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ClickHouse Cloud — Compatibility Guide

This guide provides an overview of what to expect functionally and operationally in ClickHouse Cloud. While ClickHouse Cloud is based on the open-source ClickHouse distribution, there may be some differences in architecture and implementation. You may find this blog on how we built ClickHouse Cloud interesting and relevant to read as background.

ClickHouse Cloud Architecture

ClickHouse Cloud significantly simplifies operational overhead and reduces the costs of running ClickHouse at scale. There is no need to size your deployment upfront, set up replication for high availability, manually shard your data, scale up your servers when your workload increases, or scale them down when you are not using them — we handle this for you.

These benefits come as a result of architectural choices underlying ClickHouse Cloud:

  • Compute and storage are separated and thus can be automatically scaled along separate dimensions, so you do not have to over-provision either storage or compute in static instance configurations.
  • Tiered storage on top of object store and multi-level caching provides virtually limitless scaling and good price/performance ratio, so you do not have to size your storage partition upfront and worry about high storage costs.
  • High availability is on by default and replication is transparently managed, so you can focus on building your applications or analyzing your data.
  • Automatic scaling for variable continuous workloads is on by default, so you don’t have to size your service upfront, scale up your servers when your workload increases, or manually scale down your servers when you have less activity
  • Seamless hibernation for intermittent workloads is on by default. We automatically pause your compute resources after a period of inactivity and transparently start it again when a new query arrives, so you don’t have to pay for idle resources.
  • Advanced scaling controls provide the ability to set an auto-scaling maximum for additional cost control or an auto-scaling minimum to reserve compute resources for applications with specialized performance requirements.


ClickHouse Cloud provides access to a curated set of capabilities in the open source distribution of ClickHouse. Tables below describe some features that are disabled in ClickHouse Cloud at this time.

DDL syntax

For the most part, the DDL syntax of ClickHouse Cloud should match what is available in self-managed installs. A few notable exceptions:

  • Support for CREATE AS SELECT, which is currently not available. As a workaround, we suggest using CREATE ... EMPTY ... AS SELECT and then inserting into that table (see this blog for an example).
  • Some experimental syntax may be disabled, for instance, ALTER TABLE … MODIFY QUERY statement.
  • Some introspection functionality may be disabled for security purposes, for example, the addressToLine SQL function.
  • Do not use ON CLUSTER parameters in ClickHouse Cloud - these are not needed. While these are mostly no-op functions, they can still cause an error if you are trying to use macros. Macros often do not work and are not needed in ClickHouse Cloud.

Database and table engines

ClickHouse Cloud provides a highly-available, replicated service by default. As a result, all database and table engines are "Replicated":

  • ReplicatedMergeTree (default, when none is specified)
  • ReplicatedSummingMergeTree
  • ReplicatedAggregatingMergeTree
  • ReplicatedReplacingMergeTree
  • ReplicatedCollapsingMergeTree
  • ReplicatedVersionedCollapsingMergeTree
  • MergeTree (converted to ReplicatedMergeTree)
  • SummingMergeTree (converted to ReplicatedSummingMergeTree)
  • AggregatingMergeTree (converted to ReplicatedAggregatingMergeTree)
  • ReplacingMergeTree (converted to ReplicatedReplacingMergeTree)
  • CollapsingMergeTree (converted to ReplicatedCollapsingMergeTree)
  • VersionedCollapsingMergeTree (converted to ReplicatedVersionedCollapsingMergeTree)
  • URL
  • View
  • MaterializedView
  • GenerateRandom
  • Null
  • Buffer
  • Memory
  • Deltalake
  • Hudi
  • MySQL
  • MongoDB
  • NATS
  • PostgreSQL
  • S3

Please note: in ClickHouse Cloud, you do not need to add the "Replicated" term to your specified database or table engine. All *MergeTree tables are replicated in ClickHouse Cloud automatically.


ClickHouse Cloud supports HTTPS and Native interfaces. Support for more interfaces such as MySQL and Postgres is coming soon.


Dictionaries are a popular way to speed up lookups in ClickHouse. ClickHouse Cloud currently supports dictionaries from PostgreSQL, MySQL, remote and local ClickHouse servers, Redis, MongoDB and HTTP sources.

Federated queries

We support federated ClickHouse queries for cross-cluster communication in the cloud, and for communication with external self-managed ClickHouse clusters. ClickHouse Cloud currently supports federated queries using the following integration engines:

  • Deltalake
  • Hudi
  • MySQL
  • MongoDB
  • NATS
  • PostgreSQL
  • S3

Federated queries with some external database and table engines, such as SQLite, ODBC, JDBC, Redis, RabbitMQ, HDFS and Hive are not yet supported.

User defined functions

User-defined functions are a recent feature in ClickHouse. ClickHouse Cloud currently supports SQL UDFs only.

Experimental features

Experimental features can be self-enabled by users in Development services. They are disabled in ClickHouse Cloud Production services by default to ensure the stability of production deployments. If you would like to enable an experimental feature in one of your Production services, please reach out to ClickHouse support to discuss.


The Kafka Table Engine is not generally available in ClickHouse Cloud. Instead, we recommend relying on architectures that decouple the Kafka connectivity components from the ClickHouse service to achieve a separation of concerns. We recommend ClickPipes for pulling data from a Kafka stream. Alternatively, consider the push-based alternatives listed in the Kafka User Guide

Operational Defaults and Considerations

The following are default settings for ClickHouse Cloud services. In some cases, these settings are fixed to ensure the correct operation of the service, and in others, they can be adjusted.

Operational limits

max_parts_in_total: 10,000

The default value of the max_parts_in_total setting for MergeTree tables has been lowered from 100,000 to 10,000. The reason for this change is that we observed that a large number of data parts is likely to cause a slow startup time of services in the cloud. A large number of parts usually indicate a choice of too granular partition key, which is typically done accidentally and should be avoided. The change of default will allow the detection of these cases earlier.

max_concurrent_queries: 1,000

Increased this per-server setting from the default of 100 to 1000 to allow for more concurrency. This will result in 2,000 concurrent queries for development services and 3,000 for production.

max_table_size_to_drop: 1,000,000,000,000

Increased this setting from 50GB to allow for dropping of tables/partitions up to 1TB.

System settings

ClickHouse Cloud is tuned for variable workloads, and for that reason most system settings are not configurable at this time. We do not anticipate the need to tune system settings for most users, but if you have a question about advanced system tuning, please contact ClickHouse Cloud Support.

Advanced security administration

As part of creating the ClickHouse service, we create a default database, and the default user that has broad permissions to this database. This initial user can create additional users and assign their permissions to this database. Beyond this, the ability to enable the following security features within the database using Kerberos, LDAP, or SSL X.509 certificate authentication are not supported at this time.


The table below summarizes our efforts to expand some of the capabilities described above. If you have feedback, please submit it here.

CapabilityComing soon?
Dictionary support: PostgreSQL, MySQL, remote and local ClickHouse servers, Redis, MongoDB and HTTP sourcesAdded in GA
SQL user-defined functions (UDFs)Added in GA
MySQL and PostgreSQL engineAdded in GA
Engines for SQLite, ODBC, JDBC, Redis, RabbitMQ, HDFS, and Hive
MySQL & Postgres interfaces
Kafka Table EngineNot recommended; see alternatives above
EmbeddedRocksDB EngineEvaluating demand
Executable user-defined functionsEvaluating demand