advanced database
play

ADVANCED DATABASE SYSTEMS Parallel Join Algorithms (Hashing) @ - PowerPoint PPT Presentation

Nov 21, 2022 •560 likes •1.81k views

Lect ure # 17 ADVANCED DATABASE SYSTEMS Parallel Join Algorithms (Hashing) @ Andy_Pavlo // 15- 721 // Spring 2020 2 Background Parallel Hash Join Hash Functions Hashing Schemes Evaluation 15-721 (Spring 2020) 3 PARALLEL J O IN ALGO

  1. 20 BUILD PH ASE The threads are then to scan either the tuples (or partitions) of R . For each tuple, hash the join key attribute for that tuple and add it to the appropriate bucket in the hash table. → The buckets should only be a few cache lines in size. 15-721 (Spring 2020)

  2. 21 H ASH TABLE Design Decision #1: Hash Function → How to map a large key space into a smaller domain. → Trade-off between being fast vs. collision rate. Design Decision #2: Hashing Scheme → How to handle key collisions after hashing. → Trade-off between allocating a large hash table vs. additional instructions to find/insert keys. 15-721 (Spring 2020)

  3. 22 H ASH FUN CTIO N S We do not want to use a cryptographic hash function for our join algorithm. We want something that is fast and will have a low collision rate. → Best Speed: Always return ' 1 ' → Best Collision Rate: Perfect hashing See SMHasher for a comprehensive hash function benchmark suite. 15-721 (Spring 2020)

  4. 23 H ASH FUN CTIO N S CRC-64 (1975) → Used in networking for error detection. MurmurHash (2008) → Designed to a fast, general purpose hash function. Google CityHash (2011) → Designed to be faster for short keys (<64 bytes). Facebook XXHash (2012) → From the creator of zstd compression. Google FarmHash (2014) → Newer version of CityHash with better collision rates. 15-721 (Spring 2020)

  5. 24 H ASH FUN CTIO N BEN CH M ARK Intel Core i7-8700K @ 3.70GHz crc64 std::hash MurmurHash3 CityHash FarmHash XXHash3 28000 128 Throughput (MB/sec) 64 192 21000 32 14000 7000 0 1 51 101 151 201 251 Key Size (bytes) Source: Fredrik Widlund 15-721 (Spring 2020)

  6. 25 H ASH IN G SCH EM ES Approach #1: Chained Hashing Approach #2: Linear Probe Hashing Approach #3: Robin Hood Hashing Approach #4: Hopscotch Hashing Approach #5: Cuckoo Hashing 15-721 (Spring 2020)

  7. 26 CH AIN ED H ASH IN G Maintain a linked list of buckets for each slot in the hash table. Resolve collisions by placing all elements with the same hash key into the same bucket. → To determine whether an element is present, hash to its bucket and scan for it. → Insertions and deletions are generalizations of lookups. 15-721 (Spring 2020)

  8. 27 CH AIN ED H ASH IN G hash(key) A B C D E F 15-721 (Spring 2020)

  9. 27 CH AIN ED H ASH IN G hash(key) A B | A hash(A) C Buckets D E F 15-721 (Spring 2020)

  10. 27 CH AIN ED H ASH IN G | B hash(B) hash(key) A B | A hash(A) C Buckets D E F 15-721 (Spring 2020)

  11. 27 CH AIN ED H ASH IN G | B hash(B) hash(key) A B | A hash(A) C Buckets D | C hash(C) E F 15-721 (Spring 2020)

  12. 27 CH AIN ED H ASH IN G | B hash(B) hash(key) A B | A hash(A) C Buckets D | C hash(C) E F 15-721 (Spring 2020)

  13. 27 CH AIN ED H ASH IN G | B hash(B) hash(key) A B | A | D hash(A) hash(D) C D | C hash(C) E F 15-721 (Spring 2020)

  14. 27 CH AIN ED H ASH IN G | B hash(B) hash(key) A B | A | D hash(A) hash(D) C D | C | E hash(C) hash(E) E F 15-721 (Spring 2020)

  15. 27 CH AIN ED H ASH IN G | B hash(B) hash(key) A B | A | D hash(A) hash(D) C D | C | E hash(C) hash(E) E F | F hash(F) 15-721 (Spring 2020)

