Choosing between ClickHouse and PostgreSQL often comes down to a single question: are you building features that aggregate millions of rows, or are you managing transactions that update individual records? Both databases use SQL, but they're optimized for fundamentally different workloads, and picking the wrong one can mean slow queries, expensive infrastructure, or complicated migrations later.
This article compares storage models, performance characteristics, scalability approaches, and the impact of PostgreSQL extensions on the decision. You'll also learn practical migration paths if you're already running PostgreSQL and considering ClickHouse for analytics.
Storage model and workload fit
PostgreSQL stores data in rows, where each record sits sequentially on disk with all its columns together. ClickHouse stores data in columns, where values from the same column are grouped together regardless of which row they belong to. This difference determines which database fits your workload better.
Row storage works well when you need to read or update entire records at once, like fetching a user profile or processing an order. Column storage excels when you're scanning millions of rows but only need a few columns, like calculating total revenue or counting active users. The storage model affects everything from query speed to disk usage to how you scale.
OLTP row storage in PostgreSQL
PostgreSQL optimizes for Online Transaction Processing (OLTP), where applications frequently insert, update, and delete individual records. OLTP systems handle things like user registrations, inventory updates, and payment processing.
Row-based storage makes OLTP efficient because:
- Complete record access: Updating a user's email address or phone number reads all fields from one disk location instead of jumping between column files
- ACID guarantees: PostgreSQL ensures transactions either complete fully or roll back entirely, preventing partial updates that corrupt data
- Write efficiency: Adding a new order writes all fields in one operation rather than appending to multiple column files
- Concurrency control: Multiple users can update different records simultaneously without blocking each other
PostgreSQL handles workloads where data integrity matters more than raw analytical speed. E-commerce platforms, content management systems, and authentication services typically fit this pattern.
OLAP column storage in ClickHouse
ClickHouse targets Online Analytical Processing (OLAP), where queries aggregate large datasets to produce reports, dashboards, or metrics. OLAP systems analyze user behavior, track system performance, or generate business intelligence.
Columnar storage provides specific advantages:
- Compression efficiency: Storing similar values together compresses better than mixing data types, often achieving 10× to 50× compression on real-world datasets
- Vectorized execution: Processing entire columns in single CPU instructions (SIMD) runs faster than row-by-row operations
- Selective reads: Queries needing three columns read only those three from disk instead of scanning full rows
- Parallel processing: Different CPU cores can process separate columns simultaneously
ClickHouse handles workloads where query speed across large datasets matters more than individual record updates. Event tracking, log analysis, and real-time dashboards typically fit this pattern.
ClickHouse vs PostgreSQL performance comparison
Performance differences grow as datasets scale from thousands to billions of rows. PostgreSQL handles analytical queries reasonably well on smaller datasets, but ClickHouse becomes meaningfully faster starting around 1 million rows, maintaining sub-second latency even when scanning massive tables.
The gap comes from storage architecture and query execution. PostgreSQL reads entire rows even when queries only need a few columns. ClickHouse reads specific columns and processes them with CPU-level optimizations unavailable to row-based systems.
Aggregate query latency benchmarks
Aggregation queries reveal the clearest performance differences. Computing COUNT(DISTINCT user_id) or SUM(revenue) across 100 million events might take 30 seconds in PostgreSQL but under 1 second in ClickHouse on identical hardware.
This advantage compounds with dataset size. ClickHouse only reads columns needed for aggregation, while PostgreSQL scans entire rows. On a table with 20 columns where your query uses 3, ClickHouse reads 85% less data from disk.
Compression amplifies this benefit. ClickHouse might read 1 GB of compressed data representing 50 GB uncompressed, while PostgreSQL scans closer to the full uncompressed size.
Concurrency and throughput tests
PostgreSQL handles concurrent transactional operations better because it was built for that pattern. When multiple users update different records simultaneously, PostgreSQL's MVCC (Multi-Version Concurrency Control) prevents lock contention and maintains consistency.
