Architecture / Ideal Workloads
Distributed SQL, shared-nothing, peer-to-peer architecture. All nodes symmetrical; any node can handle reads/writes. Cluster uses distributed consensus: No matter where data lives, every node can access data anywhere in cluster
SYSTEM OF RECORD. Optimized for transactional workloads that require strong consistency and global distribution, such as AI innovators, cybersecurity, eCommerce & retail, financial services, fintech/payments, gaming, quant/trading & research, and online travel
Distributed NoSQL wide-column store using a masterless ring architecture, consistent hashing, and log-structured storage (SSTables)
WRITE-HEAVY, HIGH-THROUGHPUT WORKLOADS with predictable access patterns such as telecom, adtech/martech, consumer Internet, and IoT
Auto-Sharding (Dynamic Re-Sharding Online)
NATIVE & AUTOMATIC. Automatically shards data into ranges and dynamically splits, merges, and rebalances online across nodes based on load and size. Zero downtime, fully transparent
Sharding is based on partition keys via consistent hashing; rebalancing occurs automatically on topology changes, but schema must be designed around shard keys upfront
Automatic Geo-Partitioning (Multi-Region Data Affinity)
NATIVE AND AUTOMATIC. Declarative SQL schema adjustments automatically route, partition, and anchor data close to the user's location, automatically moving data to the region where it is most frequently accessed. Supports geo-partitioning with zone configurations for data locality, compliance, and low latency
NO AUTOMATIC GEO-PARTITIONING. Data affinity is achieved through manual partition key and replica placement decisions; requires workload-specific schema design
Availability including Multi-Cloud and Hybrid
Available on all public clouds, e.g., AWS-Google Cloud-Azure; can run a single logical cluster spanning multiple clouds. Can run on prem/local, and cloud + prem hybrid deployments
Can be deployed across datacenters and clouds, but topology, consistency, and failover patterns require operator configuration. No native single-cluster multi-cloud abstraction; multi-region deployments are managed as separate datacenters within the ring
Change Data Capture (CDC)
NATIVE. CHANGEFEED command enables scalable, resilient streaming of data changes to Kafka, cloud storage, and webhooks; no third-party CDC tool needed. CDC Queries enable SQL-based filtering and transformation of streams
CDC is not native in open-source Cassandra; implemented via commit-log reading or third-party tools such as Debezium
Data Anomalies
ZERO under Serializable isolation: all standard SQL anomalies (dirty reads, non-repeatable reads, phantom reads, lost updates, and write skew) are prevented by default with no additional developer configuration. Read Committed is also available for workloads where some consistency relaxation is an acceptable tradeoff for reduced latency
MODERATE TO HIGH. Tunable consistency exposes anomalies at lower consistency levels, including dirty reads, non-repeatable reads, and lost updates. Even at QUORUM, certain anomalies remain possible without application-level safeguards. No ANSI SQL isolation semantics
Data Integrity & Foreign Keys Support
Strict ACID enforcement at the storage layer and full referential integrity; natively validates foreign keys, explicit CHECKs, and transactional constraints to ensure absolute global correctness across global nodes
No data integrity enforcement at the database layer. No foreign keys or CHECK constraints; integrity is managed at the application layer
Data Model Complexity
LOW. Relational model with strict schemas, normalized tables, joins, and referential integrity. Ideal for managing complex relationships and transactional systems of record; adapts easily to microservices and enterprise legacy systems
MODERATE. Wide-column model organized by partition and clustering keys. Schema must be designed query-first, and cross-entity relationships must be handled manually, not at the data layer but in the application layer
Data Residency
STRONG, INTUITIVE, AND LOCALITY-AWARE. Helps fulfill compliance (e.g., GDPR, CCPA) with Row-Level Control: can pin specific rows to specific geographic regions using REGIONAL BY ROW command, while preserving single logical data platform. Business and compliance teams can use simple SQL commands to ensure customer data never leaves specific geographic borders
Residency is handled via partition key design and replica placement, using separate keyspaces or tables per geography, or per-region clusters
Developer Tools / Experience / Ease of Use
Rich ecosystem: Local CLI, web console UI, ORMS, BI tools, SQL clients, native DB migration toolkits, language‑specific drivers, and compatibility with standard PostgreSQL developer tools like psql
PostgreSQL wire protocol-compatible; feels exactly like developing on standard PostgreSQL. Fits effortlessly into existing ORMs, drivers, and frameworks
Can be spun up instantly in any environment (AWS, GCP, on-prem) with the exact same management interface. The cluster manages its own data balancing, scaling, and hardware survival automatically; DBAs do not need to be distributed systems experts to keep it running smoothly.
