Category: HDP 2.6

Adding service to HDP using REST API

One way of adding new service to the HDP is by using a graphical interface Ambari. In this post, it is explained how the same is done using Ambari’s REST API. The service added in this post is Pig. Pig is not a “classical” service, rather a package, but from REST API’s point of view, it is a service.

The documentation on how to do this is dated April 21 2014. I have followed it and eventually made it work. The documentation can be found here.

The cluster

I am using AWS EC2 services, operating system is Centos7.

I have an Ambari server, version 2.6.2 and an HDP cluster version 2.6.5. This should work on other versions as well.

My cluster has one NameNode, on which Ambari is installed as well, and one DataNode. The services installed are the bare minimum – HDFS, YARN, MapReduce2, Zookeeper and Hive.

The goal

Install Pig client on the NameNode and on the DataNode.

Adding Pig to the cluster

Variables

export AMBARI_SERVER=PUBLIC_IP
export MASTER_DNS=NAMENODE_PRIVATE_DNS
export SLAVE_DNS=DATANODE_PRIVATE_DNS
export CLUSTER_NAME=mincluster2

Create service on the Cluster

curl -u admin:admin -H "X-Requested-By:ambari" -i -X POST -d '{"ServiceInfo":{"service_name":"PIG"}}' 'http://'$AMBARI_SERVER':8080/api/v1/clusters/'$CLUSTER_NAME'/services'

Pig service is added to the list of services in Ambari.

Pig added - Configs
Pig added - Summary

Check for service on the cluster

curl -k -u admin:admin -H "X-Requested-By:ambari" -i -X GET 'http://'$AMBARI_SERVER':8080/api/v1/clusters/'$CLUSTER_NAME'/services/PIG'

curl - service info
The service is registered on the cluster.

Add components to the service

curl -k -u admin:admin -H "X-Requested-By:ambari" -i -X POST -d '{"RequestInfo":{"context":"Install PIG"}, "Body":{"HostRoles":{"state":"INSTALLED"}}}' 'http://'$AMBARI_SERVER':8080/api/v1/clusters/'$CLUSTER_NAME'/services/PIG/components/PIG'

Running the curl command from previous step to check if the component is added returns the following:

curl - service info after component.png
The component has been added according to the “components” element in the JSON output. The state of the service is still “UNKNOWN”.

Creating configuration is on the next page.

Adding service Druid to HDP 2.6 stack

Druid is a “fast column-oriented distributed data store”, according to the description in Ambari. It is a new service, added in HDP 2.6. The service is Technical Preview and the version offered is 0.9.2. Druid’s website is druid.io.

!!! Hortonworks Data Platform 2.6 is needed in order to install and use Druid !!!

Hortonworks has a very intriguing three-part series on ultra fast analytics with Hive and Druid. The first blog post can be found here.

This blog post describes how Druid is added to the HDP 2.6 stack with Ambari. The documentation I used is here. According to my experience, it does not hold water. I had to make some adjustment in order to start all Druid services.

Requirements

  • Zookeeper: Druid requires installation of Zookeeper. This service is already installed on my cluster.
  • Deep storage: deep storage layer for Druid in HDP can either be HDFS or S3. Parameter “druid.storage.type” is used to define this. Installation default is HDFS.
  • Metadata storage: for holding information about Druid segments and tasks. MySql is my metadata storage of choice.
  • Batch execution engine: resource manager is YARN, execution engine is MapReduce2. Druid hadoop index tasks use MapReduce jobs for distributed ingestion of data.

All these requirements are taken care of in Ambari, most of them with a sufficient default value.

Services within Druid

  • Broker – interface between users and Druid’s historical and realtime nodes.
  • Overlord – maintain a task queue that consists of user-submitted tasks.
  • Coordinator – serve to assign segments to historical nodes, handle data replication, and to ensure that segments are distributed evenly across the historical nodes.
  • Druid Router – serve as a mechanism to route queries to multiple broker nodes.
  • Druid Superset – if you know Superset, you know Druid Superset – data visualization tool.

Pre-work in metadata storage

As mentioned, my metadata storage is MySql. There are some objects that have to be created manually for the Druid installation to go through.

Log in to MySql as root.

Create druid database

CREATE DATABASE druid DEFAULT CHARACTER SET utf8;
CREATE USER 'druid'@'%' IDENTIFIED BY 'druid';
GRANT ALL PRIVILEGES ON druid.* TO 'druid'@'%';
FLUSH PRIVILEGES;

Create superset database

The superset objects in the database have to be created even though the documentation does not mention this. The installation will not go through unless it can connect to superset database to create tables in superset schema.

CREATE DATABASE superset DEFAULT CHARACTER SET utf8;
CREATE USER 'superset'@'%' IDENTIFIED BY 'druid';
GRANT ALL PRIVILEGES ON superset.* TO 'superset'@'%';
FLUSH PRIVILEGES;

Adding service

In Ambari, click on Add Service and check Druid service.

add service druid

In the next step, you are asked to define which Druid service is going to be installed on which node in the cluster. Remember, you can always move/add services.

assign masters to nodes

The Broker is on the Client node, since that service is the gateway to external world.

In the next step – Assigning Slaves and Clients – the following two needs to be defined where they will be installed:

  • Druid Historical: Loads data segments.
  • Druid MiddleManager: Runs Druid indexing tasks.