ClickHouse handles concurrent analytical queries differently. Instead of optimizing write concurrency, it maximizes read throughput where hundreds of users run aggregation queries simultaneously. ClickHouse achieves this through efficient resource utilization and parallel query execution.
For analytical workloads specifically, ClickHouse serves more concurrent dashboard queries than PostgreSQL. This matters for customer-facing analytics where many users query data at the same time.
Compression and disk IO
Columnar compression in ClickHouse typically reduces storage by 10x-100x compared to PostgreSQL's row-based storage. This compression directly impacts query performance by reducing disk reads.
PostgreSQL offers compression through extensions, but row-based data doesn't compress as well. Adjacent values in a row often have different data types and ranges, limiting compression effectiveness. ClickHouse columns contain homogeneous data types with similar value ranges, which compress efficiently using algorithms like LZ4 and ZSTD.
Lower disk IO translates to faster queries. Reading 1 GB of compressed data that represents 50 GB uncompressed completes much faster than scanning 50 GB of less-compressed row data.
Scalability and high-availability differences
Both databases scale, but they take different approaches reflecting their workload priorities. PostgreSQL focuses on vertical scaling and read replicas for OLTP workloads. ClickHouse emphasizes horizontal scaling and distributed query execution for analytical workloads.
The scaling approach affects infrastructure planning as data grows. PostgreSQL handles analytical workloads at smaller scales, but scaling for large analytical queries becomes complex and expensive. ClickHouse scales more naturally for analytical workloads but isn't built for transactional patterns.
Vertical and read replica scaling in Postgres
PostgreSQL scales vertically by adding more CPU, memory, and faster storage to a single server. This works well for OLTP workloads because transactions operate on small data sets, and vertical scaling handles increased load without architectural changes.
For read-heavy workloads, PostgreSQL uses read replicas where writes go to a primary server and reads distribute across multiple replica servers. This offloads analytical queries from the transactional primary, but each replica contains a complete data copy, which gets expensive as datasets grow.
Horizontal scaling for writes typically requires sharding, where you partition data across multiple independent PostgreSQL instances. This adds application complexity because your code determines which shard contains which data, and cross-shard queries become difficult.
Shared-nothing sharding in ClickHouse
ClickHouse uses a shared-nothing architecture where data distributes across multiple servers, and each server processes its local data independently. Queries run in parallel across all servers, with each server processing only its data portion.
Sharding in ClickHouse is transparent to queries. You write queries against a distributed table, and ClickHouse automatically routes the query to all shards, executes in parallel, and combines results. This makes horizontal scaling simpler because applications don't need to know about the sharding scheme.
ClickHouse also supports replication within shards for high availability. Each shard can have multiple replicas, and if one replica fails, queries automatically route to healthy replicas.
Replication and failover models
PostgreSQL provides synchronous and asynchronous replication for high availability. Synchronous replication ensures writes confirm on replicas before acknowledging to the client, guaranteeing no data loss during failover but adding latency. Asynchronous replication is faster but can lose recent writes if the primary fails.
ClickHouse uses asynchronous replication by default, which fits analytical workloads where eventual consistency is acceptable and write throughput matters more than immediate durability. ClickHouse's ReplicatedMergeTree engine handles replication automatically, syncing data across replicas in the background.
Failover in PostgreSQL typically requires external tools like Patroni to promote a replica to primary. ClickHouse handles replica failures automatically; if a replica goes down, queries route to other replicas without manual intervention.
Update, delete, and mutability trade-offs
How each database handles updates and deletes reveals core architectural differences. PostgreSQL treats updates and deletes as first-class operations with immediate consistency. ClickHouse handles them as background operations that eventually take effect.
This difference matters when choosing between databases. If your application frequently updates individual records and needs those changes visible immediately, PostgreSQL fits better. If you primarily append new data and occasionally update or delete older data, ClickHouse's approach works well.
Postgres MVCC strengths
PostgreSQL uses Multi-Version Concurrency Control (MVCC) to handle concurrent updates without locking. When you update a row, PostgreSQL creates a new version rather than modifying it in place, allowing other transactions to continue reading the old version until the update commits.