Ecosystem includes cqlsh CLI, drivers for major languages, and integrations with streaming and analytics tools such as Spark and Kafka. Management tooling varies by commercial distribution
Requires query-first schema design and understanding of partition key mechanics. Workable for key-value-style access patterns; difficult for relational or ad hoc query workloads
Node additions, topology changes, and compaction tuning require distributed systems expertise. Operational complexity increases at scale
Distributed ACID Transactions
Fully distributed, multi-row, multi-table ACID transactions out-of-the-box. Fully supported with serializable isolation using distributed consensus (Raft Protocol) across tables, ranges, and regions; strong ACID guarantees
NO. No multi-partition or multi-table ACID transactions. Limited to per-partition atomicity; lightweight transactions (LWT) provide compare-and-set but are expensive and still partition-scoped
Enterprise Support
Dedicated 24/7/365 enterprise support directly from Cockroach Labs with strict SLAs and custom engineering channels. Offers global follow-the-sun support (TSE+SRE) with proven reliability and global partnerships with industry leaders. Single Global Incident Management integrates Engineering + Support + Customer Success in one channel for consistency/immediacy
Apache Cassandra is community-supported with no official SLAs. Commercial support available via third-party vendors (DataStax, Instaclustr, others); SLA depth and quality vary by vendor
FinOps Support
HIGH. Straightforward pricing based on predictable node usage or consumption metrics. Avoids hidden, fluctuating network traps when moving data across different infrastructure regions. Supports financial governance/FinOps
MODERATE. Open-source core is free, but JVM tuning, compaction management, and large node counts add operational cost. Commercial distributions and managed services add licensing fees. Egress costs across datacenters are operator-managed
Follower Reads
SUPPORTED. Supports follower/replica reads with Bounded (controlled) Staleness, allowing low‑latency local reads from nearby replicas while keeping strong global ordering
NO DEDICATED FOLLOWER READ ABSTRACTION. Replica reads are governed by consistency level and replication lag; staleness is inherent at lower consistency levels
FREEDOM
ZERO VENDOR LOCK-IN. Runs on any public or private cloud, across multiple clouds, via CockroachDB's Bring Your Own Cloud (BYOC) offering, on-premises, bare metal, Kubernetes, self-hosted, or in a hybrid deployment encompassing some or all of these. Business Source License (BSL) but Source Available. Full commercial-grade support directly from CockroachDB
ZERO VENDOR LOCK-IN. Apache-licensed open source for core Cassandra gives freedom to use, modify, and redistribute—but users relying on the open-source version must depend on community-based, non-guaranteed support
Joins
Executes fully distributed hash, merge, and lookup joins across arbitrary nodes with CockroachDB's advanced Cost-based Optimizer. Full standard SQL support for complex INNER, OUTER, LEFT, RIGHT joins across distributed tables
NO SERVER-SIDE JOINS. Join-like behavior must be denormalized into the schema or implemented in the application layer
LDAP Support
NATIVE. Direct native support for external authentication systems like LDAP, Active Directory, GSSAPI, and OIDC
NOT NATIVELY SUPPORTED in open-source Apache Cassandra. LDAP authentication is available via commercial distributions such as DataStax Enterprise
Migrations
Uses MOLT (Migration Off Legacy Technology) Toolkit & change data capture (CDC): MOLT handles schema conversion/verification and CDC moves data out. PostgreSQL wire protocol compatibility enables lift-and-shift; shadow mode testing
Migration to or from relational systems requires ETL and schema redesign tailored to partition keys and expected query patterns
Multi-Active
YES: FULLY MULTI-ACTIVE/MULTI-REGION; read/write and handle connection requests from any node in the cluster. All nodes are equal and active; any node can accept read and write traffic simultaneously.
