多级嵌套子查询以及混合Join的SQL调优

操作场景

本章节介绍在多级嵌套以及混合Join SQL查询的调优建议。

前提条件

例如有一个复杂的查询样例如下：

select
s_name,
count(1) as numwait
from (
select s_name from (
select
s_name,
t2.l_orderkey,
l_suppkey,
count_suppkey,
max_suppkey
from
test2 t2 right outer join (
select
s_name,
l_orderkey,
l_suppkey from (
select
s_name,
t1.l_orderkey,
l_suppkey,
count_suppkey,
max_suppkey
from
test1 t1 join (
select
s_name,
l_orderkey,
l_suppkey
from
orders o join (
select
s_name,
l_orderkey,
l_suppkey
from
nation n join supplier s
on
s.s_nationkey = n.n_nationkey
and n.n_name = 'SAUDI ARABIA'
join lineitem l
on
s.s_suppkey = l.l_suppkey
where
l.l_receiptdate > l.l_commitdate
and l.l_orderkey is not null
) l1 on o.o_orderkey = l1.l_orderkey and o.o_orderstatus = 'F'
) l2 on l2.l_orderkey = t1.l_orderkey
) a
where
(count_suppkey > 1)
or ((count_suppkey=1)
and (l_suppkey <> max_suppkey))
) l3 on l3.l_orderkey = t2.l_orderkey
) b
where
(count_suppkey is null)
or ((count_suppkey=1)
and (l_suppkey = max_suppkey))
) c
group by
s_name
order by
numwait desc,
s_name 
limit 100;

操作步骤

1. 分析业务。

   从业务入手分析是否可以简化SQL，例如可以通过合并表去减少嵌套的层级和Join的次数。

2. 如果业务需求对应的SQL无法简化，则需要配置DRIVER内存：
   • 使用spark-submit或者spark-sql运行SQL语句，执行3。
   • 使用spark-beeline运行SQL语句，执行4。

3. 执行SQL语句时，需要添加参数“--driver-memory”，设置内存大小，例如：

   /spark-sql --master=local[4] --driver-memory=512M -f /tpch.sql

4. 在执行SQL语句前，请使用MRS集群管理员用户修改内存大小配置。
   1. 登录FusionInsight Manager，选择“集群 > 待操作集群的名称 > 服务 > Spark2x > 配置”。
   2. 单击“全部配置”，并搜索“SPARK_DRIVER_MEMORY”。
   3. 修改参数值适当增加内存大小。仅支持整数值，且需要输入单位M或者G。例如输入512M。

参考信息

DRIVER内存不足时，查询操作可能遇到以下错误提示信息：

2018-02-11 09:13:14,683 | WARN  | Executor task launch worker for task 5 | Calling spill() on RowBasedKeyValueBatch. Will not spill but return 0. | org.apache.spark.sql.catalyst.expressions.RowBasedKeyValueBatch.spill(RowBasedKeyValueBatch.java:173)
2018-02-11 09:13:14,682 | WARN  | Executor task launch worker for task 3 | Calling spill() on RowBasedKeyValueBatch. Will not spill but return 0. | org.apache.spark.sql.catalyst.expressions.RowBasedKeyValueBatch.spill(RowBasedKeyValueBatch.java:173)
2018-02-11 09:13:14,704 | ERROR | Executor task launch worker for task 2 | Exception in task 2.0 in stage 1.0 (TID 2) | org.apache.spark.internal.Logging$class.logError(Logging.scala:91)
java.lang.OutOfMemoryError: Unable to acquire 262144 bytes of memory, got 0
        at org.apache.spark.memory.MemoryConsumer.allocateArray(MemoryConsumer.java:100)
        at org.apache.spark.unsafe.map.BytesToBytesMap.allocate(BytesToBytesMap.java:791)
        at org.apache.spark.unsafe.map.BytesToBytesMap.<init>(BytesToBytesMap.java:208)
        at org.apache.spark.unsafe.map.BytesToBytesMap.<init>(BytesToBytesMap.java:223)
        at org.apache.spark.sql.execution.UnsafeFixedWidthAggregationMap.<init>(UnsafeFixedWidthAggregationMap.java:104)
        at org.apache.spark.sql.execution.aggregate.HashAggregateExec.createHashMap(HashAggregateExec.scala:307)
        at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.agg_doAggregateWithKeys$(Unknown Source)
        at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
        at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
        at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:381)
        at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
        at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:126)
        at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
        at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
        at org.apache.spark.scheduler.Task.run(Task.scala:99)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:325)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
        at java.lang.Thread.run(Thread.java:748)