## TLDR; {#tldr}

With 26.2, we are introducing a new distribution method: ClickStack UI embedded in ClickHouse. The ClickStack UI is now distributed and embedded directly in the ClickHouse binary, making it easy to experiment with observability on your local instance, explore your own datasets, and even inspect ClickHouse itself.  Simply navigate to https://localhost:8123, select "ClickStack", and start exploring.

## Introduction {#introduction}

Historically, ClickStack has been available through Docker-based distributions. You can run the [full stack in a single container](https://clickhouse.com/docs/use-cases/observability/clickstack/deployment/all-in-one) for testing and experimentation, or deploy [each component independently](https://clickhouse.com/docs/use-cases/observability/clickstack/deployment/hyperdx-only) when moving to production - running the UI, collector, and ClickHouse separately using [tools such as Helm](https://clickhouse.com/docs/use-cases/observability/clickstack/deployment/helm).

More recently, [we introduced a managed ClickStack offering](https://clickhouse.com/blog/introducing-managed-clickstack-beta) in ClickHouse Cloud, where we host both the UI and ClickHouse. Users benefit from integrated authentication along with ClickHouse Cloud's separation of storage and compute - enabling both long-term data retention in object storage while allowing independent scaling of compute to minimize the cost per GB.

**From 26.2, we are adding a 3rd option.**

The ClickStack UI now comes embedded in the ClickHouse binary itself. At first glance, embedding a web application into a high-performance C++ database might sound like it would significantly increase the binary size. In practice, we have kept the additional footprint under 4.1 MB, ensuring installation remains lightweight and fast.

This means that installing ClickHouse now gives you ClickStack out of the box. Whether you use Docker, download the binary, or install via your favorite package manager, ClickStack is immediately available. Simply install ClickHouse, navigate to [http://localhost:8123](http://localhost:8123), and select ClickStack from the menu. Within seconds, you can begin exploring your logs, traces, and metrics.

<iframe width="768" height="432" src="https://www.youtube.com/embed/s1MGNcVNvNg?si=vO78XutzyvwGbHZM" 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>

## Built for Exploration and Local Development {#built-for-exploration-and-local-development}

The embedded version of the ClickStack UI  is designed for local exploration, learning, and exploring your own ClickHouse data with an observability UI. It is not intended for production deployments.

This distribution makes it easy to experiment with observability on your local ClickHouse instance, explore your own datasets, and even inspect ClickHouse itself. ClickHouse already exposes rich internal logs and metrics that are invaluable for diagnosing and optimizing performance. The ClickStack UI provides convenient ways to visualize this data and better understand how your instance behaves.

large_clickstack_mar2026_image4.png

medium_clickstack_mar2026_image4.png

small_clickstack_mar2026_image4.png

thumbnail_clickstack_mar2026_image4.png

clickstack_mar2026_image4.png

large_clickstack_mar2026_image7.png

medium_clickstack_mar2026_image7.png

small_clickstack_mar2026_image7.png

thumbnail_clickstack_mar2026_image7.png

clickstack_mar2026_image7.png

large_clickstack_mar2026_image1.png

medium_clickstack_mar2026_image1.png

small_clickstack_mar2026_image1.png

thumbnail_clickstack_mar2026_image1.png

clickstack_mar2026_image1.png

large_clickstack_mar2026_image9.png

medium_clickstack_mar2026_image9.png

small_clickstack_mar2026_image9.png

thumbnail_clickstack_mar2026_image9.png

clickstack_mar2026_image9.png

> The ClickHouse preset dashboard can be useful for diagnosing local issues and performance problems. Users can also search their local logs and build visualizations over system metric tables.

For larger or production deployments, we always recommend running ClickStack components separately.

The embedded version also intentionally omits certain capabilities to keep the distribution size small and the experience simple. There is no persistent state storage so alerting is disabled, as well as dashboard and querying persistence. [Event pattern](https://clickhouse.com/docs/use-cases/observability/clickstack/event_patterns) functionality is also not included to keep the size small as this requires [a WASM Python runtime](https://clickhouse.com/blog/event-patterns-clickstack#how-clickstack-implements-event-patterns).

Some of these limitations may be addressed in the future using approaches such as browser based storage, but for now the focus is simplicity and approachability.

If you plan to operate ClickStack at scale, or require alerting and persistence, the [open source docker versions](https://clickhouse.com/docs/use-cases/observability/clickstack/getting-started/oss) or [managed cloud](https://clickhouse.com/docs/use-cases/observability/clickstack/getting-started/managed) offering remain the recommended path.

## The technical challenges {#the-technical-challenges}

For the curious reader, embedding a full web application inside a C++ database binary involved some interesting engineering decisions.

### Assumptions {#assumptions}

Embedding ClickStack inside ClickHouse meant working within a strict set of conditions defined by the core team, reflecting the engineering standards expected of the ClickHouse binary:

1. Adding a [Node.js](http://node.js) dependency to ClickHouse is a non-starter
2. Files must be embedded in the binary, not lingering somewhere in the file system
3. ClickStack should not significantly inflate the binary size

### The Next.js parts {#the-nextjs-parts}

The UI that powers ClickStack, HyperDX, is a Next.js application. For those familiar, Next.js is a full-stack framework that renders pages on the server before serving, meaning that both the frontend and the backend are bundled into an application. It allows you to serve both static pages and dynamic HTML. However, serving dynamic pages without introducing a Node.js dependency would mean rewriting a lot of the Next.js internals. This meant serving dynamic pages was off the table. Luckily, ClickStack heavily uses static pages. There is still React running in the browser, but very little dynamic server-side rendering ever occurs.

ClickHouse has some existing HTML pages,  such as `play.html` for running queries in a WebUI, and `dashboards.html` for some extremely helpful dashboards to see the health of your ClickHouse instance. The `dashboards.html` page even loads some JavaScript!

However, both are a far cry from the complexity of serving a webpack output from a modern full-featured website like ClickStack. I'll get to that complexity in the [Bundling into ClickHouse](/blog/clickstack-embedded-clickhouse#bundling-into-clickhouse) section, but the existing web pages meant we could add an additional handler with just a little custom functionality.

### ClickStack existing variations and connecting to ClickHouse {#clickstack-existing-variations-and-connecting-to-clickhouse}

ClickStack already has a few  dependencies on which it relies for core functionality. The standard application includes Next.js, Express, and MongoDB. Express and MongoDB are primarily used for the CRUD persistence layer for saved searches, dashboards, and sources. The Express server also serves as a proxy for ClickHouse,  while also handling authentication to ensure DB credentials are not leaked to the frontend. It was safe to assume that the Express backend, MongoDB, and a proxy layer would not be available when embedding ClickStack in ClickHouse.

However, there already existed a [demo site](http://play-clickstack.clickhouse.com) with most capabilities we needed; connections stored in session storage, sources persisted in local storage, and ability to query ClickHouse directly without a proxy layer. The only modifications needed would be to adjust all links with the prefix "/clickstack". With this mechanism, we could attempt bundling into ClickHouse.

> We also decided to remove `pyodide`, a WASM Python runtime for the browser used for ClickStack's "Event Patterns" feature, purely because its size would inflate the ClickHouse binary size by more than we are comfortable with.

### Bundling into ClickHouse {#bundling-into-clickhouse}

ClickHouse uses many 3rd-party libraries and manages them via git submodules. This means that updating a version of a dependency is as simple as checking out a specific commit and rerunning the ClickHouse build. This provides us with the basic foundation to ensure that upgrading to a newer ClickStack version is potentially easy if we can provide it as a usable sub-module.

Building ClickHouse requires many dependent tools such as `cmake`, `ccache`, `ninja`, and many more. But NOT Node.js. For the purpose of reducing friction in local development as well as build times, this meant adding a new dependency with rather large implications like [node.js](http://node.js) was not an option. A good alternative is to generate a static bundle upon release and included directly in a git submodule. As we didn't want to clutter the ClickStack repo, this meant creating a new repo that can reproducibly check out a ClickStack version, build the static output, and update the bundle - ideally all automated upon the release of a new ClickStack version.

But we still have an issue - the files are now present in a ClickHouse build, but how can they be embedded?

C++ does have an #embed macro that allows embedding known files as raw bytes rather than as a file. Unfortunately, a feature of [Next.js](http://next.js) is that it outputs seemingly randomized file names, and lots of them. To handle this, we  generate a C++ file on the fly using some `cmake` magic. It does the following:

1. Creates a new file with some static definitions, specifically a struct definition that includes the file name, bytes, and MIME type
2. Find all files in contrib/clickstack/out
3. Sort by name
4. For each file
   1. Gzip the file (to reduce binary size + bytes shipped to browser)
   2. Generate an entry in an array

The generated file is then simply #included. Then, when a request is made to the `/clickstack` HTTP handler, there is a binary search for the file. If it is found, the file is returned with the appropriate MIME type. In the browser, the user must supply some username and password credentials to authenticate against ClickHouse - these are then used to directly query  ClickHouse over HTTP.

