sparksql语句
(1)in 不支持子查询 eg. select * from src where key in(select key from test);
支持查询个数 eg. select * from src where key in(1,2,3,4,5);
in 40000个 耗时25.766秒
in 80000个 耗时78.827
(2).union all/union
不支持顶层的union all eg. select key from src UNION ALL select key from test;
支持select * from (select key from src union all select key from test)aa;
不支持 union
支持select distinct key from (select key from src union all select key from test)aa;
3.intersect 不支持
4.minus 不支持
5.except 不支持
6.inner join/join/left outer join/right outer join/full outer join/left semi join 都支持
left outer join/right outer join/full outer join 中间必须有outer
join是最简单的关联操作,两边关联只取交集;
left outer join是以左表驱动,右表不存在的key均赋值为null;
right outer join是以右表驱动,左表不存在的key均赋值为null;
full outer join全表关联,将两表完整的进行笛卡尔积操作,左右表均可赋值为null;
left semi join最主要的使用场景就是解决exist in;
Hive不支持where子句中的子查询,SQL常用的exist in子句在Hive中是不支持的
不支持子查询 eg. select * from src aa where aa.key in(select bb.key from test bb);
可用以下两种方式替换:
select * from src aa left outer join test bb on aa.key=bb.key where bb.key <> null;
select * from src aa left semi join test bb on aa.key=bb.key;
大多数情况下 JOIN ON 和 left semi on 是对等的
A,B两表连接,如果B表存在重复数据
当使用JOIN ON的时候,A,B表会关联出两条记录,应为ON上的条件符合;
而是用LEFT SEMI JOIN 当A表中的记录,在B表上产生符合条件之后就返回,不会再继续查找B表记录了,
所以如果B表有重复,也不会产生重复的多条记录。
left outer join 支持子查询 eg. select aa.* from src aa left outer join (select * from test111)bb on aa.key=bb.a;
7. hive四中数据导入方式
1)从本地文件系统中导入数据到Hive表
create table wyp(id int,name string) ROW FORMAT delimited fields terminated by '\t' STORED AS TEXTFILE;
load data local inpath 'wyp.txt' into table wyp;
2)从HDFS上导入数据到Hive表
[wyp@master /home/q/hadoop-2.2.0]$ bin/hadoop fs -cat /home/wyp/add.txt
hive> load data inpath '/home/wyp/add.txt' into table wyp;
3)从别的表中查询出相应的数据并导入到Hive表中
hive> create table test(
> id int, name string
> ,tel string)
> partitioned by
> (age int)
> ROW FORMAT DELIMITED
> FIELDS TERMINATED BY '\t'
> STORED AS TEXTFILE;
注:test表里面用age作为了分区字段,分区:在Hive中,表的每一个分区对应表下的相应目录,所有分区的数据都是存储在对应的目录中。
比如wyp表有dt和city两个分区,则对应dt=20131218city=BJ对应表的目录为/user/hive/warehouse/dt=20131218/city=BJ,
所有属于这个分区的数据都存放在这个目录中。
hive> insert into table test
> partition (age='25')
> select id, name, tel
> from wyp;
也可以在select语句里面通过使用分区值来动态指明分区:
hive> set hive.exec.dynamic.partition.mode=nonstrict;
hive> insert into table test
> partition (age)
> select id, name,
> tel, age
> from wyp;
Hive也支持insert overwrite方式来插入数据
hive> insert overwrite table test
> PARTITION (age)
> select id, name, tel, age
> from wyp;
Hive还支持多表插入
hive> from wyp
> insert into table test
> partition(age)
> select id, name, tel, age
> insert into table test3
> select id, name
> where age>25;
4)在创建表的时候通过从别的表中查询出相应的记录并插入到所创建的表中
hive> create table test4
> as
> select id, name, tel
> from wyp;
8.查看建表语句
hive> show create table test3;
9.表重命名
hive> ALTER TABLE events RENAME TO 3koobecaf;
10.表增加列
hive> ALTER TABLE pokes ADD COLUMNS (new_col INT);
11.添加一列并增加列字段注释
hive> ALTER TABLE invites ADD COLUMNS (new_col2 INT COMMENT 'a comment');
12.删除表
hive> DROP TABLE pokes;
13.top n
hive> select * from test order by key limit 10;
14.创建数据库
Create Database baseball;
14.alter table tablename change oldColumn newColumn column_type 修改列的名称和类型
alter table yangsy CHANGE product_no phone_no string
15.导入.sql文件中的sql
insert into table CI_CUSER_20141117154351522 select mainResult.PRODUCT_NO,dw_coclbl_m02_3848.L1_01_02_01,dw_coclbl_d01_3845.L2_01_01_04 from (select PRODUCT_NO from CI_CUSER_20141114203632267) mainResult left join DW_COCLBL_M02_201407 dw_coclbl_m02_3848 on mainResult.PRODUCT_NO = dw_coclbl_m02_3848.PRODUCT_NO left join DW_COCLBL_D01_20140515 dw_coclbl_d01_3845 on dw_coclbl_m02_3848.PRODUCT_NO = dw_coclbl_d01_3845.PRODUCT_NO
insert into CI_CUSER_20141117142123638 ( PRODUCT_NO,ATTR_COL_0000,ATTR_COL_0001) select mainResult.PRODUCT_NO,dw_coclbl_m02_3848.L1_01_02_01,dw_coclbl_m02_3848.L1_01_03_01 from (select PRODUCT_NO from CI_CUSER_20141114203632267) mainResult left join DW_COCLBL_M02_201407 dw_coclbl_m02_3848 on mainResult.PRODUCT_NO = dw_coclbl_m02_3848.PRODUCT_NO
