1 database systems ( 資料庫系統 ) november 26/28, 2007 lecture #9
TRANSCRIPT
![Page 1: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/1.jpg)
1
Database Systems( 資料庫系統 )
November 26/28, 2007Lecture #9
![Page 2: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/2.jpg)
2
Announcement
• Hand in your assignment #4 on Wed.
![Page 3: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/3.jpg)
3
African Poverb
Every morning, a zebra wakes up,it knows that it must run faster than
the fastest lion, or it will be eaten;every morning, a lion wakes up,it knows that it must run faster than
the slowest zebra, or it will starve to death;
it does not matter whether you are a zebra or a lion,
when the sun comes up, you better run fast.
![Page 4: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/4.jpg)
4
Hash-Based Indexing
Chapter 11
![Page 5: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/5.jpg)
5
Introduction
• Hash-based indexes are best for equality selections. Cannot support range searches.– Equality selections are useful for natural join
operations.
![Page 6: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/6.jpg)
6
Review: Tree Index
• ISAM vs. B+ trees.– What is the main difference between them?
• Static vs. dynamic structure– What is the tradeoff between them?
• Graceful table growth/shrink vs. concurrency performance
![Page 7: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/7.jpg)
7
Similar tradeoff for hashing
• Possible to exploit the tradeoff between dynamic structure for better performance?
• Static hashing technique• Two dynamic hashing techniques
– Extendible Hashing– Linear Hashing
![Page 8: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/8.jpg)
8
Basic Static Hashing• # primary pages fixed, allocated sequentially, never de-
allocated; overflow pages if needed.• h(k) mod N = bucket to which data entry with key k belongs.
(N = # of buckets)– N = 2 ^ i (or the bucket number is the i-least significant bits)
h(key) mod N
hkey
Primary bucket pages
Overflow pages
3*, 5*
97*
0123..
N-1
4*, 6*
0*
11*
![Page 9: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/9.jpg)
9
Static Hashing (Contd.)
• Buckets contain data entries.• Hash function takes search key field of record r and
distribute values over range 0 ... N-1.– h(key) = (a * key + b) usually works well.– a and b are constants; lots known about how to tune h.
• Cost for insertion/delete/search: 2/2/1 disk page I/Os (no overflow chains).
![Page 10: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/10.jpg)
10
What is the performance problem for static hashing?
• Long overflow chains can develop and degrade performance.
– Why poor performance? Scan through overflow chains linearly.
• How to fix this problem?– Extendible and Linear Hashing: Dynamic techniques to fix t
his problem.
![Page 11: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/11.jpg)
11
Extendible Hashing
• Simple Solution – remove overflow chain.• When bucket (primary page) becomes full, ..
– Re-organize file by adding (doubling) buckets. • What is the cost concern in doubling buckets?
– High cost: rehash all entries - reading and writing all pages is expensive!
h(key) mod N
hkey
Primary bucket pages Overflow pages
3*, 5*
97*
012...
N-1
4*, 6*
0*
NN+1
…
2N-1
![Page 12: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/12.jpg)
12
How to minimize the rehashing cost?
• Use another level of indirection
– Directory of pointers to buckets
• Insert 0 (00)– Double buckets only
double the directory (no rehashing)
– Split just the bucket that overflowed!
• What is cost here?
00011011
Directory
Bucket A
Bucket B
Bucket C
Bucket D
10*
4* 12* 32* 8*
1* 5* 21* 13*
15* 7* 19*
Doubled Directory
Bucket A1
Doubled Directory
Bucket A1
![Page 13: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/13.jpg)
13
Extendible Hashing
• Directory much smaller than file, so doubling much cheaper. Only one page of data entries is split.
• How to adjust the hash function? – Before doubling directory
• h(r) → 0..N-1 buckets. – After doubling directory?
• h(r) → 0 .. 2N-1 (the range is doubled)
![Page 14: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/14.jpg)
14
Example
• Directory is array of size 4.• What info this hash data
structure maintains?– Need to know current #bits
used in the hash function• Global Depth
– May need to know #bits used to hash a specific bucket
• Local Depth – Can global depth < local depth?
