Configuring and using Hadoop and Spark on Ubuntu 22.04 LTS (with Canada 2021 Census data)
This tutorial works for Ubuntu 22.04 LTS both as VM (tested using VirtualBox) and using WSL2.
I’ve made this because I haven’t found a single guide that explains every step needed to configure and use Hadoop and Spark and actually use it for something meaningful like census data.
Install and configure dependencies
sudo apt-get install default-jre
sudo apt-get install openjdk-11-jre-headless
sudo apt-get install openjdk-8-jre-headless
sudo apt-get install openjdk-8-jdk
sudo apt-get install openssh-client openssh-server
sudo apt-get install ssh pdsh
ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa
cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
chmod 0600 ~/.ssh/authorized_keys
Download and configure Hadoop
https://hadoop.apache.org/releases.html
wget https://dlcdn.apache.org/hadoop/common/hadoop-3.3.6/hadoop-3.3.6.tar.gz
tar -xvzf hadoop-3.3.6.tar.gz
ubuntu@LAPTOP-JBell:~/hadoop-3.3.6/etc/hadoop$ ls
capacity-scheduler.xml httpfs-env.sh mapred-site.xml
configuration.xsl httpfs-log4j.properties shellprofile.d
container-executor.cfg httpfs-site.xml ssl-client.xml.example
core-site.xml kms-acls.xml ssl-server.xml.example
hadoop-env.cmd kms-env.sh user_ec_policies.xml.template
hadoop-env.sh kms-log4j.properties workers
hadoop-metrics2.properties kms-site.xml yarn-env.cmd
hadoop-policy.xml log4j.properties yarn-env.sh
hadoop-user-functions.sh.example mapred-env.cmd yarn-site.xml
hdfs-rbf-site.xml mapred-env.sh yarnservice-log4j.properties
hdfs-site.xml mapred-queues.xml.template
core-site.xml
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://localhost:9000</value>
</property>
</configuration>
hdfs-site.xml
<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
</configuration>
mapred-site.xml
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<property>
<name>mapreduce.application.classpath</name>
<value>$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/*:$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/lib/*</value>
</property>
</configuration>
yarn-site.xml
<configuration>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
<property>
<name>yarn.nodemanager.env-whitelist</name>
<value>JAVA_HOME,HADOOP_COMMON_HOME,HADOOP_HDFS_HOME,HADOOP_CONF_DIR,CLASSPATH_PREPEND_DISTCACHE,HADOOP_YARN_HOME,HADOOP_HOME,PATH,LANG,TZ,HADOOP_MAPRED_HOME</value>
</property>
</configuration>
Add to environment variables; either for single user with ~/.bashrc or for system with /etc/environment.
export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-amd64
export HADOOP_HOME=~/hadoop-3.3.6
export PATH=$PATH:$HADOOP_HOME/bin
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export HADOOP_CLASSPATH=/usr/lib/jvm/java-8-openjdk-amd64/lib/tools.jar
hdfs namenode -format
start-all.sh
CSV dataset
Canada 2021 Census:
https://www12.statcan.gc.ca/census-recensement/2021/dp-pd/prof/details/download-telecharger.cfm
We use the most geographically granular data, for Aggregate Dissemination Areas (ADA).
