sparksql系列(十) hive map嵌套struct、struct嵌套array、array嵌套struct

hive简单的数据结构像基本类型一样，处理起来没有难度。
但是hive有复杂的数据结构如struct、map、array等，处理起来较为复杂了，下面简单介绍一下用spark处理hive复杂数据结构。

struct结构

 熟悉C语言的同学可能会对这个比较熟悉。C语言中稍微复杂的类型都是由struct构成的。sttuct可以包含基本类型，也可以包含复杂类型。是较为常用的hive数据类型之一

hive建表语句

drop table appopendetail;
create table if not exists appopendetail
(
username String,
appname String,
opencount INT
)
row format delimited fields terminated by '|'
location '/hive/table/appopendetail';

drop table appopentable;
create table if not exists appopentable
(
username String,
appopen struct<appname:String,opencount:INT>
)
row format delimited fields terminated by '|'
COLLECTION ITEMS TERMINATED BY ':'
location '/hive/table/appopen';

sql生成struct

insert into appopentable
select username,named_struct('appname',appname,'opencount',opencount) as appopen from appopendetail;

sql解析struct

select appopen.appname as appname,appopen.opencount as opencount from appopentable ;

spark生成struct

import java.util
import org.apache.spark.api.java.JavaSparkContext
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._

val sparkSession= SparkSession.builder().master("local").appName("AppName").enableHiveSupport().getOrCreate()
val javasc = new JavaSparkContext(sparkSession.sparkContext)
val nameRDD = javasc.parallelize(util.Arrays.asList("{'username':'wangwu','opencount':'18','appname':'weixin'}",
  "{'username':'sunliu','opencount':'19','appname':'qq'}","{'username':'zhangsan','opencount':'20','appname':'brower'}"));
val namedf = sparkSession.read.json(nameRDD)
namedf.select(col("username"),concat_ws(":",col("appname"),col("opencount")))
  .write.option("sep","|").csv("/software/java/data/mm")

简单描述一下就是拼接列将拼接的字段用“：”链接起来 。

sparksql有直接的struct函数，但是hive文件最终的数据是文本格式的，sparksql不支持将struct保存为文本格式。

spark解析struct

1.spark.sql(“select appopen.appname as appname,appopen.opencount as opencount from appopentable”)

map结构

其实本质上和struct结构是差不多的

hive建表语句

drop table appopendetail;
create table if not exists appopendetail
(
username String,
appname String,
opencount INT
)
row format delimited fields terminated by '|'
location '/hive/table/appopendetail';

drop table appopentable;
create table if not exists appopentable
(
username String,
appopen map<String,INT>
)
row format delimited fields terminated by '|'
COLLECTION ITEMS TERMINATED BY ':'
MAP KEYS TERMINATED BY ' '
location '/hive/table/appopen';

sql生成map

insert into appopentable select username,map('appname',appname,'opencount',opencount) as appopen from appopendetail;

sql解析map

select appopen["appname"],appopen["opencount"] from appopentable ;

spark生成map

import java.util
import org.apache.spark.api.java.JavaSparkContext
import org.apache.spark.sql.{SaveMode, SparkSession}
import org.apache.spark.sql.functions._

val sparkSession= SparkSession.builder().master("local").appName("AppName").getOrCreate()
val javasc = new JavaSparkContext(sparkSession.sparkContext)
val nameRDD = javasc.parallelize(util.Arrays.asList("{'username':'wangwu','opencount':'18','appname':'weixin'}",
  "{'username':'sunliu','opencount':'19','appname':'qq'}","{'username':'zhangsan','opencount':'20','appname':'brower'}"));
val namedf = sparkSession.read.json(nameRDD).select(col("appname"),lit("appname") as "appnamekey",col("opencount"),lit("opencount") as "opencountkey" ,col("username"))

namedf.select(col("username"),concat_ws(":",
  ("	",col("appnamekey"),col("appname")),
  concat_ws("	",col("opencountkey"),col("opencount"))
))

  .write.mode(SaveMode.Overwrite).option("sep","|").csv("/software/java/data/mm")

sparksql有直接的map函数，但是hive文件最终的数据是文本格式的，sparksql不支持将map保存为文本格式。

spark解析map

1.spark.sql(“select appopen["appname"],appopen["opencount"] from appopentable”)

array结构

hive建表语句

drop table appopendetail;
create table if not exists appopendetail
(
username String,
appname String,
opencount INT
)
row format delimited fields terminated by '|'
location '/hive/table/appopendetail';

drop table appopentable;
create table if not exists appopentable
(
username String,
array array<String>
)
row format delimited fields terminated by '|'
COLLECTION ITEMS TERMINATED BY ':'
MAP KEYS TERMINATED BY ' '
location '/hive/table/appopen';

sql生成array

insert into appopentable select username,collect_list(appname) as appopen from appopendetail group by username;

sql解析array

select appopen[0] from appopentable ;

spark生成array

方法一：

import java.util
import org.apache.spark.api.java.JavaSparkContext
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._

val sparkSession= SparkSession.builder().master("local").appName("AppName").getOrCreate()
val javasc = new JavaSparkContext(sparkSession.sparkContext)
val nameRDD = javasc.parallelize(util.Arrays.asList("{'username':'wangwu','opencount':'18','appname':'weixin'}",
  "{'username':'wangwu','opencount':'19','appname':'qq'}","{'username':'wangwu','opencount':'20','appname':'brower'}"));
val namedf = sparkSession.read.json(nameRDD).select("username","appname")
  .groupBy("username")
  .agg(collect_list("appname") as "appname")
  .select(col("username"),concat_ws(":",col("appname")))
  
