I’m using HDFS 2.7.3 and Spark2 2.0.0 in Hadoop cluster. When I start Spark2 Thrift Server it successfully starts but automatically from Hive user a job starts to run and it seems to hang forever. If I manually kill the job, again it starts a new job with a new applicationId.
But if I stop the Spark2 Thrift Server the it kills the job. Can you please help me to understand this issue?
Thanks in advance.
i have met the same issue like you, spark 2.0 is not stable of thriftserver module, it is better to upgrade to spark 2.1
We have a spark streaming job which writes output to ElasticSearch. When elasticsearch is slow due to any reason, spark job waits indefinitely which causes it to accumulate data and has a snowballing effect on the streaming job itself. The only solution to make the streaming job stable then is to restart it.
Is there a way to specify spark writes to elastic search to be asynchronous? I tried looking at https://www.elastic.co/guide/en/elasticsearch/hadoop/current/configuration.html and do not see any option to have async writes.
I am trying to create an ORC table in Hive by importing from a text file in HDFS. I have tried multiple different ways, searched online for help, and regardless the insert job won't start.
I can get the text file to HDFS, I can read the text file to Hive, but I cannot convert from that to ORC.
I tried many different variations, including this one that can be used as a reference to this question:
http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.3.0/bk_dataintegration/content/moving_data_from_hdfs_to_hive_external_table_method.html
I have a single-node HDP cluster (being used for development) - version:
HDP-2.3.2.0
(2.3.2.0-2950)
And here are the relevant service versions:
Service Version Status Description
HDFS 2.7.1.2.3 Installed Apache Hadoop Distributed File System
MapReduce2 2.7.1.2.3 Installed Apache Hadoop NextGen MapReduce (YARN)
YARN 2.7.1.2.3 Installed Apache Hadoop NextGen MapReduce (YARN)
Tez 0.7.0.2.3 Installed Tez is the next generation Hadoop Query Processing framework written on top of YARN.
Hive 1.2.1.2.3 Installed Data warehouse system for ad-hoc queries & analysis of large datasets and table & storage management service
What happens when I run a SQL like this (again, I've tried many variations including directly from online tutorials):
INSERT OVERWRITE TABLE mycars SELECT * FROM cars;
My job stays like this:
Total number of applications (application-types: [] and states:
[SUBMITTED, ACCEPTED, RUNNING]):1
Application-Id Application-Name Application-Type User Queue State Final-State Progress Tracking-URL
application_1455989658079_0002 HIVE-3f41161c-b806-4e7d-974e-c18e028d683f TEZ hive root.hive ACCEPTED UNDEFINED 0% N/A
And it just hangs there. (Literally, I've tried a 20 row sample table and let it run for hours before killing it).
I am by no means an Hadoop expert (yet) and am sure it's probably a config issue, but I have been unable to figure it out.
All other Hive operations I've tried, such as creating dropping tables, loading a file to a text table, selects, all work fine. It's just when I create an ORC table that it does this. And I need an ORC table for my requirement.
Any advice would be helpful.
Most of the time it has to do with increasing your Yarn Scheduling capacity, but if your resources are already capped you can also reduce the amount of memory requested by individual TEZ tasks, through adjusting the following property in TEZ configuration :
task.resource.memory.mb
In order to increase the Cluster's capacity you can do it in the configuration settings of YARN or directly through Ambari or Cloudera Manager
In order to monitor what is happening behind the hoods you can run Yarn Resource Manager UI and check the diagnostics tab of the specific Application there are useful explicit messages about resource allocation especially when the job is accepted and keeps pending.
In regards to being able to run machine learning jobs with Spark. Which is a better choice the Yarn scheduler or the Spark Standalone scheduler?
There is no difference when it comes to run the actual spark job.
Yarn/Mesos helps you to schedule resources if you have different spark applictions running and/or other components running in your cluster (which support Yarn/Mesos of course).
The Spark standalone cluster cannot manage resources. That is if you start a Spark application and it uses all the ressources, the second application will not find any resources left. That means you have to do this by yourself (e.g. adapting Spark config accordingly)
Are there any dependencies between Spark and Hadoop?
If not, are there any features I'll miss when I run Spark without Hadoop?
Spark is an in-memory distributed computing engine.
Hadoop is a framework for distributed storage (HDFS) and distributed processing (YARN).
Spark can run with or without Hadoop components (HDFS/YARN)
Distributed Storage:
Since Spark does not have its own distributed storage system, it has to depend on one of these storage systems for distributed computing.
S3 – Non-urgent batch jobs. S3 fits very specific use cases when data locality isn’t critical.
Cassandra – Perfect for streaming data analysis and an overkill for batch jobs.
HDFS – Great fit for batch jobs without compromising on data locality.
