Hive

Hive Catalog

Create Catalog

Sloth平台兼容Flink社区Create Catalog语法

• 连接hive 数据源语法如下：（读取和写入使用同样的配置连接即可）
  需要注意的是在任务中使用的集群配置必须先上传至平台，再在任务依赖处引用。具体步骤可参考文件管理

目前不支持多套kerberos认证

开启kerberos 认证的连接配置：
create catalog myhive WITH (
    'type' = 'hive',        --requird 
    'default-database'='sloth', --required, hive database
    'hive-version'='2.1.1', --required hive version
    'hive-site'='mammut-hive-site.xml', --required, hive-site.xml file name
    'hdfs-site'='mammut-hdfs-site.xml', --required, hdfs-site.xml file name
    'core-site'='mammut-core-site.xml', --required, core-site.xml file name
    'warehouse'='hdfs://bdms-test/user/sloth/hive_db', --required, hive warehouse
    'krb.keytab'='sloth.keytab', -- optional, if auto.method=kerberos it is required.
    'krb.conf'='krb5.conf',  -- optional, if auto.method=kerberos it is required.
    'krb.principal'='sloth/dev@BDMS.163.COM',  -- optional, if auto.method=kerberos it is required.
    'auth.method'='kerberos',  -- optional, kerberos or simple or using_env_kerberos
);

未开启kerberos 认证的连接配置：
create catalog myhive WITH (
    'type' = 'hive',        --requird 
    'default-database'='sloth', --required, hive database
    'hive-version'='2.1.1', --required hive version
    'hive-site'='mammut-hive-site.xml', --required, hive-site.xml file name
    'hdfs-site'='mammut-hdfs-site.xml', --required, hdfs-site.xml file name
    'core-site'='mammut-core-site.xml', --required, core-site.xml file name
    'warehouse'='hdfs://bdms-test/user/sloth/hive_db', --required, hive warehouse
    'auth.method'='simple',  -- optional, kerberos or simple or using_env_kerberos
    'simple.user.name' = 'sloth', --用户名，非必填
);

同时也可以使用metahub 的方式在数据源处引入，使用catalog.库名.表名的方式在任务中使用即可，同时所依赖的配置文件需要通过set 语法设置

使用Hive数据源

官方文档

Hive Read

• Hive Connector支持流/批的方式读取hive表。
• 流任务可以定时监控新数据，并emit到下游。
• 流任务支持读取分区表和非分区表，对于分区表，监控分区生成；对于非分区表，监控新文件生成，然后读取文件。

• hive 读默认会自动推导source并行度，如不需要，可将table.exec.hive.infer-source-parallelism设为false。如果因为推导出的并行度过大，可通过配置table.exec.hive.infer-source-parallelism.max，限制最大并行度。

  维表

可以在Flink Sql中Join Hive Dim表。

• 对于dim表是分区表来说，flink可以像读取无解数据一样读取，每个分区时生效触发Hive Source自动读取dim表数据并加载在flink内存中。（仅在流任务有效）
• 对于dim表是非分区表的话，会一次性加载所有dim表数据到flink内存
• 对于任务是批任务的场景，会一次性加载所有分区数据到flink内存

Properties:

Source并行度:

DIM Cache TTL:

当配置streaming-source.partition.include=all加载整个hive表数据加载到内存进行Dim Join时，可以配置lookup.join.cache.ttl=60 min, 每六十分钟对cache过期，并且过期后会重新load最新的hive数据到内存。

查询优化:

Dim Demo:

kafka流表与hive维表关联，设置 streaming-source.enable=true 和 streaming-source.partition.include=latest 以支持维表关联最新分区的数据。

CREATE TABLE orders_table (
   order_id STRING,
   order_amount DOUBLE,
   product_id STRING,
   log_ts TIMESTAMP(3),
   proctime as PROCTIME()
 ) WITH (...);

 -- streaming sql, kafka temporal join a hive dimension table. Flink will automatically reload data from the
 -- configured latest partition in the interval of 'streaming-source.monitor-interval'.

