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For most tables, yes. ClickHouse can keep a table on its ordinary MergeTree definition and SQL interface while a storage policy decides where that table’s data parts live, including on S3-backed disks. The query text does not have to change. What does change is the I/O path: a query that needs parts sitting on object storage waits for remote reads unless that data is already cached locally. Whether the move lowers your bill depends on how much data is queried, how often, and what requests, transfer, cache capacity and compute the new layout adds. This article separates those three things so you can test each one.
What stays the same and what changes
- Stays the same: the table definition (
ENGINE = MergeTree), the table name, and the SQL you run against it. - Changes: where data parts are stored, how long a read takes when it reaches cold parts, how the local cache behaves, and the shape of your storage and request charges.
The title’s phrase “without touching a query” is accurate about syntax and table access. It is not a promise that every query performs the same. A cold read on S3-resident data can be slower than a read from local disk, and the official guidance is explicit that S3-backed storage suits workloads where cold-data query speed matters less.
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How ClickHouse moves data between storage tiers
Three concepts control placement. ClickHouse’s MergeTree documentation describes them as disks, volumes, and storage policies.
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Disks and volumes
A disk is a storage target, either a local path or an S3 bucket configured as a disk. A volume is an ordered group of one or more disks. S3 disks can take part in multi-disk and multi-volume policies just like local disks, which is what makes a hot-to-cold arrangement possible, for example moving data from local SSD to S3 as it ages.
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Storage policies
A storage policy names the volumes a table may use and their order. The table opts in with a setting. The ClickHouse storage-compute guide creates a table this way, with an S3 policy named s3_main:
CREATE TABLE events
(
event_time DateTime,
payload String
)
ENGINE = MergeTree
ORDER BY event_time
SETTINGS storage_policy = 's3_main';The guide notes that the table does not need a special engine name such as S3BackedMergeTree. ClickHouse converts the engine internally when the table uses S3 storage. Its example assumes ClickHouse 22.8 or later, so confirm that your deployed release meets that assumption and read the release notes for any changes that apply to it. The example creates a new table. The sources do not give a full runbook for converting an existing populated table, so test any conversion on a copy first.
Moving parts
The smallest unit that moves is a MergeTree data part. Parts can migrate between disks in the background according to user settings, or you can move them explicitly with ALTER queries. Movement is per part, so a table can hold recent parts on local disks and older parts on S3 at the same time.
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Confirm where your data actually sits first
The title says EBS, and that needs care. ClickHouse’s BYOC cost reference for AWS describes EBS gp3 volumes attached to worker nodes for the operating system, container images and ClickHouse logs. In that same model, S3 holds table data and backups in the customer’s bucket. That describes the vendor’s managed topology. It does not by itself prove that your MergeTree parts sit on EBS and can be tiered from it.
Check the layout on your own cluster before you plan anything. These queries use system tables that exist in current ClickHouse releases; confirm the column names on your version:
SELECT name, type, path FROM system.disks;
SELECT disk_name, count() AS parts, formatReadableSize(sum(bytes_on_disk)) AS size
FROM system.parts
WHERE active
GROUP BY disk_name;If the second query shows your active parts on an EBS-backed path, you have a real tiering candidate. If it shows only system and log paths, the EBS-to-S3 move is a different project.
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How queries behave on cold and warm data
When a query needs a part that lives on S3, ClickHouse reads it remotely. ClickHouse’s cache article explains that object-store reads can be cached on local disk, so a repeat read avoids another download. In the architecture that article describes, each node keeps its own cache. A query routed to a different node can therefore miss and fetch the data again, which makes cache hit rate a function of routing as well as cache size.
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| Axis | What to measure | Why it matters |
|---|---|---|
| Cold versus warm latency | Same query run with an empty cache and then again with a populated one | Shows the real penalty of the remote read path |
| Scan volume and selectivity | Bytes read per query and the fraction of parts a typical filter touches | Queries that prune most parts pay little for cold storage |
| Repeated-read frequency | How often the same cold parts are queried in a day or week | Frequent re-reads make cache hit rate the main factor |
| Cache size and hit rate | Cache capacity against the working set, and hits across nodes | Per-node caches miss when queries land on a different node |
| S3 request volume | GET and related requests generated by queries and background moves | Requests are billed separately from storage |
| Data transfer | Bytes crossing regions or leaving the AWS network | Inter-region and egress transfer add cost |
| Cost per retained compressed byte | Total monthly cost divided by compressed data retained | Gives a like-for-like comparison between layouts |
Caches are a performance aid, not a guarantee. A local SSD can hold object-store data that is read repeatedly, and ClickHouse documents that option, but the cache must be sized to the working set and tested under your query mix.
