Introduction This article is the final part in the series Scaling the Namenode (See  part 1, part 2, part 3 and part 4) In part 4 we discussed about monitoring Namenode Logs for Excessive Skews. In this part, we will look at a few optimizations around logging, access check, and block reports. Audience This article is for Hadoop administrators who are familiar with HDFS and its components. Audit log specific operations only when debug is enabled. By default, the following property is set to blank so none of the Namenode operations are restricted from making an entry into Audit log. Operations like  getfileinfo results in fetching the metadata associated with a file, and in a large/read-heavy cluster, it can generate too much audit log. So, it is recommended  to audit log  getfileinfo  only when audit log debug is enabled.   Change in hdfs-site.xml   <property> <name>dfs.namenode.audit.log.debug.cmdlist<\name> <value>getfileinfo<\value> <description>A comma separated list of NameNode commands that are written to the HDFS namenode audit log only if the audit log level is debug. <\description> <\property>​   In Cloudera Manager you can add the property under "NameNode Advanced Configuration Snippet (Safety Valve) for hdfs-site.xml".   Further, the BlockStateChange and the StateChange related logging are really only useful when those operations have failed i.e. the log entry for those classes is ERROR. At the default INFO level, these two classes generate a large amount of log entry in the Namenode logs. You can reduce the frequency of logging by adding the following lines in log4j.properties file in your Hadoop configurations. Under Cloudera Manager these properties can be added under " NameNode Logging Advanced Configuration Snippet (Safety Valve)".   log4j.logger.BlockStateChange=ERROR log4j.logger.org.apache.hadoop.hdfs.StateChange=ERROR   Avoid recursive call to external authorizer for getContentSummary getContentSummary is an expensive operation in general. It becomes even more expensive in a secured environment where the security is managed by an external component like Apache Ranger as the permission check is performed via a recursive call to check for all descendants in a path. HDFS-14112 introduced an improvement to make just  one call with  subaccess , because often they don't have to evaluate for each and every component of the path.   Change in hdfs-site.xml   <property> <name>dfs.permissions.ContentSummary.subAccess</name> <value>false</value> <description> If "true", the ContentSummary permission checking will use subAccess. If "false", the ContentSummary permission checking will NOT use subAccess. subAccess means using recursion to check the access of all descendants. </description> </property>   Again in Cloudera Manager place the property under "NameNode Advanced Configuration Snippet (Safety Valve) for hdfs-site.xml"   It is recommended to set this property to true so as to use subAccess.  Note: This improvement is only available in CDP releases, the older CDH/HDP releases do not have this improvement so adding this configuration on CDH/HDP releases is not recommended. Optimizing Block Reports In busy and large clusters (say 200 Datanodes), it is very important to not overwhelm NameNode with too frequent full block reports from the datanodes. If the NameNodes are already degraded, the block reports add further stress on the NameNodes. The NameNodes might be so slow to process the block reports that you would eventually see messages like 'Block report queue is full' in the NameNode logs. It is interesting to note that while the default block report queue size is set to 1024, we can see this 'Block report queue is full' message even during a NameNode startup in what we call a block report flood event and also when your NameNode's RPC processing time is too high to indicate a severely degraded NameNode, thereby having a backlog of reports to process and eventually overflowing the queue.  While the block report queue size is configurable and you could essentially increase the queue size, a better approach is to optimize the way the data nodes send blocks reports.   