Clickhouse Enterprise Rbac

ClickHouse Enterprise RBAC

Overview

Implement enterprise-grade role-based access control in ClickHouse using SQL-based user management, hierarchical roles, row-level policies, column grants, quotas, and settings profiles. The workflow builds least-privilege access from the ground up: create authenticated users, compose reusable roles, then narrow visibility with row and column policies and cap resource use with quotas.

Follow the seven steps below at a high level from this file; drill into the full implementation for every SQL statement, and worked examples for two end-to-end scenarios plus audit queries.

Prerequisites

• ClickHouse with access_management = 1 enabled (default in Cloud)
• Admin user with GRANT OPTION

Instructions

The build-out is seven steps. Steps 1–3 (users, roles, row security) carry the core skeleton here; Steps 4–7 (column grants, quotas, settings profiles, and the application wrapper) are summarized here and fully specified in references/implementation.md.

Step 1: Create Users with Authentication

Pick an authentication method per user: sha256_password (standard), double_sha1_password (MySQL wire protocol), or bcrypt_password (strongest — use for admin accounts). Restrict network reach with HOST IP and cap per-user resources inline with SETTINGS.

CREATE USER app_backend
    IDENTIFIED WITH sha256_password BY 'strong-password-here'
    DEFAULT DATABASE analytics
    HOST IP '10.0.0.0/8'           -- Restrict to VPC
    SETTINGS max_memory_usage = 10000000000,   -- 10GB per query
             max_execution_time = 60;          -- 60s timeout

SHOW CREATE USER app_backend;      -- Verify

Step 2: Create Role Hierarchy

Build leaf-level base roles (data_reader, data_writer, schema_manager), then compose them into job roles (analyst, developer, platform_admin). Grant roles to users and set a default role that activates on connect.

CREATE ROLE data_reader;
GRANT SELECT ON analytics.* TO data_reader;

CREATE ROLE analyst;
GRANT data_reader TO analyst;      -- Composite inherits base

GRANT analyst TO app_backend;
SET DEFAULT ROLE analyst TO app_backend;
SHOW GRANTS FOR app_backend;       -- Verify the full chain

Step 3: Row-Level Security

Isolate multi-tenant data with row policies — each user sees only rows matching its USING predicate. A permissive USING 1 = 1 policy lets an admin role see everything.

CREATE ROW POLICY acme_isolation ON analytics.events
    FOR SELECT
    USING tenant_id = 1
    TO tenant_acme;

SELECT * FROM system.row_policies;  -- List all policies

Steps 4–7: Column Grants, Quotas, Profiles, App Wrapper

• Step 4 — Column-level grants: GRANT SELECT(col, ...) to hide PII columns and GRANT INSERT(col, ...) to prevent metadata injection.
• Step 5 — Quotas: cap queries, read_rows, result_rows, and execution_time per interval so one user cannot exhaust the cluster.
• Step 6 — Settings profiles: enforce readonly, memory, thread, and concurrency ceilings; a separate ETL profile enables async_insert.
• Step 7 — Application wrapper: a per-role client factory in the app layer so read, write, and admin operations use distinct ClickHouse users.

Full SQL and the TypeScript wrapper: references/implementation.md.

Output

Running this workflow produces, in the target ClickHouse instance:

• Users with scoped authentication, network restrictions, and per-user resource caps.
• A role hierarchy — base roles composed into job roles, assigned as default roles.
• Row policies enforcing tenant/row isolation, visible in system.row_policies.
• Column grants hiding PII, verifiable via SHOW GRANTS FOR <role>.
• Quotas and settings profiles bounding resource use per user/role.

Verify the deployment with SHOW ACCESS, SHOW GRANTS FOR <user>, and the audit queries in references/examples.md.

Error Handling

Examples

Two end-to-end scenarios — a multi-tenant SaaS isolation setup and a PII-safe analyst role — plus the access-control audit queries live in references/examples.md. The core of Example 1:

-- Each tenant reads only its own rows from a shared table
CREATE ROW POLICY acme_isolation   ON analytics.events FOR SELECT USING tenant_id = 1 TO tenant_acme;
CREATE ROW POLICY globex_isolation ON analytics.events FOR SELECT USING tenant_id = 2 TO tenant_globex;
-- Connected as tenant_acme, this returns ONLY tenant_id = 1:
SELECT tenant_id, count() FROM analytics.events GROUP BY tenant_id;

Resources

• Access Control Docs
• CREATE USER
• GRANT Statement
• Row Policies
• Quotas

Next Steps

For schema migrations, see the clickhouse-migration-deep-dive skill in this pack.