13*00
01
10
11
LOCAL DEPTH
GLOBAL DEPTH
DIRECTORY
Bucket A
Bucket B
Bucket C
Bucket D
DATA PAGES
10*
1* 21*
4* 12* 32*16*
15* 7* 19*
2
2
2
2
2
5*
![Page 15: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/15.jpg)
15
Insert 20 (10100): Double Directory
19*
2
2
2
000
001010
011100
101
110111
3
3
3DIRECTORY
Bucket A
Bucket B
Bucket C
Bucket D
Bucket A2(`split image'of Bucket A)
32*
1* 5* 21*13*
16*
10*
15*7*
4* 20*12*
LOCAL DEPTH
GLOBAL DEPTH00011011
2 2
2
LOCAL DEPTH 2
DIRECTORY
GLOBAL DEPTHBucket A
Bucket B
Bucket C
Bucket D
1* 5* 21*13*
32*16*
10*
15*7* 19*
4* 12*
2
Know the difference between double directory & split bucket.double directory:-Increment global depth-Rehash bucket A-Increment local depth, why track local depth?
![Page 16: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/16.jpg)
16
Insert 9 (1001): only Split Bucket
19*
3
2
2
000
001010
011100
101
110111
3
3
3DIRECTORY
Bucket A
Bucket B
Bucket C
Bucket D
Bucket A2
32*16*
10*
15*7*
4* 20*12*
LOCAL DEPTH
GLOBAL DEPTH
19*
2
2
2
000
001010
011100
101
110111
3
3
3DIRECTORY
Bucket A
Bucket B
Bucket C
Bucket D
Bucket A2
32*
1* 5* 21*13*
16*
10*
15*7*
4* 20*12*
LOCAL DEPTH
GLOBAL DEPTH
1* 9*
321* 13* Bucket B2
(split image of Bucket B)5*
Only split bucket:-Rehash bucket B-Increment local depth
![Page 17: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/17.jpg)
17
Points to Note• What is the global depth of directory?
– Max # of bits needed to tell which bucket an entry belongs to.• What is the local depth of a bucket?
– # of bits used to determine if an entry belongs to this bucket.• When does bucket split cause directory doubling?
– Before insert, bucket is full & local depth = global depth. • How to efficiently double the directory?
– Directory is doubled by copying it over and `fixing’ pointer to split image page.
– Note that you can do this only by using the least significant bits in the directory.
![Page 18: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/18.jpg)
18
Directory Doubling
00
01
10
11
2
Why use least significant bits in directory? Allows for doubling via copying!
3
vs.
000
001
010
011100
101
110
111
00
10
01
11
2
3
Least Significant Most Significant
000
001
010
011100
101
110
111
Split buckets
![Page 19: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/19.jpg)
19
Directory Copying via Insertion 20 (10100)
2
2
2
000
001010
011100
101
110111
3
3
3DIRECTORY
Bucket A
Bucket B
Bucket C
Bucket D
Bucket A2(`split image'of Bucket A)
10*
LOCAL DEPTH
GLOBAL DEPTH
00011011
2 2
2
LOCAL DEPTH 2
DIRECTORY
GLOBAL DEPTHBucket A
Bucket B
Bucket C
Bucket D
10*
4* 12* 32* 6*
2
1* 5* 21* 13*
15* 7* 19*
1* 5* 21* 13*
15* 7* 19*
4* 12* 32* 6*
![Page 20: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/20.jpg)
20
Comments on Extendible Hashing
• If directory fits in memory, equality search answered with one disk access; else two.
• What is the problem with extendible hashing?– Consider if the distribution of hash values is skewed
(concentrates on a few buckets)– Directory can grow large.
• Can you come up with one insertion leading to multiple splits?
![Page 21: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/21.jpg)
21
Skewed data distribution (multiple splits)
• Assume each bucket holds two data entries
• Insert 2 (binary 10) – how many times of DIR doubling?
• Insert 16 (binary 10000) – how many times of DIR doubling?
• How to solve this data skew problem?
