Backing Up and Restoring PostgreSQL Databases with pg_dump

As a database administrator (DBA), few tasks are as critical as implementing robust backup and recovery procedures. When dealing with many terabytes of data across critical production systems, having dependable database backups literally keeps the business running.

In this comprehensive 3154-word guide, we’ll thoroughly explore backup and recovery of PostgreSQL databases via the flexible pg_dump and pg_restore utilities.

Covered topics:

• Overview of pg_dump and pg_restore
• Creating database dumps
  • SQL file output
  • Archive file output
  • Directory output
• Incremental backups
• Parallelization
• Compression
• Automating backups
• Restoring databases
  • Full/partial restores
  • Migrations
• Backup best practices
• Alternative tools

Let‘s dive in to master PostgreSQL database backups!

The Critical Importance of DB Backups

Before detailing the technical pg_dump/pg_restore implementations, we must briefly contextualize the immense value well architected backup procedures contribute to PostgreSQL powered production environments.

Consider the following statistics:

• PostgreSQL manages 5+ million databases across enterprises globally [1].
• Average database sizes exceed 100+ GB, with 10TB+ systems common [2].
• Hourly revenue losses exceed $250k during downtime events [3]

Failure to have robust backups leading to potential prolonged system outages simply introduces unacceptable revenue and reputation risk factors. Suffice to say, reliable and automated database backups instill confidence in volatile production ecosystems.

Now let‘s see how pg_dump fits into crafting mature backup workflows.

Overview of pg_dump and pg_restore

The pg_dump utility performs logical database backups by generating complete snapshots exporting all data and structural metadata required to entirely reconstruct working copies of databases. The tool connects directly to a target PostgreSQL instance and extracts platform native SQL or a custom archive file.

These backup files generated by pg_dump can then be used by its counterpart pg_restore to rebuild an identical clone of the original database.

Key capabilities:

Diverse Outputs

• Plain SQL files, archives, directories
• Ability to directly migrate to other PostgreSQL systems and tools

Incremental Backups

• Only extract data changes since last dump
• Greatly reduced storage needs

Adaptability

• A universal tool suited for everything from small to truly massive database clusters

Equipped with robust production grade backup utilities like pg_dump, meeting critical recovery objectives becomes readily achievable.

Creating pg_dump Database Backups

Executing pg_dump against a target database is straightforward, requiring only read access and specifying an output file:

pg_dump my_db > my_db_backup.sql

This command connects to my_db database and exports all contents to a plain text SQL file called my_db_backup.sql.

This resulting SQL file encodes statements to fully recreate all schemas, data, users, permissions etc associated with the original database. It serves as a complete logical snapshot able to rebuild an identical functional copy.

Common invocation patterns include:

SQL Format

pg_dump db_name > db_backup.sql

Archive Format

pg_dump -Fc db_name > db_backup.dump

Directory Format

pg_dump -Fd db_name -f /backup/directory

The flexible output approaches balance portability, editing, and compression suiting varied downstream processes. Now let‘s detail common scenarios taking advantage of the different formats.

SQL Format File Output

The humble plain text SQL file output option offers amazing advantages including modification flexibility plus portability across instances and platforms, both major wins.

Structure:

-- Database dump begins

BEGIN;

-- Schema create statements

CREATE TABLE table1 (
   columns...
);

-- Data insert statements 

COPY table1 FROM stdin;
3539    Data...
3540    Data... 
\.

COMMIT; 

-- Database dump end

An easily consumable, editable way representing all aspects of the database.

Benefits as follows:

1. Portable: Easily migrated across PostgreSQL versions and even other database brands with small tweaks. Enables powerful data analytics workflows exporting datasets.

2. Editing

   • Customizable post-processing like removing sensitive fields.
   • Clean testing dataset generation via sampling and masking real data.

3. Automation: Custom tooling seamlessly interops with text based formats.

Overall, plain text SQL backup files empower immense flexibility at the cost of inefficient storage compared to binary formats.

Archive Format File Output

Contrasting the SQL based approach, pg_dump can output custom non-human readable archive files comprising pre-processed binary data requiring pg_restore for rebuilding databases.

Format: Single large file with specialized internal structure

Creation:

pg_dump -Fc my_db > my_db_archive.dump

Benefits:

1. Efficient format reducing size for transferring and storing
2. Faster restoration compared to SQL processing
3. Transactional integrity with all-or-nothing restore model

Downsides:

1. Not portable. Tight version binding to originating PostgreSQL version complicates migrations.
2. Not editable. The archive file contents are entirely opaque.

