Choosing between ClickHouse and Oxla means understanding two different approaches to analytical database design. ClickHouse prioritizes speed for high-velocity ingestion and large-scale aggregations, while Oxla focuses on efficiency for complex queries with multiple joins.
This guide compares their performance characteristics, operational requirements, and cost structures to help you determine which database fits your workload. You'll learn where each database excels, how they handle different query types, and what tradeoffs matter for your specific use case.
Benchmark results at a glance
ClickHouse is an open-source columnar database known for fast analytical queries and high-velocity data ingestion, while Oxla is a self-hosted data warehouse optimized for complex queries involving multiple joins. ClickHouse typically excels at bulk updates and simpler query patterns with large-scale aggregations, while Oxla can outperform ClickHouse on queries with multiple joins, particularly when working with smaller datasets where query startup overhead matters less.
ClickHouse generally outperforms Oxla on ClickBench queries:
Star Schema Benchmark findings
The Star Schema Benchmark measures analytical database performance using a schema with fact tables and dimension tables, similar to what you'd find in a typical data warehouse. Fact tables contain measurable events like sales transactions, while dimension tables hold descriptive attributes like customer details or product categories. This benchmark simulates real business intelligence workloads where you're joining transaction data with descriptive information.
Oxla's published benchmark results show approximately 6x faster performance on JOIN operations and roughly 3x faster GROUP BY operations when aggregated data is relatively large. ClickHouse maintains advantages in scenarios with simpler aggregations across single large tables, where its columnar storage and vectorized execution deliver exceptional speed.
Real-time dashboard workload findings
Dashboard queries typically involve high concurrency, where multiple users run similar analytical queries simultaneously. ClickHouse handles concurrent readers well due to its MergeTree engine design, which allows multiple queries to read from the same data without blocking each other.
Oxla's architecture focuses on resource isolation, which helps prevent large queries from monopolizing system resources. This design choice reduces the "noisy neighbor" problem, where one heavy query slows down other concurrent operations. For workloads with predictable query patterns and high user concurrency, this isolation can provide more consistent response times.
Test methodology and dataset
Fair database comparisons require consistent hardware, representative data volumes, and realistic query patterns. The methodology used in published benchmarks affects which database appears faster, so understanding the test setup helps interpret results correctly.
Dataset size and shape
The Star Schema Benchmark uses a fact table containing order line items, with dimension tables for customers, parts, suppliers, and dates. The dataset scales from gigabytes to terabytes, allowing tests at different sizes. Most published comparisons between ClickHouse and Oxla use datasets in the tens to hundreds of gigabytes range.
ClickHouse's columnar compression typically achieves 10x to 50x compression ratios on analytical data, meaning a 100GB uncompressed dataset might occupy only 2GB to 10GB on disk. Oxla also uses columnar storage with compression, though published compression ratios vary by dataset and configuration.
Query mix definition
Analytical benchmarks test different query types to simulate real-world workloads:
- Aggregations: Summing sales, counting events, or averaging metrics across large datasets
- Filtering: Selecting subsets of data based on date ranges, categories, or other criteria
- Joins: Combining fact tables with dimension tables to enrich analytical results
- Group by operations: Organizing results by dimensions like region, product category, or time period
The Star Schema Benchmark includes 13 queries of increasing complexity. Early queries scan single tables with simple filters, while later queries involve multiple joins and complex aggregations.
Measurement process
Benchmark tests typically run each query multiple times and report the median or average execution time. The first run is often excluded because databases cache data and metadata after initial access, making subsequent runs faster. This "warm cache" scenario reflects typical production usage, where the same queries run repeatedly.
Hardware consistency matters for fair comparisons. Published benchmarks specify the instance type, CPU count, memory size, and storage configuration.
Performance deep dive: joins, group by, and concurrency
Understanding where each database excels requires looking at how they execute different query types. The architectural choices each database makes create tradeoffs that affect specific workloads differently.
Join strategies and latency
ClickHouse uses hash joins for most queries, building a hash table from the smaller table and probing it with rows from the larger table. This approach works well when one table fits comfortably in memory. For joins where neither table fits in memory, ClickHouse can spill to disk, though this impacts performance significantly, and in some cases you can avoid joins entirely using WHERE clauses.
Oxla's join implementation appears optimized for cases with multiple join operations in a single query. The database's distributed query engine can execute joins across nodes more efficiently than ClickHouse in certain scenarios, particularly when joining multiple dimension tables to a fact table simultaneously.
