Page 2 of 28. For this talk... ⢠I will explain current trends in distributed database design. ⢠The demands of spee
DISTRIBUTED DATABASE SYSTEMS Alternatively: Database or Electric Disgrace? Angus Macdonald
[email protected]
13/12/2010
Angus Macdonald
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For this talk… • I will explain current trends in distributed database design
• The demands of
• From small scale to
13/12/2010
speed and scale
internet scale
Angus Macdonald
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Speed • How do we make databases faster? • Specialization • Removing un-needed features • Focusing on what’s needed
• Create Databases for specific tasks • Data Warehousing • On-line Transaction Processing
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Specialization
DATA WAREHOUSING
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Specialization: Data Warehousing • Many reads, few writes • So, optimize for reads
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Data Warehouse Example • Most databases store tables by row
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Name
Age
Address
Favourite Colour
Judith
21
North Street
Blue
Sonny
21
Market Street
Red
Lisa
21
South Street
Green
Jon
52
The Scores
Red
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Column Storage • Data stored by column Name
Age
Address
Favourite Colour
Judith
21
North Street
Blue
Sonny
21
Market Street
Red
Lisa
21
South Street
Green
Jon
52
The Scores
Red
• Avoid un-needed data • More opportunity for compression
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Everything’s Distributed • To provide higher availability all of the table data is replicated
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Name
Age
Address
Favourite Colour Blue
Judith
21
North Street
Sonny
21
Market Street
Red
Lisa
21
South Street
Green
Jon
52
The Scores
Red
Name
Age
Address
Favourite Colour Blue
Judith
21
North Street
Sonny
21
Market Street
Red
Lisa
21
South Street
Green
Jon
52
The Scores
Red
Angus Macdonald
Name
Age
Address
Favourite Colour Blue
Judith
21
North Street
Sonny
21
Market Street
Red
Lisa
21
South Street
Green
Jon
52
The Scores
Red
8
Projections • Copies of tables can be stored together in different groups, sorted in different forms Name
Age
Name
Address
Favourite Colour
Jon
52
Judith
North Street
Blue
Judith
21
Lisa
South Street
Green
Lisa
21
Sonny
Market Street
Red
Sonny
21
Jon
The Scores
Red
• Queries take advantage of the most appropriate projection
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The Result • A product called Vertica • Formerly C-Store
• Extremely fast reads • Reasonably fast writes • Size Comparison* • C-Store (1.9Gb), Row Store (4.4Gb) • With ‘projections’: C-Store(1.9Gb), Row Store (11.9Gb)
• Speed Comparison* • 164x faster than a conventional row store • 21x faster than a conventional column store
*C-store: a column-oriented DBMS 13/12/2010
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Our Second Specialization
TRANSACTION PROCESSING
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Specialization: Transaction Processing • What features are actually needed to support transaction processing? • ACID semantics, fault tolerance
• What percentage of instructions in a transaction come from each database component? Disk Buffer Pool Crash Recovery Multi-threading Locking Other
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What’s needed? • What functionality can we afford to lose? JDBC
StoredJDBC Procedures
Query Parsing
Query Parsing Parsing Query
Locking
Locking
Latching Disk Buffer Pool
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Disk
Memory Disk
Traditional Database
New Database
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The Result • A product called VoltDB • Formerly H-Store
• 100x performance improvement over MySQL (single-node)* • 15x faster than a memcached-mysql combination*
* http://voltdb.com/content/mike-stonebraker-sql-urban-myths-webinar-recording 13/12/2010
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Speed and…
SCALE
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Scale • Big web companies need to store petabytes of data • Fighting against the limits of distribution
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The limits of distribution • All distributed systems face a fundamental problem • CAP Theorem • You can have two of these three properties
Consistency
Availability
Partition Tolerance
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Consistency and Partition Tolerance
Consistency
Availability
Partition Tolerance
A A
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Availability and Partition Tolerance
Consistency
Availability
Partition Tolerance
A A` A
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Consistency and Availability
Consistency
Availability
Partition Tolerance
A A
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Eventual Consistency • Most large-scale databases sacrifice consistency • Network partitions happen, and availability is a top priority
• VoltDB sacrifices partition tolerance • Network partitions in a cluster are rare
• Examples of eventually consistent databases: • Dynamo (Amazon) • Cassandra
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Dynamo • Aims for Availability and Partition Tolerance • Run over many data centres
• Key-Value Interface • Partitioned and replicated using consistent hashing
• 0.00057% of requests see two versions 13/12/2010
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MY OWN RESEARCH
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Speed, Scale, and… • …efficiency?
Power Usage (relative to peak)
100% 90% 80% 70% 60% 50%
Power
40%
Energy Efficiency
30% 20%
10% 0%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Utilization
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Use Case (for the Organisation) • Massive amount of resources in the enterprise are unused • 10,000 machines at the University of St Andrews alone
• Could we make use of resources to run a database system?
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Use Case (for the user) • You want to create a database • How would you make that database available to someone else?
• How would you ensure the database is available, when machines can will fail? • How will you scale up to handle increased load?
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Requirements We need something which is: • Resource-aware
• Adaptive
500
CPU Utilization 220
1024
HD Space
C
A
Memory
C
A
CPU Utilization
1353 200
HD Space
B
B
3950 Memory
• Highly-available
• Autonomic Resources Latency Coherency
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Replication
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The Result • A research project called H2O • Sacrifices Availability • Aims for ACID semantics • Assumes partitions are rare Able to run on a constantly changing set of machines
• Currently running evaluations on its performance
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