apache-pig - 如何在 PIG 拉丁语中优化 group by 语句？

Question

我有一个倾斜的数据集，我需要按操作进行分组，然后对其进行嵌套的 foreach。由于数据倾斜，很少有 reducer 需要很长时间，而其他 reducer 则不需要时间。我知道存在倾斜连接，但是 group by 和 foreach 有什么？这是我的 pig 代码(重命名变量):

foo_grouped = GROUP foo_grouped by FOO;
FOO_stats = FOREACH foo_grouped 
{ 
a_FOO_total = foo_grouped.ATTR; 
a_FOO_total = DISTINCT a_FOO_total; 

bar_count = foo_grouped.BAR; 
bar_count = DISTINCT bar_count; 

a_FOO_type1 = FILTER foo_grouped by COND1=='Y';
a_FOO_type1 = a_FOO_type1.ATTR; 
a_FOO_type1 = DISTINCT a_FOO_type1;

a_FOO_type2 = FILTER foo_grouped by COND2=='Y' OR COND3=='HIGH'; 
a_FOO_type2 = a_FOO_type2.ATTR; 
a_FOO_type2 = DISTINCT a_FOO_type2; 

generate group as FOO, 
COUNT(a_FOO_total) as a_FOO_total, COUNT(a_FOO_type1) as a_FOO_type1, COUNT(a_FOO_type2)     as a_FOO_type2, COUNT(bar_count) as bar_count; }

Answer 1

在您的示例中，FOREACH 中有很多嵌套的 DISTINCT 运算符，它们在 reducer 中执行，它依赖于 RAM 来计算唯一值，并且此查询仅生成一个作业。如果组中的唯一元素太多，您也可能会遇到与内存相关的异常。

幸运的是，PIG Latin 是一种数据流语言，您可以编写某种执行计划。为了利用更多的 CPU，您可以更改代码以强制执行更多可以并行执行的 MapReduce 作业。为此，我们应该在不使用嵌套 DISTINCT 的情况下重写查询，诀窍是执行不同的操作，而不是像只有一列一样分组，然后合并结果。它非常像 SQL，但它有效。这里是:

records = LOAD '....' USING PigStorage(',') AS (g, a, b, c, d, fd, s, w);
selected = FOREACH records GENERATE g, a, b, c, d;
grouped_a = FOREACH selected GENERATE g, a;
grouped_a = DISTINCT grouped_a;
grouped_a_count = GROUP grouped_a BY g;
grouped_a_count = FOREACH grouped_a_count GENERATE FLATTEN(group) as g, COUNT(grouped_a) as a_count;

grouped_b = FOREACH selected GENERATE g, b;
grouped_b = DISTINCT grouped_b;
grouped_b_count = GROUP grouped_b BY g;
grouped_b_count = FOREACH grouped_b_count GENERATE FLATTEN(group) as g, COUNT(grouped_b) as b_count;

grouped_c = FOREACH selected GENERATE g, c;
grouped_c = DISTINCT grouped_c;
grouped_c_count = GROUP grouped_c BY g;
grouped_c_count = FOREACH grouped_c_count GENERATE FLATTEN(group) as g, COUNT(grouped_c) as c_count;

grouped_d = FOREACH selected GENERATE g, d;
grouped_d = DISTINCT grouped_d;
grouped_d_count = GROUP grouped_d BY g;
grouped_d_count = FOREACH grouped_d_count GENERATE FLATTEN(group) as g, COUNT(grouped_d) as d_count;

mrg = JOIN grouped_a_count BY g, grouped_b_count BY g, grouped_c_count BY g, grouped_d_count BY g;
out = FOREACH mrg GENERATE grouped_a_count::g, grouped_a_count::a_count, grouped_b_count::b_count, grouped_c_count::c_count, grouped_d_count::d_count;
STORE out into '....' USING PigStorage(',');

执行后，我得到以下摘要，表明不同的操作没有受到第一个作业处理的数据倾斜的影响:

Job Stats (time in seconds):
      JobId            Maps    Reduces MaxMapTime      MinMapTIme      AvgMapTime      MaxReduceTime   MinReduceTime   AvgReduceTime   Alias   Feature Outputs
job_201206061712_0244   669     45      75      8       13      376     18      202     grouped_a,grouped_b,grouped_c,grouped_d,records,selected        DISTINCT,MULTI_QUERY
job_201206061712_0245   1       1       3       3       3       12      12      12      grouped_c_count GROUP_BY,COMBINER
job_201206061712_0246   1       1       3       3       3       12      12      12      grouped_b_count GROUP_BY,COMBINER
job_201206061712_0247   5       1       48      27      33      30      30      30      grouped_a_count GROUP_BY,COMBINER
job_201206061712_0248   1       1       3       3       3       12      12      12      grouped_d_count GROUP_BY,COMBINER
job_201206061712_0249   4       1       3       3       3       12      12      12      mrg,out HASH_JOIN       ...,
Input(s):
Successfully read 52215768 records (44863559501 bytes) from: "...."

Output(s):
Successfully stored 9 records (181 bytes) in: "..."

从 Job DAG 我们可以看到 groupby 操作是并行执行的:

Job DAG:
job_201206061712_0244   ->      job_201206061712_0248,job_201206061712_0246,job_201206061712_0247,job_201206061712_0245,
job_201206061712_0248   ->      job_201206061712_0249,
job_201206061712_0246   ->      job_201206061712_0249,
job_201206061712_0247   ->      job_201206061712_0249,
job_201206061712_0245   ->      job_201206061712_0249,
job_201206061712_0249

它在我的数据集上运行良好，其中一个组键值(在 g 列中)构成了 95% 的数据。它还摆脱了与内存相关的异常。