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The cost model
Two bills in the BYOC model
For ClickHouse BYOC on AWS, ClickHouse describes two independent bills. ClickHouse Cloud charges based on total memory allocation. AWS charges your account directly for the infrastructure provisioned in it. These statements describe that vendor model and should not be applied to every self-managed ClickHouse cluster.
Cost drivers to include
The BYOC cost reference orders typical cost drivers roughly as follows. Your ordering will differ with instance choices, data volume and traffic.
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- EC2 compute, usually first
- S3 storage, requests and inter-region transfer
- EBS volumes
- NAT and cross-availability-zone transfer
- EKS and load balancing
- Smaller variable services
ClickHouse Cloud’s pricing page meters storage on compressed object-storage data plus backups. It lists possible extra charges for backups, ClickPipes, public-internet egress and cross-region egress. The page is live and changes over time, so check it on the day you model costs at https://clickhouse.com/pricing.
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Figures and their qualifications
| Figure | Source and date | What it does and does not establish |
|---|---|---|
| ClickHouse 22.8 or later | ClickHouse storage-compute guide, accessed 2026 | The version the guide’s example assumes; not a statement about every release |
| 10× compression; 1 TB raw data billed as roughly 100 GB | ClickHouse pricing FAQ, accessed 2026 | Vendor guidance for typical analytical data; not a guaranteed ratio for your tables |
The sources do not establish a general percentage saving from moving EBS-resident parts to S3. A per-gigabyte storage price comparison is not a total-cost comparison. A monthly model for a tiered layout needs at least these terms:
- Retained compressed gigabytes on each tier, multiplied by the storage rate for that tier
- Request charges from queries and part movement
- Inter-region and egress transfer
- Cache volume capacity and the compute needed to run it
- Backups and any replication you keep
- Compute sized for the slower cold path, plus operational time
Keep AWS lifecycle rules off ClickHouse-managed data
ClickHouse’s storage-compute guide is direct about this: “Don’t configure any AWS/GCS life cycle policy. This isn’t supported and could lead to broken tables.” Use ClickHouse storage policies to decide when parts move. Do not add bucket lifecycle rules that transition or expire objects under the same prefix, because ClickHouse tracks its own part layout and an external rule can break it.
A practical sequence for the move
- Confirm the ClickHouse version and read the release notes for the S3 disk and storage policy behaviour that applies to it.
- Run the placement queries above and record which tables and parts sit on EBS.
- Define the S3 disk and a storage policy following the guide, and test on a non-production copy.
- Decide the movement rule for each table, using background movement settings or explicit ALTER queries, and apply it to a single table first.
- Benchmark representative queries with cold and warm caches, using the axes in the table above.
- Build the monthly model from the cost terms listed earlier and compare it with the current layout, including the cost of the slower path.
- Keep AWS and GCS lifecycle rules off the ClickHouse data prefix.
What the evidence does not settle
Published sources here cover the architecture, the storage-policy mechanics, the vendor’s cost categories, and one lifecycle warning. They do not provide a savings percentage, a query-latency penalty for this pattern, current AWS service prices, region-specific bills, or a complete migration runbook for existing tables. Those numbers depend on your data, access pattern and region, so measure them on your own workload before committing to a layout. For the caching mechanics, see the ClickHouse engineering post on https://clickhouse.com/blog/building-a-distributed-cache-for-s3, and for the storage policy reference, see https://github.com/ClickHouse/ClickHouse/blob/master/docs/en/engines/table-engines/mergetree-family/mergetree.md.
The BYOC cost categories are documented at https://github.com/ClickHouse/clickhouse-docs/blob/main/docs/cloud/guides/infrastructure/01_deployment_options/byoc/08_reference/06_cost_model_aws.md, and the separation of storage and compute is covered at https://github.com/ClickHouse/ClickHouse/blob/master/docs/guides/oss/deployment-and-scaling/separation-storage-compute.mdx.
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