Write Optimization of
Column-Store Databases in
Out-of-Core Environment
YOUNGSTOWN STATE UNIVERSITY
Dr. Feng “George” Yu
Assistant Professor
Department of Computer Science and Information Systems
Youngstown State University
fyu@ysu.edu

Outline
1. Part I: Write-Optimization
2. Part II: Data Cleaning
3. Part III: Application on Big Data
YOUNGSTOWN STATE UNIVERSITY, OH, USA

What is Column-Store
Database?
Column-store database is also known as columnar database
or column-oriented database
The history of column-store database can be traced back to
1970s. Not until about 2005 when many open-source and
commercial implementations of column-store databases
took off.
Well-known column-store databases:
YOUNGSTOWN STATE UNIVERSITY, OH, USA

Features of Column-Store
Databases
Fits well into the write-once-and-read-many environment.
• Works especially well for OLAP and data mining queries
• Retrieve many records but need only a few attributes.
Higher data compression rate
• Low data-entropy
• Much better than row-based storage
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Row-Based to Column-Store
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Fig. 1 customer Data in Row-Based and Column-Store (BAT) Format
id name balance
1 Alissa 100.00
2 Bob 200.00
3 Charles 300.00
(a) Row-Based Table customer
oid int
101 1
102 2
103 3
(b) BAT customer id
o
1
1
1
(c)
Figure 1: customer Data in Row-Based and
much faster in a column-store database.
Another featured benefit of the column-store
database is data compression, which can reach a higher
compression rate and higher speed than traditional
row-based database. One of the major reasons is that
the information entropy in the data of one column is
lower compared to that of row-based data.
Optimizing write operations in a column-store
sec
wo
2
e
omer
oid int
101 1
102 2
103 3
(b) BAT customer id
oid varchar
101 Alissa
102 Bob
103 Charles
(c) BAT customer name
oid float
101 100.00
102 200.00
103 300.00
(d) BAT customer balance
customer Data in Row-Based and Column-Store (BAT) Format
A BUN consists of
(oid, value)
Mapping Rules
Relational Data
Column-Store

Challenge
•Optimizing write operations in a
column-store database has always
been a challenge because:
• Data is vertically decomposed into BATs
and randomly distributed over the
storage.
• The writing on a column-store database
will be significantly delayed by ad hoc
access to large BATs across multiple
pages.
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Out-Of-Core (OOC)?
Existing works majorly focus on write
optimizations for main-memory column-store
database.
To the best of our knowledge, very few works
focus on optimizing the write performance on
the Out-Of-Core (OOC or external memory)
column-store databases.
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Traditional Update on BAT
In traditional BAT, an update by a given OID
involves in 2 phases:
1. Search the location in BAT by OID (Time-
consuming)
2.Update the value at the target location.
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Motivation
1. Avoid Searching!
2. Allow multi-values for a given OID.
3. Keep data consistent.
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Timestamped Binary
Association Table (TBAT)
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oid float
101 100.00
102 200.00
103 300.00
optime oid float
time1 101 100.00
time1 102 200.00
time1 103 300.00
customer_balance customer_balance
BAT TBAT
Suppose the existing
records were inserted
in one batch at time1.

The principle of AOC update is to avoid OOC
searching and writing in every effort and to use the
timestamp field of TBAT to label.
In AOC update, the newly updated data that is
directly appended to the end of a TBAT.
In such a manner, we don't have to frequently
perform ad hoc data searching.
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AOC Update Example
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Example:
Uupdate query on customer table:
update customer set balance=201.00
where id=2
Current timestamp is time2 (>time1).
The newest TBUN for 201.00 is appended to the end of TBAT customer_balance
New update ->
inal value to 201.00. Instead of seeking the position
to the record with oid=102, AOC update directly ap-
pends at the end of the TBAT a new tuple as (time2,
102, 201.00). The timestamp when AOC update is
performed is assumed to be time2, and 201.00 is the
newly updated value. The TBAT customer balance
after the AOC update is illustrated in Table 3.
Table 3: TBAT customer balance after AOC Update
optime oid float
time1 101 100.00
time1 102 200.00
time1 103 300.00
time2 102 201.00
3.2.2 Cost Analysis of the AOC Update
Body
Appendix

Selection after AOC Update
The data consistency will be intact in a TBAT after AOC update.
After the TBAT of customer has applied AOC updates, we run the
following query:
SELECT balance FROM customer WHERE id=2
In the updated TBAT customer_balance, two tuples will be returned:
t1=(time1, 102, 200.00)
t2=(time2, 102, 201.00)
We compare the timestamps, time2 > time1. Then 201.00 is returned
which is consistent with the last update value.
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AOC Update Experiment
Preliminary experiment results are designed in order to compare the
speed performance between AOC updates on TBATs and traditional
updates on BATs.
The experiment is performed on a CentOS 6.5 workstation with Intel
Core i7-3700 3.4GHz CPU, 16GB memory, and 250GB SATA 7200RPM
hard disk.
The experiment test code is implemented in Python 2.7.
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AOC Update Experiment
(cont.)
2.27
4.71
7.13
9.59
12.01
1.63E-03 3.25E-03 4.81E-03 6.41E-03 7.95E-03
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
10% 20% 30% 40% 50%
ElapasedTime(sec)
Update Percentage
BAT TBAT
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AOC Update and Traditional Update Running Time

