Operating Data in Avro Format
Overview
You can use HBase as data sources in Spark applications. In this example, data is stored in HBase in Avro format. Data is read from the HBase, and the read data is filtered.
Preparing Data
On the client, run the hbase shell command to go to the HBase command line and run the following command to create HBase tables to be used in the sample code:
create 'ExampleAvrotable','rowkey','cf1'
create 'ExampleAvrotableInsert','rowkey','cf1'
Development Guidelines
- Create an RDD.
- Perform operations on HBase to treat it as the data source and write the generated RDD into HBase tables.
- Read data from HBase tables and perform simple operations on the data.
Preparations
For clusters with the security mode enabled, the Spark Core sample code needs to read two files (user.keytab and krb5.conf). The user.keytab and krb5.conf files are authentication files in the security mode. Download the authentication credentials of the user principal on the FusionInsight Manager page. The user in the sample code is super, change the value to the prepared development user name.
Packaging the Project
- Use the Maven tool provided by IDEA to pack the project and generate a JAR file. For details, see Commissioning a Spark Application in a Linux Environment.
- Upload the JAR package to any directory (for example, $SPARK_HOME) on the server where the Spark client is located.
- Upload the user.keytab and krb5.conf files to the server where the client is installed (The file upload path must be the same as the path of the generated JAR file).
To run the Spark on HBase example program, set spark.yarn.security.credentials.hbase.enabled (false by default) in the spark-defaults.conf file on the Spark client to true. Changing the spark.yarn.security.credentials.hbase.enabled value does not affect existing services. (To uninstall the HBase service, you need to change the value of this parameter back to false.) Set configuration item spark.inputFormat.cache.enabled to false.
Submitting Commands
Assume that the JAR package name of the case code is spark-hbaseContext-test-1.0.jar that is stored in the $SPARK_HOME directory on the client. Run the following commands in the $SPARK_HOME directory.
- yarn-client mode:
Java/Scala version (The class name must be the same as the actual code. The following is only an example.)
bin/spark-submit --master yarn --deploy-mode client --jars /opt/female/protobuf-java-2.5.0.jar --conf spark.yarn.user.classpath.first=true --class com.huawei.bigdata.spark.examples.datasources.AvroSource SparkOnHbaseJavaExample.jar
Python version. (The file name must be the same as the actual one. The following is only an example.) Assume that the package name of the corresponding Java code is SparkOnHbaseJavaExample.jar and the package is saved to the current directory.
bin/spark-submit --master yarn --deploy-mode client --conf spark.yarn.user.classpath.first=true --jars SparkOnHbaseJavaExample.jar,/opt/female/protobuf-java-2.5.0.jar AvroSource.py
- yarn-cluster mode:
Java/Scala version (The class name must be the same as the actual code. The following is only an example.)
bin/spark-submit --master yarn --deploy-mode cluster --jars /opt/female/protobuf-java-2.5.0.jar --conf spark.yarn.user.classpath.first=true --class com.huawei.bigdata.spark.examples.datasources.AvroSource --files /opt/user.keytab,/opt/krb5.conf SparkOnHbaseJavaExample.jar
Python version. (The file name must be the same as the actual one. The following is only an example.) Assume that the package name of the corresponding Java code is SparkOnHbaseJavaExample.jar and the package is saved to the current directory.
bin/spark-submit --master yarn --deploy-mode cluster --files /opt/user.keytab,/opt/krb5.conf --conf spark.yarn.user.classpath.first=true --jars SparkOnHbaseJavaExample.jar,/opt/female/protobuf-java-2.5.0.jar AvroSource.py
Java Sample Code
The following code snippet is only for demonstration. For details about the code, see the AvroSource file in SparkOnHbaseJavaExample.
