ClickHouse^® vs ParadeDB: Can the parade keep up?

Choosing an analytical database means weighing architectural tradeoffs that affect query speed, operational complexity, and how far your system can scale. ClickHouse^® and ParadeDB take fundamentally different approaches: one is a standalone columnar database built for petabyte-scale analytics, while the other extends PostgreSQL with columnar storage to avoid managing separate infrastructure.

This article compares their architectures, performance characteristics, and operational requirements, then examines when each system makes sense for different workloads and team constraints.

Why choose ClickHouse^® over ParadeDB?

ClickHouse^® is a standalone columnar database built specifically for analytical workloads. ParadeDB is a PostgreSQL extension that adds columnar storage and search capabilities to an existing Postgres installation. The core difference comes down to architecture: ClickHouse^® was designed from scratch for OLAP queries, while ParadeDB extends a row-oriented transactional database to handle analytics.

This architectural split creates measurable differences in query speed, data compression, and how far each system can scale. ClickHouse^® handles real-time aggregations across billions of rows, particularly when data arrives continuously and rarely updates. ParadeDB offers simpler operations for teams already running PostgreSQL who want analytics without managing a separate database.

Column-oriented engine built for OLAP

ClickHouse^® stores data in columns rather than rows. When you run a query that sums revenue across millions of transactions, ClickHouse^® reads only the revenue column, not entire rows. This approach speeds up aggregations and improves compression compared to row-based storage.

ParadeDB adds columnar storage to PostgreSQL through extensions, but it sits on top of a row-oriented foundation. PostgreSQL wasn't designed for the kind of columnar scans and vectorized execution that ClickHouse^® performs natively. For queries that touch many columns or perform complex aggregations, this architectural difference shows up in query times.

Proven at petabyte scale in production

ClickHouse^® runs at petabyte scale across organizations like Yandex, Uber, and Cloudflare, with its market share growing to 5.7% in 2025 from 1.2% the previous year. Production deployments handle billions of events per day with sub-second query latency. The database includes mature features for distributed queries, replication, and sharding that have been tested under heavy production loads.

ParadeDB is newer with fewer large-scale production deployments, though its search extension reached stable release v0.16.4 in July 2025. While it performs well for smaller analytical workloads, the track record for handling multi-terabyte datasets with high concurrency is still developing. Teams considering ParadeDB for large-scale analytics face more uncertainty about performance at scale.

When does ParadeDB still make sense?

ParadeDB offers real advantages when its PostgreSQL foundation becomes an asset rather than a limitation. Teams with existing PostgreSQL expertise, compliance requirements tied to Postgres, or modest analytical needs may find ParadeDB's simplicity more valuable than ClickHouse^®'s raw performance.

The decision often comes down to operational overhead versus query speed. ParadeDB lets you avoid managing a separate analytical database, while ClickHouse^® requires dedicated infrastructure but delivers faster performance for large-scale analytics.

Small analytics workloads under one node

For analytical workloads that fit on a single PostgreSQL instance, ParadeDB can deliver good performance without distributed systems complexity. If your data volumes measure in gigabytes rather than terabytes, and query concurrency stays below a few dozen simultaneous users, ParadeDB's overhead stays manageable.

This approach works well for internal dashboards, reporting tools, or analytics features that supplement a transactional application. You avoid the operational burden of syncing data between a transactional database and an analytical one, since both workloads run on the same Postgres instance.

Teams locked into Postgres tooling

Organizations with strict compliance requirements, existing PostgreSQL expertise, or deep integration with Postgres-specific features may find ParadeDB more practical than ClickHouse^®. If your backup procedures, monitoring tools, and access control policies are all built around PostgreSQL, extending Postgres with ParadeDB requires less organizational change than adopting a new database system.

ParadeDB also preserves full ACID transaction semantics. A transaction is a sequence of database operations that either all succeed or all fail together, maintaining data consistency. This matters when analytical queries join against frequently updated transactional tables. ClickHouse^® offers eventual consistency by default, which works well for append-only event data but can complicate scenarios requiring strong transactional guarantees.

Architecture differences that drive performance

The performance gap between ClickHouse^® and ParadeDB comes from fundamental architectural decisions made when each system was designed. ClickHouse^® was purpose-built for analytical queries, while ParadeDB adapts a transactional database for analytical workloads.

Storage format and compression

ClickHouse^® uses specialized columnar formats like MergeTree that organize data by column and apply aggressive compression algorithms. Each column can use a different compression codec optimized for its data type and distribution. This flexibility typically achieves compression ratios of 10x to 100x on analytical workloads.

