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Building message queues and streaming systems directly in PostgreSQL demonstrates the database's powerful capabilities for data synchronization and real-time processing. While PostgreSQL provides the foundational primitives for sophisticated messaging architectures, implementing production-ready systems reveals significant operational complexity that organizations must carefully evaluate against purpose-built solutions.
Postgres CDC is driven by its Write-Ahead Logging. CDC tools read from this log, capturing changes as they occur, getting real-time change streams without placing strain on the Postgres server. [1] This foundation enables custom queue and streaming implementations, though operational challenges emerge at enterprise scale.
Amazon SQS provides exactly-once delivery within visibility timeouts—a pattern achievable in PostgreSQL through careful implementation of atomic message claiming and state management.
The foundational message queue requires handling concurrent access and visibility timeouts:
CREATE TABLE messages (
seq SERIAL PRIMARY KEY,
data JSONB NOT NULL,
not_visible_until TIMESTAMP,
deliver_count INTEGER NOT NULL DEFAULT 0,
last_delivered_at TIMESTAMP,
inserted_at TIMESTAMP NOT NULL DEFAULT NOW(),
updated_at TIMESTAMP NOT NULL DEFAULT NOW()
);
CREATE INDEX idx_messages_visibility
ON messages (not_visible_until, inserted_at ASC);
Operational Complexity: This basic structure requires sophisticated error handling, dead letter queue management, and concurrent access control that scales with message volume and consumer count.
Implementing exactly-once delivery requires complex query patterns:
WITH available_messages AS (
SELECT seq FROM messages
WHERE not_visible_until IS NULL
OR (not_visible_until <= NOW())
ORDER BY inserted_at ASC
LIMIT $1
FOR UPDATE SKIP LOCKED
)
UPDATE messages m
SET
not_visible_until = $2,
deliver_count = deliver_count + 1,
last_delivered_at = NOW(),
updated_at = NOW()
FROM available_messages am
WHERE m.seq = am.seq
RETURNING m.seq, m.data;
Technical Challenge: The FOR UPDATE SKIP LOCKED mechanism prevents race conditions but requires careful transaction management and error recovery logic that becomes increasingly complex in high-throughput scenarios.
First-in-first-out message ordering introduces significant architectural challenges:
WITH processing_keys AS (
SELECT key FROM messages
WHERE not_visible_until >= NOW()
),
next_message_seqs AS (
SELECT DISTINCT ON (key) seq
FROM messages
WHERE key NOT IN (SELECT key FROM processing_keys)
ORDER BY key, inserted_at
LIMIT $1
),
next_messages AS (
SELECT m.*
FROM messages m
JOIN next_message_seqs nmi ON m.seq = nmi.seq
WHERE m.not_visible_until IS NULL OR m.not_visible_until <= NOW()
FOR UPDATE SKIP LOCKED
)
UPDATE messages m
SET
not_visible_until = $2,
deliver_count = m.deliver_count + 1,
last_delivered_at = NOW(),
updated_at = NOW()
FROM next_messages nm
WHERE m.seq = nm.seq
RETURNING m.seq, m.data, m.key;
Operational Burden: This implementation requires manual management of message key sequences, complex bookkeeping for FIFO ordering, and sophisticated error handling when race conditions reduce batch sizes below requested limits.
Eliminating race conditions requires additional infrastructure:
CREATE TABLE message_key_seqs (
key TEXT NOT NULL,
seq BIGINT NOT NULL,
PRIMARY KEY (key, seq)
);
Maintenance Overhead: This approach eliminates race conditions but introduces complex upsert logic, requires transactional coordination across multiple tables, and demands sophisticated cleanup processes during message acknowledgment.
Production implementations require automated handling of persistently failing messages:
WITH messages_to_move AS (
SELECT seq FROM messages
WHERE (
not_visible_until IS NULL
OR (not_visible_until <= NOW())
)
AND deliver_count >= $1
ORDER BY inserted_at
LIMIT $2
FOR UPDATE SKIP LOCKED
),
deleted_messages AS (
DELETE FROM messages m
USING messages_to_move mtm
WHERE m.seq = mtm.seq
RETURNING m.seq, m.data, m.key, m.deliver_count, m.last_delivered_at
)
INSERT INTO dead_messages (message_seq, message_data, message_key, message_deliver_count, message_last_delivered_at)
SELECT seq, data, key, deliver_count, last_delivered_at
FROM deleted_messages;
Engineering Investment: Dead letter queue management requires additional table structures, monitoring systems, and operational procedures that increase system complexity exponentially.
Kafka's streaming architecture provides message persistence and consumer group management—concepts implementable in PostgreSQL through careful design of offset tracking and partition management.
Implementing Kafka-like streaming requires separation of message storage from consumption state:
CREATE SEQUENCE messages_seq;
CREATE TABLE messages (
seq BIGINT NOT NULL DEFAULT nextval('messages_seq'),
key TEXT NOT NULL,
data TEXT NOT NULL,
inserted_at TIMESTAMP NOT NULL DEFAULT NOW(),
partition INT GENERATED ALWAYS AS (abs(hashtext(key) % 10)) STORED,
PRIMARY KEY (seq, partition)
);
CREATE TABLE consumer_group_offsets (
consumer_name TEXT NOT NULL,
partition INT NOT NULL,
seq BIGINT NOT NULL,
updated_at TIMESTAMP DEFAULT NOW() NOT NULL,
inserted_at TIMESTAMP DEFAULT NOW() NOT NULL,
PRIMARY KEY (consumer_name, partition)
);
Architectural Complexity: This design requires manual partition management, careful coordination between message storage and offset tracking, and sophisticated consumer group coordination that scales with system complexity.
