一、实验目的

本实验通过模拟一个典型的应用场景和实际数据量，测试并对比HAWQ内部表、外部表与Hive的查询性能。

二、硬件环境

1. 四台VMware虚机组成的Hadoop集群。
2. 每台机器配置如下：
（1）15K RPM SAS 100GB
（2）Intel(R) Xeon(R) E5-2620 v2 @ 2.10GHz，双核双CPU
（3）8G内存，8GSwap
（4）10000Mb/s虚拟网卡

三、软件环境

1. Linux：CentOS release 6.4，核心2.6.32-358.el6.x86_64
2. Ambari：2.4.1
3. Hadoop：HDP 2.5.0
4. Hive（Hive on Tez）：2.1.0
5. HAWQ：2.1.1.0
6. HAWQ PXF：3.1.1

四、数据模型

1. 表结构

实验模拟一个记录页面点击数据的应用场景。数据模型中包含日期、页面、浏览器、引用、状态5个维度表，1个页面点击事实表。表结构和关系如图1所示。

图1

2. 记录数

各表的记录数如表1所示。

表1

五、建表并生成数据

1. 建立hive库表

create database test;
use test;

create table browser_dim(
  browser_sk bigint,
  browser_nm varchar(100),
  browser_version_no varchar(100),
  flash_version_no varchar(100),
  flash_enabled_flg int,
  java_version_no varchar(100),
  platform_desc string,
  java_enabled_flg int,
  java_script_enabled_flg int,
  cookies_enabled_flg int,
  user_language_cd varchar(100),
  screen_color_depth_no varchar(100),
  screen_size_txt string)
row format delimited
  fields terminated by ‘,‘
stored as orc; 

create table date_dim(
  cal_dt date,
  day_in_cal_yr_no int,
  day_of_week_no int,
  start_of_month_dt date,
  start_of_quarter_dt date,
  start_of_week_dt date,
  start_of_year_dt date)
row format delimited
  fields terminated by ‘,‘
stored as orc;

create table page_dim(
  page_sk bigint,
  domain_nm varchar(200),
  reachability_cd string,
  page_desc string,
  protocol_nm varchar(20))
row format delimited
  fields terminated by ‘,‘
stored as orc;

create table referrer_dim(
  referrer_sk bigint,
  referrer_txt string,
  referrer_domain_nm varchar(200))
row format delimited
  fields terminated by ‘,‘
stored as orc;

create table status_code_dim(
  status_cd varchar(100),
  client_error_flg int,
  status_cd_desc string,
  server_error_flg int)
row format delimited
  fields terminated by ‘,‘
stored as orc;

create table page_click_fact(
  visitor_id varchar(100),
  detail_tm timestamp,
  page_click_dt date,
  page_sk bigint,
  client_session_dt date,
  previous_page_sk bigint,
  referrer_sk bigint,
  next_page_sk bigint,
  status_cd varchar(100),
  browser_sk bigint,
  bytes_received_cnt bigint,
  bytes_sent_cnt bigint,
  client_detail_tm timestamp,
  entry_point_flg int,
  exit_point_flg int,
  ip_address varchar(20),
  query_string_txt string,
  seconds_spent_on_page_cnt int,
  sequence_no int,
  requested_file_txt string)
row format delimited
  fields terminated by ‘,‘
stored as orc;

说明：hive表使用ORCfile存储格式。

2. 用Java程序生成hive表数据

ORC压缩后的各表对应的HDFS文件大小如下：

2.2 M   /apps/hive/warehouse/test.db/browser_dim
641     /apps/hive/warehouse/test.db/date_dim
4.1 G   /apps/hive/warehouse/test.db/page_click_fact
16.1 M  /apps/hive/warehouse/test.db/page_dim
22.0 M  /apps/hive/warehouse/test.db/referrer_dim
1.1 K   /apps/hive/warehouse/test.db/status_code_dim

3. 分析hive表

analyze table date_dim compute statistics;
analyze table browser_dim compute statistics;
analyze table page_dim compute statistics;
analyze table referrer_dim compute statistics;
analyze table status_code_dim compute statistics;
analyze table page_click_fact compute statistics;

