Procedure to change Journal Node edit directory Hadoop - Name Node issue

Procedure to change Journal Node directory:

1. Create/add drive/disk (/data06)  and /data06/dfs/jn folders for journal node only iin all Nodes where journal node service installed.

2. Update hdfs-site.xml file in all Nodes where journal node service installed
 
    dfs.journalnode.edits.dir
    /data06/dfs/jn
 

3.    Copy existing edit folder from /data01/dfs/jn/mas (considering this folder having old edit logs) to new directory in all Nodes where journal node service installed ( this will take some time to copy edit logs) and change the permissions as below
cp -ar /data01/dfs/jn/*  /data06/dfs/jn /.
chown -R hdfs:hdfs /data06
chmod 700 /data06/dfs

4.  Stop standby name node and journal node
service hadoop-hdfs-namenode stop
service hadoop-hdfs-journalnode stop

5.         Start journal node and standby name node. Both services should be up and running
service hadoop-hdfs-journalnode start
service hadoop-hdfs-namenode start

6.         Stop active name node and journal node. Stand by should come up now
service hadoop-hdfs-namenode stop
service hadoop-hdfs-journalnode stop

7.         Start journal node and active name node
service hadoop-hdfs-journalnode start
service hadoop-hdfs-namenode start

8.        Restart 3rd journal node
service hadoop-hdfs-journalnode stop
service hadoop-hdfs-journalnode start

Hive Table not found even table exist

If we get table not found in hive table even if you have drop and create before inserting data into table. you can fix with below two options

option1: set SYNC_DDL=1;  only if you are using impala
option2: create table with select statement

HDFS Balancer and DataNode Space Usage Considerations:

HDFS Balancer and DataNode Space Usage Considerations:
Symptoms
It may take days or weeks to re-distribute data among DataNodes.
Newly added nodes use less space compared to existing DataNodes.
Some DataNodes use more space than other DataNodes in a cluster.

Resolution:
The balancer is useful immediately after adding some new nodes to the cluster. You might also want to consider running this as a cron job every week or so.
sudo -u hdfs hdfs balancer -threshold 10

You can start the balancer in the background by running one of the following commands:
/usr/lib/hadoop/bin/start-balancer.sh

Hadoop NameNode down or crash randomly

ERROR /Symptoms:
2017-07-27 18:50:32,405 WARN org.apache.hadoop.hdfs.qjournal.client.QuorumJournalManager: Took 5605ms to send a batch of 12 ed
its (841 bytes) to remote journal 206.46.37.113:8485
2017-07-27 18:50:38,693 WARN org.apache.hadoop.hdfs.qjournal.client.QuorumJournalManager: Took 6212ms to send a batch of 18 ed
its (1245 bytes) to remote journal 206.46.37.113:8485
2017-07-27 18:50:48,318 WARN org.apache.hadoop.hdfs.qjournal.client.QuorumJournalManager: Took 9492ms to send a batch of 4 edi
ts (1049 bytes) to remote journal 206.46.37.112:8485
2017-07-27 18:51:16,909 WARN org.apache.hadoop.hdfs.qjournal.client.QuorumJournalManager: Took 11283ms to send a batch of 8 ed
its (1672 bytes) to remote journal 206.46.37.112:8485
2017-07-27 18:51:22,765 WARN org.apache.hadoop.hdfs.qjournal.client.QuorumJournalManager: Remote journal 206.46.37.114:8485 fa
iled to write txns 32683796-32683796. Will try to write to this JN again after the next log roll.
org.apache.hadoop.ipc.RemoteException(java.io.IOException): IPC's epoch 34 is less than the last promised epoch 35
…..
…..
2017-07-27 18:51:30,993 FATAL org.apache.hadoop.hdfs.server.namenode.FSEditLog: Error: flush failed for required journal (JournalAndStream(mgr=QJM to [206.46.37.112:8485, 206.46.37.113:8485, 206.46.37.114:8485], stream=QuorumOutputStream starting at txid 32683792))
org.apache.hadoop.hdfs.qjournal.client.QuorumException: Got too many exceptions to achieve quorum size 2/3. 3 exceptions thrown:
206.46.37.114:8485: IPC's epoch 34 is less than the last promised epoch 35