We recommend a 3 prong approach to change the following in hdfs-site.xml: Split block report by volume (Default value  1000000) <property> <name>dfs.blockreport.split.threshold</name> <value>0</value> <description> If the number of blocks on the DataNode is below this threshold then it will send block reports for all Storage Directories in a single message. If the number of blocks exceeds this threshold then the DataNode will send block reports for each Storage Directory in separate messages. Set to zero to always split. </description> </property> Reduce full block report frequency from a default 6 hours to 12 hours <property> <name>dfs.blockreport.intervalMsec</name> <value>43200000</value> <description> Determines block reporting interval in milliseconds. </description> </property> Batch incremental reports (Default value 0 disables batching) <property> <name>dfs.blockreport.incremental.intervalMsec</name> <value> 100 </value> <description> If set to a positive integer, the value in ms to wait between sending incremental block reports from the Datanode to the Namenode. </description> </property>     All 3 belong under "NameNode Advanced Configuration Snippet (Safety Valve) for hdfs-site.xml" in Cloudera Manager. Conclusion This wraps up the series on getting the best performance possible out of your NameNode. We hope these tips will keep your cluster running at its best and your users happy. ... View more

When using Ambari Metrics in Distributed Mode,after HDP-3/Ambari-2.7 upgrade, HBase metrics are not emitted due to an issue which will likely be fixed after Ambari-2.7.4.   We will see similar kind of messages in AMS Collector Log. Error : 2019-06-10 02:42:59,215 INFO timeline timeline.HadoopTimelineMetricsSink: No live collector to send metrics to. Metrics to be sent will be discarded. This message will be skipped for the next 20 Debug Error shows this : 2019-06-14 20:35:29,538 DEBUG main timeline.HadoopTimelineMetricsSink: Trying to find live collector host from : exp5.lab.com,exp4.lab.com 2019-06-14 20:35:29,538 DEBUG main timeline.HadoopTimelineMetricsSink: Requesting live collector nodes : http://exp5.lab.com,exp4.lab.com:6188/ws/v1/timeline/metrics/livenodes 2019-06-14 20:35:29,557 DEBUG main timeline.HadoopTimelineMetricsSink: Unable to connect to collector, http://exp5.lab.com,exp4.lab.com:6188/ws/v1/timeline/metrics/livenodes 2019-06-14 20:35:29,557 DEBUG main timeline.HadoopTimelineMetricsSink: java.net.UnknownHostException: exp5.lab.com,exp4.lab.com 2019-06-14 20:35:29,558 DEBUG main timeline.HadoopTimelineMetricsSink: Collector exp5.lab.com,exp4.lab.com is not longer live. Removing it from list of know live collector hosts : [] 2019-06-14 20:35:29,558 DEBUG main timeline.HadoopTimelineMetricsSink: No live collectors from configuration. Basically, it's incorrectly parsing hostnames when there are more than one Metrics collector.   In the meantime, there is a very easy workaround.   Add  *.sink.timeline.zookeeper.quorum=<ZK_QUORUM_ADDRESS> Example: *.sink.timeline.zookeeper.quorum=zk_host1:2181,zk_host2:2181,zk_host3:2181   to the following files on Ambari Server host: /var/lib/ambari-server/resources/stacks/HDP/3.0/services/HBASE/package/templates/hadoop-metrics2-hbase.properties-GANGLIA-MASTER.j2 /var/lib/ambari-server/resources/stacks/HDP/3.0/services/HBASE/package/templates/hadoop-metrics2-hbase.properties-GANGLIA-RS.j2 Restart Ambari Server for changes to take effect and soon you will be able to see metrics on the Grafana HBase Dashboards. ... View more

In HDP-2.6/Ambari-2.6, it was not mandatory enable HS2 metrics explicitly. Thus, all metrics would be emitted without defining any configs explicitly.   In HDP-3/Ambari-2.7, we will see similar erros in AMS Collector Log: Error : 2019-06-10 02:42:59,215 INFO timeline timeline.HadoopTimelineMetricsSink: No live collector to send metrics to. Metrics to be sent will be discarded. This message will be skipped for the next 20 Debug Error shows this : 2019-06-14 20:35:29,538 DEBUG main timeline.HadoopTimelineMetricsSink: Trying to find live collector host from : exp5.lab.com,exp4.lab.com 2019-06-14 20:35:29,538 DEBUG main timeline.HadoopTimelineMetricsSink: Requesting live collector nodes : http://exp5.lab.com,exp4.lab.com:6188/ws/v1/timeline/metrics/livenodes 2019-06-14 20:35:29,557 DEBUG main timeline.HadoopTimelineMetricsSink: Unable to connect to collector, http://exp5.lab.com,exp4.lab.com:6188/ws/v1/timeline/metrics/livenodes 2019-06-14 20:35:29,557 DEBUG main timeline.HadoopTimelineMetricsSink: java.net.UnknownHostException: exp5.lab.com,exp4.lab.com 2019-06-14 20:35:29,558 DEBUG main timeline.HadoopTimelineMetricsSink: Collector exp5.lab.com,exp4.lab.com is not longer live. Removing it from list of know live collector hosts : [] 2019-06-14 20:35:29,558 DEBUG main timeline.HadoopTimelineMetricsSink: No live collectors from configuration.   You need to ensure the following properties exist. If not, first add them in the respective custom section via Ambari >Hive> Configs.    Next, if you are using Ambari Metrics with more than one collector, then you need to make one more change due a BUG, which will likely be fixed after Ambari-2.7.4.   Add  *.sink.timeline.zookeeper.quorum=<ZK_QUORUM_ADDRESS> Example: *.sink.timeline.zookeeper.quorum=zk_host1:2181,zk_host2:2181,zk_host3:2181   to all the 4 files under  /var/lib/ambari-server/resources/stacks/HDP/3.0/services/HIVE/package/templates/ located on Ambari Server host.   Restart Ambari Server & Hive for changes to take effect. Now the metrics will be emitted and you should be able to see data on your Grafana Dashboard. ... View more

