kandi X-RAY | hadoop Summary
kandi X-RAY | hadoop Summary
This repository is based on Apache Hadoop 2.7.1 source code. It is used to make Naver's large scale multi-tenant hadoop cluster, which is called C3. The C3 users can execute several data processing jobs with MapReduce, Spark and Hive on CPU, and execute Deep Learning algorithms on GPU. And also they can run long-live applications on docker container. Recently hadoop's new features is adding to Hadoop 2.8 or Hadoop 3.0. However if you are using hadoop cluster for years in production, your hadoop version maybe is not hadoop 2.8 or 3.0, because these versions is not recommended for production cluster yet. Thus you can't use very useful new features: GPU scheduler, docker container, several resource isolations(e.g. network outbound, disk). We're applying and developing new features to this repository. You can see histories in commit logs.
Top functions reviewed by kandi - BETA
- Process a timeline event
- Receives a packet from the stream .
- Generate the startup shutdown message .
- Creates an application submission context .
- Generate splits for the given node .
- Process a line line .
- Reload the allocation configuration file .
- Dumps properties of the TFile
- Converts a path to a byte array .
- Compute replication work for blocks .
hadoop Key Features
hadoop Examples and Code Snippets
Trending Discussions on hadoop
I followed the instructions at Structured Streaming + Kafka and built a program that receives data streams sent from kafka as input, when I receive the data stream I want to pass it to SparkSession variable to do some query work with Spark SQL, so I extend the ForeachWriter class again as follows:...
ANSWERAnswered 2021-Jun-15 at 04:42
do some query work with Spark SQL
You wouldn't use a ForEachWriter for that
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.
ANSWERAnswered 2021-Jun-14 at 09:36
You need to add scala-compiler configuration to your
pom.xml. The problem is without that there is nothing to compile your SparkTrans.scala file into java classes.
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)....
ANSWERAnswered 2021-Jun-14 at 07:42
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......
ANSWERAnswered 2021-Jun-12 at 07:08
To override a default value for a property, specify the new value within the tags, inside
mapred-default.xml, using the following format:
I had used the below command in GCP Shell terminal to create a project wordcount...
ANSWERAnswered 2021-Jun-10 at 21:48
I'd suggest finding an archetype for creating MapReduce applications, otherwise, you need to add
hadoop-client as a dependency in your pom.xml
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:
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 ?...
ANSWERAnswered 2021-Jun-08 at 17:56
Unless you expose port 9870, your personal laptop on another network will not be able to access the web interface.
You can check to see if it is exposed by trying :9870 to see if it is exposed. IP-address here has to be the global IP-address, not the local (10.* ) address.
To get the NameNode's IP address, ssh into the NameNode server, and type ifconfig (sudo apt install ifconfig if not already installed - I'm assuming Ubuntu/Linux here). ifconfig should give you a global IP address (not the 255.* - that is a mask).
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:
- I read a file -> I get the RDD_ONE
- I use the map on the RDD_ONE -> I get the RDD_TWO
- I use any other transformation on the RDD_TWO
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....
ANSWERAnswered 2021-Jun-09 at 06:13
In spark there are two types of operations: transformations and actions. A transformation on a dataframe will return another dataframe and an action on a dataframe will return a value.
Transformations are lazy, so when a transformation is performed spark will add it to the DAG and execute it when an action is called.
Suppose, you read a file into a dataframe, then perform a filter, join, aggregate, and then count. The count operation which is an action will actually kick all the previous transformation.
If we call another action(like show) the whole operation is executed again which can be time consuming. So, if we want not to run the whole set of operation again and again we can cache the dataframe.
Few pointers you can consider while caching:
- Cache only when the resulting dataframe is generated from significant transformation. If spark can regenerate the cached dataframe in few seconds then caching is not required.
- Cache should be performed when the dataframe is used for multiple actions. If there are only 1-2 actions on the dataframe then it is not worth saving that dataframe in memory.
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:
ANSWERAnswered 2021-Jun-07 at 03:19
if you don't have index on your fk columns , you should add them for sure , here is my suggestion:
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.What I've tried
Increasing memory and workes were the first things I've tried, but the number of partitions were changed as well, from 4 to 16.Script of Execution ...
ANSWERAnswered 2021-Jun-08 at 11:11
Just upgraded the version to Delta.io 1.0.0 and it stopped happening.
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:...
ANSWERAnswered 2021-Jun-03 at 20:31
hadoop-azure pulls in
hadoop-common, which pulls in Jersey. In the version of
hadoop-azure you're using,
hadoop-common is in
. In new version, it is in
provided scope. So you can just upgrade the
hadoop-azure dependency to the latest one. If you need
hadoop-common to compile, then you can redeclare
hadoop-common and put it in
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