Workaround:
dfs.qjournal.write-txns.timeout.ms default value is 20000 - Write timeout in milliseconds when writing to a quorum of remote journals.

increase dfs.qjournal.write-txns.timeout.ms default value to 30000

Procedure to apply dfs.qjournal.write-txns.timeout.ms property
1. Add below property in hdfs-site.xml file where NameNode installed (mostly core worker 1 and core worker 2)

dfs.qjournal.write-txns.timeout.ms
30000

2. Restart Standby NameNode. Standby NameNode should come up and running
service hadoop-hdfs-namenode stop
service hadoop-hdfs-namenode start
service hadoop-hdfs-namenode status

3. Restart active NameNode if Standby is Running
service hadoop-hdfs-namenode stop
service hadoop-hdfs-namenode start
service hadoop-hdfs-namenode status

Tune Yarn - CPU assignment by YARN should limit CPU number for each job

Tune Yarn

You can set CPU limit using yarn.scheduler.maximum-allocation-vcores and yarn.nodemanager.resource.cpu-vcores in yarn-site.xml. decription is below

yarn.scheduler.maximum-allocation-vcores: controls the maximum vcores that any submitted job can request.

yarn.nodemanager.resource.cpu-vcores: controls how many vcores can be scheduled on a particular NodeManager instance.

Apache Spark Mlib - Sparse Vector

What is a Apache Spark Sparse Vector

A vector is a one-dimensional array of elements. So in a programming language, an implementation of a vector is as a one-dimensional array. A vector is said to be sparse when many elements of a have zero values. And when we write programs it will not be a good idea from storage perspective to store all these zero values in the array.

So the best way of representation of a sparse vector will be by just specifying the location and value.

Example: 3 1.2 2800 6.3 6000 10.0 50000 5.7

This denotes at position:

• 3 the value is 1.2
• 2800 holds value 6.3
• 6000 holds 10.0
• 50000 holds value 5.7

When you use the sparse vector in a programming language you will also need to specify a size. In the above example the size of the sparse vector is 4.

Representation of Sparse Vector in Spark

The Vector class of org.apache.spark.mllib.linalg has multiple methods to create your own dense and sparse Vectors.

The most simple way of creating one is by using:

sparse(int size, int[] indices, double[] values)

This method creates a sparse vector where the first argument is the size, second the indices where a value exists and the last one is the values on these indices.

Rest of the elements of this vector have values zero.

Example:

Let’s say we have to create the following vector {0.0, 5.0, 0.0, 3.0, 4.0}. By using the sparse vector API of Spark this can be created as stated below:

Vector sparseVector = Vectors.sparse(5, new int[] {1,3, 4}, new double[] {5.0,3.0, 4.0});

If the same vector can also be created using the dense vector API

Vector denseVector = Vectors.dense(0.0, 5.0, 0.0, 3.0, 4.0);

Order of stopping and starting Cloudera Hadoop services

Order of stopping and starting Cloudera Hadoop services

Order of stopping Hadoop services:
service hue stop
service livy_server stop
service spark-history-server stop
service flume-ng-agent stop
service kafka stop
service impala-catalog stop
service impala-state-store stop
service impala-server stop
service llama stop
service hive-webhcat-server stop
service hive-server2 stop
service hive-metastore stop
service hbase-rest stop
service hbase-thrift stop
service hbase-regionserver stop
service hbase-master stop
service hadoop-hdfs-zkfc stop
service hadoop-mapreduce-historyserver stop
service hadoop-yarn-nodemanager stop
service hadoop-yarn-resourcemanager stop
service hadoop-httpfs stop
service hadoop-hdfs-datanode stop
service hadoop-hdfs-namenode stop
service hadoop-hdfs-journalnode stop
service zookeeper-rest stop
service zookeeper-server stop

starting will be reverse order of above list

Apache Phoenix Bulk Loading

Here is an example:

Create us_population.sql file
CREATE TABLE IF NOT EXISTS us_population ( state CHAR(2) NOT NULL, city VARCHAR NOT NULL, population BIGINT CONSTRAINT my_pk PRIMARY KEY (state, city));
Create us_population.csv file
NY,New York,8143197 CA,Los Angeles,3844829 IL,Chicago,2842518 TX,Houston,2016582 PA,Philadelphia,1463281 AZ,Phoenix,1461575 TX,San Antonio,1256509 CA,San Diego,1255540 TX,Dallas,1213825 CA,San Jose,912332
Create us_population_queries.sql file

