ClickHouse VS Cassandra: A Detailed Comparison

Here are the architectural highlights of ClickHouse:

• Columnar storage in ClickHouse improves query speed for analytical workloads since it only reads the required columns instead of scanning entire rows.
• It uses advanced compression algorithms (e.g., LZ4, ZSTD) to minimize storage requirements and enhance query speed.
• ClickHouse follows an append-only approach, meaning data is mostly immutable. Instead of updating existing records, it inserts new data and later merges it in the background.
• ClickHouse uses a shared-nothing architecture, where each node operates independently. Data can be distributed across nodes using sharding, but this requires manual configuration and the use of ClickHouse Keeper.
• Supports asynchronous replication for fault tolerance, but replication is not as seamless or automatic as in Cassandra.

Here’s how ClickHouse performs:

• Optimized for fast reads due to its columnar storage, which allows it to scan only the required columns instead of full rows.
• Uses vectorized query execution, in which data is processed in batches for better CPU efficiency and faster analytics.
• Supports complex queries like joins, aggregations, and filtering. This makes it a strong choice for analytical workloads.
• Batch inserts work well, but frequent updates and deletes are slow due to its append-only storage model.
• Consumes more CPU and RAM for real-time queries, but compression helps reduce storage overhead.
• Scales horizontally, but requires manual sharding and replication setup for distributed workloads.

Here’s what you need to know about maintaining and operating ClickHouse:

• As touched on above, ClickHouse requires manual configuration for sharding and replication, unlike Cassandra, which automates this. It uses ClickHouse Keeper to manage distributed setups, which can have a steep learning curve for some.
• Replication in ClickHouse is asynchronous, meaning there is a chance of data lag between nodes. While this works well for analytical workloads, it’s less suitable for scenarios that need real-time consistency.
• Since ClickHouse enforces a strict schema, modifications like adding or removing columns can be expensive and complicated operations.
• ClickHouse supports incremental backups to local disks or S3 buckets, but setting up a backup routine requires extensive configuration. You can also use third-party tools like clickhouse-backup to automate the entire process.
• ClickHouse provides internal system tables for monitoring queries, performance, and storage usage. However, logging and debugging can be complex, especially in a distributed setup.
• Since ClickHouse follows an append-only storage model, updates and deletes require background merging processes. Over time, storage use can grow significantly if not managed properly.

Key availability aspects of ClickHouse include:

• Asynchronous replication means there can be delays in data consistency across nodes, which can affect availability in real-time applications.
• Single-node failures can impact performance, especially in sharded setups where queries depend on multiple nodes.
• Can handle read-heavy workloads well, but ensuring high availability requires a well-configured distributed cluster with replicas.
• ClickHouse can automatically recover from small data differences, but if discrepancies are too large (e.g., due to misconfiguration), manual intervention is required.

ClickHouse is generally considered a secure database. Here are some highlights:

• ClickHouse supports basic password-based authentication for users. The cloud variant supports other options like SSO and multi-factor authentication. It’s also possible to integrate it with external authentication systems (e.g., LDAP).
• ClickHouse supports RBAC-based access management, allowing permissions to be assigned to users, roles, row policies, settings profiles, and quotas.
• It supports SSL/TLS encryption for secure communication between clients and servers.
• ClickHouse provides built-in functions for encrypting and decrypting data using AES (Advanced Encryption Standard). It’s worth noting that these encryption functions were slow in versions before ClickHouse 21.1.
• ClickHouse provides logging for query execution and user activity, but advanced auditing features are limited.
• Integration with external logging and monitoring tools (like the ClickHouse monitoring solution by Site24x7) is necessary for comprehensive auditing.
• Firewall rules and network isolation are recommended in order to restrict access to ClickHouse servers.
• ClickHouse Cloud offers enhanced network security features, including VPC peering and private endpoints.

• Columnar storage and optimized indexing make ClickHouse great for analytical workloads.
• Advanced compression algorithms reduce storage costs and speed up query execution.
• Can handle large data sets efficiently with distributed query execution.
• Supports RBAC, SSL/TLS encryption, encryption at rest, and authentication integrations.
• If self-hosted, ClickHouse is open-source and cost-effective compared to traditional data warehouses.

• Built for distributed environments with no single point of failure.
• Designed for write-heavy applications with fast insert speeds.
• Multi-datacenter and fault-tolerant replication without manual intervention.
• Easily scales horizontally by adding new nodes.
• Supports SSL/TLS encryption, RBAC, and integration with external authentication systems.

• You need fast, complex analytical queries with aggregations.
• Your workload is mostly read-heavy (i.e., business intelligence, dashboards, real-time analytics).
• You want efficient compression to reduce storage costs.
• You are okay with data immutability (i.e., minimal updates/deletes).
• You have the expertise to handle manual sharding and data distribution.