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 by   apache Java Version: Current License: Apache-2.0

 by   apache Java Version: Current License: Apache-2.0

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kandi X-RAY | hadoop Summary

hadoop is a Java library typically used in Big Data, Spark, Hadoop applications. hadoop has build file available, it has a Permissive License and it has high support. However hadoop has 9285 bugs and it has 95 vulnerabilities. You can download it from GitHub.
For the latest information about Hadoop, please visit our website at:.
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Support
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Quality
Security
Security
License
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Reuse
Reuse

kandi-support Support

  • hadoop has a highly active ecosystem.
  • It has 12457 star(s) with 7772 fork(s). There are 1010 watchers for this library.
  • It had no major release in the last 12 months.
  • hadoop has no issues reported. There are 569 open pull requests and 0 closed requests.
  • It has a negative sentiment in the developer community.
  • The latest version of hadoop is current.
hadoop Support
Best in #Java
Average in #Java
hadoop Support
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Average in #Java

quality kandi Quality

  • hadoop has 9285 bugs (360 blocker, 185 critical, 1521 major, 7219 minor) and 44440 code smells.
hadoop Quality
Best in #Java
Average in #Java
hadoop Quality
Best in #Java
Average in #Java

securitySecurity

  • hadoop has 3 vulnerability issues reported (1 critical, 2 high, 0 medium, 0 low).
  • hadoop code analysis shows 92 unresolved vulnerabilities (74 blocker, 5 critical, 1 major, 12 minor).
  • There are 1156 security hotspots that need review.
hadoop Security
Best in #Java
Average in #Java
hadoop Security
Best in #Java
Average in #Java

license License

  • hadoop is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
hadoop License
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hadoop License
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buildReuse

  • hadoop releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
hadoop Reuse
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hadoop Reuse
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Top functions reviewed by kandi - BETA

kandi has reviewed hadoop and discovered the below as its top functions. This is intended to give you an instant insight into hadoop implemented functionality, and help decide if they suit your requirements.

  • Receives a packet from the replica .
  • Increments the invoked method .
  • Generate a random word
  • Process a timeline event .
  • Collect a summary of the blocks in the block .
  • Attempt to delete a file or directory .
  • Generate real time tracking metrics .
  • Check if a block is corrupt
  • This method creates a list of splits for each node .
  • Process a job line .

hadoop Key Features

Apache Hadoop

I can't pass parameters to foreach loop while implementing Structured Streaming + Kafka in Spark SQL

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.selectExpr("CAST(value AS STRING)")
          .as(Encoders.STRING());  // or parse your JSON here using a schema 

data.select(...)  // or move this to a method / class that takes the Dataset as a parameter

// await termination 

Getting java.lang.ClassNotFoundException when I try to do spark-submit, referred other similar queries online but couldnt get it to work

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<project>
  <build>
    <sourceDirectory>src/main/scala</sourceDirectory>
    <testSourceDirectory>src/test/scala</testSourceDirectory>
    <plugins>
      <plugin>
        <groupId>net.alchim31.maven</groupId>
        <artifactId>scala-maven-plugin</artifactId>
        <version>4.5.2</version>
        <executions>
          <execution>
            <goals>
              <goal>compile</goal>
              <goal>testCompile</goal>
            </goals>
          </execution>
        </executions>
      </plugin>
    </plugins>
  </build>
</project>

Indexing of Spark 3 Dataframe into Apache Solr 8

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import pysolr
import json

def solrIndexer(row):
    solr = pysolr.Solr('http://localhost:8983/solr/spark-test')
    obj = json.loads(row)
    solr.add(obj)

#load data to dataframe from HDFS
csvDF = spark.read.load("hdfs://hms/data/*.csv", format="csv", sep=",", inferSchema="true", header="true")

csvDF.toJSON().map(solrIndexer).count()

Update to mapred-default.xml not visible in web UI configuration

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<property>
 <name>mapreduce.map.memory.mb</name>
 <value>1024</value>
</property>

Hive: Query executing from hours

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create index idx_TABLE2 on table DB_MYDB.TABLE2 (SDNT_ID,CLSS_CD,BRNCH_CD,SECT_CD,GRP_CD,GRP_NM) AS 'COMPACT' WITH DEFERRED REBUILD;

create index idx_TABLE3 on table DB_MYDB.TABLE3(SDNT_ID,CLSS_CD,BRNCH_CD,SECT_CD,GRP_CD,GRP_NM) AS 'COMPACT' WITH DEFERRED REBUILD;
-----------------------
set hive.exec.reducers.bytes.per.reducer=67108864; --example only, check your current settings 
                                                   --and reduce accordingly to get twice more reducers on Reducer 2 vertex

