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Table Schema and Design Analysis

altinity-expert-clickhouse-schema

Analyze ClickHouse table structure, partitioning, ORDER BY keys, materialized views, and identify schema design anti-patterns. Use for table design issues and optimization.

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SKILL.md

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Table Schema and Design Analysis

Analyze table structure, partitioning, ORDER BY, materialized views, and identify design anti-patterns.


Diagnostics

Run all queries from the file checks.sql and analyze the results.


Deep Dive Queries (Placeholder-Based)

Partition Distribution for Specific Table

select
    database,
    table,
    count() as partitions,
    sum(rows) as total_rows,
    formatReadableSize(sum(bytes_on_disk)) as total_size,
    formatReadableSize(median(bytes_on_disk)) as median_partition_size,
    min(partition) as oldest_partition,
    max(partition) as newest_partition
from system.parts
where active and database = '{database}' and table = '{table}'
group by database, table, partition
order by partition desc
limit 100

Column Compression Analysis for Specific Table

select
    name,
    type,
    formatReadableSize(data_compressed_bytes) as compressed,
    formatReadableSize(data_uncompressed_bytes) as uncompressed,
    round(data_uncompressed_bytes / nullIf(data_compressed_bytes, 0), 2) as ratio,
    compression_codec
from system.columns
where database = '{database}' and table = '{table}'
order by data_compressed_bytes desc
limit 50

Look for:

  • Columns with ratio < 2 → consider better codec or data transformation
  • Large columns without codec → add CODEC(ZSTD) or LZ4HC
  • String columns with low cardinality → consider LowCardinality(String)

Index Usage Analysis for Specific Database

select
    database,
    table,
    name as index_name,
    type,
    expr,
    granularity
from system.data_skipping_indices
where database = '{database}'
order by database, table

Schema Design Recommendations

Partition Key Guidelines

Data VolumeRecommended GranularityExample
< 10GB/​monthNo partitioning or yearlytoYear(ts)
10-100GB/​monthMonthlytoYYYYMM(ts)
100GB-1TB/​monthWeekly or dailytoMonday(ts)
> 1TB/​monthDailytoDate(ts)

ORDER BY Guidelines

  1. First column: Low cardinality, frequently filtered (e.g., tenant_id, region)
  2. Second column: Time-based if range queries common
  3. Subsequent: Other filter columns by selectivity (most selective last)

Anti-patterns:

  • UUID/​hash as first column
  • High-cardinality ID without tenant prefix
  • DateTime64 with microseconds as first column

Compression Codec Recommendations

Data TypeRecommended Codec
Integers (sequential)Delta, ZSTD
Integers (random)ZSTD or LZ4HC
FloatsGorilla, ZSTD
TimestampsDoubleDelta, ZSTD
Strings (long)ZSTD(3)
Strings (repetitive)LowCardinality + ZSTD

Cross-Module Triggers

FindingLoad ModuleReason
Many small partitionsaltinity-expert-clickhouse-ingestionCheck batch sizing
Oversized partitionsaltinity-expert-clickhouse-mergesMerge can't complete
High PK memoryaltinity-expert-clickhouse-memoryMemory pressure
MV performance issuesaltinity-expert-clickhouse-reportingQuery analysis
Too many parts per partitionaltinity-expert-clickhouse-mergesMerge backlog

Settings Reference

SettingDefaultRecommendation
index_granularity8192Lower for point lookups, higher for scans
ttl_only_drop_parts0Set to 1 if TTL deletes entire partitions
min_bytes_for_wide_part10MBIncrease if many small parts

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