Master PostgreSQL Effortlessly with Your Oracle Expertise

or seasoned Oracle professionals, diving into PostgreSQL can feel like entering a familiar yet distinct realm. While both databases are powerful, their architectures, terminologies, and tuning approaches differ in ways that can initially seem challenging.

But here’s the good news: if you know Oracle well, you already have a strong foundation to grasp PostgreSQL. By understanding how concepts like RowID maps to ctid, SCN translates to LSN, or how Undo is replaced by MVCC, you can quickly align your expertise with PostgreSQL’s ecosystem. This guide bridges the gap, offering a comprehensive comparison of terminology, tools, and configurations to help you seamlessly transition from Oracle to PostgreSQL. Whether you’re exploring backup and recovery, SQL tuning, or performance optimization, this blog will show you how PostgreSQL mirrors — and innovates upon — the Oracle features you know so well. Let’s decode PostgreSQL, one familiar Oracle concept at a time.

Lets dive deeper into Oracle-to-PostgreSQL comparison, focusing on areas like Backup and Recovery, SQL Tuning, EXPLAIN Plans, and Performance Tuning. This version dives deeper into these critical areas to help Oracle DBAs understand and adopt PostgreSQL effectively.

Oracle vs. PostgreSQL: A Comprehensive Comparison

Backup and Recovery

Key Considerations:

• Oracle’s RMAN is a robust, integrated tool, while PostgreSQL relies on combinations of pg_basebackup, WAL, and manual scripting.
• PostgreSQL’s PITR requires careful management of WAL archives, using parameters like archive_timeout for timely WAL generation.

When comparing Oracle’s RMAN (Recovery Manager) to PostgreSQL’s backup and recovery mechanisms, it’s important to highlight how PostgreSQL achieves similar functionality using a combination of tools and configurations. Here’s a deeper dive into the PostgreSQL equivalent for RMAN:

PostgreSQL’s Backup and Recovery Compared to Oracle RMAN

1. Full Database Backup

• Oracle RMAN: Provides an integrated, incremental, and highly automated backup solution. RMAN offers features like block-level backups, compression, and encryption.
• PostgreSQL Alternative:
• pg_basebackup: Performs full physical backups, similar to RMAN’s full database backups. It is straightforward to use and integrates well with WAL archiving for point-in-time recovery (PITR).
• Tuning Parameters:
• — wal-method: Determines how WAL is archived (fetch or stream).
• — checkpoint: Ensures a consistent snapshot of the database during backup.

2. Incremental Backups

• Oracle RMAN: Supports incremental backups by only backing up changed blocks since the last backup, reducing backup size and time.
• PostgreSQL Alternative: PostgreSQL doesn’t natively support incremental backups, but you can simulate this using:
• WAL Archiving: Continuously archiving WAL files ensures that changes since the last full backup are captured.
• Third-Party Tools: Tools like pgBackRest and Barman offer incremental backup features for PostgreSQL.

3. Point-In-Time Recovery (PITR)

• Oracle RMAN: Allows seamless recovery to a specific SCN, timestamp, or transaction, using its tightly integrated features.

PostgreSQL Alternative:

• Combines pg_basebackup and WAL replay for PITR.
• Relevant Configuration Parameters:

archive_mode = on: Enables WAL archiving.

4. Backup Compression and Encryption

• Oracle RMAN: Supports built-in compression and encryption for backups.
• PostgreSQL Alternative:

Use external tools like gzip, zstd, or pgBackRest for compression.

5. Backup Scheduling

• Oracle RMAN: Offers built-in scheduling with job management via Oracle Enterprise Manager or SQL*Plus.
• PostgreSQL Alternative:

Use cron jobs, systemd timers, or tools like pgBackRest for automated backup scheduling.

0 2 * /usr/bin/pg_basebackup -D /backups/pg -F tar -z -P

6. Consistency and Recovery Validation

Oracle RMAN: Ensures backups are consistent and can validate backup integrity.

PostgreSQL Alternative:

pg_verifybackup: Verifies the integrity of a backup created by pg_basebackup.

