CockroachDB vs Amazon Aurora and DSQL

How enterprise requirements shape distributed database selection

What enterprise companies do varies widely. When it comes to their data, though, what enterprises need is remarkably similar:

– Resilience to maintain uptime

– Scalability to manage growth

– Deployment that matches their operational parameters

– A straightforward and well-supported database experience

An enterprise database must have few resource constraints to make it capable of delivering strong consistency, low latency, and rock-solid resilience in a manner that matches today’s distributed application architectures. The choice between CockroachDB and AWS Amazon Aurora Global Database or Aurora DSQL ultimately comes down to which solution best delivers on these universal enterprise priorities: resilience, scalability, flexible deployment, and customer experience.

How traditional databases fail modern distributed applications

Enterprise organizations with a global footprint – or even those in a single country but spanning multiple cloud provider availability zones (AZs) – need data architectures that deliver always fast, always correct transactional data, and that also scale organically and deploy flexibly. But traditional RDBMSs were never designed to support modern distributed applications, forcing companies to make difficult tradeoffs between performance, availability, cost, and data integrity.

How distributed SQL enables modern enterprise architectures

Distributed SQL eliminates these tradeoffs. This mature technology combines enterprise-grade relational database capabilities with horizontal scalability and cloud-native resilience, providing:

Always-on operations in a 24/7 world: Distributed SQL databases are designed to be resilient and highly available. They continue to operate even if one or more nodes fail, surviving availability zone and even regional data center outages.

Global scale without compromise: Distributed SQL databases scale natively and horizontally simply by adding more nodes to handle increased load, or removing them when traffic is light. Truly distributed SQL databases also eliminate the traditional PostgreSQL bottleneck of routing all writes to a single primary server. And, they should be capable of deployment on any infrastructure, whether in the cloud, on-premise, or a hybrid implementation.

Declarative data placement and survival goals: native data placement capabilities help ensure data compliance and the ability to place data near to users for improved performance.

How distributed SQL databases differ across architectures and capabilities

This paper compares and analyzes differences between three major enterprise distributed SQL databases. First, let’s meet the candidates. A truly global, natively distributed SQL database, CockroachDB’s active-active multi-region architecture enables writes and strongly consistent reads from any region, without need for a single primary.

PostgreSQL compatible database – designed from the ground up for cloud-native resilience, horizontal scalability, and automated operations.

Enterprise-grade with minimal operational overhead – automatic sharding, rebalancing, and recovery with no manual intervention required for scaling or failover. No downtime for schema changes or software upgrades.

Strong, global consistency – CockroachDB’s serializable isolation means all nodes and regions can serve consistent reads and writes.

How Aurora Global Database works and its architectural tradeoffs

Aurora Global Database is an advanced Aurora feature extending a single Aurora database to span multiple AWS regions, achieved by bolting a distribution layer on top of Aurora RDS to add read-only clusters in other regions.

Multi-region PostgreSQL-compatible database – active-passive architecture spanning up to 10 secondary regions, optimized for disaster recovery and global read access.

Single-writer design – all writes must go to the primary region, with secondary regions providing eventually consistent (~<1s lag) read replicas and managed cross-region failover.

Eventually-consistent database – built on Aurora's decoupled storage and compute architecture with 6x storage replication, offering familiar PostgreSQL compatibility and RDS tooling.

Despite its name, Amazon Aurora Global Database is mostly an option for single-region deployments. It's optimized for read-heavy workloads when write scalability can be limited to a single master node in a single region. No matter how it's deployed, Aurora Global Database will suffer latency issues with global writes as its single write node is always tied to a single region.

How Aurora DSQL is designed and where it is limited

Aurora DSQL is not just Amazon’s recently-launched distributed SQL database offering (GA as of May 2025). It’s also an acknowledgement from AWS that distributed SQL is a long-term solution that solves many business-critical problems.

Serverless distributed SQL service – multi-region active-active architecture, introduces AWS's newest approach to always-on, globally distributed online transaction processing (OLTP) databases.

Fully managed serverless platform – automatic scaling across separated architectural layers and requiring zero infrastructure management.

Limited availability and significant restrictions – currently restricted to specific region pairs (not truly global), with notable feature limitations including no foreign keys, triggers, or extensions, and a 10TB default storage cap.

How database architecture determines distributed SQL capabilities

The fundamental architecture of a distributed SQL database determines not just whether it can survive failures, but how quickly it recovers, how much data might be lost, and whether your business continues operating seamlessly during outages.

How CockroachDB architecture enables scalability and resilience

CockroachDB's architecture is a layered, distributed system designed for scalability, strong consistency, and high availability (HA), inspired by Google's Spanner and F1. CockroachDB has just three components, an efficient design that separates storage and compute, with a SQL layer on top.

SQL layer: This is the highest level, providing a familiar Postgres-compatible SQL API to developers. It handles parsing SQL statements, optimizing queries and translating them into low-level key-value operations.

Transaction layer: This compute layer ensures ACID (Atomicity, Consistency, Isolation, Durability) properties for all transactions. It manages distributed transactions across multiple nodes and uses a concurrency control mechanism to maintain strong consistency.

Key-value store: A distributed, replicated key-value (KV) store is the foundation of CockroachDB. This layer is responsible for:

Data distribution: Data is automatically sharded into "ranges," which are small, contiguous chunks of the key-space. These ranges are distributed across the cluster's nodes.

Replication and consensus: Each range is replicated across multiple nodes (typically 3 or 5) for fault tolerance. CockroachDB utilizes the Raft consensus protocol to ensure that all replicas of a range are consistent and to handle leader election and failure recovery.

Data storage: The KV store uses a persistent storage engine to store data on disk within each node.

Shared-nothing architecture: Each node is independent and can handle requests. Adding more nodes to the cluster automatically distributes data and workload, allowing for seamless horizontal scaling.

Strong consistency: Raft consensus and a distributed transaction manager ensure that all nodes always see the same, up-to-date and transactionally consistent data.

High availability and fault tolerance: Data replication and the Raft consensus protocol enable the system to survive node, rack, or even data center failures with minimal disruption.

