hive操作总结DML

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本文链接： https://blog.csdn.net/weixin_42411818/article/details/100150793

Loading files into tables(将数据加载到表中)

CREATE TABLE employee LIKE sensor.employee;
LOAD DATA LOCAL INPATH '/home/hadoop-jrq/hive-course/employee.txt' OVERWRITE INTO TABLE employee;
LOAD DATA LOCAL INPATH '/home/hadoop-jrq/hive-course/employee.txt' INTO TABLE employee;   ----不加OVERWRITE就是追加数据
CREATE TABLE sensor_format LIKE sensor.sensor_format;
--1、path可以是一个文件目录
--2、原始的文件将move到表所在的LOCATION中
LOAD DATA INPATH 'hdfs://master:8020/user/hadoop-jrq/hive-course/omneo' OVERWRITE INTO TABLE sensor_format; 
LOAD DATA INPATH 'hdfs://master:8020/user/hadoop-jrq/hive-course/omneo' INTO TABLE sensor_format;

CREATE TABLE sensor_managed_partition LIKE sensor.sensor_managed_partition;
LOAD DATA INPATH 'hdfs://master:8020/user/hadoop-jrq/hive-course/omneo' OVERWRITE INTO TABLE sensor_managed_partition PARTITION(year=2019, month=201908, day=20190830);
LOAD DATA INPATH 'hdfs://master:8020/user/hadoop-jrq/hive-course/omneo' INTO TABLE sensor_managed_partition PARTITION(year=2019, month=201908, day=20190830);

LOAD DATA [LOCAL] INPATH 'filepath' [OVERWRITE] INTO TABLE tablename [PARTITION (partcol1=val1, partcol2=val2, ...)]

INSERT data into Hive tables from queries

从查询中插入数据到Hive表
LOAD files into table的方法会删除了原始文件，如果不想删除原始文件的话，则可以通过INSERT data的方式导数据
– ETL会经常用到的一种方式,创建临时表来导数据

--创建一张外部表，但是表的存储位置是在hive默认的位置，存储格式是PARQUET
CREATE EXTERNAL TABLE IF NOT EXISTS dml.sensor(
  id STRING, 
  event_id STRING, 
  event_type STRING, 
  part_name STRING, 
  part_number STRING, 
  version STRING, 
  payload STRING) 
STORED AS PARQUET;

--创建一张一样的临时的外部表
CREATE EXTERNAL TABLE IF NOT EXISTS sensor_temp (
  id STRING, 
  event_id STRING, 
  event_type STRING, 
  part_name STRING, 
  part_number STRING, 
  version STRING, 
  payload STRING) 
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' 
STORED AS TEXTFILE
LOCATION 'hdfs://master:8020/user/hadoop-jrq/hive-course/omneo-temp/';

--将sensor_tmp中的数据导入到sensor中,如果sensor中有数据的话则删除，使用sensor_tmp数据覆盖掉原来的数据
-- 一种目的,原始文件不会被删除,另一种目的是文件类型的转换
INSERT OVERWRITE TABLE sensor SELECT * FROM sensor_temp;
--将sensor_tmp中的数据追加到sensor表中
INSERT INTO TABLE sensor SELECT * FROM sensor_temp;

--语法：
INSERT OVERWRITE|INTO TABLE tablename [PARTITION (partcol1=val1, partcol2=val2, ...) [IF NOT EXISTS]] SELECT select_statement FROM from_statement

--创建一个分区表
CREATE EXTERNAL TABLE IF NOT EXISTS sensor_partition(
  id STRING, 
  event_id STRING, 
  event_type STRING, 
  part_name STRING, 
  part_number STRING, 
  version STRING, 
  payload STRING) 
PARTITIONED BY(year INT, month INT, day INT) 
STORED AS PARQUET;

--将sensor_tmp中的数据导入到sensor_partition中的数据导入到sensor分区year=2019, month=201908, day=20190830中
INSERT OVERWRITE TABLE sensor_partition PARTITION (year=2019, month=201908, day=20190830) SELECT * FROM sensor_temp; -- 导数据到指定的分区
--将sensor_tmp中的数据追加到sensor_partition表的某个分区中
INSERT INTO TABLE sensor_partition PARTITION (year=2019, month=201908, day=20190830) SELECT * FROM sensor_temp;

– 另一个例子:创建一张表

CREATE EXTERNAL TABLE IF NOT EXISTS sensor_group_20190830(
  id STRING, 
  event_type STRING, 
  part_name STRING, 
  cnt INT) 
STORED AS AVRO;