wget --content-disposition "https://www12.statcan.gc.ca/census-recensement/2021/dp-pd/prof/details/download-telecharger/comp/GetFile.cfm?Lang=E&FILETYPE=CSV&GEONO=012"
ubuntu@LAPTOP-JBell:~$ unzip -v 98-401-X2021012_eng_CSV.zip
Archive: 98-401-X2021012_eng_CSV.zip
Length Method Size Cmpr Date Time CRC-32 Name
-------- ------ ------- ---- ---------- ----- -------- ----
2415200889 Defl:N 211587436 91% 2022-12-07 16:41 f32de755 98-401-X2021012_English_CSV_data.csv
240208 Defl:N 44892 81% 2022-12-07 10:34 2eb21c80 98-401-X2021012_English_meta.txt
218562 Defl:N 41420 81% 2022-12-07 10:35 e0bd4f6c 98-401-X2021012_Geo_starting_row.CSV
1421 Defl:N 673 53% 2022-07-30 15:56 e7f8363b README_meta.txt
-------- ------- --- -------
2415661080 211674421 91% 4 files
ubuntu@LAPTOP-JBell:~$ unzip 98-401-X2021012_eng_CSV.zip
Archive: 98-401-X2021012_eng_CSV.zip
inflating: 98-401-X2021012_English_CSV_data.csv
inflating: 98-401-X2021012_English_meta.txt
inflating: 98-401-X2021012_Geo_starting_row.CSV
inflating: README_meta.txt
ubuntu@LAPTOP-JBell:~$ head 98-401-X2021012_English_CSV_data.csv
CENSUS_YEAR,DGUID,ALT_GEO_CODE,GEO_LEVEL,GEO_NAME,TNR_SF,TNR_LF,DATA_QUALITY_FLAG,CHARACTERISTIC_ID,CHARACTERISTIC_NAME,CHARACTERISTIC_NOTE,C1_COUNT_TOTAL,SYMBOL,C2_COUNT_MEN+,SYMBOL,C3_COUNT_WOMEN+,SYMBOL,C10_RATE_TOTAL,SYMBOL,C11_RATE_MEN+,SYMBOL,C12_RATE_WOMEN+,SYMBOL
2021,"2021S051610010001","10010001","Aggregate dissemination area","10010001",3.1,4.6,"00000",1,"Population, 2021",1,8881,"",,"...",,"...",,"...",,"...",,"..."
2021,"2021S051610010001","10010001","Aggregate dissemination area","10010001",3.1,4.6,"00000",2,"Population, 2016",1,9334,"",,"...",,"...",,"...",,"...",,"..."
2021,"2021S051610010001","10010001","Aggregate dissemination area","10010001",3.1,4.6,"00000",3,"Population percentage change, 2016 to 2021",,-4.9,"",,"...",,"...",-4.9,"",,"...",,"..."
2021,"2021S051610010001","10010001","Aggregate dissemination area","10010001",3.1,4.6,"00000",4,"Total private dwellings",2,5737,"",,"...",,"...",,"...",,"...",,"..."
2021,"2021S051610010001","10010001","Aggregate dissemination area","10010001",3.1,4.6,"00000",5,"Private dwellings occupied by usual residents",3,4121,"",,"...",,"...",,"...",,"...",,"..."
2021,"2021S051610010001","10010001","Aggregate dissemination area","10010001",3.1,4.6,"00000",6,"Population density per square kilometre",,9.4,"",,"...",,"...",9.4,"",,"...",,"..."
2021,"2021S051610010001","10010001","Aggregate dissemination area","10010001",3.1,4.6,"00000",7,"Land area in square kilometres",,941.33,"",,"...",,"...",,"...",,"...",,"..."
2021,"2021S051610010001","10010001","Aggregate dissemination area","10010001",3.1,4.6,"00000",8,"Total - Age groups of the population - 100% data",,8880,"",4365,"",4515,"",100,"",100,"",100,""
2021,"2021S051610010001","10010001","Aggregate dissemination area","10010001",3.1,4.6,"00000",9," 0 to 14 years",,925,"",460,"",465,"",10.4,"",10.5,"",10.3,""
ubuntu@LAPTOP-JBell:~$ head -n 16 98-401-X2021012_English_meta.txt
***********************************************************************************
** General Information
***********************************************************************************
Census Profile, 2021 - Aggregate Dissemination Areas
Catalogue number: 98-401-X2021012 Release date: 2022-12-15
Note on data quality and disclosure control
For information on data quality, as well as random rounding, data suppression, and other methods of disclosure control, please consult the Guide to the Census of Population, 2021, Catalogue no. 98-304-X, and the reference guides for the individual topics.
