大數據技術的對決——Spark對Impala對Hive對Presto
作者：佚名
在大數據浪潮全面來襲的歷史背景下，我們一直面臨著同一類難題的困擾——該選擇哪款工具解決相關問題?這項挑戰在大數據SQL引擎領域同樣存在。
在大數據浪潮全面來襲的歷史背景下，我們一直面臨著同一類難題的困擾——該選擇哪款工具解決相關問題?這項挑戰在大數據SQL引擎領域同樣存在。作為大數據報告工具開發商，AtScale公司通過基準測試為我們帶來了如下答案：
1. Spark 2.0在大規模查詢性能方面可達1.6版本的2.4倍。二者的小規模查詢性能基本持平。
Spark 2.0 improved its large query performance by an average of 2.4X over Spark 1.6 (so upgrade!). Small query performance was already good and remained roughly the same.
2. Impala 2.6版本在大規模查詢性能可達2.3版本的2.8倍，小規模查詢基本持平。
Impala 2.6 is 2.8X as fast for large queries as version 2.3. Small query performance was already good and remained roughly the same.
3. Hive 2.1配合LLAP在大規模查詢場景下可實現1.2版本性能的3.4倍，小規模查詢性能則為2倍。
Hive 2.1 with LLAP is over 3.4X faster than 1.2, and its small query performance doubled. If you're using Hive, this isn't an upgrade you can afford to skip.
責任編輯：武曉燕
來源：
網絡大數據
