Event-Driven Scheduling with ClickHouse

Event-driven scheduling allows businesses to act on data immediately as events occur, rather than relying on fixed time intervals. ClickHouse, a high-speed columnar database, is a powerful tool for these workflows due to its ability to handle real-time data ingestion and execute analytical queries rapidly. For example, companies like Instacart and Cloudflare use ClickHouse to process millions of records per second for fraud detection and log analysis.

Key Takeaways:

• What it is: Event-driven scheduling processes data in real time, triggered by events like transactions or system alerts.

• Why ClickHouse: It’s optimized for speed, handling trillions of rows and enabling real-time analytics.

• Benefits: Faster decision-making, automation, and improved customer experiences.

• Limitations: Lacks built-in workflow orchestration; external tools like Apache Airflow or Prefect are often required.

• Real-world examples: Instacart analyzes fraud patterns, while Cloudflare processes 6 million records per second.

ClickHouse is ideal for businesses prioritizing speed and scalability in real-time data workflows, though it requires external tools for managing complex task dependencies.

Prefect and ClickHouse: Real-time event driven orchestration

ClickHouse® Features for Event-Driven Workflows

ClickHouse® stands out as a powerful tool for real-time analytics, and its features make it a solid choice for event-driven workflows. Its architecture is built to meet the rigorous demands of these systems, combining columnar storage, distributed processing, and specialized engines to deliver high performance.

Concurrency and Thread Pool Management

ClickHouse® uses advanced concurrency mechanisms to optimize performance, relying on intelligent thread scheduling and efficient job distribution. This allows it to handle massive concurrent operations. For instance, one organization managed to run over 1,000 active replicas and process hundreds of millions of rows per second [2].

Its vectorized query execution engine enables parallel processing across all available CPU cores, ensuring high throughput. Add in JIT compilation, and even complex analytical queries maintain efficiency under heavy workloads.

Event-Based Data Ingestion

ClickHouse® excels at handling continuous data ingestion, processing millions of rows per second through native integrations [2]. Features like asynchronous inserts and streaming ingestion make it ideal for real-time event handling.

A great example is Mux, which uses ClickHouse® as both a stream-processing engine and a persistent data store, replacing tools like Postgres and Flink. Their architecture leverages Kafka Table Engines for data ingestion, combined with cascading Materialized Views and Null Tables for pre-aggregation and data transformation [4].

The numbers speak for themselves: with a cluster scaled to 60 vCPUs across 4 nodes, Mux achieves nearly 500,000 writes per second while keeping consumer lag under a minute [4]. This performance highlights ClickHouse®'s ability to handle high-volume event ingestion without compromising query speed.

For users of ClickHouse Cloud, ClickPipes offers direct integration with remote Kafka brokers, enabling seamless data ingestion into ClickHouse® without the need for separate pipelines [3]. This simplifies setup while maintaining the high performance necessary for event-driven workflows.

The MergeTree storage engine, the default engine in ClickHouse®, is optimized for workloads with heavy inserts. It employs partitioning, sorting, and background merging to ensure efficient handling of incoming events while keeping historical data easily accessible for analysis.

Limitations and External Scheduler Requirements

While ClickHouse® is exceptional at analytics and data processing, it isn't a full-fledged orchestration solution. It lacks native job scheduling capabilities for managing multi-step workflows, retries, or dependencies between tasks.

This limitation became evident for Prefect, a company that processes over one million flow runs per day, with each flow generating 200+ events. In February alone, they handled between 150 to 200 million events daily [5]. As Chris Guidry, Staff Software Engineer at Prefect, noted:

"We needed a new database...For us, ClickHouse's strength is looking at massive volumes of time-oriented data, it's very much made for event streams and we're very happy with how ClickHouse approaches the challenge." [5]

However, managing such complexity required external orchestration tools to complement ClickHouse®'s analytical capabilities.

ClickHouse® also has some architectural trade-offs that impact workflow design. For example, instead of traditional UPDATE/DELETE operations, it uses MUTATION operations. Adding nodes to a cluster requires manual data rebalancing, which can temporarily affect query performance.

For JOIN operations, ClickHouse®'s rule-based query planner can struggle with complex queries, especially when working with large tables. Without distributed JOIN algorithms, performance can suffer if data doesn’t fit into memory on a single node. This often forces organizations to denormalize their data, which reduces the need for JOINs but limits flexibility for exploratory analysis.

These design choices reflect ClickHouse®'s focus on analytical workloads rather than general-purpose workflow management. To handle complex workflows, organizations often pair ClickHouse® with external orchestration tools like Apache Airflow, Prefect, or Temporal. This allows them to leverage ClickHouse®'s strengths in data processing while addressing broader workflow needs. The next section will dive into these tools and best practices for integrating them with ClickHouse®.