Source: Statistics Canada, 2021 Census, Catalogue no. 98-316-X2021001.
How to cite: Statistics Canada. 2022. Census Profile. 2021 Census. Statistics Canada Catalogue no. 98-316-X2021001. Ottawa. Released December 15, 2022. https://www12.statcan.gc.ca/census-recensement/2021/dp-pd/prof/index.cfm?Lang=E
Also from 98-401-X2021012_English_meta.txt:
Aggregate dissemination area (ADA)
An aggregate dissemination area (ADA) is a dissemination geography created for the Census. ADAs cover the entire country and, where possible, have a population between 5,000 and 15,000 based on the previous census population counts. ADAs are created by grouping existing dissemination geographic areas, including census tracts (CTs), census subdivisions (CSDs) or dissemination areas (DAs). ADA boundaries respect provincial, territorial, census division (CD), census metropolitan area (CMA) and census agglomeration (CA) boundaries.
The intent of the ADA geography is to ensure the availability of census data, where possible, across all regions of Canada.
Putting CSV into HDFS
ubuntu@LAPTOP-JBell:~$ hadoop fs -mkdir /census2021
ubuntu@LAPTOP-JBell:~$ hadoop fs -mkdir /census2021/ada
ubuntu@LAPTOP-JBell:~$ hadoop fs -put ./98-401-X2021012_English_CSV_data.csv /census2021/ada/ada.csv
ubuntu@LAPTOP-JBell:~$ hadoop fs -ls -R /census2021
drwxr-xr-x - ubuntu supergroup 0 2024-02-09 09:50 /census2021/ada
-rw-r--r-- 1 ubuntu supergroup 2415200889 2024-02-09 09:50 /census2021/ada/ada.csv
ubuntu@LAPTOP-JBell:~$ hadoop fs -head /census2021/ada/ada.csv
CENSUS_YEAR,DGUID,ALT_GEO_CODE,GEO_LEVEL,GEO_NAME,TNR_SF,TNR_LF,DATA_QUALITY_FLAG,CHARACTERISTIC_ID,CHARACTERISTIC_NAME,CHARACTERISTIC_NOTE,C1_COUNT_TOTAL,SYMBOL,C2_COUNT_MEN+,SYMBOL,C3_COUNT_WOMEN+,SYMBOL,C10_RATE_TOTAL,SYMBOL,C11_RATE_MEN+,SYMBOL,C12_RATE_WOMEN+,SYMBOL
2021,"2021S051610010001","10010001","Aggregate dissemination area","10010001",3.1,4.6,"00000",1,"Population, 2021",1,8881,"",,"...",,"...",,"...",,"...",,"..."
2021,"2021S051610010001","10010001","Aggregate dissemination area","10010001",3.1,4.6,"00000",2,"Population, 2016",1,9334,"",,"...",,"...",,"...",,"...",,"..."
2021,"2021S051610010001","10010001","Aggregate dissemination area","10010001",3.1,4.6,"00000",3,"Population percentage change, 2016 to 2021",,-4.9,"",,"...",,"...",-4.9,"",,"...",,"..."
2021,"2021S051610010001","10010001","Aggregate dissemination area","10010001",3.1,4.6,"00000",4,"Total private dwellings",2,5737,"",,"...",,"...",,"...",,"...",,"..."
2021,"2021S051610010001","10010001","Aggregate dissemination area","100100
ubuntu@LAPTOP-JBell:~$ hadoop fs -cat /census2021/ada/ada.csv | wc -l
14294224
(We remind ourselves that hadoop fs -head displays the first kilobyte of a file, not a fixed number of lines.)