If you have started using Hive LLAP, you would have noticed that by default its configured to use log4j2. Default configuration makes use of advanced features from log4j2 like Rolling Over logs based on time interval and size. With time, a lot of old log files would have accumulated and typically you would compress those files manually or add additional jars and change configuration when using log4j1 to achieve the same With log4j2, a simple change in configuration can ensure that every time a log file is rolled over, it gets compressed for optimal use of storage space. Default configuration: To automatically compress the rolled over log files, update the highlighted line to: appender.DRFA.filePattern = ${sys:hive.log.dir}/${sys:hive.log.file}.%d{yyyy-MM-dd}-%i.gz -%i will ensure that in a rare scenario when there has been increased logging and the threshold size can be been reached more than once in the specified interval, the previously rolled over file won't get over written. .gz will ensure that files are compressed using gzip To understand the finer details about log4j2 appenders, you may check out the official documentation. Similarly you can also make similar changes to llap-cli log settings: ... View more

I am trying to compile and build hadoop source on MacOS. Here is the complete error trace. [exec] Scanning dependencies of target rpc_obj [exec] [ 32%] Building CXX object main/native/libhdfspp/lib/rpc/CMakeFiles/rpc_obj.dir/rpc_connection_impl.cc.o [exec] [ 32%] Building CXX object main/native/libhdfspp/lib/rpc/CMakeFiles/rpc_obj.dir/rpc_engine.cc.o [exec] [ 32%] Building CXX object main/native/libhdfspp/lib/rpc/CMakeFiles/rpc_obj.dir/namenode_tracker.cc.o [exec] [ 33%] Building CXX object main/native/libhdfspp/lib/rpc/CMakeFiles/rpc_obj.dir/request.cc.o [exec] [ 33%] Building CXX object main/native/libhdfspp/lib/rpc/CMakeFiles/rpc_obj.dir/sasl_protocol.cc.o [exec] [ 34%] Building CXX object main/native/libhdfspp/lib/rpc/CMakeFiles/rpc_obj.dir/sasl_engine.cc.o [exec] [ 34%] Building CXX object main/native/libhdfspp/lib/rpc/CMakeFiles/rpc_obj.dir/cyrus_sasl_engine.cc.o [exec] [ 34%] Built target rpc_obj [exec] Scanning dependencies of target rpc [exec] [ 35%] Linking CXX static library librpc.a [exec] [ 35%] Built target rpc [exec] [ 35%] Building CXX object main/native/libhdfspp/lib/common/CMakeFiles/common_obj.dir/ioservice_impl.cc.o [exec] In file included from /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/lib/common/ioservice_impl.cc:19: [exec] In file included from /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/lib/common/ioservice_impl.h:22: [exec] /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/include/hdfspp/ioservice.h:109:30: error: no type named 'function' in namespace 'std' [exec] virtual void PostTask(std::function<void(void)> asyncTask) = 0; [exec] ~~~~~^ [exec] /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/include/hdfspp/ioservice.h:109:38: error: expected ')' [exec] virtual void PostTask(std::function<void(void)> asyncTask) = 0; [exec] ^ [exec] /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/include/hdfspp/ioservice.h:109:24: note: to match this '(' [exec] virtual void PostTask(std::function<void(void)> asyncTask) = 0; [exec] ^ [exec] /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/include/hdfspp/ioservice.h:117:10: error: no member named 'function' in namespace 'std' [exec] std::function<void(void)> typeEraser = func; [exec] ~~~~~^ [exec] /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/include/hdfspp/ioservice.h:117:28: error: expected '(' for function-style cast or type construction [exec] std::function<void(void)> typeEraser = func; [exec] ~~~~^ [exec] /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/include/hdfspp/ioservice.h:117:31: error: use of undeclared identifier 'typeEraser' [exec] std::function<void(void)> typeEraser = func; [exec] ^ [exec] In file included from /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/lib/common/ioservice_impl.cc:19: [exec] /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/lib/common/ioservice_impl.h:45:54: error: non-virtual member function marked 'override' hides virtual member function [exec] void PostTask(std::function<void(void)> asyncTask) override; [exec] ^ [exec] /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/include/hdfspp/ioservice.h:109:16: note: hidden overloaded virtual function 'hdfs::IoService::PostTask' declared here: type mismatch at 1st parameter ('int' vs 'std::function<void ()>') [exec] virtual void PostTask(std::function<void(void)> asyncTask) = 0; [exec] ^ [exec] /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/lib/common/ioservice_impl.cc:34:14: error: allocating an object of abstract class type 'hdfs::IoServiceImpl' [exec] return new IoServiceImpl(); [exec] ^ [exec] /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/include/hdfspp/ioservice.h:109:16: note: unimplemented pure virtual method 'PostTask' in 'IoServiceImpl' [exec] virtual void PostTask(std::function<void(void)> asyncTask) = 0; [exec] ^ [exec] In file included from /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/lib/common/ioservice_impl.cc:19: [exec] In file included from /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/lib/common/ioservice_impl.h:22: [exec] In file included from /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/include/hdfspp/ioservice.h:61: [exec] /Library/Developer/CommandLineTools/usr/include/c++/v1/memory:2143:9: error: field type 'hdfs::IoServiceImpl' is an abstract class [exec] _T2 __second_; [exec] ^ [exec] /Library/Developer/CommandLineTools/usr/include/c++/v1/memory:2302:15: note: in instantiation of template class 'std::__1::__libcpp_compressed_pair_imp<std::__1::allocator<hdfs::IoServiceImpl>, hdfs::IoServiceImpl, 1>' requested here [exec] : private __libcpp_compressed_pair_imp<_T1, _T2> [exec] ^ [exec] /Library/Developer/CommandLineTools/usr/include/c++/v1/memory:3816:36: note: in instantiation of template class 'std::__1::__compressed_pair<std::__1::allocator<hdfs::IoServiceImpl>, hdfs::IoServiceImpl>' requested here [exec] __compressed_pair<_Alloc, _Tp> __data_; [exec] ^ [exec] /Library/Developer/CommandLineTools/usr/include/c++/v1/memory:4444:26: note: in instantiation of template class 'std::__1::__shared_ptr_emplace<hdfs::IoServiceImpl, std::__1::allocator<hdfs::IoServiceImpl> >' requested here [exec] ::new(__hold2.get()) _CntrlBlk(__a2, _VSTD::forward<_Args>(__args)...); [exec] ^ [exec] /Library/Developer/CommandLineTools/usr/include/c++/v1/memory:4810:29: note: in instantiation of function template specialization 'std::__1::shared_ptr<hdfs::IoServiceImpl>::make_shared<>' requested here [exec] return shared_ptr<_Tp>::make_shared(_VSTD::forward<_Args>(__args)...); [exec] ^ [exec] /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/lib/common/ioservice_impl.cc:38:15: note: in instantiation of function template specialization 'std::__1::make_shared<hdfs::IoServiceImpl>' requested here [exec] return std::make_shared<IoServiceImpl>(); [exec] ^ [exec] In file included from /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/lib/common/ioservice_impl.cc:19: [exec] In file included from /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/lib/common/ioservice_impl.h:22: [exec] In file included from /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/include/hdfspp/ioservice.h:61: [exec] /Library/Developer/CommandLineTools/usr/include/c++/v1/memory:4447:28: error: assigning to 'std::__1::__shared_weak_count *' from incompatible type 'pointer' (aka 'std::__1::__shared_ptr_emplace<hdfs::IoServiceImpl, std::__1::allocator<hdfs::IoServiceImpl> > *') [exec] __r.__cntrl_ = __hold2.release(); [exec] ~~~~~~~~^~~~~~~~~ [exec] /Library/Developer/CommandLineTools/usr/include/c++/v1/memory:4810:29: note: in instantiation of function template specialization 'std::__1::shared_ptr<hdfs::IoServiceImpl>::make_shared<>' requested here [exec] return shared_ptr<_Tp>::make_shared(_VSTD::forward<_Args>(__args)...); [exec] ^ [exec] /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/src/main/native/libhdfspp/lib/common/ioservice_impl.cc:38:15: note: in instantiation of function template specialization 'std::__1::make_shared<hdfs::IoServiceImpl>' requested here [exec] return std::make_shared<IoServiceImpl>(); [exec] ^ [exec] 9 errors generated. [exec] make[2]: *** [main/native/libhdfspp/lib/common/CMakeFiles/common_obj.dir/ioservice_impl.cc.o] Error 1 [exec] make[1]: *** [main/native/libhdfspp/lib/common/CMakeFiles/common_obj.dir/all] Error 2 [exec] make: *** [all] Error 2 [INFO] ------------------------------------------------------------------------ [INFO] Reactor Summary: [INFO] [INFO] Apache Hadoop HDFS Native Client ................... FAILURE [ 29.737 s] [INFO] Apache Hadoop HttpFS ............................... SKIPPED [INFO] Apache Hadoop HDFS-NFS ............................. SKIPPED [INFO] Apache Hadoop HDFS-RBF ............................. SKIPPED [INFO] Apache Hadoop HDFS Project ......................... SKIPPED [INFO] Apache Hadoop YARN ................................. SKIPPED [INFO] Apache Hadoop YARN API ............................. SKIPPED [INFO] Apache Hadoop YARN Common .......................... SKIPPED [INFO] Apache Hadoop YARN Registry ........................ SKIPPED [INFO] Apache Hadoop YARN Server .......................... SKIPPED [INFO] Apache Hadoop YARN Server Common ................... SKIPPED [INFO] Apache Hadoop YARN NodeManager ..................... SKIPPED [INFO] Apache Hadoop YARN Web Proxy ....................... SKIPPED [INFO] Apache Hadoop YARN ApplicationHistoryService ....... SKIPPED [INFO] Apache Hadoop YARN Timeline Service ................ SKIPPED [INFO] Apache Hadoop YARN ResourceManager ................. SKIPPED [INFO] Apache Hadoop YARN Server Tests .................... SKIPPED [INFO] Apache Hadoop YARN Client .......................... SKIPPED [INFO] Apache Hadoop YARN SharedCacheManager .............. SKIPPED [INFO] Apache Hadoop YARN Timeline Plugin Storage ......... SKIPPED [INFO] Apache Hadoop YARN TimelineService HBase Backend ... SKIPPED [INFO] Apache Hadoop YARN TimelineService HBase Common .... SKIPPED [INFO] Apache Hadoop YARN TimelineService HBase Client .... SKIPPED [INFO] Apache Hadoop YARN TimelineService HBase Servers ... SKIPPED [INFO] Apache Hadoop YARN TimelineService HBase Server 1.2 SKIPPED [INFO] Apache Hadoop YARN TimelineService HBase tests ..... SKIPPED [INFO] Apache Hadoop YARN Router .......................... SKIPPED [INFO] Apache Hadoop YARN Applications .................... SKIPPED [INFO] Apache Hadoop YARN DistributedShell ................ SKIPPED [INFO] Apache Hadoop YARN Unmanaged Am Launcher ........... SKIPPED [INFO] Apache Hadoop MapReduce Client ..................... SKIPPED [INFO] Apache Hadoop MapReduce Core ....................... SKIPPED [INFO] Apache Hadoop MapReduce Common ..................... SKIPPED [INFO] Apache Hadoop MapReduce Shuffle .................... SKIPPED [INFO] Apache Hadoop MapReduce App ........................ SKIPPED [INFO] Apache Hadoop MapReduce HistoryServer .............. SKIPPED [INFO] Apache Hadoop MapReduce JobClient .................. SKIPPED [INFO] Apache Hadoop Mini-Cluster ......................... SKIPPED [INFO] Apache Hadoop YARN Services ........................ SKIPPED [INFO] Apache Hadoop YARN Services Core ................... SKIPPED [INFO] Apache Hadoop YARN Services API .................... SKIPPED [INFO] Apache Hadoop YARN Site ............................ SKIPPED [INFO] Apache Hadoop YARN UI .............................. SKIPPED [INFO] Apache Hadoop YARN Project ......................... SKIPPED [INFO] Apache Hadoop MapReduce HistoryServer Plugins ...... SKIPPED [INFO] Apache Hadoop MapReduce NativeTask ................. SKIPPED [INFO] Apache Hadoop MapReduce Uploader ................... SKIPPED [INFO] Apache Hadoop MapReduce Examples ................... SKIPPED [INFO] Apache Hadoop MapReduce ............................ SKIPPED [INFO] Apache Hadoop MapReduce Streaming .................. SKIPPED [INFO] Apache Hadoop Distributed Copy ..................... SKIPPED [INFO] Apache Hadoop Archives ............................. SKIPPED [INFO] Apache Hadoop Archive Logs ......................... SKIPPED [INFO] Apache Hadoop Rumen ................................ SKIPPED [INFO] Apache Hadoop Gridmix .............................. SKIPPED [INFO] Apache Hadoop Data Join ............................ SKIPPED [INFO] Apache Hadoop Extras ............................... SKIPPED [INFO] Apache Hadoop Pipes ................................ SKIPPED [INFO] Apache Hadoop OpenStack support .................... SKIPPED [INFO] Apache Hadoop Amazon Web Services support .......... SKIPPED [INFO] Apache Hadoop Kafka Library support ................ SKIPPED [INFO] Apache Hadoop Azure support ........................ SKIPPED [INFO] Apache Hadoop Aliyun OSS support ................... SKIPPED [INFO] Apache Hadoop Client Aggregator .................... SKIPPED [INFO] Apache Hadoop Scheduler Load Simulator ............. SKIPPED [INFO] Apache Hadoop Resource Estimator Service ........... SKIPPED [INFO] Apache Hadoop Azure Data Lake support .............. SKIPPED [INFO] Apache Hadoop Image Generation Tool ................ SKIPPED [INFO] Apache Hadoop Tools Dist ........................... SKIPPED [INFO] Apache Hadoop Tools ................................ SKIPPED [INFO] Apache Hadoop Client API ........................... SKIPPED [INFO] Apache Hadoop Client Runtime ....................... SKIPPED [INFO] Apache Hadoop Client Packaging Invariants .......... SKIPPED [INFO] Apache Hadoop Client Test Minicluster .............. SKIPPED [INFO] Apache Hadoop Client Packaging Invariants for Test . SKIPPED [INFO] Apache Hadoop Client Packaging Integration Tests ... SKIPPED [INFO] Apache Hadoop Distribution ......................... SKIPPED [INFO] Apache Hadoop Client Modules ....................... SKIPPED [INFO] Apache Hadoop Cloud Storage ........................ SKIPPED [INFO] Apache Hadoop Cloud Storage Project ................ SKIPPED [INFO] ------------------------------------------------------------------------ [INFO] BUILD FAILURE [INFO] ------------------------------------------------------------------------ [INFO] Total time: 31.310 s [INFO] Finished at: 2018-05-11T00:27:30-04:00 [INFO] Final Memory: 67M/557M [INFO] ------------------------------------------------------------------------ [ERROR] Failed to execute goal org.apache.maven.plugins:maven-antrun-plugin:1.7:run (make) on project hadoop-hdfs-native-client: An Ant BuildException has occured: exec returned: 2 [ERROR] around Ant part ...<exec failonerror="true" dir="/Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/target" executable="make">... @ 9:131 in /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/target/antrun/build-main.xml [ERROR] -> [Help 1] org.apache.maven.lifecycle.LifecycleExecutionException: Failed to execute goal org.apache.maven.plugins:maven-antrun-plugin:1.7:run (make) on project hadoop-hdfs-native-client: An Ant BuildException has occured: exec returned: 2 around Ant part ...<exec failonerror="true" dir="/Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/target" executable="make">... @ 9:131 in /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/target/antrun/build-main.xml at org.apache.maven.lifecycle.internal.MojoExecutor.execute(MojoExecutor.java:213) at org.apache.maven.lifecycle.internal.MojoExecutor.execute(MojoExecutor.java:154) at org.apache.maven.lifecycle.internal.MojoExecutor.execute(MojoExecutor.java:146) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject(LifecycleModuleBuilder.java:117) at org.apache.maven.lifecycle.internal.LifecycleModuleBuilder.buildProject(LifecycleModuleBuilder.java:81) at org.apache.maven.lifecycle.internal.builder.singlethreaded.SingleThreadedBuilder.build(SingleThreadedBuilder.java:51) at org.apache.maven.lifecycle.internal.LifecycleStarter.execute(LifecycleStarter.java:128) at org.apache.maven.DefaultMaven.doExecute(DefaultMaven.java:309) at org.apache.maven.DefaultMaven.doExecute(DefaultMaven.java:194) at org.apache.maven.DefaultMaven.execute(DefaultMaven.java:107) at org.apache.maven.cli.MavenCli.execute(MavenCli.java:993) at org.apache.maven.cli.MavenCli.doMain(MavenCli.java:345) at org.apache.maven.cli.MavenCli.main(MavenCli.java:191) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at org.codehaus.plexus.classworlds.launcher.Launcher.launchEnhanced(Launcher.java:289) at org.codehaus.plexus.classworlds.launcher.Launcher.launch(Launcher.java:229) at org.codehaus.plexus.classworlds.launcher.Launcher.mainWithExitCode(Launcher.java:415) at org.codehaus.plexus.classworlds.launcher.Launcher.main(Launcher.java:356) Caused by: org.apache.maven.plugin.MojoExecutionException: An Ant BuildException has occured: exec returned: 2 around Ant part ...<exec failonerror="true" dir="/Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/target" executable="make">... @ 9:131 in /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/target/antrun/build-main.xml at org.apache.maven.plugin.antrun.AntRunMojo.execute(AntRunMojo.java:355) at org.apache.maven.plugin.DefaultBuildPluginManager.executeMojo(DefaultBuildPluginManager.java:134) at org.apache.maven.lifecycle.internal.MojoExecutor.execute(MojoExecutor.java:208) ... 20 more Caused by: /Users/dc/hadoop/hadoop-hdfs-project/hadoop-hdfs-native-client/target/antrun/build-main.xml:9: exec returned: 2 at org.apache.tools.ant.taskdefs.ExecTask.runExecute(ExecTask.java:646) at org.apache.tools.ant.taskdefs.ExecTask.runExec(ExecTask.java:672) at org.apache.tools.ant.taskdefs.ExecTask.execute(ExecTask.java:498) at org.apache.tools.ant.UnknownElement.execute(UnknownElement.java:291) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at org.apache.tools.ant.dispatch.DispatchUtils.execute(DispatchUtils.java:106) at org.apache.tools.ant.Task.perform(Task.java:348) at org.apache.tools.ant.Target.execute(Target.java:390) at org.apache.tools.ant.Target.performTasks(Target.java:411) at org.apache.tools.ant.Project.executeSortedTargets(Project.java:1399) at org.apache.tools.ant.Project.executeTarget(Project.java:1368) at org.apache.maven.plugin.antrun.AntRunMojo.execute(AntRunMojo.java:327) ... 22 more [ERROR] [ERROR] [ERROR] For more information about the errors and possible solutions, please read the following articles: [ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/MojoExecutionException I have done the following steps: Installed XCode and Command Line Developer tools Installed Protobuf 2.5.0 Followed this HCC article for all other installations: https://community.hortonworks.com/articles/36832/setting-hadoop-development-environment-on-mac-os-x.html Clone github.com/apache/hadoop cd /.../hadoop mvn clean package -DskipTests mvn package -Pdist -Pnative -Dtar -DskipTests It appears like the C++ code is failing to compile. Appreciate any help ... View more

