c3.gif"  src="/uploads/clickstack_sql_1eb4e0e9a2.gif" alt="catalogue_lakehouse.png" class="h-auto w-auto max-w-full"  style="width: 100%;">

We've also introduced new features to make debugging and exploration easier. One example is event deltas, which help users quickly identify anomalies and performance regressions. By sampling data across unique values of a given attribute, the UI surfaces performance differences and deviations, making it easier to understand what changed and why.

<img preview="/uploads/event_deltas_simple_3bcfb720b3.gif"  src="/uploads/event_deltas_6e27437203.gif" alt="catalogue_lakehouse.png" class="h-auto w-auto max-w-full"  style="width: 100%;">

Perhaps most importantly, the stack is now simpler. With OpenTelemetry emerging as the ubiquitous standard, all data is now ingested through an OTel collector. The default setup uses an opinionated schema designed for quick adoption, but users can modify or extend the schema as needed. The stack is **OpenTelemetry-native** but not **OpenTelemetry-exclusive**, thanks to the open schema, HyperDX can work with your existing data pipelines and schemas as well.

## Conclusion & Looking Forward

ClickStack represents the next evolution in ClickHouse's increased investment in the observability ecosystem, delivering an intuitive and opinionated full-stack experience powered by open source and open standards. By unifying ClickHouse's high-performance columnar engine, OpenTelemetry's instrumentation standards, and HyperDX's purpose-built UI into a cohesive solution, we're finally making the modern observability approach accessible to everyone.

Our commitment to open source ensures ClickStack remains accessible to everyone—from single-service deployments to multi-petabyte systems. We’ll continue to invest in both the core database for high performance observability while continuing to invest in integrations with established tools like Grafana, ensuring seamless interoperability with existing observability stacks.

With ClickStack, we deliver more than just another tool - we provide a unified foundation where all telemetry signals converge in a high-performance columnar database, complete with natural language querying, session replay, and alerting capabilities right out of the box.

Start your journey with ClickStack by checking out our [Getting Started](https://clickhouse.com/docs/use-cases/observability/clickstack/getting-started) guide in the docs.




We're delighted to announce ClickStack: the open-source observability stack built on ClickHouse - logs, metrics, traces, and session replay in one blazing-fast, developer-friendly platform.

large_ClickStack blog cover.png

medium_ClickStack blog cover.png

small_ClickStack blog cover.png

thumbnail_ClickStack blog cover.png

ClickStack blog cover.png

ClickStack: A High-Performance OSS Observability Stack on ClickHouse

Today we are thrilled to announce that the [Postgres CDC connector](https://clickhouse.com/cloud/clickpipes/postgres-cdc-connector) for ClickPipes is now generally available. With this customers can easily replicate their Postgres databases to ClickHouse Cloud within just a few clicks.

This announcement represents a major milestone. [PeerDB](https://www.peerdb.io/) — a leading Postgres CDC company we [joined forces](https://clickhouse.com/blog/clickhouse-welcomes-peerdb-adding-the-fastest-postgres-cdc-to-the-fastest-olap-database) with last year, is now fully integrated into ClickHouse Cloud as the Postgres CDC connector in ClickPipes. The connector is ready to support enterprise-grade Postgres use cases.  
  
>[Sign up for ClickHouse Cloud](https://console.clickhouse.cloud/signup) today to try out the [Postgres CDC connector for ClickPipes](https://clickhouse.com/docs/integrations/clickpipes/postgres)! 

## Postgres + ClickHouse = “the default data stack”

Over the past few years, we are seeing a consistent pattern emerge across businesses incl. [GitLab](https://about.gitlab.com/blog/2022/04/29/two-sizes-fit-most-postgresql-and-clickhouse/), [CloudFlare](https://blog.cloudflare.com/http-analytics-for-6m-requests-per-second-using-clickhouse?glxid=25d1a1c5-1ce0-4589-8305-177654961925&pagePath=%2Fblog%2Fclickhouse-welcomes-peerdb-adding-the-fastest-postgres-cdc-to-the-fastest-olap-database&origPath=%2Fblog%2Fclickhouse-acquires-peerdb-to-boost-real-time-analytics-with-postgres-cdc-integration&utm_ga=GA1.1.1147851049.1729970044/) and [Instacart](https://tech.instacart.com/real-time-fraud-detection-with-yoda-and-clickhouse-bd08e9dbe3f4): Using Postgres and ClickHouse together to solve most of their data challenges.

* **Postgres** serves as the system of record powering transactional web apps.
* **ClickHouse** powers real-time analytical and reporting workloads, serving as the system of analysis.