Using External Tools for Event-Driven Scheduling

ClickHouse® doesn't come with built-in workflow orchestration features, so many organizations turn to external scheduling tools to create event-driven systems. By combining ClickHouse®'s analytical power with these tools, you can build a cohesive real-time workflow architecture. The challenge lies in selecting the right tools and implementing them effectively to manage complex workflows while maximizing ClickHouse®'s strengths.

Common Orchestration Tools for Event-Driven Workflows

Apache Airflow is a leading tool in workflow orchestration, with an impressive 320 million downloads in 2024[11]. It’s particularly strong in managing complex data pipelines that involve dependencies, retries, and scheduling. Integration with ClickHouse® is seamless using the Airflow ClickHouse Plugin. For example, you can import the operator with:
from airflow_clickhouse_plugin.operators.clickhouse import ClickHouseOperator[8].

Astronomer’s Astro Observe is a great example of Airflow’s capabilities, managing millions of tasks monthly while significantly reducing downtime[9].

Apache Kafka plays a key role in many event-driven architectures as the backbone for event streaming. ClickHouse® supports Kafka through various integration options, such as ClickPipes, Kafka Connect Sink, and the native Kafka table engine[7].

Prefect is part of a newer wave of orchestration platforms designed for event-driven workflows. Sarah Krasnik Bedell, Prefect’s Director of Growth Marketing, highlights its flexibility:

"We want to be able to handle a wide variety of different data pipelines and code tasks. Ultimately, the goal is to enable whatever deployment and trigger patterns developers, data engineers, platform engineers, and software engineers need"[5].

As the industry moves toward event-driven and real-time processing, newer tools like Prefect are simplifying setup processes for startups and mid-sized businesses[11].

Connecting ClickHouse® to External Schedulers

Kafka Integration forms a solid base for event-driven workflows. Building on ClickPipes and other Kafka tools, external schedulers can trigger workflows based on data availability and processing needs.

GlassFlow and Altinity.Cloud enhance stream processing. GlassFlow, for instance, handles deduplication and joins upstream in Kafka streams before data reaches ClickHouse®. This reduces computational load and improves query speeds[6].

Airbyte simplifies data integration with its pre-built connectors, enabling smooth data transfers from ClickHouse® to other destinations. When paired with Airflow, this setup eases the workload on data engineering teams[10].

Best Practices for Monitoring and Scaling

To ensure smooth operation, integrating external schedulers with ClickHouse® requires careful monitoring and scaling strategies.

• Cost Management: Prefect’s shift from BigQuery and PostgreSQL to ClickHouse® illustrates the potential savings. Monthly costs dropped from $12,000 on CloudSQL and BigQuery to under $8,000 after switching to ClickHouse®[5].

• Materialized Views: These are crucial for real-time performance. Christine Shen, Software Engineer at Astronomer, shares:

  "ClickHouse's materialized views have been extremely helpful in allowing us to precompute all of those at insertion time instead of at read time, which allows us to query a lot faster"[9].

• Error Handling and Monitoring: Distributed systems require careful error tracking. Log errors at both the orchestration and ClickHouse® levels, and monitor metrics like ingestion rates, query performance, and resource usage. Setting up alerts for issues like failed workflows or unusual latency spikes is essential.

• Scaling Strategies: Monitor ClickHouse®’s CPU, memory, and disk I/O usage, and scale orchestration tools based on workflow complexity and frequency. Managed services like ClickHouse Cloud can also simplify operations. Julian LaNeve, CTO at Astronomer, notes:

  "ClickHouse Cloud has been great to work with. Managing it is super easy – we hardly have to think about it, and their team has been very helpful and responsive"[9].

• Data Pipeline Architecture: Keep transformations in the streaming layer (e.g., using GlassFlow) instead of handling them in ClickHouse®. Use orchestration tools for managing workflows and dependencies, leaving ClickHouse® to focus on fast analytics over large datasets.

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ClickHouse® vs. Tinybird for Event-Driven Scheduling

When designing event-driven scheduling systems, choosing between ClickHouse® and Tinybird often comes down to a trade-off between developer efficiency and control. While both platforms leverage ClickHouse®'s high-performance analytics engine, their approaches to implementation and management differ significantly.

Feature Comparison: ClickHouse® and Tinybird

Your choice will depend on whether you prioritize rapid deployment or the ability to fine-tune your database environment. Here's a breakdown of their key features:

This table illustrates how each platform caters to distinct needs, making it easier to align your choice with your project’s requirements.

Tinybird focuses on simplifying infrastructure management to speed up development. It offers managed data connectors for popular sources like Kafka, S3, and DynamoDB, and its one-click API generation feature converts SQL queries into REST endpoints, complete with OpenAPI documentation.