7. Advanced Backup Features

• Oracle RMAN:
• Fine-grained backups (e.g., tablespaces or specific datafiles).
• Block media recovery for specific corrupted blocks.
• PostgreSQL Alternative:

Use logical backups via pg_dump for table-level or schema-level backups.

Key Considerations for PostgreSQL Backup and Recovery

1. Flexibility: PostgreSQL’s backup ecosystem is modular, allowing customization but requiring a mix of tools.
2. Scalability: For large databases, third-party tools like pgBackRest, Barman, or WAL-G offer enterprise-grade solutions with incremental backups, compression, and retention policies.
3. Ease of Use: Oracle RMAN is more integrated and user-friendly, while PostgreSQL’s approach demands more hands-on scripting and configuration.

Example PostgreSQL PITR Workflow

1. Perform a full backup using pg_basebackup:

pg_basebackup -D /backups/full -F tar -z -P — wal-method=stream

2. Enable WAL archiving:

archive_mode = on

3. Recover to a specific point in time: — Restore the base backup. — Set recovery parameters in postgresql.conf: recovery\_target\_time \= ‘2024–11–15 10:30:00’ restore\_command \= ‘cp /backups/wal/%f %p’ — Start the database in recovery mode: pg\_ctl \-D /data/pgdata start

While PostgreSQL’s backup and recovery tools might lack the integration of Oracle’s RMAN, their modularity and extensibility offer powerful capabilities when configured correctly. Leveraging tools like pgBackRest or Barman can close the gap, making PostgreSQL a strong contender for enterprise-grade backup and recovery solutions.

SQL Tuning

Key Considerations:

• PostgreSQL doesn’t use optimizer hints but allows cost-based tuning through parameters.
• For better SQL tuning:

Use random_page_cost to reflect storage speed (lower for SSDs).

Key Considerations for SQL Tuning in PostgreSQL

PostgreSQL’s approach to SQL tuning differs from Oracle in its reliance on a cost-based query planner and absence of optimizer hints. However, PostgreSQL provides powerful tools and parameters to optimize query execution. Here are the key considerations for SQL tuning in PostgreSQL, mapped to Oracle concepts where relevant.

1. Query Plan Analysis

Oracle: Uses EXPLAIN PLAN and AUTOTRACE to analyze execution plans.

PostgreSQL:

• EXPLAIN and EXPLAIN ANALYZE: Use EXPLAIN to view query plans and EXPLAIN ANALYZE for actual runtime statistics.

Key Features:

• Highlights the cost of each operation, including I/O, CPU, and memory.
• Reveals whether sequential or index scans are used.

Example:

EXPLAIN ANALYZE SELECT * FROM employees WHERE department_id = 10;

Use the VERBOSE keyword for detailed output, including planner decisions and rewrites.

Cost-Based Planner Configuration

Oracle: Optimizer hints guide the execution plan.

PostgreSQL:

• PostgreSQL doesn’t use hints but allows configuration of cost-based planner parameters.

Key Parameters:

• random_page_cost: Cost of random I/O (default is 4; lower for SSDs or fast storage).
• seq_page_cost: Cost of sequential I/O (default is 1; adjust based on I/O throughput).
• cpu_tuple_cost: Cost of processing a single tuple (row).
• cpu_index_tuple_cost: Cost of processing an indexed tuple.

Tuning Strategy:

• Lower random_page_cost for SSDs to encourage index scans.
• Adjust cpu_tuple_cost for CPU-intensive workloads.

3. Statistics and Data Distribution

Oracle: Uses optimizer statistics to choose the best execution path.

PostgreSQL:

• The query planner relies heavily on accurate statistics.

Key Commands:

• ANALYZE: Collects statistics for query planning.
• VACUUM ANALYZE: Cleans up dead tuples and updates statistics.
• CREATE STATISTICS: Generates extended statistics for column correlations.

Example:

CREATE STATISTICS stats_name (dependencies)
ON col1, col2
FROM table_name;
ANALYZE table_name;        

Tuning Tip:

• Increase default_statistics_target (default 100; higher for complex queries).

