JethroData Index based SQL on Hadoop engine. Architecture comparison of MPP / Full-Scan sql engines such as Impala and Hive to index-based access such as Jethro. SQL and NoSQL NYC meetup Oct 20 2014 Boaz Raufman

1. JethroData
Indexed Based SQL-on-
Hadoop - An Architectural
Comparison of Tools
Simpler. Faster. Cheaper.

2. About the presenter
Boaz Raufman – Co-Founder / CTO
• Over 25 years experience in software design & mgmt
• Expertise in database architecture, information
retrieval and search technologies
• Led numerous information retrieval projects for
various Israeli intelligence agencies as well as for
commercial companies
• Started JethroData in 2010 with the idea of integrating
database and search technologies to accelerate big
data analytics
• Bachelor's degree in Computer Science and Philosophy
from the Tel-Aviv University

3. SQL-on-Hadoop
Hadoop uses the same parallel design pattern as
the parallel databases from last decade
Frameworks
• MapReduce
• Tez
• Spark
Reborn on
Hadoop
• Pivotal
HAWQ
• IBM BigSQL
• Teradata
Aster
• Actian
New Comers
• Hive
• Impala
• Presto
• Tajo
• Drill
• Spark SQL

4. Data
Node
Full-Scan Execution
Client: SELECT day, sum(sales) FROM t1 WHERE prod=‘abc’ GROUP BY day
Data
Node
Data
Node
Data
Node
Data
Node
Query
Executor
Query
Executor
Query
Executor
Query
Executor
Query
Executor
Query
Planner
/Mgr
Query
Planner/
Mgr
Query
Planner/
Mgr
Query
Planner/
Mgr
Query
Planner/
Mgr
Performance and resources based on the size of the dataset

5. Shared-Nothing MPP Design
Principles
Parallel Processing
• Divide the work across
many nodes
• Try to minimize inter-node
communication
• Work should be evenly
distributed
Full Data Scanning
• Full sequential scan -
massive I/O
• Data locality and local
processing
• Minimize amount of
data being read
– Columnar data store
– Partition by specific key
– Block stats
Performance and resource requirement
based on the dataset size

6. MPP Complex queries processing
Result
Merge
Global
Aggregation
(join, distinct,
group by,
order by,
sub-query)
Local
Aggregation
Example:
SELECT
DAY,
COUNT( DISTINCT ITEM)
FROM T1
WHERE
PRODUCT=‘abc’
GROUP BY DAY

7. Index based SQL-on-Hadoop

8. Client: SELECT day, sum(sales) FROM t1 WHERE prod=‘abc’ GROUP BY day
Data
Node
The Index-Access Design
Data
Node
Data
Node
Data
Node
Data
Node
Jethro
Query
Node
Query
Node
1. Index Access 2. Read data only for require rows
Performance and resources based on the size of the result-set

9. Index-Based Design
• Surgical scan – minimum I/O
• Performance and required resources based
on the result set size
• Extremely efficient for Interactive SQL use
cases
• Pay at load time

10. Architecture – Contrarian Concepts
• Index everything
– Every column is indexed
• Colum oriented
– Columnar or row-groups
– Append only data model
• Everything is stored in HDFS
– Can also work with S3 or Posix
• Shared Everything
– Separate compute and storage,
each scales-out independently
– Minimize cross-node operations
– Stateless Query nodes
• Parallelized multi-threaded
execution
– multiple parallelization dimensions:
columns, row ranges, partitions,
pipelining and bucketing
Jethro
Node
Jethro
Node
Client
Processing Layer
Storage Layer
HDFS/Posix/S3

11. Jethro Indexes
 Indexes map each column
value to a list of rows
 Jethro stores indexes as
Value Rows
FR rows 5,9,10,11,14
IL rows 1,3,7,12,13
US rows 2,4,6,8,15
hierarchical compressed bitmaps
 Very fast query operations – AND / OR / NOT
 Processed the entire WHERE clause to a final list of rows
 Patent pending:
http://www.google.com/patents/WO2013001535A3?cl=en
 INSERT Performance
– Load is very fast: files are appended, no random read/write, no locks
– Jethro Indexes are append-only. If needed, duplicate entries are allowed
– Periodic background merge (non-blocking)
– Compatible with HDFS

12. Built in optimizations
• Code is written in C++
• Column store and true column processing
• Vectorization for expression evaluation
• Multi-threaded and parallelized execution
• Planner using indexes meta data - index-based
queries
• Server-side cache in memory and local disk