Active-active multi-region architecture: Data is synchronously replicated and transactionally consistent across all regions.

How Aurora Global Database architecture works

Aurora Global Database uses a primary-secondary replication model: a key primary region handles all write operations, with one or more secondary regions as read-only replicas. Aurora Global Database’s design rests on four pillars:

Primary region: This is the only region where write operations can be performed. The writer database cluster for the global database resides here.

Secondary region(s): Data is replicated to secondary read-only clusters with typical latency of ~1 second, providing only eventual consistency for read replicas.

Global database cluster: This is a logical container that links the primary and secondary clusters together across different regions.

Replication layer: Dedicated network infrastructure replicates data between regions as an asynchronous process.

Aurora Global Database key architectural limitations

Single-writer architecture: The database has a single primary Amazon Aurora database cluster in one AWS region that processes all write operations.

Up to ten read regions: The primary cluster's data can be asynchronously replicated to as many as ten secondary, read-only database clusters in different AWS regions.

Storage redundancy: Within each region, the shared storage volume is automatically replicated six ways across three availability zones. With a recovery point objective (RPO) of typically one second, most data is preserved during an outage.

Localized data access: Each secondary region can support up to 16 read-only database instances that can provide low-latency access to read-only data for local users.

How Aurora DSQL architecture is designed

Aurora DSQL's architectural design distributes functions into six functional components:

Transaction and session router: This frontend component handles incoming client connections and the PostgreSQL protocol, routing queries to the appropriate processors.

Query processor: Responsible for parsing and planning SQL queries, it uses the Amazon Time Sync Service for snapshots and executes queries directly on the storage layer.

Adjudicator: This component implements optimistic concurrency control and ensures global consistency, validating transaction commits and managing conflict resolution.

Journal: A distributed log that maintains a record of all transaction changes to provide durability and enable point-in-time recovery.

Storage layer: A shared-nothing, distributed system that manages data persistence and partitioning, automatically rebalances data across nodes, and handles data replication.

Control plane: Coordinates the various components, maintains redundancy across multiple AZs, and manages automatic scaling and self-healing in case of failures.

Aurora DSQL key architectural characteristics

Serverless and fully managed: Aurora DSQL is designed to operate with zero infrastructure management for users and usage-based billing with no charges when idle.

Optimistic concurrency control: Instead of traditional locking mechanisms, Aurora DSQL uses an OCC model to handle concurrent transactions.

Single database, multiple regions: Aurora DSQL presents as one logical database spanning multiple regions within a single continent (North America, Europe, etc.), with writes propagating instantly via journal log replication.

Independent scaling: DSQL’s component architecture allows the compute, read/write, and storage layers to scale separately, providing flexibility for varying workload demands.

Key takeaway: Architecture determines database outcomes

Amazon Aurora DSQL's disaster recovery capabilities approach CockroachDB's architectural sophistication with synchronous replication and no single point of failure. However, its deliberate geographic limitations prevent truly global deployment – a constraint that may never change, given Amazon’s design choice to limit cross-continent clusters.

Amazon Aurora Global Database's slower failover, potential data loss, and single-writer bottleneck are all factors that could represent material business risks in disaster scenarios.

How disaster recovery is determined in distributed SQL systems

Traditional SQL databases once forced enterprises to treat downtime as an acceptable business risk, until distributed SQL arose to virtually eliminate that risk. But a distributed SQL database’s resilience varies according to how its architecture dictates availability and disaster recovery.

Disaster recovery focuses on restoring database operations after major incidents. This includes data loss prevention and restoration, backup strategies, and recovery time objectives.

How CockroachDB delivers self-healing disaster recovery

CockroachDB's self-healing capabilities leverage automatic rebalancing and data replication, continuously monitoring node health and redistributing data across surviving nodes when failures occur. Beyond built-in Raft replication and fault tolerance, additional capabilities minimize downtime and data loss:

Multi-active, multi-region design: Architected for resilience with deployment across multiple availability zones, regions, cloud platforms, or hybrid environments to eliminate single points of failure.

Point-in-time data recovery: Incremental, locality-aware backups and precise point-in-time recovery (PITR) with granular restore at the table, database, or cluster level deliver faster recovery times and lower RPO than traditional snapshot-based approaches.

Physical cluster replication (PCR): Continuously replicates all cluster-level data from primary to independent standby cluster asynchronously for two data center deployments. Failover stops replication, resets the standby to a consistent point in time, and makes it ready to accept application traffic.

Automatic recovery without intervention: CockroachDB's self-healing architecture automatically rebalances data and maintains availability when nodes fail – no manual intervention or complex failover procedures required.

Strong consistency across all regions: CockroachDB’s default SERIALIZABLE transaction isolation ensures all reads and writes are consistent across all nodes. This eliminates the data reconciliation challenges that plague eventually-consistent systems during disaster recovery.

Aurora limitations: Snapshot-based recovery and manual failover

Because it requires explicit planning for failover, failback, and reconciliation, Aurora does not self-heal across regions.

Daily Snapshots with manual PITR setup: Aurora Global Database relies on daily snapshots for backup, with manual intervention needed for point-in-time recovery setup. This traditional approach requires planning and additional operational overhead.

Data loss risk during unplanned failover: Aurora achieves less than one minute RTO with zero data loss during planned switchovers when promoting a secondary region to primary. However, unplanned failover can cause data loss due to asynchronous replication, requiring application teams to implement reconciliation processes to verify data was copied between regions – a time-consuming and error-prone task.

Operational complexity: Regional failover with Aurora requires ensuring both application and database can failover and failback successfully. Failback is a manual process and teams must maintain reconciliation processes to verify data consistency.

How Aurora DSQL handles disaster recovery and its limitations

Synchronous replication without data loss: Aurora DSQL provides physical, synchronous replication with strong consistency across regions. All writes are replicated via consensus using a three-region quorum, eliminating the data loss concerns of Aurora Global Database's asynchronous model.