ParadeDB relies on PostgreSQL's TOAST mechanism for compression, which was designed for large text fields and binary objects rather than columnar analytics. While ParadeDB adds some columnar optimizations through extensions, it can't match the compression efficiency of a database designed around columnar storage from the start. Better compression means lower storage costs and faster queries, since less data moves from disk to memory.

Vectorized execution and parallelism

ClickHouse^® processes data in batches using SIMD instructions, a technique called vectorized execution. Instead of processing one row at a time, it operates on thousands of rows simultaneously using specialized CPU instructions. This approach saturates modern CPU pipelines and memory bandwidth more effectively than row-by-row processing.

ParadeDB processes queries through PostgreSQL's row-by-row execution model, even when reading from columnar storage. While PostgreSQL has added parallel query execution in recent versions, it wasn't designed for the tight loops and cache-friendly access patterns that vectorized execution enables. For aggregations across millions of rows, this architectural difference translates to query times that differ by orders of magnitude.

Query performance and compression benchmarks

Real-world query patterns reveal where ClickHouse^®'s architectural advantages matter most. The performance gap widens as data volumes grow and query complexity increases.

ClickHouse^® generally outperforms ParadeDB on ClickBench queries, though ParadeDB keeps up on certain tests:

Wide table aggregations

Analytical dashboards often aggregate across many dimensions, creating queries with multiple GROUP BY columns and aggregate functions. ClickHouse^® handles queries like this efficiently because it reads only the columns referenced in the query and processes them in parallel using vectorized execution:

SELECT
country,
device\_type,
browser,
COUNT(\*) as sessions,
AVG(session\_duration) as avg\_duration,
SUM(revenue) as total\_revenue
FROM user\_sessions
WHERE date >= '2024-01-01'
GROUP BY country, device\_type, browser
ORDER BY total\_revenue DESC

This query pattern stresses both storage efficiency and execution speed. ClickHouse^® typically executes queries like this in under a second on billions of rows, while ParadeDB performance degrades more quickly as table width increases.

Time-series rollups

Monitoring systems and IoT applications frequently aggregate time-series data into buckets. ClickHouse^® includes specialized functions like toStartOfInterval() that make rollup queries concise and fast:

SELECT
toStartOfInterval(timestamp, INTERVAL 5 MINUTE) as time\_bucket,
sensor\_id,
AVG(temperature) as avg\_temp,
MAX(temperature) as max\_temp
FROM sensor\_readings
WHERE timestamp >= now() - INTERVAL 24 HOUR
GROUP BY time\_bucket, sensor\_id
ORDER BY time\_bucket

ClickHouse^®'s columnar storage and compression work particularly well for time-series data, where adjacent rows often have similar values. Delta encoding and other compression techniques can reduce storage by 100x or more for sensor data.

Ingestion speed and streaming capabilities

Getting data into your analytical database quickly matters for real-time dashboards and operational analytics. ClickHouse^® was designed for high-volume ingestion, while ParadeDB relies on PostgreSQL's insert performance.

Kafka and event streams

ClickHouse^® includes native Kafka table engines that continuously consume events from Kafka topics and write them to ClickHouse^® tables. The database batches incoming events and applies compression before writing to disk, achieving ingestion rates of millions of events per second per node.

High-volume HTTP batching

Application backends often send analytics events via HTTP for simplicity and reliability. ClickHouse^® accepts bulk inserts of JSON, CSV, or other formats through its HTTP interface, achieving high throughput with minimal client complexity:

curl -X POST 'http://localhost:8123/?query=INSERT%20INTO%20events%20FORMAT%20JSONEachRow' \
--data-binary @events.json

PostgreSQL's insert performance degrades more quickly under high-volume batching, even with COPY commands and prepared statements. The overhead of maintaining transaction logs and indexes for real-time updates slows bulk loading compared to ClickHouse^®'s append-only storage model.

SQL compatibility and limitations

ClickHouse^® and ParadeDB both support SQL, but they implement different subsets of the SQL standard. Understanding differences like this helps you evaluate migration effort and feature compatibility.

Materialized views and projections

Both systems support materialized views for pre-computing expensive aggregations. ClickHouse^® materialized views update automatically as new data arrives, making them ideal for real-time rollups and aggregations.

