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Clickhouse vs CouchDB: What are the differences?
# Introduction
This Markdown will compare the key differences between Clickhouse and CouchDB.
1. **Data storage model**: Clickhouse is column-oriented, optimized for fast retrieval of large amounts of data. It excels in analytical queries over huge datasets. In contrast, CouchDB is a document-oriented database, storing data in JSON documents. It is designed for ease of use and flexibility in handling different data structures.
2. **Query language**: Clickhouse uses SQL for querying data, making it familiar to users with SQL experience. On the other hand, CouchDB uses a RESTful HTTP API for data manipulation, allowing for seamless integration with web services and applications.
3. **Replication and scaling**: Clickhouse supports replication, sharding, and clustering for high availability and scalability. It is suitable for data warehousing and analytics workloads requiring horizontal scaling. In comparison, CouchDB provides built-in multi-master replication, making it easier to distribute and synchronize data across multiple nodes.
4. **Consistency model**: Clickhouse prioritizes speed and performance over strict consistency, offering eventual consistency for distributed queries. CouchDB, on the other hand, emphasizes data consistency and provides strong consistency guarantees for single-node deployments.
5. **Built-in features**: Clickhouse offers native support for time-series data and integrates well with other big data tools in the ecosystem. On the contrary, CouchDB provides built-in conflict resolution and versioning for document updates, ensuring data integrity in distributed environments.
In Summary, Clickhouse and CouchDB differ in their data storage models, query languages, replication capabilities, consistency models, and built-in features, catering to different use cases and preferences in data management and analytics.```
Decisions about Clickhouse and CouchDB
Gabriel Pa
CEO at NaoLogic Inc · | 10 upvotes · 582.1K views
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.
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Learn MorePros of Clickhouse
Pros of CouchDB
Pros of Clickhouse
- Fast, very very fast21
- Good compression ratio11
- Horizontally scalable7
- Utilizes all CPU resources6
- RESTful5
- Open-source5
- Great CLI5
- Great number of SQL functions4
- Buggy4
- Server crashes its normal :(3
- Highly available3
- Flexible connection options3
- Has no transactions3
- ODBC2
- Flexible compression options2
- In IDEA data import via HTTP interface not working1
Pros of CouchDB
- JSON43
- Open source30
- Highly available18
- Partition tolerant12
- Eventual consistency11
- Sync7
- REST API5
- Attachments mechanism to docs4
- Multi master replication4
- Changes feed3
- REST interface1
- js- and erlang-views1
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Cons of Clickhouse
Cons of CouchDB
Cons of Clickhouse
- Slow insert operations5
Cons of CouchDB
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What is Clickhouse?
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.
What is CouchDB?
Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.
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What companies use Clickhouse?
What companies use CouchDB?
What companies use Clickhouse?
What companies use CouchDB?
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What tools integrate with Clickhouse?
What tools integrate with CouchDB?
What tools integrate with Clickhouse?
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What are some alternatives to Clickhouse and CouchDB?
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.
Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
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.
InfluxDB
InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running.
InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.
Druid
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.