On the other hand, ClickHouse Cloud gives you direct access to its database environment. This means you can adjust database settings, connect native ClickHouse clients, and configure the system to suit complex, specific use cases. While this approach requires more expertise, it provides unmatched flexibility for advanced architectures.

When to Choose Tinybird Over ClickHouse®

Deciding between the two often depends on your team’s goals and technical expertise. Cost is a key factor: Tinybird reports that 93% of its non-enterprise customers on paid plans spend less than $100 per month on production workspaces, with a median cost under $10 [12].

Tinybird is the better choice when your focus is on analytics rather than infrastructure management. It’s ideal for scenarios where you need to:

• Reduce development time: Tinybird eliminates the need to manage database infrastructure, letting developers focus on building business logic.

• Quickly create user-facing analytics: Pre-optimized for high-concurrency, real-time queries, Tinybird is well-suited for dashboards and customer-facing applications.

• Seamlessly integrate with event streams: With managed queues, automatic REST API generation, and support for real-time data ingestion, Tinybird simplifies deployment and integration.

As Tinybird puts it:

"Fast databases make queries fast. Tinybird makes developers fast" [12].

When to Choose ClickHouse Cloud

ClickHouse Cloud is the better fit for teams that need full control over their database environment. It’s best for use cases where:

• Custom configurations are required: Direct access to database settings allows you to fine-tune performance for unique workloads.

• Your team has ClickHouse expertise: If your developers are familiar with ClickHouse, they can optimize the system beyond what managed platforms offer.

• Specialized client connections are necessary: Native ClickHouse client support enables seamless integration with existing tools and workflows.

For event-driven scheduling, Tinybird simplifies the process with managed batch queues that streamline data imports and exports. Its Git integration also makes it easy to automate CI/CD workflows, reducing the manual effort involved in updating scheduling logic.

Ultimately, the decision comes down to whether your team values development speed (Tinybird) or granular control (ClickHouse Cloud). Both platforms are capable of handling event-driven workloads, but they cater to different developer priorities and operational needs.

Optimizing ClickHouse® for Event-Driven Workflows

Now that we’ve covered ClickHouse®'s core features and its integration with external schedulers, let’s dive into how to fine-tune its performance and design for event-driven workflows. By optimizing configurations, schema design, and operations, you can significantly cut down latency in managing high-volume, real-time data streams.

Performance Optimization Strategies

One of the key factors in maintaining performance in high-concurrency environments is thread pool management. Properly managing thread pools reduces kernel overhead by reusing and caching threads.

"Effective thread pool management reduces kernel overhead through thread reuse and caching."

• Shiv Iyer [14]

For instance, ChistaDATA implemented thread pool enhancements that led to a 15% reduction in median latency and a 30% boost in 99th percentile latency for workloads with high concurrency [14]. Adaptive scaling and maintaining a pool of idle threads play a critical role in these improvements. Adjusting parameters like max_threads, max_block_size, and max_insert_threads to suit your hardware and workload can further enhance performance [17]. Increasing max_thread_pool_free_size ensures that more threads are prepped for incoming tasks, pushing efficiency to 99.5% while keeping job management delays minimal [15].

Another essential strategy is workload scheduling, especially when multiple event streams are competing for resources. ClickHouse’s scheduling capabilities allow you to allocate resources like disk I/O and CPU separately. Using settings like inflight_limit ensures maximum resource utilization, while bandwidth_limit prevents overloading [16].

Upgrading to ClickHouse version 24.10 or later provides access to the latest thread pool optimizations [15]. Metrics such as GlobalThreadPoolThreadCreationMicroseconds and GlobalThreadPoolLockWaitMicroseconds are invaluable for monitoring thread pool performance and spotting potential issues [15].

While these performance tweaks are vital, optimizing your schema design is just as critical for accelerating query execution.

Schema Design for Event-Driven Workflows

A well-thought-out schema is the backbone of efficient event-driven analytics. Choices around data types, ordering keys, and column structures directly impact both compression and query speed.

Data type optimization is a good starting point. Use strict data types to ensure accurate filtering and aggregation. Avoid nullable columns unless the distinction between empty and null values is meaningful, as they can increase storage needs and slow down queries. For numeric fields, choose the smallest precision necessary, and for date fields, opt for the coarsest precision that still meets your requirements [13].

Ordering key selection is another crucial factor. Focus on columns that are frequently used in WHERE clauses and those with high correlation to improve compression ratios. Ordering keys in ascending order of cardinality is generally beneficial, but keep in mind that filtering on columns later in the key can be less efficient. For categorical data with fewer than 10,000 unique values, use LowCardinality to save space and improve performance. Enums can also be helpful for data validation while taking advantage of natural ordering [13].

These optimizations aren’t just about internal performance - they also need to align with operational standards in the United States.