Distributed processing:
You can run Spark in three different modes: Standalone, YARN and Mesos
Have a look at the below SE question for a detailed explanation about both distributed storage and distributed processing.
Which cluster type should I choose for Spark?
Spark can run without Hadoop but some of its functionality relies on Hadoop's code (e.g. handling of Parquet files). We're running Spark on Mesos and S3 which was a little tricky to set up but works really well once done (you can read a summary of what needed to properly set it here).
(Edit) Note: since version 2.3.0 Spark also added native support for Kubernetes
By default , Spark does not have storage mechanism.
To store data, it needs fast and scalable file system. You can use S3 or HDFS or any other file system. Hadoop is economical option due to low cost.
Additionally if you use Tachyon, it will boost performance with Hadoop. It's highly recommended Hadoop for apache spark processing.
As per Spark documentation, Spark can run without Hadoop.
You may run it as a Standalone mode without any resource manager.
But if you want to run in multi-node setup, you need a resource manager like YARN or Mesos and a distributed file system like HDFS,S3 etc.
Yes, spark can run without hadoop. All core spark features will continue to work, but you'll miss things like easily distributing all your files (code as well as data) to all the nodes in the cluster via hdfs, etc.
Yes, you can install the Spark without the Hadoop.
That would be little tricky
You can refer arnon link to use parquet to configure on S3 as data storage.
http://arnon.me/2015/08/spark-parquet-s3/
Spark is only do processing and it uses dynamic memory to perform the task, but to store the data you need some data storage system. Here hadoop comes in role with Spark, it provide the storage for Spark.
One more reason for using Hadoop with Spark is they are open source and both can integrate with each other easily as compare to other data storage system. For other storage like S3, you should be tricky to configure it like mention in above link.
But Hadoop also have its processing unit called Mapreduce.
Want to know difference in Both?
Check this article: https://www.dezyre.com/article/hadoop-mapreduce-vs-apache-spark-who-wins-the-battle/83
I think this article will help you understand
what to use,
when to use and
how to use !!!
Yes, of course. Spark is an independent computation framework. Hadoop is a distribution storage system(HDFS) with MapReduce computation framework. Spark can get data from HDFS, as well as any other data source such as traditional database(JDBC), kafka or even local disk.
Yes, Spark can run with or without Hadoop installation for more details you can visit -https://spark.apache.org/docs/latest/
Yes spark can run without Hadoop. You can install spark in your local machine with out Hadoop. But Spark lib comes with pre Haddop libraries i.e. are used while installing on your local machine.
You can run spark without hadoop but spark has dependency on hadoop win-utils. so some features may not work, also if you want to read hive tables from spark then you need hadoop.
Not good at english,Forgive me!
TL;DR
Use local(single node) or standalone(cluster) to run spark without Hadoop,but stills need hadoop dependencies for logging and some file process.
Windows is strongly NOT recommend to run spark!
Local mode
There are so many running mode with spark,one of it is called local will running without hadoop dependencies.
So,here is the first question:how to tell spark we want to run on local mode?
After read this official doc,i just give it a try on my linux os:
Must install java and scala,not the core content so skip it.
Download spark package
There are "without hadoop" and "hadoop integrated" 2 type of package
The most important thing is "without hadoop" do NOT mean run without hadoop but just not bundle with hadoop so you can bundle it with your custom hadoop!
Spark can run without hadoop(HDFS and YARN) but need hadoop dependency jar such as parquet/avro etc SerDe class,so strongly recommend to use "integrated" package(and you will found missing some log dependencies like log4j and slfj and other common utils class if chose "without hadoop" package but all this bundled with hadoop integrated pacakge)!
Run on local mode
Most simple way is just run shell，and you will see the welcome log
# as same as ./bin/spark-shell --master local[*]
./bin/spark-shell
Standalone mode
As same as blew,but different with step 3.
# Starup cluster
# if you want run on frontend
# export SPARK_NO_DAEMONIZE=true
./sbin/start-master.sh
# run this on your every worker
./sbin/start-worker.sh spark://VMS110109:7077
# Submit job or just shell
./bin/spark-shell spark://VMS110109:7077
On windows?
I kown so many people run spark on windown just for study,but here is so different on windows and really strongly NOT recommend to use windows.
The most important things is download winutils.exe from here and configure system variable HADOOP_HOME to point where winutils located.
At this moment 3.2.1 is the most latest release version of spark,but a bug is exist.You will got a exception like Illegal character in path at index 32: spark://xxxxxx:63293/D:\classe when run ./bin/spark-shell.cmd,only startup a standalone cluster then use ./bin/sparkshell.cmd or use lower version can temporary fix this.
For more detail and solution you can refer for here
No. It requires full blown Hadoop installation to start working - https://issues.apache.org/jira/browse/SPARK-10944