Hydrolix vs ClickHouse for High-Volume Log Data

ClickHouse is a powerful online analytical processing (OLAP) tool with robust features for working with high-cardinality, high-dimensionality log data. We regard it highly, and we maintain our own fork of open source ClickHouse, which we use in some components of our system. In Hydrolix, ClickHouse helps support complex queries and features like summary tables, which take advantage of ClickHouse intermediate states. We even have an internal ClickHouse tricks Slack channel where practitioners can share ClickHouse queries for use cases like detecting anomalistic error rates, standard deviations, and Z scores.

Hydrolix shares a number of similarities with ClickHouse—both use columnar storage and work best with append-only workloads. But while ClickHouse is an all-purpose OLAP platform that works with many use cases, Hydrolix is designed specifically for long-term, cost-effective retention, ingest, and querying of high-volume log and event data.

Learn how Hydrolix ingested nearly 200 terabytes of data for FOX during the 2025 Super Bowl with 5-10 second time to glass, sub-second query performance, and 94% compression on incoming data.

This post will take a closer look at some of the underlying design decisions that Hydrolix has made to optimize for high-volume, cost-effective log data, and how they differ from ClickHouse’s more general, all-purpose design. Then we’ll drill down into one specific area: ingesting high volumes of data in real time, an area where Hydrolix excels and ClickHouse can sometimes struggle.

Check out our Hydrolix and ClickHouse comparison page for a high-level comparison of the two technologies.

How Hydrolix Differs From ClickHouse

On the surface, Hydrolix and ClickHouse have many similarities. Both are effective tools for high-volume, append-only workloads. And Hydrolix uses Clickhouse-flavored SQL (along with providing support for other SQL dialects such as KQL), so you can query your data in the same way regardless of whether you are using Hydrolix or ClickHouse.

Under the hood, though, Hydrolix has made specific design decisions that make it a good choice for high-volume workloads built around log and event data. These are just a few of the design decisions that make Hydrolix fundamentally different from ClickHouse.

Fully-Decoupled Architecture

Hydrolix is built on fully-decoupled, stateless architecture that uses cloud-native Kubernetes. Components like query, merge, and ingest can all be scaled independently of each other (or even scaled down completely) so that resources are always right-sized. This is in contrast to ClickHouse, where services such as MergeTree can lead to resource contention with other services such as query, leading to performance degradation.

Managed ClickHouse providers such as ClickHouse Cloud and Altinity (with Antalya) are working to build partially decoupled services on ClickHouse, but they are not fully decoupled like Hydrolix.

Cost-Effective, Performant Cloud Storage

The volume of log and event data is rapidly climbing for many enterprises, leading to skyrocketing costs. Enterprises must either discard data after a short period of time or use tiered systems that quickly move data into more cost-effective (but less accessible) cold storage. 

From the very beginning, Hydrolix was designed to maximize the read and write performance of commodity cloud storage, which is much more cost-effective than closely-coupled storage solutions like SSDs. Our rationale: enterprises need sub-second, near-real-time query latency for use cases like observability. It’s possible to achieve these goals with commodity cloud storage by using techniques such as extreme parallelism, partition pruning, and micro-indexing.

For true real-time latency—think 20 milliseconds—ClickHouse is a better choice. But for high volume workloads, the costs will be extreme. For many use cases, the incremental increase in performance is not worth the much higher costs.

Again, managed ClickHouse providers (and many other enterprises providing SaaS services) are increasingly offering decoupled cloud storage to reduce costs. However, Hydrolix is architected based on cloud storage, in contrast to platforms that are rearchitecting their platforms around decoupled storage, which can present its own design challenges.

High-Density Compression

Both Hydrolix and ClickHouse use compression to dramatically reduce the size of data in storage. Since both use columnar storage, they can achieve much better rates of compression than row-based storage formats. Compression is absolutely critical for high-volume use cases, and not just because it lowers costs—it also supports more efficient reads and writes.

ClickHouse supports ZSTD and other widely-used compression algorithms. On the other hand, Hydrolix uses a specialized high-density compression algorithm that supports higher rates of compression—typically 90-99% compression rates. It achieves these levels of compression through sorting, optimized compression algorithms, and specialized compression techniques for each data type.

For enterprises storing petabytes of data, the increased compression can make a huge difference in terms of storage footprint. As an example, an enterprise with 20 petabytes of raw data would have two petabytes of compressed data at 90% compression, and just one petabyte at 95% compression—a 50% further reduction and much less data to store, query, and pay for. Every percentage point matters when it comes to high volumes of data. While ClickHouse can achieve admirable rates of compression, you’ll typically achieve even greater cost savings and storage efficiency with Hydrolix.