![](/uploads/clickstack_mar2026_image3_ab1eabc337.png)

The result is a very lean addition, around 4.2mb embedded directly into the actual binary itself.

## Getting Started {#getting-started}

To try ClickStack embedded in ClickHouse, install ClickHouse as you normally would. You can use the one-line installer:

<pre><code type='click-ui' language='bash'>
curl https://clickhouse.com/ | sh
</code></pre>

Or see the Open Source Quick Start [guide](https://clickhouse.com/docs/getting-started/quick-start/oss) for alternatives.

For this example, we will enable internal logs so you can explore your own ClickHouse instance, including executed queries and resource usage.

After downloading the binary, move to the directory where you want ClickHouse to store its data. Then create a configuration snippet that enables the query and metric logs:

<pre><code type='click-ui' language='bash'>
mkdir -p config.d && echo "&lt;clickhouse&gt;&lt;query_log&gt;&lt;database&gt;system&lt;/database&gt;&lt;table&gt;query_log&lt;/table&gt;&lt;/query_log&gt;&lt;query_thread_log&gt;&lt;database&gt;system&lt;/database&gt;&lt;table&gt;query_thread_log&lt;/table&gt;&lt;/query_thread_log&gt;&lt;query_views_log&gt;&lt;database&gt;system&lt;/database&gt;&lt;table&gt;query_views_log&lt;/table&gt;&lt;/query_views_log&gt;&lt;metric_log&gt;&lt;database&gt;system&lt;/database&gt;&lt;table&gt;metric_log&lt;/table&gt;&lt;/metric_log&gt;&lt;asynchronous_metric_log&gt;&lt;database&gt;system&lt;/database&gt;&lt;table&gt;asynchronous_metric_log&lt;/table&gt;&lt;/asynchronous_metric_log&gt;&lt;/clickhouse&gt;" | sudo tee ./config.d/query_logs.xml &gt; /dev/null
</code></pre>


This appends to the default configuration and enables system log tables.

Start the server and open your browser at [http://localhost:8123/clickstack](http://localhost:8123/clickstack)

<pre><code type='click-ui' language='bash'>
./clickhouse server
</code></pre>

A connection to the local instance is created automatically. If you already have OpenTelemetry data loaded, ClickStack will detect it and create sources automatically. On a fresh installation, you will be prompted to create a source. For this example, create a new **Log Source** that points to `system.query_log`.

![](/uploads/clickstack_mar2026_image8_3e2b3fa28f.png)

> Config: `Name: Query Logs`
> 
> `Database: system`
> 
> `Table: query_log`
> 
> `Timestamp Column: event_time`
> 
> `Default Select: event_time, query_kind, query, databases, tables, initial_user, projections, memory_usage, written_rows, read_rows, query_duration_ms`.*

Save the source. You will be redirected to the search view, where query logs should immediately begin appearing.

At this point, you are observing your own ClickHouse instance.

![](/uploads/clickstack_mar2026_image5_4ac157885b.png)

Feel free to open the play UI at [http://localhost:8213/play](http://localhost:8213/play), run a few queries and watch them appear in ClickStack. With the default selection you can view the execution time, the memory usage and other useful metadata such as the projections used.

We also include a built-in ClickHouse preset dashboard, available from the left navigation menu. It provides insights into query latency and slow queries, highlights the most time consuming query patterns, shows query counts per table, and surfaces key system metrics such as CPU usage, memory consumption, S3 requests, and insert activity. Together, these views give you immediate observability into your ClickHouse instance, powered entirely by the embedded ClickStack UI.

![](/uploads/clickstack_mar2026_image2_42373b1362.png)

To continue exploring ClickStack and learning its features, we recommend trying it with one of our [sample datasets](https://clickhouse.com/docs/use-cases/observability/clickstack/sample-datasets). These include sample [observability data](https://clickhouse.com/docs/use-cases/observability/clickstack/getting-started/sample-data) from the [OpenTelemetry demo](https://github.com/ClickHouse/opentelemetry-demo), along with examples for [exploring session replay functionality](https://clickhouse.com/docs/use-cases/observability/clickstack/example-datasets/session-replay-demo) and monitoring your [local infrastructure and ClickHouse instance](https://clickhouse.com/docs/use-cases/observability/clickstack/getting-started/local-data).

## Conclusion {#conclusion}

With ClickStack now embedded directly in ClickHouse, getting started with observability is as simple as installing the database itself. There is no additional setup, no separate services to run, and no external UI to deploy. Within seconds, you can begin exploring logs, traces, metrics, and even ClickHouse's own internal behavior.

Our hope is that this distribution lowers the barrier to entry and makes it easier to discover the value of ClickStack using your own local datasets. It provides a practical environment for learning the product, running demos, training teams, experimenting with queries, and understanding how ClickHouse behaves during development.

We look forward to seeing how the community uses it, and contributes to its evolution.


Interested in seeing how ClickHouse works on your data? Get started with ClickHouse Cloud in minutes and receive $300 in free credits.

Get started today

With ClickHouse 26.2, the ClickStack observability UI is embedded directly in the binary. Install ClickHouse, open localhost:8123, and start exploring your logs, traces, and metrics - no extra setup required.

large_clickstack_mar2026_image6.png

medium_clickstack_mar2026_image6.png

small_clickstack_mar2026_image6.png

thumbnail_clickstack_mar2026_image6.png

clickstack_mar2026_image6.png

Introducing ClickStack embedded in ClickHouse

Sign up for the newsletter above for updates, and hop on to our #olly-clickstack Slack channel  to ask questions, share feedback, or help shape what we build next.


Welcome to the January edition of What’s New in ClickStack, the open source observability stack built for ClickHouse. Each month, we highlight the latest ClickStack improvements that help teams explore their observability data with more speed, clarity, and confidence.
January was a busy month for ClickStack. We shipped a mix of high-impact improvements that unlock entirely new workloads, alongside smaller changes that are just plain fun to use. As always, we aim to cover the full spectrum. A big thank you to our users for the constant feedback that helps shape these features and make ClickStack better for everyone.

## New contributors

Building ClickStack in the open means it is shaped by the people who use it every day. We are thankful for the continued momentum from the community, and especially for the new contributors who jumped in this month. Your ideas, code, and feedback directly influence where ClickStack goes next and help make it stronger for everyone.

[motsc](https://github.com/motsc), [yeldiRium](https://github.com/yeldiRium), [gyancr7](https://github.com/gyancr7), [gingerwizard](https://github.com/gingerwizard), [adri](https://github.com/adri), [akalitenya](https://github.com/akalitenya)

## Managed ClickStack

Early this month we [announced Managed ClickStack](https://clickhouse.com/blog/introducing-managed-clickstack-beta). Managed ClickStack is our fully managed observability offering built on ClickHouse Cloud, designed for teams that want ClickStack’s performance and scale without operating the underlying infrastructure. This delivers the same fast queries across logs, metrics, traces, and session data that our open source users enjoy, while fundamentally changing the cost model for observability. 

By pricing on just compute and storage, rather than events, hosts, or users, Managed ClickStack lets teams ingest and retain full-fidelity, high-cardinality data at massive scale, with total costs measured in cents per gigabyte per month. Separation of storage and compute means ingest and query workloads scale independently, so you only pay for the resources you actually use.

![managed_clickstack.png](/uploads/managed_clickstack_6a0350499a.png)

More importantly, Managed ClickStack reflects where we believe observability is headed. By running ClickStack on ClickHouse Cloud, we can unlock long-term retention on low-cost object storage, elastic compute for investigations and alerts, and a foundation for consolidating observability with analytics and other workloads. 

![clickstack-architecture.png](/uploads/clickstack_architecture_b0aea63687.png)

This aligns with our broader view that observability is ultimately a data problem, and one best solved on a platform built for large-scale analytical workloads. If you want to go deeper on the vision and economics behind Managed ClickStack, check out our [launch post introducing the beta](https://clickhouse.com/blog/introducing-managed-clickstack-beta).


Interested in seeing how Managed ClickStack works for your observability data? Get started in minutes and receive $300 in free credits.

## Better index support

In ClickStack, we are constantly looking for ways to better leverage ClickHouse features, whether they are newly introduced or long-standing capabilities that can be used more effectively. This month is a great example of that approach, with focused investments in improving index support to significantly accelerate query performance on large datasets.

### Improving bloom filter support

Users running ClickStack for logs and traces are already familiar with how heavily we use Bloom filters to accelerate text search. Bloom filters provide a lightweight skipping index for keyword queries, allowing ClickHouse to avoid scanning large portions of data. 

