PostgreSQL Replication, High Availability HA and Scalability

Learn to configure PostgreSQL for streaming and asynchronous replication by initializing a primary database cluster with initdb, creating data directories, system tables, and the default Postgres database.

Configure the primary for replication by editing postgresql.conf to enable remote access with listen_addresses, create a replication user with replication flag, update pg_hba.conf, and restart.

Understand PostgreSQL logical replication, with publisher and subscriber roles, enabling selective table replication, streaming decoded statements, and efficient migrations with less data transfer than physical replication.

Set up two PostgreSQL database clusters on the same machine to demonstrate logical replication. Configure the publisher and subscriber ports and enable logical level, then start both instances to test.

Perform a selective copy of data by creating a test database and table, using pg_dump to transfer the schema to a subscriber via port 5434.

Create a publication for table one, or specify multiple tables or all tables, and pair it with a subscription to enable replication.

Create a subscription to pull changes from publications, enable replication slots and initial snapshot, and verify replicated inserts, updates, deletes, and truncates on the subscriber.

Understand the limitations of logical replication in PostgreSQL: schema and sequences are not replicated, changes require manual syncing with PGD, and only regular and partition tables are supported.

Monitor PostgreSQL replication with the PDA starter application table to view key metrics, including lag between master and standby in seconds or milliseconds, and set alerts.

Use logical replication for flexibility; it doesn't require schemas, supports major versions, Windows to Linux replication, multiple subscriptions, selective operations, and incremental changes with triggers, boosting performance on slow networks.

Handle hundreds to thousands of concurrent connections in large applications by using a connection pooler such as PGE Bouncer to boost PostgreSQL performance beyond 350 transactions per second.

PGE bouncer acts as a proxy between the application and PostgreSQL, maintaining an array of open connections to quickly serve requests with a minimal memory footprint.

Build a PgBouncer setup to front a PostgreSQL test database, run a stress test, and note its 1-to-1 connections and the need for external load balancing for high availability.

Install PgBouncer using your OS package manager—yum on Red Hat or CentOS, apt-get on Ubuntu or Debian, or brew on macOS; or download and build from sources.

Install pgbouncer and copy the default config from the OS directory. Edit the file to configure backend servers, alias test_tb, authentication, and a dummy superuser.

Launch the PGA bouncer using the configuration file to start the system. Connect to the progress database on port 6432 using alias B and user progress, with log entries.

Boost performance by tuning the PGE bouncer: set mean_proposal_size, keep the default pool size per user per database at 20, cap the queue with max_client_connections, and keep nodes nearby.

Explore pool modes for PostgreSQL connections, including session, transaction, and statement modes, and learn how each affects connection lifecycle, concurrency, and application behavior.

The benchmark shows that PgBouncer dramatically increases throughput and lowers connection time for many short-lived connections, from 3.21 ms to 0.23 ms, using 20 clients and 1000 transactions.

Implement a load balancing solution for PostgreSQL replicas using Google Cloud, enabling horizontal scaling and improved high availability.

Balance load for grid connections to improve read availability and move toward high availability. Route read requests via the energy proxy to replicas, while writes connect to the primary.

Create a primary PostgreSQL instance with two read replicas on Google Cloud to boost read capacity, using private IPs in the same region and a least-privilege test user.

Create a Google Cloud engine instance to run HAProxy, install the load balancer, and configure the Cloud SDK and APIs to enable PostgreSQL high availability.

Partitioning can dramatically boost performance when done right. Identify partition keys from workflows and joins, and apply one of four methods to minimize scanned data.

Demonstrate range partitioning in PostgreSQL by splitting a customers table into age-based partitions, migrating with minimal downtime, and identifying each row’s partition via a system column.

Explore partitioning methods to optimize PostgreSQL performance, including range and list partitions, hash partitioning, and multilevel partitioning, with examples using sales dates, departments, regions, and product keys.

Explore strategies to handle growing write traffic by sharding data into logical partitions distributed across physical nodes, and employ functional partitioning to scale PostgreSQL databases efficiently.

Identify a partition key from the data model, such as user ID, and implement horizontal partitioning with data mapping table to route queries and maintain unique primary keys across shards.

Explore second level sharding with multiple partition keys, such as user ID and article ID, to view data efficiently from different angles and cross the boundaries between shards for comments.

Organizations must plan for a wide range of failure scenarios to achieve high availability and business continuity, ensuring the database remains available even when some parts fail.

Achieve high availability by replicating data to a standby replica, exploring log shipping, streaming, and logical replication at a high level, and choosing manual or automatic failover with failback considerations.

PostgreSQL uses warm standby or log shipping to archive changes and recover on standby servers. Hot standby reduces delay, while streaming and logical replication now cover most replication use cases.

Explore cascading replication in PostgreSQL, streaming changes from the primary to standbys. Compare topologies: primary-fed standbys, or one-secondary-per-standby, or a secondary feeding multiple targets for faster failover and balanced load.

Explore a high-availability example with one primary and one standby, where client connects to the primary for reads and writes; failover is manual via a trigger file or Paramount Command.

Implement high availability by using PostgreSQL built-in streaming replication to synchronize data, then use BGP for load balancing and automatic failover, with pgpool routing writes to the primary and reads to the replica.

Learn to configure PostgreSQL streaming replication to enable read load balancing, write separation, and failover by setting up a primary, creating a replica using base backup, and configuring replication access.

Configure Pgpool-II to balance load across a primary and standby in a streaming replication setup, directing inserts and deletes to the primary and selects to the standby.