Other Optimizations for Log and Event Data

Hydrolix also ships with many built-in features that make it easier to ingest and analyze high volumes of log and event data. This post won’t include an exhaustive list (otherwise, it would be a white paper!), but here are a few critical features and how they support high-volume log data.

• Support for late-arriving data: Log and event data often arrives late—sometimes very late. Many solutions, including ClickHouse, have challenges dealing with late-arriving data. Hydrolix treats late-arriving data like all other data, writing it to partitions at ingest time and then merging with existing partitions for optimized querying. You don’t need to take any additional steps to account for late-arriving data—ingest and merge services will handle it for you.
• Summary tables for metrics and aggregations: Metrics give you a high-level view into your systems, but they become increasingly challenging to generate at scale. That includes ensuring they are accurate. Hydrolix summary tables use ClickHouse intermediate states, but they also have Hydrolix-specific optimizations. For example, data is added to summary tables at ingest time (including late-arriving data), and is processed using the same ingest and merge services as raw data. This ensures that summary table data remains accurate. It also means that summary tables don’t need to query the underlying raw data, so they are extremely fast and do not contend for query resources.
• Support for ingesting multiple data sources into a single table: With Hydrolix, you can stream data from multiple sources into a single table, even if they have different attributes (Hydrolix has flexible schema). This is especially useful for comparing log data from logically-grouped data sources. For instance, you might want to ingest data from different CDN sources into a single table. Storing this data in a single table allows users to easily compare data from different sources without needing complex, compute-intensive join statements.

Feeding the Beast: Streaming Ingest at Petabyte Scale

Hydrolix is designed for streaming and transforming massive volumes of data in real time. These events include Black Friday, the Super Bowl, the Olympics, and AAA video game launches.

ClickHouse can also ingest massive volumes of data, but the process can come with considerable headaches due to the closely-coupled nature of its components. During periods of massive data ingest, services like MergeTree can dramatically slow down performance, leading to increases in both ingest and query latency.

Let’s take a look at a few reasons why ClickHouse can have challenges when it comes to streaming massive volumes of data, and then see how Hydrolix has tackled the same challenges.

Complex Tuning and Data Cleaning

ClickHouse requires right-sized batch inserts (large, well-structured blocks) to minimize the compute overhead of merging blocks. At high volumes of data, this forces complex ETL pipelines to clean and reorganize data before ingestion, with each pipeline adding hits to performance as well as further operational burden.

Additional Overhead for Distributed Coordination

In multi-node deployments, replication and ZooKeeper (or ClickHouse Keeper) add significant synchronization steps that can bottleneck ingestion at the extreme throughput levels that are typical with high levels of scale.

Merge Tradeoffs

Spikes in ingestion volume will flood the system with many smaller “parts” that require merges. If merge is too aggressive, there can be CPU/IO contention or out-of-memory issues. However, if merge is too passive, there will be higher latency retrieving data from many smaller parts.

Resource Contention From MergeTree Engine

MergeTree is a crucial part of ClickHouse, and one of its jobs is to insert data into tables as small parts and then merge those smaller parts into larger parts that have improved query performance. However, MergeTree’s continuous merging can starve query resources because reads and writes can’t be decoupled in ClickHouse. During periods of heavy data ingestion, merge overhead will spike, increasing query latency.

How Hydrolix Solves the Problems of Ingest at Petabyte Scale

Now let’s take a closer look at architectural decisions that allow Hydrolix to stream high volumes of data into cloud storage with low latency—all while including a full ETL (extract—load—transform) process.

Decoupled Cloud-Native Model

Unlike standard ClickHouse deployments (including managed deployments), Hydrolix has a unique intake-head architecture that buffers and processes data in separate pods, using a flexible partitioning format that bypasses ClickHouse’s built-in distribution layer.

Hydrolix performs cloud-based partitioning and background merging to ensure that ingestion and query workloads don’t collide. This includes automatically processing transformations and partial aggregations on raw data without forcing customers to constantly fine-tune cluster settings for petabyte-scale volumes.

Built-In ETL Process

Hydrolix includes a built-in ETL process that transforms raw data in real time. This is in contrast to ClickHouse, which doesn’t natively support ETL. ETL is crucial for standardizing, normalizing, and optimizing log data.

With Hydrolix, the ETL process adds minimal overhead in terms of ingest latency. In the case of the 2025 Super Bowl, time to glass for actionable insights was under ten seconds, including ETL—even with ingest peaking at 17.4 GB/second during the event. This ETL process performs many crucial steps, such as compressing and optimizing data for query—along with custom steps specified by the user such as adding context and standardizing data.