This pattern is accelerating in the AI era, with companies like [LangChain](https://clickhouse.com/blog/langchain-why-we-choose-clickhouse-to-power-langchain), [LangFuse](https://langfuse.com/blog/2024-12-langfuse-v3-infrastructure-evolution) and [Vapi](https://neon.tech/blog/vapi-voice-agents-neon) adopting the same architecture. We believe Postgres + ClickHouse is becoming the default data stack for modern businesses. We're committed to making that integration magical. The Postgres CDC connector is **the first major step**—enabling seamless, real-time analytics by bringing your Postgres data into ClickHouse effortlessly. 

The image below shows the reference architecture for combining Postgres and ClickHouse using Postgres CDC, with Postgres handling low-latency transactions and ClickHouse powering blazing-fast analytics.

<img preview="/uploads/Postgres_Click_House_reference_architecture_61fe52239a.png"  src="/uploads/Postgres_Click_House_reference_architecture_61fe52239a.png" alt="catalogue_lakehouse.png" class="h-auto w-auto max-w-full"  style="width: 100%;">

## Some metrics and reference customers

Over the past six months, the connector has gone through an extensive beta phase, evolving rapidly based on user feedback. It now supports **hundreds of mission-critical workloads**, moving **100TB+ of data per month** into ClickHouse.

A few reference customers include. [**Ashby**](https://www.ashbyhq.com/)—a top recruitment company in the US, [**Seemplicity**](https://seemplicity.io/) – one of the fastest-growing cybersecurity startups, and [**AutoNation**](https://www.autonation.com/) – a well-renowned automotive retailer in the US. Here are a few testimonials from our reference customers.  

>“ClickHouse powers Ashby’s customer-facing analytics, delivering lightning-fast fully dynamic insights, while Postgres handles core transactions. With Postgres CDC via ClickPipes, we seamlessly replicated terabytes of data—speeding up our real-time analytics without disrupting operations. Reports that once took minutes now finish within a second. Our enterprise customers' comprehensive recruiting dashboards, which are being filtered and shared in real-time as they make decisions that impact their organization, are their source of truth. Complementing Postgres with ClickHouse enables us to give them a fully reliable experience and handle even larger data as we scale.” - [Elenie Godzaridis](https://www.linkedin.com/in/elenie-godzaridis/), Director of Engineering, [Ashby](https://www.ashbyhq.com/)

>“We first tried to implement Postgres CDC in-house using Debezium but it was way too complex. I knew a managed product built by engineers, whose goal in life is to transform bits from Postgres into ClickHouse, would be better than anything we could do ourselves.” - [Tal Shargal](https://www.linkedin.com/in/tal-shargal-29388671/), Chief Architect at [Seemplicity](https://seemplicity.io/),  [*read the full customer story*](https://clickhouse.com/blog/seemplicity-scaled-real-time-security-analytics-with-postgres-cdc-and-clickhouse)

![Chart.png](https://cms.clickhouse-dev.com:1337/uploads/Chart_7c2a541127.png)

**This graph shows the growth we witnessed in Postgres CDC to ClickHouse usage over the past year,** [**reference**](https://www.linkedin.com/feed/update/urn:li:activity:7310749593296588800/)**.**

## Product Highlights

<iframe width="660" height="371" src="https://www.youtube.com/embed/s1hREjN02S0?si=J2ByKIkTTU9Mw94m" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>



The Postgres CDC connector is purpose-built for Postgres and ClickHouse and comes with a vast suite of features. Here are a few noteworthy ones:

1. **10x Faster Initial Loads and Resyncs** – Achieved through [parallel snapshotting](https://blog.peerdb.io/parallelized-initial-load-for-cdc-based-streaming-from-postgres), enabling parallel loading of single large tables. Terabytes of data can now be moved in hours instead of days.
    
2. **Replication Latencies as Low as 10 Seconds** – The replication slot is [continuously consumed without reconnections](https://clickhouse.com/blog/enhancing-postgres-to-clickhouse-replication-using-peerdb#efficiently-flush-the-replication-slot), and ClickHouse's [ReplacingMergeTree](https://clickhouse.com/blog/postgres-to-clickhouse-data-modeling-tips-v2) ensures minimal end-to-end latency.
    
3. **Automatic Replication of Schema Changes**– [Supports](https://clickhouse.com/docs/integrations/clickpipes/postgres/schema-changes) operations like ADD COLUMN and DROP COLUMN with zero manual intervention.
    
4. **Table and Column Exclusions** – Allows fine-grained control for better security (e.g., handling PII) and optimized network throughput.
    
5. **Secure Connectivity** – Supports [**AWS PrivateLink**](https://clickhouse.com/docs/integrations/clickpipes/aws-privatelink) and [**SSH tunneling**](https://clickhouse.com/docs/integrations/clickpipes/postgres#optional-setting-up-ssh-tunneling) for private, secure connections to source Postgres databases.
    
6. **Support for Native Postgres Features** – Includes replication for [partitioned tables,](https://blog.peerdb.io/real-time-change-data-capture-for-postgres-partitioned-tables) TOAST columns, and [advanced data types](https://clickhouse.com/docs/integrations/clickpipes/postgres/faq#how-are-postgres-data-types-mapped-to-clickhouse) such as arrays and JSON.
    