4. 建立HAWQ外部表

create schema ext;
set search_path=ext;

create external table date_dim(
cal_dt              date,
day_in_cal_yr_no    int4,
day_of_week_no      int4,
start_of_month_dt   date,
start_of_quarter_dt date,
start_of_week_dt    date,
start_of_year_dt    date
)
location (‘pxf://hdp1:51200/test.date_dim?profile=hiveorc‘)
format ‘custom‘ (formatter=‘pxfwritable_import‘); 

create external table browser_dim(
browser_sk              int8,
browser_nm              varchar(100),
browser_version_no      varchar(100),
flash_version_no        varchar(100),
flash_enabled_flg       int,
java_version_no         varchar(100),
platform_desc           text,
java_enabled_flg        int,
java_script_enabled_flg int,
cookies_enabled_flg     int,
user_language_cd        varchar(100),
screen_color_depth_no   varchar(100),
screen_size_txt         text
)
location (‘pxf://hdp1:51200/test.browser_dim?profile=hiveorc‘)
format ‘custom‘ (formatter=‘pxfwritable_import‘); 

create external table page_dim(
page_sk             int8,
domain_nm           varchar(200),
reachability_cd     text,
page_desc           text,
protocol_nm         varchar(20)
)
location (‘pxf://hdp1:51200/test.page_dim?profile=hiveorc‘)
format ‘custom‘ (formatter=‘pxfwritable_import‘); 

create external table referrer_dim(
referrer_sk         int8,
referrer_txt        text,
referrer_domain_nm  varchar(200)
)
location (‘pxf://hdp1:51200/test.referrer_dim?profile=hiveorc‘)
format ‘custom‘ (formatter=‘pxfwritable_import‘); 

create external table status_code_dim(
status_cd           varchar(100),
client_error_flg    int4,
status_cd_desc      text,
server_error_flg    int4
)
location (‘pxf://hdp1:51200/test.status_code_dim?profile=hiveorc‘)
format ‘custom‘ (formatter=‘pxfwritable_import‘); 

create external table page_click_fact(
visitor_id                varchar(100),
detail_tm                 timestamp,
page_click_dt             date,
page_sk                   int8,
client_session_dt         date,
previous_page_sk          int8,
referrer_sk               int8,
next_page_sk              int8,
status_cd                 varchar(100),
browser_sk                int8,
bytes_received_cnt        int8,
bytes_sent_cnt            int8,
client_detail_tm          timestamp,
entry_point_flg           int4,
exit_point_flg            int4,
ip_address                varchar(20),
query_string_txt          text,
seconds_spent_on_page_cnt int4,
sequence_no               int4,
requested_file_txt        text
)
location (‘pxf://hdp1:51200/test.page_click_fact?profile=hiveorc‘)
format ‘custom‘ (formatter=‘pxfwritable_import‘);

说明：HAWQ外部表使用PXF协议，指向相应的hive表。

5. 建立HAWQ内部表

set search_path=public;

create table date_dim(
cal_dt              date,
day_in_cal_yr_no    int4,
day_of_week_no      int4,
start_of_month_dt   date,
start_of_quarter_dt date,
start_of_week_dt    date,
start_of_year_dt    date) with (compresstype=snappy,appendonly=true); 

create table browser_dim(
browser_sk              int8,
browser_nm              varchar(100),
browser_version_no      varchar(100),
flash_version_no        varchar(100),
flash_enabled_flg       int,
java_version_no         varchar(100),
platform_desc           text,
java_enabled_flg        int,
java_script_enabled_flg int,
cookies_enabled_flg     int,
user_language_cd        varchar(100),
screen_color_depth_no   varchar(100),
screen_size_txt         text
) with (compresstype=snappy,appendonly=true); 

create table page_dim(
page_sk             int8,
domain_nm           varchar(200),
reachability_cd     text,
page_desc           text,
protocol_nm         varchar(20)
) with (compresstype=snappy,appendonly=true); 