-- 从一个表的分区中导数据进去
INSERT OVERWRITE TABLE sensor_group_20190830 
SELECT 
id, event_type, part_name, count(*) AS cnt 
FROM sensor_partition WHERE day = 20190830
GROUP BY id, event_type, part_name;  -- 按照这三个字段进行分区


--相当于上面的CREATE + INSERT, 但是只能创建内部表
CREATE TABLE IF NOT EXISTS sensor_group_20190830_a 
STORED AS AVRO
AS SELECT 
id, event_type, part_name, count(*) AS cnt 
FROM sensor_partition WHERE day = 20190830
GROUP BY id, event_type, part_name;


-- 使用多个 INSERT 字句
CREATE EXTERNAL TABLE IF NOT EXISTS sensor_alert LIKE sensor_partition;

FROM sensor_temp
INSERT OVERWRITE TABLE sensor_partition PARTITION (year=2019, month=201908, day=20190830) SELECT id, event_id, event_type, part_name, part_number, version, payload
INSERT OVERWRITE TABLE sensor_alert PARTITION (year=2019, month=201908, day=20190830) SELECT id, event_id, event_type, part_name, part_number, version, payload WHERE event_type='ALERT';


FROM from_statement 
INSERT OVERWRITE TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2, ...) [IF NOT EXISTS]] SELECT select_statement1
[INSERT OVERWRITE TABLE tablename2 [PARTITION (partcol1=val1, partcol2=val2, ...) [IF NOT EXISTS]] SELECT select_statement2]
[INSERT INTO TABLE tablename2 [PARTITION (partcol1=val1, partcol2=val2, ...)] SELECT select_statement2]
...;

动态分区与静态分区

备注:
如果只有动态分区,没有静态分区,在执行的时候会报错,这时候设置下面两个参数就行了
set hive.mapred.supports.subdirectories=true;
set mapred.input.dir.recursive=true;
–静态分区

INSERT OVERWRITE TABLE sensor_partition PARTITION (year=2019, month=201908, day=20190809) SELECT * FROM sensor_temp;

CREATE EXTERNAL TABLE IF NOT EXISTS sensor_event_type_partition (
  id STRING, 
  event_id STRING, 
  part_name STRING, 
  part_number STRING, 
  version STRING, 
  payload STRING) 
PARTITIONED BY(year INT, month INT, event_type STRING) 
STORED AS PARQUET;

动态的查找分区数据 year静态的,后面两个是动态的

 INSERT OVERWRITE TABLE sensor_event_type_partition PARTITION (year=2019, month, event_type)  SELECT id, event_id, part_name, part_number, version, payload, month, event_type FROM sensor_partition;  -- month, event_type必须加上,而且是在最后两列

我的理解就是,例如year=2019就是静态的,不变的,而如果变成year,那么就是动态分区了

Writing data into the filesystem from queries

从查询中将数据写入文件系统

数据量大的话则写到HDFS的文件中
    INSERT OVERWRITE DIRECTORY '/user/hadoop-jrq/hive-course/employee'
    ROW FORMAT DELIMITED 
          FIELDS TERMINATED BY '\t' 
          COLLECTION ITEMS TERMINATED BY ',' 
          MAP KEYS TERMINATED BY ':'
    STORED AS TEXTFILE
    SELECT * FROM dml.employee;
    
--数据量比较小的话则写到本地文件中
INSERT OVERWRITE LOCAL DIRECTORY '/home/hadoop-jrq/hive-course/employee'
ROW FORMAT DELIMITED 
      FIELDS TERMINATED BY '\t' 
      COLLECTION ITEMS TERMINATED BY ',' 
      MAP KEYS TERMINATED BY ':'
STORED AS TEXTFILE
SELECT * FROM dml.employee;

-- 将hive中的数据导入到hdfs的某个文件中去
INSERT OVERWRITE DIRECTORY '/user/hadoop-jrq/hive-course/employee-parquet'
STORED AS PARQUET
SELECT * FROM dml.employee;

Inserting values into tables from SQL

从SQL中将值插入表中

-- 往一个分区中写一条记录  value对应表的每一个字段
INSERT INTO TABLE sensor_partition PARTITION (year=2019, month=201908, day=20190830) VALUES("testid", "testEventId", "event_type", "part_name", "part_number", "version", "payload");

提高hive性能的两个点

bucket

引入:

--下面的查询虽然在日期的过滤上查询非常快，但是一个分区的数据量比较到，那么过滤id的话就会比较慢了
SELECT * FROM dml.sensor_partition WHERE day = 20190830 AND id = 'f277';

