shc测试环境的搭建参考：
spark读写HBase之使用hortonworks的开源框架shc（一）：源码编译以及测试工程创建
读写HBase需要两个核心的元素：
- 用户描述数据结构的schema字符串
- 与schema字符串相对应的实体类
1. 定义schema字符串
object Catalog {
val schema = s"""{
| "table":{"namespace":"default", "name":"test1", "tableCoder":"PrimitiveType"},
| "rowkey":"key",
| "columns":{
| "col0":{"cf":"rowkey", "col":"key", "type":"string"},
| "col1":{"cf":"cf1", "col":"col1", "type":"boolean"},
| "col2":{"cf":"cf2", "col":"col2", "type":"double"},
| "col3":{"cf":"cf3", "col":"col3", "type":"float"},
| "col4":{"cf":"cf4", "col":"col4", "type":"int"},
| "col5":{"cf":"cf5", "col":"col5", "type":"bigint"},
| "col6":{"cf":"cf6", "col":"col6", "type":"smallint"},
| "col7":{"cf":"cf7", "col":"col7", "type":"tinyint"},
| "col8":{"cf":"cf8", "col":"col8", "type":"byte"}
| }
|}""".stripMargin
}
schema字符串说明：
2. 定义与schema字符串对应的实体类
case class HBaseRecord(
col0: String, // sql: string
col1: Boolean, // sql: boolean
col2: Double, // sql: double
col3: Float, // sql: float
col4: Int, // sql: int
col5: Long, // sql: bigint
col6: Short, // sql: smallint
col7: Byte, // sql: tinyint
col8: Array[Byte]) // sql: byte
object HBaseRecord {
def apply(i: Int): HBaseRecord = {
HBaseRecord(i + "",
i % 2 == 0,
i.toDouble,
i.toFloat,
i,
i.toLong,
i.toShort,
i.toByte,
(i + "").getBytes("UTF-8"))
}
}
3. 写数据到HBase表完整代码
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.execution.datasources.hbase.HBaseTableCatalog
/**
* Author: YangYunhe
* Description:
* Create: 2018/8/17 12:54
*/
object WriteHBase {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder().appName("WriteHBase").getOrCreate()
val sc = spark.sparkContext
val sqlContext = spark.sqlContext
import sqlContext.implicits._
// 模拟一批数据
val data = (0 to 9).map(HBaseRecord(_))
// 写数据
sc.parallelize(data)
.toDF
.write
.options(Map(HBaseTableCatalog.tableCatalog -> Catalog.schema, HBaseTableCatalog.newTable -> "5"))
.format("org.apache.spark.sql.execution.datasources.hbase")
.save()
spark.stop()
}
}
说明：
Map(HBaseTableCatalog.tableCatalog -> Catalog.schema, HBaseTableCatalog.newTable -> "5")
这个代码意味着HBase表是不存在的，也就是我们在schema字符串中定义的"test1"这个表不存在，程序帮我们自动创建，5是region的个数，如果你提前创建好了表，那么这里的代码是这样的：
Map(HBaseTableCatalog.tableCatalog -> Catalog.schema)
当表存在的时候，数据会追加进去。
执行完以上程序后，检查一下hbase表中的内容：
hbase(main):003:0> scan 'test1'
ROW COLUMN+CELL
0 column=cf1:col1, timestamp=1534732543615, value=xFF
0 column=cf2:col2, timestamp=1534732543615, value=x00x00x00x00x00x00x00x00
0 column=cf3:col3, timestamp=1534732543615, value=x00x00x00x00
0 column=cf4:col4, timestamp=1534732543615, value=x00x00x00x00
0 column=cf5:col5, timestamp=1534732543615, value=x00x00x00x00x00x00x00x00
0 column=cf6:col6, timestamp=1534732543615, value=x00x00
0 column=cf7:col7, timestamp=1534732543615, value=x00
0 column=cf8:col8, timestamp=1534732543615, value=0
1 column=cf1:col1, timestamp=1534732543615, value=x00
1 column=cf2:col2, timestamp=1534732543615, value=?xF0x00x00x00x00x00x00
......
说明：程序本地执行会报以下错误
java.lang.IllegalArgumentException: Pathname /C:/Users/bonc/AppData/Local/Temp/spark-9fa1e56c-ce87-43e8-a936-f947b62e1af5/outputDataset/.spark-staging-5
from C:/Users/bonc/AppData/Local/Temp/spark-9fa1e56c-ce87-43e8-a936-f947b62e1af5/outputDataset/.spark-staging-5 is not a valid DFS filename.
这是因为本地运行把临时文件夹创建在本地，而删除临时文件夹时认为这个文件夹是一个HDFS的路径，所以报错，这个错误不影响读写数据，当在集群上跑这个程序就不会报错
4. 从HBase表读数据完整代码
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.execution.datasources.hbase.HBaseTableCatalog
/**
* Author: YangYunhe
* Description:
* Create: 2018/8/20 10:41
*/
object ReadHBase {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder().appName("ReadHBase").master("local").getOrCreate()
val sc = spark.sparkContext
val sqlContext = spark.sqlContext
import sqlContext.implicits._
val df: DataFrame = sqlContext
.read
.options(Map(HBaseTableCatalog.tableCatalog -> Catalog.schema))
.format("org.apache.spark.sql.execution.datasources.hbase")
.load()
df.createOrReplaceTempView("test1")
spark.sql("select * from test1").show()
spark.stop()
}
}
执行结果如下：
+----+-----+----+----+----+----+----+----+----+
|col0| col1|col2|col3|col4|col5|col6|col7|col8|
+----+-----+----+----+----+----+----+----+----+
| 0| true| 0.0| 0.0| 0| 0| 0| 0| 48|
| 1|false| 1.0| 1.0| 1| 1| 1| 1| 49|
| 2| true| 2.0| 2.0| 2| 2| 2| 2| 50|
| 3|false| 3.0| 3.0| 3| 3| 3| 3| 51|
| 4| true| 4.0| 4.0| 4| 4| 4| 4| 52|
| 5|false| 5.0| 5.0| 5| 5| 5| 5| 53|
| 6| true| 6.0| 6.0| 6| 6| 6| 6| 54|
| 7|false| 7.0| 7.0| 7| 7| 7| 7| 55|
| 8| true| 8.0| 8.0| 8| 8| 8| 8| 56|
| 9|false| 9.0| 9.0| 9| 9| 9| 9| 57|
+----+-----+----+----+----+----+----+----+----+