4. Indexing Strategies

Oracle: Supports various index types like B-Tree, Bitmap, and Function-Based Indexes.

PostgreSQL: PostgreSQL offers flexible indexing options:

• B-Tree Index: Default and suitable for equality and range queries.
• GIN/GIN Index: For full-text search or JSONB data.
• BRIN Index: Efficient for large tables with sequentially ordered data.
• Expression Index: Similar to Oracle’s Function-Based Index.

Example:

CREATE INDEX idx_salary ON employees (salary);
CREATE INDEX idx_upper_last_name ON employees (UPPER(last_name));        

Tuning Tips:

• Use indexes judiciously; too many can degrade performance on writes.
• Monitor index usage with pg_stat_user_indexes.

5. Parallel Query Execution

Oracle: Parallel query execution is built into the optimizer.

PostgreSQL:

• PostgreSQL supports parallel query execution for certain operations like scans, joins, and aggregates.

Key Parameters:

• max_parallel_workers_per_gather: Number of parallel workers for a query.
• parallel_setup_cost: Cost of initializing a parallel query.
• parallel_tuple_cost: Cost of processing a tuple in parallel.

Example:

SET max_parallel_workers_per_gather = 4;
SELECT SUM(salary) FROM employees;        

Tuning Tip:

• Increase max_parallel_workers_per_gather for large datasets but monitor CPU usage.

6. Temporary Tables and Work Memory

Oracle: Manages temporary tables and session memory through PGA.

PostgreSQL:

• PostgreSQL uses work_mem and temporary tables for intermediate query results.

Key Parameters:

• work_mem: Memory allocated for sort operations and hash joins. Default is 4MB; increase for large datasets.
• temp_buffers: Memory allocated for temporary tables.

Tuning Tip:

• Set work_mem based on session workload:

SET work_mem = '16MB';        

7. Query Caching

• Oracle: Uses a query result cache for frequently executed queries.
• PostgreSQL:
• PostgreSQL doesn’t have a built-in query result cache but caches query plans and pages in memory.

Relevant Settings:

• shared_buffers: Cache frequently accessed pages (default 25% of total memory).
• effective_cache_size: Estimates OS-level caching to guide planner decisions.

8. Monitoring and Identifying Bottlenecks

• Oracle: Uses TKPROF and dynamic performance views.

PostgreSQL:

• Use pg_stat_statements for tracking query performance:
• Install the extension:

CREATE EXTENSION pg_stat_statements;        

• Query slow statements:

SELECT query, calls, total_exec_time
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 5;        

Use EXPLAIN ANALYZE and pg_stat_activity to monitor query execution.

Key Takeaways for SQL Tuning in PostgreSQL

1. Understand the Query Planner: PostgreSQL relies on accurate statistics and cost-based planning rather than hints.
2. Optimize Memory Usage: Adjust work_mem and shared_buffers to improve query performance.
3. Index Wisely: Leverage the appropriate index type for your workload but avoid over-indexing.
4. Leverage Parallelism: Enable parallel queries for complex aggregations and large data scans.
5. Monitor and Analyze: Use tools like pg_stat_statements and EXPLAIN ANALYZE to identify and resolve performance bottlenecks.

These considerations empower Oracle DBAs to approach PostgreSQL SQL tuning with confidence, leveraging familiar concepts while adapting to PostgreSQL’s unique strengths.

EXPLAIN and Query Execution Plans

Key Considerations:

• PostgreSQL’s EXPLAIN ANALYZE is more analogous to Oracle’s AUTOTRACE.
• Use pg_stat_statements for global query performance tracking, similar to Oracle’s TKPROF.

Key Considerations for EXPLAIN and Query Execution Plans in PostgreSQL

PostgreSQL’s query execution plans, generated using the EXPLAIN and EXPLAIN ANALYZE commands, provide a comprehensive view of how queries are executed. While Oracle professionals might rely on tools like EXPLAIN PLAN and AUTOTRACE, PostgreSQL offers similar yet unique insights. Here’s how to make the most of PostgreSQL’s tools for analyzing and optimizing queries.

