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SWISSBOX REDESIGNING SYSTEMS FROM THE GROUND UP
Gustavo Alonso Systems Group
Dept. of Computer Science ETH Zürich, Switzerland
SwissBox – IBM– March-2013
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Systems Group = www.systems.ethz.ch Enterprise Computing Center = www.ecc.ethz.ch
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SWISSBOX
Gustavo Alonso, Donald Kossmann, Timothy Roscoe: SWissBox: An Architecture for Data Processing Appliances. CIDR 2011: 32-37
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The SwissBox project
Build an open source data appliance
• Hardware
• Software
What is a DB appliance?
• Database in a box
Funny database
Funny box
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• Intelligent storage manager • Massive caching • RAC based architecture • Fast network interconnect
ORACLE EXADATA
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NETEZZA (IBM) TWINFIN
• No storage manager • Distributed disks (per node) • FPGA processing • No indexing
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SAP HANA
• Main memory database • Column store • No indexing (automatic)
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SwissBox themes
System co-design
• OS/DB co-design
• HW/SW co-design
Data processing on modern hardware
New system architectures
• Databases (Crescando, SharedDB)
• Operating systems (Barrelfish)
• Intelligent storage engines (Ibex)
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Swissbox mantras
Everything is a distributed system
• Multicore = cluster
Everything is heterogeneous:
• Computing nodes
• Memory
• Links/networks
Hardware can be tailored
Performance must be predictable
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How it all started: The Amadeus use case
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Amadeus Workload
Passenger-Booking Database
• ~ 600 GB of raw data (two years of bookings)
• single table, denormalized
• ~ 50 attributes: flight-no, name, date, ..., many flags
Query Workload
• up to 4000 queries / second
• latency guarantees: 2 seconds
• today: only pre-canned queries allowed
Update Workload • avg. 600 updates per second
(1 update per GB per sec) • peak of 12000 updates per
second • data freshness guarantee: 2
seconds
Problems with State-of-the Art • Simple queries work only
because of mat. views multi-month project to
implement new query / process
• Complex queries do not work at all
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Better the devil you know …
Performance depends on workload parameters
• changes in load (updates, columns accessed) -> huge variance
• Unpredictable performance, impossible to tune correctly
0
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MySQL Query 99th
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1.251.51.752
Qu
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mse
c
Synthetic Workload Parameter s
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Hardware killed the software star
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Hardware dominates the game
Hardware evolving faster than software
Performance gains no longer for free
Machines becoming far more complex
Design assumptions no longer hold
• Multicore heterogeneity
• Hardware acceleration
• Effect of networking
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Hardware is going crazy
Gustavo Alonso - Systems Group - ETH Zürich 15
P1
P0
P2
P3
P4
P6
P5
P7
Each die has: • 6 cores • 4HT ports • 2 memory channels
Each package has: • 12 cores • 4HT ports • 4 memory channels
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Multicore challenge
Min Cores Partition Size [GB]
Intel Nehalem 2 4
AMD Barcelona 5 1.6
AMD Shanghai 3 2.6
AMD MagnyCours 2 2
Experiment setup • 8GB datastore size • SLA latency requirement 8s • 4 different machines
Example: deployment on multicores
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Adding resources does not help
Gustavo Alonso - Systems Group - ETH Zürich 17
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Th
oru
gh
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t(T
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Clients
MySQL TPC-WB 20 GB DB
MYSQL-48 MYSQL-24 MYSQL-12
8 cores
48 cores
24 cores
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Load interaction (multicore)
Gustavo Alonso - Systems Group - ETH Zürich 18
System X
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Load interaction (virtualized)
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Th
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Increasing load [#requests]
System Performance -Throughput
48 cores - isolated
48 cores - noisy
Experiment setup
• AMD MagnyCours • 4 x 2.2GHz AMD Opteron 6174 processors • total Datastore size 53GB • Noise: another CPU-intensive task running on core 0
What we actually get
What we expect to get
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SWISSBOX: storage engine Software part
Philipp Unterbrunner, Georgios Giannikis, Gustavo Alonso, Dietmar Fauser, Donald Kossmann: Predictable Performance for Unpredictable Workloads. PVLDB 2(1): 706-717 (2009)
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Crescando: the Amadeus use case
Remove load interaction
Remove unpredictability
Simplify design for scalability and modeling
Treat a multicore machine as a collection of individual nodes (not as a parallel machine)
Run only on main memory
One thread per core
Highly tune the code at each core
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Scan on a core
READ CURSOR
WRITE CURSOR DATA IN
CIRCULAR BUFFER
(WIDE TABLE)
BUILD QUERY INDEX FOR NEXT SCAN QUERIES
UPDATES
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Crescando on 1 Machine (N Cores)
...