MVCC provides several benefits:
- No read locks: Readers never block writers, and writers never block readers, enabling high concurrency for transactional workloads
- Snapshot isolation: Each transaction sees a consistent data snapshot as it existed when the transaction started
- Rollback efficiency: Because old versions are preserved, rolling back a transaction is simple and fast
The tradeoff is that PostgreSQL accumulates dead rows over time, requiring VACUUM operations to reclaim space. For workloads with frequent updates, this maintenance overhead is acceptable because the concurrency benefits outweigh the costs.
ClickHouse merge-tree and TTL work-arounds
ClickHouse's MergeTree engine is append-only by design. When you insert data, it writes new data parts to disk. Updates and deletes happen through background merge operations that eventually rewrite affected data parts.
The ALTER TABLE UPDATE and ALTER TABLE DELETE statements in ClickHouse are mutations. Mutations mark rows for modification but don't immediately change the data. Background processes merge data parts and apply mutations asynchronously, which means updated or deleted rows remain visible until the merge completes.
For time-based data expiration, ClickHouse offers TTL (Time To Live) settings that automatically delete old data based on timestamp columns. This works well for log retention where you keep the last 90 days and automatically drop older data without explicit delete statements.
Advanced architectural and performance design
Storage engines and internal architecture
Both databases handle demanding workloads efficiently, but their internal architectures follow entirely different design principles. ClickHouse’s MergeTree family serves as its core storage engine, built for append-only data, replication, partitioning, and automatic TTL-based retention.Variants like ReplacingMergeTree, SummingMergeTree, and AggregatingMergeTree allow flexible approaches to deduplication and materialized aggregation without complex ETL pipelines.PostgreSQL, by contrast, uses a heap-based storage model supported by WAL (Write-Ahead Logging) for durability and crash recovery. This ensures ACID compliance, but also introduces write amplification and heavier I/O under analytical workloads.In ClickHouse, background merges continually combine smaller parts into optimized segments. Proper configuration of parameters such as max_part_size, max_bytes_to_merge_at_once, and background_merges_mutations_concurrency keeps ingestion throughput stable even during massive batch inserts.***
Query execution models
ClickHouse processes queries using a vectorized execution engine, scanning data in CPU cache-sized blocks instead of row-by-row. This design unlocks SIMD-level parallelism, achieving far greater throughput for aggregations and scans.PostgreSQL executes tuple-by-tuple, which enables complex procedural logic and transactional consistency but limits CPU efficiency for analytical workloads. On large datasets, ClickHouse’s approach results in dramatically faster execution thanks to columnar caching, while PostgreSQL depends on index filtering and buffer reuse to mitigate slower reads.***
Compression and data encoding
ClickHouse offers per-column compression codecs, including LZ4, ZSTD, Delta, and Gorilla, giving developers fine-grained control over space savings and query performance. With proper tuning, ClickHouse routinely achieves 80–95% data reduction. Numeric and time-series data compress best with Delta or Gorilla, while ZSTD excels for text-heavy workloads.PostgreSQL only compresses large values through TOAST, which offers limited benefit for analytical queries. Its row-based structure mixes multiple data types within blocks, reducing compression efficiency and increasing disk I/O. In contrast, ClickHouse’s homogeneous column segments compress more effectively and accelerate decompression at query time.***
Background operations and lifecycle management
ClickHouse executes merges, mutations, and TTL deletions asynchronously in the background. These maintenance operations ensure compacted storage and fast read access without blocking ingestion. For manual optimization, developers can run OPTIMIZE TABLE to consolidate parts immediately.PostgreSQL uses VACUUM and autovacuum to reclaim dead tuples created by MVCC. While essential for consistency, this introduces I/O overhead and must be tuned carefully using parameters like autovacuum_vacuum_cost_limit and autovacuum_naptime to maintain throughput in mixed workloads.***
Parallelism and ingestion tuning
ClickHouse supports asynchronous inserts and buffer tables that allow multi-threaded ingestion across nodes. Parameters like async_insert and max_insert_threads enable sustained throughput of millions of rows per second.PostgreSQL improves bulk loading via COPY and UNLOGGED tables, but transactional overhead still limits write speed. Even when partitioned, PostgreSQL’s ingestion typically caps an order of magnitude below ClickHouse’s sustained rates on similar hardware.***
Query planning and optimization
ClickHouse optimizes execution through primary key sorting and data skipping indexes, drastically reducing scanned ranges. Additional structures—min-max, Bloom filters, or token indexes—further speed up selective queries.PostgreSQL’s cost-based planner offers strong flexibility with B-tree, GIN, GiST, and BRIN indexes. BRIN improves sequential analytical scans but can’t offset the inherent overhead of row-based reads.For complex joins and correlated subqueries, PostgreSQL still holds an advantage, but ClickHouse dominates on pure aggregation speed.***
Transaction semantics and durability layers
PostgreSQL guarantees full ACID compliance, using synchronous WAL commits to ensure zero data loss under any condition.