13. Use-Case Analysis
Full-Scan: Performance depends on size of dataset
Index-Access: Performance depends on size of result-set

14. Comparing Recent Benchmarks – Jun 2014
Impala Parquet Vs. Hive/Tez, Presto, Shark
Source
Jethro Vs. Impala/Parquet
Source
Impala
Using the same queries in Jun-2014
Impala benchmarks, we compared
Impala with Jethro (TPC-DS, SF 1,000)

15. Benchmark – Jethro vs. Impala – Oct 2014
103
TPC-DS Interactive queries
Oct 2014
39.8 39.4 39.9
73.4
188.4
84.3 85.2
6.4 5 4.9 4
12.3 11.7 10.3 4.4
200
180
160
140
120
100
80
60
40
20
0
q19 q42 q52 q55 q63 q68 q73 q98
Impala 1.4.2 Jethro 0.9
* Queries use original TPC-DS filter criteria
*

16. DEMO
Go to Tableau demo
1. Point browser at: http://54.245.114.83/
2. Login as try-jethro/jethro123
3. Edit workbooks:
1. Jethro: Jethro sd – save
2. Impala: Impala sd save

17. Side-By-Side Implementation
Jethro Query
Node
MapReduce / Impala
▪
Jethro Indexer
Existing
Hadoop tables
are untouched
Data
Stream
Jethro Query
Nodes
▪
▪
▪
▪ ▪
▪▪▪ ▪▪
▪
▪▪ ▪▪
▪▪ ▪
▪
▪ ▪
Hive / Pig
▪ ▪ ▪ ▪
▪▪
▪▪▪
BI Tools
SQL
▪
Indexes are
added to select
tables. ~30%
incremental
storage

18. 1. Installing JethroData
• Existing Hadoop cluster
– CDH 4.x, CDH 5.x, HDP 2.x, EMR 3.x
• Designated Jethro server
– Can be inside or outside the cluster
– HW: CPU: 16+ cores, Mem: 64GB+, Net: 1GB / 10GB, SSD
for cache
• Install Jethro – download package
– rpm install
– Install HDFS client (if needed)
– Create /jethro dir in HDFS
• Start Jethro
– service jethro start

19. 2. Load Data into Jethro
• Run create instance script
– JethroAdmin create-instance demo /Jethro/demo
• Define a new table
– JethroClient demo localhost 9111
• Create table sales_demo (…);
• Run JethroLoader process
– JethroLoader demo sales_demo.desc
sales_demo.csv &
• Start Querying – ODBC, JDBC, JethroClient
That’s it!

20. Road Map
• Jethro S3
• Analytic functions
• UDF
• Cascading optimizer
• Function indexes
• Light weight text search
• Rows group format (Parquet/ORC)
• Integration with YARN for resource management
• Sync with Hive Metastore/HCatalog
• Nested data
• Materialized views
• Distributes query

21. Functional Indexes
Problem
How to accelerate this query:
Select count(*) from T where year(birthdate)=2007;
Solution
• Function index created for commonly
used functions
• Some function indexes are automatically
created for specific data types. Example:
year function for timestamp
• Query optimizer will identify scenarios
where functional indexes should be used
• Function index can also be user defined
or created on the fly via adaptive
optimization
Base Index Function YAER index
Value Rows Value Rows
02/04/2007 5,9,10,11,14 2007
1,2,3,4,5,6,7,
8,9,10,11,12,
13,14,15
03/05/2007 1,3,7,12,13
10/10/2007 2,4,6,8,15
01/02/2008 15,18 2008 15,16,17,18
05/03/2008 16,17
Query uses function index examples:
year(c1)=2007  explicit use for year index
C1 between 2007-01-01 and 2007-12-31  implicit use for year index
C1 between 2007-01-01 and 2008-02-15  Mix: take year index for 2007 and
rest from base index

22. Jethro’s Benefits
Simple to use
• Implemented side by
side with existing
Hadoop system
• Access via SQL or
your favorite BI tool
• Integrates with
Hadoop eco system
10X Faster queries
• Interactive analysis
with Sub second
latency
• Access to data as it
arrives
• Analyze granular,
raw data
50% Cheaper to operate
• Significantly less
computing resources
• No dual systems, costly
ETL
• Elastically scalable on
commodity hardware

23. Try it Today
• Point browser at:
http://www.jethrodata.com/home
• Click
• Register:

24. Jethro – Big Data Analytics. Real-Time.
Thank You!