Analytical Databases in the Cluster: ClickHouse and TimescaleDB for High-Volume Data

In an industrial concept, millions of data points are generated daily. When these data flow into Apache Kafka, the next critical question arises: Where do we store them so that engineers and data scientists can efficiently query them? A conventional relational database quickly reaches its limits with billions of rows. Queries over periods of months often take minutes there - unacceptable for interactive dashboards or AI models.

The solution on our Kubernetes platform is the use of specialized analytical databases like ClickHouse and TimescaleDB. These systems are designed to aggregate and analyze massive amounts of data (Big Data) at lightning speed.

1. TimescaleDB: The Power of SQL for Time Series

Many industrial data are classic time series (temperatures, pressures, speeds). TimescaleDB is an extension for PostgreSQL, specifically optimized for these workloads:

• Hypertables: Data is automatically divided into small “chunks” by time partitions. This massively accelerates write operations and queries, as only relevant time ranges are searched.
• Continuous Aggregations: Average values per hour or day are calculated and cached in the background. A dashboard thus loads instantly, no matter how much raw data is underlying.
• SQL Standard: Since it is based on PostgreSQL, existing tools and the team’s existing knowledge can be directly utilized.

2. ClickHouse: Insane Speed for Ad-hoc Analyses

When it comes to filtering and grouping billions of records in milliseconds, ClickHouse is the tool of choice. It uses a columnar storage model:

• Efficiency: Instead of reading entire rows, ClickHouse only accesses the columns needed for the query. This drastically reduces the I/O load.
• Compression: Since data in columns is often similar, ClickHouse achieves extreme compression rates. This saves expensive storage space in the cluster.
• Parallel Processing: ClickHouse utilizes all available CPU cores of the Kubernetes nodes to process a query.

3. Orchestration in the Kubernetes Cluster

Operating these databases on Kubernetes offers crucial advantages for resource management:

• Storage Classes: We use powerful, replicated storage (e.g., via CEPH) to ensure the databases are highly available. If a node fails, the database pod restarts on another node and immediately reconnects to its data.
• Isolated Resources: We ensure that a compute-intensive analysis in ClickHouse does not impact the performance of the ingestion pipeline. Through Resource Quotas, we allocate each database guaranteed CPU and RAM capacities.
• Scalability: As the data volume grows, we simply add more worker nodes and storage space to the cluster. The databases can be scaled horizontally (ClickHouse cluster) or vertically.

Conclusion: Data Query Without Waiting

By combining TimescaleDB for precise time series and ClickHouse for massively parallel analyses, we create a powerhouse for industrial data. Engineers no longer have to wait for reports; they can test hypotheses in real-time. This is the foundation for data-driven decisions in production and the prerequisite for successful advanced analytics projects.

FAQ

When should I use TimescaleDB and when ClickHouse? TimescaleDB is ideal if you are already using PostgreSQL, need complex joins, or require classic time series features like automatic data retention (deleting old data). ClickHouse is unbeatable when it comes to maximum speed with huge data volumes and complex analytical queries over many dimensions.

How do the data get from Kafka to the databases? We use connectors or small specialized services (consumers) that read the data streams from the Kafka topics and write them into the respective tables. In Kubernetes, these “ingestor pods” can be perfectly scaled.

Don’t analytical databases consume an extreme amount of RAM? Analytical databases use RAM very efficiently for caching. Due to columnar storage and compression, the overall footprint is often smaller than traditional systems trying to handle the same volume.

Is data security guaranteed in the event of a node failure? Yes. By using Persistent Volumes (PVs) and a stable storage backend like CEPH, the data remains intact. Kubernetes ensures that the database instance is immediately ready for use again without manual data migration.

How does ayedo support selection and setup? We analyze your data structure and query scenarios to develop the appropriate database strategy. We implement the cluster instances on Kubernetes, optimize the storage connection, and ensure a consistent backup concept.