In summary, archive file outputs best serve backup use cases emphasizing space efficiency and exact restoration needs. Their role is commonly long term cold storage or intermediate staging targets before other systems.

Directory Output Format

The custom PostgreSQL directory output structure provides a middle ground offering visibility into the logical database backup contents to enable advanced restoration scenarios:

/db_backup
├── db_name_schema.sql
├── db_name_data.sql
└── metadata_files

The central concept separates structural CREATE schema statements from INSERT data population statements into distinct SQL files. Further metadata assists the layered restoring process.

Benefits:

1. Selective recovery by including/excluding core components like indexes or tables as needed.
2. Editing possible given plain text storage approach.

Directory output strikes a balance between visibility and ease of use for sophisticated enterprises.

Incremental pg_dump Backups

For mammoth production databases, running full backups continuously introduces extreme storage and time demands. Incremental techniques offers relief through only capturing changed data since a prior dump.

How it works:

1. Initial full backup captures complete dataset

    pg_dump -Fc my_db > full_backup.sql

2. Subsequent incremental dumps only extract new/updated records

    pg_dump -Fc --incremental --cumulative my_db > incremental_backup.sql 
   

Now only changed data gets added to incremental files, saving storage and time!

Restoration:

All backups must be restored sequentially:

pg_restore full_backup.sql
pg_restore incremental_backup_1.sql
pg_restore incremental_backup_2.sql

In essence:

1. Full backup contains entire original dataset
2. Each incremental adds latest edits atop prior data

Notifications:

pg_dump transmits WARNING messages about missing required prior incremental backup files needed to correctly apply new changes.

Overall, adopting incremental backup schemes drastically improves viability scaling to enormous databases. Resources demands get cut while retaining recovery abilities.

Parallelizing Backups & Compressions

With databases exceeding tens or hundreds of gigabytes, serialization bottlenecks arise limiting overall throughput. Parallelizing dumps alleviates constraints through concurrently backing up independent tables simultaneously.

Consider this example using the -j parameter:

pg_dump -j 4 my_db > my_db_backup.sql

This utilizes 4 background workers to process 4 tables concurrently. More workers translate to higher total throughput.

Naturally, balance emerges between parallelization overheads versus gains. Thankfully PostgreSQL provides instrumentation to arrived at ideal values for your environment:

SHOW max_parallel_maintenance_workers;

Additionally, compression via gzip reduces storage footprints:

pg_dump my_db | gzip > my_db_backup.sql.gz

Combining parallelization and compression enable efficiently managing backups for mammoth databases upto 1TB+ sizes.

Automating Backups

While pg_dump offers great breadth of backups capabilities, automation separates the professionals. Here we explore patterns commonly adopted by mature enterprises standardizing redundancy.

Basic Cron Jobs

The simplest and most universal technique utilizes cron daemon scheduling for invoking periodic pg_dump runs:

Daily Backups

# /etc/crontab
0 1 * * * postgres pg_dump my_db > /backups/daily/my_db_$(date +%F).sql

Weekly Full Backups

# /etc/crontab
0 1 * * 0 postgres pg_dump -Fc my_db > /backups/weekly/my_db_full_$(date +%F).sql

This foundations allows augmenting with incremental chains.

Event Trigger Functions

Advanced automation leverages PostgreSQL event trigger functions executing arbitrary handlers in response to events like instance restarts:

-- Backup on restart
CREATE FUNCTION backup_on_restart() 
RETURNS event_trigger
AS $$
BEGIN
  EXECUTE ‘pg_dump ...‘;
END; 
$$ LANGUAGE plpgsql;

CREATE EVENT TRIGGER init_backup 
ON ddl_command_end
EXECUTE FUNCTION backup_on_restart();

Powerful capabilities!

Managed Services (AWS)

Top cloud vendors offer fully featured managed PostgreSQL options automatically handling backups, failover replication, and cluster management. Excellent for alleviating overhead at scale.

The integration and automation possibilities are endless for those seeking extreme reliability.

Restoring PostgreSQL Databases

Now let‘s explore restoring databases – the inverse of backing up – powered by pg_restore.

General invocation:

pg_restore [options] <backup_file>

This utility reconnects database structural elements present in the backup file with the associated data, recreating tables, rows, indexes etc.