SELECT state as “State”,count(city) as “City Count”,sum(population) as “Population Sum” FROM us_population GROUP BY state ORDER BY sum(population) DESC;
Execute the following command from a command terminal

/opt//psql.py us_population.sql us_population.csv us_population_queries.sql

Output:

csv columns from database.
CSV Upsert complete. 10 rows upserted
Time: 0.181 sec(s)

St City Count Population Sum
-- ---------------------------------------- ----------------------------------------
NY 1 8143197
CA 3 6012701
TX 3 4486916
IL 1 2842518
PA 1 1463281
AZ 1 1461575
Time: 0.03 sec(s)

For more information click here.

HBASE Tools

Apache phoenix 

Apache phoenix is an SQL interface on the top of HBASE. For more information click here. 

You can connect by executing the following command under bin.

python sqlline.py zookeeper_url 

The Phoenix client driver can be found here. You can  download and setup SQuirrel as your SQL client so you can issue adhoc SQL against your HBase cluster

HBASE Stargate  

HBASE startgate is the rest interface of HBASE. for more information click here.

Apache Phoenix Bulk Loading 

Here is an example:

Create us_population.sql file
CREATE TABLE IF NOT EXISTS us_population ( state CHAR(2) NOT NULL, city VARCHAR NOT NULL, population BIGINT CONSTRAINT my_pk PRIMARY KEY (state, city));
Create us_population.csv file
NY,New York,8143197 CA,Los Angeles,3844829 IL,Chicago,2842518 TX,Houston,2016582 PA,Philadelphia,1463281 AZ,Phoenix,1461575 TX,San Antonio,1256509 CA,San Diego,1255540 TX,Dallas,1213825 CA,San Jose,912332
Create us_population_queries.sql file

SELECT state as “State”,count(city) as “City Count”,sum(population) as “Population Sum” FROM us_population GROUP BY state ORDER BY sum(population) DESC;
Execute the following command from a command terminal

/opt//psql.py us_population.sql us_population.csv us_population_queries.sql

Output:

csv columns from database.
CSV Upsert complete. 10 rows upserted
Time: 0.181 sec(s)

St City Count Population Sum
-- ---------------------------------------- ----------------------------------------
NY 1 8143197
CA 3 6012701
TX 3 4486916
IL 1 2842518
PA 1 1463281
AZ 1 1461575
Time: 0.03 sec(s)

For more information click here.

HBASE ImportTSV 

ImportTsv is a utility that will load data in TSV format into HBase. It can be used for the HBASE tables those are not managed by phoenix.

Please use this link for more information.

Enabling Jmx for Tomcat

Create (or append to) the file /opt/mas/tomcat/bin/setenv.sh

#!/bin/sh CATALINA_OPTS="$CATALINA_OPTS -Dcom.sun.management.jmxremote.port=1701 -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.local.only=false \  -Dcom.sun.management.jmxremote.password.file=/opt/mas/jmx/tomcat.password -Dcom.sun.management.jmxremote.access.file=/opt/mas/jmx/jmx.access"

then run the following commands:

chown tomcat:tomcat /opt/mas/jmx/tomcat.password /opt/mas/tomcat/bin/setenv.sh /etc/init.d/tomcat_8080 stop /etc/init.d/tomcat_8080 start

Enable JMX metrics for Spark.

Steps to enable JMX metrics for Spark.

1. For basic JVM metrics, append the following line to the file /etc/spark/conf.mas/spark-env.sh:

# Enable HistoryServer JMX connections on port 9991

HADOOP_JOB_HISTORYSERVER_OPTS="-Dcom.sun.management.jmxremote.port=9991 -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.local.only=false -Dcom.sun.management.jmxremote.password.file=/opt/mas/jmx/mapred.password -Dcom.sun.management.jmxremote.access.file=/opt/mas/jmx/jmx.access"

2. For Spark Mater webUI update spark-env.sh

           export SPARK_MASTER_WEBUI_PORT=18080

       3.   For more metrics update metrics.properties

*.sink.jmx.class=org.apache.spark.metrics.sink.JmxSink

*.source.jvm.class=org.apache.spark.metrics.source.JvmSource

Configuring Zabbix Monitoring For All Hadoop Services (Zookeeper,Spark, namenode, datanode , job history server , hdfs journal node, hive and HBase)

Below info document Zabbix monitoring configuration for all Hadoop services like Zookeeper,Spark, namenode, datanode , job history server , hdfs journal node, hive and HBase with respect file changes.