MapReduce code for line count
We make new file /home/ubuntu/hadoop-3.3.6/bin/LineCount.java with following contents:
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class LineCount{
public static class LineCntMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
Text keyEmit = new Text("Total Lines");
private final static IntWritable one = new IntWritable(1);
public void map(LongWritable key, Text value, Context context){
try {
context.write(keyEmit, one);
}
catch (IOException e) {
e.printStackTrace();
System.exit(0);
}
catch (InterruptedException e) {
e.printStackTrace();
System.exit(0);
}
}
}
public static class LineCntReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterable<IntWritable> values, Context context){
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
try {
context.write(key, new IntWritable(sum));
}
catch (IOException e) {
e.printStackTrace();
System.exit(0);
}
catch (InterruptedException e) {
e.printStackTrace();
System.exit(0);
}
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "line count2");
job.setJarByClass(LineCount.class);
job.setMapperClass(LineCntMapper.class);
job.setCombinerClass(LineCntReducer.class);
job.setReducerClass(LineCntReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
ubuntu@LAPTOP-JBell:~/hadoop-3.3.6/bin$ ls
LineCount.java hadoop hdfs mapred oom-listener yarn
container-executor hadoop.cmd hdfs.cmd mapred.cmd test-container-executor yarn.cmd
ubuntu@LAPTOP-JBell:~/hadoop-3.3.6/bin$ hadoop com.sun.tools.javac.Main LineCount.java
ubuntu@LAPTOP-JBell:~/hadoop-3.3.6/bin$ jar cf lc.jar LineCount*.class
ubuntu@LAPTOP-JBell:~/hadoop-3.3.6/bin$ hadoop jar lc.jar LineCount /census2021/ada/ada.csv /LineCount
2024-02-09 09:53:23,449 INFO client.DefaultNoHARMFailoverProxyProvider: Connecting to ResourceManager at /0.0.0.0:8032
2024-02-09 09:53:23,793 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
2024-02-09 09:53:23,804 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/ubuntu/.staging/job_1707010689166_0003
2024-02-09 09:53:23,975 INFO input.FileInputFormat: Total input files to process : 1
2024-02-09 09:53:24,022 INFO mapreduce.JobSubmitter: number of splits:18
2024-02-09 09:53:24,127 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1707010689166_0003
2024-02-09 09:53:24,127 INFO mapreduce.JobSubmitter: Executing with tokens: []
2024-02-09 09:53:24,248 INFO conf.Configuration: resource-types.xml not found
2024-02-09 09:53:24,248 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
2024-02-09 09:53:24,593 INFO impl.YarnClientImpl: Submitted application application_1707010689166_0003
2024-02-09 09:53:24,625 INFO mapreduce.Job: The url to track the job: http://LAPTOP-JBell.:8088/proxy/application_1707010689166_0003/
2024-02-09 09:53:24,626 INFO mapreduce.Job: Running job: job_1707010689166_0003
2024-02-09 09:53:29,730 INFO mapreduce.Job: Job job_1707010689166_0003 running in uber mode : false
2024-02-09 09:53:29,731 INFO mapreduce.Job: map 0% reduce 0%
2024-02-09 09:54:17,390 INFO mapreduce.Job: map 33% reduce 0%
2024-02-09 09:54:24,509 INFO mapreduce.Job: map 67% reduce 0%
2024-02-09 09:54:31,629 INFO mapreduce.Job: map 94% reduce 0%
2024-02-09 09:54:36,652 INFO mapreduce.Job: map 100% reduce 100%
2024-02-09 09:54:37,663 INFO mapreduce.Job: Job job_1707010689166_0003 completed successfully
2024-02-09 09:54:37,776 INFO mapreduce.Job: Counters: 54
File System Counters
FILE: Number of bytes read=330
FILE: Number of bytes written=5240478
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=2415272483
HDFS: Number of bytes written=21
HDFS: Number of read operations=59
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
HDFS: Number of bytes read erasure-coded=0