Backup features recently added: DSQL now includes centralized backup management, full cluster backups, automated schedules, cross-region and cross-account support, and WORM (write-once, read-many) protection for compliance scenarios.

Full snapshot limitations: Despite these additions, Aurora DSQL relies on full snapshot backup/restore via AWS Backup with no point-in-time recovery (PITR) capability, potentially impacting both RPO and RTO during disaster scenarios.

How high availability works in distributed databases

High availability (HA) ensures continuous database operations despite component failures, minimizing downtime and maintaining service continuity.

CockroachDB can achieve up to five nines (99.999%) availability, which is equivalent to approximately 5 minutes of downtime per year. This high availability is a core feature, especially for multi-region deployments, and is achieved through CockroachDB's multi-active replication with automatic rebalancing, creating a system that continuously monitors node health and automatically redistributes data across surviving nodes if one goes down. CockroachDB’s natively distributed architecture and the use of the Raft consensus protocol ensure data consistency and fault tolerance even during an availability zone – or even full region – outage.

How CockroachDB achieves high availability and fast failover

Sub-10 second failover: If you lose a node in CockroachDB, automatic failover occurs in less than 10 seconds with zero data loss.

Advanced fault tolerance: CockroachDB can withstand multiple simultaneous node failures while maintaining operations, as long as quorum is maintained for each replica and data range across the cluster.

Logical data replication (LDR): For more complex availability scenarios, CockroachDB supports logical data replication. This enables sophisticated data distribution patterns and additional resilience options for enterprise architectures in self-hosted environments.

The upshot: CockroachDB’s always-on, multi-active design avoids data loss and provides near-instant continuity. Availability is architected into every node, not bolted on.

Aurora limitations: High availability tradeoffs and failover delays

Aurora Global Database’ single-write-master replication model means that only one node in the cluster can receive writes. While on the surface it may appear that Aurora offers high availability because it can run across data centers, the reality is very different.

Choice between manual failover or data loss: Teams using Aurora might choose manual failover because Aurora’s automatic cutover is vulnerable to potential data loss.

Failover latency: Within a region, Aurora auto-failovers in approximately 30 seconds. Cross-region failovers take 1-2 minutes – up to ten times longer as compared to CockroachDB.

Multi-AZ failover with service disruption: Aurora Global Database provides multi-AZ failover capability, but failover takes time and can cause downtime.

Single-region constraint: As David Phillips, Starburst co-CTO, noted: "Amazon Aurora is tied to a single region. While it provides a standard SQL Database and nice features, if the region goes down the application goes down and it also cannot provide the latency guarantees like CockroachDB can."

The upshot: Amazon Aurora Global Database provides solid read availability but limited write availability; automatic failovers incur latency, delays, and potential data loss.

How Aurora DSQL approaches high availability and its risks

Aurora DSQL features an active-active, no-manual-failover architecture with stateless design that gives no possible single point of failure. If one region or AZ goes down, transactions can continue in others with no data loss due to quorum replication at the journal level.

Availability claims: DSQL claims to be designed for 99.99% availability in single-region deployments and 99.999% availability in multi-region deployments, matching CockroachDB claims. The failover process entails simply switching to an available region endpoint with all data current.

Geographic constraints: DSQL cluster regions are confined to a single region set (e.g., all in North America OR all in Europe). This is a significant limitation for enterprise applications with users all around the world.

Lack of production validation: Critically, DSQL is an immature product with no proven track record. While the architectural design appears sound, the lack of extensive production validation in diverse real-world scenarios means its high availability and enterprise capabilities remain largely unproven.

The upshot: Aurora DSQL’s HA model closely resembles CockroachDB in design goals (active-active, zero-loss). However it’s new, geographically limited, and lacks real-world validation.

Key takeaway: Resilience and availability comparison across databases

In summary, CockroachDB delivers both disaster recovery and high availability as native, always-on capabilities: Every node is active for reads and writes, data is synchronously replicated across regions, and recovery from node or region failure occurs automatically with no data loss or downtime.

Amazon Aurora Global Database, by contrast, relies on asynchronous replication, manual failover, and snapshot-based backups. This means outages or region failures could result in service disruption and potential data loss.

Aurora DSQL narrows this gap with active-active architecture and synchronous replication, but its limited regional scope and lack of proven production resilience could make it less reliable for enterprises requiring global continuity and deployment.

How Performance under Adversity measures real-world database resilience

Current industry benchmarks like the TPC-C (initially established in 1992) evaluate maximum performance under controlled laboratory conditions, isolated from real-world unpredictability.

These benchmarks presume single-data center setups, overlooking the complexities of globally distributed applications and data. They may incorporate durability testing but typically don't directly assess performance during outages or planned maintenance, nor do they gauge how quickly systems recover following failures. When the stable operational phase is interrupted by something like a simulated power outage, the benchmark is deemed complete. That’s not how production works.

How CockroachDB measures resilience under real-world conditions

CockroachDB has released a novel "Performance under Adversity" pressure-testing methodology that measures the impact of real-world conditions faced by application and infrastructure teams every day.

The benchmark includes six escalating phases of adversity beyond baseline level in a single continuous run, each introducing progressively harsher failure conditions and operational stress:

Baseline performance: Measure steady-state throughput under normal conditions

Internal operational stress: Simulate resource-intensive operations such as change data capture, full backups, schema changes, and rolling upgrades

Disk stalls: Randomly inject I/O freezes to evaluate storage resilience

Network failures: Simulate partial and full network failures preventing one partition from communicating with nodes in another partition

Node restarts: Unpredictably reboot database nodes (one at a time) to test recovery time and impact to performance

Zone outages: Take down an entire availability zone (AZ)

Regional outages: Take down an entire cloud provider region

Through all these phases, the methodology measures throughput in transactions per minute (tpmC), latency (90th and 95th percentile), and recovery time to baseline.

Peak performance in perfect conditions is meaningless if your database fails when it matters most. A database must prove it can survive the unexpected (hardware failures, network partitions, regional or even entire zone outages) while maintaining consistent performance. In creating and constantly measuring its own performance under real-world stress, CockroachDB is setting the standard for databases.

