PostgreSQL vs. ScyllaDB vs. ClickHouse: Which Database Dominates Modern Workloads?

In today’s high-speed digital landscape, database selection shapes the performance, scalability, and resilience of applications—from enterprise systems to real-time analytics platforms. PostgreSQL, ScyllaDB, and ClickHouse represent distinct paradigms: PostgreSQL as a robust, ACID-compliant relational powerhouse; ScyllaDB, a high-throughput, distributed NoSQL designed for write-heavy workloads; and ClickHouse, a columnar database engineered for blazing-fast analytical queries. Each system excels in different use cases, but choosing the right one demands deep understanding of their architectures, performance traits, and operational trade-offs.

This detailed comparison reveals how these databases stack up across critical dimensions—security, scalability, query speed, consistency models, and real-world applicability.

Core Architectures and Data Models

PostgreSQL operates as a full-featured relational database managed system (RDBMS), built on a multi-version concurrency control (MVCC) engine that ensures transactional integrity and isolation levels compliant with ANSI SQL standards. Its schema-driven, row-store architecture supports complex joins, foreign keys, and nested transactions—making it ideal for applications requiring data coupling and strict consistency.

In contrast, ScyllaDB is a distributed, NoSQL database built as a compatible alternative to Apache Cassandra with a focus on low-latency writes and high availability. It uses an API identical to Cassandra but runs on a multi-datacenter, consistent, strongly partitioned architecture with eventual consistency by default (with tunable read consistency). ScyllaDB stores data as large key-value pairs optimized for Cartesian-style reads—perfect for use cases demanding rapid ingestion and horizontal scalability.

ClickHouse, conversely, implements a columnar storage engine optimized for analytical query performance. Columnar storage groups similar data columns together, enabling efficient compression and fast aggregation—critical for large-scale data aggregations and reporting. Unlike row-store or document models, ClickHouse processes queries by scanning only relevant column segments, drastically reducing I/O.

It supports hybrid transactional-analytical processing (HTAP) but remains primarily a read-optimized system for analytics, not OLTP.

Performance Benchmarks: Speed, Scale, and Throughput

When measuring raw throughput, ScyllaDB consistently outperforms both PostgreSQL and ClickHouse in write-intensive scenarios. Benchmarks show ScyllaDB handling tens of thousands of writes per millisecond with minimal latency, thanks to its asynchronous, multicore-friendly design and elimination of lock contention.

This makes it the preferred choice for IoT telemetry, clickstream analytics, and event logging systems generating continuous high-volume writes. PostgreSQL, while powerful for transactional workloads, struggles under extreme write throughput. Its MVCC model incurs overhead during dense concurrent writes, and scaling write operations across nodes requires additional infrastructure like logical replication or external drivers.

Nevertheless, PostgreSQL maintains dominance in low-to-moderate write scenarios, especially where ACID guarantees and complex queries are non-negotiable. ClickHouse excels in read performance, particularly for analytical queries involving aggregations and filters across petabytes of data. Its columnar storage and predicate pushdown allow faster query execution even on massive datasets.

For example, aggregating billion-row time-series data in seconds—impossible in PostgreSQL without complex joins and pre-materialization—takes milliseconds in ClickHouse. However, write performance lags behind ScyllaDB, with disk and network I/O becoming bottlenecks as write volume increases.

Scalability and High Availability

Scalability defines competitive advantage in distributed systems.

PostgreSQL supports vertical scaling effectively via hardware upgrades, but horizontal scaling demands complex replication setups like streaming replication or logical replication, often requiring manual orchestration and leading to data consistency trade-offs. Clustering solutions like Patroni can decouple management from storage, yet full horizontal scalability remains challenging and complex. ScyllaDB is architected from the ground up for horizontal scalability.

Built on Cassandra-style partitioning, it automatically splits data across nodes in a consistent, tunable strategy that balances load and replicates data for fault tolerance. ScyllaDB’s partitioning is lightweight and automatically rebalanced, supporting seamless scaling from a single node to hundreds without downtime. Combined with pluggable storage options—including AWS Storage Gateway and managed services—it suits cloud-native deployments and microservices architectures where agility matters most.

ClickHouse’s scalability is columnar-specific: adding nodes increases storage and compute capacity but does not inherently support distributed transactional writes across shards. Query execution spans partitions, requiring careful data modeling—such as flattening hierarchies or pre-aggregating at ingestion time—to maximize parallel processing. While ClickHouse handles petabyte-scale data efficiently, true bidirectional scaling for write-heavy, interactive workloads remains limited compared to ScyllaDB.