  16. 27 CH AIN ED H ASH IN G | B hash(B) hash(key) 64-bit Bucket Pointers ¤ 48-bit Pointer A 16-bit Bloom Filter B | A | D hash(A) hash(D) C D | C | E hash(C) hash(E) E F | F hash(F) 15-721 (Spring 2020)

  17. 28 LIN EAR PRO BE H ASH IN G Single giant table of slots. Resolve collisions by linearly searching for the next free slot in the table. → To determine whether an element is present, hash to a location in the table and scan for it. → Must store the key in the table to know when to stop scanning. → Insertions and deletions are generalizations of lookups. 15-721 (Spring 2020)

  18. 29 LIN EAR PRO BE H ASH IN G hash(key) A B | A hash(A) C D E F 15-721 (Spring 2020)

  19. 29 LIN EAR PRO BE H ASH IN G hash(key) | B hash(B) A B | A hash(A) C D E F 15-721 (Spring 2020)

  20. 29 LIN EAR PRO BE H ASH IN G hash(key) | B hash(B) A B | A hash(A) C D E F 15-721 (Spring 2020)

  21. 29 LIN EAR PRO BE H ASH IN G hash(key) | B hash(B) A B | A hash(A) C | C hash(C) D E F 15-721 (Spring 2020)

  22. 29 LIN EAR PRO BE H ASH IN G hash(key) | B hash(B) A B | A hash(A) C | C hash(C) D | D E hash(D) F 15-721 (Spring 2020)

  23. 29 LIN EAR PRO BE H ASH IN G hash(key) | B hash(B) A B | A hash(A) C | C hash(C) D | D E hash(D) F 15-721 (Spring 2020)

  24. 29 LIN EAR PRO BE H ASH IN G hash(key) | B hash(B) A B | A hash(A) C | C hash(C) D | D E hash(D) F | E hash(E) 15-721 (Spring 2020)

  25. 29 LIN EAR PRO BE H ASH IN G hash(key) | B hash(B) A B | A hash(A) C | C hash(C) D | D E hash(D) F | E hash(E) | F hash(F) 15-721 (Spring 2020)

  26. 30 O BSERVATIO N To reduce the # of wasteful comparisons during the join, it is important to avoid collisions of hashed keys. This requires a chained hash table with ~2 × the number of slots as the # of elements in R . 15-721 (Spring 2020)

  27. 31 RO BIN H O O D H ASH IN G Variant of linear probe hashing that steals slots from "rich" keys and give them to "poor" keys. → Each key tracks the number of positions they are from where its optimal position in the table. → On insert, a key takes the slot of another key if the first key is farther away from its optimal position than the second key. ROBIN HOOD H HASHING FOUNDATIONS O OF COMPUTER SCIENCE 1985 15-721 (Spring 2020)

  28. 32 RO BIN H O O D H ASH IN G hash(key) A B | A [0] # of "Jumps" From First Position hash(A) C D E F 15-721 (Spring 2020)

  29. 32 RO BIN H O O D H ASH IN G hash(key) | B [0] hash(B) A B | A [0] hash(A) C D E F 15-721 (Spring 2020)

  30. 32 RO BIN H O O D H ASH IN G hash(key) | B [0] hash(B) A B | A [0] hash(A) A[0] == C[0] C D E F 15-721 (Spring 2020)

  31. 32 RO BIN H O O D H ASH IN G hash(key) | B [0] hash(B) A B | A [0] hash(A) A[0] == C[0] C | C [1] hash(C) D E F 15-721 (Spring 2020)

  32. 32 RO BIN H O O D H ASH IN G hash(key) | B [0] hash(B) A B | A [0] hash(A) C | C [1] C[1] > D[0] hash(C) D E F 15-721 (Spring 2020)

  33. 32 RO BIN H O O D H ASH IN G hash(key) | B [0] hash(B) A B | A [0] hash(A) C | C [1] C[1] > D[0] hash(C) D | D [1] E hash(D) F 15-721 (Spring 2020)

  34. 32 RO BIN H O O D H ASH IN G hash(key) | B [0] hash(B) A B | A [0] hash(A) A[0] == E[0] C | C [1] hash(C) D | D [1] E hash(D) F 15-721 (Spring 2020)

