PostgreSQL Enhancement
PopSQL
Daniel Basilio, Eril Berkok
Julia Canella, Mark Fischer
Misiu Godfrey, Andrew Heard
Presentation Overview
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Problem
Enhancement
Implications of our proposal
SAAM Analysis
o Chosen Approach
o Alternate Approach
Use Case
Concurrency and Team Issues
Testing Impact
Lessons Learned
Limitations
Conclusion
Enhancement: The Problem
• A commonly referenced problem of PostgreSQL is its
limited scalability
• Because the postmaster MUST be on the same machine as
all back end instances, PostgreSQL cannot scale out back
end instance processing. Currently, the only solution is to
scale up.
• This limits the amount of users who can connect to a
PostgreSQL database at any given time.
Enhancement: The Solution
• PostgreSQL already allows data to be spread out over
machines. This functionality is controlled primarily by the
access subsystem.
• Our idea: Allow postgreSQL to distribute query processing
across multiple machines.
• Because every machine needs to access the same data, act
on the same table locks, and return the same results several changes will have to be made to the PostgreSQL
architecture
Enhancement: Implications
• In order to realize our enhancement, several changes need
to be made to the existing architecture, primarily:
o The Client Communications Manager and Server Process
subsystems needs to be able to remotely create Backend
Instances and connect the client to remote machines
o The Data Control subsystem needs to be replicated
through all machines with processing capabilities, and
kept up to date, so that all machines know where the
data are
New Conceptual Architecture
Diagram 1: New
Conceptual
Architecture
Changes to Postmaster
• After implementing our enhancement, there are two cases
the postmaster must consider:
o If the backend instance is created on the same machine
as the postmaster, then nothing changes.
o If the backend instance is created on a new machine,
then the postmaster has to forward new connection
information to the client so that they can re-connect to
a new machine
• How does the postmaster know where to create new
backend instances? Answer: Load Balancing
Load Balancer Subsystem
• A new subsystem designed to balance the clients and
workload assigned to each machine
• The Load Balancer receives CPU usage stats from each
machine (generated within the new Data Sync Subsystem
within Database Control) periodically so it is aware of the
state each machine is in
• The Load Balancer will replace the Postmaster in talking to
the client. When connected to, it chooses the machine
with the smallest CPU usage and tells that machine to
create a new Backend Instance. It then forwards contact
information back to the client and disconnects.
Load Balancer and Creating a New
Backend Instance
Diagram 2:
Shows the data
flow amongst
the Load
Balancer and
the Server
Processes
scaled out on
different
machines
CPU Maxing Issues
• If a machine's CPU consumption reaches its configured
maximum, it will not be given new clients by the Load
Balancer.
• Machines which are being used more heavily than first
assumed, can put in a request with the Load Balencer to
have one of its current users re-connect to another
machine. This request is activated by the Data
Synchronization subsystem.
• Requests are granted or denied depending on the number
of other machines and their respective workloads.
What happens if a machine dies?
• If a machine dies the Load Balancer will not receive the
CPU usage stats from the machine.
• It will then know that the machine has died and needs to
be fixed and that no new clients should be directed there
• This works if the machine is dedicated only to processing
• If the machine holds necessary storage data, other
machines will be unable to access it and will return a
server error
• If the central server machine (the initial point of access)
dies, the system, like the pre-enhancement system, will
not be able to receive any connections.
The States of a Machine
Diagram 3: The
states of a
machine
SAAM Analysis
Stakeholders
Non-Functional
Requirements
PostgreSQL
Development Group
Maintainability,
Scalability, Portability,
Manageability
Companies that use
PostgreSQL &
Stakeholders in that
company
Reliability, Scalability,
Performance, Security,
Usability
User of PostgreSQL
powered software
Reliability,
Performance
First Approach: Forward Pointers
• As an alternative to the bulletin board, it was proposed
that if a machine did not know where certain data live, it
would instead ask its neighbor, who would perform the
same process
• This system avoids the use of shared memory, but has the
"worst case scenario" of a machine needing to query every
other machine before it can update its map of the data
system.
• This could cause significant time lag and it was decided
that a small portion of shared memory would be necessary
• Doesn't include a convenient way to implement
synchronized statistics.
Advantages/Disadvantages
Performance - Worst case: if the data do not exist will go through
every machine. Very slow.