MULTI-ACTIVE, TO A POINT. All nodes are peers in the ring and can serve partition-local reads and writes. Multi-active but within the bounds of eventual or tunable consistency
Multi-Data-Center Support
FULL. Connects geographically isolated, heterogeneous data centers (AWS, GCP, Azure, on-prem) into a single logical cluster, supported by features such as Physical Cluster Replication (PCR) and Logical Data Replication (LDR)
Supports multi-datacenter deployments via Network Topology Strategy replication, with configurable replication factors per datacenter. Topology, failover, and cross-datacenter consistency require manual configuration and ongoing operational management
Multi-region Functionality / Multi-region Writes
ACTIVE-ACTIVE: Read/Write from any node in any region; built-in low-latency local access patterns and Survival Goals (e.g., ALTER DATABASE...SURVIVE REGION FAILURE) commands configure fault tolerance intent
True multi‑region, multi‑active writes: any node in any region can serve reads and writes while preserving serializable consistency guarantees
ACTIVE-ACTIVE, TO A POINT. Achieved via multi-datacenter replication in the ring. Consistency and latency vary depending on chosen replication and consistency levels
Writes can be accepted in any region owning a partition, but global ordering and cross-region consistency are not guaranteed
Replication
Built-in, automatic consensus replication using the Raft protocol; data is divided into ranges and replicated across nodes
Asynchronous, tunable replication using configurable consistency levels (ONE, QUORUM, ALL, etc.). Replication factor set per keyspace. Periodic repair operations required to maintain consistency across replicas
Required Downtime
ZERO. Online schema changes, rolling upgrades, and cluster expansion occur without taking the data platform offline
LOW. Rolling upgrades and node additions can occur without full-cluster downtime, but major schema changes or topology changes can require careful coordination and may impact availability
Resilience
Five 9s availability: Survives node/disk/rack/region failures automatically via Raft consensus, with zero data loss (RPO=0). Naturally resilient to outages with granular row-level control
Uses tunable replication to survive node and datacenter failures while prioritizing availability and partition tolerance over consistency
Scale
Virtually unlimited horizontal scale-out. Automatic, seamless handling of growing datasets; increase storage and throughput capacity linearly simply by adding more nodes
Scales linearly, especially for partition-key-centric, write-heavy workloads
Schema Changes
FULLY ONLINE & NON-BLOCKING. Online transactional schema changes (add/alter columns, indexes, constraints) run in the background without locking tables with zero downtime. Designed for always‑on services
Schema changes are supported but can affect performance at scale and require planning to avoid hotspots and compaction overhead
Security-Privacy-Compliance
RBAC, Encryption at Rest with Customer Managed Encryption Keys (CMEK), TLS encryption in transit, IAM integrations, column-level encryption, and robust data-masking natively. Fine-grained encryption at cluster, database, table, or partition levels. Certified SOC 2 Type II and SOC 3, PCI-DSS, HIPAA, and ISO 27001-27017-27018 compliant, with ISO 42001 (Responsible, Ethical, and Safe AI Governance) pending. CockroachDB CIS Benchmarks to deploy hardened CockroachDB configurations. Comprehensive support for GDPR and DORA compliance
Supports TLS encryption in transit, password or certificate-based authentication, and role-based access control. Encryption at rest is available via OS-level or third-party tooling. Audit logging and advanced enterprise security features are generally limited to commercial distributions
SQL Compatibility
HIGH. PostgreSQL Wire Compatible: Uses PG wire protocol; strong ANSI SQL with complex queries, joins, window functions, triggers, stored procedures, and UDFs. Supports spatial data, extensions, syntax; most apps connect with minimal or no changes
LOW. Uses CQL (Cassandra Query Language), which resembles SQL in syntax but lacks joins, subqueries, and many SQL operators. Query capabilities are largely constrained by partition key design
Storage Engine
Built on Pebble, a highly tuned, GO-based, LSM-tree key-value storage engine developed by Cockroach Labs and inspired by RocksDB specifically for distributed SQL
Built on log-structured storage using SSTables with periodic compaction, optimized for high write throughput on commodity hardware
Stored Procedures
SUPPORTED AND MATURE. PL/pgSQL and other languages such as Python and Perl support deep procedural logic, autonomous transactions, and complex business rule enforcement. Supports user-defined stored procedures
NO TRADITIONAL STORED PROCEDURES. Business logic is embedded in application services, with limited server-side scripting available
Transaction Performance / Isolation Levels
Optimized for OLTP with strong consistency; cross‑region transactions maintain data correctness. Optimizations like Parallel Commits drop distributed execution overhead to a single network round-trip for most transactions
Enforces strict Serializable isolation exclusively, the strongest isolation level, to ensure zero data anomalies under heavy parallel traffic, and Read Committed
Does not offer ANSI SQL isolation levels; behavior is governed by consistency levels and partition-level semantics
Single-partition writes are fast—but result from trading consistency for speed
Triggers & Deferrable Constraints
FULLY SUPPORTED. Supports triggers and deferrable constraints across all deployment models
NO RELATIONAL TRIGGERS OR DEFERRABLE CONSTRAINTS. Behavior is modeled via schema design and managed at the application layer
Vector Search
BUILT-IN NATIVE VECTOR SEARCH, scalable distributed HNSW/IVF indexing, and pgvector (the industry standard for vector similarity search). CockroachDB's C-SPANN provides distributed vector indexing (ANN) at scale; available across all tiers. Suited for AI/ML inference and RAG applications where vectors and transactional data coexist in one engine without a separate vector database
NO NATIVE VECTOR SEARCH CAPABILITIES. Requires integration with external or ecosystem components
Writes and Query Routing
Every node is a gateway to the entirety of the database for unlimited reads and writes in any region. Any node can accept SQL queries; a Distributed Optimizer routes work to the right ranges/replicas based on locality and cost
Client drivers route queries to nodes responsible for the partition key. Poor partition key design can cause unbalanced load, hotspots, and scatter-gather query patterns
PRICING
SIMPLE. Commercial Enterprise: Simple, straightforward pricing, plus the ability to tie data to a location to avoid egress costs. Free for single-node/dev. Free Community Tier
SIMPLE. Apache Cassandra is open-source and free to run. Commercial distributions and managed services (DataStax, Instaclustr, Amazon Keyspaces) add licensing and support costs