A materialized view is a database object that stores the results of a query and updates them automatically when underlying data changes. In ClickHouse^®, materialized views can transform and aggregate data as it's inserted, enabling real-time analytics without expensive recomputation:

CREATE MATERIALIZED VIEW daily\_revenue\_mv
ENGINE = SummingMergeTree()
ORDER BY (date, product\_id)
AS SELECT
toDate(timestamp) as date,
product\_id,
sum(amount) as revenue
FROM purchases
GROUP BY date, product\_id;

ParadeDB uses PostgreSQL's materialized view implementation, which requires manual or scheduled refreshes. This works well for dashboards that update hourly or daily, but it adds latency for real-time analytics.

Operational overhead and scaling paths

Managing database infrastructure consumes engineering time that could be spent building features. The operational complexity of ClickHouse^® versus ParadeDB differs significantly.

Sharding and replicas

ClickHouse^® distributes data across multiple nodes using distributed tables and sharding. Each shard contains a subset of the data, and queries automatically fan out across shards and aggregate results. Setting up and maintaining a ClickHouse^® cluster requires understanding ZooKeeper or ClickHouse® Keeper for coordination, configuring replication, and managing cluster topology.

ParadeDB relies on PostgreSQL's sharding extensions like Citus or pg\_partman. Tools like this add complexity to an already complex database system, and they don't match ClickHouse^®'s native distributed query capabilities. For workloads that outgrow a single node, ClickHouse^®'s architecture scales more naturally.

Dev workflow and CI/CD

ClickHouse^® queries and table schemas can be version-controlled as SQL files and deployed through standard CI/CD pipelines. However, testing ClickHouse^® queries locally requires running a ClickHouse^® instance, which adds setup time for new developers.

Tinybird simplifies this workflow by providing a local development environment that mirrors production ClickHouse^® infrastructure. Developers can define data sources and queries as code, test them locally using the Tinybird CLI, and deploy to production with a single command. This approach eliminates much of the operational complexity associated with self-hosted ClickHouse^®.

Total cost of ownership in production

The true cost of a database includes infrastructure, storage, compute, and engineering time. ClickHouse^® and ParadeDB have different cost profiles that become more apparent at scale.

Storage cost per TB: ClickHouse^®'s compression efficiency directly reduces storage costs. A dataset that occupies 10TB in PostgreSQL might compress to 500GB in ClickHouse^®, cutting storage costs by 95%. In cloud environments where storage is billed by the gigabyte, this difference compounds monthly.

Compute per query: ClickHouse^® executes analytical queries faster, which means each query consumes less CPU time. When running thousands of queries per hour, this efficiency translates to lower compute costs. Faster queries also improve user experience, since dashboards load more quickly.

DevOps hours vs managed SaaS: Self-hosting ClickHouse^® requires dedicated engineering time for cluster management, performance tuning, and troubleshooting. Organizations typically assign at least one engineer to database operations, and larger deployments require full-time database reliability engineering teams.

Managed services eliminate this operational burden by handling infrastructure, scaling, and maintenance. Tinybird provides a managed ClickHouse^® platform designed for developers who want to integrate ClickHouse^® into their applications without managing clusters. The platform handles ingestion, query optimization, and API hosting, letting developers focus on building features rather than managing infrastructure.

Sign up for a free Tinybird account to start building with managed ClickHouse^® in minutes.

Tinybird's managed ClickHouse^® for fast shipping

Tinybird provides a managed ClickHouse^® platform that eliminates infrastructure complexity while preserving ClickHouse^®'s performance advantages. Developers can define data sources and queries as code, test them locally, and deploy to production with the Tinybird CLI.

The platform handles ingestion from Kafka, HTTP, and other sources, and it automatically generates REST APIs from SQL queries. This approach lets backend developers integrate ClickHouse^® into their applications in hours rather than weeks. Tinybird manages cluster scaling, replication, and performance tuning, so engineering teams can focus on building features rather than managing databases.

FAQs about ClickHouse^® vs ParadeDB

Is ParadeDB production ready for multi terabyte datasets?

ParadeDB is relatively new and primarily tested on smaller workloads, while ClickHouse^® has proven scalability at petabyte scale across many organizations. For datasets measured in multiple terabytes with high query concurrency, ClickHouse^® offers more confidence based on production track record.

Can I run ClickHouse^® and ParadeDB together?

Yes, many teams use both systems for different use cases, with ParadeDB handling smaller analytical queries and ClickHouse^® for large-scale analytics. This approach lets you leverage PostgreSQL's transactional capabilities while using ClickHouse^® for performance-intensive analytical workloads.

How do Citus or other Postgres extensions interact with ParadeDB?

ParadeDB works alongside most Postgres extensions, though some advanced sharding or columnar extensions may conflict with ParadeDB's storage optimizations. Testing compatibility in a staging environment before production deployment helps identify any issues with specific extension combinations.

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