Consistency, ACID Compliance, and Data Integrity

Maintaining data consistency is critical in enterprise environments. PostgreSQL enforces ACID compliance with serializable isolation levels and robust recovery mechanisms, ensuring every transaction is durable and consistent even in failure scenarios. This makes it a cornerstone for financial systems, inventory management, and order processing platforms where accuracy cannot be compromised.

ScyllaDB prioritizes availability and partition tolerance over strict consistency, using an ultimately consistent model with tunable read consistency. By default, it supports quorum reads (e.g., all-node confirmation), but developers can opt for eventually consistent reads to improve latency—a deliberate design choice favoring performance in globally distributed systems. While this trades off immediate consistency, ScyllaDB maintains full ACID semantics for individual transactions, combining speed with reliability.

ClickHouse sacrifices ACID guarantees entirely in favor of speed and scale. Designed for analytical reporting, it does not support row-level updates or strict transactions. Instead, it assumes immutable datasets: every query operates on a snapshot, and data is immutable after ingestion.

While unsuitable for transactional systems, this model enables high-speed aggregations and real-time dashboards without locking or rollback overhead—ideal for log analytics and monitoring platforms where data is refreshed nightly.

Security, Operations, and Ecosystem Maturity

Security implementation varies significantly across the trio. PostgreSQL offers comprehensive, enterprise-grade security features: row-level security policies, fine-grained authentication (LDAP, LDAP integration), encryption at rest via transparent data encryption (TDE), and role-based access control (RBAC) with extended capabilities through extensions like pg_row_level_security.

Its mature ecosystem includes robust auditing and compliance reporting, making it a standard in regulated industries. ScyllaDB delivers solid security with TLS encryption in transit, granular role-based access control, and integration with HashiCorp Vault for dynamic secrets. While powerful, its native security tooling is less mature than PostgreSQL’s, requiring third-party extensions for advanced features like audit logging or row-level masking.

Operationally, ScyllaDB benefits from familiar Cassandra deployment patterns but lacks PostgreSQL’s broad enterprise support ecosystem. ClickHouse emphasizes simplicity in secure deployment, especially cloud-managed versions like AWS Redshift Spectrum or Snowflake-managed instances. Encryption at rest and in transit is core, with built-in column-level masking and secure data routing.

However, since ClickHouse evolved more recently, its ecosystem of plugins and integrations—while growing—is still narrower, particularly for hybrid transactional analytics and external service orchestration.

Real-World Use Cases and Industry Fit

PostgreSQL’s versatility makes it the go-to choice for applications demanding complex queries, rich data types (JSON, arrays, geometric), and transactional integrity. It powers content management systems, e-commerce order engines, and financial analytics platforms where joined data relationships matter.

Its extensibility via SQL/JSON and native support for geographic information systems (PostGIS) further broaden its appeal. ScyllaDB thrives in high-throughput, latency-sensitive environments. Use cases include real-time clickstream processing, IoT data ingestion, live dashboards, and social media feed generation—where millions of writes per second must surface instantly.

Companies like LinkedIn and Tesla rely on ScyllaDB for telemetry pipelines requiring persistent, turnkey scalability. ClickHouse dominates analytics and business intelligence. Data warehouses, ad-hoc reporting platforms, and customer behavior analytics platforms leverage its accelerated aggregation and compression.

Financial firms use it for real-time fraud detection dashboards, while retailers deploy it for dynamic inventory forecasting. Its query performance enables “self-service” analytics without heavy ETL overhead.

Choosing the Right Fit: A Quick Guide

Selecting between PostgreSQL, ScyllaDB, and ClickHouse hinges on three pillars: workload type, performance requirements, and consistency needs.

- Choose PostgreSQL when you require ACID transactions, complex querying, or data integrity guarantees—ideal for OLTP systems, financial transactions, and applications with rich data schemas. - Opt for ScyllaDB if high write throughput, horizontal scaling, and low-latency data ingestion define your bottlenecks—perfect for IoT, real-time logging, and streaming analytics platforms. - Pick ClickHouse when analytical performance, columnar optimization, and quick aggregation speed matter most—ideal for business intelligence, time-series analytics, and dashboards.

In short: PostgreSQL delivers relational robustness and vers