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Couchbase vs HBase: What are the differences?

Introduction

Couchbase and HBase are both popular NoSQL databases used for managing large amounts of data. However, they have several key differences in terms of data model, scalability, data consistency, query language, indexing, and performance.

  1. Data Model: Couchbase is a document-oriented database, which means it stores data in JSON-like documents. On the other hand, HBase is a column-oriented database that stores data in tables with rows and columns.

  2. Scalability: Couchbase offers seamless scalability with built-in support for sharding and replication. It allows horizontal scaling by distributing data across multiple nodes. In contrast, HBase uses Hadoop Distributed File System (HDFS) and Hadoop's MapReduce for horizontal scalability.

  3. Data Consistency: Couchbase provides eventual consistency by default, which means that the data may take some time to propagate across all nodes. However, it also offers strong consistency as an option. HBase, on the other hand, provides strong consistency by default.

  4. Query Language: Couchbase uses the N1QL query language, which is SQL-like and allows querying data using both structured and unstructured queries. HBase uses HBase shell and Java API for querying data, which requires more technical expertise.

  5. Indexing: Couchbase supports secondary indexes, allowing efficient querying on non-primary key attributes. It also supports various types of indexes like GSI, spatial, and full-text search. HBase uses sparse indexes based on row keys, which limits the types of queries that can be performed.

  6. Performance: Couchbase is known for its high performance and low latency due to its scalable architecture and in-memory caching. It provides automatic data caching and efficient data replication. HBase also offers good performance but may require additional configuration and tuning for optimal results.

In summary, Couchbase and HBase differ in terms of their data model, scalability, data consistency, query language, indexing, and performance.

Advice on Couchbase and HBase
Needs advice
on
HBaseHBaseMilvusMilvus
and
RocksDBRocksDB

I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!

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Replies (1)
Recommends

You've probably come to a decision already but for those reading...here are some resources we put together to help people learn more about Milvus and other databases https://zilliz.com/comparison and https://github.com/zilliztech/VectorDBBench. I don't think they include RocksDB or HBase yet (you could could recommend on GitHub) but hopefully they help answer your Elastic Search questions.

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Ilias Mentzelos
Software Engineer at Plum Fintech · | 9 upvotes · 243.9K views
Needs advice
on
CouchbaseCouchbase
and
MongoDBMongoDB

Hey, we want to build a referral campaign mechanism that will probably contain millions of records within the next few years. We want fast read access based on IDs or some indexes, and isolation is crucial as some listeners will try to update the same document at the same time. What's your suggestion between Couchbase and MongoDB? Thanks!

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Replies (2)
Jon Clarke
Enterprise Account Exec at ScyllaDB · | 4 upvotes · 88.6K views
Recommends
on
CouchbaseCouchbaseScyllaDBScyllaDB

I am biased (work for Scylla) but it sounds like a KV/wide column would be better in this use case. Document/schema free/lite DBs data stores are easier to get up and running on but are not as scalable (generally) as NoSQL flavors that require a more rigid data model like ScyllaDB. If your data volumes are going to be 10s of TB and transactions per sec 10s of 1000s (or more), look at Scylla. We have something called lightweight transactions (LWT) that can get you consistency.

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Recommends
on
MongoDBMongoDB

I have found MongoDB highly consistent and highly available. It suits your needs. We usually trade off partion tolerance fot this. Having said that, I am little biased in recommendation as I haven't had much experience with couchbase on production.

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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 · 218.4K 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 · 218.5K 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 Couchbase and HBase
Gabriel Pa

After using couchbase for over 4 years, we migrated to MongoDB and that was the best decision ever! I'm very disappointed with Couchbase's technical performance. Even though we received enterprise support and were a listed Couchbase Partner, the experience was horrible. With every contact, the sales team was trying to get me on a $7k+ license for access to features all other open source NoSQL databases get for free.

Here's why you should not use Couchbase

Full-text search Queries The full-text search often returns a different number of results if you run the same query multiple types

N1QL queries Configuring the indexes correctly is next to impossible. It's poorly documented and nobody seems to know what to do, even the Couchbase support engineers have no clue what they are doing.

Community support I posted several problems on the forum and I never once received a useful answer

Enterprise support It's very expensive. $7k+. The team constantly tried to get me to buy even though the community edition wasn't working great

Autonomous Operator It's actually just a poorly configured Kubernetes role that no matter what I did, I couldn't get it to work. The support team was useless. Same lack of documentation. If you do get it to work, you need 6 servers at least to meet their minimum requirements.

Couchbase cloud Typical for Couchbase, the user experience is awful and I could never get it to work.

Minimum requirements The minimum requirements in production are 6 servers. On AWS the calculated monthly cost would be ~$600. We achieved better performance using a $16 MongoDB instance on the Mongo Atlas Cloud

writing queries is a nightmare While N1QL is similar to SQL and it's easier to write because of the familiarity, that isn't entirely true. The "smart index" that Couchbase advertises is not smart at all. Creating an index with 5 fields, and only using 4 of them won't result in Couchbase using the same index, so you have to create a new one.

Couchbase UI The UI that comes with every database deployment is full of bugs, barely functional and the developer experience is poor. When I asked Couchbase about it, they basically said they don't care because real developers use SQL directly from code

Consumes too much RAM Couchbase is shipped with a smaller Memcached instance to handle the in-memory cache. Memcached ends up using 8 GB of RAM for 5000 documents! I'm not kidding! We had less than 5000 docs on a Couchbase instance and less than 20 indexes and RAM consumption was always over 8 GB

Memory allocations are useless I asked the Couchbase team a question: If a bucket has 1 GB allocated, what happens when I have more than 1GB stored? Does it overflow? Does it cache somewhere? Do I get an error? I always received the same answer: If you buy the Couchbase enterprise then we can guide you.

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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 Couchbase
Pros of HBase
  • 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
  • 9
    Performance
  • 5
    OLTP
  • 1
    Fast Point Queries

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Cons of Couchbase
Cons of HBase
  • 3
    Terrible query language
    Be the first to leave a con

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    - No public GitHub repository available -

    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.

    What is HBase?

    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.

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    What companies use Couchbase?
    What companies use HBase?
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    Blog Posts

    Jun 24 2020 at 4:42PM

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    What are some alternatives to Couchbase and HBase?
    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.
    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.
    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.
    Redis
    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
    Oracle
    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.
    See all alternatives