column-stores basics
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1 class 3 column-stores basics prof.
2 Goetz Graefe Google Research
3 guest lecture Justin Levandoski Microsoft Research
4 projects option 1: systems project (now online) basic key-value store functionality - work individually single machine - multi-core design basic design as in Facebook, LinkedIn, Mongo, etc. can lead to research option 2: research project self-designing data systems + shape-shifting access methods research with DASlab researchers - groups of 3 available for cs165 students or otherwise advanced students next generation adaptive Key-value stores (with Facebook)
5 C/C++ no libraries unless we explicitly allow it we expect you build everything from scratch so you can control storage and access 100% Basic Readings for Projects Modern B-Tree Techniques by Goetz Graefe, Foundations and Trends in Databases, 2011 The Log-Structured Merge-Tree (LSM-Tree) by Patrick E. O'Neil, Edward Cheng, Dieter Gawlick, Elizabeth J. O'Neil Acta Inf. 33(4): ,1996
6 midway check-in (10%) special class (2-3 hour?) in mid March: 1) design docs 2) at least one performance example 3) presentation/poster
7 workload? two reading sessions and two hacking sessions per week? so maybe a minimum of 15 hours per week (if you already have decent hacking and data structure/algorithms experience)
8 Action steps: 1) Read the syllabus & website carefully, 2) Register to Piazza, 3) Do P0 if you have not taken CS165 and check self-test, 4) Register for paper presentation (week 2), 5) Start submitting your paper reviews (week 3) web site: piazza: piazza.com/harvard/spring2017/cs265/home office hours: Stratos: Wed/Thur/Fri, 3-4pm, MD139 TF office hours: Mon 4-5pm, Tue, 3-4pm, MD 136 textbook: nope research papers will be available from the Harvard network
9 how can I prepare? 1) start browsing some basic texts Get familiar with the very basics of traditional database architectures: Architecture of a Database System. By J. Hellerstein, M. Stonebraker and J. Hamilton. Foundations and Trends in Databases, 2007 Get familiar with very basics of modern database architectures: The Design and Implementation of Modern Column-store Database Systems. By D. Abadi, P. Boncz, S. Harizopoulos, S. Idreos, S. Madden. Foundations and Trends in Databases, 2013 Get familiar with the very basics of modern large scale systems: Massively Parallel Databases and MapReduce Systems. By Shivnath Babu and Herodotos Herodotou. Foundations and Trends in Databases, ) play with basic data structures implementation in C (linked list/hash table/tree)
10 class attendance!= participating in discussion expectation: on average everyone tries to speak 3-4 times do not obsess about correctness, derailing class, etc.
11 design logical design physical design system design
12 essential steps in using a database system experts/system admins clean schema load tune query user/apps
13 declarative interface ask what you want so do db systems just work? db system
14 declarative interface ask what you want DBA indexes/views/tuning knobs db system
15 declarative interface ask what you want indexes/views/tuning knobs DBA but db cracking, adaptive* ideas db system
16 design logical design physical design system design
17 select min(a) from R where B<10 and C<80 algorithms/operators database kernel data data data parser optimizer execution storage
18 a simple example build a key-value store similar to the ones Facebook, Google, etc use interface supported: put, get, scan, count, get range, load unique key-value pairs, r>>w but w>>0 data how to store and access
19 cheaper faster CPU registers on chip cache on board cache memory disk SRAM DRAM cache miss: looking for something which is not in the cache ~1ns ~10ns ~100ns speed memory wall memory miss: looking for something which is not in memory cpu mem time
20 100Kx disk Pluto 2 years Jim Gray, IBM, Tandem, DEC, Microsoft ACM Turing award ACM SIGMOD Edgar F. Codd Innovations award 100x memory 10x on board cache 2x on chip cache registers New York 1.5 hours this building 10 min this room 1 min my head ~0
21 random access & page-based access CPU need to only read x but have to read all of page 1 registers data value x on chip cache page1 page2 page3 data move on board cache memory disk
22 dbms block size os block size device block size os and db will typically refer to pages
23 employee (id:int, name:varchar(50), office:char(5), telephone:char(10), city:varchar(30), salary:int) data storage blocks < pages < files (1, name1, office1, tel1, city1, salary1) (2, name2, office2, tel2, city2, salary2) (3, name3, office3, tel3, city3, salary3) (4, name4, office4, tel4, city4, salary4) (5, name5, office5, tel5, city5, salary5) (6, name6, office6, tel6, city6, salary6) (7, name7, office7, tel7, city7, salary7) (8, name8, office8, tel8, city8, salary8) (9, name9, office9, tel9, city9, salary9) file remember: the way we store data defines the best possible way we can access it