create table referrer_dim(
referrer_sk         int8,
referrer_txt        text,
referrer_domain_nm  varchar(200)
) with (compresstype=snappy,appendonly=true); 

create table status_code_dim(
status_cd           varchar(100),
client_error_flg    int4,
status_cd_desc      text,
server_error_flg    int4
) with (compresstype=snappy,appendonly=true); 

create table page_click_fact(
visitor_id                varchar(100),
detail_tm                 timestamp,
page_click_dt             date,
page_sk                   int8,
client_session_dt         date,
previous_page_sk          int8,
referrer_sk               int8,
next_page_sk              int8,
status_cd                 varchar(100),
browser_sk                int8,
bytes_received_cnt        int8,
bytes_sent_cnt            int8,
client_detail_tm          timestamp,
entry_point_flg           int4,
exit_point_flg            int4,
ip_address                varchar(20),
query_string_txt          text,
seconds_spent_on_page_cnt int4,
sequence_no               int4,
requested_file_txt        text
) with (compresstype=snappy,appendonly=true);

说明：内部表结构定义与hive表等价，使用snappy压缩的行存储格式。

6. 生成HAWQ内部表数据

insert into date_dim select * from hcatalog.test.date_dim;
insert into browser_dim select * from hcatalog.test.browser_dim;
insert into page_dim select * from hcatalog.test.page_dim;
insert into referrer_dim select * from hcatalog.test.referrer_dim;
insert into status_code_dim select * from hcatalog.test.status_code_dim;
insert into page_click_fact select * from hcatalog.test.page_click_fact;

说明：通过HCatalog直接查询hive表，插入到HAWQ内部表中。snappy压缩后的各表对应的HDFS文件大小如下：

6.2 K   /hawq_data/16385/177422/177677
3.3 M   /hawq_data/16385/177422/177682
23.9 M  /hawq_data/16385/177422/177687
39.3 M  /hawq_data/16385/177422/177707
1.8 K   /hawq_data/16385/177422/177726
7.9 G   /hawq_data/16385/177422/177731

7. 分析HAWQ内部表

analyze date_dim;
analyze browser_dim;
analyze page_dim;
analyze referrer_dim;
analyze status_code_dim;
analyze page_click_fact;

六、执行查询

分别在hive表、HAWQ外部表、HAWQ内部表上执行以下5个查询语句，记录执行时间。

1. 查询给定周中support.sas.com站点上访问最多的目录

-- hive查询
select top_directory, count(*) as unique_visits
  from (select distinct visitor_id, substr(requested_file_txt,1,10) top_directory
          from page_click_fact, page_dim, browser_dim
         where domain_nm = ‘support.sas.com‘
           and flash_enabled_flg=1
           and weekofyear(detail_tm) = 19
           and year(detail_tm) = 2017
       ) directory_summary
 group by top_directory
 order by unique_visits;  

-- HAWQ查询，只是用extract函数代替了hive的weekofyear和year函数，与hive的查询语句等价。
select top_directory, count(*) as unique_visits
  from (select distinct visitor_id, substr(requested_file_txt,1,10) top_directory
          from page_click_fact, page_dim, browser_dim
         where domain_nm = ‘support.sas.com‘
           and flash_enabled_flg=1
           and extract(week from detail_tm) = 19
           and extract(year from detail_tm) = 2017
       ) directory_summary
 group by top_directory
 order by unique_visits;

2. 查询各月从www.google.com访问的页面

-- hive查询
select domain_nm, requested_file_txt, count(*) as unique_visitors, month
  from (select distinct domain_nm, requested_file_txt, visitor_id, month(detail_tm) as month
          from page_click_fact, page_dim, referrer_dim
         where domain_nm = ‘support.sas.com‘
           and referrer_domain_nm = ‘www.google.com‘
       ) visits_pp_ph_summary
 group by domain_nm, requested_file_txt, month
 order by domain_nm, requested_file_txt, unique_visitors desc, month asc;  