Repo Description This script triggers service checks for components which are not in maintenance mode. Once service checks are triggered, you can monitor their progress from Ambari UI. It can be used with following arguments: -u <ambariAdminUser> -p <ambariAdminPassword> -s <all|comma-separated list> Optional arguments: [-t <ambariServerHost or IP Address>] [-n <ambariServerPort>] [-c <empty or path to cert file>] to be used when Ambari SSL is enabled When using -c Mention -c at the end of the command as shown in the examples Mention the path to the CAcerts file (preferably) or the Ambari certs file (if selfsigned) to trust the https connectivity to Ambari. If either paths are not available, you may use -c without any path. If not specified, default value for -t : localhost If not specified, default value for -n : 8080(when SSL disabled) and 8443(when SSL enabled) For KERBEROS service check, script will prompt for an option to skip service check. You may skip the check for KERBEROS if you do not have KDC Admin principal and password. If executing this script on Ambari Server host, you do not need to specify -t and -p options Example: Trigger Service Check for all components sh ambari-service-check.sh -u admin -p admin -s all Example: Trigger Service Check for only for HIVE, HDFS, and KNOX sh ambari-service-check.sh -u admin -p admin -s hive,hdfs,knox Example: Trigger Service Check for only for HIVE, HDFS, and KNOX when ssl is enabled sh ambari-service-check.sh -u admin -p admin -s hive,hdfs,knox -c Example: Trigger Service Check for only for HIVE, HDFS, and KNOX when ssl is enabled and you want to specify cert file sh ambari-service-check.sh -u admin -p admin -s hive,hdfs,knox -c /path/to/cert/file Repo Info Github Repo URL https://github.com/dineshchitlangia/Ambari-Service-Check Github account name dineshchitlangia Repo name Ambari-Service-Check ... View more

When running a custom Java application that connects via JDBC to Hive, after migration to HDP-2.6.x, the application now fails to start with a NoClassDefFoundError or ClassNotFoundException related to a Hive class, like: Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/hive/service/cli/thrift/TCLIService$Iface at org.apache.hive.jdbc.HiveDriver.connect(HiveDriver.java:105) at java.sql.DriverManager.getConnection(DriverManager.java:664) at java.sql.DriverManager.getConnection(DriverManager.java:270) Root Cause Prior to HDP-2.6.x, the hive-jdbc.jar is a symlink which points to the "standalone" jdbc jar (the one intended to be used for non-hadoop apps, like a generic app that has JDBC driver DB accessibility), for example in HDP 2.5.0: /usr/hdp/current/hive-client/lib/hive-jdbc.jar -> hive-jdbc-1.2.1000.2.5.0.0-1245-standalone.jar But from newer versions, HDP-2.6.x onwards, the hive-jdbc.jar now points to the "hadoop env" JDBC driver, which has dependencies on many other Hadoop JARs, for example in HDP 2.6.2: /usr/hdp/current/hive-client/lib/hive-jdbc.jar -> hive-jdbc-1.2.1000.2.6.2.0-205.jar or in HDP-2.6.3 /usr/hdp/current/hive-client/lib/hive-jdbc.jar -> hive-jdbc-1.2.1000.2.6.3.0-235.jar Does this mean the HDP stack no longer includes a standalone JAR ? No. The standalone jar has been moved to this path: /usr/hdp/current/hive-client/jdbc Two ways to solve this: 1. Change the custom Java application's classpath to use the hive-jdbc-*-standalone.jar explicitly As noted above, the standalone jar is now available in a different path. For example in HDP-2.6.2: /usr/hdp/current/hive-client/jdbc/hive-jdbc-1.2.1000.2.6.2.0-205-standalone.jar In HDP-2.6.3 /usr/hdp/current/hive-client/jdbc/hive-jdbc-1.2.1000.2.6.3.0-235-standalone.jar 2. Add the following to the HADOOP_CLASSPATH of the custom Java application if it uses other Hadoop components/JARs /usr/hdp/current/hive-client/lib/hive-metastore-*.jar:/usr/hdp/current/hive-client/lib/hive-common-*.jar:/usr/hdp/current/hive-client/lib/hive-cli-*.jar:/usr/hdp/current/hive-client/lib/hive-exec-*.jar:/usr/hdp/current/hive-client/lib/hive-service.jar:/usr/hdp/current/hive-client/lib/libfb303-*.jar:/usr/hdp/current/hive-client/lib/libthrift-*.jar:/usr/hdp/current/hadoop-client/lib/log4j*.jar:/usr/hdp/current/hadoop-client/lib/slf4j-api-*.jar:/usr/hdp/current/hadoop-client/lib/slf4j-log4j12-*.jar:/usr/hdp/current/hadoop-client/lib/commons-logging-*.jar ... View more