Implementing consumer groups requires complex query patterns:
BEGIN;
WITH locked_offset AS (
SELECT consumer_name, seq, partition
FROM consumer_group_offsets
WHERE consumer_name = $1
FOR UPDATE SKIP LOCKED
LIMIT 1
ORDER BY updated_at ASC
)
SELECT
lo.consumer_name,
lo.seq AS current_seq,
lo.partition,
m.seq,
m.key,
m.data,
m.inserted_at
FROM locked_offset lo
LEFT JOIN messages m ON m.partition = lo.partition AND m.seq > lo.seq
WHERE lo.consumer_name IS NOT NULL
ORDER BY m.seq ASC
LIMIT $2;
COMMIT;
Operational Challenges: Consumer group management requires long-running transactions, sophisticated timeout handling, and manual coordination of partition assignment across multiple consumer instances.
PostgreSQL's MVCC architecture creates sequence gaps that can cause message loss:
Technical Issue: Think of it as a streaming representation of a Write-Ahead Log (WAL). Since WAL captures and records all data changes in the Postgres database, these changes are considered logical decoding streams and are categorized as a logical replication slot at the database level. [2] However, sequences can commit out-of-order, creating gaps that break streaming consistency.
Resolution Complexity: Solving sequence gaps requires implementing register_sequence_transaction and max_safe_seq functions that add significant complexity to message consumption logic.
Scaling concurrent processing requires careful partition design:
Scaling Constraints:
Combining message persistence with flexible consumption patterns requires sophisticated data modeling:
CREATE TABLE consumer_messages (
id SERIAL PRIMARY KEY,
consumer_name TEXT NOT NULL,
message_seq TEXT NOT NULL,
not_visible_until TIMESTAMP,
deliver_count INTEGER NOT NULL DEFAULT 0,
last_delivered_at TIMESTAMP,
inserted_at TIMESTAMP NOT NULL DEFAULT NOW(),
updated_at TIMESTAMP NOT NULL DEFAULT NOW(),
UNIQUE (consumer_name, message_seq)
);
Implementation Burden: This approach requires:
Rather than building and maintaining complex PostgreSQL messaging infrastructure, Stacksync provides purpose-built bi-directional data synchronization that eliminates the operational overhead demonstrated in these custom implementations.
Eliminated Concurrency Complexity: Use Stacksync to build real-time, bi-directional syncs, orchestrate workflows, and observe every data pipeline. True bidirectional sync offers a fundamentally different approach. Changes in any connected system—whether CRM, database, or other platform—propagate instantly to all other systems.
Automated Error Handling: Immediately get alerted about record syncing issues over email, slack, Pagerduty and Whatsapp. Resolve issues from a centralized dashboard with retry and revert options. Stacksync eliminates the need for custom dead letter queue implementation and manual error recovery procedures.
Enterprise-Scale Reliability: Whether you sync 50k or 100M+ records, Stacksync handles all the dirty plumbing of infrastructure, queues and code so you don't have to. Integrations are non-invasive and do not require any setup on your systems.
PostgreSQL Integration: Stacksync provides seamless PostgreSQL integration that maintains database-centric workflows while eliminating custom queue complexity.
Multi-System Coordination: Stacksync provides a purpose-built solution for connecting Snowflake Postgres with enterprise business systems through real-time, bi-directional data synchronization. Unlike traditional one-way ETL processes, Stacksync maintains true bi-directional synchronization between Snowflake Postgres and connected systems. Changes in any system propagate instantly to all connected endpoints, ensuring data consistency across the entire ecosystem.
Engineering Resource Optimization: The system automatically handles real-time event processing and data updates across all connected platforms, allowing technical teams to focus on developing valuable features rather than maintaining integration "dirty plumbing".
Enterprise Security and Compliance: As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer.
Custom implementations may be justified when:
Stacksync eliminates custom implementation complexity when:
Automate any process across all systems. Connect, enrich and combine data with workflow automation. Stacksync provides sophisticated workflow capabilities that would require extensive custom development in PostgreSQL-based solutions.
Database Synchronization: Stacksync supports comprehensive PostgreSQL synchronization patterns including cross-region replication and multi-environment consistency.
Enterprise Integration: While Snowflake Postgres handles database operations, Stacksync eliminates the complexity of integration development: Real-time customer profile synchronization between Salesforce and Snowflake Postgres · Automated product usage data flow from application databases to both systems · Bi-directional synchronization of customer and vendor records · Real-time transaction data flow for immediate financial reporting · Automated reconciliation processes triggered by data changes
Building PostgreSQL-based message queues and streaming systems demonstrates the database's powerful capabilities while revealing significant operational complexity. The implementations shown require extensive engineering investment in concurrency control, error handling, partition management, and operational monitoring.
Custom Implementation Reality:
Stacksync Alternative:
Two-way sync delivers immediate business value by enabling rapid deployment of mission-critical integrations without complex infrastructure management. Organizations can seamlessly scale their operations while maintaining enterprise-grade data accuracy and security.
For organizations evaluating PostgreSQL-based streaming solutions, the technical implementations demonstrate both PostgreSQL's capabilities and the operational complexity of production systems. Stacksync transforms this complexity into competitive advantage through purpose-built data synchronization that eliminates custom infrastructure maintenance while providing enterprise-scale reliability.
Ready to eliminate messaging infrastructure complexity? Explore how Stacksync's enterprise data integration platform delivers the benefits of custom PostgreSQL implementations without the operational overhead.
