Example: Using the HBase-Spark connector

In this example we want to store personal data in an HBase table. We want to store name, email address, birth date and height as a floating point number. The contact information (email) is stored in the c column family and personal information (birth date, height) is stored in the p column family. The key in HBase table will be the name attribute.

	Spark	HBase
Type/Table	Person	person
Name	name: String	key
Email address	email: String	c:email
Birth date	birthDate: Date	p:birthDate
Height	height: Float	p:height

Use the following command to create the HBase table:

shell> create 'person', 'p', 'c'

Use the following spark code in spark-shell or spark3-shell to insert data into our HBase table:

val sql = spark.sqlContext

import java.sql.Date

case class Person(name: String,
                 email: String,
                 birthDate: Date,
                 height: Float)

var personDS = Seq(
 Person("alice", "alice@alice.com", Date.valueOf("2000-01-01"), 4.5f),
 Person("bob", "bob@bob.com", Date.valueOf("2001-10-17"), 5.1f)
).toDS


if (true) {
  personDS.write.format("org.apache.hadoop.hbase.spark")
   .option("hbase.columns.mapping",
     "name STRING :key, email STRING c:email, " +
       "birthDate DATE p:birthDate, height FLOAT p:height")
   .option("hbase.table", "person")
   .option("hbase.spark.use.hbasecontext", false)
   .save()
}

Use the following spark code in pyspark or pyspark3 to insert data into our HBase table:

from datetime import datetime
from pyspark.sql.types import StructType, StructField, StringType, DateType, FloatType

personDS.write.format("org.apache.hadoop.hbase.spark")
 .option("hbase.columns.mapping",
   "name STRING :key, email STRING c:email, " +
     "birthDate DATE p:birthDate, height FLOAT p:height")
 .option("hbase.table", "person")
 .option("hbase.spark.use.hbasecontext", false)
 .save()

The previously inserted data can be tested with a simple scan:

shell> scan ‘person’
ROW		COLUMN+CELL
 alice		column=c:email, timestamp=1568723598292, value=alice@alice.com
 alice		column=p:birthDate, timestamp=1568723598292, value=\x00\x00\x00\xDCl\x87 \x00
 alice		column=p:height, timestamp=1568723598292, value=@\x90\x00\x00
 bob		column=c:email, timestamp=1568723598521, value=bob@bob.com
 bob		column=p:birthDate, timestamp=1568723598521, value=\x00\x00\x00\xE9\x99u\x95\x80
 bob		column=p:height, timestamp=1568723598521, value=@\xA333
2 row(s)

Use the following snippet in spark-shell or spark3-shell to read the data back:

val sql = spark.sqlContext
var df = spark.emptyDataFrame

if (true) {
  df = sql.read.format("org.apache.hadoop.hbase.spark") 
 .option("hbase.columns.mapping",
   "name STRING :key, email STRING c:email, " +
     "birthDate DATE p:birthDate, height FLOAT p:height")
 .option("hbase.table", "person")
 .option("hbase.spark.use.hbasecontext", false)
 .load()
 }

df.createOrReplaceTempView("personView")val results = sql.sql("SELECT * FROM personView WHERE name = 'alice'")
results.show()

The result of this snippet is the following Data Frame:

+-----+------+---------------+----------+
| name|height|          email| birthDate|
+-----+------+---------------+----------+
|alice|   4.5|alice@alice.com|2000-01-01|
+-----+------+---------------+----------+

HBase configuration can be altered in these cases:

import org.apache.hadoop.hbase.spark.HBaseContext
import org.apache.hadoop.hbase.HBaseConfiguration

val conf = new HBaseConfiguration()
conf.set("hbase.zookeeper.quorum", "hbase-1.example.com")

// the latest HBaseContext will be used afterwards
new HBaseContext(spark.sparkContext, conf)

val df = sql.read.format("org.apache.hadoop.hbase.spark")
 .option("hbase.columns.mapping",
   "name STRING :key, email STRING c:email, " +
     "birthDate DATE p:birthDate, height FLOAT p:height")
 .option("hbase.table", "person")
 .load()
df.createOrReplaceTempView("personView")

val results = sql.sql("SELECT * FROM personView WHERE name = 'alice'")
results.show()