CREATE TABLE ci_cuser_yymmddhhmisstttttt_tmp(product_no string) row format serde 'com.bizo.hive.serde.csv.CSVSerde' ;
LOAD DATA LOCAL INPATH '/home/ocdc/coc/yuli/test123.csv' OVERWRITE INTO TABLE test_yuli2;
创建支持CSV格式的testfile文件
CREATE TABLE test_yuli7 row format serde 'com.bizo.hive.serde.csv.CSVSerde' as select * from CI_CUSER_20150310162729786;
不依赖CSVSerde的jar包创建逗号分隔的表
"create table " +listName+ " ROW FORMAT DELIMITED FIELDS TERMINATED BY ','" +
" as select * from " + listName1;
create table aaaa ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LINES TERMINATED BY '\n' STORED AS TEXTFILE as select * from
ThriftServer 开启FAIR模式
SparkSQL Thrift Server 开启FAIR调度方式:
1. 修改$SPARK_HOME/conf/spark-defaults.conf,新增
2. spark.scheduler.mode FAIR
3. spark.scheduler.allocation.file /Users/tianyi/github/community/apache-spark/conf/fair-scheduler.xml
4. 修改$SPARK_HOME/conf/fair-scheduler.xml(或新增该文件), 编辑如下格式内容
5. <?xml version="1.0"?>
6. <allocations>
7. <pool name="production">
8. <schedulingMode>FAIR</schedulingMode>
9. <!-- weight表示两个队列在minShare相同的情况下,可以使用资源的比例 -->
10. <weight>1</weight>
11. <!-- minShare表示优先保证的资源数 -->
12. <minShare>2</minShare>
13. </pool>
14. <pool name="test">
15. <schedulingMode>FIFO</schedulingMode>
16. <weight>2</weight>
17. <minShare>3</minShare>
18. </pool>
19. </allocations>
20. 重启Thrift Server
21. 执行SQL前,执行
22. set spark.sql.thriftserver.scheduler.pool=指定的队列名
等操作完了 create table yangsy555 like CI_CUSER_YYMMDDHHMISSTTTTTT 然后insert into yangsy555 select * from yangsy555
创建一个自增序列表,使用row_number() over()为表增加序列号 以供分页查询
二. API
Spark SQL的API方案:3种
SQL
the DataFrames API
the Datasets API.
但会使用同一个执行引擎
the same execution engine is used
(一)数据转为Dataframe
1、(半)格式化数据(HDFS文件)
SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc)
Load默认是parquet格式,通过format指定格式
DataFrame df = sqlContext.read().load("examples/src/main/resources/users.parquet");
DataFrame df = sqlContext.read().format("json").load("main/resources/people.json");
旧API 已经被废弃
DataFrame df2 =sqlContext.jsonFile("/xxx.json");
DataFrame df2 =sqlContext.parquetFile("/xxx.parquet");
2、RDD数据
SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc)
a. 通过类 利用Java类的反射机制
已有:JavaRDD<Person> people
DataFrame df= sqlContext.createDataFrame(people, Person.class);
b. 通过schema转换RDD
已有:StructType schema = DataTypes.createStructType(fields);
和JavaRDD<Row> rowRDD
- DataFrame df= sqlContext.createDataFrame(rowRDD, schema);
3、 Hive数据(HDFS文件在数据库中的表(schema) 对应关系)
HiveContext sqlContext = new org.apache.spark.sql.hive.HiveContext(sc); DataFrame df = sqlContext.sql("select count(*) from wangke.wangke where ns_date=20161224"); sqlContext.refreshTable("my_table") //(if configured,sparkSQL caches metadata) sqlContext.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)"); sqlContext.sql("LOAD DATA LOCAL INPATH 'resources/kv1.txt' INTO TABLE src"); Row[] results = sqlContext.sql("FROM src SELECT key, value").collect();
4、特殊用法
DataFrame df = sqlContext.sql("SELECT * FROM parquet.`main/resources/users.parquet`"); //查询临时表people DataFrame teenagers = sqlContext.sql("SELECT name FROMpeople WHERE age >= 13 AND age <= 19")
1、展示
df.show();
df.printSchema();
2、过滤选择
df.select("name").show(); df.select(df.col("name"), df.col("age").plus(1)).show(); df.filter(df.col("age").gt(21)).show(); df.groupBy("age").count().show();
3、写文件
df.select("name", "favorite_color").write().save("namesAndFavColors.parquet"); df.select("name", "age").write().format("parquet").save("namesAndAges.parquet"); df.write().parquet("people.parquet");
4、注册临时表
df.registerTempTable("people");
之后就可以用SQL在上面去查了
DataFrame teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
5、保存Hive表
When working with a HiveContext, DataFrames can also be saved as persistent tables using the saveAsTable command
只有HiveContext生成的Dataframe才能调用saveAsTable去持久化hive表
(三)、直接SQL操作sqlContext.sql("create table xx.tmp like xx.xx"); sqlContext.sql("insert into table xx.tmp partition(day=20160816) select * from xx.xx where day=20160816"); sqlContext.sql("insert overwrite table xx.xx partition(day=20160816) select * from xx.tmp where day=20160816");