<blockquote style="font-size: 15px;">
<p>In ClickHouse, Bloom filters are used as skip indices to quickly rule out data blocks that cannot possibly match a query. When data is written, values or derived tokens are hashed and stored in a compact probabilistic structure at the granule level. At query time, ClickHouse checks the Bloom filter before reading a granule. If the filter indicates a value is not present, that granule is skipped entirely. Bloom filters guarantee no false negatives, meaning matching data is never missed, but they may produce false positives, which results in additional granules being scanned. This tradeoff allows ClickHouse to significantly reduce IO and CPU for text and high cardinality searches while keeping index size relatively small.</p>
</blockquote>

Until now, ClickStack used the [tokenbf_v1 index](https://clickhouse.com/docs/optimize/skipping-indexes/examples#token-bloom-filter-tokenbf-v1-for-word-based-search) for log body searches. This index pre-tokenizes text by splitting on non-alphanumeric characters and stores the resulting tokens in a Bloom filter. While convenient, it exposed several tuning parameters that were difficult to reason about in practice. Users had to choose the filter size, number of hash functions, random seed, and granularity. Although a granularity of 8 generally worked well, the other parameters often required trial-and-error or direct assistance from ClickHouse experts.

In this release, we have now also support the simpler [`bloom_filter`](https://clickhouse.com/docs/optimize/skipping-indexes/examples#generic-bloom-filter-scalar) index combined with explicit tokenization using the [`tokens`](https://clickhouse.com/docs/sql-reference/functions/splitting-merging-functions#tokens) function. This index takes a single optional parameter: the acceptable false positive rate between 0 and 1, with a default of 0.01. Tokenization is handled separately, using the same non alphanumeric splitting behavior by default, but now with the flexibility to adjust tokenization explicitly if needed (this opens some interesting opportunities we plan to explore!).

This change dramatically reduces the tuning surface area. Instead of juggling multiple low level parameters, users now only need to think about granularity and false positive rate. We still recommend a granularity of 8 for log body searches, and for most workloads the default false positive rate is sufficient. When tuning is needed, it becomes much simpler to evaluate values slightly above or below the default and observe the impact on query performance.

<blockquote style="font-size: 15px;">
<p>Note that lowering the false positive rate too aggressively, with the aim of skipping more granules, is not always beneficial. Doing so increases the filter size, which can slow evaluation and increase load times - although this is mitigated somewhat with [recent changes to stream skip indices](https://clickhouse.com/blog/streaming-secondary-indices). Allowing slightly higher false positives can actually improve performance by reducing index overhead. The easiest approach is to experiment with values near the default, sticking with the default if no clear improvement emerges.</p>
</blockquote>

The new approach delivers other meaningful gains. At a granularity of 8, index sizes are typically around 15% smaller. Index analysis can be up to 50% faster, and in some datasets pruning selectivity improves by as much as 7x, although this varies significantly with data characteristics. The net result is searches up to 5x faster with far simpler tuning.

As an example, consider the simple example using our playground OpenTelemetry demo dataset, where we search for “Failed to place”:


The above shows an improvement in query performance of around 50%, with pruning more than 4x more effective (1.76m vs 10.66m rows processed) for the `bloom_filter` index. However, note how performance is appreciably better for both vs  using no indexes. 

An astute reader will notice the query syntax has also changed. 

To ensure the index is used, we have updated how text search queries are generated in the ClickStack UI. Previously, ClickStack UI lowercased the log body and query text (we prefer case-insensitive search for logs), relying on implicit tokenization in the Bloom filter.  The UI would also tokenize the query, using `hasToken` function with conjunctions relying on boolean logic e.g. `(((hasToken(lower(Body), lower('Failed'))) AND (hasToken(lower(Body), lower('to'))) AND (hasToken(lower(Body), lower('place')))))`

With the new filter, queries use the `tokens` and `lower` functions at query time, deferring tokenization to ClickHouse, with the `hasAll` function used to check token presence i.e. `hasAll(tokens(lower(Body)), tokens(lower('Failed to place')))`. This ensures the index is used correctly while preserving existing search behavior. 

<blockquote style="font-size: 15px;">
<p>ClickStack automatically checks the index type in use and uses the correct querying approach.</p>
</blockquote>

### Supporting text indices

Bloom filters are still a powerful tool, but they are not without limitations. False positives are unavoidable, and even simplified tuning introduces tradeoffs. For some workloads, especially large-scale log search, inverted indices can offer compelling advantages (although with tradeoffs).

ClickHouse’s inverted text index has undergone significant rework and is now available in beta. Recent improvements have focused on making it viable at large scale, particularly in ClickHouse Cloud on object storage. Internally, the implementation still uses Bloom filters as a lightweight existence check, allowing ClickHouse to skip expensive index evaluation when a term is guaranteed not to be present in a granule.

Our benchmarking on logging datasets has been especially encouraging, with inverted indices outperforming Bloom filters in both selectivity and query latency. 

As a result, ClickStack now supports this index type directly in the UI. Enabling this required further updates to our Lucene-to-SQL compilation pipeline so that `hasAllTokens` is used when a text index is present. Note that the index is responsible for tokenization as well as pre-processing - expressions defined on index creation. For ClickStack, we use the `splitByNonAlpha` function for the tokenizer and `lower` for the pre-processor - thus replicating current search behavior.

```text
INDEX idx_body Body TYPE text(tokenizer = 'splitByNonAlpha', preprocessor = lower(Body))
```

There are a few constraints to be aware of. The [`hasAllTokens`](https://clickhouse.com/docs/sql-reference/functions/string-search-functions#hasAllTokens) function supports a maximum of 64 tokens per call, so longer search terms are split into batches and combined with substring matching. It also requires [`enable_full_text_index`](https://clickhouse.com/docs/operations/settings/settings#enable_full_text_index) to be enabled, which is only available in ClickHouse v25.12 and newer. For this reason, users running OSS deployments need to explicitly enable this setting in their ClickHouse user profile.

Currently, we only support the [`splitByNonAlpha`](https://clickhouse.com/docs/sql-reference/functions/splitting-merging-functions#splitByNonAlpha) tokenizer. If a text index is created with a different tokenizer, ClickStack will fall back to existing search behavior. Exploring additional tokenizers for log search is an area we are actively interested in - such as [`sparseGrams`](https://clickhouse.com/jp/videos/sparsegrams), which has shown [potential for improving text search speed](https://clickhouse.com/blog/clickhouse-release-26-01#text-index-improvements).

As an example, consider our earlier search. Note that we now use the `hasAllTokens` deferring tokenization and lower case to the index:




The query performance is almost 9x faster than the bloom filter for this example, with the volume of data read over 100x less. We would encourage the curious reader to read about the [direct-read optimisations made to achieve this](https://clickhouse.com/docs/engines/table-engines/mergetree-family/textindexes#direct-read), as well as the [caching options on offer for tuning text indices](https://clickhouse.com/docs/engines/table-engines/mergetree-family/textindexes#caching).

Next, we will be benchmarking inverted indices across a wider range of scales and workloads and publishing guidance in our [new performance tuning documentation](https://clickhouse.com/docs/use-cases/observability/clickstack/performance_tuning). While inverted indices can be extremely powerful, they come with additional storage and resource costs, and they may not be the right choice for every workload.

Stay tuned. We will be sharing more data and insights soon as we continue to answer a key question for observability at scale: when do inverted indices make sense?

## Alerts use materialized views

Alerts now benefit directly from materialized views in ClickStack. Last month, we introduced [support for materialized views in ClickStack](https://clickhouse.com/blog/whats-new-in-clickstack-december-2025#materialized-views-arrive-in-clickstack). Any materialized view created in ClickHouse and registered with a source in the ClickStack UI, HyperDX, is automatically used to accelerate aggregation queries throughout the product - including charts, search histograms, and preset experiences. In this release, alerting has been fully integrated into the same query path, allowing alerts to take advantage of those same optimizations.

Materialized views represent a natural fit to power alerting. Alerts almost always query recent data, while materialized views perform aggregations at insert time, shifting work from query execution to ingestion. As data is inserted, aggregate states are computed and stored in a much smaller target table, making subsequent aggregation queries dramatically faster. By exploiting materialized views, alert evaluations become lightweight and efficient, even at high alert volumes.

![accelerated_clickstack.png](/uploads/accelerated_clickstack_f899326c47.png)

The result is that users can safely run hundreds or even thousands of alerts with minimal impact on overall cluster performance. The cost is paid incrementally at insert time and amortized across all incoming data, keeping per-insert overhead low. When building an alert visualization in HyperDX, users can tell if their alert will be accelerated with an icon showing that a materialized view will be used automatically.

## Some smaller things

Alongside these larger wins around index usage, we continue to ship a steady stream of smaller improvements that make ClickStack easier to use day to day. These changes focus on usability, smoother workflows, and simpler deployment, helping teams get value faster while steadily improving the overall experience.

### Filtering tables with HAVING

Users with a data analytics background will immediately recognize the value of the SQL `HAVING` clause for querying observability data. Unlike `WHERE`, which filters rows before aggregation, `HAVING` allows result sets to be filtered based on aggregate values computed during the query. For example, you might calculate the average latency and 99th percentile per service, then return only those services whose average latency exceeds 100 milliseconds. This pattern is especially useful for reducing large result sets down to something more meaningful and actionable.


The latest version of ClickStack now supports `HAVING` when querying tables in the UI. This allows users to filter results using any aggregate expression, not just raw columns. In practice, this means you can filter by an alias such as `avg_duration > 100`, or define an entirely new aggregate in the filter itself, for example `median(Duration) > 100`, giving you far more flexibility when exploring and refining aggregated results.