Set spark context configuration prioritizing spark-submit

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config = my_config_dict
sc = SparkContext()
conf = sc.getConf()
for option in my_config_dict.keys():
    conf.setIfMissing(option, my_config_dict[option])

Error when building docker image for jupyter spark notebook

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docker build --rm --force-rm \
  --build-arg spark_version=3.0.2 \
  -t jupyter/pyspark-notebook:3.0.2 .
docker build --rm --force-rm \
  --build-arg spark_version=3.1.1 \
  --build-arg hadoop_version=2.7 \
  -t jupyter/pyspark-notebook:3.1.1 .  
-----------------------
docker build --rm --force-rm \
  --build-arg spark_version=3.0.2 \
  -t jupyter/pyspark-notebook:3.0.2 .
docker build --rm --force-rm \
  --build-arg spark_version=3.1.1 \
  --build-arg hadoop_version=2.7 \
  -t jupyter/pyspark-notebook:3.1.1 .  

Map-reduce functional outline

copy iconCopydownload iconDownload
    xA           xB
     |           |
  xform(xA)   ​xform(xB)
       ​\       /
aggregator(xform(xA), xform(xB))
           ​|
         ​value
    xA           xB               xC
     |           |                |
  xform(xA)   ​xform(xB)         xform(xC)
     |           |                |
     yA          yB               yC
       ​\       /                  |
aggregator(yA, yB)                |
           ​|                     /
         ​value                  /
           |                   /
          aggregator(value, yC)
                   |
              next_value
import functools

# Combiner
def add(a, b):
    return a + b

# Transformer
def square(a):
    return a * a

one_to_ten = range(1, 11)

functools.reduce(add, map(square, one_to_ten), 0)
(require '[clojure.core.reducers :as r])

(defn square [x] (* x x))

(r/fold + (pmap square (range 1 11)))
-----------------------
    xA           xB
     |           |
  xform(xA)   ​xform(xB)
       ​\       /
aggregator(xform(xA), xform(xB))
           ​|
         ​value
    xA           xB               xC
     |           |                |
  xform(xA)   ​xform(xB)         xform(xC)
     |           |                |
     yA          yB               yC
       ​\       /                  |
aggregator(yA, yB)                |
           ​|                     /
         ​value                  /
           |                   /
          aggregator(value, yC)
                   |
              next_value
import functools

# Combiner
def add(a, b):
    return a + b

# Transformer
def square(a):
    return a * a

one_to_ten = range(1, 11)

functools.reduce(add, map(square, one_to_ten), 0)
(require '[clojure.core.reducers :as r])

(defn square [x] (* x x))

(r/fold + (pmap square (range 1 11)))
-----------------------
    xA           xB
     |           |
  xform(xA)   ​xform(xB)
       ​\       /
aggregator(xform(xA), xform(xB))
           ​|
         ​value
    xA           xB               xC
     |           |                |
  xform(xA)   ​xform(xB)         xform(xC)
     |           |                |
     yA          yB               yC
       ​\       /                  |
aggregator(yA, yB)                |
           ​|                     /
         ​value                  /
           |                   /
          aggregator(value, yC)
                   |
              next_value
import functools

# Combiner
def add(a, b):
    return a + b

# Transformer
def square(a):
    return a * a

one_to_ten = range(1, 11)

functools.reduce(add, map(square, one_to_ten), 0)
(require '[clojure.core.reducers :as r])

(defn square [x] (* x x))

(r/fold + (pmap square (range 1 11)))
-----------------------
    xA           xB
     |           |
  xform(xA)   ​xform(xB)
       ​\       /
aggregator(xform(xA), xform(xB))
           ​|
         ​value
    xA           xB               xC
     |           |                |
  xform(xA)   ​xform(xB)         xform(xC)
     |           |                |
     yA          yB               yC
       ​\       /                  |
aggregator(yA, yB)                |
           ​|                     /
         ​value                  /
           |                   /
          aggregator(value, yC)
                   |
              next_value
import functools

# Combiner
def add(a, b):
    return a + b

# Transformer
def square(a):
    return a * a

one_to_ten = range(1, 11)

functools.reduce(add, map(square, one_to_ten), 0)
(require '[clojure.core.reducers :as r])

(defn square [x] (* x x))

(r/fold + (pmap square (range 1 11)))

