Quantrail Data

ClickHouse® is designed for high-performance analytical workloads, but like any database system, it produces errors when queries, schemas, configurations, or resources are misused.

Many of these errors are straightforward once you understand what causes them. This article covers some of the most common ClickHouse® errors, why they happen, and how to resolve them.

1. Memory Limit Exceeded

Error

Code: 241. DB::Exception: Memory limit exceeded

Why It Happens

This is one of the most common ClickHouse® errors.

A query attempts to consume more memory than the configured limit. Typical causes include:

• Large joins
• High-cardinality aggregations
• Sorting large datasets
• Processing too much data without filtering

Example:

SELECT *
FROM events
ORDER BY timestamp

If events contains billions of rows, ClickHouse® may need substantial memory to perform the sort.

How to Fix It

Reduce Data Scanned

Add filters whenever possible:

SELECT *
FROM events
WHERE event_date >= today() - 7
ORDER BY timestamp;

Increase Memory Limits

SET max_memory_usage = 10000000000;

Only do this if sufficient system memory is available.

Enable External Processing

For large aggregations:

SET max_bytes_before_external_group_by = 1000000000;

For large sorts:

SET max_bytes_before_external_sort = 1000000000;

This allows ClickHouse® to spill data to disk instead of keeping everything in memory.

2. Too Many Parts

Error

Code: 252. DB::Exception: Too many parts

Why It Happens

ClickHouse® stores data in immutable parts.

If applications continuously insert tiny batches, the number of parts grows faster than background merges can process them.

Example:

for row in rows:
    insert(row)

This pattern can create thousands of small parts.

How to Fix It

Batch Inserts

Instead of inserting individual rows:

INSERT INTO events VALUES (...)

Insert larger batches:

INSERT INTO events FORMAT JSONEachRow

with thousands of rows per request.

Check Active Parts

SELECT
    table,
    count()
FROM system.parts
WHERE active
GROUP BY table;

Review Merge Activity

SELECT *
FROM system.merges;

If merges are constantly behind, ingestion patterns need adjustment.

3. Unknown Identifier

Error

Code: 47. DB::Exception: Unknown identifier

Why It Happens

A query references a column that does not exist.

Example:

SELECT username
FROM users;

while the actual column is:

user_name

How to Fix It

Inspect the schema:

DESCRIBE TABLE users;

or:

SHOW CREATE TABLE users;

Verify:

• Column names
• Aliases
• Case sensitivity assumptions
• Materialized columns

4. Type Mismatch

Error

Code: 53. DB::Exception: Type mismatch

Why It Happens

ClickHouse® cannot automatically reconcile incompatible data types.

Example:

SELECT *
FROM orders
WHERE order_id = '123';

If order_id is an integer column, type conversion may fail.

How to Fix It

Use explicit conversions:

SELECT *
FROM orders
WHERE order_id = toUInt64('123');

Useful conversion functions:

toUInt32()
toUInt64()
toInt32()
toFloat64()
toDate()
toDateTime()

Always verify column types:

DESCRIBE TABLE orders;

5. Cannot Parse Input

Error

Code: 27. Cannot parse input

Why It Happens

The incoming data format does not match the table schema.

Example:

Table:

id UInt64,
created_at DateTime

Incoming data:

{
  "id": "abc",
  "created_at": "invalid_date"
}

ClickHouse® cannot convert these values.

How to Fix It

Validate incoming data before insertion.

Test parsing manually:

SELECT
    toUInt64('abc');

Review ingestion pipelines and serialization logic.

For JSON workloads:

FORMAT JSONEachRow

is generally easier to debug than CSV.

6. Table Already Exists

Error

Code: 57. Table already exists

Why It Happens

The table creation statement targets a table that already exists.

Example:

CREATE TABLE events (...)

when the table already exists.

How to Fix It

Use:

CREATE TABLE IF NOT EXISTS events (...)

or verify existing tables:

SHOW TABLES;

7. Unknown Table

Error

Code: 60. Table does not exist

Why It Happens

The referenced table cannot be found.

Example:

SELECT *
FROM event;

while the table is:

events

How to Fix It

Check available tables:

SHOW TABLES;

Check the current database:

SELECT currentDatabase();

Fully qualify the table if necessary:

SELECT *
FROM analytics.events;

8. Readonly Mode

Error

Code: 164. Cannot execute query in readonly mode

Why It Happens

The user account is configured with read-only permissions.

Attempting operations such as:

INSERT
UPDATE
DELETE
CREATE
DROP

will fail.

How to Fix It

Check user permissions:

SHOW GRANTS;

Grant appropriate privileges:

GRANT INSERT ON analytics.events TO app_user;

Follow the principle of least privilege.

9. Timeout Exceeded

Error

Code: 159. Timeout exceeded

Why It Happens

A query takes longer than the configured timeout.

Common causes:

• Full table scans
• Expensive joins
• Poor filtering
• Resource contention

How to Fix It

Review Query Plan

EXPLAIN indexes = 1
SELECT ...

Reduce Scanned Data

Use partition filters:

WHERE event_date >= today() - 30

Increase Timeout

SET max_execution_time = 300;

Only increase limits after understanding why the query is slow.

10. Too Many Simultaneous Queries

Error

Code: 202. Too many simultaneous queries

Why It Happens

The server has reached its configured concurrency limits.

This commonly occurs when:

• BI dashboards execute many parallel queries
• Applications open excessive connections
• Long-running queries accumulate

How to Fix It

Inspect active queries:

SELECT *
FROM system.processes;

Terminate problematic queries:

KILL QUERY WHERE query_id = '2f7e3d5d-7f4d-4d48-a4d0-9f4c5f7e8abc';

Review concurrency settings and workload patterns.

11. Distributed Query Failures

Error

All connection tries failed

or

Connection refused

Why It Happens

A Distributed table cannot reach one or more remote nodes.

Possible causes:

• Network failures
• Incorrect cluster configuration
• DNS issues
• Downstream server outages

How to Fix It

Verify cluster configuration:

SELECT *
FROM system.clusters;

Check node connectivity:

SELECT *
FROM remote('host', system.one);

Ensure every node can communicate with the others.

Exploring ClickHouse® for Your Analytics?

At Quantrail Data, we help teams run ClickHouse® reliably for real-time analytics – from Kubernetes deployments and migrations to performance tuning in production.

We see these challenges firsthand while supporting demanding analytics workloads. In one recent engagement, a customer achieved near bare-metal performance with ClickHouse® in production – a story we’ve shared here:

Success Story: Quantrail Bare-Metal ClickHouse® Deployment
If you’re evaluating ClickHouse® or trying to get more out of an existing setup, we’re happy to share practical lessons from real-world deployments.

Contact
Quantrail Data

Conclusion

Most ClickHouse® errors fall into four categories:

1. Resource limits (memory, CPU, concurrency)
2. Schema and data type issues
3. Ingestion problems
4. Cluster and replication failures

When troubleshooting, avoid guessing. Start with ClickHouse® system tables:

system.parts
system.merges
system.processes
system.query_log
system.replicas
system.replication_queue

These tables provide the information needed to identify the root cause rather than treating symptoms.

The fastest way to resolve ClickHouse® issues is to understand what the database is trying to tell you. The error code is usually the first clue.

References

https://clickhouse.com/docs/troubleshooting

https://clickhouse.com/blog/common-getting-started-issues-with-clickhouse