When I run a local spark2x program from eclipse, I am getting belo error:
Exception in thread "main" org.apache.spark.sql.AnalysisException: When hive.metastore.uris is setted, please set spark.sql.authorization.enabled and hive.security.authorization.enabled to true to enable authorization.;
Code used:
System.setProperty("hadoop.home.dir", "D:/winutils");
//kerberos releated
val ZKServerPrincipal = "zookeeper/hadoop.hadoop.com";
val ZOOKEEPER_DEFAULT_LOGIN_CONTEXT_NAME = "Client";
val ZOOKEEPER_SERVER_PRINCIPAL_KEY = "zookeeper.server.principal";
val hadoopConf: Configuration = new Configuration();
LoginUtil.setZookeeperServerPrincipal(ZOOKEEPER_SERVER_PRINCIPAL_KEY, ZKServerPrincipal);
LoginUtil.login(userPrincipal, userKeytabPath, krb5ConfPath, hadoopConf);
//creating spark session
val spark = SparkSession .builder() .appName("conenction").config("spark.master", "local") .config("spark.sql.authorization.enabled","true") .enableHiveSupport() .getOrCreate()
val df75 = spark.sql("select * from dbname.tablename limit 10")
I tried to use Sparksession with hive tables.
I had used the following code:
val spark= SparkSession.builder().appName("spark").master("local").enableHiveSupport().getOrCreate()
spark.sql("select * from data").show()
Shows table not found, but the table exists in hive. Please help me with this.
spark.sql("select * from databasename.data").show() - will work
Hello you have to provide the path of warehouse, like:
// warehouseLocation points to the default location for managed databases and tables
val warehouseLocation = new File("spark-warehouse").getAbsolutePath
val spark = SparkSession
.builder()
.appName("Spark Hive Example")
.config("spark.sql.warehouse.dir", warehouseLocation)
.enableHiveSupport()
.getOrCreate()
For more information, You can see here: Hive Tables with Spark
I want to read data from an Hbase table using get command while I have also the key of the row..I want to do that in my Spark streaming application, Is there any source code someone can share?
You can use Spark newAPIHadoopRDD to read Hbase table, which returns and RDD.
For example:
val sparkConf = new SparkConf().setAppName("Hbase").setMaster("local")
val sc = new SparkContext(sparkConf)
val conf = HBaseConfiguration.create()
val tableName = "table"
conf.set("hbase.master", "localhost:60000")
conf.set("hbase.zookeeper.quorum", "localhost:2181")
conf.set("zookeeper.znode.parent", "/hbase-unsecure")
conf.set(TableInputFormat.INPUT_TABLE, tableName)
val rdd = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat], classOf[ImmutableBytesWritable], classOf[Result])
println("Number of Records found : " + rdd.count())
sc.stop()
Or you can use any Spark Hbase connector like HortonWorks Hbase connector.
https://github.com/hortonworks-spark/shc
You can also use Spark-Phoenix API.
https://phoenix.apache.org/phoenix_spark.html
val sql = "select time from table"
val data = sql(sql).map(_.getTimeStamp(0).toString)
In the hive table,time's type is timestamp.when i run this program,it throws NullPointerException.
val data = sql(sql).map(_.get(0).toString)
When I change to the above code,the same Exception be threw.
Is anyone can tell me how to get TimeStamp data in hive using Spark?
Tks.
If you are trying to read data from Hive table you should use, HiveContext instead of SQLContext.
