The document presents a meetup led by Rahul Jain discussing emerging technologies and frameworks in big data, particularly focusing on Apache Parquet, Apache Drill, and Apache Flink. It provides insights into columnar storage systems, the Dremel paper for real-time data analysis, and the unique features of each technology, such as storage efficiency and query flexibility. The talk emphasizes the performance benefits and use-cases of these frameworks in handling large datasets in modern data analytics environments.

1. Emerging
Technologies/Frameworks
in Big Data
Rahul Jain
@rahuldausa
Meetup Sep 2015

2. About Me
• Independent Big data/Search Consultant
• 8+ years of learning experience.
• Worked (got a chance) on High volume
distributed applications.
• Still a learner (and beginner)

3. Quick Questionnaire
How many people know/heard Apache Parquet ?
How many people know/heard Apache Drill ?
How many people Know/heard Apache Flink ?

4. What we are going to
learn/see today ?
• Columnar Storage (overview)
• Apache Parquet (with Demo)
• Dremel (Basic overview)
• Apache Drill (with Demo)
• Apache Flink (with Demo)

5. Let’s discuss
Columnar Storage

6. Lets say we have a Employee table
RowId EmpId Lastname Firstname Salary
001 10 Smith Joe 40000
002 12 Jones Mary 50000
003 11 Johnson Cathy 44000
004 22 Jones Bob 55000

7. table storage in row oriented system
In Row-oriented systems,
It will be stored as
001:10,Smith,Joe,40000;
002:12,Jones,Mary,50000;
003:11,Johnson,Cathy,44000;
004:22,Jones,Bob,55000;
RowId EmpId Lastname Firstname Salary
001 10 Smith Joe 40000
002 12 Jones Mary 50000
003 11 Johnson Cathy 44000
004 22 Jones Bob 55000

8. table storage in column oriented system
In Row-oriented systems,
It will be stored as
001:10,Smith,Joe,40000;
002:12,Jones,Mary,50000;
003:11,Johnson,Cathy,44000;
004:22,Jones,Bob,55000;
RowId EmpId Lastname Firstname Salary
001 10 Smith Joe 40000
002 12 Jones Mary 50000
003 11 Johnson Cathy 44000
004 22 Jones Bob 55000
But In Column-oriented systems,
It will be stored as
10:001,12:002,11:003,22:004;
Smith:001,Jones:002,Johnson:003,Jones:004;
Joe:001,Mary:002,Cathy:003,Bob:004;
40000:001,50000:002,44000:003,55000:004;

9. Row vs Column Storage
Row-oriented storage
001:10,Smith,Joe,40000;002:12,Jones,Mary,50000;003:11,Johnson,Cathy,440
00;004:22,Jones,Bob,55000;
Column-oriented storage
10:001,12:002,11:003,22:004;Smith:001,Jones:002,Johnson:003,Jones:004;Joe:001,
Mary:002,Cathy:003,Bob:004;40000:001,50000:002,44000:003,55000:004;

10. Apache Parquet
(Columnar Storage for Hadoop ecosystem)

11. About Apache Parquet
• Columnar based Storage format
• Initially started by Twitter and Cloudera
• stores nested data structures in a flat columnar format using a technique
outlined in the Dremel paper from Google.
• Can store very-2 large dataset with very high compression rate.
• Due to compression, less IO and Faster Processing.
• Provides high level APIs in Java
• Integration with Hadoop and its eco-system
• http://parquet.apache.org

12. Parquet Design
• required: exactly one occurrence
• optional: 0 or 1 occurrence
• repeated: 0 or more occurrences
For e.g, an address book schema:
message AddressBook {
required string owner;
repeated string ownerPhoneNumbers;
repeated group contacts {
required string name;
optional string phoneNumber;
}
}

13. Size Comparison
$ du -sch test.*
407M test.csv (1 million records, 4 columns)
70M test.csv.gz (~83% reduction)
35M test.parquet (~92% reduction)