Description During HDP Upgrade, Hive Metastore restart step fails with message - "ValueError: time data '2017-05-10 19:08:30' does not match format '%Y-%m-%d %H:%M:%S.%f'" Following is the stack trace: Traceback (most recent call last): File "/var/lib/ambari-agent/cache/common-services/HIVE/0.12.0.2.0/package/scripts/hive_metastore.py", line 211, in <module> HiveMetastore().execute() File "/usr/lib/python2.6/site-packages/resource_management/libraries/script/script.py", line 329, in execute method(env) File "/usr/lib/python2.6/site-packages/resource_management/libraries/script/script.py", line 841, in restart self.pre_upgrade_restart(env, upgrade_type=upgrade_type) File "/var/lib/ambari-agent/cache/common-services/HIVE/0.12.0.2.0/package/scripts/hive_metastore.py", line 118, in pre_upgrade_restart self.upgrade_schema(env) File "/var/lib/ambari-agent/cache/common-services/HIVE/0.12.0.2.0/package/scripts/hive_metastore.py", line 150, in upgrade_schema status_params.tmp_dir) File "/usr/lib/python2.6/site-packages/resource_management/libraries/functions/security_commons.py", line 242, in cached_kinit_executor if (now - datetime.strptime(last_run_time, "%Y-%m-%d %H:%M:%S.%f") > timedelta(minutes=expiration_time)): File "/usr/lib64/python2.6/_strptime.py", line 325, in _strptime (data_string, format)) ValueError: time data '2017-05-10 19:08:30' does not match format '%Y-%m-%d %H:%M:%S.%f' Root cause During the upgrade, the data will be read from a file, such as *_tmp.txt, under the /var/lib/ambari-agent/tmp/kinit_executor_cache directory. This issue occurs if this file is not updated and points to an older date. Solution 1. Login to Hive Metastore host 2. Move *_tmp.txt files mv /var/lib/ambari-agent/tmp/kinit_executor_cache/*_tmp.txt /tmp 3. Retry Restart Hive Metastore step from Ambari Upgrade screen ... View more

During upgrade, if Namenode restarts timeout, it may not appear to be a problem as the request times out from the Ambari UI but the restart process continue to run in the background. However, this can lead to inconsistencies in Ambari database and cause further issues at Finalize upgrade step. Note: This article is only useful upto Ambari-2.5.x version and must be performed before starting the upgrade process. Ambari-2.6 onwards it is a one step change where you only need to update the ambari.properties file instead of all the xml changes listed below. For Ambari-2.6 onwards: refer this document https://docs.hortonworks.com/HDPDocuments/Ambari-2.6.0.0/bk_ambari-upgrade/content/preparing_to_upgrade_ambari_and_hdp.html Caution: The steps described below are a hack! It is not recommended that we go about making changes to upgrade XML files based on our needs. Please exercise caution and measure your risks before following the steps. You can increase the timeouts for namenode restart using the following steps before you start the upgrade process: Step 1: Locate the upgrade file on Ambari server host. If we are upgrading from HDP-2.5 to HDP-2.6 then : /var/lib/ambari-server/resources/stacks/HDP/2.5/upgrades/nonrolling-upgrade-2.6.xml [Express Upgrade] /var/lib/ambari-server/resources/stacks/HDP/2.5/upgrades/upgrade-2.6.xml [Rolling Upgrade] Step 2: Change <service name="HDFS"> <component name="NAMENODE"> <upgrade> <task xsi:type="restart-task"/> </upgrade> </component> to <service name="HDFS"> <component name="NAMENODE"> <upgrade> <task xsi:type="restart-task" timeout-config="upgrade.parameter.nn-restart.timeout"/> </upgrade> </component> Step 3: Add this to ambari.properties upgrade.parameter.nn-restart.timeout=XXXXXX where XXXXXX is the time in seconds Step 4: Restart Ambari Server Step 5: Now you can move on to your upgrade process ... View more

For this article, I am using Ambari-2.5.2.0 and trying to upgrade HDP-2.5.3 to HDP-2.6.2. If you have a large HBase cluster, it can take a long time to do a HBase snapshot. As part of upgrade, this is one of the steps which Ambari will perform for you. However, for a large cluster, this step can actually lead to a timeout from Ambari UI and may result in further inconsistencies just before the Finalize Upgrade step. To overcome this, a lot of people have started performing a manual HBase Snapshot before the upgrade. However, not many have found a way to force Ambari to skip this step and save some time instead of waiting for it to timeout in order to proceed to next step during the upgrade. Here is how you can skip HBase Snapshot step altogether (in case you want to perform it manually before the upgrade): Caution: The steps described below are a hack! It is not recommended that we go about making changes to upgrade XML files based on our needs. Please exercise caution and measure your risks before following the steps. The following steps must be performed before starting the upgrade. Step 1: Locate the upgrade XML file on ambari-server host /var/lib/ambari-server/resources/stacks/HDP/2.5/upgrades/upgrade-2.6.xml (for Rolling Upgrade) /var/lib/ambari-server/resources/stacks/HDP/2.5/upgrades/nonrolling-upgrade-2.6.xml (for Express Upgrade) Step 2: Comment out the following piece of code in the upgrade XML file and save it <execute-stage service="HBASE" component="HBASE_MASTER" title="Snapshot HBASE"> <task xsi:type="execute" hosts="master"> <script>scripts/hbase_upgrade.py</script> <function>take_snapshot</function> </task> </execute-stage> Step 3: Restart Ambari Server for it to pick up the changes. Step 4: Now you can start your upgrade. ... View more