Beginners Guide to High Availability for Postgres

Beginners Guide to
High Availability for
Postgres
Presented by:
Gaby Schilders, Sr. Sales Engineer
18 November 2020

Slides and recording will be available in next 48 hours
Please do submit your questions through the chat – answers follow during Q&A
We will be sharing information about EDB and PostgreSQL later on in the session
Welcome – Housekeeping Items

© Copyright EnterpriseDB Corporation, 2020. All rights reserved.3
Agenda
1. Concepts of High Availability
2. RPO, RTO and Uptime in High Availability
3. How does High Availability work?
4. High Availability for Postgres using
• Streaming Replication
• Logical Replication
5. Postgres parameters for High Availability (Streaming
Replication)
6. EDB tools for High Availability management and
monitoring

High Availability (formally)
High availability (HA) is a characteristic of a system, which aims to ensure an agreed level of
operational performance, usually uptime, for a higher than normal period.
Key principles:
• Eliminate single point of failure
• Reliable crossover
• Detection of failures
Ref: https://en.wikipedia.org/wiki/High_availability

High Availability (practically)
High availability (HA) in computing is the continuous and consistently correct functioning of a
system or service as provided to (end)users
But what is this factor measured on?
• Hardware
• IT infrastructure
• Business application

Scheduled/Unscheduled downtime
• Scheduled/planned downtime is a result of maintenance that is disruptive to system
operation and usually cannot be avoided with a currently installed system design.
• It include patches to system software that require a reboot or system configuration
changes that only take effect upon a reboot.
• Unscheduled/Unplanned downtime is the result of downtime events due to some physical
failures/events, such as hardware or software failure or environmental anomaly.
• For example, power outages, failed CPU or RAM components (or possibly other
hardware components failure), network failure, security breaches, or various
applications, middleware, and operating system failures result in Unplanned
outage/Unscheduled downtime.

Availability calculation
Calculated/expressed as a percentage of uptime in a given year based on the service level
agreements. Some companies exclude the planned outage/scheduled downtime based on their
agreements with customers on the availability of their services.
Availability % Downtime per year Downtime per month
Downtime per
week Downtime per day
99.99% ("four nines") 52.60 minutes 4.38 minutes 1.01 minutes 8.64 seconds
99.995% ("four and a half
nines") 26.30 minutes 2.19 minutes 30.24 seconds 4.32 seconds
99.999% ("five nines") 5.26 minutes 26.30 seconds 6.05 seconds
864.00
milliseconds

Recovery Point Objective (RPO)
RPO is a measurement of time from the failure, disaster or comparable loss-causing event as seen
from the system being measured over.
RPO is described as:
• The period of time from the data-loss event back to the last point where data is in a usable
format
• How frequently you need to back-up your data (RPO doesn’t represent additional factors like
restore time and recovery time as we’ll discuss later on)
• How much data is lost following a disaster or loss-causing event (in volume, for instance)

Recovery Time Objective (RTO)
The amount of time an application can be down and not result in significant damage to a business
and the time that it takes for the system to go from loss to recovery
Recovery process includes
• The steps that IT must take to return the application
• And its data to its pre-disaster state.

RPO vs. RTO
RPOs and RTOs are key concepts for maintaining business continuity and function as business
metrics for calculating how often your business needs to perform data backups.
• RTOs coincide with recovery point objectives (RPOs), a measurement of time from the
failure, disaster or similar loss-causing event.
• RPOs calculate back in time to when your data was last usable, probably the most recent
backup.

Mean Time To Recover (MTTR)
The average time that a device will take to recover from any failure. systems which have to be
repaired or replaced.
• Examples of such devices range from self-resetting fuses (where the MTTR would be very
short, probably seconds), up to whole systems which have to be repaired or replaced.
• Usually part of a maintenance contract, where the user would pay more for a system MTTR
of which was 24 hours, than for one of, say, 7 days
• Does not mean the supplier is guaranteeing to have the system up and running again within
24 hours (or 7 days) of being notified of the failure.

Geography Recovery Objectives (GRO)
If datacenter becomes unavailable, how long it takes for the service to become available again.
• It covers RPO/RTO for making services available across the geography.