Disaster recovery comparison: Backup type, PITR support, RPO, and RTO differences

Backup type:
CockroachDB — Incremental, locality-aware
Amazon Aurora Global Database — Daily snapshots
Aurora DSQL — Full snapshots

Recovery complexity:
CockroachDB — Automated
Amazon Aurora Global Database — Manual intervention required
Aurora DSQL — Simplified but limited options

PITR support:
CockroachDB — Yes, native
Amazon Aurora Global Database — Manual setup required
Aurora DSQL — No

Granular restore:
CockroachDB — Table/database/cluster level
Amazon Aurora Global Database — Cluster level
Aurora DSQL — Cluster level

Data loss risk:
CockroachDB — Zero
Amazon Aurora Global Database — Possible during unplanned failover
Aurora DSQL — Zero (for replication)

RPO:
CockroachDB — Minimal (PITR + incremental backups)
Amazon Aurora Global Database — ~1 second (replication lag)
Aurora DSQL — Zero (replication), higher (backup-only)

RTO:
CockroachDB — Fast (targeted recovery)
Amazon Aurora Global Database — Slower (full snapshot restore)
Aurora DSQL — Slower (full snapshot restore)

High availability comparison: Failover behavior and downtime differences

Architecture:

CockroachDB — Multi-active replication
Amazon Aurora Global Database — Single-primary with read replicas
Aurora DSQL — Active-active

Failover type:

CockroachDB — Automatic, self-healing
Amazon Aurora Global Database — Manual (to prevent data loss) or automatic (with data loss)
Aurora DSQL — Automatic

Failover time:

CockroachDB — <10 seconds
Amazon Aurora Global Database — 30 seconds (intra-region), minutes inter-region
Aurora DSQL — Near-instant

Data loss on failover:

CockroachDB — Never
Amazon Aurora Global Database — Possible
Aurora DSQL — No

Node failure response:

CockroachDB — Automatic rebalancing
Amazon Aurora Global Database — Replica promotion
Aurora DSQL — Endpoint switching

Regional failure:

CockroachDB — Survives with no interruption
Amazon Aurora Global Database — Requires manual intervention
Aurora DSQL — Survives within region set

How scaling challenges impact distributed SQL database performance

Scaling a database to handle today’s large and ephemeral data loads is challenging. Scaling a relational database with strict ACID transaction requirements in a highly distributed environment is even more so, because guaranteeing data consistency and isolation gets increasingly difficult as nodes are added to handle increasing demand.

Just as architecture determines database resilience, it also determines how (and how well) a database scales. Let’s examine how CockroachDB, Amazon Aurora Global Database, and Aurora DSQL each handle scaling for writes, reads, data volume, and scaling operations for the database itself.

How CockroachDB scales writes across regions without bottlenecks

CockroachDB's multi-active architecture enables multi-node writes across all nodes in any region with simultaneous writes in multiple regions for true active-active global deployments. Strong consistency is maintained through consensus-based replication using the Raft protocol. Write capacity scales horizontally by adding nodes with automatic data rebalancing. With no single primary dependency, all nodes can accept writes, enabling elastic scaling for write- or read-intensive workloads without manual sharding.

Aurora limitations: Single-writer bottlenecks and manual sharding

Aurora Global Database has a primary instance in one region accepting writes. Secondary regions are read-only. Write scaling requires vertically increasing instance sizes and manual sharding as workloads increase. While Aurora scales reads globally, clients in different regions experience high write latencies, and fine-grained geographic data placement is not supported.

How Aurora DSQL scales writes and where it falls short

Aurora DSQL improves on Aurora Global Database's write limitations with an active-active distributed architecture supporting multi-region writes and automatic sharding. However, DSQL is constrained to only two active regions plus one witness region in multi-region configurations, and is limited to single-continent deployments.

This architectural difference explains why customers migrate from Aurora to CockroachDB when they hit scaling limits: CockroachDB's design eliminates the write bottleneck entirely rather than just moving or minimizing it.

How CockroachDB enables global read scaling with locality control

CockroachDB provides unlimited global read distribution with no practical limits on replicas or regions. Follower reads enable low-latency local access, while GLOBAL table locality serves infrequently updated data needing worldwide access and REGIONAL BY ROW tables enable geo-partitioned, granular data placement.

How Aurora handles read scaling across regions

Aurora Global Database has strong multi-region read scaling capabilities, with up to 16 read replicas per region and up to five secondary read-only database clusters in different regions. Fast read failover to other regions is supported, with storage-level replication maintaining typically less than one-second lag and independent cluster sizing for each region.

How Aurora DSQL approaches read scaling with strong consistency

Aurora DSQL provides improved read scaling over standard Aurora with automatic scaling across regions and multi-region architecture maintaining strong consistency without stale reads. Synchronous replication uses three-region quorum consensus, while serverless autoscaling adjusts capacity based on load.

How CockroachDB scales data volume without limits

CockroachDB removes data capacity constraints with no hard data limits, growing continuously as needed. Automatic sharding and data distribution require no manual intervention, with data automatically splitting into smaller ranges and rebalancing across nodes as the cluster expands, significantly reducing operational complexity and eliminating planned downtime.

Aurora limitations: Storage caps and manual sharding complexity

Aurora Global Database has hard capacity limits of 256 TB maximum per cluster and max table size of 32TB. Once limits are reached, users must manually shard the database (causing planned downtime), necessitating:

added application logic to route queries

operational overhead from monitoring and rebalancing shards

data consistency challenges maintaining transactional integrity across shards

performance management difficulties when uneven data distribution creates "hot spots."

Aurora DSQL limitations: Data size and workload restrictions

Aurora DSQL has restrictive capacity limits with a default limit of 10 TiB of total storage and maximum 128 TiB with approved limit increase. Additional limitations include caps on database and column counts, DML capped at 3,000 rows per transaction, connections timing out after one hour, and single built-in database only.

This explains why customers migrate from Aurora to CockroachDB to avoid manual sharding at scale.