![having_table_clickstack.png](/uploads/having_table_clickstack_21e0b63ee7.png)

### Legend filtering

Legend filtering for charts represents a small and thoughtful quality of life improvement for charts. You can now filter time series directly from the chart legend. Clicking a legend item instantly isolates that series in the chart, making it easy to focus on a single signal without touching the query. Click it again, and the chart resets to show everything.


series_filtering.mp4

We added a bit of extra polish here too. By holding Shift, you can select multiple series at once and compare them side by side. All legend entries remain visible, with clear visual cues showing what is selected and what is temporarily dimmed, and the Y axis automatically rescales to fit only the visible data. It is a small feature, but one that makes exploring charts faster, more interactive, and a little more satisfying to use.

### Collector improvements

ClickStack is designed to make getting started with observability as simple as possible. It ships with a pre-configured OpenTelemetry Collector that just works with ClickHouse, with sensible defaults for batching, custom processing, and schema enforcement already in place. 

By default, the collector is orchestrated using OpAMP, which secures the OTLP ingestion endpoint with an ingestion key exposed directly in the UI. Users can copy this key and send data to the collector with the appropriate authorization header, providing a secure and frictionless onboarding experience from the very first event.

As ClickStack has grown, and with the emergence of the managed offering, more users want to run the OpenTelemetry Collector independently of the UI while still benefiting from the ClickStack distribution and its ongoing improvements. To support this, ingestion can now be decoupled from the UI. If the `OPAMP_SERVER_URL` environment variable is omitted or set to an empty value, communication with the OpAmp server is disabled. The collector will continue to run independently, without relying on the UI for configuration.

To secure ingestion in this mode, users can provide an `OTLP_AUTH_TOKEN`. When set, this token is required for all data sent to its OTLP endpoint. This change makes it easier to get started with Managed ClickStack, while also enabling advanced users to operate their own collectors and still take advantage of ClickStack’s standardized processing and schema enforcement.

```sh
# Example command for running the ClickStack OpenTelemetry collector
docker run -e CLICKHOUSE_ENDPOINT=https://myhost:8443 -e CLICKHOUSE_USER="default" - e CLICKHOUSE_PASSWORD="<password>" -p 4317 -p 4318 -e OTLP_AUTH_TOKEN "a_secure_string_value" clickhouse/clickhouse-otel-collector:latest
```

## And finally, a new theme

Users reading the post above regarding Managed ClickStack may have noticed that the HyperDX interface looks a little different! 
With the launch of Managed ClickStack, we took the opportunity to introduce an alternative ClickStack theme. Since ClickStack’s launch, there has been occasional confusion around whether HyperDX is the product or the UI - the UI’s HyperDX branding not helping with this. 
You will increasingly see us refer to the UI as just the “ClickStack UI” - while still acknowledging its original identity as HyperDX. 


new_theme.mp4

The new ClickStack theme, which is used by default in our managed offering, aligns more closely with the broader ClickHouse branding - emphasizing the familiar white, yellow and blacks used across our products. 
Open source users can switch to this theme through user preferences, or keep the original HyperDX look if green is more your style. No hard feelings. Both themes are available in light and dark variants, so you can choose what works best for you.

## Conclusion

That wraps up January for ClickStack. We have some exciting updates coming over the next few weeks, with APIs and RBAC firmly at the top of our roadmap as we continue to focus on making ClickStack easier to integrate and operate at scale. As always, we would love to hear what you want to see next. 


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

Subscribe to our observability newsletter

January’s ClickStack update brings Managed ClickStack, dramatically faster text search with new indexing options, materialized view powered alerts, and a wave of usability upgrades across the stack.

large_whats-new-clickstack-jan-2026.png

medium_whats-new-clickstack-jan-2026.png

small_whats-new-clickstack-jan-2026.png

thumbnail_whats-new-clickstack-jan-2026.png

whats-new-clickstack-jan-2026.png

What's new in ClickStack. January '26.

We're excited to announce the availability of Managed ClickStack for ClickHouse Cloud in beta. 

Managed ClickStack makes it easier than ever to take advantage of ClickHouse for observability, delivering fast queries and powerful analytics across logs, metrics, traces, and session replays without the overhead of running infrastructure. 

<iframe width="768" height="432" src="https://www.youtube.com/embed/pa_dyNdAHC4?si=GpTwP7UlycCFL_Vp" 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>

With market-leading cost efficiency at its core, Managed ClickStack provides a simple path to production-grade observability at any scale - whether you're already using ClickHouse Cloud for real-time analytics or warehousing, coming from [ClickStack Open Source](https://clickhouse.com/clickstack) or just starting to explore the benefits of ClickHouse for observability.

Built on ClickHouse Cloud, Managed ClickStack is priced on infrastructure, not events. Store full-fidelity, high-cardinality OpenTelemetry data indefinitely **for less than three cents per gigabyte per month, with no per-user, per-host, or other extraneous fees**. Scale your logs, metrics and traces without reduced retention periods, sampling or rollups. Scale ingest and query independently, pay only for what you use, and get ClickHouse Cloud reliability out of the box. The result is a managed observability platform that completely changes the economics of observability, while readying users for emerging agentic workloads and consolidating observability with other use cases where it belongs.

![clickstack-ui.png](/uploads/clickstack_ui_cd5b5e7259.png)

<blockquote style="font-size: 15px;">
<p>We remain fully committed to ClickStack Open Source and will continue to invest in new features, performance improvements, and tooling for the community. Users who choose to run ClickStack self-managed will continue to have full access to the same open source foundation that powers Managed ClickStack.</p>
</blockquote>

## Getting started with Managed ClickStack

To get started with Managed ClickStack, new users can begin with a ClickHouse Cloud trial, which includes $300 of credits - enough for about ~10TB of OTel data based on our recommended sizing. Create a service, select ‘ClickStack’ and you’ll be guided through loading your data, with step-by-step instructions provided as part of the onboarding flow.

![clickstack-managed.png](/uploads/clickstack_managed_356c59dc4f.png)

In this initial beta release, Managed ClickStack includes guided ingestion for **OpenTelemetry** and **Vector**, with more options to follow. Users are also free to use any of the supported ClickHouse Cloud ingestion methods. While ClickStack is optimized for OpenTelemetry schemas and provides the fastest path to value out of the box, it is not limited to them. Any event-based data with a timestamp can be ingested and explored in the ClickStack UI (HyperDX). Existing ClickHouse Cloud users can enable ClickStack on new or existing services and follow the same process to start using Managed ClickStack immediately.

## Why we built managed ClickStack

When we [launched ClickStack last year](https://clickhouse.com/blog/clickstack-a-high-performance-oss-observability-stack-on-clickhouse), we did so with the goal of democratizing access to ClickHouse for observability. ClickHouse had already proven itself as an almost de facto store for observability data, showing that columnar databases are a natural fit for the workload - as demonstrated by users already running [observability on ClickHouse at unprecedented scale](https://clickhouse.com/blog/how-anthropic-is-using-clickhouse-to-scale-observability-for-ai-era).

ClickStack set out to make this accessible to everyone by providing a more opinionated, OpenTelemetry-based observability stack with the ClickStack UI (HyperDX) built in. By standardizing on OpenTelemetry and pairing them with a purpose-built UI, ClickStack made it dramatically easier to adopt ClickHouse for logs, metrics, traces, and session replays, and to benefit from the performance and cost advantages of a columnar store without building everything from scratch.

Teams have begun running ClickStack at larger and larger scales, retaining more data, increasing cardinality, and moving away from the sampling and short retention windows that had become the norm in observability. For many users, this shift was transformational, changing the economics of their observability. Meanwhile, we've been busy adding new features and making sure that ClickStack scales better than ever, bringing improved cost efficiency and performance by adding support for [features like materialized views](https://clickhouse.com/blog/whats-new-in-clickstack-december-2025#materialized-views-arrive-in-clickstack).

![clickstack_feb_2026_timeline.png](/uploads/clickstack_feb_2026_timeline_f8a108ea4b.png)

As usage has grown, so has the demand for a managed experience. Many teams want the benefits of ClickStack and ClickHouse without operating the underlying infrastructure themselves. Others are already using ClickHouse Cloud for analytics, warehousing, or AI workloads and wanted to **consolidate observability into the same platform, avoiding data silos and duplication **while unlocking deeper correlation across their datasets - aligning with our vision that **observability is really just another data problem**.

Managed ClickStack is the natural next step in this journey. By running ClickStack on ClickHouse Cloud, we can deliver the same open source foundation with a simpler operational model, and access to capabilities like separation of storage and compute, low-cost object storage for long-term retention, and enterprise-grade reliability and security out of the box. 

While "separation of storage and compute" and "object storage" might seem like implementation details for many, they change the cost economics of observability.

