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GlobeTP: Template-Based Database Replication for Scalable Web Applications

Tobias Groothuyse, Swaminathan Sivasubramanian, and Guillaume Pierre. In procedings of WWW 2007, May 8-12, 2007, Banff, Alberta, Canada. Dina Adel Said

dsaid@vt.edu

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Problem Definition

• How to provide a scalable infrastructure

for hosting dynamically generated web content?

• Past Solutions:
• 1. Cache generated pages
• 2. Distribute

the computational across multiple application servers

• 3. Cache the results of DB queries.
• Problems:

Bottleneck resides in the throughput of the origin DB.

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Problem Definition (cont.)

• Solution: Use DB Replication.
• Problem: Doesn’t scale linearly because

all update, delete, insert (UDI) queries are performed to each DB relipca.

• Past solutions:
• 1. Increase the throughput of each indi-

vidual sever

• 2. Partial Replication

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Partial Replication

• Past Solutions:

– Depending on the application program- mer Gao et al. [2003] – GlobeDB: Sivasubramanian et al. [2005].

∗ Record-level replication granularity ∗ Provides excellent query latency ∗ A central sever maintains all the updates then sends batch updates to other servers. ∗ Does not improve the thoughput because the central server provides a bottleneck.

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DBTP: Template-Based solution

• The nature of web applications belong to

small number of query templates.

• Query

template: parameterized SQL query where parameters are passed at run time.

• By knowing these templates, table place-

ments are selected to insure maximum throughput and reasonable latency.

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Models

• Application Model:

– The application programmer is required to specify explicity the application templates.

• System Model:

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Main problems to consider

• 1. Cluster Identification:

Ensure that the placement of tables would find at least

• ne server to execute each query tem-

plate.

• 2. Consider all the defined templates, read
• r UDI, and determine the best place-

ment to provide the maximum through- put.

• 3. Define a load balancing algorithm that al-

lows read queries to distribute efficiently.

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Data Placement: Cluster Identification

• Goal: Determines the set of tables that is

needed to be replicated together so that templates function correctly. Meanwhile, number of servers that must execute the UDI query should be minimized.

• Characterize each query template:
• 1. Whether it is read or UDI
• 2. The set of tables that it accesses.

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Data Placement: Load Analysis

• Determines the load received by each of

the cluster.

• Determines the load on Table Clusters:

– Read or UDI query – Frequency of template occurrence – Computational complexity for executing this query: ∗ Use DB systems tools to estimate the actual execution time. ∗ Run the query in a live system.

• Determines the load on DB servers (Read or UDI

query)

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Data Placement: Cluster Placement

• Determines the placement of the cluster

across the set of DB servers

load achieved by each replica is minimized.

• Using exhaustive search O(2N∗T/N!),

where T is No. of tables and N number of Nodes.

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Query Routing

• Round Robin (RR): Efficient if all coming

queries have the same cost.

• RR-QID: RR by Query ID

– Each Query template is identified by its QID. – Each queue is associated with the set of DB servers that can server a certain QID. – RR fashion is implemented for each queue.

• Cost-based Routing

– Upon arrival of incoming query, the query router estimates the current load on each DB server. – The Query is scheduled to the least loaded DB server (that can serve the query).

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Experiments

• Compare Globe-TP with full DB replica-

tion using:

– TPC-W: standard e-commerce benchmark – RUBBoS: bulletin-board benchmark modeled after slashdot.org

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Experiments (cont.)

• Query

latency distributions using 4 servers.

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Experiments (cont.)

• Maximum achievable throughputs with

90% of queries processed within 100ms.

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Advantages

• Easily coupled with a distributed DB

query cache.

• Does not require any modification in the

application itself.

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Disadvantages

• Does not support transactions. However,

it can be implemented through query router.

• Limitation due to table granularity par-

tial replication.

• Fault Tolerance issues.
• Does not take into consideration the long-

term load variations that must be ex- pected when operating a popular dy- namic web site.

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References

Lei Gao, Mike Dahlin, Amol Nayate, Jiandan Zheng, and Arun Iyengar. Application specific data replication for edge services. In WWW ’03: Proceedings of the 12th international conference on World Wide Web, 449–460, Budapest, Hungary. 2003. ISBN 1-58113-680-3. Swaminathan Sivasubramanian, Gustavo Alonso, Guillaume Pierre, and Maarten van Steen. Globedb: autonomic data replication for web applications. In WWW ’05: Proceedings of the 14th international conference on World Wide Web, 33–42, Chiba, Japan. 2005. ISBN 1-59593-046-9.

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Thank you dsaid@vt.edu