Group by spill behavior
GROUP BY operations aggregate rows based on one or more columns, like counting sales by region and product. When the number of unique groups is small, the aggregation state fits in memory and completes quickly. When there are millions of unique groups, the aggregation state grows large and may exceed available memory.
ClickHouse handles large GROUP BY operations by spilling to disk when memory is exhausted. The database uses an external aggregation algorithm that writes intermediate results to temporary storage. Oxla’s reported 3x advantage on large GROUP BY operations suggests more efficient memory management or better disk I/O patterns during spill operations.
Concurrent readers impact
ClickHouse's MergeTree engine allows multiple queries to read the same data simultaneously without locking. Each query operates on a consistent snapshot of the data, even as new data is being inserted.
Resource contention can still occur when multiple large queries run simultaneously. Each query consumes CPU, memory, and disk I/O bandwidth, and without careful configuration, one heavy query can slow down others. ClickHouse provides settings to limit resource usage per query, though tuning these requires understanding your workload patterns.
Ingest speed and storage efficiency
Getting data into your database quickly and storing it efficiently affects both operational costs and query performance. Both databases use columnar storage with compression, but their ingestion approaches differ.
Streaming insert throughput
ClickHouse handles high-velocity data ingestion through its MergeTree engine, which batches incoming rows into parts and periodically merges smaller parts into larger ones. This design allows the database to accept up to 4 million rows per second while maintaining query performance. The tradeoff is that data isn't immediately visible in queries until the current batch is flushed.
Oxla also supports streaming ingestion, though published benchmarks focus more on bulk loading scenarios. The database can ingest data while serving queries, with independent scaling of storage and compute helping isolate ingestion load from query workload.
Compression ratios and disk usage
Columnar compression works by storing values from the same column together, which often contain similar or repeated values. ClickHouse supports multiple compression codecs, including LZ4 (fast), ZSTD (better compression), and specialized codecs for specific data types like Delta encoding for timestamps or T64 for integers.
Storage efficiency impacts total cost of ownership, especially for large datasets. A 10x compression ratio means storing 10TB of raw data requires only 1TB of disk space.
Hardware requirements and scaling models
Choosing the right hardware and scaling approach affects both performance and cost. Both databases can run on a single server or scale to distributed clusters, though their architectures differ.
Single-node specifications
For development and testing, both ClickHouse and Oxla can run on modest hardware. A server with 4-8 CPU cores, 16-32GB RAM, and SSD storage handles datasets in the tens of gigabytes range.
For production workloads with hundreds of gigabytes to terabytes of data, more substantial hardware is recommended:
- CPU: 16-32 cores for parallel query execution
- Memory: 64-256GB depending on working set size and concurrency
- Storage: NVMe SSDs for low-latency disk I/O, with capacity based on compressed data size
ClickHouse's performance scales roughly linearly with CPU core count for most queries. Memory requirements depend on the size of intermediate results during aggregations and joins.
Cluster topology options
ClickHouse uses a sharding model where data is distributed across multiple servers, with each shard holding a subset of the data. Queries that scan all data run across all shards in parallel, with results combined at the end. Replication provides high availability by keeping copies of each shard on multiple servers.
Oxla's distributed architecture decouples storage and compute, allowing you to scale each independently. This design means you can add more query processing capacity without moving data, or increase storage without adding compute resources.
SQL feature coverage and ecosystem tooling
SQL compatibility and integration with existing tools affects how easily you can adopt a new database. Both ClickHouse and Oxla support SQL, though with different feature sets and extensions.
Window functions and advanced SQL
ClickHouse provides comprehensive support for analytical SQL, including window functions like ROW\_NUMBER(), RANK(), and moving averages. The database supports common table expressions (CTEs) for organizing complex queries, and array functions for working with nested data structures.
Oxla offers basic window function support, though with fewer advanced features compared to ClickHouse. For workloads that rely heavily on window functions or complex analytical SQL, ClickHouse's more mature implementation provides more options.
Materialized views and index types
Materialized views pre-compute and store query results, which can dramatically speed up repeated queries. ClickHouse's materialized views automatically update as new data arrives, making them useful for real-time dashboards and aggregations.
The database also supports multiple index types, including primary keys, skip indexes, and full-text search indexes. Oxla's pre-aggregation capabilities help accelerate queries on large datasets, though the implementation differs from ClickHouse's materialized views.
Backup, restore, and external integrations
ClickHouse integrates with a wide ecosystem of tools, including business intelligence platforms like Tableau and Looker, data pipeline tools like Apache Kafka and dbt, and monitoring systems like Grafana. The database's popularity means more third-party integrations and community-contributed connectors.