Job Counters
Launched map tasks=18
Launched reduce tasks=1
Data-local map tasks=18
Total time spent by all maps in occupied slots (ms)=346566
Total time spent by all reduces in occupied slots (ms)=11939
Total time spent by all map tasks (ms)=346566
Total time spent by all reduce tasks (ms)=11939
Total vcore-milliseconds taken by all map tasks=346566
Total vcore-milliseconds taken by all reduce tasks=11939
Total megabyte-milliseconds taken by all map tasks=354883584
Total megabyte-milliseconds taken by all reduce tasks=12225536
Map-Reduce Framework
Map input records=14294224
Map output records=14294224
Map output bytes=228707584
Map output materialized bytes=432
Input split bytes=1962
Combine input records=14294224
Combine output records=18
Reduce input groups=1
Reduce shuffle bytes=432
Reduce input records=18
Reduce output records=1
Spilled Records=36
Shuffled Maps =18
Failed Shuffles=0
Merged Map outputs=18
GC time elapsed (ms)=50184
CPU time spent (ms)=88410
Physical memory (bytes) snapshot=8216698880
Virtual memory (bytes) snapshot=49281294336
Total committed heap usage (bytes)=8874622976
Peak Map Physical memory (bytes)=475123712
Peak Map Virtual memory (bytes)=2596765696
Peak Reduce Physical memory (bytes)=242405376
Peak Reduce Virtual memory (bytes)=2599333888
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=2415270521
File Output Format Counters
Bytes Written=21
ubuntu@LAPTOP-JBell:~$ hadoop fs -ls -R /LineCount
-rw-r--r-- 1 ubuntu supergroup 0 2024-02-09 09:54 /LineCount/_SUCCESS
-rw-r--r-- 1 ubuntu supergroup 21 2024-02-09 09:54 /LineCount/part-r-00000
ubuntu@LAPTOP-JBell:~$ hadoop fs -cat /LineCount/part-r-00000
Total Lines 14294224
Total lines are 14294224. This includes the header, thus there are 14294223 records.
Spark
https://spark.apache.org/downloads.html
wget https://dlcdn.apache.org/spark/spark-3.3.4/spark-3.3.4-bin-hadoop3.tgz
tar -xvzf spark-3.3.4-bin-hadoop3.tgz
export SPARK_HOME=/home/ubuntu/spark-3.3.4-bin-hadoop3
export PATH=$SPARK_HOME/bin:$PATH
export SPARK_DIST_CLASSPATH=$(hadoop classpath)
cp $SPARK_HOME/conf/spark-defaults.conf.template $SPARK_HOME/conf/spark-defaults.conf
spark.driver.host localhost
run-example SparkPi 10
24/02/08 17:42:53 INFO DAGScheduler: Job 0 finished: reduce at SparkPi.scala:38, took 0.770038 s
Pi is roughly 3.1433631433631435
24/02/08 17:42:53 INFO SparkUI: Stopped Spark web UI at http://localhost:4040
ubuntu@LAPTOP-JBell:~$ spark-shell
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/ubuntu/spark-3.3.4-bin-hadoop3/jars/log4j-slf4j-impl-2.17.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/ubuntu/hadoop-3.3.6/share/hadoop/common/lib/slf4j-reload4j-1.7.36.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]
24/02/09 16:54:19 WARN Utils: Your hostname, LAPTOP-JBell resolves to a loopback address: 127.0.1.1; using 172.17.194.43 instead (on interface eth0)
24/02/09 16:54:19 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
24/02/09 16:54:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Spark context Web UI available at http://localhost:4040
Spark context available as 'sc' (master = local[*], app id = local-1707515665178).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.3.4
/_/
Using Scala version 2.12.15 (OpenJDK 64-Bit Server VM, Java 1.8.0_392)
Type in expressions to have them evaluated.
Type :help for more information.
scala> val df = spark.read.option("header", "true").csv("/census2021/ada/ada.csv")
df: org.apache.spark.sql.DataFrame = [CENSUS_YEAR: string, DGUID: string ... 21 more fields]
scala> val distinctGeoNameCount = df.select("GEO_NAME").distinct().count()
distinctGeoNameCount: Long = 5433
That is, the number of ADA is 5433.
