authors: stavros harizopoulos @ hp daniel j. abadi @ yale samuel madden @ mit michael stonebraker @...
TRANSCRIPT
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Authors:
Stavros Harizopoulos @ HP
Daniel J. Abadi @ Yale
Samuel Madden @ MIT
Michael Stonebraker @ MIT
Supervisor: Dr Benjamin Kao
Presenter: For
Sigmod 2008 paper
OLTP through the looking glass, and what we found there
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What is Online Transaction Processing(OLTP)?
• OLTP, refers to a class of systems that facilitate and manage transaction-oriented applications, typically for data entry and retrieval transaction processing (wiki)
• OLTP database was optimized for computer technology of late 1970s, 30 years ago
2B3L
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Main feature of OLTP
• Buffer Management to facilitate data transfer between memory and disk
• B-Tree for on-disk data storage• Logging for recovery• Locking to support concurrency • Latching for accessing shared data structure
2B3L
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Motivation of the studies• Is the OLTP database optimized nowadays,
given the hardware advancement?• Request from outside the DB community for
alternative DB architecture
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Motivation of studiesHardware advancement
30 Years ago Nowadays
DB cost In millions Few thousands
Storage size DB size >> Memory
Memory >DB size
Processing time for most of the transactions
\ In microseconds
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Motivation of studies Request from outside the DB community
• “database-like” storage system proposal from Operating System and networking conference – varying forms of
• concurrency, • consistency, • reliability, • replication, • queryability
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Trends in OLTP – (1/5) Cluster Computing
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Trend in OLTP-(2/5) Memory resident Databases
Buffer
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Trend in OLTP-(2/5) Memory resident Databases
• Data doesn’t grow as fast as memory size
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Trend in OLTP-(3/5)Single Threading in OLTP System
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• A step backward from multithread to single thread ?
• Why multithreading?– Prevent idle of CPU while waiting data from disk– Prevent long-running transactions from blocking
short transaction
• Not valid for memory resident DB– No disk wait– Long-running transactions run in ware house
Trend in OLTP-(3/5)Single Threading in OLTP System
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• What about multi processors ?– Achieve shared-nothing processor by virtual
machine
• What about network disk?– Feasible to partition transaction to run in “single-
site”
Trend in OLTP-(3/5)Single Threading in OLTP System
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Trend in OLTP-(4/5) High Availability vs. Logging
• 24x7 service achieved by using multiple set of hardware.
• Perform recovery by copying missing states from other database replicas.
• Log for recovery can be avoided
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Trend in OLTP-(4/5) High Availability vs. Logging
Production Server Standby Server
Recovery from24x7 service
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Trend in OLTP-(5/5)Transaction Variants
• Why transaction variants?– 2 phase commit protocol harm large scale
distributed DB system performance– 2 phase commit involves commit-request and
commit phase which involves all server to participate.
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Trend in OLTP-(5/5)Transaction Variants
• Trade consistency with performance• Eventual consistency, all writes propagate
among the database servers.
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Trend in OLTP-(5/5)Transaction Variants
• Eventual consistency example: AmazonSx, Sy, Sz are different servers
D1, D2 add item to cartD3 delete an item from cartD4 add another item to cart
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Research groups interested in new DB architecture
• Amazon, • HP, • NYU, • MIT
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Trend in OLTP - Summary
Eventual consistency
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DBMS modification by OLTP trends
• (1) memory resident DB can get rid of buffer mgt
Buffer
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DBMS modification by OLTP trends
• (2) single thread can avoid locking and latching
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DBMS modification by OLTP trends
• (3) Cluster computing avoid locking, instead of single processor multithread, each processor is responsible for each own thread
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DBMS modification by OLTP trends
• (4) high availability can avoid using log for recovery purpose
Production Server Standby Server
Recovery from
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DBMS modification by OLTP trends
• (5) transaction less avoid book keeping, i.e. logging
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Case Study of DBMS, Shore
• Shore was developed at the University of Wisconsin in the early 1990’s
• It was designed to be a typed, persistent object system borrowing from both file system and object-oriented database technologies
http://www.cs.wisc.edu/shore
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Basic components of Shore
2B3L
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Removing Shore’s components
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Remove Shore’s logging(1)
• To increase log buffer size so that it will not flush to disk
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Remove Shore’s locking(2)
• To configure Lock Manager always granting lock
• To remove codes that handle ungranted lock request
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Remove Shore’s latching(3)
• To add if-else statement to avoid request for latch
• To replace the original latching intensive B-tree with a latch free B-tree
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Remove Shore’s Buffer Mgt (4)
• To replace Buffer Mgt by directly invoking malloc for memory allocation
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Shore after components removal
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Bench mark for comparison (TPC-C)
• TPC-C is industry standard used to measure ecommerce performance
• TPC-C is designed to represent any industry that must manage, sell, or distribute a product or service
• Vendors includes Microsoft, Oracle, IBM, Sybase, Sun, HP, DELL etc.
http://www.tpc.org/tpcc/default.asp
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Bench mark for comparison
• 1 warehouse(~100M) serves 10 districts, and each district serve 3000 customers.
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Bench mark for comparison
• 5 concurrent transactions in TPC-C– New Order Transaction– Payment – Deliver Order– Check status of Order– Monitor Stock Level of warehouse
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Experiment setup and measurement
• Single-core Pentium 4, 3.2GHz, with 1MB L2 cache, hyper threading disabled, 1GB RAM, running Linux 2.6.
• 40,000 transactions of types New Order Transaction and Payment are run
• Results measured in – 1) Throughput (Time/ # Transaction completed) – 2) Instruction count
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Results after removing the components (in throughput)
• Memory resident Shore DB provided 640 transactions per second.
• Stripped-down Shore DB provided 12,700 transactions per second.
• The Stripped-down Shore DB gave a 20 times improvement in throughput
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Results after removing the components (in # instruction)
• Instruction of useful work is only <2% of a memory resident DB
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Effect of removing different components for payment (1/6)
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Effect of removing different components for payment (2/6)
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Effect of removing different components for payment (3/6)
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Effect of removing different components for payment (4/6)
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Effect of removing different components for payment (5/6)
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Effect of removing different components for payment (6/6)
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Effect of removing different components for both order
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Instruction and cycle comparsion
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Implication for future OLTP engineConcurrency Control
• Single threaded transaction allow concurrency control to be turned off
• But many DBMS applications are not sufficiently well behave for Single threaded transaction.
• Dynamic locking was experimentally the best concurrency control with disk.
• What concurrency control protocol is best?
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Implication for future OLTP engineMulti-core support
• Virtualization, each core is a single-threaded machine
• Intra-query parallelism, each processor running part of a single query
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Implication for future OLTP engineReplication Management
• Active-passive replication scheme with log• Replica may not be consistent with the primary
unless on two-phase commit protocol• Log is required
• Active-active replication scheme with transactions
• Two-phase commit introduce large latency for distributed replication
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Implication for future OLTP engineWeak Consistency
• Eventual Consistency - Data is not immediately propagated across all nodes.
• To study the effect of different degree of workload to the consistency level of data
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Implication for future OLTP engine Cache conscious B-Trees
• Cache misses in the B-tree code may well be the new bottleneck for the stripped down system.
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Conclusion
• Most significant overhead contributors are buffer management and locking operation, followed by logging and latching.
• A fully stripped down system’s performance is orders of magnitude better than an unmodified system.
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Thank you !