Altinity Expert Clickhouse Ingestion

Insert Performance and Ingestion Analysis

Diagnose INSERT performance, batch sizing, part creation patterns, and ingestion bottlenecks.

Quick Diagnostics (Run First)

1. Current Insert Activity

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

2. Recent Insert Performance (Last Hour)

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

3. Part Creation Rate by Table

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 small
• avg_rows_per_part < 10000 → Micro-batches, will cause merge pressure

4. Insert vs Merge Balance

select
    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

Problem-Specific Queries

Slow Inserts Investigation

-- 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

Insert with MV Overhead

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

Failed Inserts

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-memory
• 252 (TOO_MANY_PARTS) → Load altinity-expert-clickhouse-merges
• 319 (UNKNOWN_PACKET_FROM_CLIENT) → Client/network issue

Batch Size Analysis

-- 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-batched
• avg_batch_rows < 10000 → Could improve
• avg_batch_rows > 100000 → Good batching
• Ideal: 10K-1M rows per insert

Data Pipeline Patterns

Kafka Engine Ingestion

-- 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 Flush Patterns

-- Buffer table status
select
    database,
    name,
    total_rows,
    total_bytes
from system.tables
where engine = 'Buffer'

Ad-Hoc Query Guidelines

Required Safeguards

-- 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

Useful Filters

-- 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

Cross-Module Triggers

Key Settings Reference

-- Check current settings for a user/profile
select name, value, changed
from system.settings
where name like '%insert%'
order by name

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