The document provides an overview of the Hadoop ecosystem, detailing various systems that work with or replace MapReduce, including Hive, Pig, Cascading, and Spark. It discusses their functionalities, advantages, and typical usage scenarios. Additionally, it covers complementary databases like HBase and Parquet, as well as utility tools such as Flume, Avro, and Oozie, highlighting the flexibility and performance improvements that these components bring to data processing workflows.

1. Hadoop
Ecosystem
Ran
Silberman
Dec. 2014

2. What types of ecosystems exist?
● Systems that are based on MapReduce
● Systems that replace MapReduce
● Complementary databases
● Utilities
● See complete list here

3. Systems based
on MapReduce

4. Hive
● Part of the Apache project
● General SQL-like syntax for querying HDFS or other
large databases
● Each SQL statement is translated to one or more
MapReduce jobs (in some cases none)
● Supports pluggable Mappers, Reducers and SerDe’s
(Serializer/Deserializer)
● Pro: Convenient for analytics people that use SQL

5. Hive Architecture

6. Hive Usage
Start a hive shell:
$hive
create hive table:
hive> CREATE TABLE tikal (id BIGINT, name STRING, startdate TIMESTAMP, email
STRING)
Show all tables:
hive> SHOW TABLES;
Add a new column to the table:
hive> ALTER TABLE tikal ADD COLUMNS (description STRING);
Load HDFS data file into the dable:
hive> LOAD DATA INPATH '/home/hduser/tikal_users' OVERWRITE INTO TABLE tikal;
query employees that work more than a year:
hive> SELECT name FROM tikal WHERE (unix_timestamp() - startdate > 365 * 24 *
60 * 60);

7. Pig
● Part of the Apache project
● A programing language that is compiled into one or
more MaprRecuce jobs.
● Supports User Defined functions
● Pro: More Convenient to write than pure MapReduce.

8. Pig Usage
Start a pig Shell. (grunt is the PigLatin shell prompt)
$ pig
grunt>
Load a HDFS data file:
grunt> employees = LOAD 'hdfs://hostname:54310/home/hduser/tikal_users'
as (id,name,startdate,email,description);
Dump the data to console:
grunt> DUMP employees;
Query the data:
grunt> employees_more_than_1_year = FILTER employees BY (float)rating>1.
0;
grunt> DUMP employees_more_than_1_year;
Store query result to new file:
grunt> store employees_more_than_1_year into
'/home/hduser/employees_more_than_1_year';

9. Cascading
● An infrastructure with API that is compiled to one or
more MapReduce jobs
● Provide graphical view of the MapReduce jobs workflow
● Ways to tweak setting and improve performance of
workflow.
● Pros:
○ Hides MapReduce API and joins jobs
○ Graphical view and performance tuning

10. MapReduce workflow
● MapReduce framework operates exclusively on
Key/Value pairs
● There are three phases in the workflow:
○ map
○ combine
○ reduce
(input) <k1, v1> =>
map => <k2, v2> =>
combine => <k2, v2> =>
reduce => <k3, v3> (output)

11. WordCount in MapRecuce Java API
private class WordCount {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}

12. WordCount in MapRecuce Java Cont.
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}

13. WordCount in MapRecuce Java Cont.
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.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);
}
}

14. MapReduce workflow example.
Let’s consider two text files:
$ bin/hdfs dfs -cat /user/joe/wordcount/input/file01
Hello World Bye World
$ bin/hdfs dfs -cat /user/joe/wordcount/input/file02
Hello Hadoop Goodbye Hadoop

15. Mapper code
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}

16. Mapper output
For two files there will be two mappers.
For the given sample input the first map emits:
< Hello, 1>
< World, 1>
< Bye, 1>
< World, 1>
The second map emits:
< Hello, 1>
< Hadoop, 1>
< Goodbye, 1>
< Hadoop, 1>

17. Set Combiner
We defined a combiner in the code:
job.setCombinerClass(IntSumReducer.class);

18. Combiner output
Output of each map is passed through the local combiner
for local aggregation, after being sorted on the keys.
The output of the first map:
< Bye, 1>
< Hello, 1>
< World, 2>
The output of the second map:
< Goodbye, 1>
< Hadoop, 2>
< Hello, 1>

19. Reducer code
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}

20. Reducer output
The reducer sums up the values
The output of the job is:
< Bye, 1>
< Goodbye, 1>
< Hadoop, 2>
< Hello, 2>
< World, 2>

21. The Cascading core components
● Tap (Data resource)
○ Source (Data input)
○ Sink (Data output)
● Pipe (data stream)
● Filter (Data operation)
● Flow (assembly of Taps and Pipes)

22. WordCount in Cascading
Visualization
source (Document Collection)
sink (Word Count)
pipes (Tokenize, Count)

23. WodCount in Cascading Cont.
// define source and sink Taps.
Scheme sourceScheme = new TextLine( new Fields( "line" ) );
Tap source = new Hfs( sourceScheme, inputPath );
Scheme sinkScheme = new TextLine( new Fields( "word", "count" ) );
Tap sink = new Hfs( sinkScheme, outputPath, SinkMode.REPLACE );
// the 'head' of the pipe assembly
Pipe assembly = new Pipe( "wordcount" );
// For each input Tuple
// parse out each word into a new Tuple with the field name "word"
// regular expressions are optional in Cascading
String regex = "(?<!pL)(?=pL)[^ ]*(?<=pL)(?!pL)";
Function function = new RegexGenerator( new Fields( "word" ), regex );
assembly = new Each( assembly, new Fields( "line" ), function );
// group the Tuple stream by the "word" value
assembly = new GroupBy( assembly, new Fields( "word" ) );