ClickHouse prioritizes throughput and scalability, providing eventual consistency through ReplicatedMergeTree. Each part includes checksums and metadata hashes to maintain reliability. Asynchronous replication ensures fault tolerance with minimal latency impact, fitting analytical use cases where real-time durability is less critical.***
Operational performance, observability, and scalability patterns
System observability and diagnostics
ClickHouse exposes detailed system tables like system.query_log, system.metrics, and system.parts, offering real-time visibility into query plans, merge operations, and resource utilization. Developers can trace I/O pressure, CPU consumption, or replication lag directly through SQL.PostgreSQL provides extensive monitoring through pg_stat_statements and pg_stat_activity, ideal for transactional visibility but less granular for large analytical clusters. Integrating ClickHouse with Prometheus and Grafana enables dashboards tracking latency histograms, merge queues, and compression ratios out of the box.***
Backup and recovery mechanisms
PostgreSQL’s Point-In-Time Recovery (PITR) uses WAL archiving to restore databases to precise states, a robust but storage-heavy solution for analytical workloads.
ClickHouse introduces incremental, part-level backups, reducing storage footprint and recovery times. Snapshots integrate with S3, Azure Blob, or GCS, allowing remote object storage as a resilient backup target. Clusters can restore full or partial datasets without downtime, keeping ingestion active during recovery.***
Deployment flexibility and elasticity
ClickHouse operates as a shared-nothing distributed system, scaling horizontally across nodes. Distributed tables automatically parallelize queries, and adding capacity simply involves joining new servers to the cluster.PostgreSQL primarily scales vertically. Extensions like Citus and PostgresXL provide sharding but require application-level awareness and complex orchestration. ClickHouse scales naturally within containerized or Kubernetes environments, offering operator-based automation for provisioning, scaling, and failover.***
High availability and replication models
PostgreSQL offers synchronous and asynchronous replication with configurable durability trade-offs. Failover often depends on tools like Patroni for automatic promotion.
ClickHouse’s ReplicatedMergeTree handles replication natively, maintaining asynchronous replicas per shard. When one replica fails, queries automatically route to healthy ones, ensuring non-blocking availability. The eventual consistency model maximizes performance without sacrificing reliability.
Cost efficiency and resource utilization
ClickHouse’s columnar compression and vectorized execution deliver significant hardware savings. Analytical workloads at petabyte scale can run with up to 5× lower compute and 10× less storage than equivalent PostgreSQL deployments.
PostgreSQL’s cost grows linearly with data volume, as larger tables demand more memory, CPU, and storage for indexes and WAL logs. Even with optimizations like BRIN indexing, the total cost of ownership increases sharply at analytical scale.ClickHouse also supports tiered storage—keeping hot data on SSD and cold data in object storage—queried seamlessly through S3 or GCS tables, enabling cost-optimized long-term retention without complex data tiering logic.***
Operational simplicity and maintenance
ClickHouse’s append-only architecture eliminates common pain points such as index bloat, manual partitioning, and heavy vacuum cycles. Background merges automatically compact data, reducing operational effort.