Python subprocess with apostrophes, removes them

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subprocess.run([
    'docker', 'exec', 'hbase', 'bash', '-c',
    '''echo 'create "myTable", "R"' | hbase shell'''])

Exception in thread &quot;JobGenerator&quot; java.lang.NoSuchMethodError: 'scala.collection.mutable.ArrayOps scala.Predef$.refArrayOps(java.lang.Object[])'

copy iconCopydownload iconDownload
<properties>
    <scala.minor.version>2.11</scala.minor.version>
    <spark.version>2.4.2</spark.version>
</properties>
         <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_${scala.minor.version}</artifactId>
            <version>${spark.version}</version>
            <scope>provided</scope>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-kafka-0-10 -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_${scala.minor.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.minor.version}.8</version>
        </dependency>
-----------------------
<properties>
    <scala.minor.version>2.11</scala.minor.version>
    <spark.version>2.4.2</spark.version>
</properties>
         <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_${scala.minor.version}</artifactId>
            <version>${spark.version}</version>
            <scope>provided</scope>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-kafka-0-10 -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_${scala.minor.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.minor.version}.8</version>
        </dependency>
-----------------------
<project xmlns="http://maven.apache.org/POM/4.0.0"
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>TikiData</groupId>
    <artifactId>TikiData</artifactId>
    <version>V1</version>
    <dependencies>
        <!-- https://mvnrepository.com/artifact/com.google.code.gson/gson -->
        <dependency>
            <groupId>com.google.code.gson</groupId>
            <artifactId>gson</artifactId>
            <version>2.8.6</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-client -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>3.3.0</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>3.1.1</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.12</artifactId>
            <version>3.1.1</version>
            <scope>provided</scope>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-kafka-0-10 -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_2.12</artifactId>
            <version>3.1.1</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.12</artifactId>
            <version>3.1.1</version>
            <scope>provided</scope>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.scala-lang/scala-library -->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>2.12.2</version>
        </dependency>

    </dependencies>
    <build>
        <sourceDirectory>src</sourceDirectory>
        <plugins>
            <plugin>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.8.1</version>
                <configuration>
                    <release>11</release>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-assembly-plugin</artifactId>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                        <configuration>
                            <archive>
                                <manifest>
                                    <mainClass>
                                        demo.KafkaDemo
                                    </mainClass>
                                </manifest>
                            </archive>
                            <descriptorRefs>
                                <descriptorRef>jar-with-dependencies</descriptorRef>
                            </descriptorRefs>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

Community Discussions

Trending Discussions on hadoop
  • I can't pass parameters to foreach loop while implementing Structured Streaming + Kafka in Spark SQL
  • Getting java.lang.ClassNotFoundException when I try to do spark-submit, referred other similar queries online but couldnt get it to work
  • Indexing of Spark 3 Dataframe into Apache Solr 8
  • Update to mapred-default.xml not visible in web UI configuration
  • Import org.apache statement cannot be resolved in GCP Shell
  • Hadoop NameNode Web Interface
  • RDD in Spark: where and how are they stored?
  • Hive: Query executing from hours
  • Cannot Allocate Memory in Delta Lake
  • Webapp fails with &quot;JBAS011232: Only one JAX-RS Application Class allowed&quot; after adding a maven dependency to hadoop-azure
Trending Discussions on hadoop

QUESTION

I can't pass parameters to foreach loop while implementing Structured Streaming + Kafka in Spark SQL

Asked 2021-Jun-15 at 04:42

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:

package stream;

import java.io.FileNotFoundException;
import java.io.PrintWriter;

import org.apache.spark.sql.ForeachWriter;
import org.apache.spark.sql.SparkSession;
import org.json.simple.JSONObject;
import org.json.simple.parser.JSONParser;
import org.json.simple.parser.ParseException;

import dataservices.OrderDataServices;
import models.SuccessEvent;

public class MapEventWriter extends ForeachWriter<String>{
private SparkSession spark;

public MapEventWriter(SparkSession spark) {
    this.spark = spark;
}

private static final long serialVersionUID = 1L;

@Override
public void close(Throwable errorOrNull) {
    // TODO Auto-generated method stub
    
}

@Override
public boolean open(long partitionId, long epochId) {
    // TODO Auto-generated method stub
    return true;
}

