Performance Tradeoffs in ReadOptimized Databases - PowerPoint PPT Presentation

Title: Performance Tradeoffs in ReadOptimized Databases


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Performance Tradeoffs in Read-Optimized Databases

• Stavros Harizopoulos
• MIT CSAIL
• joint work with
• Velen Liang, Daniel Abadi, and Sam Madden

massachusetts institute of technology
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Read-optimized databases
1
2
Joe

SQL Server DB2 Oracle
Sybase IQ MonetDB CStore
Sue
45

37

column stores
row stores
Read optimizations
Materialized views, multiple indices, compression
How does column-orientation affect performance?
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Rows vs. columns
column data
row data
seek
1 Joe 45
2 Sue 37
single file

Study performance tradeoffs solely in data storage
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Performance study

• Methodology
• Built storage manager from scratch
• Sequential scans
• Analyze CPU, disk, memory
• Findings
• Columns are generally more I/O efficient
• Competing traffic favors columns
• Conditions where columns are CPU-constrained
• Conditions where rows are MemBW-constrained

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Talk outline

• System architecture
• Workload and Experiments
• Analysis
• Conclusions

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System architecture

• Block-iterator operators
• Single-threaded, C, Linux AIO
• No buffer pool
• Use filesystem, bypass OS cache
• Compression
• Dense-pack

100 full
60 full
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Compression methods

• Dictionary
• Bit-pack
• Pack several attributes inside a 4-byte word
• Use as many bits as max-value
• Delta
• Base value per page
• Arithmetic differences

low high low normal
00 10 00 01
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Storage engine
row scanner
column scanner
SELECT name, age WHERE age gt 40
Joe 45

Joe 45

S
apply predicate(s)
S
POS 45
POS
name
apply predicate 1
S
age
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Platform
CPU
L2
RAM
DISKS
(striped)
3.2 GB/sec
3.2GHz
180 MB/sec
1GB
1MB
prefetching
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Workload

• LINEITEM (wide)
• 60m rows ? 9.5 GB
• ORDERS (narrow)
• 60m rows ? 1.9 GB
• Query

150 bytes
50 bytes
32 bytes
12 bytes
SELECT a1, a2, a3, WHERE a1 yields variable
selectivity
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Wide tuple 10 selectivity
Column
Row
time (sec)
Column (CPU only)
Row (CPU only)
selected bytes per tuple

• Large prefetch hides disk seeks in columns

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Wide tuple 10 sel. (CPU)
attributes selected column store

• Row-CPU suffers from memory stalls

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Wide tuple 10 sel. (CPU)
0.1
attributes selected column store

• Column-CPU efficiency with lower selectivity

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Narrow tuple 10 selectivity
time (sec)
row store
column store
selected bytes per tuple
attributes selected

• Memory stalls disappear in narrow tuples
• Compression similar to narrow (not shown)

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Varying prefetch size
no competingdisk traffic
Column 2
Column 8
time (sec)
Column 16
Column 48 (x 128KB)
Row (any prefetch size)
selected bytes per tuple

• No prefetching hurts columns in single scans

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Varying prefetch size
no competingdisk traffic
with competing disk traffic
time (sec)
selected bytes per tuple

• No prefetching hurts columns in single scans
• Under competing traffic, columns outperform rows
  for any prefetch size

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Analysis

• Central parameter in analysis
• cycles per disk byte (cpdb)
• What can it model
• More / fewer disks
• More / fewer CPUs
• CPU / disk competing traffic
• Trends in cpdb
• 10 ? 30 from 1995 to 2006
• Further increase with multicore chips

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Analysis
speedup of cols over rows
2
10 selectivity
50 projection
1.6 2
1.2 1.6
(cpdb)
cycles per disk byte
0.8 1.2
0.4 0.8
tuple width

• Rows favored by narrow tuples and low cpdb
• Disk-bound workloads have higher cpdb

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See our paper for the rest

• CPU time breakdowns, L2 prefetcher
• Disk prefetching implementation
• Compression results
• Non-pipelined column scanner
• Analysis

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Conclusions

• Given enough space for prefetching, columns
  outperform rows in most workloads
• Competing traffic favors columns
• Memory-bandwidth bottleneck in rows
• Future work
• Column scanners, random I/O, write performance

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Thank you
db.csail.mit.edu/projects/cstore
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Analysis
parameter
what it can model