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Clickhouse

418
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85
Couchbase

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110
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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.

Advice on Clickhouse and Couchbase
Needs advice
on
CouchbaseCouchbase
and
MongoDBMongoDB

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.

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Replies (3)
Petr Havlicek
Freelancer at havlicekpetr.cz · | 12 upvotes · 224.4K views
Recommends
on
MongoDBMongoDB

I prefer MongoDB due to own experience with migration of old archive of pdf and meta-data to a new “archive”. The biggest advantage is speed of filters output - a new archive is way faster and reliable then the old one - but also the the easy programming of MongoDB with many code snippets and examples available. I have no personal experience so far with Couchbase. From the architecture point of view both options are OK - go for the one you like.

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Ivan Begtin
Founder - Dateno, Director - NGO "Informational Culture" / Ambassador - OKFN Armenia at Infoculture · | 7 upvotes · 224.5K views
Recommends
on
ArangoDBArangoDB

I would like to suggest MongoDB or ArangoDB (can't choose both, so ArangoDB). MongoDB is more mature, but ArangoDB is more interesting if you will need to bring graph database ideas to solution. For example if some data or some documents are interlinked, then probably ArangoDB is a best solution.

To process tables we used Abbyy software stack. It's great on table extraction.

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OtkudznamDamir Radinović-Lukić
Recommends
on
LinuxLinux

If you can select text with mouse drag in PDF. Use pdftotext it is fast! You can install it on server with command "apt-get install poppler-utils". Use it like "pdftotext -layout /path-to-your-file". In same folder it will make text file with line by line content. There is few classes on git stacks that you can use, also.

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Decisions about Clickhouse and Couchbase
Gabriel Pa

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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Pros of Clickhouse
Pros of Couchbase
  • 21
    Fast, very very fast
  • 11
    Good compression ratio
  • 7
    Horizontally scalable
  • 6
    Utilizes all CPU resources
  • 5
    RESTful
  • 5
    Open-source
  • 5
    Great CLI
  • 4
    Great number of SQL functions
  • 4
    Buggy
  • 3
    Server crashes its normal :(
  • 3
    Highly available
  • 3
    Flexible connection options
  • 3
    Has no transactions
  • 2
    ODBC
  • 2
    Flexible compression options
  • 1
    In IDEA data import via HTTP interface not working
  • 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
  • 5
    Equal nodes in cluster, allowing fast, flexible changes
  • 5
    Both a key-value store and document (JSON) db
  • 5
    Open source, community and enterprise editions
  • 4
    Automatic configuration of sharding
  • 4
    Local cache capability
  • 3
    Easy setup
  • 3
    Linearly scalable, useful to large number of tps
  • 3
    Easy cluster administration
  • 3
    Cross data center replication
  • 3
    SDKs in popular programming languages
  • 3
    Elasticsearch connector
  • 3
    Web based management, query and monitoring panel
  • 2
    Map reduce views
  • 2
    DBaaS available
  • 2
    NoSQL
  • 1
    Buckets, Scopes, Collections & Documents
  • 1
    FTS + SQL together

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Cons of Clickhouse
Cons of Couchbase
  • 5
    Slow insert operations
  • 3
    Terrible query language

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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 Couchbase?

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.

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What companies use Clickhouse?
What companies use Couchbase?
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What are some alternatives to Clickhouse and Couchbase?
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.
See all alternatives