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You can keep the same ClickHouse table definition and the same SQL while a storage policy places older data parts on S3-backed disks. What does not carry over automatically is the I/O profile. A query that needs parts sitting in object storage waits on remote reads unless a local cache already holds them, so the saving is a storage-tier decision that has to be checked against how often that data is actually queried.
What “without touching a query” actually covers
The phrase describes SQL and table access. A table declared with ENGINE = MergeTree keeps its name, its columns, and its SELECT and INSERT statements. Dashboards, application code, and scheduled jobs do not need new query text. The phrase does not describe latency. ClickHouse’s own storage-compute guide presents S3-backed storage as most suitable where slower queries on cold data are acceptable (ClickHouse, “Separation of storage and compute”), and its article on caching for S3 explains that reads not found in the local cache are fetched remotely (ClickHouse, “Building a Distributed Cache for S3”).
How ClickHouse moves data between disks
ClickHouse organizes storage into disks and ordered volumes, and a storage policy is built from them. The movement unit is the MergeTree data part, not the table and not an individual row. Parts can move between disks in the background according to user settings, or when you run an ALTER query that targets them. S3 disks can take part in multi-disk and multi-volume policies alongside local disks, so a policy can keep recent parts on a local SSD volume and let older parts move to an S3 volume as they age (ClickHouse, MergeTree table engine documentation).
That mechanism is the technical basis for a hot and cold tier. It is not a separate archive system. Every part on S3 is still a part of the same table, which is why the SQL surface does not change.
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Setting up an S3 tier
The ClickHouse storage-compute guide assumes version 22.8 or later and walks through defining an S3 disk, wrapping it in a storage policy, and creating a table that points to that policy. Follow its example for the exact disk configuration in your server’s storage settings. The sequence is:
- Confirm that the ClickHouse release you run is 22.8 or later, since that is the version the guide assumes.
- Define the S3 disk and a storage policy in the server storage configuration, using the guide’s example. The guide’s policy name is
s3_main. - Create the table with the ordinary engine and point it at the policy. The guide does not require a special engine name, because ClickHouse converts the engine internally when the table uses S3 storage:
CREATE TABLE events ( ts DateTime, user_id UInt64, payload String ) ENGINE = MergeTree ORDER BY ts SETTINGS storage_policy = 's3_main'; - Check where parts are placed by querying
system.partsand reading itsdisk_namecolumn for the table’s active parts. This shows whether the policy has moved anything yet. - Leave part placement to the storage policy and its movement settings. Do not attach AWS or GCS lifecycle rules to the same bucket (see the section on lifecycle policies below).
Operational configuration still changes. The table definition stays the same, but the server configuration, bucket permissions, and monitoring for the new disk are new work.
Confirm where your data lives before modeling savings
The title says EBS, and that premise needs checking. 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 the same reference, S3 holds ClickHouse table data and backups in the customer’s buckets (ClickHouse, BYOC cost model (AWS)). That is the vendor’s description of its managed layout, and it does not necessarily match a self-managed cluster where MergeTree parts sit on EBS.
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Before you model any saving, run the disk query above and a check of system.disks to see which disks hold table parts. If your parts already sit on S3, or your EBS volumes hold only the operating system and logs, the EBS-to-S3 move is not the change you are planning. If parts sit on EBS volumes, the tiering described here applies to them.
Query behavior: cold reads versus warm reads
A local cache can hold object-store data on each node, so repeated reads avoid repeat downloads. The cache is per node in the architecture ClickHouse describes. A query routed to another node can hit a cold cache and fetch the same parts again. Local SSD caching is documented as an option for object-store data (ClickHouse, “Building a Distributed Cache for S3”), but the documentation does not establish a generic latency penalty or speedup for any workload.
The table below lists what to measure. Public documentation does not give benchmark values for this exact EBS-to-S3 configuration, so these are the numbers to collect on your own data.