-- HAWQ查询，只是用extract函数代替了hive的month函数，与hive的查询语句等价。
select domain_nm, requested_file_txt, count(*) as unique_visitors, month
  from (select distinct domain_nm, requested_file_txt, visitor_id, extract(month from detail_tm) as month
          from page_click_fact, page_dim, referrer_dim
         where domain_nm = ‘support.sas.com‘
           and referrer_domain_nm = ‘www.google.com‘
       ) visits_pp_ph_summary
 group by domain_nm, requested_file_txt, month
 order by domain_nm, requested_file_txt, unique_visitors desc, month asc;

3. 给定年份support.sas.com站点上的搜索字符串计数

-- hive查询
select query_string_txt, count(*) as count
  from page_click_fact, page_dim
 where query_string_txt <> ‘‘
   and domain_nm=‘support.sas.com‘
   and year(detail_tm) = ‘2017‘
 group by query_string_txt
 order by count desc;

-- HAWQ查询，只是用extract函数代替了hive的year函数，与hive的查询语句等价。
select query_string_txt, count(*) as count
  from page_click_fact, page_dim
 where query_string_txt <> ‘‘
   and domain_nm=‘support.sas.com‘
   and extract(year from detail_tm) = ‘2017‘
 group by query_string_txt
 order by count desc;

4. 查询使用Safari浏览器访问每个页面的人数

-- hive查询
select domain_nm, requested_file_txt, count(*) as unique_visitors
  from (select distinct domain_nm, requested_file_txt, visitor_id
          from page_click_fact, page_dim, browser_dim
         where domain_nm=‘support.sas.com‘
           and browser_nm like ‘%Safari%‘
           and weekofyear(detail_tm) = 19
           and year(detail_tm) = 2017
       ) uv_summary
 group by domain_nm, requested_file_txt
 order by unique_visitors desc;  

-- HAWQ查询，只是用extract函数代替了hive的weekofyear和year函数，与hive的查询语句等价。
select domain_nm, requested_file_txt, count(*) as unique_visitors
  from (select distinct domain_nm, requested_file_txt, visitor_id
          from page_click_fact, page_dim, browser_dim
         where domain_nm=‘support.sas.com‘
           and browser_nm like ‘%Safari%‘
           and extract(week from detail_tm) = 19
           and extract(year from detail_tm) = 2017
       ) uv_summary
 group by domain_nm, requested_file_txt
 order by unique_visitors desc;

5. 查询给定周中support.sas.com站点上浏览超过10秒的页面

-- hive查询
select domain_nm, requested_file_txt, count(*) as unique_visits
  from (select distinct domain_nm, requested_file_txt, visitor_id
          from page_click_fact, page_dim
         where domain_nm=‘support.sas.com‘
           and weekofyear(detail_tm) = 19
           and year(detail_tm) = 2017
           and seconds_spent_on_page_cnt > 10
       ) visits_summary
 group by domain_nm, requested_file_txt
 order by unique_visits desc;  

-- HAWQ查询，只是用extract函数代替了hive的weekofyear和year函数，与hive的查询语句等价。
select domain_nm, requested_file_txt, count(*) as unique_visits
  from (select distinct domain_nm, requested_file_txt, visitor_id
          from page_click_fact, page_dim
         where domain_nm=‘support.sas.com‘
           and extract(week from detail_tm) = 19
           and extract(year from detail_tm) = 2017
           and seconds_spent_on_page_cnt > 10
       ) visits_summary
 group by domain_nm, requested_file_txt
 order by unique_visits desc;

七、测试结果

Hive、HAWQ外部表、HAWQ内部表查询时间对比如表2所示。每种查询情况执行三次取平均值。

表2

从图2中的对比可以看到，HAWQ内部表比Hive on Tez快的多（4-50倍）。同样的查询，在HAWQ的Hive外部表上执行却很慢。因此，在执行分析型查询时最好使用HAWQ内部表。如果不可避免地需要使用外部表，为了获得满意的查询性能，需要保证外部表数据量尽可能小。同时要使查询尽可能简单，尽量避免在外部表上执行聚合、分组、排序等复杂操作。

图2