24 slotted pages employee (id:int, name:varchar(50), office:char(5), telephone:char(10), city:varchar(30), salary:int) page (1, name1, office1, tel1, city1, salary1) (2, name2, office2, tel2, city2, salary2) (3, name3, office3, tel3, city3, salary3) (4, name4, office4, tel4, city4, salary4) (5, name5, office5, tel5, city5, salary5) (6, name6, office6, tel6, city6, salary6) (7, name7, office7, tel7, city7, salary7) (8, name8, office8, tel8, city8, salary8) (9, name9, office9, tel9, city9, salary9) header row2 row1 row3
25 slotted page free_offset, N, offset1-length1, offset2-lenght2, free space scan null update var length
26 row-store select A,B,C,D select A A BC D one page contains all fields of multiple attributes stored continuously file
27 select A,B,C,D select A row-store column-store A BC D A B C D one page contains fields of a single attribute stored continuously
28 the way we store data defines the possible (efficient) access methods
29 ~1960s 1970: column storage ideas start appearing monetdb ~2000: open source complete system rows rows rows rows rows rows rows history/timeline 1985: first rather complete column-store model 2005-now: more ideas and industry adoption of columnstore designs c-store,vertica,vectorwise and then ibm,microsoft,oracle, and more
30 virtual ids/ positional alignment A B C columns do not need to have the same width tuple 1 tuple 2 tuple 3 tuple 4 tuple 5 tuple 6 a1 a2 a3 a4 a5 a6 b1 b2 b3 b4 b5 b6 c1 c2 c3 c4 c5 c6 fixed-width + dense positional lookups/joins A(i) = A + i * width(a)
31 ok so now we can selectively read columns but how do we process them? disk memory A A B C D option1 columnstore engine option2 A BC early tuple reconstruction/materialization row-store engine
32 late reconstruction/materialization select min(c) from R where A<10 & B<20 disk memory A B C D
33 late reconstruction/materialization select min(c) from R where A<10 & B<20 disk A B C D A<10 memory
34 late reconstruction/materialization select min(c) from R where A<10 & B<20 disk A B C D A<10 memory 1: int *input=a 2: for (i=0;i<tuples;i++,input++) 3: if *input<10 4: *output=i 5: output++ A<10
35 late reconstruction/materialization select min(c) from R where A<10 & B<20 disk memory A B C D A<10 IDs
36 late reconstruction/materialization select min(c) from R where A<10 & B<20 disk memory A B C D A<10 IDs B
37 late reconstruction/materialization select min(c) from R where A<10 & B<20 disk memory A B C D A<10 IDs B B<20
38 late reconstruction/materialization select min(c) from R where A<10 & B<20 disk memory A B C D A<10 IDs B B<20 IDs C
39 late reconstruction/materialization select min(c) from R where A<10 & B<20 disk memory A B C D A<10 IDs B B<20 IDs C minc
40 late reconstruction/materialization select min(c) from R where A<10 & B<20 disk memory A B C D A<10 IDs B B<20 IDs C minc always sequential access patterns memory contains only what is needed at any point in time
41 working over fixed width & dense columns select for (i=0;i<size;i++) if column[i]>v res[j++]=i no function calls, no indirections, no auxiliary data, min ifs easy to prefetch next data values fetch for (i=0;i<size;i++) inter2[j++]=column[inter1[i]]
42 A<10 IDs B B<20 IDs C minc alt1) start with B alt2) scan A & B independently and merge alt3) store intermediates as bit vectors - not positions
43 A<10 IDs B B<20 IDs C minc late tuple reconstruction/materialization only reconstruct to present results no need to assemble tuples minimize memory footprint minimize data we are moving up the memory hierarchy but requires new processing engine
44 A<10 IDs B B<20 IDs C minc possible data flow patterns tuple at a time block/vector at a time column at a time
45 select min(c) from R where A<10 & B<20 A B C D A<10 IDs B B<20 IDs C minc column- A B C D A<10 IDs B B<20 IDs C minc vector-
46 essential column-stores features virtual ids late tuple reconstruction (if ever) vectorized execution compression fixed-width columns 45" 40" Performance'of'Column3Oriented'Op$miza$ons' Late" Materializa:on" Run$me'(sec)' 35" 30" 25" 20" 15" 10" 5" Compression" Join"Op:miza:on" Baseline" 0" Column"Store" Row"Store" Column-stores vs. row-stores: how different are they really? D. Abadi, S. Madden, and N. Hachem ACM SIGMOD Conference on Management of Data, 2008
47 but why now weren t all those design options obvious in the past as well? moving data from disk moving data from memory computation 1) big memories 2) cpu vs memory speed
48 class 3 column-stores basics BIG DATA SYSTEMS prof.
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