How CockroachDB enables zero-downtime scaling operations

CockroachDB can be upgraded one node at a time with no application impact. The system scales seamlessly with zero downtime by adding nodes on-the-fly with automatic rebalancing. Changes and maintenance operations like rolling upgrades, rolling repaves for OS/security updates, and online schema changes can all take place without taking the database offline.

Aurora limitations: Scaling operations require downtime

Aurora Global Database requires planned downtime for schema changes, updates, and major operations. Rolling upgrades require planning and architecture changes, with the single-writer model adding complexity for rolling operations. Database sharding adds manual work and complexity for updates or upgrades.

How Aurora DSQL handles operational scaling and tradeoffs

Aurora DSQL, fully managed and serverless, provides autoscaling for compute and storage independently and automatic sharding with no manual intervention. However, scaling DSQL requires cost monitoring and management. And scaling geographically is limited by DSQL’s limited regional support.

Key takeaway: Scaling comparison across CockroachDB, Aurora, and DSQL

CockroachDB scales both reads and writes seamlessly; elastically scales and rebalances as data loads increase; and requires no downtime for database scaling operations

These characteristics mean CockroachDB can scale in tandem with all your SQL applications across their entire lifecycle, for all types of data. This includes mission-critical workloads for financial services organizations like account transactions and payment processing systems, but also working with a broader array of operational data when, for example, running dev/test applications. CockroachDB can be used for existing SQL applications that rely on PostgreSQL compatibility including isolation level, or for net-new applications that need high availability and strongly consistent data.

✅ Strong for: Write-heavy workloads, horizontal scaling, high-growth applications, global distribution
✅ Strong for: Zero-downtime operations, automatic data distribution, operational simplicity
✅ Strong for: Multi-region consistency, eliminating single points of failure

CockroachDB's distributed architecture is purpose-built for applications that must scale writes and reads simultaneously across global deployments, without operational overhead or architectural limitations.

Amazon Aurora Global Database scales reads well, especially if eventually consistent data is acceptable, but scales writes poorly, has hard capacity limits, and requires planned downtime for scaling operations

Aurora's single-write-master architecture makes it unsuitable for write-intensive or high-growth applications that need to scale both reads and writes globally. Many customers don't discover this limitation until they hit the scale wall, and customers have migrated specifically because they hit scale limitations.

✅ Strong for: Read-heavy workloads, global read distribution, fast regional reads
❌ Weak for: Write-heavy workloads, horizontal write scaling, avoiding manual sharding, limiting planned downtime for maintenance operations like schema changes or software updates
⚠️ Watch out for: Write bottlenecks, 256 TB storage limit, operational complexity (including data sharding) at scale

Amazon Aurora DSQL scales better than Aurora Global Database for writes, but is limited for large-scale workloads and requires refactoring applications for retries and for limited PostgreSQL compatibility

While Aurora DSQL scales by automatically distributing data, the use of OCC and snapshot isolation level can cause performance issues for some workloads, including concurrent writes. Additional schema planning and design is required to ensure isolation and mitigation of snapshot-related rollbacks. OCC aborts will occur when two or more concurrent transactions attempt to write to the same keys, impacting overall database performance and latency, and also requires applications to be rewritten to add application retries. Because PostgreSQL compatibility is limited, application-level changes may be required when migrating to DSQL.

✅ Strong for: AWS-native apps, bursty/low baseline workloads, eliminating single-writer bottleneck
⚠️ Limited for: Cross-continent deployments, large-scale data (>10 TiB default), high transaction volumes
❌ Weak for: Multi-cloud/hybrid strategies, mature production use cases, PostgreSQL feature parity

How database architecture determines deployment flexibility

Database architecture fundamentally determines where and how you can deploy your applications. The choices made in a database's core design – single-writer vs multi-active, cloud-agnostic vs cloud-provider-specific, monolithic vs distributed – create constraints and opportunities that ripple through every aspect of your infrastructure strategy. A database architected around a single primary writer can’t be transformed into a multi-region active-active system, and one created for a single cloud provider can’t be configured to support hybrid or multi-cloud environments. These foundational design choices are lasting, meaning your database decisions today shape your deployment flexibility tomorrow.

Deployment flexibility matters now more than ever. Case in point: the October 2025 AWS outage that took down more than 70 AWS cloud services, including Aurora DSQL. The extended outage disrupted more than 1,000 businesses for 15 hours and caused an estimated $100bn in economic damage. This is a stark illustration of concentration risk in digital infrastructure, where a single DNS resolution failure in one AWS region cascaded globally and paralyzed everything from airline operations to major financial institutions to healthcare organizations (not to mention Snapchat).

Organizations operating solely within the affected AWS region had no failover option when the outage struck, leaving them completely helpless, but the blast radius was much larger than aws-us-east-1. Organizations with multi-region AWS deployments still experienced service degradation and rolling outages, including overseas users like the UK government, but many were able to recover more quickly with fewer service disruptions. For enterprise companies that want to guarantee operational resilience, outages like these only cement the need for multi-cloud implementation options and vendor-agnostic data architectures.

2DC vs 3DC deployments: Active-passive vs active-active architecture

When comparing an Amazon Aurora two-data center deployment (2DC) with a CockroachDB three-data center (3DC) deployment, the key distinction is between an active-passive and an active-active architecture. Amazon Aurora Global Database uses a single-writer model, while CockroachDB is a fully distributed, multi-writer database designed for multi-region active-active operations.

Amazon Aurora Global Database functions as primary-secondary replication with only one region having write capability. In a 2DC setup, Aurora cannot distribute write load between locations, forcing vertical scaling by adding more machines to the primary cluster.

CockroachDB uses the Raft protocol, a quorum-based consensus model, to tolerate complete data center failures while continuing to accept reads and writes and maintaining strong consistency through majority consensus. This 3DC+ configuration enables self-healing with zero operator intervention and distributed writes across all data centers simultaneously. However, CockroachDB also enables 2DC configurations through physical cluster replication (PCR).

Aurora limitations: Multi-region deployments with single-writer constraints

Aurora Global Database trades off eventual consistency for geographic reach.