14. Let’s discuss first
Dremel: Interactive Analysis of
Web-Scale Datasets

15. What is Dremel
• A Published a Paper in 2010 by Google
• Interactive Analysis of Web-Scale Datasets
– An adhoc query on a very large scale dataset (in Petabytes)
– Near Real time
– MR (Map-Reduce) works but that is meant for Batch Processing
• SQL like Query Interface
• Nested Data (with a Column storage representation)
• Paper:
– http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36632.pdf
• Projects (Implementation):
– Google Big Query (Cloud based)
– Apache Drill (Open source)

16. Why Dremel: Speed Matters
Credit: http://www.slideshare.net/robertlz/dremel-interactive-analysis-of-webscale-datasets

17. Widely used inside Google
Credit: http://www.slideshare.net/robertlz/dremel-interactive-analysis-of-webscale-datasets

18. Tree based structure
Credit: http://www.alberton.info/images/articles/papers/dremel1.png

19. Column striped representation
Credit: http://www.alberton.info/images/articles/papers/dremel2.png

20. Query Processing
Credit: http://farm9.staticflickr.com/8426/7843420938_9cb23a4cb0_b.jpg

21. Let’s move to
Apache Drill

22. About Apache Drill
• Based on Google’s Dremel Paper
• Supports data-intensive distributed applications for
interactive analysis of large-scale datasets
• Have a Datastore aware optimizer
– which constructs the query plan based on datastore’s
processing capabilities.
• Supports Data locality.
• http://drill.apache.org/

23. So Why Drill?
• Flexible Data Model
• Fixed Schema(Avro)/Dynamic Schema(JSON)/Schema less SQL
• Schema can be discovered on the Fly
• Built-in optimistic query execution engine.
• Doesn’t require a particular storage or execution system
(Map-Reduce, Spark, Tez)
• Better Performance and Manageability
• Cluster of commodity servers
• Daemon (drillbit) on each data node
• Works with Hadoop, CSV, JSON, Avro/Parquet, MongoDB, HBase,
Solr etc.

24. Query any non-relational datastore

25. Distributed SQL query engine
Credit: http://www.slideshare.net/MapRTechnologies/drill-highperformancesqlenginewithjsondatamodel

26. Designed to support wide set of
use-cases
Credit: http://www.slideshare.net/MapRTechnologies/drill-highperformancesqlenginewithjsondatamodel

27. Querying
CSV:
0: jdbc:drill:> select count(*) from dfs.`/tmp/test.csv`;
+-----------+
| EXPR$0 |
+-----------+
| 10000001 |
+-----------+
1 row selected (5.771 seconds)
Parquet:
0: jdbc:drill:> select count(*) from dfs.`/tmp/test.parquet`;
+-----------+
| EXPR$0 |
+-----------+
| 10000001 |
+-----------+
1 row selected (0.257 seconds)

28. Drill Shell
./bin/drill-embedded
It will start Drill in Embedded Mode. You will see output like this,
org.glassfish.jersey.server.ApplicationHandler initialize
INFO: Initiating Jersey application, version Jersey: 2.8 2014-04-29
01:25:26...
apache drill 1.0.0
"say hello to my little drill"
0: jdbc:drill:zk=local>
For windows: This will start the shell with Drill in embedded Mode.
./bin/sqlline.bat –u "jdbc:drill:schema=dfs;zk=local"

29. Terminology
• Drillbit
– Drillbit runs on each data node in the cluster, Drill
maximizes data locality during query execution.
Movement of data over the network or between
nodes is minimized or eliminated when possible.

30. Drill Configuration
drill.exec:{
cluster-id: "<cluster_name>",
zk.connect:
"<zkhostname1>:<port>,<zkhostname2>:<port>,<zkhostname3>:<port>“
}
Configuration: $DRILL_HOME/conf/drill-override.conf
Default configuration:
drill.exec: {
cluster-id: "drillbits1",
zk.connect: "localhost:2181"
}