  35. 32 RO BIN H O O D H ASH IN G hash(key) | B [0] hash(B) A B | A [0] hash(A) A[0] == E[0] C | C [1] C[1] == E[1] hash(C) D | D [1] E hash(D) F 15-721 (Spring 2020)

  36. 32 RO BIN H O O D H ASH IN G hash(key) | B [0] hash(B) A B | A [0] hash(A) A[0] == E[0] C | C [1] C[1] == E[1] hash(C) D | D [1] D[1] < E[2] E hash(D) F 15-721 (Spring 2020)

  37. 32 RO BIN H O O D H ASH IN G hash(key) | B [0] hash(B) A B | A [0] hash(A) A[0] == E[0] C | C [1] C[1] == E[1] hash(C) D | E [2] D[1] < E[2] E hash(E) F | D [2] hash(D) 15-721 (Spring 2020)

  38. 32 RO BIN H O O D H ASH IN G hash(key) | B [0] hash(B) A B | A [0] hash(A) C | C [1] hash(C) D | E [2] E hash(E) F | D [2] D[2] > F[0] hash(D) | F [1] hash(F) 15-721 (Spring 2020)

  39. 33 H O PSCOTCH H ASH IN G Variant of linear probe hashing where keys can move between positions in a neighborhood . → A neighborhood is contiguous range of slots in the table. → The size of a neighborhood is a configurable constant. A key is guaranteed to be in its neighborhood or not exist in the table. HOPSCOTCH HASHING SYMPOSIUM ON DISTRIBUTED COMPUTING 2008 15-721 (Spring 2020)

  40. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) A B C D E F 15-721 (Spring 2020)

  41. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) Neighborhood #1 A B C D E F 15-721 (Spring 2020)

  42. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) Neighborhood #1 A Neighborhood #2 B Neighborhood #3 C D ⋮ E F 15-721 (Spring 2020)

  43. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) A B Neighborhood #3 C D E F 15-721 (Spring 2020)

  44. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) A B | A Neighborhood #3 hash(A) C D E F 15-721 (Spring 2020)

  45. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) Neighborhood #1 A B | A hash(A) C D E F 15-721 (Spring 2020)

  46. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B Neighborhood #1 hash(B) A B | A hash(A) C D E F 15-721 (Spring 2020)

  47. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A Neighborhood #3 hash(A) C D E F 15-721 (Spring 2020)

  48. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A Neighborhood #3 hash(A) C D E F 15-721 (Spring 2020)

  49. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A Neighborhood #3 hash(A) C | C hash(C) D E F 15-721 (Spring 2020)

  50. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A hash(A) C | C hash(C) D E F 15-721 (Spring 2020)

  51. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A hash(A) C | C hash(C) D E F 15-721 (Spring 2020)

  52. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A hash(A) C | C hash(C) D | D E hash(D) F 15-721 (Spring 2020)

  53. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A hash(A) C | C hash(C) D | D E hash(D) F 15-721 (Spring 2020)

  54. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A Neighborhood #3 hash(A) C | C hash(C) D | D E hash(D) F 15-721 (Spring 2020)

  55. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A Neighborhood #3 hash(A) C | C hash(C) D | D E hash(D) F 15-721 (Spring 2020)

  56. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A Neighborhood #3 hash(A) C | C hash(C) D | D E hash(D) F 15-721 (Spring 2020)

  57. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A hash(A) C | C hash(C) D E F | D hash(D) 15-721 (Spring 2020)

  58. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A Neighborhood #3 hash(A) C | C hash(C) D | E E hash(E) F | D hash(D) 15-721 (Spring 2020)

  59. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A hash(A) C | C hash(C) D | E E hash(E) F | D Neighborhood #6 hash(D) 15-721 (Spring 2020)

  60. 34 H O PSCOTCH H ASH IN G Neighborhood Size = 3 hash(key) | B hash(B) A B | A hash(A) C | C hash(C) D | E E hash(E) F | D Neighborhood #6 hash(D) | F hash(F) 15-721 (Spring 2020)

  61. 35 CUCKO O H ASH IN G Use multiple tables with different hash functions. → On insert, check every table and pick anyone that has a free slot. → If no table has a free slot, evict the element from one of them and then re-hash it find a new location. Look-ups are always O(1) because only one location per hash table is checked. 15-721 (Spring 2020)

