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B17 Eliminating the database bottleneck
1.
Eliminating the Database Bottleneck What
makes Vectorwise so fast Mark Van de Wiel Director Product Management, Vectorwise Thursday, November 01, 2012 1 of 9 1 of 9 Confidential © 2012 Actian Corporation
2.
Agenda Why traditional
RDBMSs are slow for analytics Why Vectorwise is fast The I/O challenge Efficient updates Confidential © 2012 Actian Corporation 2
3.
100x (+) Performance
Difference – 2003 Custom C versus Relational Database TPC-H 1 GB query 1 (runtime in s) 30 28.1 26.2 25 20 MySQL 15 DBMS 'X' C program 10 Vectorwise 5 0.2 0.6 0 MySQL DBMS 'X' C program Vectorwise Confidential © 2012 Actian Corporation 3
4.
Traditional Relational Database
for Analytics Inefficiencies Inefficient storage Inefficient processing Confidential © 2012 Actian Corporation 4
5.
Inefficient Storage for
Analytics Row-based storage model Predominant in 2003, still very common today Works well for OLTP 101 Joe 27 Black 103 Edward 21 Scissorhand Confidential © 2012 Actian Corporation 5
6.
Inefficient Storage –
Row-based Pages on disk – example 101 27 Joe Black 103 21 Edward Scissorhand Var-width attribute pointers pointers to tuples Confidential © 2012 Actian Corporation 6
7.
Issues with Row-based
Storage Always read all attributes Poor bandwidth Poor use of memory buffer Complex row structure and navigation E.g. compressing out null fields E.g. row chaining Confidential © 2012 Actian Corporation 7
8.
Efficient Storage for
Analytics Columnar storage: store attributes separtely Retrieve only attributes required by the query Used by “traditional” column stores, e.g. Sybase IQ, Vertica Confidential © 2012 Actian Corporation 8
9.
Inefficient Processing How a
traditional database runs a query Query: SELECT name, salary*.19 AS tax FROM employee WHERE age > 25 Confidential © 2012 Actian Corporation 9
10.
Inefficient Processing How a
traditional database runs a query Tuple-at-a-time iterator interface: - open() - next(): tuple - close() next() is called: - for each operator - for each tuple Complex code repeated over and over Confidential © 2012 Actian Corporation 10
11.
Inefficient Processing How a
traditional database runs a query Data-specific computational functionality Called once for every operation on every tuple Worse for complex tuple representations Confidential © 2012 Actian Corporation 11
12.
Inefficient Processing (Part
1 of 2) Lots of repeated, unnecessary code Operator logic Function calls Attribute access Most instructions interpreting a query Very few instructions processing actual data! Many instructions per tuple Confidential © 2012 Actian Corporation 12
13.
CPU Features –
Inefficient Processing Part 2 In the last 20 years… Chip cache because RAM access is too slow and congested Branch-sensitive CPU pipelines Superscalar features SIMD instructions (SSE and AVX) Great for multimedia processing, scientific computing… … but NOT for traditional relational databases Complex code: function calls, branches Poor use of CPU cache (both data and instructions) Processing one value at a time Confidential © 2012 Actian Corporation 13
14.
Inefficient Processing Traditional RDBMS
Many instructions per tuple Many cycles per instruction Very many cycles per tuple Confidential © 2012 Actian Corporation 14
15.
Vectorwise – Vector-based
Processing Query: SELECT name, salary*.19 AS tax FROM employee WHERE age > 25 Confidential © 2012 Actian Corporation 15
16.
Vectorwise – Vector-based
Processing Vector contains data of multiple tuples (1024) All operations consume and produce entire vectors Effect: much less operator.next() and primitive calls. AND: pipelined query evaluation Confidential © 2012 Actian Corporation 16
17.
Why is Vectorwise
so Fast? Reduced interpretation overhead 100+ times fewer function calls Good CPU cache use High locality in primitives Cache-conscious algorithms No tuple navigation Primitives only see arrays Vectorization allows algorithmic optimization CPU and compiler-friendly function bodies Multiple work units, loop-pipelining, SIMD… BONUS: PARALLEL QUERY Confidential © 2012 Actian Corporation 17
18.
Some Numbers Traditional
RDBMS: <200 MB/s per core Vectorwise (lab environment): >1.5 GB/s per core Confidential © 2012 Actian Corporation 18
19.
Addressing the I/O
Challenge Columnar storage Smart column buffer (memory) Data compression On disk: less I/O In memory: best use of column buffer Ultra-efficient decompression algorithms to get sufficient throughput Large contiguous data blocks for optimum disk I/O In-memory min-max indexes per block (i.e. per column) Eliminate data blocks based on implicit/explicit filter criteria Confidential © 2012 Actian Corporation 19
20.
Efficient Updates in
a Column Store Positional Delta Trees (PDTs) In-memory representation of small data changes Efficiently merged with on-disk data Periodically propagated to disk Provide snapshot read consistency ACID compliant Confidential © 2012 Actian Corporation 20
21.
Agenda Why traditional
RDBMSs are slow for analytics Why Vectorwise is fast The I/O challenge Efficient updates Confidential © 2012 Actian Corporation 21
22.
Confidential © 2012
Actian Corporation
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