Oxla, as a newer product, has fewer integrations available. The database supports standard SQL interfaces, which allows basic connectivity with many tools, though specialized integrations may require custom development.
Operational overhead: self-hosted, cloud, and managed options
Running a database in production involves more than just query performance. Monitoring, maintenance, and high availability all require time and expertise.
Monitoring, alerting, and observability
ClickHouse exposes detailed metrics through system tables, showing query execution times, memory usage, disk I/O, and other operational data. The database also provides query logs that help identify slow queries and optimization opportunities.
Oxla provides monitoring capabilities, though the tooling ecosystem is less mature than ClickHouse's. The database includes basic observability features, with Docker and YAML deployment options simplifying initial setup.
Upgrades, patching, and failover
ClickHouse releases new versions regularly, with both minor updates and major feature releases. Upgrading a distributed ClickHouse cluster requires careful planning to avoid downtime, typically involving rolling upgrades where nodes are updated one at a time, with clusters scaling linearly to handle 65.33 billion rows across multiple nodes.
Oxla's self-hosting model simplifies some maintenance tasks through containerized deployment. The database's architecture supports independent scaling of storage and compute, which can make capacity adjustments easier than with traditional databases.
Total cost of ownership and licensing
Beyond software licensing, the true cost of running a database includes infrastructure, engineering time, and operational overhead. Both databases are open-source, but deployment costs vary.
Infrastructure cost drivers
- Compute requirements: CPU and memory needs scale with query volume and complexity
- Storage costs: Both raw data storage and space for indexes and materialized views contribute to total storage costs
- Network bandwidth: Distributed deployments transfer data between nodes, and queries that return large result sets consume outbound bandwidth
ClickHouse's efficiency on single-node deployments can reduce costs for smaller workloads, while Oxla's decoupled storage and compute can provide cost advantages for large datasets with variable query loads.
Engineering head-count and support
Self-hosting either database requires engineers familiar with database operations, Linux systems, and distributed systems concepts. ClickHouse's larger community means more available documentation, tutorials, and engineers with ClickHouse experience. Oxla's newer status means fewer resources available and potentially longer learning curves.
Managed services reduce operational burden by handling infrastructure setup, monitoring, backups, and upgrades. For teams that want to focus on building applications rather than managing databases, managed services can provide better time-to-value even if per-unit costs are higher.
When to choose ClickHouse with Tinybird
Tinybird provides a managed ClickHouse platform that eliminates infrastructure complexity for developers integrating ClickHouse into their applications. The service handles cluster management, scaling, and operations, allowing you to focus on building features rather than managing databases.
Rapid prototyping and API delivery
Tinybird's pipe files let you define SQL queries as code, with built-in support for parameterization and API generation. You write your ClickHouse SQL, define parameters, and deploy the pipe as a REST API endpoint in minutes.
The platform supports local development with the Tinybird CLI and a Docker-based local runtime. You can test queries locally, validate results, and deploy to production with confidence.
SaaS pricing transparency
Tinybird uses usage-based pricing that scales with your data volume and query load. You pay for what you use rather than provisioning fixed infrastructure that may sit idle.
The managed service model means you don't pay for idle capacity, and you don't spend engineering time monitoring and maintaining ClickHouse clusters. Sign up for a free Tinybird plan to start building ClickHouse-powered APIs without infrastructure setup.
FAQs about ClickHouse vs Oxla
Does Oxla support window functions?
Oxla provides basic window function support, though with fewer advanced features compared to ClickHouse's comprehensive analytical SQL capabilities. For queries that rely heavily on complex window functions, ClickHouse offers more options.
Can I migrate data from ClickHouse to Oxla?
Data migration between ClickHouse and Oxla requires exporting data from ClickHouse and reimporting it to Oxla, as there are no direct migration tools between the databases. The process typically involves exporting to CSV or Parquet format and loading into the target database.
Which database handles time-series data better?
ClickHouse offers specialized time-series functions and partitioning strategies that make it well-suited for time-series workloads. The database can partition data by time period and drop old partitions efficiently. Oxla focuses on general analytical workloads with strong join performance rather than time-series-specific optimizations.
What are the main architectural differences between the databases?
ClickHouse uses a MergeTree storage engine with column-oriented design and a sharding model for distributed deployments. Oxla employs a distributed query engine optimized for complex joins and aggregations, with decoupled storage and compute that scale independently.
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