Apache Tajo is a SQL-on-Hadoop system that provides both fast interactive analysis and stable long-running extract-transform-load (ETL) jobs. It supports various data formats and storage systems. Companies like SK Telecom and Bluehole Studio use Tajo for tasks such as data warehousing, game log analysis, and music streaming data discovery. Tajo is optimized for performance and supports features like cost-based query optimization and off-heap processing. Benchmark tests show it outperforms other SQL-on-Hadoop systems like Hive and Spark SQL.

1. Introduction to Apache Tajo:
Future of Data Warehouse
Jihoon Son / Gruter Inc.

2. I am
● Jihoon Son (@jihoonson)
○ Ph.D at Korea Univ.
○ Tajo project co-founder
○ Committer and PMC member of Apache Tajo
○ Research engineer at Gruter
○ Linkedin
■ https://www.linkedin.com/in/jihoonson
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3. Today's Topic: Tajo
● What is Tajo?
○ Tajo / tάːzo / 타조
○ Ostrich in Korean
■ Fastest two-legged animal in
the world
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4. Today's Topic: Tajo
● What is Apache Tajo?
○ Our Ostrich can do SQL
processing on big data!
■ SQL-on-Hadoop system
■ Apache Top-level project
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5. Maybe You Think ...
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SQL-on-Hadoop?
Boring..

6. This Ostrich is Different!
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7. SQL-on-Hadoop Systems
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8. SQL-on-Hadoop Systems
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9. SQL-on-Hadoop Systems
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Long-running
ETL jobs
Low-latency
interactive analysis

10. SQL-on-Hadoop Systems
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● Requirements
○ Stable query execution
■ Fault-tolerance
● Can avoid query
resubmission
○ Adaptation to dynamic
environment
■ Available resources,
unpredictable delays, ...
Long-running
ETL jobs

11. SQL-on-Hadoop Systems
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● Requirements
○ Fast query execution
■ Several query execution
techniques
■ In-memory processing Low-latency
interactive analysis

12. Tajo is designed for Both Workloads
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Long-running
ETL jobs
Low-latency
interactive analysis

13. Who are using Tajo?
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14. Use Cases: SK Telecom
● Data warehousing & analysis
○ 1st
telco in South Korea
■ 40 TB/day compressed data (2014)
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15. ETLETLETL
Integration
Layer
Data
Warehouse
Operational
Systems
SK Telecom: Before Tajo
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Marketing
Sales
ERP
SCM
ODS
Staging
Area
Data
Vault
Data Marts
Strategic
Marts
Hadoop MPP DBMS

16. ETLETLETL
Integration
Layer
Data
Warehouse
Operational
Systems
SK Telecom: After Tajo
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Marketing
Sales
ERP
SCM
ODS
Staging
Area
Data
Vault
Data Marts
Strategic
Marts

17. ETLETLETL
Integration
Layer
Data
Warehouse
Operational
Systems
SK Telecom: After Tajo
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Marketing
Sales
ERP
SCM
ODS
Staging
Area
Data
Vault
Data Marts
Strategic
Marts
● Long-running ETL jobs
● Ad-hoc analysis

