Clickhouse vs HBase

Overview

HBase

Stacks511

Followers498

Votes15

GitHub Stars5.5K

Forks3.4K

Clickhouse

Stacks431

Followers543

Votes85

Clickhouse vs HBase: What are the differences?

Introduction

In this article, we will explore the key differences between Clickhouse and HBase, two popular distributed databases.

Data Model: Clickhouse is a columnar database that stores data in columns rather than rows, making it highly efficient for analytical queries. On the other hand, HBase is a key-value store that organizes data in rows and columns, allowing flexible schema design.
Scalability: Clickhouse is designed to handle large amounts of data and can horizontally scale by adding more servers to the cluster. It is well-suited for applications that require high throughput and low latency for analytical queries. In contrast, HBase is built on Apache Hadoop and can scale petabytes of data across a distributed cluster. It provides automatic sharding, replication, and load balancing to ensure high availability.
Consistency Model: Clickhouse is an eventually consistent database, meaning that it sacrifices consistency for achieving high availability and low latency. It supports real-time data ingestion and allows for near-instantaneous query results. HBase, on the other hand, provides strong consistency guarantees and supports atomic reads and writes. It ensures that data is consistent across all replicas before returning the results.
Query Language: Clickhouse has its own SQL-like query language that allows users to perform complex analytics on large datasets. It provides various built-in analytical functions, supports subqueries, and has extensive support for aggregations and joins. HBase, on the other hand, uses HBase Shell or client APIs to interact with the database. It supports a limited set of operations, mainly focused on key-value operations.
Integrations: Clickhouse integrates well with other data processing frameworks like Apache Kafka, Apache Spark, and Apache Hadoop. It supports ingestion from various data sources, including batch and streaming data. HBase, being based on Apache Hadoop, integrates seamlessly with the Hadoop ecosystem. It can read and write data from and to Hadoop distributed file system (HDFS) and is often used for real-time analytics alongside MapReduce and Apache Hive.
Data Storage: Clickhouse stores data in a compressed format, utilizing efficient data compression algorithms. This allows it to store and process large data volumes efficiently. HBase, on the other hand, stores data in a distributed file system and provides built-in compression options. It supports both in-memory and on-disk storage, offering flexibility based on the use case.

In Summary, Clickhouse is a columnar database optimized for analytical queries with eventual consistency, while HBase is a key-value store designed for scalability and strong consistency.

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Detailed Comparison

HBase	Clickhouse
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.	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 5.5K	GitHub Stars -
GitHub Forks 3.4K	GitHub Forks -
Stacks 511	Stacks 431
Followers 498	Followers 543
Votes 15	Votes 85
Pros & Cons
Pros 9 Performance 5 OLTP 1 Fast Point Queries	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 HBase, 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.

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Clickhouse vs HBase: What are the differences?

Introduction

In this article, we will explore the key differences between Clickhouse and HBase, two popular distributed databases.

Data Model: Clickhouse is a columnar database that stores data in columns rather than rows, making it highly efficient for analytical queries. On the other hand, HBase is a key-value store that organizes data in rows and columns, allowing flexible schema design.
Scalability: Clickhouse is designed to handle large amounts of data and can horizontally scale by adding more servers to the cluster. It is well-suited for applications that require high throughput and low latency for analytical queries. In contrast, HBase is built on Apache Hadoop and can scale petabytes of data across a distributed cluster. It provides automatic sharding, replication, and load balancing to ensure high availability.
Consistency Model: Clickhouse is an eventually consistent database, meaning that it sacrifices consistency for achieving high availability and low latency. It supports real-time data ingestion and allows for near-instantaneous query results. HBase, on the other hand, provides strong consistency guarantees and supports atomic reads and writes. It ensures that data is consistent across all replicas before returning the results.
Query Language: Clickhouse has its own SQL-like query language that allows users to perform complex analytics on large datasets. It provides various built-in analytical functions, supports subqueries, and has extensive support for aggregations and joins. HBase, on the other hand, uses HBase Shell or client APIs to interact with the database. It supports a limited set of operations, mainly focused on key-value operations.
Integrations: Clickhouse integrates well with other data processing frameworks like Apache Kafka, Apache Spark, and Apache Hadoop. It supports ingestion from various data sources, including batch and streaming data. HBase, being based on Apache Hadoop, integrates seamlessly with the Hadoop ecosystem. It can read and write data from and to Hadoop distributed file system (HDFS) and is often used for real-time analytics alongside MapReduce and Apache Hive.
Data Storage: Clickhouse stores data in a compressed format, utilizing efficient data compression algorithms. This allows it to store and process large data volumes efficiently. HBase, on the other hand, stores data in a distributed file system and provides built-in compression options. It supports both in-memory and on-disk storage, offering flexibility based on the use case.

In Summary, Clickhouse is a columnar database optimized for analytical queries with eventual consistency, while HBase is a key-value store designed for scalability and strong consistency.