At the end of our tutorial, we calculate the number of ADA another way.
ubuntu@LAPTOP-JBell:~/hadoop-3.3.6/bin$ spark-shell
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/ubuntu/spark-3.3.4-bin-hadoop3/jars/log4j-slf4j-impl-2.17.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/ubuntu/hadoop-3.3.6/share/hadoop/common/lib/slf4j-reload4j-1.7.36.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]
24/02/09 09:58:14 WARN Utils: Your hostname, LAPTOP-JBell resolves to a loopback address: 127.0.1.1; using 172.17.194.43 instead (on interface eth0)
24/02/09 09:58:14 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
24/02/09 09:58:19 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Spark context Web UI available at http://localhost:4040
Spark context available as 'sc' (master = local[*], app id = local-1707490699967).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.3.4
/_/
Using Scala version 2.12.15 (OpenJDK 64-Bit Server VM, Java 1.8.0_392)
Type in expressions to have them evaluated.
Type :help for more information.
scala> val df = spark.read.option("header", "true").csv("/census2021/ada/ada.csv")
df: org.apache.spark.sql.DataFrame = [CENSUS_YEAR: string, DGUID: string ... 21 more fields]
scala> val rowCount = df.count()
rowCount: Long = 14294223
scala> df.columns.foreach(println)
CENSUS_YEAR
DGUID
ALT_GEO_CODE
GEO_LEVEL
GEO_NAME
TNR_SF
TNR_LF
DATA_QUALITY_FLAG
CHARACTERISTIC_ID
CHARACTERISTIC_NAME
CHARACTERISTIC_NOTE
C1_COUNT_TOTAL
SYMBOL12
C2_COUNT_MEN+
SYMBOL14
C3_COUNT_WOMEN+
SYMBOL16
C10_RATE_TOTAL
SYMBOL18
C11_RATE_MEN+
SYMBOL20
C12_RATE_WOMEN+
SYMBOL22
scala> val df = spark.read.option("header", "true").csv("/census2021/ada/ada.csv")
df: org.apache.spark.sql.DataFrame = [CENSUS_YEAR: string, DGUID: string ... 21 more fields]
scala> val dfRenamed = df.withColumnRenamed("C2_COUNT_MEN+", "C2_COUNT_MEN").withColumnRenamed("C3_COUNT_WOMEN+", "C3_COUNT_WOMEN").withColumnRenamed("C11_RATE_MEN+", "C11_RATE_MEN").withColumnRenamed("C12_RATE_WOMEN+", "C12_RATE_WOMEN")
dfRenamed: org.apache.spark.sql.DataFrame = [CENSUS_YEAR: string, DGUID: string ... 21 more fields]
scala> dfRenamed.columns.foreach(println)
CENSUS_YEAR
DGUID
ALT_GEO_CODE
GEO_LEVEL
GEO_NAME
TNR_SF
TNR_LF
DATA_QUALITY_FLAG
CHARACTERISTIC_ID
CHARACTERISTIC_NAME
CHARACTERISTIC_NOTE
C1_COUNT_TOTAL
SYMBOL12
C2_COUNT_MEN
SYMBOL14
C3_COUNT_WOMEN
SYMBOL16
C10_RATE_TOTAL
SYMBOL18
C11_RATE_MEN
SYMBOL20
C12_RATE_WOMEN
SYMBOL22
scala> val df = spark.read.option("header", "true").csv("/census2021/ada/ada.csv")