@Override
public void process(String input) {     
    OrderDataServices services = new OrderDataServices(this.spark);
}
}

however in the process function, if I use spark variable, the program gives an error, the program passes in my spark as follows:

package demo;

import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.concurrent.TimeoutException;

import org.apache.hadoop.fs.Path;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.streaming.StreamingQuery;
import org.apache.spark.sql.streaming.StreamingQueryException;
import org.json.simple.parser.ParseException;

import dataservices.OrderDataServices;
import models.MapperEvent;
import models.OrderEvent;
import models.SuccessEvent;
import stream.MapEventWriter;
import stream.MapEventWriter1;

public class Demo {
    public static void main(String[] args) throws TimeoutException, StreamingQueryException, ParseException, IOException {
        try (SparkSession spark = SparkSession.builder().appName("Read kafka").getOrCreate()) {
            Dataset<String> data = spark
                    .readStream()
                    .format("kafka")
                    .option("kafka.bootstrap.servers", "localhost:9092")
                    .option("subscribe", "tiki-1")
                    .load()
                    .selectExpr("CAST(value AS STRING)")
                    .as(Encoders.STRING());
            
            MapEventWriter eventWriter = new MapEventWriter(spark);
            
            StreamingQuery query = data
                    .writeStream()
                    .foreach(eventWriter)
                    .start();
            
            query.awaitTermination();
            
        }
    }
    
    
}

The error is NullPointerException at the spark call location, that is, no spark variable is initialized. Hope anyone can help me, I really appreciate it.

Caused by: java.lang.NullPointerException
    at org.apache.spark.sql.SparkSession.sessionState$lzycompute(SparkSession.scala:151)
    at org.apache.spark.sql.SparkSession.sessionState(SparkSession.scala:149)
    at org.apache.spark.sql.DataFrameReader.<init>(DataFrameReader.scala:998)
    at org.apache.spark.sql.SparkSession.read(SparkSession.scala:655)
    at dataservices.OrderDataServices.<init>(OrderDataServices.java:18)
    at stream.MapEventWriter.process(MapEventWriter.java:38)
    at stream.MapEventWriter.process(MapEventWriter.java:15)

ANSWER

Answered 2021-Jun-15 at 04:42

do some query work with Spark SQL

You wouldn't use a ForEachWriter for that

.selectExpr("CAST(value AS STRING)")
          .as(Encoders.STRING());  // or parse your JSON here using a schema 

data.select(...)  // or move this to a method / class that takes the Dataset as a parameter

// await termination 

Source https://stackoverflow.com/questions/67972167

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

In Apache Hadoop 3.2.0 to 3.2.1, 3.0.0-alpha1 to 3.1.3, and 2.0.0-alpha to 2.10.0, WebHDFS client might send SPNEGO authorization header to remote URL without proper verification.
Web endpoint authentication check is broken in Apache Hadoop 3.0.0-alpha4, 3.0.0-beta1, and 3.0.0. Authenticated users may impersonate any user even if no proxy user is configured.
In Apache Hadoop versions 3.0.0-alpha2 to 3.0.0, 2.9.0 to 2.9.2, 2.8.0 to 2.8.5, any users can access some servlets without authentication when Kerberos authentication is enabled and SPNEGO through HTTP is not enabled.
In Apache Hadoop versions 3.0.0-alpha1 to 3.1.0, 2.9.0 to 2.9.1, and 2.2.0 to 2.8.4, a user who can escalate to yarn user can possibly run arbitrary commands as root user.
Apache Hadoop 3.1.0, 3.0.0-alpha to 3.0.2, 2.9.0 to 2.9.1, 2.8.0 to 2.8.4, 2.0.0-alpha to 2.7.6, 0.23.0 to 0.23.11 is exploitable via the zip slip vulnerability in places that accept a zip file.
In Apache Hadoop versions 2.6.1 to 2.6.5, 2.7.0 to 2.7.3, and 3.0.0-alpha1, if a file in an encryption zone with access permissions that make it world readable is localized via YARN's localization mechanism, that file will be stored in a world-readable location and can be shared freely with any application that requests to localize that file.
In Apache Hadoop 3.1.0 to 3.1.1, 3.0.0-alpha1 to 3.0.3, 2.9.0 to 2.9.1, and 2.0.0-alpha to 2.8.4, the user/group information can be corrupted across storing in fsimage and reading back from fsimage.
HDFS clients interact with a servlet on the DataNode to browse the HDFS namespace. The NameNode is provided as a query parameter that is not validated in Apache Hadoop before 2.7.0.
The HDFS web UI in Apache Hadoop before 2.7.0 is vulnerable to a cross-site scripting (XSS) attack through an unescaped query parameter.

Install hadoop

You can download it from GitHub.
You can use hadoop like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the hadoop component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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