  62. 36 CUCKO O H ASH IN G Hash Table #1 Hash Table #2 ⋮ ⋮ 15-721 (Spring 2020)

  63. 36 CUCKO O H ASH IN G Hash Table #1 Hash Table #2 Insert X hash 1 (X) hash 2 (X) ⋮ ⋮ 15-721 (Spring 2020)

  64. 36 CUCKO O H ASH IN G Hash Table #1 Hash Table #2 Insert X hash 1 (X) hash 2 (X) hash 1 (X) | X ⋮ ⋮ 15-721 (Spring 2020)

  65. 36 CUCKO O H ASH IN G Hash Table #1 Hash Table #2 Insert X hash 1 (X) hash 2 (X) hash 1 (X) | X Insert Y hash 1 (Y) hash 2 (Y) ⋮ ⋮ 15-721 (Spring 2020)

  66. 36 CUCKO O H ASH IN G Hash Table #1 Hash Table #2 Insert X hash 2 (Y) | Y hash 1 (X) hash 2 (X) hash 1 (X) | X Insert Y hash 1 (Y) hash 2 (Y) ⋮ ⋮ 15-721 (Spring 2020)

Recommend

Advanced Database CS 525: Organization? Advanced Database =Database Implementation

Advanced Database CS 525: Organization? Advanced Database =Database Implementation

Advanced Database CS 525: Organization? Advanced Database =Database Implementation Organization =How to implement a database system and have fun doing it ;-) 01: Introduction Boris Glavic Slides: adapted from a course taught by

199 views • 7 slides
Lect ure # 11 ADVANCED DATABASE SYSTEMS System Catalogs and Database Compression @

Lect ure # 11 ADVANCED DATABASE SYSTEMS System Catalogs and Database Compression @

Lect ure # 11 ADVANCED DATABASE SYSTEMS System Catalogs and Database Compression @ Andy_Pavlo // 15- 721 // Spring 2018 DATABASE TALK Oracle In-Memory Database Engine Monday February 26 th @ 4:30pm GHC 4401

709 views • 60 slides
Advanced Database Technologies Advanced Database Technologies in a Diabetic Healthcare System in

Advanced Database Technologies Advanced Database Technologies in a Diabetic Healthcare System in

Advanced Database Technologies Advanced Database Technologies in a Diabetic Healthcare System in a Diabetic Healthcare System Wynne Hsu, Mong Li Lee, Beng Chin Ooi, Pranab Kumar Mohanty, Keng Lik Teo, Chenyi Xia National University of

444 views • 7 slides
Database Management System (DBMS) DBMS contains information about a particular enterprise

Database Management System (DBMS) DBMS contains information about a particular enterprise

Advanced Database System Architectures Advanced Topics in Database Management (INFSCI 2711) Textbook: Database System Concepts - 6 th Edition, 2010 Vladimir Zadorozhny, DINS, SCI University of Pittsburgh 1 Database Management System (DBMS) DBMS

545 views • 20 slides
ADVANCED DATABASE SYSTEMS Database Compression @ Andy_Pavlo // 15- 721 // Spring 2019 CMU

ADVANCED DATABASE SYSTEMS Database Compression @ Andy_Pavlo // 15- 721 // Spring 2019 CMU

Lect ure # 10 ADVANCED DATABASE SYSTEMS Database Compression @ Andy_Pavlo // 15- 721 // Spring 2019 CMU 15-721 (Spring 2019) 2 UPCO M IN G DATABASE EVEN TS Splice Machine Tech Talk Thursday Feb 21 st @ 12:00pm CIC 4 th Floor

1.11k views • 73 slides
15-721 ADVANCED DATABASE SYSTEMS Lecture #01 Course Introduction &amp; History of Database

15-721 ADVANCED DATABASE SYSTEMS Lecture #01 Course Introduction &amp; History of Database

15-721 ADVANCED DATABASE SYSTEMS Lecture #01 Course Introduction &amp; History of Database Systems @Andy_Pavlo // Carnegie Mellon University // Spring 2017 Who are the richest people in the world? 3 WHY YOU SHOULD TAKE THIS COURSE

1.21k views • 67 slides
Advanced Database Management Systems Database Management Systems Alvaro A A Fernandes School of

Advanced Database Management Systems Database Management Systems Alvaro A A Fernandes School of

