smithery/treasure-data

trino-optimizer

TD Trino performance optimization including CTAS (5x faster), UDP bucketing for ID lookups, magic comments for join distribution, REGEXP_LIKE vs LIKE, and approx functions.

Installation

$ npx skills add smithery/treasure-data --skill trino-optimizer

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Agent compatibility

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Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,725 B
  • docs SUMMARY.md 195 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

TD Trino Query Optimizer

Output Optimization

CTAS is 5x faster than SELECT (skips JSON serialization):

create table results as
select td_time_string(time, 'd!', 'JST') as date, count(*) as events
from events
where td_interval(time, '-1M', 'JST')
group by 1

User-Defined Partitioning (UDP)

Hash partition for fast ID lookups on large tables (>100M rows):

create table customer_events with (
  bucketed_on = array['customer_id'],
  bucket_count = 512
) as
select * from raw_events
where td_interval(time, '-30d', 'JST')

Accelerated queries (equality on all bucketing columns):

select * from customer_events
where customer_id = 12345
  and td_interval(time, '-7d', 'JST')

NOT accelerated (missing bucketing column):

select * from customer_events
where td_interval(time, '-7d', 'JST')

UDP for colocated joins:

-- set session join_distribution_type = 'PARTITIONED'
-- set session colocated_join = 'true'
select a.*, b.*
from customer_events_a a
join customer_events_b b on a.customer_id = b.customer_id

Magic Comments for Join Distribution

-- BROADCAST: Small right table fits in memory
-- set session join_distribution_type = 'BROADCAST'
select * from large_table, small_lookup
where large_table.id = small_lookup.id

-- PARTITIONED: Both tables large or memory issues
-- set session join_distribution_type = 'PARTITIONED'
select * from large_table_a, large_table_b
where large_table_a.id = large_table_b.id

REGEXP_LIKE vs Multiple LIKE

-- BAD: Multiple LIKE clauses
where column like '%android%' or column like '%ios%' or column like '%mobile%'

-- GOOD: Single REGEXP_LIKE
where regexp_like(column, 'android|ios|mobile')

Approximate Functions

approx_distinct(user_id)              -- vs count(distinct user_id)
approx_percentile(response_time, 0.95)
approx_set(column)                    -- HyperLogLog sketch

~2% error rate, dramatically reduced memory.

Common Errors

Error Fix
Query exceeded memory limit Narrow time range, use approx functions, try PARTITIONED join
PAGETRANSPORTTIMEOUT Reduce columns, use CTAS, process in smaller chunks
Query timeout Add time filters, use limit for testing

Optimization Checklist

  • Time filter (tdinterval/tdtime_range)
  • Specific columns (not select *)
  • approx_distinct for unique counts
  • regexp_like for pattern matching
  • CTAS for large results
  • UDP for frequent ID lookups