Firebolt vs ClickHouse

Firebolt vs ClickHouse

Get the speed you need, without the complexity 

Firebolt is built from the ground up to deliver sub-second latency and runs as a cloud native SaaS offering, thus eliminating the operational overhead of building and operating a data warehouse. If you know relational databases, you know Firebolt.

ClickHouse is a specialized accelerator that requires operational heavy lift due to the need for proprietary skills, customization and performance tuning.  Firebolt, on the other hand, is a general purpose data warehouse that provides high performance without the operational heavy lift.

ClickHouse Cloud is a new managed offering in the public cloud. While infrastructure management is abstracted from the user, this offering has limited scalability and still requires proprietary skills to customize and tune.

Architecture

Architecture

ClickHouse leverages a clustered, shared nothing architecture with compute and storage tightly coupled together. This tight coupling introduces cluster management and scaling challenges.

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Purpose-built with decoupled compute and storage that allows for elastic scaling to address high performance ingest, low latency and high concurrency workloads.

Decoupled Compute and Storage - Workload Isolation

Decoupled Compute and Storage - Workload Isolation

Workload and resource isolation are not currently not available in ClickHouse or ClickHouse Cloud.

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Firebolt architecture allows spinning up isolated resources enabling workload and resource isolation when needed.  This provides scale-up, scale-out, scale-in and scale-to-zero capabilities for mixed workload environments.

Operational Overhead 

Operational Overhead 

Installation, management, upgrades and scaling of a ClickHouse cluster require extensive amount of resources. Requires careful planning and execution around server, networking, os and storage maintenance.

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As a SaaS offering, Firebolt abstracts away the complexity of managing infrastructure. Scaling with cloud based resources is performed through Firebolt UI, SQL or APIs.

Query Optimization and SQL Support

Query Optimization and SQL Support

ClickHouse lacks a full fledged query optimizer. While, ClickHouse provides a SQL-like dialect, it is not ANSI-SQL compliant. 

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Firebolt’s built-in cost based optimizer finds the most efficient way to query data by reordering operations, identifying indexes, pruning data and leveraging push down and tiered storage to delivery fast response times. On top of that, knowledge of ANSI-SQL is all you need to start using Firebolt. 

Materialized Views 

Materialized Views 

ClickHouse materialized views are updated on insert trigger. However, customers need to understand the complexities of configuring and using them.

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Firebolt’s aggregating indexes are defined once, incrementally auto-synced at ingest and data can be queried at index speed immediately. No query rewrites needed.

Joins and Mutations

Joins and Mutations

While ClickHouse supports Joins, updates and deletes, these are all expensive operations. To work around join challenges, denormalization is recommended. ClickHouse recommends avoiding updates and deletes due to the performance overhead. 

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Firebolt customers do not need to denormalize data to drive performance. Join acceleration and multistage query execution are built into Firebolt. Furthermore, Firebolt offers updates/deletes designed with performance in mind.

Pricing Model

Pricing Model

ClickHouse relies on shared nothing architecture that requires careful planning of compute and storage resources. Inherently, the tight coupling between compute and storage introduces cost challenges. Additionally, block storage requirements drive higher costs.

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Firebolt lowers TCO through granular choice of instances without the complexity of cluster configuration, scale to zero with auto-stop and optimized object storage format.

The Firebolt Advantage 

SaaS

No infrastructure to manage
Focus on your datasets and queries
Simplify tuning with cost based optimizer

Decoupled Storage and Compute

Elastic Scaling
Easy Workload Isolation
Spin-up/ Spin-down environments on the fly

Transparent Consumption Based  Pricing

Granular compute provisioning
Save costs with Auto Stop
Lower TCO with scalable object storage

Integration with Modern Data Stack

REST APIs / SDK 
Ecosystem Integrations
ANSI-SQL compliant

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