PostgreSQL requires continuous autovacuum tuning, index maintenance, and log rotation, especially in hybrid workloads. Analytical deployments often require additional maintenance windows and custom scripts to sustain consistent performance.***
Integration and hybrid architectures
ClickHouse connects directly to Kafka, S3, GCS, and even PostgreSQL using table engines and table functions, enabling real-time ingestion and federated queries. This allows joining live transactional data from PostgreSQL with aggregated analytical tables in ClickHouse without ETL pipelines.PostgreSQL’s FDWs and procedural extensions remain powerful for mixed workloads but add complexity and latency. ClickHouse’s native connectors make it simpler to handle streaming pipelines, batch ingestion, and Postgres CDC flows simultaneously, supporting diverse analytics scenarios such as those found in the Internet of Things.***
Deployment automation and managed services
Modern ClickHouse deployments run via BYOC, fully managed SaaS, or Kubernetes operators. These models separate control and data planes, providing secure isolation and independent scaling. Managed environments automatically handle node orchestration, replication, and version upgrades, as described in Managed ClickHouse options.Managed PostgreSQL services like AWS RDS, Azure Database, and Cloud SQL are robust for transactional workloads but less optimized for distributed analytics. Scaling beyond a single node often means manual sharding and replica coordination.***
Performance consistency at scale
Under heavy concurrency, ClickHouse maintains predictable latency by parallelizing reads and minimizing locks. Its stateless query execution and partition pruning make performance almost independent of total data volume.
PostgreSQL’s MVCC guarantees consistency but adds lock overhead and dead tuple buildup, which slow down long-running analytical queries. Beyond a certain scale, performance degradation becomes exponential, whereas ClickHouse continues to deliver sub-second aggregations on billions of rows.
ClickHouse vs PostgreSQL analytics comparison use cases
Different analytical workloads favor one database over the other based on data volume, query patterns, and update frequency. Recognizing these patterns helps you choose the right tool for your specific use case.
In many production systems, both databases coexist: PostgreSQL serves as the system of record for transactional data, while ClickHouse provides analytical capabilities. Data flows from PostgreSQL to ClickHouse through Change Data Capture (CDC) or periodic batch exports.
Product analytics data storage
Product analytics tracks user events like page views, clicks, form submissions, and feature usage. These workloads generate high volumes of immutable event data that get queried for funnel analysis, retention cohorts, and user behavior patterns.
ClickHouse excels here because event data is immutable (once logged, events rarely change), fitting ClickHouse's append-only model. High insert throughput handles millions of events per second without impacting query performance. Aggregation speed completes queries like "show me daily active users over the last 90 days" in milliseconds even with billions of events.
PostgreSQL handles product analytics at smaller scales (millions of events), but query performance degrades as event volume grows into billions. Row-based storage becomes inefficient when analytical queries only need a few columns from each event.
Real-time monitoring and alerting
Log aggregation and metrics collection generate continuous streams of time-series data. These workloads require both fast ingestion and low-latency queries for dashboards and alerting systems.
ClickHouse handles this workload well because it ingests and queries data simultaneously without performance degradation. Background merges consolidate data parts while queries continue running, and the columnar format makes time-range queries (like "show me error rates for the last hour") fast.
PostgreSQL works for monitoring at smaller scales, especially when using the TimescaleDB extension for time-series optimization. However, as metric cardinality and retention periods grow, ClickHouse's compression and query performance advantages become significant.
Machine learning feature stores
Feature stores for machine learning serve historical feature values for training and real-time feature values for inference. These workloads combine batch analytical queries (for training data) with point lookups (for inference).
ClickHouse works well for the batch analytical portion, where you compute aggregate features over large historical datasets. Columnar storage and aggregation speed make feature engineering queries fast, even when computing features across billions of rows.
