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Drive Team Excellence with PostgreSQL Performance Tuning Corporate Training

PostgreSQL Performance Tuning training empowers database professionals and engineers to optimize PostgreSQL databases for speed, reliability, and scalability. Participants explore query planning, indexing strategies, vacuuming, partitioning, caching, and advanced monitoring to resolve performance bottlenecks in production environments.

Edstellar's PostgreSQL Performance Tuning Instructor-led course offers virtual/onsite training options, enabling teams to apply performance tuning best practices directly to their organizational database infrastructure with guidance from experienced PostgreSQL experts.

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Skills Your Employees Will Gain

These are the core, hands-on capabilities your team builds during the program.

  • Query Optimization
  • Index Design
  • Execution Plan Analysis
  • Vacuum and Bloat Management
  • Connection Pooling
  • Table Partitioning
  • Performance Monitoring

What Your Team Will Achieve After This Training

  • Master query optimization techniques and interpret PostgreSQL execution plans to eliminate bottlenecks
  • Gain proficiency in designing and managing indexes to maximize read and write performance
  • Develop skills in vacuuming, bloat management, and storage optimization for healthy databases
  • Learn connection pooling, memory configuration, and resource management strategies
  • Build expertise in table partitioning, caching, and replication for scalable architectures
  • Apply monitoring and diagnostic tools to identify, profile, and resolve performance issues

Topics & Program Outline

The curriculum is organized into focused modules built by industry experts and delivered virtually or on-premise. Interactive sessions reflect the evolving demands of the workplace, keeping the learning both relevant and practical.

