The document describes a middleware for implementing highly available distributed databases called HDDBRS. It has the following key points: 1. HDDBRS uses a 3-tier architecture with clients, middleware, and backend database servers. The middleware handles redundancy, recovery, and failure detection in a transparent way. 2. The middleware uses erasure coding techniques to stripe and encode data across multiple database servers for redundancy while minimizing storage overhead. It can recover lost data or servers. 3. A demonstration showed it could recover a failed server with 6% overhead, and recover a 3KB record in 130ms with 0.18ms for decoding. The middleware provides high availability, scalability, and performance.

1. HDDBRS MIDDLEWARE FOR IMPLEMENTING HIGHLY AVAILABLE DISTRIBUTED DATABASES
RIM MOUSSA, PHD
UTIC LAB. , TUNISIA
Project Goals
Implement
a
reliable,
scalable,
portable,
full-featured
high
availability
solution
for
distributed
databases, conformant with open standards.
3-tier distributed architecture
1. Client
more than
$1MpH, 8%
 Not aware of data distribution
 Not aware of data redundancy
DBCTm-1 Work.
Queue
DBCTm Work. Queue
DBCTi Work. Queue
JDBC Driver
 DB group k-available composed of m source
DB instances and k parity DB instances.
 Tables horizontally fragmented.
 Surjective function for record grouping.
between $51KpH and
$250KpH, 28%
High-availability Methods
REED SOLOMON
ERASURE
CODES
Data Stripping
Load Balancing
Encoding/
Decoding
Overhead
Quick Recovery
Minimal Storage
Overhead
Spare
Fig. 1. Middleware Architecture.
 Testbed: Oracle DBMS, 1.7GHz CPU on DB
DB connection Thread for each DB instance
Query Handler Thread
Distributed Transaction Handler Thread
Recovery Thread
RMI Thread …
Threads communicate through concurrent
queues (working and response queue for
each thread) and sleep and notify primitives.
backends, 2.7GHz mid-tier, all connected through
a100Mbps router,
 Insert Performances: 65ms, 140ms, 160ms for
respectively k = 0, 1, 2.
 Record Recovery Performances:
 130ms for a 3KB record,
 only 0.18ms for decoding.
 Fragments Recovery:
 JDBC Interface with DB backends.
 XA/open standard (2-PC protocol) for
distributed transaction management.
 RMI for client transaction processing.
High Storage
Cost
 One data fragment of 7.52MB recovered at a
rate of 720KBps.
 Two data fragments of 15.04MB recovered at
a rate of 690KBps.
 Decoding overhead is 6% of recovery time.
Demonstration Outline
Demonstrated Configuration:
 2-available group of 4 source
DB instances and 2 parity DB
instances (m = 4, k = 2) .
 Item
• Script to create
table fragments fon
each DB instance.
• DB population.
 Each item is 3KB.
DB Set up &
Population
Key Search
• Search item with
key i_id
• Either by deleting of
up to k fragments
contents or by
shutting down
corresponding DB
instances
Record
Recovery
• Recover item with
key i_id
•
•
•
•
Simulate k
Servers Failure
Set up k spares
Query alive servers
Decode
Insert recovered
data into spare
servers
Recover k
Servers
 Oracle DBMS instances
Future Work
References
Automatic
increase of
Performance a group
high
Test using
TPC-C bench availability
in both failure level
and safe
modes
[Khediri MSc
Project]
TH
18
Distributed
highly
available
DB which
autoscale
over a
cluster
.
.
.
Performances
 Multithreading






JDBC Driver
Spare
Middleware Architecture
REPLICATION
DBCTj Work. Queue
DBCTn Work. Queue
3. DB backends
JDBC Driver
 Redundant data management
 Recovery process (records and fragments)
 Failure detection …
up to $50KpH, 46%
Optimize
parity
updates
using
Jserver
.
.
.
2. HDDBRS Mid-tier
JDBC Driver
between $251KpH and
$1MpH, 18%
DBCT0 Work. Queue
DB Backends
A survey conducted by the CPR and ERA, in
2001 shows important downtime cost per
hour for questionned companies [1].
JDBC Driver
System Design
JDBC Driver
Downtime Cost
1. CPR, EAR, http://www.contingencyplanningresearch.com/cod.htm
2. Litwin, W., Moussa, R., Schwarz, T.J.E.: LH*RS - a highly available scalable
distributed data structure. ACM Trans., (2005)
3. Weatherspoon, H., Kubiatowicz, J.D.: Erasure Coding vs. Replication: A
quantitative Comparison. Proc. of the 1st International Workshop on P2P
Systems, (2002)
4. Cecchet, E., Marguerite, J., Zwaenepoel, W.: C-JDBC Flexible Database
Clustering Middleware. USENIX, (2004)
Further Information
URL: http://rim.moussa.googlepages.com/hddbrs_mid_project.html
Email: rim.moussa@googlepages.com
ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, HONG KONG, 2009