PostgreSQL fits better for the real-time inference portion, where you need fast point lookups for individual user or entity features. Row-based storage makes retrieving all features for a single entity efficient.
Many ML systems use both: ClickHouse for offline feature computation and training data generation, PostgreSQL (or a key-value store) for online feature serving during inference.
Impact of popular PostgreSQL extensions
PostgreSQL's extension ecosystem adds specialized capabilities that can change the database comparison for specific workloads. Extensions like PostGIS, TimescaleDB, and pgvector give PostgreSQL functionality that would otherwise require separate specialized databases.
Extensions matter when evaluating PostgreSQL against ClickHouse because they can tip the balance for certain use cases. Understanding what each extension provides helps you assess whether PostgreSQL with extensions handles your analytical workload or whether ClickHouse's native capabilities are still necessary.
PostGIS spatial workloads
PostGIS adds geographic information system (GIS) capabilities to PostgreSQL, enabling storage and queries for spatial data like points, lines, and polygons.
For applications combining transactional data with geospatial queries (like ride-sharing apps tracking driver locations and trip histories), PostgreSQL with PostGIS offers a single database for both workloads. You can store trip data and query "find all available drivers within 2 miles of this location" without maintaining separate databases.
ClickHouse has geographic functions, but they're less comprehensive than PostGIS. If your workload requires complex spatial operations alongside transactional data, PostgreSQL with PostGIS is typically the better choice. If you're doing large-scale geospatial analytics (like analyzing billions of GPS points), ClickHouse's performance advantages might outweigh PostGIS's richer function set.
TimescaleDB time-series APIs
TimescaleDB extends PostgreSQL with time-series specific optimizations, including automatic partitioning by time, compression, and continuous aggregates. This extension makes PostgreSQL more competitive for time-series workloads where it would otherwise struggle, with benchmarks showing up to 1,000× faster queries compared to vanilla PostgreSQL.
TimescaleDB provides features like hypertables (automatic partitioning that makes large time-series tables perform like many smaller tables), continuous aggregates (materialized views that incrementally update as new data arrives), and compression (columnar compression for older time-series data that reduces storage and improves query performance).
For time-series workloads at moderate scale (millions to low billions of data points), TimescaleDB can deliver acceptable performance while maintaining PostgreSQL's transactional capabilities. At larger scales (hundreds of billions of data points), ClickHouse's native columnar architecture typically performs better. For a detailed comparison of how ClickHouse and TimescaleDB differ across architecture, performance, and operational complexity, see our ClickHouse vs TimescaleDB comparison.
Pgvector for embeddings
The pgvector extension adds vector similarity search to PostgreSQL, enabling AI and machine learning use cases like semantic search, recommendation systems, and retrieval-augmented generation (RAG) for large language models.
Pgvector stores vector embeddings (arrays of floats) and provides functions for similarity search using distance metrics like cosine similarity, L2 distance, and inner product. This allows you to store both structured data and vector embeddings in the same database, simplifying application architecture.
ClickHouse doesn't have a direct equivalent to pgvector. If your application combines transactional data with vector similarity search, PostgreSQL with pgvector offers a unified solution. For analytical workloads that include vector data but don't require similarity search, ClickHouse's array functions and aggregation performance might be sufficient.
Cost and operational overhead
Total cost of ownership includes hardware, licensing, and operational complexity. Both databases are open source, but the resources required to run them and the operational expertise needed differ significantly.
Hardware and licensing
Both ClickHouse and PostgreSQL are open source (Apache 2.0 and PostgreSQL licenses respectively), meaning no licensing fees for the database software itself. Cost differences come from hardware requirements and cloud service pricing.
ClickHouse typically requires less hardware for analytical workloads because of compression and query efficiency. A ClickHouse cluster might handle the same analytical workload as a PostgreSQL cluster using one-tenth the storage and one-fifth the compute resources.
