Hive – Distinct 的实现

准备数据

语句

SELECT COUNT, COUNT(DISTINCT uid) FROM logs GROUP BY COUNT;

hive> SELECT * FROM logs;
OK
a	苹果	3
a	橙子	3
a	烧鸡	1
b	烧鸡	3

hive> SELECT COUNT, COUNT(DISTINCT uid) FROM logs GROUP BY COUNT;
1	1
3	2

根据count分组，计算独立用户数。

计算过程

1. 第一步先在mapper计算部分值，会以count和uid作为key，如果是distinct并且之前已经出现过，则忽略这条计算。第一步是以组合为key，第二步是以count为key.

2. ReduceSink是在mapper.close()时才执行的，在GroupByOperator.close()时，把结果输出。注意这里虽然key是count和uid，但是在reduce时分区是按count来的！

3. 第一步的distinct计算的值没用，要留到reduce计算的才准确。这里只是减少了key组合相同的行。不过如果是普通的count，后面是会合并起来的。

4. distinct通过比较lastInvoke判断要不要+1（因为在reduce是排序过了的，所以判断distict的字段变了没有，如果没变，则不+1）

Operator

Explain

hive> explain select count, count(distinct uid) from logs group by count;
OK
ABSTRACT SYNTAX TREE:
(TOK_QUERY (TOK_FROM (TOK_TABREF (TOK_TABNAME logs))) (TOK_INSERT (TOK_DESTINATION (TOK_DIR TOK_TMP_FILE)) (TOK_SELECT (TOK_SELEXPR (TOK_TABLE_OR_COL count)) (TOK_SELEXPR (TOK_FUNCTIONDI count (TOK_TABLE_OR_COL uid)))) (TOK_GROUPBY (TOK_TABLE_OR_COL count))))

STAGE DEPENDENCIES:
Stage-1 is a root stage
Stage-0 is a root stage

STAGE PLANS:
Stage: Stage-1
Map Reduce
Alias -> Map Operator Tree:
logs
TableScan //表扫描
alias: logs
Select Operator//列裁剪，取出uid，count字段就够了
expressions:
expr: count
type: int
expr: uid
type: string
outputColumnNames: count, uid
Group By Operator //先来map聚集
aggregations:
expr: count(DISTINCT uid) //聚集表达式
bucketGroup: false
keys:
expr: count
type: int
expr: uid
type: string
mode: hash //hash方式
outputColumnNames: _col0, _col1, _col2
Reduce Output Operator
key expressions: //输出的键
expr: _col0 //count
type: int
expr: _col1 //uid
type: string
sort order: ++
Map-reduce partition columns: //这里是按group by的字段分区的
expr: _col0 //这里表示count
type: int
tag: -1
value expressions:
expr: _col2
type: bigint
Reduce Operator Tree:
Group By Operator //第二次聚集
aggregations:
expr: count(DISTINCT KEY._col1:0._col0) //uid:count
bucketGroup: false
keys:
expr: KEY._col0 //count
type: int
mode: mergepartial //合并
outputColumnNames: _col0, _col1
Select Operator //列裁剪
expressions:
expr: _col0
type: int
expr: _col1
type: bigint
outputColumnNames: _col0, _col1
File Output Operator //输出结果到文件
compressed: false
GlobalTableId: 0
table:
input format: org.apache.hadoop.mapred.TextInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat

Stage: Stage-0
Fetch Operator
limit: -1