adaptive loading adaptive indexing dbtouch 3 Ideas for Big Data Exploration
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1 adaptive loading adaptive indexing dbtouch Ideas for Big Data Exploration Stratos Idreos CWI, INS-, Amsterdam
2
3
4
5 data is everywhere
6 daily data years
7 daily data years Eric Schmidt: Every two days we create as much data as much we did from dawn of humanity to 00
8 - exabytes daily data years 0 Eric Schmidt: Every two days we create as much data as much we did from dawn of humanity to 00
9
10 not always sure what we are looking for (until we find it)
11 not always sure what we are looking for (until we find it) data exploration
12 database systems great... declarative processing, back-end to numerous apps
13 database systems great... declarative processing, back-end to numerous apps but databases are too heavy and blind!
14 database systems great... declarative processing, back-end to numerous apps but databases are too heavy and blind! timeline
15 database systems great... declarative processing, back-end to numerous apps but databases are too heavy and blind! load timeline
16 database systems great... declarative processing, back-end to numerous apps but databases are too heavy and blind! load index timeline
17 database systems great... declarative processing, back-end to numerous apps but databases are too heavy and blind! load index query timeline
18 expert users - idle time - workload knowledge database systems great... declarative processing, back-end to numerous apps but databases are too heavy and blind! load index query timeline
19
20 systems tailored for data exploration
21 no workload knowledge systems tailored for data exploration
22 no workload knowledge no installation steps systems tailored for data exploration
23 no workload knowledge no installation steps quick response times systems tailored for data exploration
24 no workload knowledge no installation steps quick response times minimize data-to-query time systems tailored for data exploration
25 ideas adaptive indexing ACM SIGMOD Jim Gray Diss. Award - Cor Baayen Award years, papers
26 ideas adaptive indexing ACM SIGMOD Jim Gray Diss. Award - Cor Baayen Award years, papers adaptive loading In use in Hadapt and Tableau years, papers
27 ideas adaptive indexing ACM SIGMOD Jim Gray Diss. Award - Cor Baayen Award years, papers adaptive loading In use in Hadapt and Tableau years, papers dbtouch VENI 0 year, paper
28 ideas adaptive indexing ACM SIGMOD Jim Gray Diss. Award - Cor Baayen Award years, papers adaptive loading In use in Hadapt and Tableau years, papers dbtouch VENI 0 year, paper Martin Kersten, Stefan Manegold, Felix Halim, Panagiotis Karras, Roland Yap, Goetz Graefe, Harumi Kuno, Eleni Petraki, Anastasia Ailamaki, Ioannis Alagiannis, Renata Borovica, Miguel Branco, Ryan Johnson, Erietta Liarou
29 indexing load index query tune= create proper indexes offline performance 0-00X
30 indexing load index query tune= create proper indexes offline performance 0-00X but it depends on workload! which indices to build? on which data parts? and when to build them?
31 load index query
32 load index query timeline
33 load index query sample workload timeline
34 load index query sample workload analyze timeline
35 load index query sample workload analyze create indices timeline
36 load index query sample workload analyze create indices query timeline
37 load index query sample workload analyze create indices query timeline complex and time consuming process
38 load index query human administrators + auto-tuning tools sample workload analyze create indices query timeline complex and time consuming process
39 what can go wrong?