## Changing the cost economics of observability

Most managed observability platforms price around constraints. Some charge per gigabyte ingested. Others rely on more punitive user-based or host-based pricing. In practice, this forces teams to constantly decide what they can afford to monitor, how long they can retain data, and where they need to sample or drop signals to stay within budget. Cost becomes a limiting factor on observability itself.

ClickHouse has [already shown](https://clickhouse.com/user-stories?useCase=2) that this does not need to be the case. With columnar storage and aggressive compression, it is common to achieve 10–15x compression on observability data, while still supporting fast aggregations and high-cardinality queries. This alone fundamentally improves the economics of storing logs, metrics, and traces over long periods of time.

Managed ClickStack takes this a step further. Building on ClickHouse Cloud’s separation of storage and compute changes the pricing model entirely. Observability data is stored in low-cost object storage, with intelligent local and distributed caching handled automatically by ClickHouse Cloud. There are no hot or cold tiers, and no artificial boundaries based on data age. Data is treated consistently regardless of how old it is. What gets cached and queried efficiently is **driven purely by access patterns, not by retention policies**.

![managed_clickstack_architecture.png](/uploads/managed_clickstack_architecture_6a1b359914.png)

This means **retention effectively stops being a meaningful cost dimension**. High-cardinality OpenTelemetry data can be retained for years at **less than a cent per gigabyte per month**, without sampling, rollups, or forced data expiration. Frequently accessed data is cached automatically and queried at low latency. Less frequently accessed data remains fully queryable, with predictable performance and cost.

### Efficient and tunable compute

With storage and retention no longer a cost constraint, the remaining variable becomes compute -  you still need resources to ingest and query data. Here users further benefit from the separation of storage and compute, as ingest and query workloads are fully isolated and can be scaled independently. 

Ingest compute runs continuously to deliver consistent throughput, with each core capable of sustaining up to 20MB/s of writes in our benchmarks. In practice, this translates to users paying less than 1 cent per GB per month for ingest compute.

Meanwhile, independent query compute can be shaped to match how observability data is used. Small, always-on pools can power dashboards and alerts over recent data. Larger pools can be spun up on demand for investigations or historical analysis, then scaled down or idled when no longer needed. Because compute is isolated and elastic, users only pay for query **resources they use** - a concept users coming from cloud data warehousing will be familiar with.

![managed_clickstack_compute_pools.png](/uploads/managed_clickstack_compute_pools_4c2fdab30d.png)

The result is you no longer need to choose between cost, retention, or visibility. You can ingest everything, retain everything, and query everything, while keeping costs transparent and predictable.

## Unifying datastores and unlocking agentic workloads

While cost efficiency is a critical driver for observability, it is only one part of a much larger shift. ClickHouse already powers real-time application analytics and business intelligence workloads at scale. Over time, we believe these datasets will increasingly converge, and that the systems used to store and analyze them will consolidate as well. ClickHouse is uniquely positioned to support this shift.

The ability to correlate observability data with business and application data unlocks powerful new questions. Teams can go beyond detecting incidents to understanding their real impact, such as attributing outages to revenue loss, user churn, or degraded customer experience. Even simpler correlations, joining logs or traces with application or product data, become easier and more efficient when everything lives in a single database.

![clickstack_multiple_usecases.png](/uploads/clickstack_multiple_usecases_e3508350e0.png)

<blockquote style="font-size: 15px;">
<p>In the above, <strong>compute pools are used to service different use cases</strong>, rather than individual query types. For example, you might have a dedicated compute pool for observability and ClickStack workloads, another for real-time analytics, and a separate data warehousing pool that remains idle until needed. Additional workloads can be supported by introducing new compute pools as required. Within each use case, it is also common for multiple compute pools to emerge, each sized and tuned for different workload characteristics. All compute pools can access all of the data for correlation provided access policies permit.</p>
</blockquote>

Separation of storage and compute is also essential in delivering this use case consolidation effectively. Multiple workloads can safely query the same underlying data while using isolated compute resources tuned for their specific needs. Observability, analytics, BI, and investigation workloads no longer need separate systems or duplicated data, reducing both operational complexity and cost.
Historically, asking these complex questions required deep cross-domain knowledge, strong SQL experience and familiarity with multiple datasets - a rare persona for many businesses. However, with the emergence of agentic workloads and AI-driven data interaction, natural language interfaces make it far easier to ask sophisticated cross-domain questions across unified datasets.


hero-1920.mp4

SQL plays a central role here. Large language models already have a strong built-in understanding of SQL and can generate efficient queries with minimal context. SQL remains the most expressive and precise language for querying structured data, making it a natural foundation for [agentic analytics](https://clickhouse.com/blog/agent-facing-analytics). As query volumes increase and latency expectations tighten, ClickHouse’s performance characteristics become even more important.

Looking ahead, we believe this convergence of observability, analytics, and agentic interaction represents a major shift in how teams work with data. As we explore agentic capabilities across ClickStack and the broader ClickHouse Cloud platform, users who consolidate their data and workloads on ClickHouse will be well positioned to take advantage of what comes next. Stay tuned for more announcements in 2026 as we expand our [Agentic Data Stack](https://clickhouse.com/ai) across both ClickHouse Cloud and ClickStack.

## Conclusion


New users can sign up for ClickHouse Cloud, create a service, and connect their telemetry source to their ClickHouse instance, with ClickStack available directly in the console. Existing users can open ClickStack straight from their console immediately on any service.

This beta is the first step toward making Managed ClickStack broadly available. Over the coming months, we’ll expand capabilities, refine defaults, and roll out additional tooling as the offering matures.

We’d love to hear from you as we build. Join the ClickHouse Slack and hop into the [#olly-clickstack channel ](https://clickhouse.com/slack) to share feedback, ask questions, or follow along with updates about anything ClickStack related.


Announcing Managed ClickStack, a fully managed observability platform on ClickHouse Cloud that lets you ingest, retain, and query full-fidelity logs, metrics, and traces at massive scale for a fraction of the cost.

large_managed-clickstack-banner.png

medium_managed-clickstack-banner.png

small_managed-clickstack-banner.png

thumbnail_managed-clickstack-banner.png

managed-clickstack-banner.png

Introducing Managed ClickStack and redefining the economics of observability

When your mission-critical workflows span days, weeks, or even months, every data observability data point counts. That's why we're excited to announce the integration between [ClickStack](https://clickhouse.com/use-cases/observability) and [Temporal Cloud's OpenMetrics endpoint](https://docs.temporal.io/cloud/metrics/openmetrics/), bringing high performance observability to your durable execution platform.

![temporal_dashboard.png](/uploads/temporal_dashboard_ed1d16163e.png)

## What is Temporal?

Temporal is a durable execution platform that helps developers build reliable applications. Temporal lets you focus on business logic rather than writing complex error handling, retry logic, and state management code to survive failures.
Your business logic runs as a Temporal Workflow, whether that means processing payments, orchestrating agents, or managing long-running shopping cart experiences. If a server crashes, the network fails, or a service goes down, Temporal automatically recovers and resumes execution exactly where it left off. No lost progress, no orphaned processes.

### What is ClickStack?

[ClickStack](https://clickhouse.com/docs/use-cases/observability/clickstack/getting-started) is a cloud-native observability stack built on ClickHouse for high-performance storage and querying of logs, metrics, and traces. It’s designed for teams dealing with large volumes of telemetry and high-cardinality data, where traditional observability platforms either fall over or become cost-prohibitive.

ClickStack focuses on horizontal scalability, efficient compression, and predictable query performance, even as data volumes grow into the petabyte range. It’s commonly used for log analytics, operational monitoring, and deep investigation of production systems where ingestion rate and retention really matter.

Explore the ClickHouse-powered open source observability stack built for OpenTelemetry at scale.

Get started with ClickStack

ClickStack consists of:

* **HyperDX UI**, a purpose-built frontend for exploring and visualizing observability data
* **A custom-built OpenTelemetry Collector**, with an opinionated schema for logs, metrics, and traces
* **ClickHouse**, the high-performance analytical database at the core of the stack


## Why ClickStack and Temporal Cloud belong together

Running Temporal at scale means managing potentially thousands of concurrent workflows, each with its own activities, timers, and state transitions. When something goes wrong, or when you need to optimize performance, you need to be able to navigate the wealth of observability information coming out of the system.

This is where ClickStack helps. Built on ClickHouse, ClickStack handles the high cardinality metrics that Temporal generates with ease. ClickStack processes queries across Task Queues, Workflow Types, and Namespaces in milliseconds, so you can get to the needle in the haystack quickly.

For teams already running ClickStack, adding Temporal metrics means unified observability. Alternatively, for users running Temporal, ClickStack offers an out-of-box open-source observability solution that users can [get started with in minutes](https://clickhouse.com/docs/use-cases/observability/clickstack/getting-started). Understand how the health of a database affects Task processing backlogs. Track how deployment changes affect Workflow latency. Build dashboards that show the complete picture of your system's health.