Reliability - Load Balancer will prevent machines from overloading
Scalability - Splitting Backend Instances from Postmaster onto
different machines allows horizontal scaling out. No shared memory.
Manageability - Troubleshooting becomes more difficult as more
machines add complexity
Security - Still only one point of entry to system
Affordability - Ease to implement forward pointing Access Managers
Chosen Approach: Bulletin Board
Subsystem
• The bulletin board subsystem is a repository that maintains a
listing of all data locations, updates statistical changes.
• When a machine creates a new table, or shifts a data location, it
posts an update on the bulletin board for all machines to see.
• When each machine gets an update from the board, it will
increment a counter.
• When all computers have checked an update it can be removed
from the bulletin board, minimizing its size.
• Computers will poll the bulletin board at fixed intervals to remain
up to date.
• Also, if a computer does not know the location of a piece of data
it will check the bulletin board before determining that it does not
exist.
Advantages/Disadvantages
Performance - Worst case only has to check Bulletin Board
Reliability - Load Balancer will prevent machines from overloading
Scalability - Splitting Backend Instances from Postmaster onto
different machines allows horizontal scaling out. Bulletin Board still
has to be scaled up, but it is comparatively small
Manageability - Troubleshooting becomes more difficult as more
machines add complexity
Security - Still only one point of entry to system
Affordability - Difficult to implement repository style of Bulletin
Board
Changes to Access Control
• If all Backend Instances over multiple machines tried to
contact a central Access Manager a bottleneck would
occur on the machine
o so each machine will have its own Access Manager that
has control over the data on that machine
• If a different Access Manager needs to access data on a
different machine, must talk to that machine's Access
Manager
o therefore all Access Managers must be able to
communicate with each other
Data Synchronization Subsystem
• New subsystem in charge of maintaining synchronized data
• Maintains tables of where data live
• One Data Sync subsystem will contain the bulletin board
for data updates between Access Managers and every Data
Sync subsystem will be able to consult that bulletin board
• Updates statistics and keeps tables up to date
• Since locks are maintained by whichever machine the
retrieved data is sitting on, this subsystem does not have
to worry about the current lock-state of data in the system
Access Control
Diagram 4:
Shows the
data flow
within the
Database
Control
subsystem
and the
connection
between
Access
Managers on
different
machines
Concurrency Issues
• Concurrency issues were solved in the original PostgreSQL
by using MVCC and the lock manager
• The main reason to have the accesses talk to each other is
that if a piece of data is locked by its local access
(whether because that machine or others are using it) then
other's cannot also use it, which keeps MVCC and locks as
they currently are.
• This adds no new concurrency issues to PostgreSQL
Team Issues
• Team issues are handled primarily by seperation of
functionality such that different developers can change
functionality without effecting changes elsewhere.
• Since the Access subsystem already acts as a facade for the
backend processes, our changes to Access should not affect
other subsystems.
• Because separation of functionality is maintained, team
issues after our improvement is implemented should
remain the same as they were before.
Use Case
Diagram 5: Establishing connecting between user and Backend
Instance
Testing impact of enhancement with
other features
• Regression Testing is required to test that nothing has
been broken and no integration problems have occured
• Stress test to see if the Load Balancer actually works in
preventing overloaded machines
• Test that the Access Manager can handle finding data that
is not on its own machine by referring to the Bulletin
Board.
Lessons Learned
• There are many different ways to architecturally
implement one change
• Worst case scenarios for implementations must be
considered
• Choosing an exact implementation took a while since so
many possibilities and trade offs between performance and
ease of implementation had to be considered
• Effective distribution is difficult to build into a legacy
system and should typically be planned from the start
Limitations
• Difficult to know if new implementation will cause
integration issues among subsystems
• Had to assume systems could have certain functionality,
for example: Access Manager subsystems could talk to each
other across machines
• Objects such as the Bulletin Board and Load Balancer will
still need to scale up as the project scales out because
they contain an amount of information proportional to the
amount of machines in the system
Conclusions
• High level ideas for Implementation developed quickly but
deciding on low level implementation proved to be more
difficult
• The performance of the system proved to be the largest
differentiator between the two implementations we chose
between
• This improvement will be invaluable to PostgreSQL users
because the system will now be able to scale to handle
extremely large loads