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Clickhouse vs Couchbase: What are the differences?
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 Couchbase
- High performance18
- Flexible data model, easy scalability, extremely fast18
- Mobile app support9
- You can query it with Ansi-92 SQL7
- All nodes can be read/write6
- Equal nodes in cluster, allowing fast, flexible changes5
- Both a key-value store and document (JSON) db5
- Open source, community and enterprise editions5
- Automatic configuration of sharding4
- Local cache capability4
- Easy setup3
- Linearly scalable, useful to large number of tps3
- Easy cluster administration3
- Cross data center replication3
- SDKs in popular programming languages3
- Elasticsearch connector3
- Web based management, query and monitoring panel3
- Map reduce views2
- DBaaS available2
- NoSQL2
- Buckets, Scopes, Collections & Documents1
- FTS + SQL together1
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Cons of Clickhouse
- Slow insert operations5
Cons of Couchbase
- Terrible query language3