ClickHouse^® vs OrioleDB: Can Postgres extensions keep up?

When developers need fast analytical queries, they often compare purpose-built OLAP databases like ClickHouse against PostgreSQL extensions like OrioleDB that promise better performance without leaving the Postgres ecosystem. The architectural differences between these systems determine not just query speed, but also operational complexity, scaling paths, and which workloads each database handles best.

This article explains how ClickHouse and OrioleDB differ in storage architecture, query execution, and real-world performance, then provides guidance on when to choose each system for analytical workloads.

Problem these databases try to solve

ClickHouse is an OLAP database built for high-speed analytical queries on large datasets, while OrioleDB is a storage engine extension designed to improve PostgreSQL's performance for workloads with frequent updates. ClickHouse stores data by column and processes queries using vectorized execution, achieving 10-100x speedup over row-based databases for analytical tasks. OrioleDB reorganizes PostgreSQL's storage layer to reduce bloat and improve transaction processing without leaving the Postgres ecosystem.

Traditional databases store data row-by-row, which works well for transactional systems but creates bottlenecks when scanning millions of rows for aggregations. When you run a query that counts events by day across a year of data, a row-based database reads every field in every row, even if the query only touches two columns.

Architecture differences that drive performance

The performance gap between ClickHouse and OrioleDB comes down to how each system stores, retrieves, and processes data.

Columnar storage vs index-organized tables

ClickHouse stores data by column rather than by row, meaning all values for a single column are stored together on disk. When you run a query that aggregates or filters on specific columns, ClickHouse only reads the columns referenced in the query, skipping irrelevant data entirely.

OrioleDB reorganizes PostgreSQL tables using index-organized storage, where rows are stored in the order of a clustered index. This improves data locality for range scans and reduces the number of disk reads needed for queries that filter on indexed columns, achieving 22x fewer IOPS per transaction in benchmarks. However, OrioleDB still operates within PostgreSQL's row-based execution model, so it reads entire rows even when only a few columns are needed.

Vectorized execution vs Postgres executor

ClickHouse processes data in batches using SIMD (Single Instruction, Multiple Data) instructions, which allows the CPU to apply the same operation to multiple values simultaneously. This vectorized execution model takes advantage of modern CPU architectures and cache hierarchies, making aggregations extremely fast even on commodity hardware.

OrioleDB works within PostgreSQL's traditional row-by-row executor, which processes one tuple at a time through the query plan. While OrioleDB improves storage efficiency and reduces bloat, it doesn't change how PostgreSQL executes queries at the CPU level.

Compression and encoding strategies

ClickHouse applies aggressive compression to columnar data, achieving 15-20x compression ratios depending on the data type and cardinality. Because columns store similar data types together, compression algorithms like LZ4 and ZSTD work more effectively.

OrioleDB reduces storage overhead by eliminating PostgreSQL's traditional heap bloat and using an undo log for transaction management. This makes OrioleDB more space-efficient than standard PostgreSQL, but it doesn't achieve the same compression levels as columnar formats.

Benchmark results on ClickBench and beyond

ClickBench is a public benchmark that compares analytical database performance across identical hardware and queries. The benchmark includes 43 SQL queries designed to simulate real-world analytical workloads, such as aggregations, filtering, and sorting on large datasets.

ClickHouse consistently ranks near the top of ClickBench results, often completing the full query suite in under 10 seconds on a single server. OrioleDB has not been included in ClickBench results, likely because it's still in active development and not yet positioned as a direct competitor to purpose-built analytical databases.

OrioleDB is orders of magnitude slower on ClickBench queries, suggesting it is not optimized for high-concurrency, real-time analytics read patterns:

Throughput on CPU-bound queries

ClickHouse excels at CPU-bound queries like GROUP BY aggregations, ORDER BY operations, and complex joins on large fact tables. Vectorized execution and columnar storage allow ClickHouse to process billions of rows per second per core.

OrioleDB improves PostgreSQL's performance for queries that involve sequential scans or index lookups, particularly when those queries also perform updates. However, it doesn't fundamentally change PostgreSQL's query execution speed for pure analytical workloads.

Latency under high concurrency

ClickHouse handles thousands of concurrent queries by distributing load across multiple CPU cores and leveraging its efficient query execution engine. Response times typically remain under one second even with hundreds of users querying the same dataset simultaneously.

OrioleDB improves PostgreSQL's ability to handle concurrent updates by reducing lock contention and eliminating vacuum overhead. This makes it better suited for mixed workloads where reads and writes happen simultaneously, but it doesn't match ClickHouse's concurrency model for read-heavy analytical queries.

Write and ingest speed

ClickHouse supports bulk inserts at rates exceeding millions of rows per second per server, making it ideal for streaming data pipelines and high-volume log ingestion. Inserts are typically batched and written to immutable parts, which are later merged in the background.

OrioleDB improves PostgreSQL's write performance by reducing the overhead of vacuum operations and managing transaction visibility more efficiently. This makes it faster for update-heavy transactional workloads, but it doesn't reach the same ingest speeds as ClickHouse for append-only analytical data.