  1. PostgreSQL Architecture Overview
    • Understanding the PostgreSQL process model and shared memory
    • How the query lifecycle works from parsing to execution
    • Role of the buffer pool, WAL, and background workers
    • Key configuration files and their impact on performance
  2. Performance Fundamentals and Benchmarking
    • Defining performance goals and acceptable thresholds
    • Introduction to pgbench for baseline benchmarking
    • Identifying common performance anti-patterns
    • Setting up a performance testing environment
  3. Understanding PostgreSQL Statistics
    • Role of pg_statistic and pg_stats in the planner
    • How ANALYZE updates statistics for query planning
    • Configuring statistics targets for better estimates
    • Detecting stale statistics and their effects
  4. Key Performance Metrics
    • Monitoring CPU, I/O, and memory usage in PostgreSQL
    • Understanding cache hit ratios and their significance
    • Using pg_stat_activity to observe active sessions
    • Interpreting wait events for bottleneck identification
  5. PostgreSQL Logging for Performance
    • Configuring log_min_duration_statement for slow query logging
    • Using auto_explain to capture execution plans automatically
    • Analyzing log output with pgBadger
    • Setting up structured logging for observability pipelines
  6. Configuration Parameters Impacting Performance
    • Tuning shared_buffers, work_mem, and maintenance_work_mem
    • Understanding effective_cache_size and its planner influence
    • Configuring checkpoint and WAL-related parameters
    • Best practices for applying and testing configuration changes
  1. Understanding the Query Planner
    • How the PostgreSQL cost-based planner selects execution strategies
    • Role of cost parameters: seq_page_cost and random_page_cost
    • Understanding planner row estimates and their accuracy
    • Forcing and testing alternative plans with enable_ parameters
  2. Using EXPLAIN and EXPLAIN ANALYZE
    • Reading execution plan nodes: Seq Scan, Index Scan, Hash Join
    • Interpreting actual vs. estimated rows and cost differences
    • Using BUFFERS option to identify cache misses
    • Visualizing plans with explain.dalibo.com and similar tools
  3. Rewriting Inefficient Queries
    • Identifying implicit type casts that break index usage
    • Refactoring correlated subqueries into joins or CTEs
    • Simplifying overly complex WHERE clause logic
    • Using EXISTS vs. IN for better planner decisions
  4. Join Strategies and Optimization
    • Understanding nested loop, hash join, and merge join strategies
    • Controlling join order with join_collapse_limit
    • Optimizing large table joins with proper statistics
    • Detecting and resolving cross-join performance issues
  5. CTEs, Subqueries, and Window Functions
    • Understanding CTE materialization fences in older PostgreSQL versions
    • Using MATERIALIZED and NOT MATERIALIZED hints in PostgreSQL 12+
    • Optimizing window functions with proper PARTITION BY design
    • Replacing subqueries with lateral joins for performance gains
  6. Parameterized Queries and Plan Caching
    • How prepared statements affect plan reuse and performance
    • Understanding generic vs. custom plan selection
    • Detecting plan cache issues with pg_prepared_statements
    • Using plan_cache_mode to control caching behavior
  1. Index Types in PostgreSQL
    • B-Tree indexes: structure, use cases, and limitations
    • Hash indexes: when and why to use them
    • GIN and GiST indexes for full-text and geometric data
    • BRIN indexes for naturally ordered large datasets
  2. Partial and Expression Indexes
    • Creating partial indexes to reduce index size and maintenance cost
    • Building expression indexes for computed column queries
    • Common use cases: status filters, case-insensitive searches
    • Verifying that partial indexes are used by the planner
  3. Composite Indexes and Column Order
    • Rules for ordering columns in composite indexes
    • Using index-only scans to eliminate heap access
    • Covering indexes with INCLUDE clause in PostgreSQL 11+
    • Avoiding over-indexing and its write performance cost
  4. Index Maintenance and Bloat
    • Understanding index bloat and its impact on performance
    • Detecting bloat with pgstattuple and related extensions
    • Using REINDEX CONCURRENTLY to rebuild indexes online
    • Scheduling regular index maintenance in production
  5. Identifying Missing and Unused Indexes
    • Querying pg_stat_user_indexes to find unused indexes
    • Using pg_stat_user_tables to detect sequential scan patterns
    • Recommending indexes based on slow query analysis
    • Tools for automated index suggestion: HypoPG and pg_qualstats
  6. Indexing for Concurrency and Write Performance
    • Using CREATE INDEX CONCURRENTLY to avoid table locks
    • Impact of indexes on INSERT, UPDATE, and DELETE throughput
    • Balancing read and write performance with index strategy
    • Index locking behavior and conflict resolution strategies
  1. MVCC and Dead Tuples
    • How PostgreSQL MVCC creates dead tuples during updates and deletes
    • Impact of dead tuple accumulation on query performance
    • Understanding transaction ID wraparound risk
    • Monitoring table bloat with pg_stat_user_tables
  2. VACUUM and VACUUM FULL
    • Difference between VACUUM and VACUUM FULL operations
    • When to use VACUUM FULL and its locking implications
    • Monitoring vacuum progress with pg_stat_progress_vacuum
    • Using pg_repack as a lock-free alternative to VACUUM FULL
  3. Autovacuum Configuration and Tuning
    • How autovacuum thresholds and scale factors work
    • Tuning autovacuum for high-write tables individually
    • Controlling autovacuum cost delay to reduce I/O impact
    • Detecting and resolving autovacuum bottlenecks