Generally you should select Druid Historical and Druid MiddleManager for multiple nodes. Both services are on namenode to begin with.

Next step are settings. There are some passwords and MySql server that needs to be defined. Secret key is also something one needs to define. A random string of characters would do the trick.

Be sure to create the objects in the MySql before you proceed with the installation.

installation settings

!!! Superset Database port should be 3306, just like Metadata storage port.

The advanced tab (picture above) is mostly for the superset parameters – entering name, email and password is needed to proceed with the installation. This is later on used in the visualization tool Superset.

Once you click OK, you are asked to doublecheck and change some recommended values. The following ones are related to Druid installation and should be checked to accept the recommended values.

dependency configuration.jpg

In the Review step, check if everything is as it should be and click Deploy.

After the installation completes all Druid services should be up and running. If there is the need to restart any services, do so.

Tweaking MapReduce2

There is one detail not mentioned in Hortonworks documentation when Druid is installed. There are two parameters in MapReduce2 that have to be tweaked in order for Druid to successfully load data. Explanation is at the bottom.

The parameters are:

  • mapreduce.map.java.opts
  • mapreduce.reduce.java.opts

The following should be added at the end of the existing values:

-Duser.timezone=UTC -Dfile.encoding=UTF-8

How it looks in Ambari:

map java heap size parameterreduce java heap size parameter

The service MapReduce2 should now be restarted.

Explanation

Various error messages occur in the Druid Console log files when the Druid job start to load the data. The error messages vary depending on the data, but generally, they do not provide any useful information.
From my experience, one error had a problem with the first line in a valid csv file, while in another example, the error was that no data can be indexed (code below).

Caused by: java.lang.RuntimeException: No buckets?? seems there is no data to index.
	at io.druid.indexer.IndexGeneratorJob.run(IndexGeneratorJob.java:176) ~[druid-indexing-hadoop-0.9.2.2.6.0.3-8.jar:0.9.2.2.6.0.3-8]
	at io.druid.indexer.JobHelper.runJobs(JobHelper.java:349) ~[druid-indexing-hadoop-0.9.2.2.6.0.3-8.jar:0.9.2.2.6.0.3-8]
	at io.druid.indexer.HadoopDruidIndexerJob.run(HadoopDruidIndexerJob.java:94) ~[druid-indexing-hadoop-0.9.2.2.6.0.3-8.jar:0.9.2.2.6.0.3-8]
	at io.druid.indexing.common.task.HadoopIndexTask$HadoopIndexGeneratorInnerProcessing.runTask(HadoopIndexTask.java:261) ~[druid-indexing-service-0.9.2.2.6.0.3-8.jar:0.9.2.2.6.0.3-8]
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) ~[?:1.8.0_111]
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) ~[?:1.8.0_111]
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) ~[?:1.8.0_111]
	at java.lang.reflect.Method.invoke(Method.java:498) ~[?:1.8.0_111]
	at io.druid.indexing.common.task.HadoopTask.invokeForeignLoader(HadoopTask.java:201) ~[druid-indexing-service-0.9.2.2.6.0.3-8.jar:0.9.2.2.6.0.3-8]
	... 7 more

 

Upgrading HDP 2.5 to 2.6

This blog post explains how an express upgrade from HDP 2.5 to HDP 2.6 has been done.

I have a HDP 2.5 cluster on AWS, Ubuntu 14.04 is running on all instances. My metadata database of choice is MySql 5.6.

Prior to upgrading HDP, Ambari has to be upgraded to 2.5. An upgrade from Ambari 2.4 to 2.5 is described here.

Backup databases

Do a backup of all databases that are storing metadata for services installed in HDP.

Example of backing up Hive metadata:
On the server where Hive metastore database is, create a backup folder

mkdir /home/ubuntu/hive-backup

Dump the database into a file (enter password when prompted):

mysqldump -u hive -p hive > /home/ubuntu/hive-backup/hive.mysql

Backup namenode files

Create backup directory

mkdir/home/ubuntu/hdp25-backup

Backup a complete block map of the file system

sudo -u hdfs hdfs fsck / -files -blocks -locations > /home/ubuntu/hdp25-backup/dfs-old-fsck-1.log

Create a list of all the DataNodes in the cluster

sudo -u hdfs hdfs dfsadmin -report > /home/ubuntu/hdp25-backup/dfs-old-report-1.log

Capture the complete namespace of the file system

sudo -u hdfs hdfs dfs -ls -R / > /home/ubuntu/hdp25-backup/dfs-old-lsr-1.log

Go into safemode

sudo -u hdfs hdfs dfsadmin -safemode enter

Output

Safe mode is ON

Save namespace

sudo -u hdfs hdfs dfsadmin -saveNamespace

Output

Save namespace successful

Copy the checkpoint files located in ${dfs.namenode.name.dir}/current into a backup directory

sudo cp /hadoop/hdfs/namenode/current/fsimage_0000000000000485884 hdp25-backup/
sudo cp /hadoop/hdfs/namenode/current/fsimage_0000000000000485884.md5 hdp25-backup/

Store the layoutVersion for the NameNode

sudo cp /hadoop/hdfs/namenode/current/VERSION hdp25-backup/

Take the NameNode out of Safe Mode

sudo -u hdfs hdfs dfsadmin -safemode leave

Finalize any prior HDFS upgrade

sudo -u hdfs hdfs dfsadmin -finalizeUpgrade

Output

Finalize upgrade successful