18. Use Cases: SK Telecom
● Significantly reduced ETL & analysis time
○ Daily analysis becomes possible
○ More exploratory analysis is newly available
with remaining resources
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19. Use Cases: Bluehole Studio
● Game log analysis
○ Finding principal
causes of service-
quality deficiencies
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20. Use Cases: Bluehole Studio
● Tajo on EMR
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21. Use Cases: Bluehole Studio
● Their first log analysis system
○ Easy and rapid deployment of Tajo
○ Low learning curve with SQL standard
● Immediate action becomes possible for
user complaints and hidden bugs
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22. Use Cases: Melon
● Data discovery
○ Music streaming service (26 million users)
○ Analysis of purchase history for target
marketing
● Significantly reduced analysis time
○ Faster analysis by replacing Hive with Tajo
○ More analysis becomes possible
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23. So, Why should you use Tajo?
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24. So, Why should you use Tajo?
● Easy to use
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25. So, Why should you use Tajo?
● Easy to use
○ ANSI-SQL standard compliance (2003)
■ CTAS, Window functions, ...
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26. So, Why should you use Tajo?
● Easy to use
○ ANSI-SQL standard compliance (2003)
■ CTAS, Window functions, ...
○ Mature SQL features
■ Most existing queries can be executed without
modification
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27. So, Why should you use Tajo?
● Easy to use
○ ANSI-SQL standard compliance (2003)
■ CTAS, Window functions, ...
○ Mature SQL features
■ Most existing queries can be executed without
modification
○ Various data format support
■ Text, JSON, Orc, Parquet, …
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28. So, Why should you use Tajo?
● Optimized performance
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29. So, Why should you use Tajo?
● Optimized performance
○ Optimized code
■ Optimized I/O performance
● Nearly max I/O performance (~120MB/s) per disk
■ Off-heap data processing
● Mitigating GC overhead
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30. So, Why should you use Tajo?
● Optimized performance
○ Cost-based query plan optimization
■ Join ordering
■ Best algorithm selection
● According to input size
■ Progressive optimization
● Further optimize the query plan during query execution
● Especially excellent for long running queries
■ => Efficient start schema processing
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31. So, Why should you use Tajo?
● Various storage type support
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32. So, Why should you use Tajo?
● Various storage type support
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33. Logical Data Warehouse with Tajo
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Global view
Application DBMS NoSQL
Cloud
storage
On-premise
storage

34. Logical Data Warehouse with Tajo
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Global view
Application DBMS NoSQL
Cloud
storage
On-premise
storage
● Fast delivery
● Easy maintenance
● Simple data flow

35. How fast is Tajo?
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36. Evaluation on Cloud Environment
● Google Cloud Platform
○ Instance type: n1-standard-8
■ 8 core, 30GB RAM
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37. Target Systems
● Hive (0.12)
○ Baseline performance
○ Default configuration provided by GCP
■ Use the whole cpu and memory
● Tajo (0.11.0)
○ Default configuration provided by GCP
■ Use the whole cpu and memory
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38. Target Systems
● Spark-SQL (1.5.0)
○ Default configuration provided by GCP
■ Use the whole cpu and memory
■ Tungsten enabled by default
○ spark.sql.shuffle.partitions is
adjusted for better performance
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39. TPC-DS
● Data
○ 24 tables
■ Plain text format
■ Stored on Google Cloud Storage
● Query
○ Which can be executed on every system
without modifications
■ For Hive, 0.12 doesn't support implicit join, so
every query had to be changed
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40. SF 1000, 50 instances
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41. SF 1000, 50 instances
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42. SF 1000, 50 instances
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Cannot be run
on 1TB

43. SF 10000, 50 instances
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44. SF 10000, 50 instances
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45. Demo
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46. Simple Demo on EMR
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● Using TPC-H data set, but
○ Lineitem table is stored on HDFS
○ Orders table is stored on PostgreSQL
○ Other tables are stored on S3

47. Apache Tajo
● Is excellent for both long-running ETL jobs
and exploratory ad-hoc analysis
● Is very fast
● Supports query federation on diverse data
sources
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48. Get Involved!
● We are recruiting contributors!
● General
○ http://tajo.apache.org/
● Getting Started
○ http://tajo.apache.org/docs/current/getting_started.html
● Downloads
○ http://tajo.apache.org/downloads.html
● Issue tracker
○ http://issues.apache.org/jira/browse/TAJO
● Join the mailing list
○ dev-subscribe@tajo.apache.org
○ issues-subscribe@tajo.apache.org
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49. Useful Links
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● EMR bootstrap
○ https://github.com/awslabs/emr-bootstrap-
actions/tree/master/tajo
● How to setup Tajo on EMR
○ http://www.gruter.com/blog/setting-up-a-
tajo-cluster-on-amazon-emr/

50. Q & A
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