  4. Table Bloat Detection and Remediation
    • Using pgstattuple to measure live vs. dead tuple ratios
    • Identifying bloated tables requiring immediate intervention
    • Scheduling bloat remediation during low-traffic windows
    • Preventing bloat through proactive autovacuum tuning
  5. TOAST and Large Object Management
    • Understanding how PostgreSQL stores oversized columns with TOAST
    • TOAST storage strategies: PLAIN, EXTENDED, EXTERNAL, MAIN
    • Impact of large TOAST tables on vacuum and query performance
    • Optimizing schema design to minimize TOAST usage
  6. Filesystem and Storage Tuning
    • Choosing optimal filesystem settings for PostgreSQL data directories
    • Aligning storage block sizes with PostgreSQL page size
    • Using tablespaces to distribute I/O across storage devices
    • Impact of SSD vs. HDD on PostgreSQL performance characteristics
  1. PostgreSQL Connection Architecture
    • How each PostgreSQL connection spawns a backend process
    • Memory overhead of high connection counts
    • Impact of connection saturation on query latency
    • Understanding max_connections and its practical limits
  2. PgBouncer for Connection Pooling
    • Installing and configuring PgBouncer in front of PostgreSQL
    • Session, transaction, and statement pooling modes explained
    • Configuring pool_size and reserve_pool_size appropriately
    • Monitoring PgBouncer stats for pool health
  3. Pgpool-II for Load Balancing
    • Introduction to Pgpool-II and its connection pooling features
    • Configuring read/write splitting for primary-replica setups
    • Health check and failover configuration in Pgpool-II
    • Comparing PgBouncer and Pgpool-II use cases
  4. Work Memory and Resource Tuning
    • Configuring work_mem for sort and hash operations
    • Risks of setting work_mem too high in multi-user environments
    • Using SET commands to apply per-session memory limits
    • Tuning maintenance_work_mem for VACUUM and index builds
  5. Locking, Deadlocks, and Contention
    • Understanding PostgreSQL lock types and their hierarchy
    • Detecting lock waits with pg_locks and pg_blocking_pids
    • Resolving deadlocks through query redesign and retry logic
    • Minimizing lock contention in high-concurrency workloads
  6. Query Timeouts and Resource Governance
    • Setting statement_timeout to prevent runaway queries
    • Using lock_timeout to avoid long lock waits
    • Implementing idle_in_transaction_session_timeout for session hygiene
    • Applying resource governance with pg_query_settings and roles
  1. Introduction to Table Partitioning
    • Why partitioning improves query performance and maintenance
    • Range, list, and hash partitioning strategies in PostgreSQL
    • How partition pruning eliminates irrelevant data scans
    • Declarative partitioning syntax introduced in PostgreSQL 10
  2. Range Partitioning for Time-Series Data
    • Designing range-partitioned tables for date and time columns
    • Automating partition creation with pg_partman
    • Dropping old partitions for efficient data retention management
    • Verifying partition pruning with EXPLAIN output
  3. List and Hash Partitioning Strategies
    • Using list partitioning for categorical data distributions
    • Hash partitioning for even data distribution across partitions
    • Creating default partitions to handle unmatched rows
    • Choosing between partitioning strategies based on query patterns
  4. Indexes and Constraints on Partitioned Tables
    • How indexes are created on partitioned tables and child partitions
    • Applying unique constraints and primary keys to partitioned tables
    • Foreign key limitations on partitioned tables
    • Partition-wise joins and aggregates for performance gains
  5. Schema and Data Type Optimization
    • Choosing appropriate data types to minimize storage and improve speed
    • Avoiding unnecessary NULLable columns and their overhead
    • Using numeric types correctly to prevent implicit casting
    • Normalizing vs. denormalizing schema for read-heavy workloads
  6. Table Inheritance and Legacy Partitioning
    • Understanding table inheritance as a pre-declarative partitioning approach
    • Migrating inheritance-based partitioning to declarative partitioning
    • Performance differences between inheritance and declarative methods
    • When to retain inheritance-based designs for compatibility
  1. PostgreSQL Buffer Cache
    • How shared_buffers caches frequently accessed data pages
    • Monitoring buffer cache hit rates with pg_statio_user_tables
    • Sizing shared_buffers for different workload profiles
    • Using pg_buffercache to inspect buffer contents
  2. Operating System Page Cache
    • How the OS page cache supplements PostgreSQL shared_buffers
    • Configuring effective_cache_size to inform the planner
    • Impact of huge pages on PostgreSQL memory management
    • Minimizing OS swap usage for consistent query latency
  3. Query Result Caching Patterns
    • Application-level caching with Redis or Memcached for hot queries
    • Using materialized views as a server-side result cache
    • Refreshing materialized views concurrently for live data needs
    • Identifying candidates for result caching via query frequency analysis
  4. Materialized Views for Performance
    • Creating materialized views for expensive aggregation queries
    • Indexing materialized views to further accelerate lookups
    • Scheduling REFRESH MATERIALIZED VIEW CONCURRENTLY in maintenance windows
    • Trade-offs between data freshness and query performance
  5. WAL and Checkpoint Tuning
    • Understanding WAL write overhead and its performance impact
    • Tuning wal_buffers and synchronous_commit for throughput
    • Configuring checkpoint_completion_target to spread I/O load
    • Balancing durability and performance with fsync settings