0
1
LOCAL DEPTH
GLOBAL DEPTH 0* 8*
1
11
![Page 22: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/22.jpg)
22
Insert 2 (10)
0001
LOCAL DEPTH
GLOBAL DEPTH 0* 8*
101
LOCAL DEPTH
GLOBAL DEPTH 0* 8*
1
11
1011
2
2
2
2*
![Page 23: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/23.jpg)
23
Insert 16 (10000)
01
LOCAL DEPTH
GLOBAL DEPTH 0* 8*
1
11
0001
LOCAL DEPTH
GLOBAL DEPTH 0* 8*
1
1011
2
2
2
000001
LOCAL DEPTH
GLOBAL DEPTH 0* 8*
1
010011
3
3
2
100101110111
3
![Page 24: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/24.jpg)
24
Comments on Extendible Hashing
• Delete: If removal of data entry makes bucket empty, can be merged with `split image’. If each directory element points to same bucket as its split image, can halve directory.
![Page 25: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/25.jpg)
25
Delete 10*
00011011
2 2
2
LOCAL DEPTH 2
DIRECTORY
GLOBAL DEPTHBucket A
Bucket B
Bucket C
Bucket D
1* 5* 21*13*
32*16*
10*
15*7* 19*
4* 12*
2 00011011
2 2
2
LOCAL DEPTH 1
DIRECTORY
GLOBAL DEPTHBucket A
Bucket B
Bucket B2
1* 5* 21*13*
32*16*
15*7* 19*
4* 12*
![Page 26: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/26.jpg)
26
Delete 15*, 7*, 19*
00011011
2 2
2
LOCAL DEPTH 1
DIRECTORY
GLOBAL DEPTHBucket A
Bucket B
Bucket B2
1* 5* 21*13*
32*16*
15*7* 19*
4* 12*
00011011
2 1
LOCAL DEPTH 1
GLOBAL DEPTHBucket A
Bucket B1* 5* 21*13*
32*16*4* 12*
DIRECTORY
0001
1 1
LOCAL DEPTH 1
GLOBAL DEPTHBucket A
Bucket B1* 5* 21*13*
32*16*4* 12*
![Page 27: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/27.jpg)
27
Linear Hashing (LH)
• Another dynamic hashing scheme, as an alternative to Extendible Hashing.
• What are problems in static/extendible hasing?– Static hashing: long overflow chains– Extendible hashing: data skew causing large directory
• Is it possible to come up with a more balanced solution?– Shorter overflow chains than static hashing– No need for directory in extendible hashing
• How do you get rid of directory (indirection)?
![Page 28: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/28.jpg)
28
Linear Hashing (LH)
• Basic Idea: – Pages are split when overflows occur – but not necessarily
the page with the overflow.– Splitting occurs in turn, in a round robin fashion.
• one by one from the first bucket to the last bucket.
• Use a family of hash functions h0, h1, h2, ...– Each function’s range is twice that of its predecessor.– When all the pages at one level (the current hash function)
have been split, a new level is applied.– Splitting occurs gradually– Primary pages are allocated in order & consecutively.
![Page 29: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/29.jpg)
29
Linear Hashing Verbal Algorithm• Initial Stage.
– The initial level distributes entries into N0 buckets.– Call the hash function to perform this h0.
![Page 30: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/30.jpg)
30
Linear Hashing Verbal Algorithm• Splitting buckets.
– If a bucket overflows its primary page is chained to an overflow page (same as in static hashing).
– Also when a bucket overflows, some bucket is split.• The first bucket to be split is the first bucket in the file (not
necessarily the bucket that overflows).• The next bucket to be split is the second bucket in the file … and
so on until the Nth. has been split.• When buckets are split their entries (including those in overflow
pages) are distributed using h1.– To access split buckets the next level hash function (h1) is
applied.– h1 maps entries to 2N0 (or N1)buckets.
![Page 31: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/31.jpg)
31
Linear Hashing Example• Initially, the index level equal to 0
and N0 equals 4 (three entries fit on a page).
• h0 range = 4 buckets• Note that next indicates which
bucket is to split next. (Round Robin)
• Now consider what happens when 9 (1001) is inserted (which will not fit in the second bucket).
next 64 36
1 17 5
6
31 15
00
01
10
11
h0
![Page 32: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/32.jpg)
32
Insert 9 (1001)
next 64 36
1 17 5
6
31 15
00
01
10
11
h0
h1 next 64
h0 next 1 17 5 9
h0 6
h0 31 15
h1 36
![Page 33: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/33.jpg)
33
Linear Hashing Example 2
• An overflow page is chained to the primary page to contain the inserted value.