Operational model and scaling paths

Sharding and replication approaches

ClickHouse uses distributed tables to shard data across multiple nodes, allowing queries to run in parallel across the cluster. Replication is handled by writing data to multiple replicas using ClickHouse Keeper or ZooKeeper, providing fault tolerance and read scalability.

OrioleDB runs as a PostgreSQL extension, so it relies on PostgreSQL's native replication mechanisms like streaming replication or logical replication. Sharding typically requires external tools like Citus or application-level partitioning.

Backup and disaster recovery

ClickHouse supports incremental backups through its native backup system, allowing you to back up specific tables or partitions without stopping the database. Restoring data is fast because ClickHouse can copy immutable parts directly to disk.

OrioleDB uses PostgreSQL's backup tools like pg\_basebackup and point-in-time recovery (PITR). Because OrioleDB reduces bloat, backup sizes are smaller than standard PostgreSQL, but the backup and restore process follows PostgreSQL conventions.

Feature comparison for real-time analytics

When choosing a database for real-time analytics, specific features like SQL coverage, materialized views, and streaming ingestion determine how quickly you can build and scale your application.

ClickHouse supports a broad subset of SQL, including window functions, CTEs (Common Table Expressions), and advanced aggregations. However, it doesn't support all PostgreSQL extensions or procedural languages like PL/pgSQL.

OrioleDB inherits PostgreSQL's full SQL compatibility, including PostGIS for geospatial queries, full-text search, and JSON operations. This makes it easier to adopt OrioleDB if your application already relies on PostgreSQL-specific features.

Cost, licensing, and cloud options

Total cost of ownership includes not just hardware and cloud compute, but also the engineering time required to deploy, scale, and maintain a database in production.

ClickHouse is licensed under Apache 2.0, which allows commercial use, modification, and distribution without restrictions. OrioleDB is licensed under the PostgreSQL License, which is similarly permissive.

ClickHouse typically requires 16 GB of RAM or more for production workloads, with CPU and storage scaling based on data volume and query complexity. Because ClickHouse compresses data aggressively, storage costs are often lower than other databases for the same dataset.

OrioleDB reduces PostgreSQL's memory and storage footprint by eliminating bloat entirely, but it still requires similar hardware to standard PostgreSQL for comparable workloads. The main benefit is more efficient use of existing resources rather than a significant reduction in hardware requirements.

When to choose ClickHouse, OrioleDB, or both

The right database depends on your workload characteristics, existing infrastructure, and team expertise.

ClickHouse fits best for:

• Time-series analytics like log processing, observability data, and IoT telemetry where data is written once and queried many times
• Data warehousing that aggregates large datasets for business intelligence, dashboards, and reporting
• High-concurrency analytics serving real-time queries to thousands of users simultaneously
• Streaming data pipelines ingesting from Kafka, Kinesis, or other event streams in real time

OrioleDB works well for:

• Existing PostgreSQL applications where teams want better performance for update-heavy workloads
• Mixed transactional and analytical queries in a single database
• Applications relying on PostGIS, full-text search, or other PostgreSQL extensions
• Gradual migration paths that improve PostgreSQL performance without rewriting application code

Some teams run both ClickHouse and PostgreSQL in production, using PostgreSQL for transactional data and ClickHouse for analytics. Data is typically replicated from PostgreSQL to ClickHouse using CDC tools like Debezium or custom replication scripts.

Tinybird and managed ClickHouse in practice

Tinybird provides a managed ClickHouse platform designed for developers who want to integrate ClickHouse into their applications without managing infrastructure. Tinybird handles cluster scaling, observability, and security, allowing you to focus on building features rather than operating databases.

With Tinybird, you define data pipelines as code using .pipe and .datasource files, test locally using the Tinybird CLI, and deploy to production with a single command. Tinybird also provides hosted API endpoints, so you can expose ClickHouse queries as REST APIs without writing backend code.

To get started, sign up for a free Tinybird account, install the CLI, and follow the quickstart guide in the Tinybird documentation.

FAQs about ClickHouse vs OrioleDB

is OrioleDB production-ready for analytical workloads?

OrioleDB is still in active development and not recommended for production analytical workloads. It's designed to improve PostgreSQL's storage efficiency and update performance, but it doesn't fundamentally change PostgreSQL's row-based execution model. ClickHouse has been proven in production environments at companies like Cloudflare, Uber, and eBay for large-scale analytical use cases.

can I query ClickHouse and Postgres data together?

Yes, ClickHouse includes a PostgreSQL table engine that allows you to query PostgreSQL tables directly from ClickHouse without copying data. You can also use external tools like dbt or Apache Superset to federate queries across both systems, though performance will depend on network latency and query complexity.

how do I migrate historical data from Postgres to ClickHouse quickly?

Use ClickHouse's PostgreSQL table engine to perform an initial bulk copy, then set up ongoing replication using CDC tools like Debezium or custom ETL pipelines. For large tables, consider partitioning the data by time and migrating partitions incrementally to avoid downtime.

what is the development roadmap for each database project?

ClickHouse focuses on improving query performance, adding new data formats, and enhancing cloud-native features like separation of storage and compute. OrioleDB aims to become a pure PostgreSQL extension without requiring core patches, making it easier to adopt in existing PostgreSQL deployments. Both projects are actively developed, but ClickHouse has a larger community and more production deployments.

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