1. Basic Query Plan Analysis

Oracle: Uses EXPLAIN PLAN to display the execution path.

PostgreSQL:

• EXPLAIN: Displays the execution plan without running the query.
• EXPLAIN ANALYZE: Executes the query and provides the actual runtime statistics.

Tuning Tips:

• Use EXPLAIN to understand potential execution paths before running expensive queries.
• Use EXPLAIN ANALYZE to debug slow queries and compare estimated vs. actual costs.

2. Cost-Based Execution Plans

Oracle: Optimizer uses a combination of statistics and hints to determine the execution path.

PostgreSQL: PostgreSQL’s planner chooses the most cost-effective execution path based on:

• Startup Cost: Cost to fetch the first row.
• Total Cost: Total cost to fetch all rows.
• Rows: Estimated number of rows produced by each step.

Tuning Parameters:

• random_page_cost: Adjusts the perceived cost of random I/O.
• seq_page_cost: Adjusts the cost of sequential scans.

Tuning Tip: Lower random_page_cost for systems with SSDs or fast random-access storage.

3. Sequential Scans vs. Index Scans

Oracle: Displays whether full table scans or indexed access paths are used.

PostgreSQL:

• Sequential Scan: Used when scanning the entire table is more efficient.
• Index Scan: Used when index access is cost-effective.

Tuning Parameters:

• enable_seqscan: Set to off temporarily to force index usage (useful for testing).
• enable_indexscan: Set to off to test sequential scan performance.

Example:

SET enable_seqscan = off;
EXPLAIN ANALYZE SELECT * FROM employees WHERE department_id = 10;        

4. Join Strategies

Oracle: Displays nested loops, hash joins, or merge joins in plans.

PostgreSQL: PostgreSQL supports various join strategies:

• Nested Loop Join: Efficient for small datasets.
• Hash Join: Efficient for large, unsorted datasets.
• Merge Join: Efficient when both datasets are sorted.

Tuning Parameters: enable_nestloop, enable_hashjoin, enable_mergejoin: Enable or disable specific join methods.

Tuning Tip: Test different join strategies by toggling join-related planner parameters.

5. Parallel Query Execution

Oracle: Automatically evaluates and applies parallel query execution where appropriate.

PostgreSQL: PostgreSQL supports parallelism for scans, joins, and aggregates.

Relevant Parameters:

• max_parallel_workers_per_gather: Number of workers per query.
• parallel_setup_cost: Cost to initialize a parallel query.
• parallel_tuple_cost: Cost of processing each tuple in parallel.

Example:

SET max_parallel_workers_per_gather = 4;
EXPLAIN ANALYZE SELECT COUNT(*) FROM employees;        

Tuning Tip: Use parallel queries for large datasets and aggregations but monitor CPU usage.

6. Partition Pruning

Oracle: Optimizer leverages partitioning metadata to eliminate unnecessary scans.

PostgreSQL:

• Partition Pruning: Automatically skips irrelevant partitions during query execution.
• Key Feature: Declarative partitioning in PostgreSQL simplifies and enhances query planning.

Tuning Tip: Ensure partition keys are included in query predicates to enable pruning.

7. Monitoring Query Execution

Oracle: Uses dynamic performance views (e.g., v$sql_plan) for monitoring execution.

PostgreSQL:

• Use pg_stat_activity to view currently running queries and their states:

SELECT pid, usename, query, state, backend_start
FROM pg_stat_activity;        

• Use pg_stat_statements for tracking long-running or resource-intensive queries:

SELECT query, calls, total_exec_time
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 5;        

8. Understanding Plan Details

Oracle: Provides operations like INDEX RANGE SCAN, FULL TABLE SCAN.

PostgreSQL: PostgreSQL explains every step in the query execution, such as:

• Seq Scan: Sequential scan of a table.
• Index Scan: Fetch rows using an index.
• Bitmap Index Scan: Fetches row pointers using an index, then reads data pages.
• Aggregate: Performs aggregation like SUM or COUNT.

Tuning Tip: Focus on high-cost nodes in the plan output for optimization.