 SELECT * FROM orders_table AS o
 JOIN hive_catalog.hive_db.dimension_table FOR SYSTEM_TIME AS OF o.proctime AS dim
 ON o.product_id = dim.product_id;

Hive Write

注意：hive sink 使用前面的catalog 方式连接hive即可，hive表可以提前在hive 中创建，也可以在flink sql 任务中创建，字段可以直接使用。下面案列提前在hive中创建表 写hive 时支持分区表和非分区表，分区字段取值在sql 中指定即可。 默认任务创建生成的分区，在hive metastore 中不会自行创建，需要手动添加分区后可查询。 也可以通过sql hints 在任务中指定自动申报，指定后，分区会自行构建。

创建表demo:
create table sloth.ods_water_sensor(
    sensor_id String,
    ts timestamp,
    water_level double
)partitioned by (ds string)
stored as parquet;
-- hive 数据库中执行，必须提前创建。

未指定分区自行构建参数，需要手动添加分区，sql 如下(在查询数据时hive库中执行)：
alter table sloth.ods_water_sensor add if not exists partition(ds='2021-11-16');

flink sql开发demo:
--SQL
-- 注册kafkasource 
create table water_sensor(
    sensor_id VARCHAR,
    ts timestamp,
    water_level double
)with(
        'connector' = 'kafka',
        'topic' = 'demo_1',
        'scan.startup.mode' = 'latest-offset',
        'properties.bootstrap.servers' = 'xxx',
        'properties.zookeeper.connect' = 'xxx',
        'format' = 'json',
        'properties.group.id' = 'test'
        );

-- 注册hivecatalog
create catalog hive_catalog WITH (
    'type' = 'hive',       
    'default-database'='sloth', 
    'hive-version'='2.1.1', 
    'hive-site'='hive-site.xml', 
    'hdfs-site'='hdfs-site.xml', 
    'core-site'='core-site.xml', 
    'warehouse'='hdfs://bdms-test/user/sloth/hive_db', 
    'krb.keytab'='sloth.keytab', 
    'krb.conf'='krb5.conf',
    'krb.principal'='sloth/dev@BDMS.163.COM',
    'auth.method'='kerberos',
    'sys.db.url'='',
    'sys.db.user'='',
    'sys.db.password'=''
);

insert into hive_catalog.sloth.ods_water_sensor   /*+ OPTIONS('sink.partition-commit.policy.kind'='metastore,success-file') */ -- sql Hints 指定刷写数据时自动创建分区
    select 
        sensor_id,
        ts,
        water_level,
        date_format(ts, 'yyyy-MM-dd') as ds
    from water_sensor;

使用DDL 方式开发时，sys 开头参数为解析必备的系统参数，无实际含义，但构建是需要保留 在sink 过程中，可以控制表写入及刷新的一些参数。可以利用sql hints 语法进行设置 具体使用可参考sql Hints
作业查询结果如下：

Hive Formats

hive表支持以下几种数据格式：

• Text
• CSV
• SequenceFile
• ORC
• Parquet

Hive Function

可以在Flink Sql中使用Hive自定义函数，需要将Hive Udf类/Jar添加到任务依赖中， 不然flink任务会报class not find异常。

支持hive:

• UDF
• GenericUDF
• GenericUDTF
• GenericUDAF

Hive UDX:

TestHiveSimpleUDF

package com.netease.sloth.hive.exmaples.udf;

import org.apache.hadoop.hive.ql.exec.UDF;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;

/**
 * Test simple udf. Registered under name 'myudf'
 */
public class TestHiveSimpleUDF extends UDF {
   public IntWritable evaluate(IntWritable i) {
      return new IntWritable(i.get());
   }

   public Text evaluate(Text text) {
      return new Text(text.toString());
   }
}

TestHiveGenericUDF

package com.netease.sloth.hive.exmaples.udf;

import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.udf.generic.GenericUDF;
import org.apache.hadoop.hive.serde2.objectinspector.ConstantObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.IntObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.StringObjectInspector;
import org.apache.hadoop.io.IntWritable;

import static com.google.common.base.Preconditions.checkArgument;


/**
 * Test generic udf. Registered under name 'mygenericudf'
 */
public class TestHiveGenericUDF extends GenericUDF {