For Zookeeper:
vi /etc/default/zookeeper
export SERVER_JVMFLAGS="-Dcom.sun.management.jmxremote.port=1703 -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.password.file=/opt/shekhar/jmx/zookeeper.password -Dcom.sun.management.jmxremote.access.file=/opt/shekhar/jmx/jmx.access"
---------------------------------
Spark:
vi /etc/spark/conf/metrics.properties
*.sink.jmx.class=org.apache.spark.metrics.sink.JmxSink
*.source.jvm.class=org.apache.spark.metrics.source.JvmSource
---------------------------------
For namenode, datanode and job history server and hdfs journal node:
vi /etc/hadoop/conf/hadoop-env.sh

# Use YARN for all hadoop commands
export HADOOP_MAPRED_HOME=/usr/lib/hadoop-mapreduce

# Enable NameNode JMX connections on port 9980
HADOOP_NAMENODE_OPTS="-Dcom.sun.management.jmxremote.port=9980 -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.local.only=false -Dcom.sun.management.jmxremote.password.file=/opt/shekhar/jmx/hdfs.password -Dcom.sun.management.jmxremote.access.file=/opt/shekhar/jmx/jmx.access"

# Enable DateNode JMX connections on port 9981
HADOOP_DATANODE_OPTS="-Dcom.sun.management.jmxremote.port=9981 -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.local.only=false -Dcom.sun.management.jmxremote.password.file=/opt/shekhar/jmx/hdfs.password -Dcom.sun.management.jmxremote.access.file=/opt/shekhar/jmx/jmx.access"

# Enable JournalNode JMX connections on port 1704
HADOOP_JOURNALNODE_OPTS="${HADOOP_JOURNALNODE_OPTS} -Dcom.sun.management.jmxremote.port=1704 -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.local.only=false -Dcom.sun.management.jmxremote.password.file=/opt/shekhar/jmx/hdfs.password -Dcom.sun.management.jmxremote.access.file=/opt/shekhar/jmx/jmx.access"

# Enable Job History Server JMX connections on port 9991
HADOOP_JOB_HISTORYSERVER_OPTS="${HADOOP_JOB_HISTORYSERVER_OPTS} -Dcom.sun.management.jmxremote.port=9991 -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.local.only=false -Dcom.sun.management.jmxremote.password.file=/opt/shekhar/jmx/mapred.password -Dcom.sun.management.jmxremote.access.file=/opt/shekhar/jmx/jmx.access"

----------------------------
For resourcemanager, nodemanager:
vi /etc/hadoop/conf/yarn-env.sh
YARN_RESOURCEMANAGER_OPTS="-Dcom.sun.management.jmxremote.port=9983 -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.local.only=false -Dcom.sun.management.jmxremote.password.file=/opt/shekhar/jmx/yarn.password -Dcom.sun.management.jmxremote.access.file=/opt/shekhar/jmx/jmx.access"

YARN_NODEMANAGER_OPTS="-Dcom.sun.management.jmxremote.port=9984 -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.local.only=false -Dcom.sun.management.jmxremote.password.file=/opt/shekhar/jmx/yarn.password -Dcom.sun.management.jmxremote.access.file=/opt/shekhar/jmx/jmx.access"
---------------------------------
For Hive

vi /etc/hive/conf/hive-env.sh
JMX_OPTS="-Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.local.only=false -Dcom.sun.management.jmxremote.password.file=/opt/shekhar/jmx/hive.password -Dcom.sun.management.jmxremote.access.file=/opt/shekhar/jmx/jmx.access"
if [ "$SERVICE" = "hiveserver2" ]; then
  export HADOOP_OPTS="$HADOOP_OPTS $JMX_OPTS -Dcom.sun.management.jmxremote.port=1705 -Dproc_hiveserver"
elif [ "$SERVICE" = "metastore" ]; then
  export HADOOP_OPTS="$HADOOP_OPTS $JMX_OPTS -Dcom.sun.management.jmxremote.port=1706 -Dproc_metastore"
fi
----------------------------------
For HBase:

vi /etc/hbase/conf/hbase-env.sh
HBASE_JMX_BASE="-Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.local.only=false -Dcom.sun.management.jmxremote.password.file=/opt/shekhar/jmx/hbase.password -Dcom.sun.management.jmxremote.access.file=/opt/shekhar/jmx/jmx.access"
export HBASE_MASTER_OPTS="$HBASE_MASTER_OPTS $HBASE_JMX_BASE -Dcom.sun.management.jmxremote.port=10101"
export HBASE_REGIONSERVER_OPTS="$HBASE_REGIONSERVER_OPTS $HBASE_JMX_BASE -Dcom.sun.management.jmxremote.port=10102"
export HBASE_THRIFT_OPTS="$HBASE_THRIFT_OPTS $HBASE_JMX_BASE -Dcom.sun.management.jmxremote.port=10103"
#export HBASE_ZOOKEEPER_OPTS="$HBASE_ZOOKEEPER_OPTS $HBASE_JMX_BASE -Dcom.sun.management.jmxremote.port=10104"
export HBASE_REST_OPTS="$HBASE_REST_OPTS $HBASE_JMX_BASE -Dcom.sun.management.jmxremote.port=10105"

Phoenix 4.7 integration issue with Spark 2.1.0

Steps to integrate Phoenix 4.7 with spark2.1.0

step 1. coping below files into /usr/lib/spark/lib
phoenix-core-4.7.0-HBase-1.0.jar
phoenix-server-4.7.0-HBase-1.0.jar
phoenix-spark-4.7.0-HBase-1.0.jar
phoenix-HBase-client.jar

Step2. creating soft links in /usr/lib/spark/lib
phoenix-HBase-client.jar -> /usr/lib/spark/lib/phoenix-server-client-4.7.0-HBase-1.0.jar
phoenix-spark-HBase.jar -> /usr/lib/spark/lib/phoenix-spark-4.7.0-HBase-1.0.jar

I had integration issue with jackson-core-XXX.jar files. As Spark 2.1 using 2.6.5 version of Jackson apis but Phonenix-Core using 1.9.13. phoneix-core module pom.xml having true so the output of MANIFEST file has below content and Class-Path configured with older version of jackson api and start getting exception like some of the methods not available. I solved the issue by removing true and building Phonenix-Core module then Class-path attribute not generated in Manifest file so it's started using Spark version of jars in run time.