  6. Huge Pages and Kernel Memory Settings
    • Enabling huge pages on Linux for PostgreSQL shared memory
    • Configuring vm.overcommit_memory and vm.swappiness for databases
    • Setting kernel.shmmax and kernel.shmall for large shared_buffers
    • Verifying huge page usage in PostgreSQL log output
  1. Streaming Replication Fundamentals
    • How WAL-based streaming replication works in PostgreSQL
    • Configuring primary and standby servers for streaming replication
    • Monitoring replication lag with pg_stat_replication
    • Synchronous vs. asynchronous replication trade-offs
  2. Logical Replication for Selective Sync
    • Differences between physical and logical replication in PostgreSQL
    • Setting up publications and subscriptions for logical replication
    • Using logical replication for zero-downtime major version upgrades
    • Filtering replicated tables for selective data propagation
  3. Read Replicas for Query Offloading
    • Routing read-only queries to standby replicas for scalability
    • Using hot_standby_feedback to prevent query cancellations on replicas
    • Configuring recovery.conf or postgresql.conf for hot standby
    • Load balancing reads across multiple replicas with PgBouncer or HAProxy
  4. Failover and High Availability Tools
    • Introduction to Patroni for automated PostgreSQL failover
    • Using repmgr for replication management and switchover
    • Configuring virtual IPs and HAProxy for transparent failover
    • Testing failover scenarios in staging environments
  5. Replication Slots and Their Risks
    • What replication slots are and how they retain WAL segments
    • Risks of unused replication slots causing disk exhaustion
    • Monitoring slot lag with pg_replication_slots
    • Best practices for slot lifecycle management
  6. Performance Impact of Replication on Primary
    • CPU and I/O overhead of WAL generation on the primary server
    • Minimizing replication-related lock contention
    • Impact of synchronous_standby_names on write latency
    • Tuning wal_sender_timeout and wal_receiver_timeout
  1. Built-in PostgreSQL Statistics Views
    • Using pg_stat_user_tables to detect sequential scans and dead tuples
    • Analyzing pg_stat_user_indexes for index usage patterns
    • Monitoring bgwriter and checkpoint stats for I/O insights
    • Resetting statistics with pg_stat_reset for fresh baselines
  2. pg_stat_statements for Query Analysis
    • Installing and enabling the pg_stat_statements extension
    • Identifying top queries by total time, calls, and mean duration
    • Using query fingerprints to group parameterized queries
    • Integrating pg_stat_statements with Grafana dashboards
  3. Prometheus and Grafana for PostgreSQL Monitoring
    • Setting up postgres_exporter to expose metrics to Prometheus
    • Building Grafana dashboards for real-time PostgreSQL observability
    • Configuring alerts for replication lag, lock waits, and bloat
    • Using pre-built dashboards from the Grafana community library
  4. Log Analysis with pgBadger
    • Installing and running pgBadger for log report generation
    • Identifying slowest queries and most frequent lock conflicts
    • Configuring PostgreSQL logging parameters for optimal pgBadger input
    • Automating daily pgBadger reports for proactive monitoring
  5. Profiling with auto_explain and Timing
    • Enabling auto_explain to log plans for slow queries automatically
    • Using timing options to measure I/O vs. CPU time in plans
    • Sampling production workloads with auto_explain.sample_rate
    • Correlating explain output with application-level performance metrics
  6. Disk I/O and System-Level Diagnostics
    • Using iostat and iotop to identify PostgreSQL I/O bottlenecks
    • Correlating PostgreSQL wait events with OS-level I/O metrics
    • Detecting storage throughput limits affecting PostgreSQL performance
    • Using perf and strace for deep process-level profiling
  1. Advanced Query Tuning Techniques
    • Using extended statistics for multi-column correlation awareness
    • Applying row-level security without sacrificing query performance
    • Optimizing recursive CTEs and hierarchical queries
    • Using parallel query execution for large analytical workloads
  2. Useful Performance Extensions
    • HypoPG for hypothetical index testing without actual creation
    • pg_qualstats for tracking predicate usage across queries
    • pgstattuple for detailed tuple-level bloat analysis
    • timescaledb for optimized time-series workloads on PostgreSQL
  3. Parallel Query and JIT Compilation
    • Enabling and configuring parallel query with max_parallel_workers
    • Understanding when parallel plans improve vs. hurt performance
    • JIT compilation in PostgreSQL 11+: benefits and overhead
    • Disabling JIT selectively for OLTP workloads with high overhead
  4. Cloud PostgreSQL Performance Tuning
    • Performance considerations for Amazon RDS and Aurora PostgreSQL
    • Tuning Azure Database for PostgreSQL Flexible Server
    • Google Cloud SQL for PostgreSQL performance best practices
    • Comparing managed service limitations vs. self-hosted flexibility
  5. PostgreSQL Upgrades and Version-Specific Optimizations
    • Performance improvements introduced in PostgreSQL 14, 15, and 16
    • Using pg_upgrade for in-place major version upgrades
    • Logical replication approach for minimal-downtime upgrades
    • Testing performance regression after major version upgrades
  6. Capacity Planning and Long-Term Performance Strategy
    • Forecasting database growth and planning hardware scaling
    • Establishing performance baselines and regression testing pipelines
    • Building a performance runbook for on-call database engineers
    • Continuous performance improvement culture: metrics, reviews, and iterations

