configure KNOX over HiveServer2 HA on HDP-2.5

A quick guide to connect HiveServer2 to MySQL DB metastore over SSL

Apache Flink is a streaming data flow engine that provides data distribution, communication, and fault tolerance for distributed computations over data streams. this is a sample application to consume output of vmstat command as a stream, so lets get hands dirty

mkdir  flink-steaming-example
cd flink-steaming-example/
mkdir -p src/main/scala
cd src/main/scala/
vim FlinkStreamingExample.scala

import org.apache.flink.streaming.api.scala._
object FlinkStreamingExample{
    def main(args:Array[String]){
        val env = StreamExecutionEnvironment.getExecutionEnvironment
        val socketVmStatStream = env.socketTextStream("ip-10-0-0-233",9000);
        socketVmStatStream.print
        env.execute()
    }    
}

cd -
vim build.sbt
name := "flink-streaming-examples"

version := "1.0"
scalaVersion := "2.10.4"

libraryDependencies ++= Seq("org.apache.flink" %% "flink-scala" % "1.0.0",
  "org.apache.flink" %% "flink-clients" % "1.0.0",
    "org.apache.flink" %% "flink-streaming-scala" % "1.0.0")

sbt clean package

stream some test data
vmstat 1 | nc -l 9000

now submit flink job
bin/flink run /root/flink-steaming-example/target/scala-2.10/flink-streaming-examples_2.10-1.0.jar 
03/20/2016 08:04:12 Job execution switched to status RUNNING.
03/20/2016 08:04:12 Source: Socket Stream -> Sink: Unnamed(1/1) switched to SCHEDULED 
03/20/2016 08:04:12 Source: Socket Stream -> Sink: Unnamed(1/1) switched to DEPLOYING 
03/20/2016 08:04:12 Source: Socket Stream -> Sink: Unnamed(1/1) switched to RUNNING 

let see output of the job
tailf flink-1.0.0/log/flink-root-jobmanager-0-ip-10-0-0-233.out -- will see the vmstat output

Hadoop MapReduce : Enabling JVM Profiling using -XPROF

The -Xprof profiler is the HotSpot profiler. HotSpot works by running Java code in interpreted mode, while running a profiler in parallel. The HotSpot profiler looks for "hot spots" in the code, i.e. methods that the JVM spends a significant amount of time running, and then compiles those methods into native generated code.
-Xprof is very handy to profile mapreduce code, here are few configuration parameters required to turn on profiling in mapreduce using -Xprof

mapreduce.task.profile='true'
mapreduce.task.profile.maps='0-'
mapreduce.task.profile.reduces='0-'
mapreduce.task.profile.params='-Xprof'

Running Apache Kafka in Docker Container

Apache Kafka : Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system.
Docker containers : wrap up a piece of software in a complete filesystem that contains everything it needs to run: code, runtime, system tools, system libraries – anything you can install on a server. This guarantees that it will always run the same, regardless of the environment it is running in.
in this quick demo I will make use of a Docker image to run Apache Kafka in a Docker container.

Env: Single node pre-installed with RHEL7
Docker Installation:

yum update
curl -sSL https://get.docker.com/  | sh
service docker start
service docker status // to check service is up

Apache Kafka setup
1. pull the docker image for the zookeeper

docker pull dockerkafka/zookeeper

2. pull docker image for kafka

docker pull dockerkafka/zookeeper

3. Run zookeeper docker container in detach mode

docker run -d --name zookeeper -p 2181:2181 dockerkafka/zookeeper

4. Run docker container for Kafka

docker run --name kafka -p 9092:9092 --link zookeeper:zookeeper dockerkafka/kafka  &

5. get host ip where zk is running

docker inspect --format '{{ .NetworkSettings.IPAddress }}' zookeeper
172.17.0.2

6. get host ip where kafka is running

docker inspect --format '{{ .NetworkSettings.IPAddress }}' kafka

7. in the next course of action lets create a topic for that start interactive shell

docker exec -t -i exec kafka bash
kafka-topics.sh --create --topic vmstat_logs --zookeeper 172.17.0.2:2181 --replication-factor 1 --partitions 1

8. start a kafka producer which will publish vmstat output of every one second to the broker

vmstat 1 | kafka-console-producer.sh --topic vmstat_logs --broker-list 172.17.0.3:9092

9. open a different shell and start a consumer

docker exec -t -i kafka bash
kafka-console-consumer.sh --topic vmstat_logs --from-beginning --zookeeper 172.17.0.2:2181

the consumer will start running and consume the vmstat logs from the producer.

creating fat jar (uber jar) using maven shade plugin

Add the following build configuration in pom.xml and simple run mvn clean package and it will build you a fat jar including all the dependencies into the jar.