The primary in one region handles all writes, introducing 100-300ms latency for non-local users. Up to five secondary regions provide read-only replicas, with each secondary region supporting up to 16 read replicas for excellent read scalability and low-latency reads worldwide.

Data replicates asynchronously with typical lag of one second or less, meaning secondary regions may serve slightly stale data. Aurora Global Database can span continents but only for read replicas, not writes.

How Aurora DSQL handles multi-region deployments and its constraints

Aurora DSQL allows applications to write to multiple regions simultaneously.

However, DSQL is fundamentally limited to only two active regions plus one witness region. The witness region serves as a tie-breaker for consensus voting and participates in durability but doesn't serve application traffic; this provides fault tolerance benefits without performance or capacity benefits.

Also, DSQL regions are confined to the same continent because cross-continental latency would make DSQL's synchronous consensus protocol too slow for acceptable transaction commit times. This means a global SaaS company with customers in North America, Europe, and Asia would need to run three separate DSQL database clusters (one per continent) and write custom application logic to synchronize data between them. This completely defeats the purpose of a distributed database.

How CockroachDB enables global multi-region deployments without limits

CockroachDB eliminates both Aurora's single-writer constraint and DSQL's geographic limitations.

CockroachDB’s multi-region deployments put the database where the users are, automatically partitioning to ensure data locality while the database looks (and feels) like it’s running right in their region.

The system supports unlimited regions with no hard limit, and all regions can accept writes with every node accepting both read and write operations. CockroachDB clusters routinely span continents with strong consistency, maintaining serializable isolation by default across all regions, and with global ACID guarantees even when transactions span regions. Sophisticated declarative data placement includes REGIONAL BY ROW for localized customer data, GLOBAL tables for reference data replicated everywhere, and smart routing where reads use the nearest replica while writes go to nodes where data is housed.

Aurora limitations: AWS-only deployment and vendor lock-in

Enterprise companies need flexibility and choice in deploying their data infrastructure, whether cloud-hosted, on-premise, in a hybrid environment, or serverless. There are many reasons: for compliance with operational resilience regulations such as DORA, meeting air-gapped and security requirements, avoiding vendor lock-in, or even simply having the option to add or change cloud providers.

Amazon Aurora Global Database and Aurora DSQL both limit enterprise data architecture options because they can only deploy on AWS infrastructure, period. And both AWS offerings are available in fewer regions for deployment than CockroachDB.

How CockroachDB supports multi-cloud, hybrid, and on-prem deployments

CockroachDB is vendor-agnostic on the other hand, giving enterprises an array of choices beyond single cloud provider deployment. Having options for multi-cloud deployments, hybrid cloud/on-prem, and self-hosted environments flexibility ensures that a company can leverage the best features and pricing from different providers while maintaining control over their cloud and data infrastructures. CockroachDB offers:

Self-hosted deployment for teams that require complete control over the database environment, and deploy in their own private data centers (or on their own private cloud)

Public cloud deployment on AWS, GCP, and/or Azure for single and multi-region applications

Hybrid deployment in multi-cloud and hybrid cloud/on-prem, or for applications that need to run in a cloud other than AWS/GCP/Azure

Cockroach Cloud for fully-managed serverless deployments; including bring-your-own-cloud for customers who want to use their own cloud infrastructure (and pricing discounts) but have Cockroach Labs manage the database.

CockroachDB enables the utmost deployment flexibility and eliminates vendor lock-in. That leaves companies free to innovate and leverage new cloud services as they are released, and to build with the application and data infrastructures of their choice.

Key takeaway: Deployment flexibility comparison across databases

Multi-region:

Each of these databases handle geographic reach, write distribution, and consistency differently.

Aurora Global Database prioritizes geographic flexibility over write latency and strong data consistency, and lacks multi-region/multi-write capability. Aurora DSQL, conversely, trades off limited geography in favor of write distribution.

Both Aurora options impose architectural constraints (single-writer or two-region limit) that simplify operations but limit both scalability and multi-region deployments.

CockroachDB is the only choice that provides unlimited geographic reach with unlimited write distribution while maintaining strong consistency, but this does require a somewhat more sophisticated data placement strategy.

Infrastructure options:

Both Aurora Global Database and Aurora DSQL lock enterprises into AWS infrastructure exclusively and open up cloud exit concerns, especially in regulated industries.

CockroachDB is platform-agnostic. It eliminates vendor lock-in by supporting deployment across AWS, GCP, Azure, self-hosted private data centers, hybrid cloud/on-prem environments, and bring-your-own-cloud or fully-managed Cockroach Cloud serverless options.

Deployment comparison: Multi-region, multi-cloud, and write distribution differences

Portability:
CockroachDB — AWS, GCP, Azure, Oracle OCI, CockroachDB Cloud, self-hosted, and hybrid environments
Amazon Aurora Global Database — AWS
Aurora DSQL — AWS

Active write regions:
CockroachDB — Unlimited; all regions accept writes
Amazon Aurora Global Database — One only
Aurora DSQL — Two only

Multi-region writes:
CockroachDB — Native; all nodes can accept reads and writes across regions
Amazon Aurora Global Database — Writes routed through primary region
Aurora DSQL — Multi-region writes, limited to single continent

Geographic reach:
CockroachDB — Global (any continents)
Amazon Aurora Global Database — Global (read replicas only)
Aurora DSQL — Regional (same continent)

Multi-continent writes:
CockroachDB — Yes
Amazon Aurora Global Database — No
Aurora DSQL — No

Data residency:
CockroachDB — Yes; can tie data to location
Amazon Aurora Global Database — No
Aurora DSQL — No

Best for:
CockroachDB — Flexible, scalable, multi-region and hybrid applications
Amazon Aurora Global Database — Read-heavy, single-region AWS workloads
Aurora DSQL — Regional AWS-native applications with simpler workloads

How customer experience impacts database adoption and operations

Operational simplicity directly impacts developer productivity, system reliability, and total cost of ownership. Let's compare how CockroachDB, Amazon Aurora Global Database, and Aurora DSQL handle day-to-day database operations: backups, upgrades, and schema management. We’ll also examine database migration tooling, Postgres compatibility, and the technical customer support experience for each provider.