  .repartition(1).write.mode(SaveMode.Overwrite).option("sep","|").csv("/software/java/data/mm")

简单描述一下就是拼接列将拼接的字段用“：”链接起来 

spark解析array

1.spark.sql(“select appopen[0] from appopentable“)

struct组合map array 结构

hive建表语句

drop table appopendetail;
create table if not exists appopendetail
(
username String,
appname String,
opencount INT
)
row format delimited fields terminated by '|'
location '/hive/table/appopendetail';

create table if not exists appopentablestruct_map
(
struct_map struct<appname:String,opencount:map<String,String>>
)
row format delimited fields terminated by '|'
COLLECTION ITEMS TERMINATED BY ':'
MAP KEYS TERMINATED BY ' '
location '/hive/table/appopentablestruct_map';

create table if not exists appopentablestruct_array
(
struct_array struct<appname:String,opencount:array<INT>>
)
row format delimited fields terminated by '|'
COLLECTION ITEMS TERMINATED BY ':'
MAP KEYS TERMINATED BY ' '
location '/hive/table/appopentablestruct_array';

sql生成struct嵌套

insert into appopentablestruct_map select named_struct('appname',appname,'opencount',map('appname',appname,'opencount',opencount)) as struct_map from appopendetail;
insert into appopentablestruct_array select named_struct('appname',appname,'opencount',collect_list(opencount)) as struct_map from appopendetail group by appname;

sql解析struct嵌套

select struct_map.appname,struct_map.opencount,struct_map.opencount["appname"],struct_map.opencount["opencount"] from appopentablestruct_map;
select struct_array.appname,struct_array.opencount,struct_array.opencount[0] from appopentablestruct_array;

spark生成struct嵌套

方法一：

import java.util
import org.apache.spark.api.java.JavaSparkContext
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._

val sparkSession= SparkSession.builder().master("local").appName("AppName").getOrCreate()
val javasc = new JavaSparkContext(sparkSession.sparkContext)
val nameRDD = javasc.parallelize(util.Arrays.asList("{'username':'wangwu','opencount':'18','appname':'weixin'}",
  "{'username':'wangwu','opencount':'19','appname':'qq'}","{'username':'wangwu','opencount':'20','appname':'brower'}"));

val namedf = sparkSession.read.json(nameRDD)

namedf.select(col("appname"),col("username"),lit("appname") as "appnamekey",
  col("opencount"),lit("opencount") as "opencountkey")

  .select(concat_ws(":",col("appname"),
    concat_ws("	",
  concat_ws("u0004",col("appnamekey"),col("appname")),
    concat_ws("u0004",col("opencountkey"),col("opencount"))
  )))
  .repartition(1).write.mode(SaveMode.Overwrite).option("sep","|").csv("/software/java/data/appopentablestruct_map")

namedf.select(col("appname"),col("username"),lit("appname") as "appnamekey",
  col("opencount"),lit("opencount") as "opencountkey")
  .groupBy("appname")
  .agg(concat_ws(":",col("appname"),concat_ws("	",collect_list("opencount") as "opencount")) as "opencount" )
  .select("opencount")
  .repartition(1).write.mode(SaveMode.Overwrite).option("sep","|").csv("/software/java/data/appopentablestruct_array")

简单描述一下就是拼接列将拼接的字段用“：”链接起来 

spark解析struct嵌套

1.spark.sql(“select struct_map.appname,struct_map.opencount,struct_map.opencount["appname"],struct_map.opencount["opencount"] from appopentablestruct_map“)

2.spark.sql(“select struct_array.appname,struct_array.opencount,struct_array.opencount[0] from appopentablestruct_array“)

map组合struct array 结构

hive建表语句

drop table appopendetail;
create table if not exists appopendetail
(
username String,
appname String,
opencount INT
)
row format delimited fields terminated by '|'
location '/hive/table/appopendetail';

drop table appopentablemap_struct;
drop table appopentablestruct_array;
create table if not exists appopentablemap_struct
(
map_struct map<String,struct<appname:String,opencount:INT>>
)
row format delimited fields terminated by '|'
COLLECTION ITEMS TERMINATED BY ':'
MAP KEYS TERMINATED BY ' '
location '/hive/table/appopentablemap_struct';

create table if not exists appopentablestruct_array
(
map_array map<String,array<INT>>
)
row format delimited fields terminated by '|'
COLLECTION ITEMS TERMINATED BY ':'
MAP KEYS TERMINATED BY ' '
location '/hive/table/appopentablestruct_array';