U.S.-Specific Operational Considerations

Running ClickHouse in the U.S. requires careful attention to time zones, data formats, and regulatory requirements. Time zone handling is particularly important when processing events across regions or aligning analytics with U.S. business hours. Configure the timezone parameter to accommodate Eastern Time (ET) and Pacific Time (PT), especially during daylight saving changes in March and November.

For date and time formatting, while ClickHouse stores dates in ISO format internally, it’s a good practice to display timestamps in familiar U.S. formats like MM/DD/YYYY or "12/31/2024 11:59 PM EST" for local users.

Industry-specific compliance is another key consideration. For example, financial institutions must adhere to SOX regulations, while healthcare organizations need to meet HIPAA requirements. ClickHouse’s audit logging and data retention features can help maintain regulatory compliance by providing oversight and automatically purging outdated data.

When presenting numeric data to U.S. audiences, use commas for thousands and periods for decimals (e.g., 1,234.56). Currency values should include dollar signs (e.g., $1,234.56), and if temperature data is relevant, display it in Fahrenheit to align with local conventions.

Conclusion

ClickHouse® has proven itself as a standout choice for event-driven scheduling and real-time analytics workflows. As Girff aptly puts it:

"ClickHouse has earned its reputation as a lightning-fast OLAP database for real-time analytics. If you've ever dealt with slow queries on massive datasets, switching to ClickHouse often feels like turning on a jet engine" [18].

With the ability to scan billions of rows per second, execute queries more than twice as fast as Snowflake, and deliver 38% better compression efficiency [1], ClickHouse is designed for speed and efficiency. Its columnar storage architecture and vectorized query execution allow it to handle enormous volumes of event data without the slowdowns that traditional databases often face. Companies like Uber rely on it to process and analyze massive log data in real time [1].

When it comes to deployment, the choice depends heavily on your team's needs and expertise. Opt for self-managed ClickHouse or ClickHouse Cloud if you want full control and are prepared to fine-tune performance settings. On the other hand, Tinybird offers a simplified experience by managing the complexities of ClickHouse for you, letting you focus on application development. With 93% of non-enterprise customers paying under $100 per month and a median cost of less than $10 [12], Tinybird is a cost-effective solution for smaller teams aiming to move quickly. As Ben Hylak, Co-Founder & CTO at Raindrop, puts it:

"Tinybird is to ClickHouse what Supabase is to Postgres. One of my all-time favorite dev tools" [19].

Ultimately, the right platform depends on balancing developer productivity with system control. Whether you're automating data ingestion or orchestrating external workflows, making an informed choice will set the foundation for scalable, real-time analytics tailored to your project's needs.

FAQs

How does ClickHouse® enable real-time data ingestion for event-driven workflows?

ClickHouse® is built to handle real-time data ingestion with incredible speed and efficiency, making it a perfect fit for event-driven workflows. It can manage high-speed data streams through various methods, including direct streaming, batch loading, and integration with platforms like Kafka. These features enable ClickHouse to process events as they occur, delivering timely and precise analytics.

For developers working on large-scale, event-driven applications, pairing ClickHouse with tools like Prefect can be a game-changer. Prefect helps orchestrate workflows in real time, complementing ClickHouse's capabilities. Together, they provide a solid foundation for creating scalable, real-time analytics pipelines and event-driven systems.

What are the challenges of using ClickHouse® for workflow orchestration, and how can tools like Apache Airflow or Prefect address them?

ClickHouse® excels at delivering top-notch real-time analytics and lightning-fast query performance. However, it falls short when it comes to managing workflow orchestration. It doesn’t include built-in features like task scheduling, dependency management, or support for intricate workflows - key elements for automating data pipelines.

This is where tools like Apache Airflow and Prefect come into play. These tools fill the gap by offering orchestration capabilities that ClickHouse® lacks. With them, you can schedule tasks, manage dependencies, and design dynamic workflows, making it easier to integrate ClickHouse® into broader data processing ecosystems. They empower developers to scale operations effectively while focusing on building robust, event-driven analytics systems.

When should a business use Tinybird instead of ClickHouse® Cloud for event-driven scheduling?

When deciding between Tinybird and ClickHouse® Cloud, it comes down to your team's specific needs and priorities.

If you're looking for a fully managed platform that takes care of infrastructure and offers built-in tools for tasks like real-time analytics and event-driven workflows, Tinybird is a strong contender. It streamlines processes such as database upgrades, supports streaming data ingestion, and even provides API endpoints. This makes it a great choice for teams aiming to quickly build and scale data-driven applications without getting bogged down in technical complexities.

On the flip side, ClickHouse® Cloud is a better fit for teams that want greater control over their database setup. If your team prefers to manage infrastructure directly and fine-tune database performance to meet specific needs, ClickHouse® Cloud offers the flexibility and hands-on approach to make that possible.