   @Override
   public ObjectInspector initialize(ObjectInspector[] arguments) throws UDFArgumentException {
      checkArgument(arguments.length == 2);

      checkArgument(arguments[1] instanceof ConstantObjectInspector);
      Object constant = ((ConstantObjectInspector) arguments[1]).getWritableConstantValue();
      checkArgument(constant instanceof IntWritable);
      checkArgument(((IntWritable) constant).get() == 1);

      if (arguments[0] instanceof IntObjectInspector ||
            arguments[0] instanceof StringObjectInspector) {
         return arguments[0];
      } else {
         throw new RuntimeException("Not support argument: " + arguments[0]);
      }
   }

   @Override
   public Object evaluate(DeferredObject[] arguments) throws HiveException {
      return arguments[0].get();
   }

   @Override
   public String getDisplayString(String[] children) {
      return "TestHiveGenericUDF";
   }
}

TestHiveUDTF

package com.netease.sloth.hive.exmaples.udf;

import org.apache.hadoop.hive.ql.exec.UDFArgumentException;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.udf.generic.GenericUDTF;
import org.apache.hadoop.hive.serde2.objectinspector.ConstantObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory;
import org.apache.hadoop.io.IntWritable;

import java.util.Collections;

import static com.google.common.base.Preconditions.checkArgument;

/**
 * Test split udtf. Registered under name 'mygenericudtf'
 */
public class TestHiveUDTF extends GenericUDTF {

   @Override
   public StructObjectInspector initialize(ObjectInspector[] argOIs) throws UDFArgumentException {
      checkArgument(argOIs.length == 2);

      // TEST for constant arguments
      checkArgument(argOIs[1] instanceof ConstantObjectInspector);
      Object constant = ((ConstantObjectInspector) argOIs[1]).getWritableConstantValue();
      checkArgument(constant instanceof IntWritable);
      checkArgument(((IntWritable) constant).get() == 1);

      return ObjectInspectorFactory.getStandardStructObjectInspector(
            Collections.singletonList("col1"),
            Collections.singletonList(PrimitiveObjectInspectorFactory.javaStringObjectInspector));
   }

   @Override
   public void process(Object[] args) throws HiveException {
      String str = (String) args[0];
      for (String s : str.split(",")) {
         forward(s);
         forward(s);
      }
   }

   @Override
   public void close() {
   }
}

Hive Shell

hive> add jar /home/sloth/tmp_yexx/original-hive-udf-1.0.jar;
Added [/home/sloth/tmp_yexx/original-hive-udf-1.0.jar] to class path
Added resources: [/home/sloth/tmp_yexx/original-hive-udf-1.0.jar]
hive> create function myudf as 'com.netease.sloth.hive.exmaples.udf.TestHiveSimpleUDF';
OK
Time taken: 1.59 seconds
hive> create function mygenericudf as 'com.netease.sloth.hive.exmaples.udf.TestHiveGenericUDF';
OK
Time taken: 0.014 seconds
hive> create function mygenericudtf as 'com.netease.sloth.hive.exmaples.udf.TestHiveUDTF';
OK

Flink Sql

select 
    mygenericudf(myudf(name), 1) as a,
    mygenericudf(myudf(id), 1) as b,
    s
from 
    source,
    lateral table(mygenericudtf(name, 1)) as T(s)

1.14

hive 数据源

Hive Read

Properties:

示例

create catalog music with (
'type' = 'hive',
'hive-version'='2.1.1',
'hive-site'='hive-site.xml',
'hdfs-site'='hdfs-site.xml',
'core-site'='core-site.xml',
'auth.method'='kerberos',
'krb.keytab'='da_music.keytab',
'krb.conf'='krb5.conf',
'krb.principal'='da_music/dev@HADOOP.HZ.NETEASE.COM',
'default-database'='music_db_test',
'streaming-source.partition.include' = 'specific',
'streaming-source.partition-order' = 'partition-time',
--     分区时间戳的抽取格式。默认的 extractor 需要写成 yyyy-MM-dd HH:mm:ss 的形式，并用 Hive 表中相应的分区字段做占位符替换。可自定义抽取器
'partition.time-extractor.timestamp-pattern' = '$dt 00:00:00',
--   'partition.latest.size' = '2'
'partition.specific.from' = '1',
'partition.specific.end' = '1'
);