High Availability
For Postgres

Eliminate Single
Point of Failure

Eliminate Single Point of failure
• WAL shipping based replication
• Replication based on the archived WAL
• Streaming replication (SR)
• Streaming WAL files to one or more standbys
• Logical replication
• Streaming logical data modifications from the WAL.

• Identical to primary system
• Data is still mirrored in real time
• Allows READ
• On failure, can replace primary
• Approaches
• WAL shipping based
• Streaming WAL (widely used after 9.0)
Hot Standby

Hot Standby: WAL shipping

Monitor: WAL shipping
• Functions on standby
• pg_is_in_recovery()
• pg_last_xlog/wal_replay_location/lsn()
• pg_last_xact_replay_timestamp()

Hot Standby: Streaming Replication

Streaming Replication
• Asynchronous Streaming Replication
• Synchronous Streaming Replication
• synchronous_standby_names
E.g.
• FIRST 1 (standby_east, standby_west)
• ANY 3 (standby_east, standby_west, eu_standby_east, eu_standby_west)
• 'standby_east, standby_west'
• synchronous_commit - off/local/remote_write/on/remote_apply

Monitor: Streaming Replication
• Views
• Primary: pg_stat_replication
• Standby: pg_wal_receiver

Reliable
CrossOver &
Detection

Reliable Crossover & Detection
• In a redundant system, the crossover point itself becomes a single point of failure.
• Fault-tolerant systems must provide a reliable crossover or automatic switchover
mechanism to avoid failure.
• Detection of failures:
• If the above two principles are proactively monitored, then a user may never see a
system failure.

EDB Postgres Failover Manager:
• Continuously monitors your PostgreSQL service to automatically detect failures.
• After an outage is confirmed, Failover Manager automatically promotes the most up-to-date
standby database as the new master.

EDB Postgres Failover Manager:

RPO/RTO/MTTR/GPO
Backup And Recovery

RPO/RTO/MTTR/GPO
Backup And Recovery Tool

High Availability Monitoring
Postgres Enterprise Manager

Maintenance
Window/
Planned
Downtime

Maintenance Window/Planned Downtime
Software Updates/Patching
• Three reasons for software updates
• Remedy known software issues
• General stability and reliability of the software
• Security problem

Maintenance Window/Planned Downtime
Software Updates: Strategies
• Three strategies
• All Nodes Patching
• Rolling Patching
• Minimum Downtime Patching

Conclusion
• High Availability components
• Hot Standby (Streaming Replication)
• EDB Postgres Failover Manager
• Postgres Enterprise Manager
• Backup And Recovery Tool
• Design consideration
• Near zero downtime software maintenance
• RPO/RTO/GRO

Resources
• Blog series
• What Does High Availability Really Mean
• Patching Minor Version in Postgres High Availability (HA) Database Cluster
• Plans & Strategies for DBAs
• Key Parameters and Configuration for Streaming Replication in Postgres 12
• Quick and Reliable Failure Detection with EDB Postgres Failover Manager

• Enterprise PostgreSQL innovations
• 4,000+ global customers
• Recognized by Gartner Magic Quadrant for 7 years in a row
• One of the only sub-$1bn revenue companies
• PostgreSQL community leadership
2019
Challengers Leaders
Niche Players Visionaries
Abilitytoexecute
Completeness of vision
1986
The Design
of PostgreSQL
1996
Birth of
PostgreSQL
2004
EDB
is founded
2020
TodayMaterialized
Views
Parallel
Query
JIT
Compilation
Heap Only
Tuples (HOT)
Serializable
Parallel Query
We’re database fanatics who care
deeply about PostgreSQL
Expertise

Market Success

Core team Major contributors Contributors
EDB Open Source Leadership
Named EDB open source committers and contributors
Akshay Joshi Amul Sul Ashesh Vashi Ashutosh Sharma Jeevan Chalke
Dilip Kumar Jeevan Ladhe Mithun Cy Rushabh Lathia Amit Khandekar
Amit Langote Devrim Gündüz
Robert Haas
Bruce Momjian
Dave Page
Designates PostgreSQL committers

Learn More
Other resources
Thank You
Next Webinar: November 25
An overview of reference architectures for
Postgres by Michael Willer
Postgres Pulse EDB Youtube
Channel

Beginners Guide to High Availability for Postgres

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