05. Alternative Speichermodelle. Architektur von Datenbanksystemen I

Transcription

1 05. Alternative Speichermodelle Architektur von Datenbanksystemen I

2 Einführung LETZTE VORLESUNG ROW-BASED RECORD MANAGEMENT klassisches N-äres Speichermodell (NSM), auch row-store NSM = Normalized Storage Model VORTEILE gesamter Datensatz kann mit einem Seitenzugriff gelesen werden leichte Änderbarkeit einzelner Attributwerte NACHTEIL: werden nur wenige Attributwerte benötigt, müssen trotzdem immer alle Attributwerte gelesen werden à unnötiger IO-Aufwand ALTERNATIVEN: SPALTENORIENTIERTE SPEICHERMODELLE Zerlegung einer n-stelligen Relation in eine Menge von Projektionen (z.b. binäre Relation) Identifikation (und Rekonstruktion) über eine Schlüsselspalte oder Position NSM page organization 2

3 Beispiel ROW-BASED RECORD MANAGEMENT VERSUS COLUMN-BASED RECORD MANAGEMENT Datensatz als Einheit Menge von vertikalen Projektionen 3

4 Decomposition Storage Model - DSM BESCHREIBUNG alle Werte einer Spalte (Attribut) werden hintereinander gespeichert Adressierung über Position bzw. logischer ID (surrogate) Seitenaufbau (Datensatz bestehend aus 2 Attributen) G.P. Copeland, S.F. Khoshafian: A Decmposition Storage Model, In: SIGMOD 1985, pages : DSM (DECOMPOSITION STORAGE MODEL) Proposed as an alternative to NSM (Normalized Storage Model) Decomposition storage mode, decomposes relations vertically 2 indexes: clustered on ID, non-clustered on value Speeds up queries projecting few columns Disadvantages: storage overhead for storing tuple IDs, expensive tuple reconstruction costs 4

5 Decomposition Storage Model/2 EIGENSCHAFTEN Kompression einfach möglich (z.b. Run length encoding) effizientere Scanoperationen (Feldoperationen bessere Cache-Nutzung) jedoch: Updateoperationen sind komplexer, Lesen aller Spalten aufwendiger Einsatz bei leseoptimierten Datenbanken 5

6 Vergleich + easy to add/modify a record - might read unnecessary data + only need to read in relevant data - tuple writes require multiple accesses -> suitable for read-mostly, read-intensive, large data repositories 6

7 Vergleich/2 Characterisitc NSM DSM Inter-record spatial locality Low record reconstruction cost 7

8 Partition Attributes Across - PAX GOALS Maximizes inter-record spatial locality Incurs a minimal record reconstruction cost APPROACH compromise between NSM and DSM keep attributes values of each record on the same page using a cache-friendly algorithm for placing attributes values inside the page - vertically partitionsthe records within each page - storting together the valuesof each attribute in minipages 8

9 PAX-Design STORAGE DESIGN each page is partitioned in n minipages (n attributes in a relation) Page Header - pointersto the beginningof each minipage - free space information - number of records - attributes sizes (fixed length or variable) Minipages - F-minpage à fixed-length attributevalues, precence bits indicate the availability of attributesvaluesfor the records (if null, the attribute value is not present) - V-minipage à variable-length attributes values, slotted with pointersto each value, null valuesare denoted by null pointers 9

10 Evaluation QUERY PERFORMANCE (READ) BULK-LOADING 10

11 Cache Behavior 11

12 History & Development 12

13 From DSM to Column-Stores 1985: DECOMPOSITION STORAGE MODEL LATE 90S 2000S: FOCUS ON MAIN-MEMORY SOME COLUMN STORE SYSTEMS MonetDB, C-Store, Sybase IQ, SAP Business Warehouse Accelerator, Infobright, Exasol, X100/VectorWise PERFORMANCE MonetDB PAX: Partition Attributes Across - Retains NSM I/O pattern - Optimizescache-to-RAM communication 2005: THE (RE)BIRTH OF COLUMN-STORES New hardware and application realities - Faster CPUs, larger memories, disk bandwidth - Multi-terabyte Data Warehouses New approach: combine several techniques - Read-optimized, fast multi-column access, disk/cpu efficiency, light-weight compression Used in read oriented environments - OLAP 13

14 Application Characteristics OLTP (ON-LINE TRANSACTION PROCESSING) Mix between read-only and update queries Minor analysis tasks Used for data preservation and lookup Read typically only a few records at a time High performance by storing contiguous records in disk pages OLAP (ON-LINE ANALYTICAL PROCESSING) Query-intensive DBMS applications Infrequent batch-oriented updates Complex analysis on large data volumes Read typically only a few attributes of large amounts of historical data in order to partition them and compute aggregates High performance by storing contiguous values of a single attribute 14

15 Hardware Development - Memory Wall HARDWARE IMPROVEMENTS NOT EQUALLY DISTRIBUTED Advances in CPU speed have outpaced advances in RAM latency CACHE MEMORIES CAN REDUCE THE MEMORY LATENCY WHEN THE REQUESTED DATA IS FOUND IN THE CACHE. Vertically fragmented data structures optimize memory cache usage Main-memory access has become a performance bottleneck for many computer applications - Bandwidth - Latency - Adress translation (TLB) à Memory Wall 15

16 Speed in Relation... 16

17 Memory Performance Comparison 17

18 The Role of Caches CACHES THE SUNNY SIDE Memory is physically accessed at cache line granularity, e.g. 64Byte Sequential memory access: 18

19 The Role of Caches CACHES THE BAD SIDE Memory is physically accessed at cache line granularity, e.g. 64Byte Random memory access: cache miss 19

20 The Role of Caches CACHES THE UGLY Memory is physically accessed at cache line granularity, e.g. 64Byte Writes effectively turn into read-modify-write - Many memory addresses map into the same cache line(s) - Dirty cache line needs to be evicted before new one loads 20

21 The Role of Caches CACHES THE UGLY Memory is physically accessed at cache line granularity, e.g. 64Byte Writes effectively turn into read-modify-write - Many memory addresses map into the same cache line(s) - Dirty cache line needs to be evicted before new one loads 21

22 Is Memory the new Disk? IS MEMORY THE NEW DISK à IN TERMS OF BEHAVIOR? à NOT QUITE Some characteristics are very similar, e.g. random vs. sequential Memory architecture complicates things! 22

23 Architektur kommerzieller Produkte 23

24 Vertica VERTICA ANAYLYTIC DATABASE DBMS Optimized for Next-Generation Data Warehousing (OLAP) Hybrid Store consisting of two distinct storage structures - WOS (Write-Optimized Store): fits into main memoryand is desigend to efficiently support insert and update operations; WOS is unsorted and uncompressed - ROS (Read-Optimized Store): bulkof the data; sorted andcompressed; making it efficient to read and query Tuple Mover - Moves data out of the WOS and into ROS Structure - WOS and ROS are organized as DMS 24

25 SAP HANA ARCHITECTURE (2012) 25

26 SAP HANA Column Store MAIN AND DELTA STORE Main Store: main part of the data; compressed data Delte Store: all data changes are written; basic compression and optimized for write access MERGE PROCESS Moves data from delta to main store 26

27 SAP HANA Column Store/2 THE DELTA MERGE OPERATION 27