7. **Open API (Beta)** – Enables [programmatic](https://clickhouse.com/docs/integrations/clickpipes/postgres/faq#can-clickpipe-creation-be-automated-or-done-via-api-or-cli) creation and management of pipes. Ideal for ISV applications deploying isolated ClickHouse Cloud + ClickPipes environments. [**Terraform**](https://registry.terraform.io/providers/ClickHouse/clickhouse/3.2.0-alpha1/docs/resources/clickpipe) **support coming soon** for easy infrastructure-as-code automation.
    

## Pricing

As part of the GA launch—and as [mentioned](https://clickhouse.com/blog/postgres-cdc-connector-clickpipes-public-beta#pricing) earlier during the beta—we’re introducing pricing for the Postgres CDC connector. Our goal is to keep it highly competitive while staying true to our core vision: making it seamless and affordable for customers to connect their Postgres databases to ClickHouse. The connector is **over 5x more cost-effective** than external ETL tools and similar features in other database platforms. 

Note that pricing will start being metered in monthly bills **beginning September 1st, 2025 for all customers (existing and new) using Postgres CDC ClickPipes**. Customers will have a 3-month window to optimize costs if needed, though we don’t anticipate most will need to make any changes. For detailed information on the pricing structure, examples, and FAQs, click [here](https://clickhouse.com/docs/cloud/manage/billing/overview#clickpipes-for-postgres-cdc).

## How to try Postgres CDC to ClickHouse

You can follow the links below to connect your Postgres databases to ClickHouse Cloud for blazing fast analytics.

* [Ingesting data from Postgres to ClickHouse (using CDC)](https://clickhouse.com/docs/en/integrations/clickpipes/postgres)
    
* [ClickPipes for Postgres FAQ](https://clickhouse.com/docs/en/integrations/clickpipes/postgres/faq)
    
* [Try ClickHouse Cloud for free](https://clickhouse.com/docs/en/cloud/get-started/cloud-quick-start)

We’re excited to announce the general availability of the Postgres CDC connector in ClickPipes. Natively integrated with ClickHouse Cloud, this connector enables blazing-fast replication of your Postgres databases with just a few clicks.

large_Postgres CDC connector for ClickPipes is now Generally Available.png

medium_Postgres CDC connector for ClickPipes is now Generally Available.png

small_Postgres CDC connector for ClickPipes is now Generally Available.png

thumbnail_Postgres CDC connector for ClickPipes is now Generally Available.png

Postgres CDC connector for ClickPipes is now Generally Available.png

Postgres CDC connector for ClickPipes is now Generally Available

ClickHouse Cloud now has a distributed cache for object storage: shared, low-latency, and built for speed. This post dives into the architecture, why it matters, and how it performs.

large_446559747-9d77c836-9abd-458b-95fe-5de1e15bb634.png

medium_446559747-9d77c836-9abd-458b-95fe-5de1e15bb634.png

small_446559747-9d77c836-9abd-458b-95fe-5de1e15bb634.png

thumbnail_446559747-9d77c836-9abd-458b-95fe-5de1e15bb634.png

446559747-9d77c836-9abd-458b-95fe-5de1e15bb634.png

Building a Distributed Cache for S3

ClickHouse source code is published under the Apache 2.0 License. Software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

logo_without_text.svg

Stay informed on feature releases, product roadmap, support, and cloud offerings!

Subscribe to our newsletter

icon-github.svg

icon-slack.svg

icon-telegram.svg

icon-twitter.svg

May 28-29, 2025

Whats been announced

ClickStack: A High-Performance OSS Observability Stack on ClickHouse

Postgres CDC connector for ClickPipes is now Generally Available

Building a Distributed Cache for S3

Open House videos are on the way

What’s happening at Open House?

Agenda at a glance

MAY 28
Workshops

MAY 29
Conference

Speakers

Aaron Katz

Tanya Bragin

Alexey Milovidov

Srinivas Narayanan

Jeana Choi

Maruth Goyal

Krithika Balagurunathan

Alon Tal

Open House

FAQs

Get in touch

Products

Resources

Company

Join our community

Comparisons

Partners

May 28-29, 2025

Whats been announced

ClickStack: A High-Performance OSS Observability Stack on ClickHouse

Postgres CDC connector for ClickPipes is now Generally Available

Building a Distributed Cache for S3

Open House videos are on the way

What’s happening at Open House?

Agenda at a glance

MAY 28Workshops

MAY 29Conference

Speakers

Aaron Katz

Tanya Bragin

Alexey Milovidov

Srinivas Narayanan

Jeana Choi

Maruth Goyal

Krithika Balagurunathan

Alon Tal

Open House

FAQs

Get in touch

Products

Resources

Company

Join our community

Comparisons

Partners

MAY 28
Workshops

MAY 29
Conference