Diagnose ClickHouse INSERT performance, batch sizing, part creation patterns, and ingestion bottlenecks. Use for slow inserts and data pipeline issues.
Diagnose INSERT performance, batch sizing, part creation patterns, and ingestion bottlenecks.
select
query_id,
user,
elapsed,
formatReadableSize(written_bytes) as written,
written_rows,
formatReadableSize(memory_usage) as memory,
substring(query, 1, 80) as query_preview
from system.processes
where query_kind = 'Insert'
order by elapsed desc
limit 20
select
toStartOfFiveMinutes(event_time) as ts,
count() as insert_count,
round(avg(query_duration_ms)) as avg_ms,
round(quantile(0.95)(query_duration_ms)) as p95_ms,
sum(written_rows) as total_rows,
formatReadableSize(sum(written_bytes)) as total_bytes
from system.query_log
where type = 'QueryFinish'
and query_kind = 'Insert'
and event_time > now() - interval 1 hour
group by ts
order by ts desc
limit 20
select
database,
table,
toStartOfMinute(event_time) as minute,
count() as parts_created,
round(avg(rows)) as avg_rows_per_part,
formatReadableSize(avg(size_in_bytes)) as avg_part_size
from system.part_log
where event_type = 'NewPart'
and event_time > now() - interval 1 hour
group by database, table, minute
order by parts_created desc
limit 30
Red flags:
parts_created > 60 per minute (> 1/sec) → Batching too smallavg_rows_per_part < 10000 → Micro-batches, will cause merge pressureselect
database,
table,
countIf(event_type = 'NewPart') as new_parts,
countIf(event_type = 'MergeParts') as merges,
countIf(event_type = 'MergeParts') - countIf(event_type = 'NewPart') as net_reduction
from system.part_log
where event_time > now() - interval 1 hour
group by database, table
having new_parts > 10
order by new_parts desc
limit 20
If net_reduction negative → Load altinity-expert-clickhouse-merges for merge backlog analysis
-- Find slowest inserts
select
event_time,
query_id,
user,
query_duration_ms,
written_rows,
formatReadableSize(written_bytes) as written,
formatReadableSize(memory_usage) as peak_memory,
arrayStringConcat(tables, ', ') as tables,
substring(query, 1, 100) as query_preview
from system.query_log
where type = 'QueryFinish'
and query_kind = 'Insert'
and event_date = today()
order by query_duration_ms desc
limit 20
When inserts feed materialized views, slow MVs cause insert delays.
-- Find slow MVs during inserts
select
toStartOfFiveMinutes(qvl.event_time) as ts,
qvl.view_name,
count() as trigger_count,
round(avg(qvl.view_duration_ms)) as avg_mv_ms,
round(max(qvl.view_duration_ms)) as max_mv_ms,
sum(qvl.written_rows) as rows_written_by_mv
from system.query_views_log qvl
where qvl.event_time > now() - interval 1 hour
group by ts, qvl.view_name
order by avg_mv_ms desc
limit 20
-- Correlate slow insert with MV breakdown (requires query_id)
select
view_name,
view_duration_ms,
read_rows,
written_rows,
status
from system.query_views_log
where query_id = '{query_id}'
order by view_duration_ms desc
select
event_time,
user,
exception_code,
exception,
substring(query, 1, 150) as query_preview
from system.query_log
where type like 'Exception%'
and query_kind = 'Insert'
and event_date = today()
order by event_time desc
limit 30
Common exception codes:
241 (MEMORY_LIMIT_EXCEEDED) → Load altinity-expert-clickhouse-memory252 (TOO_MANY_PARTS) → Load altinity-expert-clickhouse-merges319 (UNKNOWN_PACKET_FROM_CLIENT) → Client/network issue-- Analyze actual batch sizes being inserted
select
database,
table,
count() as insert_count,
round(avg(written_rows)) as avg_batch_rows,
min(written_rows) as min_batch,
max(written_rows) as max_batch,
round(quantile(0.5)(written_rows)) as median_batch
from system.query_log
where type = 'QueryFinish'
and query_kind = 'Insert'
and event_date = today()
and written_rows > 0
group by database, table
having insert_count > 10
order by avg_batch_rows asc
limit 20
Recommendations:
avg_batch_rows < 1000 → Seriously under-batchedavg_batch_rows < 10000 → Could improveavg_batch_rows > 100000 → Good batching-- Check Kafka consumer lag (if using Kafka engine)
select
database,
name,
engine,
total_rows,
total_bytes
from system.tables
where engine like '%Kafka%'
-- Kafka-related messages in logs
select
event_time,
level,
message
from system.text_log
where logger_name like '%Kafka%'
and event_time > now() - interval 1 hour
order by event_time desc
limit 50
-- Buffer table status
select
database,
name,
total_rows,
total_bytes
from system.tables
where engine = 'Buffer'
-- Always limit results
limit 100
-- Always time-bound
where event_date = today()
-- or
where event_time > now() - interval 1 hour
-- For query_log, filter by type
where type = 'QueryFinish' -- completed
-- or
where type like 'Exception%' -- failed
-- Filter by table
where has(tables, 'database.table_name')
-- Filter by user
where user = 'producer_app'
-- Filter by insert size
where written_rows > 1000000 -- large inserts
where written_rows < 100 -- micro-batches
| Finding | Load Module | Reason |
|---|---|---|
| Part creation > 1/sec | altinity-expert-clickhouse-merges |
Merge backlog likely |
| High memory during insert | altinity-expert-clickhouse-memory |
Memory limits, buffer settings |
| Slow MV during insert | altinity-expert-clickhouse-reporting |
Analyze MV query |
| TOO_MANY_PARTS error | altinity-expert-clickhouse-merges + altinity-expert-clickhouse-schema |
Immediate action needed |
| Insert queries reading too much | altinity-expert-clickhouse-schema |
MV design issues |
| Disk slow during insert | altinity-expert-clickhouse-storage |
Storage bottleneck |
| Setting | Default | Impact |
|---|---|---|
max_insert_block_size |
1048545 | Rows per block |
min_insert_block_size_rows |
1048545 | Min rows before flush |
min_insert_block_size_bytes |
268435456 | Min bytes before flush |
async_insert |
0 | Async insert mode |
async_insert_max_data_size |
1000000 | Async batch threshold |
async_insert_busy_timeout_ms |
200 | Max wait for async batch |
-- Check current settings for a user/profile
select name, value, changed
from system.settings
where name like '%insert%'
order by name