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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Clickhouse vs Couchbase

Clickhouse vs Couchbase

OverviewDecisionsComparisonAlternatives

Overview

Couchbase
Couchbase
Stacks505
Followers606
Votes110
Clickhouse
Clickhouse
Stacks431
Followers543
Votes85

Clickhouse vs Couchbase: What are the differences?

  1. Scalability and Performance: Clickhouse is designed for high performance analytical processing and can handle massive amounts of data with low latency. It is horizontally scalable and can process billions of rows per second. On the other hand, Couchbase is a distributed NoSQL database that provides high scalability and performance for both read and write operations. It can handle large workloads and scale out horizontally by adding more nodes.

  2. Data Model: Clickhouse is a columnar database that stores data in columns rather than rows, which allows for efficient compression and faster query execution. It is optimized for analytical queries and aggregations. In contrast, Couchbase is a document-oriented database that stores data in JSON-like documents. It offers flexible schema-less data model, which allows for easy data modeling and makes it suitable for a wide range of use cases.

  3. Consistency Model: Clickhouse is an eventual consistency database, which means that it may not provide real-time consistency across all replicas in a distributed setup. It prioritizes availability and partition tolerance over strict consistency. On the other hand, Couchbase provides strong consistency by default, ensuring that every read receives the most recent write. It uses a distributed consensus protocol to ensure consistency across replicas.

  4. Query Language: Clickhouse uses its own SQL-like query language called ClickHouse SQL (CHQL) for querying and manipulating data. It supports advanced analytical features such as window functions, materialized views, and sampling. Couchbase uses N1QL (pronounced as "nickel") as its query language, which is a SQL-like query language for JSON documents. It extends SQL to provide querying flexibility over complex JSON structures.

  5. Data Replication: Clickhouse supports asynchronous data replication, where data is replicated to replicas in the background. It provides configurable replication settings for controlling data consistency and performance. Couchbase also supports data replication but offers both synchronous and asynchronous replication options. It provides replication across multiple data centers for high availability and disaster recovery.

  6. Caching: Clickhouse provides efficient caching mechanisms to improve query performance, including a block cache, page cache, and a query cache. It leverages the available RAM to cache frequently accessed data and results. Couchbase also provides a built-in caching mechanism that can be configured to cache frequently accessed data in memory. It uses an intelligent caching strategy to optimize data access and reduce latency.

In Summary, Clickhouse is a high-performance columnar database with eventual consistency, while Couchbase is a distributed NoSQL database based on a document-oriented model with strong consistency.

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Advice on Couchbase, Clickhouse

Gabriel
Gabriel

CEO at Naologic

Jan 2, 2020

DecidedonCouchDBCouchDBCouchbaseCouchbaseMemcachedMemcached

We implemented our first large scale EPR application from naologic.com using CouchDB .

Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

592k views592k
Comments
Mike
Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k
Comments

Detailed Comparison

Couchbase
Couchbase
Clickhouse
Clickhouse

Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.

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.

JSON document database; N1QL (SQL-like query language); Secondary Indexing; Full-Text Indexing; Eventing/Triggers; Real-Time Analytics; Mobile Synchronization for offline support; Autonomous Operator for Kubernetes and OpenShift
-
Statistics
Stacks
505
Stacks
431
Followers
606
Followers
543
Votes
110
Votes
85
Pros & Cons
Pros
  • 18
    High performance
  • 18
    Flexible data model, easy scalability, extremely fast
  • 9
    Mobile app support
  • 7
    You can query it with Ansi-92 SQL
  • 6
    All nodes can be read/write
Cons
  • 3
    Terrible query language
Pros
  • 21
    Fast, very very fast
  • 11
    Good compression ratio
  • 7
    Horizontally scalable
  • 6
    Utilizes all CPU resources
  • 5
    Great CLI
Cons
  • 5
    Slow insert operations
Integrations
Hadoop
Hadoop
Kafka
Kafka
Elasticsearch
Elasticsearch
Kubernetes
Kubernetes
Apache Spark
Apache Spark
No integrations available

What are some alternatives to Couchbase, Clickhouse?

MongoDB

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

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

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

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

SQLite

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

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

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

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

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

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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