If you are using Spark 2.0, you can try the following.
val spark = SparkSession
.builder()
.appName("Spark Hive Example")
.config("spark.sql.warehouse.dir", warehouseLocation)
.enableHiveSupport()
.getOrCreate()
import spark.implicits._
import spark.sql
val df = sql("select time from table")
df.select($"time").show()
I am trying to follow the instructions mentioned here...
https://www.percona.com/blog/2016/08/17/apache-spark-makes-slow-mysql-queries-10x-faster/
and here...
https://www.percona.com/blog/2015/10/07/using-apache-spark-mysql-data-analysis/
I am using sparkdocker image.
docker run -it -p 8088:8088 -p 8042:8042 -p 4040:4040 -h sandbox sequenceiq/spark:1.6.0 bash
cd /usr/local/spark/
./sbin/start-master.sh
./bin/spark-shell --driver-memory 1G --executor-memory 1g --executor-cores 1 --master local
This works as expected:
scala> sc.parallelize(1 to 1000).count()
But this shows an error:
val jdbcDF = spark.read.format("jdbc").options(
Map("url" -> "jdbc:mysql://1.2.3.4:3306/test?user=dba&password=dba123",
"dbtable" -> "ontime.ontime_part",
"fetchSize" -> "10000",
"partitionColumn" -> "yeard", "lowerBound" -> "1988", "upperBound" -> "2016", "numPartitions" -> "28"
)).load()
And here is the error:
<console>:25: error: not found: value spark
val jdbcDF = spark.read.format("jdbc").options(
How do I connect to MySQL from within spark shell?
With spark 2.0.x,you can use DataFrameReader and DataFrameWriter.
Use SparkSession.read to access DataFrameReader and use Dataset.write to access DataFrameWriter.
Suppose using spark-shell.
read example
val prop=new java.util.Properties()
prop.put("user","username")
prop.put("password","yourpassword")
val url="jdbc:mysql://host:port/db_name"
val df=spark.read.jdbc(url,"table_name",prop)
df.show()
read example 2
val jdbcDF = spark.read
.format("jdbc")
.option("url", "jdbc:mysql:dbserver")
.option("dbtable", “schema.tablename")
.option("user", "username")
.option("password", "password")
.load()
from spark doc
write example
import org.apache.spark.sql.SaveMode
val prop=new java.util.Properties()
prop.put("user","username")
prop.put("password","yourpassword")
val url="jdbc:mysql://host:port/db_name"
//df is a dataframe contains the data which you want to write.
df.write.mode(SaveMode.Append).jdbc(url,"table_name",prop)
Create the spark context first
Make sure you have jdbc jar files in attached to your classpath
if you are trying to read data from jdbc. use dataframe API instead of RDD as dataframes have better performance. refer to the below performance comparsion graph.
here is the syntax for reading from jdbc
SparkConf conf = new SparkConf().setAppName("app"))
.setMaster("local[2]")
.set("spark.serializer",prop.getProperty("spark.serializer"));
JavaSparkContext sc = new JavaSparkContext(conf);
sqlCtx = new SQLContext(sc);
df = sqlCtx.read()
.format("jdbc")
.option("url", "jdbc:mysql://1.2.3.4:3306/test")
.option("driver", "com.mysql.jdbc.Driver")
.option("dbtable","dbtable")
.option("user", "dbuser")
.option("password","dbpwd"))
.load();
It looks like spark is not defined, you should use the SQLContext to connect to the driver like this:
import org.apache.spark.sql.SQLContext
val sqlcontext = new org.apache.spark.sql.SQLContext(sc)
val dataframe_mysql = sqlcontext.read.format("jdbc").option("url", "jdbc:mysql://Public_IP:3306/DB_NAME").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "tblage").option("user", "sqluser").option("password", "sqluser").load()
Later you can user sqlcontext where you used spark (in spark.read etc)
This is a common problem for those migrating to Spark 2.0.0 from the earlier versions. The Spark documentation is not very good. To solve this, you have to define a SparkSession, like this:
import org.apache.spark.sql.SparkSession
val spark = SparkSession
.builder()
.appName("Spark SQL Example")
.config("spark.some.config.option", "some-value")
.getOrCreate()
This solution is hidden in the Spark SQL, Dataframes and Data Sets Guide located here. SparkSession is the new entry point to the DataFrame API and it incorporates both SQLContext and HiveContext and has some additional advantages, so there is no need to define either of those anymore. Further information about this can be found here.
Please accept this as the answer, if you find this useful.