## Getting started: Connecting Temporal Cloud to ClickStack

The integration uses the [OpenTelemetry Collector's Prometheus receiver](https://opentelemetry.io/docs/collector/configuration/#receivers) to scrape metrics from Temporal Cloud. [Full instructions on how to set up the integration are available](https://clickhouse.com/docs/use-cases/observability/clickstack/integrations/temporal-metrics), but the gist is once you have a Temporal Cloud API Key that has permission to read metrics, you create the configuration file in the OpenTelemetry Collector like this:


Once deployed, open the ClickStack UI, HyperDX, and navigate to the Metrics explorer. Search for metrics starting with `temporal_cloud` to confirm data is flowing. You can import the [pre-built Temporal dashboard](https://clickhouse.com/docs/use-cases/observability/clickstack/integrations/temporal-metrics#dashboards) and immediately visualize Workflow success rates, Actions consumption against your limits, and Task Queue backlogs.

## What's Next

With Temporal metrics flowing into ClickStack, you can set up alerts on critical thresholds, build custom dashboards for your specific workflow patterns, and correlate workflow performance with the rest of your observability data.

For full configuration details and troubleshooting guidance, check out the[ complete documentation](https://clickhouse.com/docs/use-cases/observability/clickstack/integrations/temporal-metrics).

Your durable workflows deserve scalable observability. With ClickStack and [Temporal Cloud](https://temporal.io/cloud) working together, you get both.


Learn how ClickStack integrates with Temporal Cloud to deliver fast, scalable observability for high-cardinality metrics, helping you spot bottlenecks, debug failures, and understand workflow health in minutes.

large_temporal_clickhouse.png

medium_temporal_clickhouse.png

small_temporal_clickhouse.png

thumbnail_temporal_clickhouse.png

temporal_clickhouse.png

Monitoring Temporal Cloud with ClickStack

## Introduction

It’s hard to believe that ClickStack only [launched in late May last year](https://clickhouse.com/blog/clickstack-a-high-performance-oss-observability-stack-on-clickhouse). The pace since then has felt more like a full year of product evolution than 7 months. ClickStack has gained [native JSON support](#july:-json-support-arrives), [dashboards import/export](#september:-dashboard-import/export,-custom-collector-config,-and-smarter-queries), [support for Materialized Views](#december:-materialized-views-unlock-new-scalability-limits), performance wins across every layer, [a full alerting system](#october:-alerting,-flexible-event-deltas,-and-dashboard-filters), and the arrival of [features like Service Maps](#november:-what’s-new---service-maps-and-attribute-highlighting).

Alongside the monthly releases, we also [brought ClickStack into ClickHouse Cloud](https://clickhouse.com/blog/announcing-clickstack-in-clickhouse-cloud), marking the first step toward a unified environment where observability and analytics live side by side. And we’re nowhere near done. 2026 will raise the ceiling again with deeper Cloud integration, a fully managed ClickStack experience, AI powered notebooks, anomaly detection, out-of-the-box integrations and even more opinionated defaults that let teams adopt observability with almost no operational effort.   

![image2.png](/uploads/image2_5a6614eac7.png)

This post looks back on our journey from May through December: the **key features** introduced each month as the stack has matured into something far more capable than the version we first announced. This list is in no way exhaustive, rather focusing on those features we’re most proud of.  We’ll close with a look ahead at where we’re investing next and what users can expect from ClickStack in 2026\.

## May: ClickStack is launched

ClickStack began as a response to a clear pattern we had seen for years. Teams at scale such as [Netflix](https://clickhouse.com/blog/netflix-petabyte-scale-logging) and [Anthropic](https://clickhouse.com/blog/how-anthropic-is-using-clickhouse-to-scale-observability-for-ai-era), were already using ClickHouse as the foundation for their observability systems, taking advantage of its ability to store and query wide events with high cardinality and deep context. What they lacked was an opinionated, end to end experience. HyperDX changed that trajectory. When the project open sourced its next generation UI in late 2024, it gave us a modern workflow layer built specifically around ClickHouse strengths: fast search, fast aggregations, and a UI that understands how to construct efficient queries without hiding the power of SQL. Bringing HyperDX and ClickHouse together created an opportunity to offer something the ecosystem had been missing, a complete open source observability stack that embraced OpenTelemetry and delivered logs, metrics, traces, and session replay in one place.  

![image6.png](/uploads/image6_29c07a439b.png)

That vision became real in May with the [launch of ClickStack](https://clickhouse.com/blog/clickstack-a-high-performance-oss-observability-stack-on-clickhouse). This first release bundled an opinionated OpenTelemetry collector, and a ClickHouse native UI through HyperDX. It delivered a turnkey workflow for ingestion, search, dashboards, and debugging while still giving advanced users full access to SQL and the ability to customize schemas as needed. What started as an integration of proven building blocks quickly became a cohesive product, built around the idea that observability is a data problem best solved by a column store and an experience tailored for it.

Most importantly, ClickStack democratized access to the advantages that only large engineering teams had previously enjoyed. Until now, the full potential of ClickHouse for observability was reserved for organizations that could afford to build custom ingestion paths, schemas, and UI layers. Open-source ClickStack removed that barrier. It delivered a unified experience where the UI, schema, and engine work together by design, offering an observability workflow that finally takes full advantage of ClickHouse performance while remaining open, accessible, and effortless to adopt.

## July: JSON support arrives

Just weeks after launch, July brought [the arrival of native JSON type support](https://clickhouse.com/blog/whats-new-clickstack-july-2025#clickstack-supports-the-json-type-for-faster-queries). JSON had been evolving inside ClickHouse for several release cycles, gradually maturing into a high performance, column aware format that preserves structure and types while enabling sub column pruning. By July, the feature was ready for production workloads, and ClickStack became one of the first large scale use cases to adopt it. Moving from Map based attribute storage to native JSON can unlock dramatic improvements for **some workloads**, including far less I/O, and the ability to work with deeply nested observability data without flattening or manual preprocessing.

![image8.png](/uploads/image8_3716078ef5.png)

Introducing JSON early in ClickStack’s lifetime promises faster queries, more expressive schemas, and an ingestion model that better reflects modern OpenTelemetry data. But the work isn’t done. We **continue to study when JSON is the right choice,** how to pair it with indexing strategies, and how it behaves under high volume observability workloads. **Expect more guidance and benchmarking in the months ahead** as we refine best practices and explore how far JSON can be pushed for real time, high cardinality telemetry at scale.

## August: ClickStack meets ClickHouse Cloud

August marked a major milestone for ClickStack, the debut of the [HyperDX powered UI directly inside ClickHouse Cloud](https://clickhouse.com/blog/announcing-clickstack-in-clickhouse-cloud). This wasn’t just about convenience. It represented the moment ClickStack moved from a self hosted open source experience to a first class, Cloud integrated product. For the first time, users could launch ClickStack with no infrastructure, no separate authentication, and no operational surface area, instantly pairing observability workflows with the same real time warehouse they already relied on for analytics. It opened ClickStack to three key groups: existing ClickStack users running on Cloud who now had one less component to manage, companies migrating large workloads from proprietary observability vendors who needed a cost efficient alternative that didn’t compromise on performance, and application teams already using ClickHouse Cloud for analytics who could now add observability with a single click.

This Cloud integration also made our broader vision tangible, unified observability and analytics in one system. Instead of treating telemetry as a separate silo, ClickStack in Cloud allows teams to join traces, logs, and metrics directly with application data, product events, and operational KPIs. It shifts observability from after the fact diagnosis to a fully correlated analytical workflow where business impact, performance regressions, and customer behavior can all be understood through one engine. At the core of this belief is the principle we’ve stated throughout the year:

**We believe observability is just another data problem.** And that it belongs in the same database as your business-critical analytics. With ClickHouse Cloud, you get the performance of a real-time warehouse, the scale and flexibility of object storage, and now the visibility of a modern observability UI \- all in one stack.

![image9.png](/uploads/image9_3cb6c255b8.png)

Since launching in Cloud, we’ve continued onboarding customers steadily and have gathered feedback from some of the largest organizations using ClickHouse for observability today. These include deeply scaled teams such as [Anthropic](https://clickhouse.com/blog/how-anthropic-is-using-clickhouse-to-scale-observability-for-ai-era) and [character.AI](http://character.AI), whose input has shaped how the UI behaves under high volume, high cardinality workloads. Their use cases have pushed the product further, influencing everything from performance optimizations to workflow simplifications. August wasn’t just a feature release, it was the start of a new chapter where ClickStack became a native part of ClickHouse Cloud and a foundation for the unified observability experience we’ll continue building in 2026\.

Alongside this, we added further [performance improvements](https://clickhouse.com/blog/whats-new-in-clickstack-august#improved-query-efficiency-for-time-based-primary-keys) for scale as well as [early support](https://clickhouse.com/blog/whats-new-in-clickstack-august#inverted-indices-support) for ClickHouse’s [new inverted index](https://clickhouse.com/blog/clickhouse-full-text-search) \-  an effort aimed at improving full text search over log bodies. While still experimental, it’s an area we’re actively still evaluating, and it will remain a focus as we move into January 2026\.