PostgreSQL requires more substantial hardware for analytical workloads at scale. The lack of columnar compression means more disk space, and row-based scanning means more CPU and memory to achieve similar query performance. For transactional workloads, PostgreSQL's hardware requirements are reasonable and often lower than what ClickHouse would need for the same transactional load.
Observability, backups, and maintenance
PostgreSQL has mature tooling for monitoring, backups, and maintenance built over decades. Tools like pg_stat_statements, pgBackRest, and pg_dump are well-documented and widely understood.
ClickHouse requires understanding of its specific operational characteristics. Monitoring merge operations, managing distributed tables, and tuning compression codecs require ClickHouse-specific expertise.
Maintenance patterns differ significantly. PostgreSQL requires regular VACUUM operations to reclaim space from updated and deleted rows. ClickHouse requires monitoring background merges and occasionally optimizing table schemas for query patterns.
Managed service pricing models
Cloud providers offer managed services for both databases with different pricing models. Managed PostgreSQL services (like AWS RDS, Google Cloud SQL, or Azure Database for PostgreSQL) typically charge based on instance size, storage, and backup retention.
Managed ClickHouse services (like ClickHouse Cloud, Altinity, or Tinybird) often charge based on compute usage and storage separately. Because ClickHouse compresses data efficiently, storage costs are typically lower than PostgreSQL, but compute costs vary based on query complexity and concurrency.
For analytical workloads, managed ClickHouse services often cost less than managed PostgreSQL services at equivalent performance levels. The compression and query efficiency mean you can run smaller instances and store less data.
Migration paths from PostgreSQL to ClickHouse
Many organizations start with PostgreSQL for all workloads and later add ClickHouse specifically for analytics. This migration doesn't mean replacing PostgreSQL entirely; it means routing analytical queries to ClickHouse while keeping PostgreSQL as the system of record for transactional data.
1. Capture-and-replicate with CDC
Change Data Capture (CDC) streams changes from PostgreSQL to ClickHouse in real-time. Tools like Debezium, Maxwell, or PostgreSQL's logical replication capture inserts, updates, and deletes from PostgreSQL and replay them in ClickHouse.
This approach works well when you want to keep PostgreSQL as the source of truth for transactional data, your analytical queries can tolerate a few seconds of replication lag, and you need to maintain historical data in ClickHouse even as records are updated or deleted in PostgreSQL.
CDC handles the complexity of translating PostgreSQL's updates and deletes into ClickHouse's append-only model. The CDC tool typically creates new rows in ClickHouse for each change, allowing you to query both current state and historical changes.
2. Dual write cut-over
Dual writing means your application writes data to both PostgreSQL and ClickHouse simultaneously. This approach gives you more control over the migration and avoids relying on external replication tools.
The typical pattern involves continuing writes to PostgreSQL as normal, adding writes to ClickHouse for new data, backfilling historical data from PostgreSQL to ClickHouse, gradually moving analytical queries from PostgreSQL to ClickHouse, and eventually stopping queries to PostgreSQL for analytics.
This approach requires application changes but gives you precise control over data format and transformation. You can optimize the ClickHouse schema for your query patterns rather than replicating PostgreSQL's schema directly.
3. Retire Postgres OLAP queries
The final step in most migrations is identifying which queries can move to ClickHouse and which stay in PostgreSQL. Analytical queries that scan large datasets, compute aggregates, or generate reports move to ClickHouse. Transactional queries that read or update individual records stay in PostgreSQL.
Some queries don't fit cleanly into either category. Queries that join transactional data with analytical aggregates might need to be split, with the aggregation happening in ClickHouse and the join happening in your application code or PostgreSQL.
The goal isn't to eliminate PostgreSQL but to use each database for its strengths. PostgreSQL continues handling transactions, user data, and operational queries, while ClickHouse handles analytics, reporting, and large-scale aggregations.
Developer experience and tooling
The ease of integrating a database into your application affects development velocity and operational reliability. Both databases offer SQL interfaces, but the surrounding ecosystem of drivers, ORMs, and development tools differs significantly.