Who Should Attend?

This program suits professionals at many levels across the organization, including:

  • Database Administrators
  • Backend Developers
  • Data Engineers
  • DevOps Engineers
  • Cloud Infrastructure Engineers
  • Solutions Architects

What are the Prerequisites?

Professionals should have basic SQL knowledge and familiarity with PostgreSQL to take the PostgreSQL Performance Tuning training course.

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        Hear from Organizations We've Trained

        "Edstellar's virtual PostgreSQL Performance Tuning training was a game-changer for our remote database team. Within weeks of completing the course, our team reduced average query execution time by 40% and eliminated several critical slow queries impacting our customer-facing application. The live online format was engaging and the expert instructor made complex concepts highly accessible."

        Rajiv Menon

        Head of Database Engineering,

        A Leading E-Commerce Company

        "We brought Edstellar onsite to train our entire backend engineering team on PostgreSQL performance tuning. The hands-on labs using our actual production schemas gave immediate, practical value. Post-training, our team successfully reduced database infrastructure costs by 30% through better indexing and connection pooling strategies. Highly recommended for any engineering organization running PostgreSQL at scale."

        Priya Subramaniam

        VP of Engineering,

        A Global SaaS Platform

        "Our team attended Edstellar's intensive PostgreSQL Performance Tuning bootcamp and came back transformed. The deep dives into vacuuming, partitioning, and replication performance were exactly what we needed. We implemented the learnings immediately and saw database p99 latency drop by over 50% in our analytics pipeline. An exceptionally well-structured program delivered by true subject matter experts."

        Samuel Okafor

        Director of Data Infrastructure,

        A Financial Services Firm

        "Edstellar's IT & Technical training programs have been instrumental in strengthening our engineering teams and building future-ready capabilities. The hands-on approach, practical cloud scenarios, and expert guidance helped our teams improve technical depth, problem-solving skills, and execution across multiple projects. We're excited to extend more of these impactful programs to other business units."

        Aditi Rao

        L&D Head,

        A Global Technology Company

        Recognition That Motivates Your Team

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