## September: Dashboard import/export, custom collector config, and smarter queries

September introduced one of the most meaningful initial quality of experience improvements in ClickStack: [dashboard import and export](https://clickhouse.com/blog/whats-new-in-clickstack-september-2025#importexport-dashboards). This feature gives teams a faster path to value by allowing them to share dashboards internally, version them alongside code, and quickly spin up known good views when onboarding new services or teams. More importantly, it lays the groundwork for a community driven ecosystem of shared dashboards. Throughout the year we’ve been [expanding our documentation with out of the box dashboards for common integrations](https://clickhouse.com/docs/use-cases/observability/clickstack/integration-guides) and getting started scenarios, and we expect import/export to make it easier for users to contribute their own patterns back to the broader ClickStack community. Our hope is simple, dashboards should be as portable and composable as the SQL behind them.

![export_dashboard.gif](/uploads/export_dashboard_e066be3f13.gif)

September also introduced a powerful capability for extending ingestion workflows: [custom OpenTelemetry collector configuration](https://clickhouse.com/blog/whats-new-in-clickstack-september-2025#custom-collector-configuration). Users can now layer their own pipelines and receivers on top of ClickStack’s default distribution, which is essential when consuming logs from sources such as Kafka, tailing host files, or adding custom processors. This change not only enables more complex production deployments, it also [underpins the richer integration guides](https://clickhouse.com/docs/use-cases/observability/clickstack/integration-guides) we’ve been publishing, making ClickStack easier to adopt across a variety of environments. 

Alongside ingestion improvements came a major step forward in query performance. We implemented [time window chunking for search](https://clickhouse.com/blog/whats-new-in-clickstack-september-2025#chunking-time-windows), allowing ClickStack to fetch recent data first and stop early once enough results are returned. This optimization has since become a foundation for other performance wins, including faster chart rendering and more responsive histogram queries. It’s a clear example of how incremental improvements in September ultimately shaped the performance story of the entire stack.

![5_clickstack_september2025.gif](/uploads/5_clickstack_september2025_a95d9a36de.gif)

Finally, September added support for [custom aggregations](https://clickhouse.com/blog/whats-new-in-clickstack-september-2025#custom-aggregations) in the UI, letting users tap directly into the full power of ClickHouse’s aggregation engine. 

## October: Alerting, flexible Event Deltas, and dashboard filters

October marked another major milestone with the introduction of [threshold based alerting in ClickHouse Cloud](https://clickhouse.com/blog/whats-new-in-clickstack-october-2025#alerting-for-clickhouse-cloud). Users can now create alerts directly from either searches or charts, allowing them to monitor latency, error rates, throughput, or any SQL derived metric. The initial release also added built in integrations with PagerDuty and Incident.io along with generic webhooks for custom pipelines. More importantly, this laid the architectural foundation for alerting at serious scale. Alert workloads can run on [dedicated compute through ClickHouse Cloud’s read-write](https://clickhouse.com/docs/cloud/reference/warehouses) separation and compute pool model, which ensures that ingest and analytical queries remain unaffected. It opens the door to thousands of alerts running reliably and independently, with future updates planned to expand to anomaly detection logic with new out-of-the-box destinations.

![image3.png](/uploads/image3_d2a83c9abb.png)

Another highlight of October was the evolution of Event Deltas. Event Deltas have become one of the most distinctive features in ClickStack. At their core, they allow users to select a subset of slow spans, compare them against a baseline population, and automatically surface the attributes and values most associated with the regression. This [offers a lightweight form of anomaly detection at query time](https://clickhouse.com/blog/%20faster-root-cause-for-slow-traces-with-clickstack-event-deltas), rooted in actual distributions rather than heuristics. In October, we made [Event Deltas fully configurable](https://clickhouse.com/blog/whats-new-in-clickstack-october-2025#configurable-event-deltas). Instead of being limited to span duration, users can now build deltas on any numeric field, such as database latency or queue delay. This makes the feature far more flexible and unlocks entirely new investigative workflows.

![image11.png](/uploads/image11_67dbecacf2.png)

We also introduced [dashboard filters](https://clickhouse.com/blog/whats-new-in-clickstack-october-2025#dashboard-filtering), a usability improvement that immediately resonated with users - before users had to type filters. Some observability tools require predefined variables to make filters work, adding friction and setup overhead. In ClickStack, dashboard filtering is as simple as choosing a column. The UI automatically determines the correct filter type and renders it inline. The filter then applies to every visualization on the dashboard that shares the same data source. This allows users to slice and refine dashboards naturally, without configuration work or template engineering. October in many ways embodied the ClickStack approach: powerful features, simple workflows, and an experience that keeps getting faster and more intuitive.Finally, September added support for [custom aggregations](https://clickhouse.com/blog/whats-new-in-clickstack-september-2025#custom-aggregations) in the UI, letting users tap directly into the full power of ClickHouse’s aggregation engine. 


clickstack-oct-2025-dashboard_filtering (1).mp4


## November: What’s New - Service Maps and attribute highlighting

November introduced one of the most anticipated features in ClickStack: [Service Maps, now available in beta](https://clickhouse.com/blog/whats-new-in-clickstack-november-2025#service-maps). Service Maps transform trace data into a visual representation of how services interact, allowing teams to understand traffic flows, dependencies, and failure patterns at a glance. Crucially, ClickStack exposes them not only as a dedicated view but also in context. When users inspect an individual trace, a focused service map appears alongside the waterfall, showing the precise path that request took through the system. While the current beta already delivers valuable insight, we plan to accelerate performance and scalability in early 2026. This includes exploring internal ClickHouse capabilities such as refreshable materialized views to support high volume, real time graph construction.

![image7.png](/uploads/image7_c629312677.png)

We also introduced configurable [attribute highlighting](https://clickhouse.com/blog/whats-new-in-clickstack-november-2025#attribute-highlighting), a lightweight but powerful feature for both logs and traces. Users can now define specific fields to surface automatically when inspecting a span or log entry. These appear immediately in the side panel or trace header, making important identifiers, labels, or metadata visible without searching through full attribute payloads. Highlighted attributes can define custom search expressions and can include automatically detected clickable links. This helps teams build opinionated, domain specific debugging workflows that reflect the unique structure of their telemetry.

![image6](/uploads/image6_c99e85f911.png)

Finally, we [added line chart comparison](https://clickhouse.com/blog/whats-new-in-clickstack-november-2025#line-chart-comparisons), a small feature that quickly became a community favorite. With a single toggle, users can overlay the previous period on any time series chart, making regressions or improvements immediately visible without building separate dashboards or calculating offsets manually. It is a simple addition that dramatically improves exploratory analysis and reinforces our focus on delivering fast, intuitive tooling that shortens the path from data to understanding.



Spin up the world's fastest and most scalable open source observability stack, in seconds.

Get started with ClickStack 

line-chart-comparisons.mp4

## December: Materialized views unlock new scalability limits 

December closed the year with the biggest performance leap yet, the [arrival of fully integrated Materialized Views inside ClickStack](https://clickhouse.com/blog/whats-new-in-clickstack-december-2025). While ClickStack had previously allowed users to materialize individual charts, the approach was limited. Any filter or change in grouping pushed the query back to the base table, making performance gains inconsistent. The new design changes everything. Users can now create Incremental Materialized Views directly in ClickHouse, register them with a source, and let ClickStack’s query layer automatically select the most efficient view for each query. The system understands each view’s time granularity, available dimensions, and exposed metrics. At query time, it uses ClickHouse’s `EXPLAIN ESTIMATE` to determine which candidate view scans the fewest granules. This **allows any query in HyperDX - searches, dashboards, histogram views, and charts - to be accelerated** at scale with no additional work from the user.

hyperdx_mv.mp4

This is more than a performance enhancement. It represents a new scalability tier for ClickStack. Incremental Materialized Views compute only changes as data arrives, shifting small amounts of work to ingestion while drastically reducing query depth for downstream workflows. As a result, deployments can maintain near real time responsiveness even at very large volumes. This feature remains in beta, and we plan to refine it through early 2026, including smarter view selection and future support for automatically generated views based on user access patterns. Ending the year with Materialized Views sets the foundation for a new class of performance optimization across the stack, and one of the clearest demonstrations of how ClickStack continues to evolve by using the full power of ClickHouse’s engine.

## Looking ahead to 2026

As we look ahead to 2026, we are focused on making ClickStack the de facto open-source stack for teams operating OpenTelemetry at scale. Many of this year’s foundations from JSON support to Materialized Views will continue to unlock new performance and workflow improvements across the stack. In early 2026, we plan to build directly on these capabilities to deliver even faster dashboards, searches, and histograms for high volume environments.

Two of the most requested features from customers, **RBAC and audit logging**, are also on the way. RBAC will allow administrators to create and manage roles directly inside ClickStack, bringing enterprise grade access control to the full observability workflow. Audit logs will bring better visibility into usage and change tracking, a key requirement for regulated environments and organizations operating at scale.

We are also investing heavily in new analysis experiences. An **AI powered notebook** experience will likely be released in private preview with select users and will offer a streamlined way to explore data, generate queries, and combine observability and analytics in the same workspace. **Anomaly detection** for alerts remains a major priority with the aim to augment threshold based alerting with statistical based models that reduce noise and help teams catch regressions earlier.