40 what can go wrong? not enough idle time to finish proper tuning
41 what can go wrong? not enough idle time to finish proper tuning by the time we finish tuning, the workload changes
42 database cracking
43 database cracking remove all tuning steps and need for human input incremental, adaptive, partial indexing
44 initialization indexing database cracking remove all tuning steps and need for human input incremental, adaptive, partial indexing
45 initialization query indexing database cracking remove all tuning steps and need for human input incremental, adaptive, partial indexing
46 initialization query indexing database cracking remove all tuning steps and need for human input incremental, adaptive, partial indexing
47 initialization query indexing database cracking remove all tuning steps and need for human input incremental, adaptive, partial indexing every query is treated as an advice on how data should be stored
48 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A
49 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A
50 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A
51 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A
52 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A
53 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A
54 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A
55 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A
56 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A
57 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Result tuples Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A
58 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Result tuples Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Dynamically/on-the-fly within the select-operator
59 Database Cracking CIDR 00 cracking example gain knowledge on how data is organized Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Result tuples Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Dynamically/on-the-fly within the select-operator
60 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Dynamically/on-the-fly within the select-operator
61 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Dynamically/on-the-fly within the select-operator
62 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Dynamically/on-the-fly within the select-operator
63 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Dynamically/on-the-fly within the select-operator
64 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Dynamically/on-the-fly within the select-operator
65 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Dynamically/on-the-fly within the select-operator
66 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Dynamically/on-the-fly within the select-operator
67 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Dynamically/on-the-fly within the select-operator
68 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Dynamically/on-the-fly within the select-operator
69 Database Cracking CIDR 00 cracking example Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Result tuples Dynamically/on-the-fly within the select-operator
70 Database Cracking CIDR 00 cracking example the more we crack, the more we learn Column A Cracker column of A Cracker column of A Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Q (copy) Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Result tuples Dynamically/on-the-fly within the select-operator
71 Database Cracking CIDR 00 cracking example the more we crack, the more we learn Q: select * from R where R.A > 0 and R.A < Q: select * from R where R.A > and R.A <= Column A Q (copy) Cracker column of A Piece : A <= 0 Q (in place) Piece : 0 < A < Piece : <= A Cracker column of A every query is treated as an advice on how data should be stored Piece : A <= Piece : < A <= 0 Piece : 0 < A < Piece : <= A <= Piece 5: < A Result tuples Dynamically/on-the-fly within the select-operator
72 Sideways Cracking, SIGMOD 0 TPC-H 0000 MonetDB Presorted Sel. Crack Sid. Crack MySQL Presorted Response time (milli secs) TPC-H Query Query sequence
73 Sideways Cracking, SIGMOD 0 TPC-H 0000 MonetDB Presorted Sel. Crack Sid. Crack MySQL Presorted Response time (milli secs) TPC-H Query 5 Normal MonetDB selection cracking Query sequence
74 Sideways Cracking, SIGMOD 0 TPC-H 0000 MonetDB Presorted Sel. Crack Sid. Crack MySQL Presorted Response time (milli secs) TPC-H Query 5 Normal MonetDB selection cracking Presorted MonetDB Preparation cost - minutes Query sequence
75 Sideways Cracking, SIGMOD 0 TPC-H 0000 MonetDB Presorted Sel. Crack Sid. Crack MySQL Presorted Response time (milli secs) Presorted MonetDB Preparation cost - minutes TPC-H Query Query sequence Normal MonetDB selection cracking MonetDB with sideways cracking
76 Sideways Cracking, SIGMOD 0 TPC-H 0000 MonetDB Presorted Sel. Crack Sid. Crack MySQL Presorted Response time (milli secs) Presorted MonetDB Preparation cost - minutes TPC-H Query Query sequence Normal MonetDB selection cracking MonetDB with sideways cracking
77 Sideways Cracking, SIGMOD 0 TPC-H 0000 MonetDB Presorted Sel. Crack Sid. Crack MySQL Presorted Response time (milli secs) Presorted MonetDB Preparation cost - minutes TPC-H Query Query sequence Normal MonetDB selection cracking MonetDB with sideways cracking
78 Sideways Cracking, SIGMOD 0 TPC-H 0000 MonetDB Presorted Sel. Crack Sid. Crack MySQL Presorted Response time (milli secs) Presorted MonetDB Preparation cost - minutes TPC-H Query Query sequence Normal MonetDB selection cracking MonetDB with sideways cracking