AOC Update Experiment
(cont.)
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1392.84
1449.65
1484.23
1495.56
1509.9
1320
1340
1360
1380
1400
1420
1440
1460
1480
1500
1520
10% 20% 30% 40% 50%
TimesFaster(x1.0)
Update Percentage
Times of AOC Update Faster than Traditional Update =
Time(Update on BAT)
Time(AOC Update on TBAT)
Average 1466.436
times faster
Overheads

Potential Problems with AOC
Update
When many AOC updates are performed, searching
becomes gradually slower because its unsorted appendix
requires a linear search; the greater the volume of updated
data, the slower the search.
YOUNGSTOWN STATE UNIVERSITY, OH, USA
TBAT customer balance. The target
change the attribute value from the orig-
to 201.00. Instead of seeking the position
rd with oid=102, AOC update directly ap-
he end of the TBAT a new tuple as (time2,
00). The timestamp when AOC update is
is assumed to be time2, and 201.00 is the
ated value. The TBAT customer balance
OC update is illustrated in Table 3.
BAT customer balance after AOC Update
optime oid float
time1 101 100.00
time1 102 200.00
time1 103 300.00
time2 102 201.00
t1=(time1,
t2=(time2,
As we comp
than time1. Then
tent with the last
3.2.4 O ine D
Update
After a period of
there will be many
same oid and di
query is issued in
will all be return
execution time.
In order to
ficient on TBAT,
Body
Appendix

Search Speed Degeneration
YOUNGSTOWN STATE UNIVERSITY, OH, USA
Selection Query Execution Overhead:
TBAT over BAT ( time(TBAT)/time(BAT) × 100%)

Outline
1. Part I: Write-Optimization
2. Part II: Data Cleaning
3. Part III: Application on Big Data
YOUNGSTOWN STATE UNIVERSITY, OH, USA

Data Cleaning After AOC
Update
Data cleaning has recently drawn a lot of attention.
Data cleaning in our context is the process by which we
merge the updated updated data from the appendix into
the body.
• Remove multi-values of the same OID
• Avoid slower linear search
During non-peak times, Offline Data Cleaning allows for
these adjustments to be made by merging into the body the
recently updated data.
YOUNGSTOWN STATE UNIVERSITY, OH, USA

Problems with the Offline Data
Cleaning Method
This method causes the database to go offline, meaning that any
incoming queries will have to wait until the database comes back
online.
This lapse in service may not be appropriate for environments that
require a constant workload; inappropriate for constant input-streams.
YOUNGSTOWN STATE UNIVERSITY, OH, USA

Online Data Cleaning
The major difference of online data cleaning is the employment of a
sophisticated data structure called snapshot. The idea of live snapshot
roots from cloud computing.
YOUNGSTOWN STATE UNIVERSITY, OH, USA
Body
Snapshot
of Body
Appendix
New
Appendix
(original)
online
merge
read
read
read & write
Body
Merged
Appendix
New
During Online Cleaning After Online Cleaning

Online Data Cleaning (cont.)
The Online Eager Data Cleaning (speed priority) method merges the
entire appendix of the TBATs into the body in one go to save on time.
The Online Progressive Data Cleaning (memory-usage priority) method
is used during more extreme cases when the full appendix may not fit
into memory. The DBA manually decides a block size, and the appendix
is split into several of those blocks and added to an appendix queue.
The above eager method is applied to these appendix files and any
streaming updates (present-time) can be added to a new split appendix
file to be queued when it fills up the block size.
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Progressive Data-Cleaning
Results
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Outline
1. Part I: Write-Optimization
2. Part II: Data Cleaning
3. Part III: Application on Big Data
YOUNGSTOWN STATE UNIVERSITY, OH, USA