How CockroachDB enables automated backups and precise recovery

CockroachDB provides incremental, locality-aware backups with point-in-time recovery (PITR) and granular restore options at table, database, or cluster level.

This enables faster, targeted recovery that reduces outage blast radius and data-loss exposure. Key capabilities include:

continuous incremental backups without performance impact

locality-aware backups optimizing storage costs and recovery time

fine-grained restore options

PITR for reconstructing database state at any point in time

AS OF SYSTEM TIME queries for dispute investigations that can cut investigation time by more than 50%, with no customer-facing downtime

Aurora limitations: Backup recovery and PITR constraints

Aurora Global Database uses automated backups through daily snapshots with typical replication lag of less than one second, though unplanned failover can lead to data loss.

Manual configuration is required for PITR, with asynchronous cross-region replication at ~1 second lag and full database restore required with no granular table-level recovery. Aurora failover requires downtime and may require client reconnection or manual intervention, with failover taking 10-30 seconds and recovery procedures more time-consuming due to full snapshot restoration.

How Aurora DSQL handles backups and its limitations

Aurora DSQL supports full snapshot backup/restore via AWS Backup.

DSQL has centralized management, automated schedules, cross-region/account support, and WORM protection, but lacks PITR. Only full cluster backups are supported, with no table or database-level granularity. Without PITR, teams cannot restore to specific points in time between snapshots, potentially increasing data loss exposure and complicating audit and compliance scenarios.

How CockroachDB enables zero-downtime upgrades and maintenance

CockroachDB can be upgraded with zero downtime as each node is self-contained and operates independently.

When a node is upgraded, it is seamlessly replaced by a new node running the latest version, transparent to the application and allowing continued database use without interruption. This allows for:

zero-downtime rolling upgrades across all nodes

zero-downtime rolling repaves for security patches or OS updates

data replicated multiple times ensuring availability during node maintenance

no application changes required during upgrades

pause/resume control for maintenance operations

Aurora limitations: Maintenance windows and upgrade constraints

Aurora Global Database’s single-write node replication model requires planned downtime to perform upgrades and changes, adding overhead and complexity. Maintenance windows are required for most upgrades, with the single-writer architecture limiting upgrade flexibility and potential failover to standby during major version upgrades. Application reconnection is often needed post-upgrade, requiring the coordination of downtime windows across teams.

How Aurora DSQL manages upgrades and tradeoffs

Aurora DSQL fully-managed database and maintenance operations occur automatically without user intervention, though long-term operational patterns are still emerging. AWS manages all infrastructure upgrades but upgrade timing is not under user control, which may conflict with business-critical periods.

PostgreSQL compatibility across CockroachDB, Aurora, and DSQL

CockroachDB is PostgreSQL compatible and the majority of PostgreSQL syntax, meaning that existing applications built on PostgreSQL can be migrated to CockroachDB with minimal, if any change to the application. Postgres compatibility covers the majority of typical use cases with core SQL features including:

triggers

stored procedures

UDFs

temporary tables

sequences

materialized views

foreign keys with full referential integrity

partial/inverted indexes

vector type and indexes

SERIALIZABLE isolation by default

LDAP/Active Directory integration

Amazon Aurora Global Database uses the native PostgreSQL engine with AWS enhancements, providing near-complete PostgreSQL compatibility with broad extension support, tools and ORMs working out-of-the-box, minimal application changes required, and standard PostgreSQL tooling and frameworks. Aurora offers the smoothest migration path for applications heavily reliant on PostgreSQL-specific features and extensions.

Aurora DSQL has the most PostgreSQL limitations, lacking foreign keys, triggers, sequences, temporary tables, materialized views, PL/pgSQL, and many extensions (including pgvector and PostGIS). Without foreign key enforcement, applications must implement integrity checks in code, risking data drift, higher testing burden, and longer audits. DSQL migration requires the most application refactoring of the three.

The upshot: Amazon Aurora Global Database wins in terms of pure PostgreSQL compatibility. However, this is mitigated by operational limitations such as AWS-only deployment, planned maintenance downtime, manual sharding at scale, and limited multi-region write capabilities.

Additionally, the fact that Aurora Global Database is a fork of the open-source PostgreSQL code creates technical debt. This is because proprietary changes must be constantly "forward ported", and reconciled with code changes every time a newer version of PostgreSQL is released. This process becomes increasingly complex and time-consuming as the fork diverges further from the main project, effectively creating a maintenance burden that slows development and updates.

In comparison, CockroachDB provides strong compatibility for the majority of common Postgres use cases. Superior operations include zero-downtime, multi-region writes, and horizontal scaling, which are valuable for applications requiring global distribution and operational resilience. Technical debt is not an issue, because CockroachDB “owns the stack”: the PostgreSQL compatibility layer native to our implementation, which will never require forward porting.

Customer support experience: CockroachDB vs Aurora

Numerous online reviews of AWS support reveal customers voicing frustration. Some customers report difficult engineering access, tiered barriers making technical escalation challenging, limited expertise on database infrastructure versus optimization, long resolution times (some AWS users say it can sometimes take weeks for AWS support to close a ticket), and generic responses.

Even some AWS Enterprise support customers have said that it can be difficult to get knowledgeable help for complex database issues like distributed scenarios, failover planning, and performance optimization.

CockroachDB support emphasizes direct access to actual database engineers, with deep problem-solving expertise plus proactive guidance for operational database tuning, like performance optimization. Customer support also includes professional services covering migration assessment with MOLT tools, architecture design for multi-region/multi-cloud scenarios, performance tuning, and disaster recovery planning. Tailored training is available for enterprises of all sizes, from two-person startups to large organizations.

CockroachDB received Gartner Customers' Choice recognition in 2023 and 2024, with 97% customer recommendation rate in the 2024 Gartner report and strong G2 reviews praising engineering accessibility.