df: org.apache.spark.sql.DataFrame = [CENSUS_YEAR: string, DGUID: string ... 21 more fields]
scala> val dfRenamed = df.withColumnRenamed("C2_COUNT_MEN+", "C2_COUNT_MEN").withColumnRenamed("C3_COUNT_WOMEN+", "C3_COUNT_WOMEN").withColumnRenamed("C11_RATE_MEN+", "C11_RATE_MEN").withColumnRenamed("C12_RATE_WOMEN+", "C12_RATE_WOMEN")
dfRenamed: org.apache.spark.sql.DataFrame = [CENSUS_YEAR: string, DGUID: string ... 21 more fields]
scala> val df_reduced = dfRenamed.select("GEO_NAME", "CHARACTERISTIC_ID", "CHARACTERISTIC_NAME", "C1_COUNT_TOTAL", "C2_COUNT_MEN", "C3_COUNT_WOMEN", "C10_RATE_TOTAL", "C11_RATE_MEN", "C12_RATE_WOMEN")
df_reduced: org.apache.spark.sql.DataFrame = [GEO_NAME: string, CHARACTERISTIC_ID: string ... 7 more fields]
scala> df_reduced.coalesce(1).write.option("header", "true").csv("/census2021/ada_reduced")
[Stage 1:> (0 + 1) / 1]
ubuntu@LAPTOP-JBell:~$ hadoop fs -ls -R /census2021
drwxr-xr-x - ubuntu supergroup 0 2024-02-09 10:29 /census2021/ada
-rw-r--r-- 1 ubuntu supergroup 2415200889 2024-02-09 09:50 /census2021/ada/ada.csv
drwxr-xr-x - ubuntu supergroup 0 2024-02-09 10:32 /census2021/ada_reduced
-rw-r--r-- 1 ubuntu supergroup 0 2024-02-09 10:32 /census2021/ada_reduced/_SUCCESS
-rw-r--r-- 1 ubuntu supergroup 763087510 2024-02-09 10:32 /census2021/ada_reduced/part-00000-f87a37db-597e-42df-bd05-bab7766f0c41-c000.csv
ubuntu@LAPTOP-JBell:~$ hadoop fs -head /census2021/ada_reduced/part-00000-f87a37db-597e-42df-bd05-bab7766f0c41-c000.csv
GEO_NAME,CHARACTERISTIC_ID,CHARACTERISTIC_NAME,C1_COUNT_TOTAL,C2_COUNT_MEN,C3_COUNT_WOMEN,C10_RATE_TOTAL,C11_RATE_MEN,C12_RATE_WOMEN
10010001,1,"Population, 2021",8881,,,,,
10010001,2,"Population, 2016",9334,,,,,
10010001,3,"Population percentage change, 2016 to 2021",-4.9,,,-4.9,,
10010001,4,Total private dwellings,5737,,,,,
10010001,5,Private dwellings occupied by usual residents,4121,,,,,
10010001,6,Population density per square kilometre,9.4,,,9.4,,
10010001,7,Land area in square kilometres,941.33,,,,,
10010001,8,Total - Age groups of the population - 100% data,8880,4365,4515,100,100,100
10010001,9,0 to 14 years,925,460,465,10.4,10.5,10.3
10010001,10,0 to 4 years,265,130,135,3,3,3
10010001,11,5 to 9 years,295,135,150,3.3,3.1,3.3
10010001,12,10 to 14 years,370,190,180,4.2,4.4,4
10010001,13,15 to 64 years,5010,2500,2510,56.4,57.3,55.6
10010001,14,15 to 19 years,370,185,180,4.2,4.2,4
10010001,15,20 to 24 years,280,155,120,3.2,3.6,2.7
10010001,16,25 to 29 years,250,130,120,2.8,3,2.7
10010001,17,30 to 34 years
(We remind ourselves that hadoop fs -head displays the first kilobyte of a file, not a fixed number of lines.)
