D. Materials Requisitions
Production control
prepares materials
requisitions to
authorize the release
of raw materials from
inventory for use in
production.
E. Job Time Cards
Job time cards are used
to document the
amount of labor time
that is spent on ea
Aries issues:
Crash during recovery do it again.
CLRs are used for escrow xacts cannot be undone multiple times. Do an example
Group commit why required
Application errors: roll forward to a specific point in time, then undo backward just not
to the prese
Modern day OLTP:
Main memory problem
No-disk stalls in a Xact
Do not allow user-stalls in a Xact (Aunt Millie will go out for lunch)
- hence no stalls.
A heavy Xact is 200 record touches less than 1 Msec.
Why not run Xact to completion single threaded! No
WAL recovery
Options for the log:
Can use a physical log. Whenever the bits change, write a log record.
Insert could cause logging of 4K worth of data
Can use a logical log record the SQL command (nobody does this too slow - and
wont work for undo)
Can us
Geometric Interpretation
Consider the following simple LP:
max
s.t.:
x2
z = x1 + 2x2 = (1, 2) (x1 , x2 ),
x1 3,
c
x1 + x2 5,
x1 , x2
0.
Each inequality constraint denes a
hyperplane, and a feasible half-space.
x1
The intersection of all feasible half
spac
3/6/00!
Notation
S, Q, R, P
Logical sentences
Background theory (a sentence).
not, and (), or (v), implies (), if and only if (iff, ).
Standard logical connectives where iff if and only if.
M(S), entails, Models of sentence S, entails, false.
A, B,
6.851 Advanced Data Structures (Spring12)
Prof. Erik Demaine
Problem 4
Sample solution
Cache-oblivious median nding. The standard median of medians algorithm is already a
cache-oblivious algorithm meeting the desired bounds, so all the remains is to prove
Shortest Path Problems on Graphs
Input: hV , E , w , s, G i:
V : set of vertices (nite, or in some cases countably innite).
E V V : set of edges.
w : E ! R+ , e 7! w (e): a function that associates to each edge a strictly
positive weight (cost, length, ti
6.830 2010 Lecture 15: C-Store (Sam Madden)
Why are we reading this paper?
C-store has standard interface, but very different design
Help us understand what choices standard DBs made
Think about different set of apps than OLTP
Paper status
Most individual
What to do with Scientific Data?
by
Michael Stonebraker
Outline
Science data what it looks like
Hardware options for deployment
Software options
RDBMS
Wrappers on RDBMS
SciDB
Courtesy of LSST. Used with permission.
O(100) petabytes
Courtesy of LSST. Used
6.830 2009 Lecture 19: BigTable
big picture
parallel db (one data center)
mix of OLTP and batch analysis
lots of data, high r/w rates, 1000s of cheap boxes thus many failures
what does paper say Google uses BigTable for?
analyzing big web crawls
analyzing
6.830 2009 Lecture 16: Parallel Databases
today's topic: parallel databases
how to get more performance than is possible w/ one computer
- MOTIVATION -why might one CPU/one disk not yield enough performance?
can only scan big tables at 50 MB/sec
can only
6.830 2010 Lecture 16: Two-Phase Commit
last time we were talking about parallel DBs
partitioned data across multiple servers
we mostly discussed read-only queries
what about read/write queries?
high-level model
a bunch of servers
rows are partitioned ove
1. Short Answer (6 points each, 4 problems)
1. What is an Operating System?
- . a m M
2. What is the difference between bu
ll} «:2 Wait
3. Which process scheduling algorithm(s) can NOT be both preemptive and non-preemptive?
V m m WM m
Egg; gif
(Mixed) Integer Linear Programming
Many problems of interest can be formulated as mathematical
programs in which some of the decision variables are constrained to
take one of a nite set of values
Typically, these represent logical decisions: visit a locat