-- 我们可以通过bucket来提升上面的SQL的性能
CREATE TABLE dml.sensor_partition_bucket(
  id STRING, 
  event_id STRING, 
  event_type STRING, 
  part_name STRING, 
  part_number STRING, 
  version STRING, 
  payload STRING)
PARTITIONED BY(year INT, month INT, day INT) 
CLUSTERED BY(id) INTO 12 BUCKETS  -- 对ID分12个桶,按照ID的hash值进行分区的
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' 
STORED AS TEXTFILE;

SET hive.enforce.bucketing = true; --需要先设置,否则查询不到数据,Hive 2.x 后就不需要了
SELECT * FROM dml.sensor_partition_bucket WHERE day = 20190830 AND id = 'f277';

-- 导数据
INSERT OVERWRITE TABLE dml.sensor_partition_bucket PARTITION (year=2019, month=201908, day=20190830) SELECT * FROM dml.sensor_temp;

--因为 "f277".hashCode() % 12 所以下面的语句只要去/user/hive/warehouse/dml.db/sensor_partition_bucket/year=2019/month=201908/day=20190830/000010_0这个文件中扫描就行
SELECT * FROM dml.sensor_partition_bucket WHERE day = 20190830 AND id = 'f277';

--加上SORTED BY(event_time)就是为了使得在一个bucket中的数据是按照event_time升序排列
CREATE TABLE sensor_partition_bucket(
  id STRING, 
  event_id STRING, 
  event_type STRING, 
  part_name STRING, 
  part_number STRING, 
  version STRING, 
  payload STRING,
  event_time STRING) 
PARTITIONED BY(year INT, month INT, day INT) 
CLUSTERED BY(id) SORTED BY(event_time) INTO 12 BUCKETS
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' 
STORED AS TEXTFILE;

bucket table的两个作用
1.数据sampling(取样)
–数据的取样
–tablesample是抽样语句，语法：TABLESAMPLE(BUCKET x OUT OF y ON bucket column)

--y必须是table总bucket数的倍数或者因子。hive根据y的大小，决定抽样的比例。
--例如，table总共分了12份，当y=6时，抽取 (12/6=)2个bucket的数据，当y=24时，抽取(12/24=)1/2个bucket的数据。
--x表示从哪个bucket开始抽取。例 如，table总bucket数为12，tablesample(bucket 4 out of 6)，表示总共抽取（12/6=）2个bucket的数据，
--分别为第4个bucket和第（4+6=）10个bucket的数据。

SELECT * FROM sensor_partition_bucket TABLESAMPLE(BUCKET 4 OUT OF 6 ON id);

2.提升某些查询操作效率，例如mapside join

Skewed Table(倾斜的表,为了解决数据倾斜问题)

有这么一种场景：一张表中的一列或者某几列的某些值占比特别的大

CREATE TABLE dml.skewed_single (key STRING, value STRING)
SKEWED BY (key) ON ("1", "5", "6")  -- 1,5,6 就是占比特别大的key
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' 
STORED AS TEXTFILE;

-- 创建外部表用以导数据
CREATE EXTERNAL TABLE skewed_test_tmp (
  key STRING,
  value STRING
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' 
STORED AS TEXTFILE
LOCATION '/user/hadoop-jrq/hive-course/skewed';

--Skewed Table：只是含有倾斜的信息的表
INSERT OVERWRITE TABLE dml.skewed_single SELECT * FROM dml.skewed_test_tmp;

使得性能更加明显的
List Bucketing Table是Skewed Table，此外，它告诉hive使用列表桶的特点：为倾斜值创建子目录。

CREATE TABLE list_bucket (key STRING, value STRING)
SKEWED BY (key) ON ("1", "5", "6") STORED AS DIRECTORIES  -- 为每一个倾斜值指定目录
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' 
STORED AS TEXTFILE;
-- 导数据,效果就是数据会分开存储
INSERT OVERWRITE TABLE dml.list_bucket SELECT * FROM dml.skewed_test_tmp;

--需要设置下面的属性才可以查询,否则查询不出来数据,因为他和分区不一样
set hive.mapred.supports.subdirectories=true;
set mapred.input.dir.recursive=true;

SELECT * FROM dml.list_bucket WHERE key=1; --只会扫描/user/hive/warehouse/dml.db/list_bucket/key=1这个文件目录

--语法：
CREATE TABLE table_name (key STRING, value STRING)
  SKEWED BY (key) ON (1,5,6) [STORED AS DIRECTORIES];