• Note that the split page is not necessary the overflow page – round robin.
• If h0 maps a value from zero to next – 1 (just the first page in this case), h1 must be used to insert the new entry.
• Note how the new page falls naturally into the sequence as the fifth page.
(000)H1 next 64
(01)H0 next 1 17 5 9
(10)H0 6
(11)H0 31 15
(100)H1 36
• The page indicated by next is split (the first one).
• Next is incremented.
![Page 34: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/34.jpg)
34
Insert 8 (1000), 7(111), 18(10010), 14(1100)
(000)H1 64 8
(01)H0 next 1 17 5 9
(10)H0 6 18
(11)H0 31 15 7
(100)H1 36 14
![Page 35: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/35.jpg)
35
Before Insert 11 (1011)
(000)H1 64 8
(01)H0 next 1 17 5 9
(10)H0 6 18
(11)H0 31 15 7
(100)H1 36 14
![Page 36: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/36.jpg)
36
After Insert 11 (1011)
(000)H1 64 8
(001)H1 next 1 17 9
(10)H0 next 6 18
(11)H0 31 15 7 11
(100)H1 36 14
(101)H1 5
Which hash function (h0 or h1) for searching 9?
How about searching for 18?
![Page 37: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/37.jpg)
37
Linear Hashing
• Insert 32, 162
• Insert 10, 13, 233 • After the 2nd. split
the base level is 1 (N1 = 8), use h1.
• Subsequent splits will use h2 for inserts between the first bucket and next-1.
2 1
h1 h1 next3 64 8 32 16
h1 h1 1 17 9
h1 h0 next1 10 18
6 18 14
h0 h0 next2 11
31 15 7 11
h1 h1 36
h1 h1 5 13
h1 - 6 14
- - 31 15 7 23
![Page 38: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/38.jpg)
38
Notes for Linear Hashing
• Why linear hashing does not need a directory but extendible hashing needs one?– Consider finding i-th bucket– New buckets are created in order (sequentially, i.e., round
robin) in linear hashing• Level progression:
– Once all Ni buckets of the current level (i) are split the hash function hi is replaced by hi+1.
– The splitting process starts again at the first bucket and hi+2 is applied to find entries in split buckets.
![Page 39: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/39.jpg)
39
LH Described as a Variant of EH
• Can you describe linear hashing using extendible hashing (EH)?– Begin with an EH index where directory has N elements.– Use overflow pages, split buckets round-robin.– First split is at bucket 0. (Imagine directory being doubled at this point.)
But elements <1,N+1>, <2,N+2>, ... are the same. So, need only create directory element N, which differs from 0, now.
• When bucket 1 splits, create directory element N+1, etc.
• So, directory can double gradually. Also, primary bucket pages are created in order. If they are allocated in sequence too (so that finding i’th is easy), we actually don’t need a directory! Voila, LH.
![Page 40: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/40.jpg)
40
![Page 41: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/41.jpg)
41
The New World is Flat• With computer and network, the playing field is being leveled
across the world.• There is no end to what can be done by whom
– Before: manufacturing products (Made in Taiwan, Made in China)– Now: anything including service industry
• This is called outsourcing• The rule of Capitalist economy:
– Work goes to the place where it can be done cheapest and most efficient
![Page 42: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/42.jpg)
42
Outsourcing Examples• Customer services moving to call centers in India
– Credit cards, banks, insurance, Microsoft, …• Radiologists in India and Australia• E-tutoring• Jetblue & homesourcing• Tax preparation outsourcing• MacDonald Flatfries and burgers• Kenichi Ohmae ( 大前研一 )
![Page 43: 1 Database Systems ( 資料庫系統 ) November 26/28, 2007 Lecture #9](https://reader033.vdocuments.us/reader033/viewer/2022061518/5697bf701a28abf838c7da02/html5/thumbnails/43.jpg)
43
Outsourcing and ME in Taiwan?• So anyone on this planet may be replaced by anyone on this
planet for cheaper and more efficiency– Yes, for any jobs – high/low tech, manufacturing/service– Scaring? Consider massive amount of smart & cheap labors in China
and India• Oops … this is happening NOW here in Taiwan!
• So one can compete or collaborate globally with anyone on this planet -> opportunity.