Advanced Database Management Systems Database Management Systems Alvaro A A Fernandes School of Computer Science, University of Manchester AAAF (School of CS, Manchester) Advanced DBMSs 2011-2012 1 / 144 Outline Database Management Systems:

2.09k views • 144 slides
Lect ure # 01 ADVANCED DATABASE SYSTEMS Course Introduction &amp; History of Database Systems

Lect ure # 01 ADVANCED DATABASE SYSTEMS Course Introduction &amp; History of Database Systems

Lect ure # 01 ADVANCED DATABASE SYSTEMS Course Introduction &amp; History of Database Systems @ Andy_Pavlo // 15- 721 // Spring 2018 2 WH Y YO U SH O ULD TAKE TH I S CO URSE DBMS developers are in demand and there are many challenging

912 views • 66 slides
Advanced SQL II Advanced Aggregation and OLAP 5DV120 Database System Principles Ume a

Advanced SQL II Advanced Aggregation and OLAP 5DV120 Database System Principles Ume a

Advanced SQL II Advanced Aggregation and OLAP 5DV120 Database System Principles Ume a University Department of Computing Science Stephen J. Hegner hegner@cs.umu.se http://www.cs.umu.se/~hegner Advanced SQL II Advanced

388 views • 35 slides
CSE 736: Advanced Topics in Database Systems YINGJING YAN

CSE 736: Advanced Topics in Database Systems YINGJING YAN

CSE 736: Advanced Topics in Database Systems YINGJING YAN INTRODUCTION Chabot is a picture retrieval system for a database that will eventually

811 views • 47 slides
DATABASE SYSTEMS Database programming in a web environment Database System Course AGENDA FOR

DATABASE SYSTEMS Database programming in a web environment Database System Course AGENDA FOR

DATABASE SYSTEMS Database programming in a web environment Database System Course AGENDA FOR TODAY The final project Advanced Mysql Database programming Recap: DB servers in the web Web programming architecture HTTP on a need-to-know basis.

688 views • 54 slides
CS 61: Database Systems Advanced SQL Adapted from https://www.mysqltutorial.org/ unless

CS 61: Database Systems Advanced SQL Adapted from https://www.mysqltutorial.org/ unless

CS 61: Database Systems Advanced SQL Adapted from https://www.mysqltutorial.org/ unless otherwise noted Review: database schema has tables for Restaurants and Inspections (and others) use nyc_inspections; One entry for each restaurant One

392 views • 23 slides
ADVANCED DATABASE SYSTEMS Optimizer Implementation (Part III) @ Andy_Pavlo // 15- 721 //

ADVANCED DATABASE SYSTEMS Optimizer Implementation (Part III) @ Andy_Pavlo // 15- 721 //

Lect ure # 21 ADVANCED DATABASE SYSTEMS Optimizer Implementation (Part III) @ Andy_Pavlo // 15- 721 // Spring 2020 2 O BSERVATIO N The best plan for a query can change as the database evolves over time. Physical design changes.

517 views • 47 slides
Distributed Transaction Management Advanced Topics in Database Management (INFSCI 2711) Some

Distributed Transaction Management Advanced Topics in Database Management (INFSCI 2711) Some

Distributed Transaction Management Advanced Topics in Database Management (INFSCI 2711) Some materials are from Database Management Systems, Ramakrishnan and Gehrke and Database System Concepts, Siberschatz, Korth and Sudarshan and Data

517 views • 24 slides
DATABASE SYSTEMS Database programming in a web environment Database System Course, 2016-2017

DATABASE SYSTEMS Database programming in a web environment Database System Course, 2016-2017

DATABASE SYSTEMS Database programming in a web environment Database System Course, 2016-2017 AGENDA FOR TODAY The final project Advanced Mysql Database programming Recap: DB servers in the web Web programming architecture HTTP on a

845 views • 53 slides
Data Linkage Advanced Topics in Database Management (INFSCI 2711) Some materials are from

Data Linkage Advanced Topics in Database Management (INFSCI 2711) Some materials are from

Data Linkage Advanced Topics in Database Management (INFSCI 2711) Some materials are from Similarity Joins in Relational Database Systems, Augsten and Bohlen and Semantic Web for Working Ontologists, Allemang and Hendler Vladimir Zadorozhny,

277 views • 14 slides
Recap CS 525: Advanced Database We have discussed Organization