Developer experience matters more as your team grows and as you integrate the database deeper into your application. A database that's fast but difficult to work with can slow down feature development and increase operational burden.
SQL dialect and drivers
Both databases support SQL, but with different dialects and extensions. PostgreSQL's SQL dialect is mature and well-documented, with extensive support for standard SQL features, window functions, CTEs (Common Table Expressions), and complex joins including LEFT JOIN.
ClickHouse's SQL dialect is similar but not identical to standard SQL. It includes extensions for array functions, approximate algorithms, and specialized aggregations that don't exist in PostgreSQL. Some standard SQL features like correlated subqueries have limitations in ClickHouse.
Driver availability favors PostgreSQL. Nearly every programming language has mature, well-maintained PostgreSQL drivers. ClickHouse has official drivers for popular languages (Python, Go, Java, Node.js), but the ecosystem is smaller and some drivers are community-maintained rather than officially supported.
Local dev and CI/CD
PostgreSQL is easy to run locally for development. Most developers can install PostgreSQL through their package manager and have it running in minutes. Docker images for PostgreSQL are well-maintained and widely used in CI/CD pipelines.
ClickHouse is also available as Docker images and through package managers, but it's less commonly part of developers' local setups. Running a ClickHouse cluster locally for testing distributed queries requires more configuration than a single PostgreSQL instance.
For CI/CD, both databases can run in containers for automated testing. PostgreSQL's smaller footprint and faster startup time make it slightly easier to use in CI pipelines.
Security and RBAC
PostgreSQL has comprehensive security features built over decades. It supports role-based access control (RBAC), row-level security policies, column-level permissions, and integration with external authentication systems like LDAP and Kerberos.
ClickHouse provides RBAC with roles, users, and permissions for databases, tables, and even specific columns. It supports row-level security through row policies and integrates with external authentication systems. The security model is less mature than PostgreSQL's but covers most production use cases.
Both databases support SSL/TLS for encrypted connections and can integrate with secrets management systems for credential storage.
Tinybird for managed ClickHouse in minutes
Tinybird provides managed ClickHouse infrastructure designed for developers who want to integrate ClickHouse into their applications without managing clusters, scaling, or DevOps. The platform handles ClickHouse infrastructure while offering a developer experience focused on APIs and data pipelines.
Unlike self-hosted ClickHouse or other managed services, Tinybird includes built-in API generation from SQL queries, streaming ingestion from various sources, and a CLI for local development and CI/CD integration.
Sign up for a free Tinybird plan
Getting started with Tinybird takes a few minutes. Sign up for a free Tinybird plan that includes a managed ClickHouse workspace, streaming ingestion, and hosted API endpoints. The free plan provides enough resources to prototype analytical features and test ClickHouse performance with your data.
Frequently asked questions about ClickHouse vs PostgreSQL
How do I evaluate ClickHouse performance compared to PostgreSQL for my workload?
Start with query patterns and data volume analysis. Export a representative sample of your data and queries, then benchmark both databases with realistic workloads to measure query latency, concurrency, and resource usage.
Can ClickHouse and PostgreSQL coexist in the same application stack?
Many organizations use PostgreSQL for transactions and ClickHouse for analytics. This hybrid approach leverages each database's strengths and is often the best architecture for applications with both transactional and analytical requirements.
Does ClickHouse support complex joins like PostgreSQL?
ClickHouse supports joins but they perform differently due to columnar storage. Complex multi-table joins with large result sets work better in PostgreSQL's row-based model, while ClickHouse excels at joins where one table is much smaller than the other.
What happens to foreign key constraints when migrating from PostgreSQL?
ClickHouse doesn't enforce foreign key constraints at the database level. Applications handle referential integrity, or you accept eventual consistency where related records might temporarily be out of sync during replication.
Is ClickHouse suitable for applications with small datasets?
ClickHouse overhead makes it less efficient for small datasets (under 100 million rows). PostgreSQL typically performs better for applications with limited data volume where the benefits of columnar storage don't outweigh the operational complexity.