在大数据处理平台中,Hive 是一个被广泛使用的工具,能够将 SQL 查询转化为 MapReduce 作业并在 Hadoop 上执行。数据分析师和工程师经常需要使用 JOIN 和 IN 语句来合并数据,但这两者之间的效率差异却很少被讨论。本文将对 Hive 中 JOIN 和 IN 的效率进行比较,同时提供代码示例,并用状态图展示其执行过程。 JOIN ...
对此,Hive 特意提供了一个环境变量:hive.mapred.mode=strict; 防止笛卡儿积的执行: 1 FAILED: SemanticException [Error 10052]: In strict mode, cartesian product is not allowed. If you really want to perform the operation, set hive.mapred.mode=nonstrict 从2 中的观察得知我们在 on 后面跟 join ...
In Hive, the join operation is used to combine data from two or more tables based on a related column between them. The INNER JOIN is one of the join types that returns only the matched rows from both tables. In some cases, you may want to perform a join operation based on the exist...
3)笛卡尔积查询数据量非常大时,笛卡尔积查询会出现不可控的情况,因此严格模式下也不允许执行。 在开启严格模式下,进行上述三种不符合要求的查询,通常会报类似FAILED: Error in semantic analysis: In strict mode, XXX is not allowed. If you really want to perform the operation,+set hive.mapred.mode=nonst...
FAILED: SemanticException [Error 10052]:Instrict mode, cartesian productisnotallowed. If you really wanttoperform the operation,sethive.mapred.mode=nonstrict 从2 中的观察得知我们在 on 后面跟 join 条件,走的是 reduce side join,如果你在 where 后跟则是走 Cartesian product,但是这里单条 sql 又没法...
The backup is also saved in the distributed cache. The small table data that the map task reads from the local disk or distributed cache by bucket is the output after mapping with the large table.Optimizing Join Sequences If the Join operation is to be performed on three or more tables ...
FAILED:SemanticException[Error10052]:In strict mode,cartesian product is not allowed.If you really want to perform the operation,sethive.mapred.mode=nonstrict 从2 中的观察得知我们在 on 后面跟 join 条件,走的是 reduce side join,如果你在 where 后跟则是走 Cartesian product,但是这里单条 sql 又没...
After many searches, i have try this in my code: set hive.auto.convert.join=false; and this resolved my NPE error but I don't understand why hive.auto.convert.join = true makes this error, knowing that this parameter checks if the smaller table file size is greater than the value spec...
Hive supports MAPJOINs, which are well suited for this scenario – at least for dimensions small enough to fit in memory. Before release 0.11, a MAPJOIN could be invoked either through an optimizer hint: select /*+ MAPJOIN(time_dim) */ count(*) from ...
Improvements to the Hive Optimizer Hive automatically recognizes various use cases and optimizes for them. Hive 0.11 improves the optimizer for these cases:hive可以自动优化,在0.11里面改进了一些优化用例 Joins where one side fits in memory. In the new optimization:join的一边适合放进内存,有新的优化方...