Recap CS 525: Advanced Database We have discussed Organization

2013-02-06 Recap CS 525: Advanced Database We have discussed Organization ConvenIonal Indices 06: Even more index B-trees structures Hashing Boris Glavic

273 views • 8 slides
ADVANCED DATABASE SYSTEMS Recovery Protocols @ Andy_Pavlo // 15- 721 // Spring 2019 CMU

ADVANCED DATABASE SYSTEMS Recovery Protocols @ Andy_Pavlo // 15- 721 // Spring 2019 CMU

Lect ure # 12 ADVANCED DATABASE SYSTEMS Recovery Protocols @ Andy_Pavlo // 15- 721 // Spring 2019 CMU 15-721 (Spring 2019) 2 DATABASE RECOVERY Recovery algorithms are techniques to ensure database consistency , atomicity and durability

1.05k views • 50 slides
ADVANCED DATABASE SYSTEMS Self-Driving Database Management Systems @ Andy_Pavlo // 15- 721 //

ADVANCED DATABASE SYSTEMS Self-Driving Database Management Systems @ Andy_Pavlo // 15- 721 //

Lect ure # 25 ADVANCED DATABASE SYSTEMS Self-Driving Database Management Systems @ Andy_Pavlo // 15- 721 // Spring 2019 CMU 15-721 (Spring 2019) 2 Autonomous DBMS History Self-Driving DBMSs Learned Components CMU 15-721 (Spring 2019) 3

918 views • 55 slides
Distributed Query Processing Advanced Topics in Database Management (INFSCI 2711) Some materials

Distributed Query Processing Advanced Topics in Database Management (INFSCI 2711) Some materials

Distributed Query Processing Advanced Topics in Database Management (INFSCI 2711) Some materials are from Database System Concepts, Siberschatz, Korth and Sudarshan Vladimir Zadorozhny, DINS, University of Pittsburgh 1 1 Banking Example

277 views • 25 slides
15-721 ADVANCED DATABASE SYSTEMS Lecture #11 Database Compression Andy Pavlo / / Carnegie

15-721 ADVANCED DATABASE SYSTEMS Lecture #11 Database Compression Andy Pavlo / / Carnegie

15-721 ADVANCED DATABASE SYSTEMS Lecture #11 Database Compression Andy Pavlo / / Carnegie Mellon University / / Spring 2016 @Andy_Pavlo // Carnegie Mellon University // Spring 2017 2 TODAYS AGENDA Background Nave Compression

827 views • 57 slides
ADVANCED DATABASE SYSTEMS Multi-Version Concurrency Control (Garbage Collection) @ Andy_Pavlo

ADVANCED DATABASE SYSTEMS Multi-Version Concurrency Control (Garbage Collection) @ Andy_Pavlo

Lect ure # 05 ADVANCED DATABASE SYSTEMS Multi-Version Concurrency Control (Garbage Collection) @ Andy_Pavlo // 15- 721 // Spring 2020 2 M VCC GARBAGE CO LLECTIO N A MVCC DBMS needs to remove reclaimable physical versions from the database

1.46k views • 57 slides
OLAP and Data Warehousing Advanced Topics in Database Management (INFSCI 2711) Some materials are

OLAP and Data Warehousing Advanced Topics in Database Management (INFSCI 2711) Some materials are

OLAP and Data Warehousing Advanced Topics in Database Management (INFSCI 2711) Some materials are from Database Management Systems, R. Ramakrishnan and J. Gehrke and from https://www.kimballgroup.com/ Vladimir Zadorozhny, DINS, University of

414 views • 22 slides
Lect ure # 24 ADVANCED DATABASE SYSTEMS Databases on New Hardware @ Andy_Pavlo // 15- 721 //

Lect ure # 24 ADVANCED DATABASE SYSTEMS Databases on New Hardware @ Andy_Pavlo // 15- 721 //

Lect ure # 24 ADVANCED DATABASE SYSTEMS Databases on New Hardware @ Andy_Pavlo // 15- 721 // Spring 2018 2 ADM IN ISTRIVIA Snowflake Guest: May 2 th @ 3:00pm Final Exam Handout: May 2 nd Code Review #2: May 2 nd @ 11:59pm We will use the

1.01k views • 73 slides

More recommend