<build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                    </execution>
                </executions>
                <configuration>
                    <finalName>uber-${artifactId}-${version}</finalName>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <configuration>
                    <source>1.7</source>
                    <target>1.7</target>
                </configuration>
            </plugin>
        </plugins>
    </build>

Apache Flume : Data ingestion from Kafka to HDFS

flume configuration to setup kafka as source and HDFS as sink.

tier1.channels = kafkachannel
tier1.sink = hdfssink

tier1.channels.kafkachannel.type = org.apache.flume.channel.kafka.KafkaChannel
tier1.channels.kafkachannel.brokerList = kafkabroker-1:9092,kafkabroker-2:9092
tier1.channels.kafkachannel.topic = logs
tier1.channels.kafkachannel.zookeeperConnect = kafkabroker-1:2181
tier1.channels.kafkachannel.parseAsFlumeEvent = false

tier1.sinks.hdfssink.type = hdfs
tier1.sinks.hdfssink.hdfs.path = /tmp/logs
tier1.sinks.hdfssink.hdfs.rollinterval = 5
tier1.sinks.hdfssink.hdfs.fileType = DataStream
tier1.sinks.hdfssink.channel =  kafkachannel

Setup 2 node Apache Kafka cluster on Mac-OSX

This is a quick start guide to setup 2 node (broker) cluster on the Mac-OSX.
Steps to install:
1. Download apache kafka from the http://kafka.apache.org/downloads.html
2. extract to some folder in my example i have created in /tmp folder
3. Create kafka logs directory

mkdir /tmp/kafka-logs-1
mkdir /tmp/kafka-logs-2

4. copy config/server.properties to config/server2.properties as I will be running the two broker on the same machine so the following property needs to be updated in server.properties

broker.id=0
port=9092
log.dirs=/tmp/kafka-logs-1

edit the server2.properties accordingly

broker.id=1
port=9091
log.dirs=/tmp/kafka-logs-2

5. now start the zookeeper with

bin/zookeeper-server-start.sh config/zookeeper.properties

6. Start kafka broker

bin/kafka-server-start.sh config/server.properties &

7. Start second kafka broker

bin/kafka-server-start.sh config/server2.properties &

8. create kafka topic

bin/kafka-topics.sh --zookeeper localhost:2181 --create --topic general_topic --partitions 2 --replication-factor 2

9. list topic

bin/kafka-topics.sh --zookeeper localhost:2181 --describe --topic general_topic

10. now its time to test kafka setup, for that setup producer and consumers

bin/kafka-console-producer.sh --broker-list  localhost:9092 --topic general_topic

bin/kafka-console-consumer.sh --zookeeper localhost:2181 --topic general_topic
bin/kafka-console-consumer.sh --zookeeper localhost:2181 --topic general_topic --from-beginning

Hadoop Simple Authentication using java

Following is the sample java program to take the advantage of Hadoop UserGroupInfomation to perform a simple authentication by impersonating a user.

import org.apache.hadoop.conf.*;
import org.apache.hadoop.security.UserGroupInformation;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.FileStatus;

public class SimpleSecurity {

    public static void main(String args[]) {

        try {
            UserGroupInformation ugi
                    = UserGroupInformation.createRemoteUser("rsingh");

            ugi.doAs(new PrivilegedExceptionAction<Void>() {

                public Void run() throws Exception {

                    Configuration conf = new Configuration();
                    conf.set("fs.defaultFS", "maprfs:///");
                    conf.set("hadoop.job.ugi", "rsingh");


                    FileSystem fs = FileSystem.get(conf);


                    fs.createNewFile(new Path("/user/rsingh/test"));


                    FileStatus[] status = fs.listStatus(new Path("/user/rsingh"));

                    for(int i=0;i<status.length;i++){

                        System.out.println(status[i].getPath());

                    }

                    return null;

                }
            });
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

Compile and Run this code

javac -cp `hadoop classpath` SimpleSecurity.java 
ava -cp .:`hadoop classpath` SimpleSecurity
16/02/17 03:08:17 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/02/17 03:08:18 INFO fs.MapRFileSystem: User root is impersonating user rsingh
maprfs:///user/rsingh/test

Oozie : Running a jobflow using java api

This is sample program to run the map-reduce job flow from oozie java client Api.copy the example directory shipped with the oozie installation on the dfs and create a sample program as follows:

import java.util.Properties;
import org.apache.oozie.client.OozieClient;
import org.apache.oozie.client.WorkflowJob;

public class OozieWFJavaApi{

public static void main(String args[]){
 OozieClient wc = new OozieClient("http://ip-10-0-0-233:11000/oozie");