public static void main(JavaSparkContext jsc) throws IOException {
LoginUtil.loginWithUserKeytab();
SQLContext sqlContext = new SQLContext(jsc);
Configuration hbaseconf = new HBaseConfiguration().create();
JavaHBaseContext hBaseContext = new JavaHBaseContext(jsc, hbaseconf);
List list = new ArrayList<AvroHBaseRecord>();
for(int i=0; i<=255 ; ++i){
list.add(AvroHBaseRecord.apply(i));
}
try{
Map<String, String> map = new HashMap<String, String>();
map.put(HBaseTableCatalog.tableCatalog(), catalog);
map.put(HBaseTableCatalog.newTable(), "5");
sqlContext.createDataFrame(list, AvroHBaseRecord.class).write().options(map).format("org.apache.hadoop.hbase.spark").save();
Dataset<Row> ds = withCatalog(sqlContext,catalog);
ds.show();
ds.printSchema();
ds.registerTempTable("ExampleAvrotable");
Dataset<Row> c= sqlContext.sql("select count(1) from ExampleAvrotable");
c.show();
Dataset<Row> filtered = ds.select("col0", "col1.favorite_array").where("col0 = 'name1'");
filtered.show();
java.util.List<Row> collected = filtered.collectAsList();
if (collected.get(0).get(1).toString().equals("number1")) {
throw new UserCustomizedSampleException("value invalid", new Throwable());
}
if (collected.get(0).get(1).toString().equals("number2")) {
throw new UserCustomizedSampleException("value invalid", new Throwable());
}
Map avroCatalogInsertMap = new HashMap<String,String>();
avroCatalogInsertMap.put("avroSchema" , AvroHBaseRecord.schemaString);
avroCatalogInsertMap.put(HBaseTableCatalog.tableCatalog(), avroCatalogInsert);
ds.write().options(avroCatalogInsertMap).format("org.apache.hadoop.hbase.spark").save();
Dataset<Row> newDS = withCatalog(sqlContext,avroCatalogInsert);
newDS.show();
newDS.printSchema();
if (newDS.count() != 256) {
throw new UserCustomizedSampleException("value invalid", new Throwable());
}
ds.filter("col1.name = 'name5' || col1.name <= 'name5'").select("col0","col1.favorite_color", "col1.favorite_number").show();
} finally{
jsc.stop();
}
}
Scala Sample Code
The following code snippet is only for demonstration. For details about the code, see the AvroSource file in SparkOnHbaseScalaExample.
def main(args: Array[String]) {
LoginUtil.loginWithUserKeytab()
val sparkConf = new SparkConf().setAppName("AvroSourceExample")
val sc = new SparkContext(sparkConf)
val sqlContext = new SQLContext(sc)
val hbaseConf = HBaseConfiguration.create()
val hbaseContext = new HBaseContext(sc, hbaseConf)
import sqlContext.implicits._
def withCatalog(cat: String): DataFrame = {
sqlContext
.read
.options(Map("avroSchema" -> AvroHBaseRecord.schemaString, HBaseTableCatalog.tableCatalog -> avroCatalog))
.format("org.apache.hadoop.hbase.spark")
.load()
}
val data = (0 to 255).map { i =>
AvroHBaseRecord(i)
}
try {
sc.parallelize(data).toDF.write.options(
Map(HBaseTableCatalog.tableCatalog -> catalog, HBaseTableCatalog.newTable -> "5"))
.format("org.apache.hadoop.hbase.spark")
.save()
val df = withCatalog(catalog)
df.show()
df.printSchema()
df.registerTempTable("ExampleAvrotable")
val c = sqlContext.sql("select count(1) from ExampleAvrotable")
c.show()
val filtered = df.select($"col0", $"col1.favorite_array").where($"col0" === "name001")
filtered.show()
val collected = filtered.collect()
if (collected(0).getSeq[String](1)(0) != "number1") {
throw new UserCustomizedSampleException("value invalid")
}
if (collected(0).getSeq[String](1)(1) != "number2") {
throw new UserCustomizedSampleException("value invalid")
}
df.write.options(
Map("avroSchema" -> AvroHBaseRecord.schemaString, HBaseTableCatalog.tableCatalog -> avroCatalogInsert,
HBaseTableCatalog.newTable -> "5"))
.format("org.apache.hadoop.hbase.spark")
.save()
val newDF = withCatalog(avroCatalogInsert)
newDF.show()
newDF.printSchema()
if (newDF.count() != 256) {
throw new UserCustomizedSampleException("value invalid")
}
df.filter($"col1.name" === "name005" || $"col1.name" <= "name005")
.select("col0", "col1.favorite_color", "col1.favorite_number")
.show()
} finally {
sc.stop()
}
}
Python Sample Code
The following code snippet is only for demonstration. For details about the code, see the AvroSource file in SparkOnHbasePythonExample.
# -*- coding:utf-8 -*-
"""
[Note]
1. PySpark does not provide HBase APIs. In this example, Python is used to invoke Java code to implement required operations.
2. If yarn-client is used, ensure that the spark.yarn.security.credentials.hbase.enabledparameter in the spark-defaults.conffile under Spark2x/spark/conf/ is set to true on the Spark2x client.
Set spark.yarn.security.credentials.hbase.enabled to true.
"""
from py4j.java_gateway import java_import
from pyspark.sql import SparkSession
# Create a SparkSession instance.
spark = SparkSession\
.builder\
.appName("AvroSourceExample")\
.getOrCreate()
# Import the required class to sc._jvm.
java_import(spark._jvm, 'com.huawei.bigdata.spark.examples.datasources.AvroSource')
# Create a class instance, invoke the method, and transfer the sc._jsc parameter.
spark._jvm.AvroSource().execute(spark._jsc)
# Stop SparkSession.
spark.stop()
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