总结，创建表的语句

CREATE [TEMPORARY] [EXTERNAL] TABLE [IF NOT EXISTS] [db_name.]table_name    -- (Note: TEMPORARY available in Hive 0.14.0 and later)
  [(col_name data_type [COMMENT col_comment], ... [constraint_specification(约束的意思)])]
  [COMMENT table_comment]
  [PARTITIONED BY (col_name data_type [COMMENT col_comment], ...)]
  [CLUSTERED BY (col_name, col_name, ...) [SORTED BY (col_name [ASC|DESC], ...)] INTO num_buckets BUCKETS]
  [SKEWED BY (col_name, col_name, ...)                  -- (Note: Available in Hive 0.10.0 and later)]
     ON ((col_value, col_value, ...), (col_value, col_value, ...), ...)
     [STORED AS DIRECTORIES]
  [
   [ROW FORMAT row_format] 
   [STORED AS file_format]
     | STORED BY 'storage.handler.class.name' [WITH SERDEPROPERTIES (...)]  -- (Note: Available in Hive 0.6.0 and later)
  ]
  [LOCATION hdfs_path]
  [TBLPROPERTIES (property_name=property_value, ...)]   -- (Note: Available in Hive 0.6.0 and later)
  [AS select_statement];   -- (Note: Available in Hive 0.5.0 and later; not supported for external tables)
 
 -- 另一个表和一个表的数据结构相似,那么久可以用like的方式进行创建
CREATE [TEMPORARY] [EXTERNAL] TABLE [IF NOT EXISTS] [db_name.]table_name
  LIKE existing_table_or_view_name
  [LOCATION hdfs_path];

Hive 和 HBase交互

1.启动HBase
2.hive和hbase的交互方式
hive启动的时候需要指定一些参数和一些依赖的jar包

    hive --auxpath $HIVE_HOME/lib/zookeeper-3.4.6.jar,$HIVE_HOME/lib/hive-hbase-handler-2.3.3.jar,$HIVE_HOME/lib/hbase-server-1.1.1.jar --hiveconf hbase.zookeeper.quorum=master,slave1,slave2

-- 创建一张表
CREATE TABLE pokes (foo INT, bar STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' 
STORED AS TEXTFILE;

LOAD DATA LOCAL INPATH '/home/hadoop-jrq/hive-course/kv3.txt' OVERWRITE INTO TABLE pokes;

CREATE TABLE hbase_table_1(key int, value string) 
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'  -- 存储的文件格式是hbase的文件格式
WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key,cf1:val")  -- 字段的映射关系 第一个字段映射成rowkey,第二个字段映射成cf1:val
TBLPROPERTIES ("hbase.table.name" = "xyz", "hbase.mapred.output.outputtable" = "xyz"); -- hbase.table.name创建的表名

-- pokes数据插入到hbase_table_1
INSERT OVERWRITE TABLE hbase_table_1 SELECT * FROM pokes;

SELECT * FROM hbase_table_1;

SCAN 'xyz' 

--Column Mapping
--多个column和多个column family的映射
CREATE TABLE hbase_table_2(key int, value1 string, value2 int, value3 int) 
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES (
"hbase.columns.mapping" = ":key,a:b,a:c,d:e"  -- 一一映射
);
-- 插入一条数据
INSERT OVERWRITE TABLE hbase_table_2 SELECT foo, bar, foo+1, foo+2 FROM pokes;

--Hive中的Map对应的是HBase中的Column Family
CREATE TABLE hbase_table_map(value map<string,int>, row_key int) 
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES (
"hbase.columns.mapping" = "cf:,:key"
);
INSERT OVERWRITE TABLE hbase_table_map SELECT map(bar, foo), foo FROM pokes;

–从已经存在的HBASE表sensor中创建一张hive表

-- avro有这个bug,3.0版本的时候才会结束,不是avro的话就不会报错
CREATE EXTERNAL TABLE sensor_hbase_avro
ROW FORMAT SERDE 'org.apache.hadoop.hive.hbase.HBaseSerDe'
STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
WITH SERDEPROPERTIES (
    "hbase.columns.mapping" = ":key,v:event_id",
    "v.event_id.serialization.type" = "avro",
    "v.event_id.avro.schema.url" = "hdfs://master:8020/user/hadoop-jrq/hive-course/sensor.avsc")
TBLPROPERTIES (
    "hbase.table.name" = "sensor",
    "hbase.mapred.output.outputtable" = "sensor",
    "hbase.struct.autogenerate"="true");

--上面的语句会报错，是hive的一个bug
https://issues.apache.org/jira/browse/HIVE-17829?src=confmacro