79 Stochastic Cracking, PVLDB skyserver experiments cracking answers queries while full indexing is still half way creating the index
80 cracking databases updates (SIGMOD0) multiple columns (SIGMOD0) storage restrictions (SIGMOD0) benchmarking (TPCTC0) algorithms (PVLDB) concurrency control (PVLDB) robustness (PVLDB)
81 cracking databases updates (SIGMOD0) multiple columns (SIGMOD0) storage restrictions (SIGMOD0) benchmarking (TPCTC0) joins disk based multi-dimensional holistic indexing optimization algorithms (PVLDB) row-stores multi-cores concurrency control (PVLDB) robustness (PVLDB) aggregations vectorwised
82 loading load index query
83 loading load index query copy data inside the database database now has full control
84 loading load index query copy data inside the database database now has full control slow process...not all data might be needed all the time
85 database vs. unix tools file, attributes, billion tuples DB Awk single query cost (secs) 0 550,00,50,00
86 database vs. unix tools file, attributes, billion tuples DB Awk single query cost (secs) 0 550,00,50,00 break down db cost Loading Query Processing % %
87 database vs. unix tools file, attributes, billion tuples DB Awk single query cost (secs) 0 550,00,50,00 break down db cost Loading Query Processing % loading is a major bottleneck %
88 database vs. unix tools file, attributes, billion tuples DB Awk single query cost (secs) 0 550,00,50,00 break down db cost Loading Query Processing % loading is a major bottleneck %... but writing/maintaining scripts is hard too
89 adaptive loading load/touch only what is needed and only when it is needed
90 but raw data access is expensive tokenizing - parsing - no indexing - no statistics
91 but raw data access is expensive tokenizing - parsing - no indexing - no statistics challenge: fast raw data access
92 query plan
93 query plan scan
94 query plan scan db
95 query plan scan db
96 query plan scan zero loading cost
97 query plan scan files access raw data adaptively on-the-fly zero loading cost
98 query plan scan files cache access raw data adaptively on-the-fly zero loading cost
99 selective parsing - file indexing file splitting - online statistics query plan scan files cache access raw data adaptively on-the-fly zero loading cost
100 NoDB SIGMOD 0 Execution Time (sec) ~500 ~500 Q0 Q Q Q Q Q5 Q Q Q Q Q0 Q Q Q Q Q5 Q Q Q Q Load 00 0 MySQL CSV Engine MySQL DBMS X DBMS X w/ external files PostgreSQL PostgresRaw PM + C 0
101 NoDB SIGMOD 0 Execution Time (sec) ~500 ~500 Q0 Q Q Q Q Q5 Q Q Q Q Q0 Q Q Q Q Q5 Q Q Q Q Load 00 0 MySQL CSV Engine MySQL DBMS X DBMS X w/ external files PostgreSQL PostgresRaw PM + C 0
102 NoDB SIGMOD 0 Execution Time (sec) ~500 ~500 Q0 Q Q Q Q Q5 Q Q Q Q Q0 Q Q Q Q Q5 Q Q Q Q Load 00 0 MySQL CSV Engine MySQL DBMS X DBMS X w/ external files PostgreSQL PostgresRaw PM + C 0
103 NoDB SIGMOD 0 Execution Time (sec) ~500 ~500 Q0 Q Q Q Q Q5 Q Q Q Q Q0 Q Q Q Q Q5 Q Q Q Q Load 00 0 MySQL CSV Engine MySQL DBMS X DBMS X w/ external files PostgreSQL PostgresRaw PM + C 0
104 NoDB SIGMOD 0 Execution Time (sec) ~500 ~500 Q0 Q Q Q Q Q5 Q Q Q Q Q0 Q Q Q Q Q5 Q Q Q Q Load 00 0 MySQL CSV Engine MySQL DBMS X DBMS X w/ external files PostgreSQL PostgresRaw PM + C 0
105 NoDB SIGMOD 0 Execution Time (sec) ~500 ~500 Q0 Q Q Q Q Q5 Q Q Q Q Q0 Q Q Q Q Q5 Q Q Q Q Load 00 0 MySQL CSV Engine MySQL DBMS X DBMS X w/ external files PostgreSQL PostgresRaw PM + C 0
106 NoDB SIGMOD 0 Execution Time (sec) ~500 ~500 Q0 Q Q Q Q Q5 Q Q Q Q Q0 Q Q Q Q Q5 Q Q Q Q Load 00 0 MySQL CSV Engine MySQL DBMS X DBMS X w/ external files PostgreSQL PostgresRaw PM + C 0
107 NoDB SIGMOD 0 Execution Time (sec) ~500 ~500 Q0 Q Q Q Q Q5 Q Q Q Q Q0 Q Q Q Q Q5 Q Q Q Q Load 00 0 MySQL CSV Engine MySQL DBMS X DBMS X w/ external files PostgreSQL PostgresRaw PM + C reducing data-to-query time 0
108 querying load index query
109 querying load index query declarative SQL interface
110 querying load index query declarative SQL interface correct and complete answers
111 querying load index query declarative SQL interface correct and complete answers
112 querying complex and slow - not fit for exploration load index query declarative SQL interface correct and complete answers
113 dbtouch dbtouch CIDR 0
114 dbtouch dbtouch CIDR 0 data
115 dbtouch dbtouch CIDR 0 data query
116 dbtouch dbtouch CIDR 0 no expert knowledge - no set-up costs interactive mode fit for exploration data query
117 dbtouch CIDR 0 challenge design database systems tailored for touch exploration
118 dbtouch CIDR 0 challenge design database systems tailored for touch exploration user drives query processing actions
119 adaptive loading adaptive indexing dbtouch Ideas for Big Data Exploration Stratos Idreos CWI, INS-, Amsterdam
120 adaptive loading adaptive indexing dbtouch Ideas for Big Data Exploration Stratos Idreos CWI, INS-, Amsterdam systems tailored for exploration
121 more in INS- data vaults sciborg cyclotron sciql holistic indexing datacell charles hardware-conscious
122 more in INS- data vaults sciborg cyclotron sciql holistic indexing Thank you! datacell charles hardware-conscious
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class 1 welcome to CS265! BIG DATA SYSTEMS prof. Stratos Idreos
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