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PostgreSQL's Multi-Version Concurrency Control delivers exceptional concurrency and performance, but introduces sequence ordering complexities that undermine real-time data synchronization between operational systems. While MVCC (multi-version concurrency control) systems like Postgres are strongly consistent, they can appear to be eventually consistent from a client's perspective [1]. This sequence skew creates critical failures in cursor-based pagination, compromising database synchronization and automated data sync between applications.
Unlike most other database systems which use locks for concurrency control, Postgres maintains data consistency by using a multiversion model. This means that while querying a database each transaction sees a snapshot of data (a database version) as it was some time ago, regardless of the current state of the underlying data [2].
Consider a typical operational table with timestamps and sequence numbers:
create table access_logs (
id serial primary key,
user_name text,
inserted_at timestamp default now()
)
Every transaction in Postgres gets a transaction ID called XID. This includes single one statement transactions such as an insert, update, or delete, as well as explicitly wrapping a group of statements together via BEGIN - COMMIT. When a transaction starts, Postgres increments an XID and assigns it to the current transaction [3].
The critical issue emerges from PostgreSQL's function resolution behavior:
now()) resolve at transaction startnextval()) execute throughout transaction durationThis creates scenarios where Transaction A starts first but commits after Transaction B, resulting in records appearing out of chronological order despite sequential identifiers.
This query volatility from the client's perspective is usually not a problem. However, there's one very notable and common situation where this causes big issues [1].
Cursor pagination failure mode:
seq=1, seq=2, seq=4seq=4 as cursor for next pageseq=3 commits after cursor advancementseq=3 is permanently skippedThis sequence skew creates unacceptable data loss in:
Traditional approaches to PostgreSQL sequence ordering introduce complexity, performance penalties, and operational overhead. Stacksync eliminates these challenges through architecture specifically designed for real-time data synchronization reliability.
Use Stacksync to build real-time, bi-directional syncs, orchestrate workflows, and observe every data pipeline . Rather than relying on potentially inconsistent sequence ordering, Stacksync implements field-level change detection that captures modifications as they occur.
Technical advantages over sequence-based approaches:
Stacksync is designed for enterprise-grade workloads. It synchronizes data in milliseconds and scales from tens of thousands to over 100 million records .
Unlike sequence serialization approaches that throttle write performance, Stacksync maintains:
Configure and sync data within minutes without code. Whether you sync 50k or 100M+ records, Stacksync handles all the dirty plumbing of infrastructure, queues and code so you don't have to .
While PostgreSQL offers several approaches to address sequence consistency, each introduces significant operational overhead compared to purpose-built synchronization platforms.
create table my_seq (
id UUID primary key,
seq BIGINT not null default 0
);
Critical limitations:
PostgreSQL provides a means for creating locks that have application-defined meanings. These are called advisory locks, because the system does not enforce their use — it is up to the application to use them correctly. Advisory locks can be useful for locking strategies that are an awkward fit for the MVCC model [4].
Transaction-level lock requests, on the other hand, behave more like regular lock requests: they are automatically released at the end of the transaction, and there is no explicit unlock operation. This behavior is often more convenient than the session-level behavior for short-term usage of an advisory lock [4].
Implementation complexity:
with max_seq as (
select min(l1.objid) as seq
from pg_locks l1
inner join pg_locks l2 on l1.pid = l2.pid
where l2.classid = 1
and l1.classid = 0
and l1.locktype = 'advisory'
)
Operational challenges:
Simple time-based filtering (where inserted_at < now() - interval '30 seconds') introduces unacceptable latency for real-time operational systems requiring immediate data consistency.
Stacksync was built from the ground up for real-time, two-way sync between CRMs (e.g., Salesforce, HubSpot), ERPs, and databases (e.g., Postgres, MySQL, BigQuery). It intelligently handles complex scenarios, such as syncing read-only fields in one direction while maintaining two-way sync for all other fields, ensuring data integrity is never compromised .
Unlike custom PostgreSQL sequence solutions that only address single-database consistency, Stacksync provides:
The platform provides data consistency. An integrated issue management dashboard gives you instant visibility into any failed syncs, with options to retry or revert with a single click. This eliminates silent failures and empowers teams to manage data flows without constant engineering oversight .
Operational benefits over custom implementations:
With a no-code interface, Stacksync reduces complex integration projects from months to minutes. It automates field mapping, data transformation, and can create new tables with ideal schemas automatically. This frees your engineering team from maintaining complex API integrations and allows them to focus on building competitive advantages .
Organizations facing PostgreSQL sequence consistency challenges have successfully eliminated these complexities through Stacksync's purpose-built architecture:
Enterprise scalability: Nautilus Solar operate solar farms and powers energy to 16k+ households integrating in real-time Netsuite, Postgres and HubSpot. Acertus delivers in real-time enriched data to Salesforce and consolidates enterprise insights connecting Salesforce, Netsuite, Snowflake product databases .
Technical reliability: Rather than implementing complex advisory lock mechanisms, these organizations achieve guaranteed data consistency through Stacksync's event-driven synchronization engine.
Traditional sequence consistency approaches become inadequate when organizations require:
To achieve true operational agility, organizations must move beyond brittle custom scripts and generic workflow tools. A purpose-built platform engineered for real-time, bi-directional synchronization provides the reliability, scalability, and efficiency required to power a modern, data-driven enterprise .
Decision criteria favoring Stacksync:
PostgreSQL sequence skew represents a fundamental challenge that undermines real-time data synchronization reliability. While technical approaches like sequence table serialization and advisory locks can address ordering issues, they introduce performance penalties, implementation complexity, and operational overhead that significantly impact business efficiency.
Bi-directional data sync is no longer a luxury—it's a necessity for organizations that depend on accurate, up-to-date information across multiple systems. The right tool delivers real-time, reliable, and secure synchronization, reduces engineering overhead, and supports business growth .
Stacksync eliminates PostgreSQL sequence ordering complexities entirely through its event-driven architecture and field-level change detection. By providing true bi-directional synchronization, guaranteed data consistency, and no-code implementation across 200+ connectors, Stacksync delivers superior reliability compared to custom PostgreSQL solutions while freeing engineering resources for competitive differentiation.
Experience sequence-agnostic data synchronization: Start a 14-day free trial to see how Stacksync's purpose-built architecture eliminates PostgreSQL ordering challenges while delivering enterprise-grade reliability for your critical data flows.
