Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/clickhouse-rate-limits/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill clickhouse-rate-limits. Copyright stays with the author (MIT).

ClickHouse Rate Limits & Concurrency

Overview

ClickHouse has no REST API rate limits like a SaaS product. Instead it enforces server-side concurrency limits, memory quotas, and per-user settings that control resource usage. This skill configures those server-side limits and pairs them with client-side controls so an application stays within them under load.

Prerequisites

• ClickHouse admin access (or Cloud console) to create quotas and settings profiles.
• The @clickhouse/client Node package for the client-side patterns.
• A rough target for peak concurrent queries and per-query memory.

Instructions

Work top-down: cap resources at the server, then make the client respect the cap.

Step 1: Know the server-side limits

The defaults you tune most often:

ClickHouse Cloud's management API (not the query interface) is separately limited to 10 requests per 10 seconds. Full table in references/implementation.md.

Step 2: Cap resources per user (essential skeleton)

Bind a quota and a settings profile to each application user:

CREATE SETTINGS PROFILE IF NOT EXISTS app_profile
    SETTINGS
        max_memory_usage = 5000000000,       -- 5GB per query
        max_execution_time = 30,             -- 30s timeout
        max_concurrent_queries_for_user = 10 -- 10 parallel queries
    TO app_user;

The full quota (CREATE QUOTA … FOR INTERVAL 1 HOUR MAX …) plus verification queries are in references/implementation.md.

Step 3: Make the client respect the cap

Four client-side patterns keep the app inside the server limits — connection pooling, an app-level concurrency queue (p-queue), retry-with-backoff on TOO_MANY_SIMULTANEOUS_QUERIES, and insert buffering to avoid TOO_MANY_PARTS. Each is a drop-in TypeScript snippet in references/implementation.md, with the concurrency queue as the smallest starting point:

import PQueue from 'p-queue';
const queryQueue = new PQueue({ concurrency: 5, timeout: 30_000, throwOnTimeout: true });
const rateLimitedQuery = <T>(sql: string) =>
  queryQueue.add(async () => (await client.query({ query: sql, format: 'JSONEachRow' })).json<T>());

Step 4: Monitor and verify

Watch live concurrency and confirm limits bind with the queries in references/examples.md (system.processes, system.metrics, system.query_log, SHOW QUOTAS).

Output

Applying this skill produces:

• A ClickHouse settings profile and quota bound to the app user, capping per-query memory, timeout, and per-user concurrency.
• Client-side guardrails — a bounded connection pool, a concurrency queue, a retry wrapper, and an insert buffer — so the app cannot exceed the server cap.
• Monitoring queries that report current running queries, queue depth, and historical peak concurrency, plus a check that the quota is applied.

Error Handling

The retry wrapper in references/implementation.md treats codes 202, 159, and network errors as retryable.

Examples

Three worked end-to-end scenarios live in references/examples.md:

1. Cap a reporting service at 5 concurrent queries — wrap every dashboard tile's query in the p-queue limiter.
2. Survive a concurrency spike — queryWithRetry absorbs code-202 bursts with exponential backoff + jitter instead of returning 500s.
3. High-throughput ingest without TOO_MANY_PARTS — InsertBuffer batches a firehose into a few large inserts.

Resources

• Server Settings
• Query Complexity Limits
• Quotas
• references/implementation.md — full server + client code
• references/examples.md — monitoring queries + worked scenarios

Next Steps

For security hardening (users, roles, TLS), see the clickhouse-security-basics skill. For query-level performance work, see clickhouse-performance-tuning.