Manifest file with true:
Manifest-Version: 1.0
Implementation-Title: Phoenix Core
Implementation-Version: 4.7.0-HBase-1.0
Archiver-Version: Plexus Archiver
Built-By: mas_user
Specification-Vendor: Apache Software Foundation
Specification-Title: Phoenix Core
Implementation-Vendor-Id: org.apache.phoenix
Class-Path: tephra-api-0.7.0.jar tephra-core-0.7.0.jar gson-2.2.4.jar
guice-3.0.jar javax.inject-1.jar aopalliance-1.0.jar guice-assistedin
ject-3.0.jar libthrift-0.9.0.jar fastutil-6.5.6.jar twill-common-0.6.
0-incubating.jar twill-core-0.6.0-incubating.jar twill-api-0.6.0-incu
bating.jar asm-all-5.0.2.jar twill-discovery-api-0.6.0-incubating.jar
twill-discovery-core-0.6.0-incubating.jar twill-zookeeper-0.6.0-incu
bating.jar metrics-core-3.1.0.jar tephra-hbase-compat-1.0-0.7.0.jar a
ntlr-3.5.jar ST4-4.0.7.jar antlr-runtime-3.5.jar stringtemplate-3.2.1
.jar antlr-2.7.7.jar jline-2.11.jar sqlline-1.1.8.jar guava-13.0.1.ja
r joda-time-1.6.jar annotations-1.3.2.jar jackson-core-asl-1.9.13.jar
jackson-mapper-asl-1.9.13.jar junit-4.12.jar hamcrest-core-1.3.jar p
rotobuf-java-2.5.0.jar httpclient-4.0.1.jar httpcore-4.0.1.jar common
s-logging-1.2.jar log4j-1.2.17.jar slf4j-api-1.6.4.jar snappy-0.3.jar
htrace-core-3.1.0-incubating.jar netty-all-4.0.23.Final.jar commons-
codec-1.7.jar commons-collections-3.2.1.jar commons-csv-1.0.jar jsr30
5-2.0.1.jar slf4j-log4j12-1.7.5.jar hadoop-auth-2.6.0-cdh5.4.8.jar ap
acheds-kerberos-codec-2.0.0-M15.jar apacheds-i18n-2.0.0-M15.jar api-a
sn1-api-1.0.0-M20.jar api-util-1.0.0-M20.jar curator-framework-2.7.1.
jar hadoop-core-2.6.0-mr1-cdh5.4.8.jar hsqldb-1.8.0.10.jar core-3.1.1
.jar hadoop-hdfs-2.6.0-cdh5.4.8.jar commons-daemon-1.0.13.jar leveldb
jni-all-1.8.jar hadoop-hdfs-2.6.0-cdh5.4.8-tests.jar findbugs-annotat
ions-1.3.9-1.jar jersey-client-1.9.jar commons-cli-1.2.jar commons-ma
th-2.1.jar commons-lang-2.6.jar hbase-common-1.0.0-cdh5.4.8.jar hbase
-annotations-1.0.0-cdh5.4.8.jar commons-io-2.4.jar jetty-util-6.1.26.
cloudera.4.jar hbase-protocol-1.0.0-cdh5.4.8.jar hbase-client-1.0.0-c
dh5.4.8.jar netty-3.6.6.Final.jar zookeeper-3.4.5-cdh5.4.8.jar jcodin
gs-1.0.8.jar hbase-server-1.0.0-cdh5.4.8.jar commons-httpclient-3.1.j
ar jersey-core-1.9.jar jersey-server-1.9.jar asm-3.1.jar jetty-6.1.26
.cloudera.4.jar jetty-sslengine-6.1.26.cloudera.4.jar jsp-2.1-6.1.14.
jar jsp-api-2.1-6.1.14.jar servlet-api-2.5-6.1.14.jar jasper-compiler
5.5.23.jar jasper-runtime-5.5.23.jar jamon-runtime-2.3.1.jar jersey
json-1.9.jar jettison-1.1.jar jaxb-impl-2.2.3-1.jar jaxb-api-2.1.jar
activation-1.1.jar hbase-server-1.0.0-cdh5.4.8-tests.jar hadoop-commo
n-2.6.0-cdh5.4.8.jar commons-math3-3.1.1.jar xmlenc-0.52.jar commons-
net-3.1.jar servlet-api-2.5.jar jsp-api-2.1.jar commons-el-1.0.jar je
ts3t-0.9.0.jar java-xmlbuilder-0.4.jar commons-configuration-1.6.jar
commons-digester-1.8.jar commons-beanutils-1.7.0.jar commons-beanutil
s-core-1.8.0.jar avro-1.7.6-cdh5.4.8.jar paranamer-2.3.jar jsch-0.1.4
2.jar curator-client-2.7.1.jar curator-recipes-2.7.1.jar htrace-core-
3.0.4.jar commons-compress-1.4.1.jar xz-1.0.jar hadoop-annotations-2.
6.0-cdh5.4.8.jar hadoop-mapreduce-client-core-2.6.0-cdh5.4.8.jar hado
op-yarn-common-2.6.0-cdh5.4.8.jar jersey-guice-1.9.jar guice-servlet-
3.0.jar hadoop-yarn-api-2.6.0-cdh5.4.8.jar joni-2.1.2.jar
Implementation-Vendor: Apache Software Foundation
Created-By: Apache Maven 3.3.9
Build-Jdk: 1.8.0_65
Specification-Version: 4.7.0-HBase-1.0

Manifest file without true:
Manifest-Version: 1.0
Implementation-Title: Phoenix Core
Implementation-Version: 4.7.0-HBase-1.0
Archiver-Version: Plexus Archiver
Built-By: mas_user
Specification-Vendor: Apache Software Foundation
Specification-Title: Phoenix Core
Implementation-Vendor-Id: org.apache.phoenix
Implementation-Vendor: Apache Software Foundation
Created-By: Apache Maven 3.3.9
Build-Jdk: 1.8.0_65
Specification-Version: 4.7.0-HBase-1.0

thanks,
Shekhar Reddy