highly available distributed databases (poster)
1
Project Goals Implement a reliable, scalable, portable, full-featured high availability solution for distributed databases, conformant with open standards. 18 TH ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT , HONG K ONG, 2009 Optimize parity updates using Jserver Performance Test using TPC-C bench in both failure and safe modes [Khediri MSc Project] Automatic increase of a group high availability level Distributed highly available DB which autoscale over a cluster Future Work • Script to create table fragments fon each DB instance. • DB population. DB Set up & Population • Search item with key i_id Key Search • Either by deleting of up to k fragments contents or by shutting down corresponding DB instances Simulate k Servers Failure • Recover item with key i_id Record Recovery • Set up k spares • Query alive servers • Decode • Insert recovered data into spare servers Recover k Servers References Further Information Demonstration Outline 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 1 st International Workshop on P2P Systems, (2002) 4. Cecchet, E., Marguerite, J., Zwaenepoel, W.: C-JDBC Flexible Database Clustering Middleware. USENIX, (2004) URL: http://rim.moussa.googlepages.com/hddbrs_mid_project.html Email: [email protected] Performances Testbed: Oracle DBMS, 1.7GHz CPU on DB 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: 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. HDDB RS MIDDLEWARE FOR IMPLEMENTING HIGHLY A VAILABLE DISTRIBUTED D ATABASES RIM MOUSSA, PHD UTIC LAB. , TUNISIA Middleware Architecture Multithreading 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. JDBC Interface with DB backends. XA/open standard (2-PC protocol) for distributed transaction management. RMI for client transaction processing. System Design 3-tier distributed architecture 1. Client Not aware of data distribution Not aware of data redundancy 2. HDDB RS Mid-tier Redundant data management Recovery process (records and fragments) Failure detection … 3. DB backends DB group k-available composed of m source DB instances and k parity DB instances. Tables horizontally fragmented. Surjective function for record grouping. Downtime Cost High-availability Methods Demonstrated Configuration: 2-available group of 4 source DB instances and 2 parity DB instances (m = 4, k = 2) . Item Each item is 3KB. Oracle DBMS instances up to $50KpH, 46% between $51KpH and $250KpH, 28% between $251KpH and $1MpH, 18% more than $1MpH, 8% A survey conducted by the CPR and ERA, in 2001 shows important downtime cost per hour for questionned companies [1]. REED SOLOMON ERASURE CODES REPLICATION High Storage Cost Quick Recovery Load Balancing Minimal Storage Overhead Encoding/ Decoding Overhead Data Stripping . . . . . . JDBC Driver JDBC Driver JDBC Driver JDBC Driver DBCT0 Work. Queue DBCTm-1 Work. Queue DBCTm Work. Queue DBCTn Work. Queue DB Backends Spare Spare JDBC Driver JDBC Driver DBCTj Work. Queue DBCTi Work. Queue Fig. 1. Middleware Architecture.
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Transcript of highly available distributed databases (poster)
Project Goals
Implement a reliable, scalable, portable, full-featured high availability solution for distributed
databases, conformant with open standards.
18TH ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, HONG KONG, 2009
Optimizeparityupdates usingJserver
Performance Test usingTPC-C benchin both failureand safemodes [Khediri MScProject]
Automaticincrease of a group highavailabilitylevel
DistributedhighlyavailableDB whichautoscaleover a cluster
Future Work
• Script to createtable fragments fon each DB instance.
• DB population.
DB Set up & Population
• Search item withkey i_id
Key Search• Either by deleting of
up to k fragments contents or by shutting down corresponding DB instances
Simulate k Servers Failure
• Recover item withkey i_id
Record Recovery • Set up k spares
• Query alive servers
• Decode
• Insert recovereddata into spareservers
Recover kServers
References
Further Information
Demonstration Outline
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)
URL: http://rim.moussa.googlepages.com/hddbrs_mid_project.html
Email: [email protected]
Performances Testbed: Oracle DBMS, 1.7GHz CPU on DB
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:
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.
HDDBRS MIDDLEWARE FOR IMPLEMENTING HIGHLY AVAILABLE DISTRIBUTED DATABASES
RIM MOUSSA, PHD
UTIC LAB. , TUNISIA
Middleware Architecture
Multithreading
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.
JDBC Interface with DB backends.
XA/open standard (2-PC protocol) for
distributed transaction management.
RMI for client transaction processing.
System Design
3-tier distributed architecture
1. Client
Not aware of data distribution
Not aware of data redundancy
2. HDDBRS Mid-tier
Redundant data management
Recovery process (records and fragments)
Failure detection …
3. DB backends
DB group k-available composed of m source
DB instances and k parity DB instances.
Tables horizontally fragmented.
Surjective function for record grouping.
Downtime Cost
High-availability Methods
Demonstrated Configuration:
2-available group of 4 source
DB instances and 2 parity DB
instances (m = 4, k = 2) .
Item
Each item is 3KB.
Oracle DBMS instances
up to $50KpH, 46%
between $51KpH and $250KpH, 28%
between $251KpH and $1MpH, 18%
more than $1MpH, 8%
A survey conducted by the CPR and ERA, in
2001 shows important downtime cost per
hour for questionned companies [1].
REED SOLOMON
ERASURE
CODES
REPLICATION
High Storage Cost
Quick Recovery
Load Balancing
Minimal Storage Overhead
Encoding/ DecodingOverhead
Data Stripping
.
.
.
.
.
.
JDB
C D
rive
rJD
BC
Dri
ver
JDB
C D
rive
rJD
BC
Dri
ver
DBCT0 Work. Queue
DBCTm-1 Work. Queue
DBCTmWork. Queue
DBCTnWork. Queue
DB
Bac
ken
ds
Spare Spare
JDBC Driver JDBC Driver
DBCTjWork. QueueDBCTiWork. Queue
Fig. 1. Middleware Architecture.