The ETL process also provides benefits when it comes to efficient merges. In Hydrolix, each incoming chunk of data is transformed (including SQL cleaning) and immediately stored in a cloud-native format, eliminating the wait for merges or co-located shards.

Purpose-Built for Petabyte Scale

ClickHouse excels in many different scenarios, but when it comes to ingesting many terabytes per day, extensive tuning is typically required. Meanwhile, Hydrolix focuses specifically on massive-scale ingestion and querying. We maintain our own fork of open-source Clickhouse so we can combine the best parts of ClickHouse while automating ingestion, compaction, and distribution details behind the scenes. Behind the scenes, Hydrolix uses Kubernetes to autoscale resources, and tools like the Hydrolix Kubernetes Operator automate scale and cluster management so that users don’t have to worry about operational burden.

Cloud-Native Distribution

By avoiding ClickHouse’s built-in distribution and replication complexities, Hydrolix removes single points of failure (like Zookeeper or ClickHouse Keeper) and performance pitfalls inherent in large-scale clusters.

When to Consider Hydrolix

Hydrolix is specifically designed to ingest, store, and analyze high volumes of log and event data cost-effectively. Because of these design choices, Hydrolix works really well for the following scenarios: 

• High-volume use cases (at least one terabyte per day): The volume of log data has gone up 5x on average over the three-year period ending in 2023, and 35% of enterprises are already ingesting at least a terabyte of log data per day. If you aren’t ingesting this much data yet, there’s a good chance you will be soon. For lower-volume use cases, consider Hydrolix for AWS.
• Immutable, timestamped data: Hydrolix is append-only and partitions data by time. This is ideal for log and event data, which typically should remain immutable sources of truth and shouldn’t be changed. These design choices allow for faster reads and writes. In the case of writing data, no expensive upserts are needed. And in the case of reading data, time-filtered queries maximize the benefits of query techniques like partition pruning to ensure fast reads even on distributed object storage.
• Observability and other near-real-time use cases: To ensure greater cost-effectiveness, Hydrolix uses distributed object stores for data storage. This dramatically reduces the cost of retaining data, especially compared to closely-coupled storage solutions like SSDs. While Hydrolix maximizes the performance of object storage using techniques like micro-indexing, parallelism, and pruning, it isn’t possible to use Hydrolix for true real-time use cases (with millisecond latency). Instead, it’s the best choice for use cases such as observability, business intelligence (BI), and cybersecurity, where teams can act on incoming data in seconds without paying the exorbitant costs associated with closely-coupled data storage solutions.

When to Consider ClickHouse

Of course, ClickHouse is an amazing solution, whether you are using open source ClickHouse or a managed alternative. There are many use cases where ClickHouse is the better choice. Here are a few of these scenarios.

• Open source: If you’re using open source ClickHouse and remain committed to building using the open source path, ClickHouse is the better option. There’s a lot of DIY and complexity involved, and you won’t have access to all the premium features that managed platforms like ClickHouse Cloud offer, but you can tailor ClickHouse to your exact specifications. As maintainers of our own ClickHouse fork, we know there are a lot of rewards (and challenges) to this approach. In contrast, Hydrolix is a proprietary, fully-managed solution.
• True real time: There is an advantage to tightly-coupled compute and storage, even if it comes with a high cost. You can get millisecond-latency queries, which some use cases absolutely require. We’ve made a design choice to favor cost-efficiency and near-real-time, but for situations where you need a response in milliseconds (programmatic advertising placement is one example), ClickHouse is the better choice.
• Data that isn’t timestamped: As an all-purpose OLAP solution, ClickHouse can work well for many kinds of data, including data that doesn’t have timestamps, because you can partition based on any attribute. In contrast, Hydrolix is designed for timestamped data. Data is partitioned by time and queries perform best when filtered by time.

Conclusion

ClickHouse is an amazing tool and we use it in our own query components. It’s a great all-purpose solution for a wide range of use cases that require true millisecond latency. But for high-volume log and event data where cost-effective, long-term storage is a priority, we believe that Hydrolix is the better choice. For enterprises dealing with huge increases in the volume of incoming data—all while trying to manage budgets that are often tight—Hydrolix can let you keep and analyze all your data for less, giving you more insights with fewer compromises.

Next Steps

• Check out our Hydrolix and ClickHouse comparison page for a high-level comparison of the two technologies.
• Interested in learning more about Hydrolix and how it compares to ClickHouse? Contact Hydrolix about a managed trial or demo.