31. Starting Drill in Distributed Mode
./bin/drillbit.sh restart
./bin/drillbit.sh [--config <conf-dir>] (start|stop|status|restart|autorestart)
It will restart the Drillbit service.
Tip:
Check the hostname on Drillbit is listening. For e.g.
2015-09-05 03:21:20,070 [main] INFO o.apache.drill.exec.server.Drillbit - Drillbit
environment: host.name=192.168.0.101
This will start the drill shell on local machine based on configuration provided in
drill-overide.conf
Start the shell:
./bin/drill-localhost (if drillbit listening on localhost)
otherwise
./bin/sqlline -u "jdbc:drill:drillbit=192.168.0.101"

32. Verify it once; and try a sample
0: jdbc:drill:zk=local> select * from sys.drillbits;
+----------------+------------+---------------+------------+----------+
| hostname | user_port | control_port | data_port | current |
+----------------+------------+---------------+------------+----------+
| 192.168.0.101 | 31010 | 31011 | 31012 | true |
+----------------+------------+---------------+------------+----------+
0: jdbc:drill:zk=local> select count(*) from `dfs`.`$DRILL_HOME/sample-
data/nation.parquet`;
+---------+
| EXPR$0 |
+---------+
| 25 |
+---------+
1 row selected (1.752 seconds)

33. Drill – Web Client
A Storage Plugin can be added/Enabled

34. Let’s move to
Apache Flink

35. About Apache Flink
• Open source framework for Big Data Analytics
• Distributed Streaming dataflow engine
• Runs Computing In-Memory.
• Executes programs in data-parallel and pipelined manner.
• Most popular for running Stream Data Processing.
• Provides high level APIs in
• Java
• Scala
• Python
• Integration with Hadoop and its eco-system and can read existing data of HDFS or
HBase.
• https://flink.apache.org

36. So Why Flink?
Credit: Compiled based on several articles,Blogs, Stackoverflow posts added in references page.
• Share a lot of Similarities with relational DBMS
• Data is serialized in byte buffers and processed a lot in binary representation
• So allows Fine grained memory control
• Uses a Pipeline based Processing Model with Cost based Optimizer to choose
the execution strategy.
• optimized for cyclic or iterative processes by using iterative transformations
on collections
• achieved by an optimization of join algorithms, operator chaining and
reusing of partitioning and sorting.
• Flink streaming processes data streams as true streams, i.e., data elements
are immediately "pipelined" though a streaming program as soon as they
arrive
• also has its own memory management system separate from Java’s garbage
collector.

37. Credit: http://www.slideshare.net/stephanewen1/apache-flink-overview

38. Flink vs Spark
(they looks to be pretty similar)
Apache Flink:
case class Word (word: String, frequency: Int)
val counts = text
.flatMap {line => line.split(" ").map(word => Word(word,1))}
.groupBy("word").sum("frequency")
Apache Spark:
val counts = text
.flatMap(line => line.split(" ")).map(word => (word, 1))
.reduceByKey{case (x, y) => x + y}

39. But….
Apache Spark: is batch processing framework that can
approximate stream processing (called as micro-batching)
Apache Flink: is primarily a stream processing framework that
can look like a batch processor.

40. Credit: http://www.slideshare.net/stephanewen1/apache-flink-overview

41. Credit: http://www.slideshare.net/stephanewen1/apache-flink-overview

42. Flink – Web Client
Arguments to program separated by spaces

43. Flink – Web Client

44. References
• https://flink.apache.org/
• https://www.quora.com/What-are-the-differences-between-Apache-Spark-and-Apache-Flink
• http://stackoverflow.com/questions/28082581/what-is-the-differences-between-apache-spark-
and-apache-flink
• http://statrgy.com/2015/06/01/best-data-processing-engine-flink-vs-spark/
• http://stackoverflow.com/questions/29780747/apache-flink-vs-apache-spark-as-platforms-for-
large-scale-machine-learning
• http://www.infoworld.com/article/2919602/hadoop/flink-hadoops-new-contender-for-
mapreduce-spark.html
• http://www.kdnuggets.com/2015/05/interview-matei-zaharia-creator-apache-spark.html

45. Thanks!
@rahuldausa on twitter and slideshare
http://www.linkedin.com/in/rahuldausa