scala> val df = spark.read.option("header", "true").csv("/census2021/ada_reduced/part-00000-f87a37db-597e-42df-bd05-bab7766f0c41-c000.csv")
df: org.apache.spark.sql.DataFrame = [GEO_NAME: string, CHARACTERISTIC_ID: string ... 7 more fields]
scala> val df_id = df.select($"CHARACTERISTIC_ID".cast("integer").alias("CHARACTERISTIC_ID"), $"CHARACTERISTIC_NAME").distinct().orderBy("CHARACTERISTIC_ID")
df_id: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [CHARACTERISTIC_ID: int, CHARACTERISTIC_NAME: string]
scala> df_id.coalesce(1).write.option("header", "true").csv("/census2021/ada_characteristic")
ubuntu@LAPTOP-JBell:~$ hadoop fs -ls -R /census2021/ada_characteristic
-rw-r--r-- 1 ubuntu supergroup 0 2024-02-09 13:31 /census2021/ada_characteristic/_SUCCESS
-rw-r--r-- 1 ubuntu supergroup 78032 2024-02-09 13:31 /census2021/ada_characteristic/part-00000-3912cf78-b4fe-4986-9e70-9a62cff91ab6-c000.csv
ubuntu@LAPTOP-JBell:~$ hadoop fs -head /census2021/ada_characteristic/part-00000-3912cf78-b4fe-4986-9e70-9a62cff91ab6-c000.csv
CHARACTERISTIC_ID,CHARACTERISTIC_NAME
1,"Population, 2021"
2,"Population, 2016"
3,"Population percentage change, 2016 to 2021"
4,Total private dwellings
5,Private dwellings occupied by usual residents
6,Population density per square kilometre
7,Land area in square kilometres
8,Total - Age groups of the population - 100% data
9,0 to 14 years
10,0 to 4 years
11,5 to 9 years
12,10 to 14 years
13,15 to 64 years
14,15 to 19 years
15,20 to 24 years
16,25 to 29 years
17,30 to 34 years
18,35 to 39 years
19,40 to 44 years
20,45 to 49 years
21,50 to 54 years
22,55 to 59 years
23,60 to 64 years
24,65 years and over
25,65 to 69 years
26,70 to 74 years
27,75 to 79 years
28,80 to 84 years
29,85 years and over
30,85 to 89 years
31,90 to 94 years
32,95 to 99 years
33,100 years and over
34,Total - Distribution (%) of the population by broad age groups - 100% data
35,0 to 14 years
36,15 to 64 years
37,65 years and over
38,85 years and over
39,Average age of the population
40,Median age of the population
41,Total - Occupied pri
We can determine the number of CHARACTERISTIC_ID in two ways: the following
ubuntu@LAPTOP-JBell:~$ hadoop fs -cat /census2021/ada_characteristic/part-00000-3912cf78-b4fe-4986-9e70-9a62c
ff91ab6-c000.csv | wc -l
2632
and the following
ubuntu@LAPTOP-JBell:~$ hadoop fs -tail /census2021/ada_characteristic/part-00000-3912cf78-b4fe-4986-9e70-9a62
cff91ab6-c000.csv
a.m.
2620,Between 7 a.m. and 7:59 a.m.
2621,Between 8 a.m. and 8:59 a.m.
2622,Between 9 a.m. and 11:59 a.m.
2623,Between 12 p.m. and 4:59 a.m.
2624,Total - Eligibility for instruction in the minority official language for the population in private households born in 2003 or later - 100% data
2625,Children eligible for instruction in the minority official language
2626,Children not eligible for instruction in the minority official language
2627,"Total - Eligibility and instruction in the minority official language, for the population in private households born between 2003 and 2015 (inclusive) - 100% data"
2628,Children eligible for instruction in the minority official language
2629,Eligible children�who have been instructed in the minority official language at the primary or secondary level in Canada
2630,Eligible children�who have not been instructed in the minority official language at the primary or secondary level in Canada
2631,Children not eligible for instruction in the minority official language
Thus there are 2631 CHARACTERISTIC_ID.
There are 14294223 records in the datasets. Thus there are $\dfrac{14294223}{2631}=5433$ ADA.
References
https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-common/SingleCluster.html
https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-common/FileSystemShell.html
https://statinfer.com/301-2-3-map-reduce-code-for-line-count/