Update on BAT in Map-Reduce
In a Map-Reduce environment, we assume the update list of OIDs are
collected and submitted in a batch of UPDATE_LIST
1. Map-Reduce Join
BAT LEFT OUTER JOIN UPDATE_LIST ON OID => (BAT combined with
UPDATE_LIST)
• Map-side join: when UPDATE_LIST is small enough to fit into
memory
• Reduce-side join: when UPDATE_LIST is large enough
2. Selective Projection (Map-Only)
FOR each record in (BAT combine UPDATE_LIST)
IF UPDATE_LIST attribute is not NULL: output updated value
(keep the most recent update)
ELSE: output original value
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TBAT (Timestamped BAT)
TBAT in HDFS:
struct TBUN{
TIMESTAMP optime,
ROWID oid,
USER_DEFINED_TYPE attrv
}
struct TBAT_slip{
TBUN[max_size_per_HDFS_slip] tbuns
}
• No need for any global pre-sorting or indexing
• ‘attrv’ is can be any user defined type that flexibly define arbitrary kinds of
schema
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AMO Update (logical)
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Example:
Update query on customer table:
update customer set balance=201.00 where id=2
Current timestamp is time2 (>time1).
The newest TBUN for 201.00 is appended to the end of TBAT customer_balance
inal value to 201.00. Instead of seeking the position
to the record with oid=102, AOC update directly ap-
pends at the end of the TBAT a new tuple as (time2,
102, 201.00). The timestamp when AOC update is
performed is assumed to be time2, and 201.00 is the
newly updated value. The TBAT customer balance
after the AOC update is illustrated in Table 3.
Table 3: TBAT customer balance after AOC Update
optime oid float
time1 101 100.00
time1 102 200.00
time1 103 300.00
time2 102 201.00
3.2.2 Cost Analysis of the AOC Update
t
t
3
A
t
s
q
w
e
fi
p
New Data
Old Data

AMO Update Experiment
Performed on a Cloudera Distributed Hadoop (CDH) cluster
• 1 master and 3 slaves
• Total HDFS capacity= 310GB (block size = 64MB)
• Interconnection is Gigabit Ethernet
Data sets: 1GB and 10GB random synthetic data in BAT and TBAT.
Update queries: from 10% to 30% of the original data.
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AMO Update Experiment
(cont.)
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1GB Update Running Time
0
50
100
150
200
250
300
350
400
450
500
10 15 20 25 30
RunningTime(sec)
Update Percentage (%)
BAT TBAT

YOUNGSTOWN STATE UNIVERSITY
10GB Update Running Time
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
10 15 20 25 30
RunningTime(sec)
Update Percentage (%)
BAT TBAT
AMO Update Experiment
(cont.)

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Relative Overhead Changing over Data Sets
0
20
40
60
80
100
120
140
160
180
10 15 20 25 30
Overhead(%)
Update Percentage (%)
1GB 10GB
AMO Update Experiment
(cont.)

Generalized Write Optimization Framework (DEXA’15)
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Atomic
Buffer
(Read In)
Atomic
Buffer
Atomic
Buffer
Atomic
Buffer
Atomic
Buffer
Read Optimized Data
Serialized
TBAT_1
Serialized
TBAT_2
Serialized
TBAT_N
Input Stream
Atomic
Buffer
(Full)
Atomic
Buffer
(Full)
Atomic
Buffer
(Full)
…
Write Queue
Buffer Pool
…
Write Optimized ModuleRead Optimized Module

Publications
1. Hastening Data Retrieval on Out-of-Core Column-Store Databases using Offset B+-Tree
F. Yu, E. S. Jones
28th International Conference on Computer Applications in Industry and Engineering (CAINE 2015),
October 12-14, 2015, Hilton San Diego/Harbor Island, San Diego, California, USA, pp. 313-318
2. A Framework of Write Optimization on Read-Optimized Out-of-Core Column-Store Databases
F. Yu, W.-C. Hou
26th International Conference on Database and Expert Systems Applications (DEXA 2015), Valencia,
Spain, September 1-4, 2015, pp. 155-169
3. Write Optimization using Asynchronous Update on Out-of-Core Column-Store Databases in Map-
Reduce
F. Yu, E. S. Jones, W.-C. Hou
2015 IEEE International Congress on Big Data, June 27 - July 2, 2015, New York, USA, pp. 720-723
4. Online Data Cleaning for Out-Of-Core Column-Store Databases with Timestamped Binary
Association Tables
F. Yu, C. Luo, W.-C. Hou, E. S. Jones
Proceeding of 30th International Conference On Computers And Their Applications (CATA 2015),
Honolulu, Hawaii, USA, March 9-11, 2015, pp. 407-412
5. Asynchronous Update on Out-of-Core Column-Store Databases Utilizing the Time stamped Binary
Association Table
F. Yu, C. Luo, W.-C. Hou, E. S. Jones
Proceeding of 27th International Conference on. Computer Applications in Industry and
Engineering (CAINE 2014), New Orleans, Louisiana, LA, October 13-15, 2014, pp. 215-220.
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Source Code
https://github.com/YSU-Data-Lab/TBAT-DEXA15
YOUNGSTOWN STATE UNIVERSITY, OH, USA

New Challenges
•New Index on C-S DBs
• Local and global
• Searching
• Data Cleaning
• Parallel Processing
•Big Data
• Searching
• Data Cleaning
• Auto Mapping
• To Index or not to index?
•Broader Applications
• Scientifics Data Management
• Big Data Analytics
• Machine Learning
• OLAP
• OLTP
• HPC
• HTC
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Thank you!
Feng “George” Yu
Computer Science and Information Systems
Youngstown State University, Youngstown, OH
fyu@ysu.edu
YOUNGSTOWN STATE UNIVERSITY