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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
YOUNGSTOWN	STATE	UNIVERSITY
Row-Based	to	Column-Store
YOUNGSTOWN	STATE	UNIVERSITY
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.
YOUNGSTOWN	STATE	UNIVERSITY
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.
YOUNGSTOWN	STATE	UNIVERSITY
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.
YOUNGSTOWN	STATE	UNIVERSITY
Motivation
1. Avoid	Searching!
2. Allow	multi-values	for	a	given	OID.
3. Keep	data	consistent.
YOUNGSTOWN	STATE	UNIVERSITY,	OH,	USA
Timestamped Binary	
Association	Table	(TBAT)
YOUNGSTOWN	STATE	UNIVERSITY
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.
YOUNGSTOWN	STATE	UNIVERSITY
AOC	Update	Example
YOUNGSTOWN	STATE	UNIVERSITY
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.
YOUNGSTOWN	STATE	UNIVERSITY
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.	
YOUNGSTOWN	STATE	UNIVERSITY
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
YOUNGSTOWN	STATE	UNIVERSITY,	OH,	USA
AOC	Update	and	Traditional	Update	Running	Time
AOC	Update	Experiment	
(cont.)
YOUNGSTOWN	STATE	UNIVERSITY
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.	
YOUNGSTOWN	STATE	UNIVERSITY,	OH,	USA
Progressive	Data-Cleaning	
Results
YOUNGSTOWN	STATE	UNIVERSITY,	OH,	USA
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
YOUNGSTOWN	STATE	UNIVERSITY
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
YOUNGSTOWN	STATE	UNIVERSITY
AMO	Update	(logical)
YOUNGSTOWN	STATE	UNIVERSITY
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.	
YOUNGSTOWN	STATE	UNIVERSITY
AMO	Update	Experiment	
(cont.)
YOUNGSTOWN	STATE	UNIVERSITY
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.)
YOUNGSTOWN	STATE	UNIVERSITY
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)
YOUNGSTOWN	STATE	UNIVERSITY
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.
YOUNGSTOWN	STATE	UNIVERSITY,	OH,	USA
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
YOUNGSTOWN	STATE	UNIVERSITY,	OH,	USA
Thank	you!
Feng	“George”	Yu
Computer	Science	and	Information	Systems
Youngstown	State	University,	Youngstown,	OH
fyu@ysu.edu
YOUNGSTOWN	STATE	UNIVERSITY

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