Key Takeaways for EXPLAIN and Query Execution Plans in PostgreSQL

1. Understand Cost-Based Plans: PostgreSQL uses startup and total cost metrics for planning. Adjust cost parameters (random_page_cost, seq_page_cost) based on hardware and workload.
2. Index Usage: Monitor and enforce index usage where necessary. Use pg_stat_user_indexes to identify underused indexes.
3. Join Optimization: Optimize join strategies by enabling/disabling planner parameters (enable_nestloop, enable_hashjoin).
4. Parallelism: Enable parallel queries for large datasets but test performance impact on multi-core systems.
5. Use EXPLAIN ANALYZE Regularly: Compare estimated and actual costs to identify inaccurate statistics or inefficiencies.
6. Leverage Monitoring Tools: Combine pg_stat_activity and pg_stat_statements to track real-time and historical query performance.

This structured approach to understanding and utilizing query plans in PostgreSQL provides Oracle professionals with actionable strategies to optimize SQL performance.

Performance Tuning

Key Considerations:

• PostgreSQL’s work memory and shared buffers correspond closely to Oracle’s PGA and SGA.
• Use parallel queries judiciously for large datasets to enhance performance.

Key Considerations for Performance Tuning in PostgreSQL

Performance tuning in PostgreSQL involves optimizing memory, I/O, query execution, and concurrency settings to achieve the best results for your workload. While Oracle has integrated tools like the Automatic Database Diagnostic Monitor (ADDM) and SQL Tuning Advisor, PostgreSQL offers flexible configuration parameters and monitoring tools. Here are key considerations for performance tuning that go beyond what we’ve covered in earlier sections.

Connection Management

Oracle: Uses connection pooling via features like Database Resident Connection Pooling (DRCP).

PostgreSQL: PostgreSQL can use external tools for connection pooling:

• pgbouncer: Lightweight connection pooling for handling high-concurrency applications.
• pgpool-II: Supports connection pooling and load balancing.

Relevant Parameters:

• max_connections: Limits the total number of database connections.
• connection_timeout: Defines the timeout for idle connections.

Tuning Tip: Use connection pooling tools to reduce overhead for short-lived queries.

2. Memory Allocation for Large Workloads

Oracle: Automatically allocates memory with the Automatic Memory Management (AMM) feature.

PostgreSQL: PostgreSQL requires manual tuning of memory parameters to match the workload.

Key Parameters:

• maintenance_work_mem: Memory for maintenance tasks like VACUUM and CREATE INDEX. Increase this for faster index creation and vacuum operations.
• temp_buffers: Temporary buffers allocated for each session. Higher values help with complex queries.
• Tuning Tip: For maintenance tasks:

SET maintenance_work_mem = '512MB';        

• Monitor memory usage to avoid swapping.

3. Disk I/O Optimization

• Oracle: Optimizes disk I/O through Automatic Storage Management (ASM).
• PostgreSQL: PostgreSQL relies on underlying storage systems but allows for fine-tuning I/O-related parameters.

Relevant Parameters:

• effective_io_concurrency: Improves performance for SSDs or RAID setups by enabling parallel I/O.
• synchronous_commit: Determines whether transactions wait for WAL writes to disk. Setting it to off improves write performance but reduces durability.
• wal_writer_delay: Controls the frequency of WAL writes to disk.

Tuning Tip: For SSDs,

effective_io_concurrency = 200        

4. Autovacuum and Bloat Management

Oracle: Manages undo and segment cleanup automatically.

PostgreSQL: PostgreSQL uses autovacuum to clean up dead tuples and manage bloat.

Key Parameters:

• autovacuum_vacuum_scale_factor: Determines when autovacuum runs based on table size.
• autovacuum_analyze_scale_factor: Triggers statistics collection for query planning.

Tuning Tip: For large tables, lower the scale factors to prevent excessive bloat:

autovacuum_vacuum_scale_factor = 0.1
autovacuum_analyze_scale_factor = 0.05        

5. Monitoring Lock Contention

Oracle: Uses V$ views to monitor locks and waits.