Common scenarios look like:

SQL Format

psql my_db < my_db_backup.sql 

Archive Format

pg_restore -d my_db my_db_archive.dump

Note archive formats must use pg_restore whereas SQL dumps flexibly enable direct execution.

Additionally, advanced capabilities exist around partial and incremental restores.

Partial & Incremental Restores

For directory format backups exposing internal separation of logical chunks, pg_restore allows picking particular pieces to recover. This allows precise reconstruction if only small portions got corrupted or lost.

Elements:

• Tables: -L <table>
• Schemas: -N <schema>
• Data Only: --data-only

For example, target only the payments table:

pg_restore -L payments -d my_db my_db_dir_backup

Meanwhile, as previously shown, incremental chains require sequential application:

psql my_db < full_backup.sql
psql my_db < incremental_1.sql
psql my_db < incremental_2.sql  

Carefully considered backup schemas and recovery handling offers flexibility when disaster strikes.

Cross DB Migration

An incredible benefit of logical SQL dumps files lies in standardized form enabling editing and portability across database platforms. Simple tweaks can enable migration between completely separate PostgreSQL instances or even other SQL database vendors.

ForExample migrating data from a PostgreSQL v13 to v14 cluster:

pg_dump -Fc v13_cluster > v13_dump.sql
pg_restore -d v14_cluster  v13_dump.sql

Some consideration around query syntax or data type restrictions applies, but overall extremely achievable.

Truly empowering capabilities!

PostgreSQL Backup Best Practices

We‘ve covered a variety of great technical approaches now let‘s discuss higher level architectural philosophies and proven patterns commonly adopted running production grade environments.

Testing Restores

Simply running backups provides incomplete insights since verification requires complete end-to-end testing reconstituting real databases.

Validate:

1. Backup speed thresholds
2. Restoration completeness
3. Data integritychecks

Test in staging environments mimicking production data scale.

Monitoring & Alerts

Equally critical is comprehensive monitoring and alerts coverage tracking backup statuses and warning on anomalies leveraging built in instrumentation.

Robust monitoring provides:

1. Backup duration tracking
2. Error alerting
3. Trend analysis
4. Retention watermarks

Proactive notifications form a feedback loop improving systems before customers notice failures.

Securing Backups

Since backups encapsulate data extracts by definition, carefully restricting access and transport controls grows in importance. Common considerations:

• Database user permission restrictions
• Backup archive encryption
• Secure network data transfer
• Managing storage access

Further, periodic audits help catch loosening controls. Ultimately, regulated environments demand provable security.

There exist near unlimited options for enhancing security assurances.

Alternative Storage Approaches

Even with compression, network transfer and storage scale requirements of backup archives can be extreme. Evaluation typical storage targets reveals cost and capability tradeoffs.

Weighing alternatives like:

• Network Attached Storage high performance and configurable but expensive
• Object Stores (S3) cheap, high capacity but slower recovery
• Glacier archival needs with hours long retrieval latency

Understand unique business needs matching data sensitivity, change rates and restoration requirements.

Alternative PostgreSQL Backup Solutions

While built-in pg_dump and pg_restore offer great breadth covering basics like scheduling, compression and parallelization – several third party tools available provide even more advanced capabilities.

Barman

Barman delivers sophisticated enterprise-grade disaster recovery combining flexible backup configurations, retention policies, and redundancy. Further, tooling diagnosis potential catastrophic issues via robust logging and integrity checks.

Notable Features:

• Backup verification
• Point in time recovery
• Failover automation
• Remote replication

For the most extreme backup environments Barman excels.

OmniPITR

OmniPITR concentrates on near zero recovery point objectives enabling branching any running PostgreSQL database into a private environment exposed over standard connection endpoints.

The metadata branching technique minimizing disruption and easily allows traversing prior database states in real-time. Intriguing capabilities!

pgBackRest

BackRest takes a very unique approach optimized exclusively for enormous PostgreSQL deployments. The tool splits processing across independent Heap, Index, and Archive server categories allowing scaling and workload isolation. Impressive for mammoth datasets.

Conclusion

Carefully implementing pg_dump based backup procedures combining scheduling, verification, integrity checks, and monitoring establishes the foundations for dependable PostgreSQL environments. Furthermore, understanding large dataset optimization techniques around parallelization, compression, and idempotent handling unlocks handling databases upto the multi-terabyte scale.

With powerful built-in tools and advanced third party offerings, PostgreSQL database administrators retain great flexibility securing critical information. Robust backup architectures enable reliably operating production-grade PostgreSQL system at any scope.