Migration tooling: MOLT vs AWS DMS

CockroachDB’s migration tooling and Amazon Aurora migration tooling serve two different purposes:

CockroachDB MOLT (Migrate Off Legacy Technology) is a seamless path for migrating PostgreSQL, Oracle, MySQL, and Microsoft SQL Server to CockroachDB.

AWS Database Migration Service (DMS) is a paid service that supports heterogenous migrations to databases in the AWS ecosystem.

The free MOLT toolkit along with our experienced professional services organization enables safe, minimal-downtime database migrations to CockroachDB:

MOLT Fetch extracts schema and data from the source database

MOLT Convert converts schemas with CockroachDB-specific optimizations

MOLT Verify validates data consistency post-migration

AWS DMS is a fully-managed paid service for database migrations to any AWS database. Components consist of a managed replication server to run migration tasks, endpoints for source and target database connections, and AWS’s SCT (schema conversion tool) for heterogeneous migrations.

Migration support services: CockroachDB vs Aurora

Amazon Aurora (Global/DSQL): AWS DMS alone doesn't offer engineering support for migrations, requiring expensive consulting engagements when an organization doesn’t have (or is unable to dedicate) experienced technical staff to design and run the migration.

CockroachDB: CockroachDB's Professional Services team combines tooling and expert guidance, using MOLT together with comprehensive migration engagements:

Assessment: Analyze source database and migration complexity

Planning: Design migration strategy and downtime windows

Schema optimization: Leverage MOLT for CockroachDB-specific tuning

Execution: Use MOLT tools with hands-on guidance

Validation: MOLT Verify ensures data integrity

Optimization: Post-migration performance tuning

Use both together for complex migrations:
Many teams leverage the strengths of both tools in tandem: DMS for initial heterogeneous conversion and CDC, then MOLT for final CockroachDB-specific optimization and cutover.

Key takeaway: Customer experience comparison

The optimal distributed SQL database choice comes down to organizational priorities. Amazon Aurora Global Database suits organizations requiring absolute PostgreSQL compatibility, if they are willing to make the tradeoff for single-writer architecture, AWS vendor lock-in, and potentially frustrating technical support experiences.

CockroachDB is the stronger choice for organizations prioritizing strong PostgreSQL compatibility for real-world applications, in step with:

operational resilience

global distribution

horizontal scalability

guaranteed-accessible engineering support

Aurora DSQL is best suited for greenfield applications with simple data models. For long-term success, choose CockroachDB: its distributed architecture, proactive support, and free migration tooling converge to deliver superior total cost of ownership.

Backup and recovery comparison: PITR support, RPO/RTO, and restore granularity

Backup type:
CockroachDB — Incremental, locality-aware
Amazon Aurora Global Database — Daily snapshots
Aurora DSQL — Full snapshot via AWS Backup

Backup frequency:
CockroachDB — Continuous
Amazon Aurora Global Database — Daily automated + manual
Aurora DSQL — Automated schedules

PITR:
CockroachDB — Yes (AS OF SYSTEM TIME)
Amazon Aurora Global Database — Manual configuration required
Aurora DSQL — No

Restore granularity:
CockroachDB — Table, database, or cluster
Amazon Aurora Global Database — Full database only
Aurora DSQL — Full cluster only

RPO:
CockroachDB — Near-zero
Amazon Aurora Global Database — <1 second
Aurora DSQL — Depends on snapshot frequency

RTO:
CockroachDB — Fast, targeted recovery
Amazon Aurora Global Database — 10–30 seconds failover + restore
Aurora DSQL — Full snapshot restore (variable)

Data loss risk:
CockroachDB — Minimal
Amazon Aurora Global Database — Possible during unplanned failover
Aurora DSQL — Between snapshots

Operational impact:
CockroachDB — Minimal, no production impact
Amazon Aurora Global Database — Downtime + planning required
Aurora DSQL — Limited flexibility

Zero-downtime upgrades:
CockroachDB — Yes (rolling upgrades)
Amazon Aurora Global Database — No (requires maintenance windows)
Aurora DSQL — AWS-managed

Rolling repaves:
CockroachDB — Yes
Amazon Aurora Global Database — No
Aurora DSQL — AWS-managed

Upgrade control:
CockroachDB — Full control
Amazon Aurora Global Database — Limited
Aurora DSQL — None

Application changes required:
CockroachDB — No
Amazon Aurora Global Database — Often required
Aurora DSQL — No

Replication model impact:
CockroachDB — Multi-active
Amazon Aurora Global Database — Single-writer
Aurora DSQL — Limited active-active

Operational maturity:
CockroachDB — 10+ years
Amazon Aurora Global Database — Mature
Aurora DSQL — Recently GA

Operational impact:
CockroachDB — Zero downtime, seamless
Amazon Aurora Global Database — Planned downtime
Aurora DSQL — Low burden, low control

Key takeaway: CockroachDB vs Aurora Global Database and DSQL

This comprehensive analysis explored fundamental architectural differences between CockroachDB, Amazon Aurora Global Database, and Amazon Aurora DSQL. Their design divergences directly impact enterprise operations across four critical database dimensions:

resilience

scalability

deployment flexibility

customer experience

Our comparative analysis shows that Amazon Aurora Global Database doesn’t live up to its name. It’s limited by its own single-region write architecture, best serving organizations that choose to prioritize absolute PostgreSQL compatibility. The tradeoffs: eventual consistency, data latency, and the potential risk of data loss in failover scenarios.

We’ve also seen how Aurora DSQL is indeed a step forward for AWS databases when it comes to supporting modern distributed application architecture. But DSQL’s far-from-complete Postgres compatibility and geographical constraints keep it from being a serious contender for truly global enterprise-scale workloads.

How CockroachDB supports modern distributed application requirements

Organizations that deploy CockroachDB demand:

unwavering dependability for critical operations

native scaling capabilities

flexible deployment options

straightforward operation with dedicated engineering support.

As the pioneering distributed SQL database, CockroachDB eliminates compromise. Enterprises can have uptime, scalability, and data consistency at the core of their database architecture.