PostgreSQL: Lock contention can cause performance bottlenecks. Monitor locks using system views:

• pg_locks: Shows active locks.
• pg_stat_activity: Tracks long-running queries that may hold locks.

Example Query:

Tuning Tip: Minimize transaction duration and use advisory locks (pg_advisory_lock) for custom concurrency control.

6. Asynchronous Query Execution

Oracle: Supports asynchronous database calls for certain operations.

PostgreSQL:

• PostgreSQL supports asynchronous query execution at the client level, which can improve throughput for applications requiring parallel data retrieval.
• Tools like libpq (C API) and Python’s asyncpg can be used for non-blocking query execution.

7. Background Worker Processes

Oracle: Leverages multiple background processes for efficient task management.

PostgreSQL:

• PostgreSQL allows custom background workers to perform tasks asynchronously.
• Use extensions like pg_cron for periodic job scheduling and TimescaleDB for time-series workloads.

Tuning Tip: Write custom background workers to offload computationally intensive tasks.

8. Query Prioritization

Oracle: Implements resource management using the Database Resource Manager (DBRM).

PostgreSQL:

• Query prioritization is achieved through manual workload isolation:
• Use separate roles or connection pools for high-priority and low-priority queries.
• Assign lower priority sessions to servers with reduced work_mem or CPU affinity.

9. Parallel Index Creation

Oracle: Offers parallel index creation natively.

PostgreSQL: PostgreSQL supports parallel index creation for large tables with the CREATE INDEX command. max_parallel_maintenance_workers: Number of parallel workers for maintenance operations.

Tuning Tip:

• Increase max_parallel_maintenance_workers to speed up index creation.

10. Extensions for Performance Tuning

Oracle: Extends functionality through optional feature packs.

PostgreSQL: PostgreSQL supports numerous extensions that enhance performance tuning:

• pg_stat_statements: Tracks query performance metrics.
• pg_partman: Simplifies partitioning for large datasets.
• HypoPG: Allows hypothetical indexes for testing query plans.

Example:

CREATE EXTENSION pg_stat_statements;        

Key Takeaways for Performance Tuning in PostgreSQL

1. Manage Connections Effectively: Use connection pooling tools like pgbouncer to handle high concurrency.
2. Optimize Memory Usage: Tune maintenance_work_mem and temp_buffers for efficient resource utilization.
3. Minimize Disk I/O Bottlenecks: Adjust effective_io_concurrency and synchronous_commit based on storage and workload.
4. Control Autovacuum Behavior: Fine-tune autovacuum_vacuum_scale_factor to prevent table bloat.
5. Monitor and Resolve Lock Contention: Use pg_locks and pg_stat_activity to identify blocking queries.
6. Leverage Extensions: Install and use extensions like pg_stat_statements and HypoPG for advanced tuning.

This framework provides a comprehensive guide for Oracle DBAs to systematically approach PostgreSQL performance tuning, leveraging tools, and strategies tailored to PostgreSQL’s architecture.

Typical PostgreSQL Tuning Workflow for Oracle DBAs

1. Memory Allocation:

• Set shared_buffers = 25% of total memory.
• Configure work_mem for sorting and hashing (e.g., 4–16MB depending on workload).

2. WAL Tuning:

• Increase wal_buffers for high write-intensive workloads.
• Adjust checkpoint_timeout and max_wal_size to balance durability and write performance.

3. Vacuum Optimization:

• Enable autovacuum with tuned parameters:

autovacuum_vacuum_cost_limit = 2000

4. Query Optimization:

• Regularly run ANALYZE to update statistics.
• Use EXPLAIN ANALYZE to debug and optimize slow queries.
• Adjust random_page_cost and cpu_tuple_cost for storage-specific tuning.

Call to action

By leveraging this detailed Oracle-to-PostgreSQL comparison and tuning strategies, Oracle DBAs can efficiently transition to PostgreSQL while maintaining a high-performance database environment. Try these relative learnings to become export in PostgreSQL because practice makes you perfect and take advantage of your Oracle expetise.

Stay tuned for more detailed articles on PostgreSQL best practices tailored for Oracle experts!