Clickhouse vs Hadoop

Overview

Hadoop

Stacks2.7K

Followers2.3K

Votes56

GitHub Stars15.3K

Forks9.1K

Clickhouse

Stacks431

Followers543

Votes85

Clickhouse vs Hadoop: What are the differences?

ClickHouse vs Hadoop

ClickHouse and Hadoop are both popular big data processing platforms, but they have some key differences that make them suitable for different use cases.

Data Processing Paradigm: ClickHouse is a columnar database that is optimized for fast analytical queries. It is designed to provide real-time analytics on large datasets. On the other hand, Hadoop is a distributed computing platform that follows the MapReduce paradigm for processing and analyzing large volumes of data.
Data Storage: ClickHouse stores data in a columnar format, which enables efficient storage and retrieval of individual columns. This storage format is ideal for analytics workloads where queries often involve aggregations and filtering of specific columns. In contrast, Hadoop uses the Hadoop Distributed File System (HDFS) to store data in a distributed manner across multiple nodes. It provides fault tolerance and high throughput for handling large files.
Scalability: ClickHouse is designed to scale horizontally by adding more servers to a cluster. It can handle heavy workloads and process data in parallel to achieve high performance. Hadoop, on the other hand, is known for its massive scalability. It can scale to thousands of nodes and process petabytes of data.
Data Processing Speed: Due to its columnar storage and optimized query execution engine, ClickHouse can provide much faster query response times compared to Hadoop. It can efficiently scan and aggregate large volumes of data in a highly parallelized manner. In Hadoop, the processing speed depends on factors like the complexity of the MapReduce job and the cluster configuration.
Ease of Use: ClickHouse is known for its simplicity and ease of use. Its SQL-like query language makes it easier for users familiar with relational databases to interact with the system. Hadoop, on the other hand, has a steeper learning curve and requires knowledge of programming languages like Java for writing MapReduce jobs.
Data Update Support: ClickHouse is primarily designed for read-heavy workloads and does not have built-in support for updating or deleting individual rows. It is optimized for fast inserts and efficient retrieval of data. In contrast, Hadoop allows for more complex data processing scenarios, including data updates and deletions, making it suitable for a wider range of use cases.

In summary, ClickHouse is a fast and scalable columnar database optimized for real-time analytics, while Hadoop is a distributed computing platform that excels in handling massive volumes of data using the MapReduce paradigm. ClickHouse offers faster query response times, easier usability, and efficient storage for column-based analytical workloads. Hadoop, on the other hand, provides massive scalability, flexibility for complex data processing, and support for data updates and deletions.

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CLI (Node.js)

Manual

Detailed Comparison

Hadoop	Clickhouse
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.	It allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query.
Statistics
GitHub Stars 15.3K	GitHub Stars -
GitHub Forks 9.1K	GitHub Forks -
Stacks 2.7K	Stacks 431
Followers 2.3K	Followers 543
Votes 56	Votes 85
Pros & Cons
Pros 39 Great ecosystem 11 One stack to rule them all 4 Great load balancer 1 Amazon aws 1 Java syntax	Pros 21 Fast, very very fast 11 Good compression ratio 7 Horizontally scalable 6 Utilizes all CPU resources 5 Open-source Cons 5 Slow insert operations

What are some alternatives to Hadoop, Clickhouse?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Clickhouse vs Hadoop: What are the differences?

ClickHouse vs Hadoop

ClickHouse and Hadoop are both popular big data processing platforms, but they have some key differences that make them suitable for different use cases.

Data Processing Paradigm: ClickHouse is a columnar database that is optimized for fast analytical queries. It is designed to provide real-time analytics on large datasets. On the other hand, Hadoop is a distributed computing platform that follows the MapReduce paradigm for processing and analyzing large volumes of data.
Data Storage: ClickHouse stores data in a columnar format, which enables efficient storage and retrieval of individual columns. This storage format is ideal for analytics workloads where queries often involve aggregations and filtering of specific columns. In contrast, Hadoop uses the Hadoop Distributed File System (HDFS) to store data in a distributed manner across multiple nodes. It provides fault tolerance and high throughput for handling large files.
Scalability: ClickHouse is designed to scale horizontally by adding more servers to a cluster. It can handle heavy workloads and process data in parallel to achieve high performance. Hadoop, on the other hand, is known for its massive scalability. It can scale to thousands of nodes and process petabytes of data.
Data Processing Speed: Due to its columnar storage and optimized query execution engine, ClickHouse can provide much faster query response times compared to Hadoop. It can efficiently scan and aggregate large volumes of data in a highly parallelized manner. In Hadoop, the processing speed depends on factors like the complexity of the MapReduce job and the cluster configuration.
Ease of Use: ClickHouse is known for its simplicity and ease of use. Its SQL-like query language makes it easier for users familiar with relational databases to interact with the system. Hadoop, on the other hand, has a steeper learning curve and requires knowledge of programming languages like Java for writing MapReduce jobs.
Data Update Support: ClickHouse is primarily designed for read-heavy workloads and does not have built-in support for updating or deleting individual rows. It is optimized for fast inserts and efficient retrieval of data. In contrast, Hadoop allows for more complex data processing scenarios, including data updates and deletions, making it suitable for a wider range of use cases.

Clickhouse vs Hadoop

Overview

Clickhouse vs Hadoop: What are the differences?