24. WodCount in Cascading
// For every Tuple group
// count the number of occurrences of "word" and store result in
// a field named "count"
Aggregator count = new Count( new Fields( "count" ) );
assembly = new Every( assembly, count );
// initialize app properties, tell Hadoop which jar file to use
Properties properties = new Properties();
FlowConnector.setApplicationJarClass( properties, Main.class );
// plan a new Flow from the assembly using the source and sink Taps
// with the above properties
FlowConnector flowConnector = new FlowConnector( properties );
Flow flow = flowConnector.connect( "word-count", source, sink, assembly );
// execute the flow, block until complete
flow.complete();

25. Diagram of Cascading Flow

26. Scalding
● Extension to Cascading
● Programing language is Scala instead of Java
● Good for functional programing paradigms in Data
Applications
● Pro: code can be very compact!

27. WordCount in Scalding
import com.twitter.scalding._
class WordCountJob(args : Args) extends Job(args) {
TypedPipe.from(TextLine(args("input")))
.flatMap { line => line.split("""s+""") }
.groupBy { word => word }
.size
.write(TypedTsv(args("output")))
}

28. Summingbird
● An open source from Twitter.
● An API that is compiled to Scalding and to Storm
topologies.
● Can be written in Java or Scala
● Pro: When you want to use Lambda Architecture and
you want to write one code that will run on both Hadoop
and Storm.

29. WordCount in Summingbird
def wordCount[P <: Platform[P]]
(source: Producer[P, String], store: P#Store[String, Long]) =
source.flatMap { sentence =>
toWords(sentence).map(_ -> 1L)
}.sumByKey(store)

30. Systems that
replace MapReduce

31. Spark
● Part of the Apache project
● Replaces MapReduce with it own engine that works
much faster without compromising consistency
● Architecture not based on Map-reduce but rather on two
concepts: RDD (Resilient Distributed Dataset) and DAG
(Directed Acyclic Graph)
● Pro’s:
○ Works much faster than MapReduce;
○ fast growing community.

32. Impala
● Open Source from Cloudera
● Used for Interactive queries with SQL syntax
● Replaces MapReduce with its own Impala Server
● Pro: Can get much faster response time for SQL over
HDFS than Hive or Pig.

33. Impala benchmark
Note: Impala is over Parquet!

34. Impala replaces MapReduce

35. Impala architecture
● Impala architecture was inspired by Google Dremel
● MapReduce is great for functional programming, but not
efficient for SQL.
● Impala replaced the MapReduce with Distributed Query
Engine that is optimized for fast queries.

36. Dermal architecture
Dremel: Interactive Analysis of Web-Scale Datasets

37. Impala architecture

38. Presto, Drill, Tez
● Several more alternatives:
○ Presto by Facebook
○ Apache Drill pushed by MapR
○ Apache Tez pushed by Hortonworks
● all are alternatives to Impala and do more or less the
same: provide faster response time for queries over
HDFS.
● Each of the above claim to have very fast results.
● Be careful of benchmarks they publish: to get better
results they use indexed data rather than sequential
files in HDFS (i.e., ORC file, Parquet, HBase)

39. Complementary
Databases

40. HBase
● Apache project
● NoSQL cluster database that can grow linearly
● Can store billions of rows X millions of columns
● Storage is based on HDFS
● API based on MapReduce
● Pros:
○ Strongly consistent read/writes
○ Good for high-speed counter aggregations

41. Parquet
● Apache (incubator) project. Initiated by Twitter &
Cloudera
● Columnar File Format - write one column at a time
● Integrated with Hadoop ecosystem (MapReduce, Hive)
● Supports Avro, Thrift and ProtBuf
● Pro: keep I/O to a minimum by reading from a disk only
the data required for the query

42. Columnar format (Parquet)

43. Advantages of Columnar formats
● Better compression as data is more homogenous.
● I/O will be reduced as we can efficiently scan only a
subset of the columns while reading the data.
● When storing data of the same type in each column,
we can use encodings better suited to the modern
processors’ pipeline by making instruction branching
more predictable.

44. Utilities

45. Flume
● Cloudera product
● Used to collect files from distributed systems and send
them to central repository
● Designed for integration with HDFS but can write to
other FS
● Supports listening to TCP and UDP sockets
● Main Use Case: collect distributed logs to HDFS

46. Avro
● An Apache project
● Data Serialization by Schema
● Support rich data structures. Defined in Json-like syntax
● Support Schema evolution
● Integrated with Hadoop I/O API
● Similar to Thrift and ProtocolBuffers

47. Oozie
● An Apache project
● Workflow Scheduler for Hadoop jobs
● Very close integration with the Hadoop API

48. Mesos
● Apache project
● Cluster manager that abstracts resources
● Integrated with Hadoop to allocate resources
● Scalable to 10,000 nodes
● Supports physical machines, VM’s, Docker
● Multi resource scheduler (memory, CPU, disk, ports)
● Web UI for viewing cluster status