sql生成map嵌套

insert into appopentablemap_struct 

select map('map_struct',map_struct) from (select named_struct('appname',appname,'opencount',opencount) as map_struct from appopendetail)t;

insert into appopentablestruct_array
select map('map_array',appopen) from (select username,collect_list(opencount) as appopen from appopendetail group by username)t;

sql解析map嵌套

select map_struct["map_struct"].appname,map_struct["map_struct"].opencount from appopentablemap_struct;
select map_array["map_array"][0],map_array["map_array"][1] from appopentablestruct_array;

spark生成map嵌套

方法一：

import java.util
import org.apache.spark.api.java.JavaSparkContext
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._

val sparkSession= SparkSession.builder().master("local").appName("AppName").getOrCreate()
val javasc = new JavaSparkContext(sparkSession.sparkContext)
val nameRDD = javasc.parallelize(util.Arrays.asList("{'username':'wangwu','opencount':'18','appname':'weixin'}",
  "{'username':'wangwu','opencount':'19','appname':'qq'}","{'username':'wangwu','opencount':'20','appname':'brower'}"));

val namedf = sparkSession.read.json(nameRDD)

namedf.select(col("appname"),col("username"),lit("appname") as "appnamekey",
  col("opencount"),lit("opencount") as "opencountkey")
  .select(concat_ws("	",col("appnamekey"),
    concat_ws("u0004",col("appname"),col("opencount")))
  )
  .repartition(1).write.mode(SaveMode.Overwrite).option("sep","|").csv("/software/java/data/appopentablemap_struct")

namedf
  .select(col("appname"),col("username"),lit("appname") as "appnamekey",
    col("opencount"),lit("opencount") as "opencountkey")
  .groupBy("appname")
  .agg(concat_ws("	",col("appname"),concat_ws("u0004",collect_list("opencount") as "opencount")) as "opencount" )
  .select("opencount")

简单描述一下就是拼接列将拼接的字段用“：”链接起来 

spark解析map嵌套

1.spark.sql(“select map_struct["map_struct"].appname,map_struct["map_struct"].opencount from appopentablemap_struct“)

2.spark.sql(“select map_array["map_array"][0],map_array["map_array"][1] from appopentablestruct_array“)

array组合struct map 结构

hive建表语句

drop table appopendetail;
create table if not exists appopendetail
(
username String,
appname String,
opencount INT
)
row format delimited fields terminated by '|'
location '/hive/table/appopendetail';

drop table appopentablearray_map;
create table if not exists appopentablearray_map
(
array_map array<map<String,String>>
)
row format delimited fields terminated by '|'
COLLECTION ITEMS TERMINATED BY ':'
MAP KEYS TERMINATED BY ' '
location '/hive/table/appopentablearray_map';
drop table appopentablearray_struct;
create table if not exists appopentablearray_struct
(
array_struct array<struct<appname:String,opencount:String>>
)
row format delimited fields terminated by '|'
COLLECTION ITEMS TERMINATED BY ':'
MAP KEYS TERMINATED BY ' '
location '/hive/table/appopentablearray_struct';

sql生成array嵌套

insert into appopentablearray_map
select collect_list(openmap) as openmap from
(select appname,map('appname',appname,'opencount',opencount) as openmap from appopendetail)t;

insert into appopentablearray_struct
select collect_list(openmap) as openmap from
(select appname,named_struct('appname',appname,'opencount',cast(opencount as string)) as openmap from appopendetail)t;

sql解析array嵌套

select array_map[0]["appname"],array_map[0]["opencount"] from appopentablearray_map;
select array_struct[0].appname,array_struct[0].opencount from appopentablearray_struct;

spark生成array嵌套

方法一：

import java.util
import org.apache.spark.api.java.JavaSparkContext
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._

val sparkSession= SparkSession.builder().master("local").appName("AppName").getOrCreate()
val javasc = new JavaSparkContext(sparkSession.sparkContext)
val nameRDD = javasc.parallelize(util.Arrays.asList("{'username':'wangwu','opencount':'18','appname':'weixin'}",
  "{'username':'wangwu','opencount':'19','appname':'qq'}","{'username':'wangwu','opencount':'20','appname':'brower'}"));

val namedf = sparkSession.read.json(nameRDD)

namedf.select(col("appname"),col("username"),lit("appname") as "appnamekey",
    col("opencount"),lit("opencount") as "opencountkey")
  .agg(concat_ws(":",collect_list(
      concat_ws("	",
        concat_ws("u0004",col("appnamekey"),col("appname")),
        concat_ws("u0004",col("opencountkey"),col("opencount"))
      )
    ))as "openmap")
  .select("openmap")

namedf.select(col("appname"),col("username"),lit("appname") as "appnamekey",
    col("opencount"),lit("opencount") as "opencountkey")
  .agg(concat_ws(":",collect_list(
        concat_ws("	",col("appname"),col("opencount"))
    ))as "openmap")
  .select("openmap")

spark解析array嵌套

1.spark.sql(“select array_map[0]["appname"],array_map[0]["opencount"] from appopentablearray_map“)

2.spark.sql(“select array_struct[0].appname,array_struct[0].opencount from appopentablearray_struct“)