Split
Scan Thread
Scan Thread
Scan Thread
Scan Thread
Scan Thread
Merge
Input Queue
(Operations)
Input Queue
(Operations)
Output Queue
(Result Tuples)
Output Queue
(Result Tuples)
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Crescando in a Data Center (N Machines)
...
Aggregation
Layers
Replication
Groups
...
...
External Clients
Crescando
...
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Why is this interesting (industry)?
Fully predictable performance
• Response time determined by design regardless of load
Only two parameters:
• Size of the scan
• Number of queries per scan
Scalable to arbitrary numbers of nodes
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Why is this interesting (research)?
Storage engine with a different interface
Can be used as intelligent (active) storage engine
Modular component
No multi-threading, no fancy parallelism, no synchronization, no shared data structures, etc.
Suitable for hardware acceleration
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SWISSBOX: data processing engine
Georgios Giannikis, Gustavo Alonso, Donald Kossmann: SharedDB: Killing One Thousand Queries With One Stone. PVLDB 5(6): 526-537 (2012)
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SharedDB
SharedDB does not run queries individually (each one in one thread). Instead, it runs operators that process queries in batches thousands of queries at a time
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Shared DB can run TPC-W!
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Predictability at scale
SharedDB can run complex joins (and shorts) in predictable time with large update loads
Linear scalability with number of processing units (cores)
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Raw performance
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Predictability, robustness
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Why is this interesting (industry)?
Optimize whole loads rather than individual queries/services
Fully predictable performance
Better use of resources and parallelism
Eliminates complex database administration problems
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Why is this interesting (research)?
One plan to
• Optimize
• Deploy NEW
• Schedule NEW
DB/OS codesign:
• The database does not know about the hardware or its state …
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Why is this interesting?
SharedDB runs on a heterogeneous storage engine:
Crescando
Key value store
Same idea can be generalized to different representations and/or hardware architectures -> see next
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SWISSBOX: hardware acceleration
Parallel Computation of Skyline Queries Louis Woods, Jens Teubner and Gustavo Alonso. IEEE FCCM, March, 2013
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ORACLE EXADATA
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Intelligent storage engine
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Inserting the FPGA in the data path
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Engine design
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So far so good
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Points of interest
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SWISSBOX: Challenges ahead
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Execution platform very complex
Optimization not trivial
• Multicore
• Load interaction
• Virtualization
• Lack of precise cost models
• Not stable/standard platforms
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Beyond plan optimization
Other aspects to optimization
• Deployment on heterogeneous architecture
• Scheduling
Parallel threads
Across queries
• Load interaction
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Hardware dictates everything
Many options enabled by hardware
• Custom hardware
• Custom configurations
• Multiplicity computing
Reasonable development cost
Can beat almost any software design by tuning the hardware
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CONCLUSIONS
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May you live in interesting times
Many radical changes in IT infrastructure
• Cloud computing
• Hardware & architecture relevant again
• Specialization / tailoring
• Large scale clusters
• Geographic distribution
Great opportunity for research