    Properties conf = wc.createConfiguration();

    conf.setProperty(OozieClient.APP_PATH, "maprfs:///user/mapr/examples/apps/map-reduce/workflow.xml");
    conf.setProperty("jobTracker", "maprfs:///");
    conf.setProperty("nameNode", "maprfs:///");
    conf.setProperty("queueName", "default");
    conf.setProperty("oozie.use.system.libpath", "true");
    conf.setProperty("oozie.wf.rerun.failnodes", "true");
    conf.setProperty("outputDir","map-reduce");
    try {
        String jobId = wc.run(conf);
        System.out.println("job, " + jobId + " submitted");

        while (wc.getJobInfo(jobId).getStatus() == WorkflowJob.Status.RUNNING) {
            System.out.println("Workflow job in Running State");
            Thread.sleep(1000);
        }
        System.out.println("WF job Completed");
        System.out.println(wc.getJobInfo(jobId));
    } catch (Exception r) {
        System.out.println("Job submission failed with exception " + r.getLocalizedMessage());
    }
}

}

Compile and run using oozie client api

[mapr@ip-10-0-0-233 tmp]$ javac -cp .:/opt/mapr/oozie/oozie-4.2.0/lib/oozie-client-4.2.0-mapr-1510.jar OozieWFJavaApi.java 
[mapr@ip-10-0-0-233 tmp]$ java -cp .:/opt/mapr/oozie/oozie-4.2.0/lib/* OozieWFJavaApi
job, 0000007-160215031423760-oozie-mapr-W submitted
Workflow job in Running State
Workflow job in Running State
Workflow job in Running State
Workflow job in Running State
WF job Completed
Workflow id[0000007-160215031423760-oozie-mapr-W] status[SUCCEEDED]

Shared Memory : Simple C program to demonstrate setting up Share memory

shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs.let see how it
actually work using simple c programs.lets create two process which will use shared memory

//process1.c
#include <stdio.h>
#include <sys/types.h>
#include <sys/socket.h>
#include <netinet/in.h>
#include <string.h>
#include <stdlib.h>
#include <sys/ipc.h>
#include <sys/shm.h>


int main(int argc,char *argv[]){


int shmid;
key_t key;
char *shm;
char *memloc;
key = 9876;

shmid = shmget(key,100,IPC_CREAT | 0666);
if(shmid <0){
    printf("unable to get shared memory area identifier");
    exit(1);
}


shm = shmat(shmid,NULL,0) ;
if (shm == (char *)-1)
{
    printf("map/unmap shared memory");
    exit(1);
}


memcpy(shm,"this is shared content",22);
memloc = shm;
memloc +=22;

*memloc = 0;

while(*shm != '!')
    sleep (1);

return 0;
}

let create an another program which will read from the shared memory created by the process1.

#include <stdio.h>
#include <sys/types.h>
#include <sys/socket.h>
#include <netinet/in.h>
#include <string.h>
#include <stdlib.h>
#include <sys/ipc.h>
#include <sys/shm.h>


int main(int argc,char *argv[]){


int shmid;
key_t key;
char *shm;
char *mloc;
key = 9876;

shmid = shmget(key,100,IPC_CREAT | 0666);
if(shmid <0){
    printf("unable to get shared memory area identifier");
    exit(1);
}


shm = shmat(shmid,NULL,0) ;
if (shm == (char *)-1)
{
    printf("map/unmap shared memory");
    exit(1);
}

for(mloc = shm; *mloc !=0;mloc++)
    printf ("%c\n",*mloc);

*shm = '!';
return 0;
}

Compilation:

gcc -o process1 process1.c 
gcc -o process2 process2.c 

Run:

./process1 -- will create and write to the shared memory and will be running
from another shell
./process2 -- will write the content from shared memory to the console and set content to the shared memory which terminate the process1.

-- 
ipcs -m
IPC status from  as of Sat Feb  6 13:24:00 IST 2016
T     ID     KEY        MODE       OWNER    GROUP
Shared Memory:
m  65536 0x00002694 --rw-rw-rw-   rsingh    staff


ipcrm -M 9876 // 9876 is hex equivalent of the shared memory key.

BTrace : Tracing a java Application

BTrace is a helpful tool that lets you run Java-like scripts on top of a live JVM to capture or aggregate any form of variable state without restarting the JVM or deploying new code. This enables you to do pretty powerful things like printing the stack traces of threads, writing to a specific file, or printing the number of items of any queue or connection pool and many more.

lets trace a simple java application to know how much time a method took to complete at runtime without modifying a code.
Installation:

-Download latest version of BTrace from https://kenai.com/projects/btrace/downloads/directory/releases and unzip it some location.
-Add the env variable as follows

export BTRACE_HOME=/root/btrace-bin
PATH=$PATH:$BTRACE_HOME/bin

This is the sample code which I want to trace

package  com.rajkrrsingh.trace.test;  
  
public  class  BtraceTest {  
  
    public  void  execute (int i) {   
        System.out.println ("executing "+i+" time");  
        try  {  
            Thread.sleep (Math.abs (( int ) (Math.random () *  1000 )));  
        }  catch  (InterruptedException e) {  
            e.printStackTrace ();  
        }  
    }  
  
    public  void  doExcute () {  
       int i=0; 
       while  ( true ) {  
           
            execute (i);
            i++;  
        }  
    }  
  
    public  static  void  main (String [] args) {  
        BtraceTest btraceTest =  new  BtraceTest ();  
        btraceTest.doExcute ();  
    }  
}  

BTrace require a tracing script which has annotated action about what to trace and can add additional capabilities.this is the user guide to know about the different annotations and what they signifies.we have a following tracing script which will trace the execute method and print its execution time. to compile this script you need to have brace-tool.jar in the class path.

package  com.rajkrrsingh.trace.test;

import com.sun.btrace.annotations.BTrace;

import static com.sun.btrace.BTraceUtils.jstack;
        import static com.sun.btrace.BTraceUtils.println;
        import static com.sun.btrace.BTraceUtils.str;
        import static com.sun.btrace.BTraceUtils.strcat;
        import static com.sun.btrace.BTraceUtils.timeMillis;

        import com.sun.btrace.annotations.BTrace;
        import com.sun.btrace.annotations.Kind;
        import com.sun.btrace.annotations.Location;
        import com.sun.btrace.annotations.OnMethod;
        import com.sun.btrace.annotations.TLS;


@BTrace
public class TraceMethodTime {

    @TLS
    static Long beginTime;

    @OnMethod(clazz="com.rajkrrsingh.trace.test.BtraceTest", method="execute")

    public static void traceExecuteBegin() {
        println("method Start!");
        beginTime = timeMillis();

    }


    @OnMethod(clazz="com.rajkrrsingh.trace.test.BtraceTest", method="execute", location=@Location(Kind.RETURN))

    public static void traceExcute() {

        println(strcat(strcat("btrace.test.MyBtraceTest.execute time is: ", str(timeMillis() - beginTime)), "ms"));
        println("method End!");
        jstack();
    }
}

now it time to trace the already running java application, to know the process id of the application use jps command which will fetch you the process id.attach btrace to process id as follows and it will start tracing the application

btrace process-id script-location
btrace 22618 com/rajkrrsingh/trace/test/TraceMethodTime.java 

Btrace will attach to the running jam and you will see the following output on the console.

method Start!
btrace.test.MyBtraceTest.execute time is: 323ms
method End!
com.rajkrrsingh.trace.test.BtraceTest.execute(BtraceTest.java:12)
com.rajkrrsingh.trace.test.BtraceTest.doExcute(BtraceTest.java:18)
com.rajkrrsingh.trace.test.BtraceTest.main(BtraceTest.java:25)
method Start!
btrace.test.MyBtraceTest.execute time is: 655ms
method End!
com.rajkrrsingh.trace.test.BtraceTest.execute(BtraceTest.java:12)
com.rajkrrsingh.trace.test.BtraceTest.doExcute(BtraceTest.java:18)
com.rajkrrsingh.trace.test.BtraceTest.main(BtraceTest.java:25)

SparkThrift Server Configuration in yarn mode

Env: MapR,Spark 1.4.1
Start spark thrift server on mapr cluster as follows (-- start thrift server non root user)

/opt/mapr/spark/spark-1.4.1/sbin/start-thriftserver.sh --master yarn --hiveconf hive.server2.thrift.bind.host `hostname` --hiveconf hive.server2.trift.port 10000

check for any error exception in the logs(in my case it is tailf /opt/mapr/spark/spark-1.4.1/logs/spark-mapr-org.apache.spark.sql.hive.thriftserver.HiveThriftServer2-1-ip-10-0-0-233.out)
if there is no error exception in the logs try connecting using the beeline ship with the spark distribution

/opt/mapr/spark/spark-1.4.1/bin/beeline 
Beeline version 1.4.1 by Apache Hive
beeline> !connect jdbc:hive2://10.0.0.233:10000/default;auth=noSasl
scan complete in 1ms
Connecting to jdbc:hive2://10.0.0.233:10000/default;auth=noSasl
Enter username for jdbc:hive2://10.0.0.233:10000/default;auth=noSasl: 
Enter password for jdbc:hive2://10.0.0.233:10000/default;auth=noSasl: 
Connected to: Spark SQL (version 1.4.1)
Driver: Spark Project Core (version 1.4.1)
Transaction isolation: TRANSACTION_REPEATABLE_READ
0: jdbc:hive2://10.0.0.233:10000/default> show tables;
+------------------------------+--------------+
|          tableName           | isTemporary  |
+------------------------------+--------------+
| b1                           | false        |
| b2                           | false        |
| bob                          | false        |
| ct                           | false        |