Overview
This article compares ClickHouse and Elasticsearch in terms of performance for large-scale data analytics, particularly focusing on `count(*)` aggregations over billions of rows. It highlights ClickHouse's superior data compression and aggregation speed, leading to significant cost savings and efficiency improvements for users.
What You'll Learn
1
How to effectively compare ClickHouse and Elasticsearch for data analytics workloads
2
Why ClickHouse is more efficient for `count(*)` aggregations over large datasets
3
When to consider migrating from Elasticsearch to ClickHouse for cost savings
Key Questions Answered
How does ClickHouse outperform Elasticsearch in data storage and query performance?
ClickHouse requires 12 to 19 times less storage space compared to Elasticsearch, enabling the use of smaller and cheaper hardware. Additionally, ClickHouse runs aggregation queries at least 5 times faster than Elasticsearch, making it a more efficient choice for large-scale data analytics.
What are the key differences in data loading between ClickHouse and Elasticsearch?
ClickHouse can natively read Parquet files and load data significantly faster than Elasticsearch, which relies on Logstash for data ingestion. In testing, ClickHouse loaded 100 billion rows in less than one day, while Elasticsearch struggled to load even 30 billion rows in a similar timeframe.
What are the storage size requirements for 1 billion rows in ClickHouse vs Elasticsearch?
For 1 billion rows, ClickHouse requires approximately 4.2 GB of storage with LZ4 compression, while Elasticsearch requires 51.3 GB with `_source` enabled. This demonstrates ClickHouse's superior data compression capabilities.
Key Statistics & Figures
Storage space reduction
12 to 19 times less storage space
ClickHouse compared to Elasticsearch for large datasets
Latency improvement
at least 5 times lower latencies
ClickHouse for `count(*
Technologies & Tools
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Database
Clickhouse
Used for efficient data storage and aggregation
Database
Elasticsearch
Used for data analytics and search capabilities
Key Actionable Insights
1Consider migrating from Elasticsearch to ClickHouse if your organization handles large-scale data analytics. The cost savings from reduced storage requirements and improved query performance can lead to significant operational efficiencies.Organizations dealing with petabyte-scale observability use cases have reported over 30% reductions in hardware costs after migrating to ClickHouse.
2Utilize ClickHouse's materialized views for continuous data summarization to further enhance performance and reduce costs.This technique allows for efficient data aggregation, making it easier to manage large datasets while maintaining fast query response times.
Common Pitfalls
1
Failing to optimize data ingestion processes can lead to significant delays and inefficiencies.
Elasticsearch's reliance on Logstash for data loading can result in longer load times compared to ClickHouse's native support for Parquet files.
Related Concepts
Data Compression Techniques
Aggregation Performance Optimization
Time-series Data Analysis