{"slug":"compressing-market-data-zstd-vs-gzip","qid":"value_repetition","label":"value_repetition","post_title":"compressing-market-data-zstd-vs-gzip","post_url":"/blog/compressing-market-data-zstd-vs-gzip#q-value_repetition","columns":["ticker","distinct_prices","trades_per_distinct_price"],"rows":[{"ticker":"NVDA","distinct_prices":22709,"trades_per_distinct_price":88},{"ticker":"KO","distinct_prices":5963,"trades_per_distinct_price":40},{"ticker":"AAPL","distinct_prices":19272,"trades_per_distinct_price":36},{"ticker":"SPY","distinct_prices":19530,"trades_per_distinct_price":25},{"ticker":"MSFT","distinct_prices":33101,"trades_per_distinct_price":12}],"shape":"ranking","sql":"SELECT\n    ticker,\n    countDistinct(price)                              AS distinct_prices,\n    toUInt32(round(count() / countDistinct(price)))   AS trades_per_distinct_price\nFROM global_markets.stocks_trades\nWHERE ticker IN ('AAPL', 'MSFT', 'NVDA', 'SPY', 'KO')\n  AND sip_timestamp >= toDateTime64('2026-09-15 04:00:00', 9, 'UTC')\n  AND sip_timestamp <  toDateTime64('2026-09-16 04:00:00', 9, 'UTC')\nGROUP BY ticker\nORDER BY trades_per_distinct_price DESC","computed_at":"2026-09-28T15:16:07.159138+00:00","elapsed":0.002350533}