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| Measurement | Why it matters | How to measure it |
|---|---|---|
| Cold versus warm latency | Uncached parts are fetched from S3, so first reads and repeat reads differ | Run the same query after clearing the cache, then repeatedly, and compare p50 and p95 |
| Bytes read per query | Selective queries that skip most parts fetch less remote data | Compare read volume from system.query_log for representative queries |
| Repeat-read frequency | A cache pays off only when the same parts are read again | Count distinct parts and repeat accesses over a week of query logs |
| Cache size and hit rate | The working set has to fit the cache to avoid refetching | Track hits and misses per node during peak periods |
| S3 request volume | Requests are billed separately from storage | Count requests per day from your AWS billing and usage reports |
| Cross-region and data transfer | Transfer charges can offset storage savings | Break transfer out by source and destination region |
| Compute time per query | Slower remote reads keep nodes busy for longer | Compare CPU-seconds and wall-clock time per query class |
The cost model
For ClickHouse BYOC on AWS, the vendor describes two independent bills. ClickHouse Cloud charges based on total memory allocation, and AWS charges the customer’s account directly for the infrastructure that is provisioned. The BYOC reference ranks typical cost drivers in this order: EC2, S3, EBS, NAT and cross-AZ transfer, EKS, load balancing, then smaller variable services. S3 charges include GB-month storage, requests, and inter-region transfer. These figures describe the vendor’s BYOC model and should not be applied as-is to every self-managed ClickHouse deployment.
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Figures that appear in ClickHouse’s own material
| Figure | Stated by | What it does and does not establish |
|---|---|---|
| Compression of about 10× and 1 TB of raw data billed as roughly 100 GB | ClickHouse pricing FAQ, checked in 2026 (ClickHouse Pricing) | Vendor guidance on typical analytical data. It is not a guaranteed ratio for your tables. |
| Storage metered on compressed object-storage data plus backups | ClickHouse Cloud pricing page, checked in 2026 | Describes ClickHouse Cloud metering. Backups, ClickPipes, public-internet egress, and cross-region egress can add charges. Check the live page before budgeting. |
| $300 in credits for 30 days | ClickHouse pricing page, checked in 2026 | A promotional offer whose terms may change. It is not a recurring discount. |
| Version 22.8 or higher | ClickHouse storage-compute guide | The version the guide assumes. Confirm it against the release you deploy. |
What the figures do not show
No public figure from ClickHouse quantifies a general saving from moving EBS-hosted parts to S3. A lower per-GB storage price is not a total-cost saving on its own. A full model needs request charges, data retrieval and transfer, cache hardware, compute time, backups, replication, and the engineering effort to run the new tier. Compression also changes the result, because the retained compressed bytes are what gets billed.
Lifecycle policies: a hard stop
ClickHouse’s storage-compute guide is explicit about one step to avoid:
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“Don’t configure any AWS/GCS life cycle policy. This isn’t supported and could lead to broken tables.”
Let the ClickHouse storage policy own part placement. An S3 lifecycle rule that expires, transitions, or rewrites objects behind ClickHouse’s back can break tables. Use the policy’s movement settings and ALTER queries for every move.
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- Parts of the table you want to tier currently sit on EBS, as shown by
system.parts. - Your ClickHouse release is 22.8 or later, and the S3 disk and policy follow the guide.
- Most of the cold data is queried rarely enough that remote reads are acceptable to the people using it.
- You have measured cold and warm latency and bytes read on representative queries.
- Your cost model includes requests, transfer, cache capacity, compute, backups, and replication, not only storage per GB.
- No AWS or GCS lifecycle rules apply to the bucket used by ClickHouse.
Limits of this assessment
This article draws on ClickHouse’s public documentation, its S3 caching article, and its pricing page as checked in 2026. It does not include current AWS list prices, region-specific bills, workload benchmarks, or a full migration runbook beyond the storage-compute guide’s example. Partner and referral program terms are also outside its scope. Any numerical total-cost estimate for your environment needs your own usage data and current prices from AWS and ClickHouse.
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Pricing and version details change, so confirm both against the live pages and your deployed release before acting on them.
Source links: MergeTree storage policies and S3 multi-volume storage, separation of storage and compute, ClickHouse Pricing, BYOC cost model (AWS), and Building a Distributed Cache for S3.
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