Comparing ClickHouse v/s Hadoop for Real-time Analytics Capability

Why is Hadoop not suitable for Real-Time Analytics?

Table of Contents

Introduction

Hadoop and ClickHouse are two different systems that serve different purposes when it comes to processing and analyzing data. Hadoop is a distributed computing platform that is designed to process and analyze large volumes of data in parallel, while ClickHouse is a columnar database management system that is optimized for fast query processing and real-time analytics.

Here are some key differences between Hadoop and ClickHouse:

  1. Data storage and processing model: Hadoop uses a distributed file system called HDFS to store and process data in a distributed manner, while ClickHouse uses a columnar storage engine to store and process data in a more optimized way for analytical queries.
  2. Query processing: Hadoop uses MapReduce to process data and execute queries, a batch processing framework unsuited for real-time analytics. ClickHouse, on the other hand, is optimized for real-time query processing and can handle complex analytical queries with low latency.
  3. Scalability: Hadoop is highly scalable and can process large volumes of data in parallel, but it is designed for batch processing, which makes it less suitable for real-time analytics. ClickHouse, on the other hand, is optimized for real-time analytics and can handle large volumes of data with low latency.
  4. Data complexity: Hadoop can handle complex, unstructured data, but it requires more processing and storage overhead to do so. ClickHouse is optimized for structured data and can handle complex queries with low latency and high throughput.
  5. Hardware requirements: Hadoop requires a large cluster of commodity hardware to operate efficiently, which can be expensive to set up and maintain. ClickHouse, on the other hand, can run on a single server or a small cluster of servers, which makes it more cost-effective.

Overall, Hadoop is only suitable for batch processing because it is designed to process and analyze data in large batches, which makes it less suitable for real-time analytics. ClickHouse, on the other hand, is designed for real-time analytics and can handle complex analytical queries with low latency, making it a better choice for real-time analytics.

Conclusion

In summary, Hadoop is a powerful distributed computing platform that is well-suited for batch processing of large volumes of data, while ClickHouse is a columnar database management system that is optimized for real-time analytics and can handle complex analytical queries with low latency. When it comes to real-time analytics, ClickHouse is a better choice than Hadoop because it is optimized for real-time query processing and can handle complex analytical queries with low latency and high throughput.

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About Shiv Iyer 222 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.