Integration work will continue to accelerate. You can expect a growing catalog of pre-built dashboards, tighter getting started guides, and simplified ingestion paths for common environments. We are particularly focused on **CSP integrations**, such as CloudWatch, and making it easier than ever to pull data from Kafka with minimal configuration. Longer term, we plan to improve our support for **Prometheus style metrics**, including the upcoming developments in **PromQL inside ClickHouse**.

But the most significant milestone on the horizon is the shift toward **a fully managed ClickStack offering in ClickHouse Cloud**. Teams will simply send telemetry and get a complete, continuously optimized observability experience powered by ClickStack. It is an ambitious step, but it represents the natural evolution of the platform and the culmination of the vision we started in May \-  observability that is fast, scalable, open, and effortless to adopt. 

Finally, happy new year to all our readers and we look forward to meeting you in events and online in 2026\.

A look back at the later half of 2025 in the world of ClickStack.

large_image12.png

medium_image12.png

small_image12.png

thumbnail_image12.png

image12.png

ClickStack: A (half) year in review

Welcome to the December edition of What’s New in ClickStack, the open-source observability stack built for ClickHouse. Each month, we showcase the latest ClickHouse capabilities and HyperDX UI improvements that help teams explore their data with greater speed, clarity, and confidence.

This release focuses on a single feature that deserves the spotlight: smarter use of Materialized Views throughout the HyperDX UI. While December included many usability refinements, this addition stands apart as a major step forward for performance and scale. Materialized Views can now be created in ClickHouse, linked to a source, and used automatically by the Query Layer to accelerate charts, searches, and dashboards for users at any scale.

## New contributors

Building an open-source observability stack is a collaborative effort, and we are grateful for the continued energy from the community. Thank you to all new contributors this month. Your work helps move ClickStack forward for everyone.

[sgarfinkel](https://github.com/sgarfinkel) [alok87](https://github.com/alok87) [daniellockyer](https://github.com/daniellockyer) [PzaThief](https://github.com/PzaThief)

## Materialized Views arrive in ClickStack

**TLDR;** ClickStack now accelerates visualizations by using ClickHouse Materialized Views, with an intelligent layer that selects the most efficient view automatically for each query issued by the HyperDX UI.

Historically, ClickStack allowed users to materialize a chart. This created a Materialized View behind the scenes, and future queries for that chart would run against the view. In practice, the feature had limited adoption because any applied filter forced the query back to the base table. For most workloads, this meant the performance gain was inconsistent.

Users can now create Materialized Views directly in ClickHouse and register them with a source inside HyperDX. Once a view is attributed to a source, HyperDX understands the views  grouping interval, its available dimensions, and the aggregation metrics it exposes. All views must group data by a time interval such as one minute. Column groupings and metrics are inferred automatically when the view is marked as usable in the source.

![config_mv.png](/uploads/config_mv_2b8da75a05.png)



After an administrator assigns a view to a source, the view becomes available throughout the HyperDX application whenever it can accelerate a query. Multiple views can be attached to a single source, requiring only a small ingestion cost to maintain these Incremental Materialized Views. The application selects views intelligently at query time, giving large deployments a significant query-time performance boost. 

Getting started with the ClickHouse-powered open source observability stack built for OpenTelemetry at scale.


This feature is currently in Beta. There are a few improvements we’d like to consider before making this feature GA, as well as considering feedback from the community as it's adopted and used in real-world workloads.

> If you're new to ClickStack and aren't aware of ClickHouse’s Incremental Materialized Views, we strongly recommend watching [this short introductory video](https://clickhouse.com/docs/materialized-views).

### How ClickStack chooses the best view

A natural question is how the system chooses between the base table and multiple eligible views. To address this, ClickStack uses the `EXPLAIN ESTIMATE` clause in ClickHouse. When a query is issued, the system asks ClickHouse to estimate the number of granules and the volume of data that would be scanned for each candidate. The view that scans the fewest granules is selected. While this heuristic may not be perfect, it reliably reduces the amount of data scanned and delivers consistently fast responses across a wide range of workloads.

Views remain effective even when charts have filters or when searches include constraints. They can also power histograms, dashboard charts, and filtered visualizations as long as the required dimensions exist within the view.

### A concrete example

Consider two Materialized Views created against our public OpenTelemetry demo data in [sql.clickhouse.com](sql.clickhouse.com) for the traces source. The first `otel_traces_1m` groups by minute `toStartOfMinute(Timestamp)`, `ServiceName`, and `StatusCode`, computing a count of rows as well as the metrics `avg(Duration)` and `max(Duration)`. The second `otel_traces_1m_v2` groups by `toStartOfMinute(Timestamp)`, `ServiceName`, `StatusCode`, and `SpanName`, and computes the same metrics in addition to `quantileTDigest(Duration)`. The latter is larger because it contains more grouping keys and therefore produces more rows.



If a user builds a visualization that shows average duration per service over time by hour, both views are technically valid. ClickStack issues an `EXPLAIN ESTIMATE` query to determine granule counts (and thus less data to scan). The smaller view that groups only by minute, ServiceName, and SeverityText is sufficient, so it is selected. As shown below, the smaller view provides faster performance for the SQL query used to compute the chart. Note how both queries are faster than querying the base table:

> The absolute timing differences here are small, 0.009 seconds for `otel_traces_1m` versus 0.015 seconds for `otel_traces_1m_v2`, and at the scale shown, a few hundred million rows, even the base table query completes in around half a second. At petabyte scale and trillions of rows, however, these seemingly minor gaps compound and become materially significant.

When we actually configure this chart in ClickHouse, you’ll see an acceleration icon indicating the use of a Materialized View. Clicking this shows the view used as well as some useful information, such as the granularity and metrics available, as well as the views that were considered (and discounted). 

On dashboards, you'll also see a small acceleration icon appear on the chart to indicate the use of a view.

![clickstack_accelerated_dashboard.png](/uploads/clickstack_accelerated_dashboard_8fb2ac7fbc.png)

If the dashboard is filtered by `StatusCode`, the same view continues to be used. However, adding a filter on `SpanName` requires the additional dimension, so the second view becomes the required match. The system, however, switches seamlessly with no user intervention.


hyperdx_multiple_mvs.mp4

If the user adds a second metric to the chart (p99), the latter view will also be used. Currently, charts are rendered with a single query, so an accelerating view must satisfy all metrics. In the future, we may make this more intelligent, where specific metrics are handled by different views. Additionally, views are assessed for each query; there is currently no caching layer that determines which views are effective for which queries. For now, we recommend keeping view counts moderate (<10) while in Beta pending the addition of further intelligence and/or caching.



hyperdx_multiple_metrics.mp4

> Note: Views are used across the entire HyperDX application where possible, with view assessment occurring at the query layer. For example, users can use the same capability to accelerate the histogram on the search view for common workflows.

### Why this matters

This feature represents a major milestone for ClickStack. Users now have a flexible mechanism to build performance optimizations that match their workloads. Everything works in open source; users only need to create views and link them to their sources.

It's important to remember that Incremental Materialized Views in ClickHouse compute only the changes to the view as data arrives. This shifts computation to insert time. Since ClickHouse is highly optimized for ingestion, the incremental cost applied to each block is very low compared to the savings at query time. The result is lower operational cost with near real-time performance gains for every downstream chart, search, and workflow - even at petabyte scale.

> This approach is very different from systems that recompute the entire view on each update or schedule periodic updates. For a deep dive into Materialized Views, how they work and how to create them we recommend [this guide](https://clickhouse.com/docs/materialized-view/incremental-materialized-view).

We are excited to build on this foundation throughout 2026. For example, in ClickHouse Cloud, we plan to extend this capability with an intelligence layer that creates views automatically based on user access patterns. 

## Conclusion

As we close out the year and ship our final release of December, we would like to thank all our users and contributors and wish you a happy and successful new year. 

Acceleration with Materialized Views concludes the year, marking a significant step forward for ClickStack and introducing a new level of performance and configurability that enables users to optimize their own workflows. The feature is available today in open source, where teams can define and tune their own views to target the parts of their system that matter most to them. 

While currently in beta, we will publish practical guidance in our documentation shortly to help teams get the most value from it. Looking ahead, in ClickStack and ClickHouse Cloud, we plan to automate the creation of many of these views based on how users actually work, further accelerating everyday experiences with no manual effort. Thank you for being part of the ClickStack community. Here's to an exciting year ahead.



ClickStack’s December update introduces the intelligent use of Materialized Views that automatically accelerate charts and dashboards, delivering faster insights at scale with zero user effort.

The ClickStack Team

Blog posts by The ClickStack Team

Introducing ClickStack embedded in ClickHouse

What's new in ClickStack. January '26.

Introducing Managed ClickStack and redefining the economics of observability

Monitoring Temporal Cloud with ClickStack

ClickStack: A (half) year in review

What's new in ClickStack. December '25.