hadoop | Apache Hadoop - Docker distribution

I am new to Spark and am trying to run on a hadoop cluster a simple spark jar file built through maven in intellij. But I am getting classnotfoundexception in all the ways I tried to submit the application through spark-submit.

My pom.xml:

I have setup a small size Hadoop Yarn cluster where Apache Spark is running. I have some data (JSON, CSV) that I upload to Spark (data-frame) for some analysis. Later, I have to index all data-frame data into Apache SOlr. I am using Spark 3 and Solr 8.8 version.

In my search, I have found a solution here but it is for different version of Spark. Hence, I have decided to ask someone for this.

Is there any builtin option for this task. I am open to use SolrJ and pySpark (not scal shell).

I have an Apache Kylin container running in docker. I was getting a Java heap space error in map reduce phase so I tried updating some parameters in Hadoop mapred-default.xml file. After making the changes, I restarted the container but, when I go to Yarn ResourceManager Web UI and then to Configuration:

An xml file is opened, looking like this:

However my new values for the properties that I set inside the mapred-default.xml are not here, it is showing the old values for those properties... Does anyone have any idea why that is happening and what I should do to make it register the new values? I tried restarting the container, but it didn't help...

I had used the below command in GCP Shell terminal to create a project wordcount

I have 3 remote computers (servers):

• computer 1 has internal IP: 10.1.7.245
• computer 2 has internal IP: 10.1.7.246
• computer 3 has internal IP: 10.1.7.247

(The 3 computers above are in the same network, these 3 computers are all using Ubuntu 18.04.5 LTS Operating System)

(My personal laptop is in another different network, my laptop also uses Ubuntu 18.04.5 LTS Operating System)

I use my personal laptop to connect to the 3 remote computers using SSH protocol and using user root : (Below ABC is a name)

• computer 1: ssh root@ABC.University.edu.vn -p 12001
• computer 2: ssh root@ABC.University.edu.vn -p 12002
• computer 3: ssh root@ABC.University.edu.vn -p 12003

I have successfully set up a Hadoop Cluster which contains 3 above computer:

• computer 1 is the Hadoop Master
• computer 2 is the Hadoop Slave 1
• computer 3 is the Hadoop Slave 2

======================================================

I starts HDFS of the Hadoop Cluster by using the below command on Computer 1: start-dfs.sh

Everything is successful:

• computer 1 (the Master) is running the NameNode
• computer 2 (the Slave 1) is running the DataNode
• computer 3 (the Slave 2) is running the DataNode

I know that the the Web Interface for the NameNode is running on Computer 1, on IP 0.0.0.0 and on port 9870 . Therefore, if I open the web browser on computer 1 (or on computer 2, or on computer 3), I will enter the 10.1.7.245:9870 on the URL bar (address bar) of the web browser to see the Web Interface of the NameNode.

======================================================

Now, I am using the web browser of my personal laptop.

How could I access to the Web Interface of the NameNode ?

I've always heard that Spark is 100x faster than classic Map Reduce frameworks like Hadoop. But recently I'm reading that this is only true if RDDs are cached, which I thought was always done but instead requires the explicit cache () method.

I would like to understand how all produced RDDs are stored throughout the work. Suppose we have this workflow:

1. I read a file -> I get the RDD_ONE
2. I use the map on the RDD_ONE -> I get the RDD_TWO
3. I use any other transformation on the RDD_TWO

QUESTIONS:

if I don't use cache () or persist () is every RDD stored in memory, in cache or on disk (local file system or HDFS)?

if RDD_THREE depends on RDD_TWO and this in turn depends on RDD_ONE (lineage) if I didn't use the cache () method on RDD_THREE Spark should recalculate RDD_ONE (reread it from disk) and then RDD_TWO to get RDD_THREE?

Thanks in advance.

I'm try to execute the below hive query on Azure HDInsight cluster but it's taking unprecedented amount of time to finish. Did implemented hive settings but of no use. Below are the details:

Table

The goal is to have a Spark Streaming application that read data from Kafka and use Delta Lake to create store data. The granularity of the delta table is pretty granular, the first partition is the organization_id (there are more than 5000 organizations) and the second partition is the date.

The application has a expected latency, but it does not last more than one day up. The error is always about memory as I'll show below.

OpenJDK 64-Bit Server VM warning: INFO: os::commit_memory(0x00000006f8000000, 671088640, 0) failed; error='Cannot allocate memory' (errno=12)

There is no persistence and the memory is already high for the whole application.

Increasing memory and workes were the first things I've tried, but the number of partitions were changed as well, from 4 to 16.

I have a webapp that runs fine in JBoss EAP 6.4. I want to add some functionality to my webapp so that it can process Parquet files that reside in AzureBlob storage. I add a single dependency to my pom.xml: