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

Couchbase vs Ehcache

OverviewDecisionsComparisonAlternatives

Overview

Couchbase
Couchbase
Stacks505
Followers606
Votes110
Ehcache
Ehcache
Stacks616
Followers160
Votes4
GitHub Stars2.1K
Forks585

Couchbase vs Ehcache: What are the differences?

Introduction:  
Couchbase and Ehcache are two popular caching solutions used in web development to improve performance and scalability of applications.
  1. Architecture: One key difference between Couchbase and Ehcache is their architecture. Couchbase is a NoSQL database that stores data in a distributed manner across multiple nodes, providing high availability and scalability. In contrast, Ehcache is an in-memory caching solution that stores data in the memory of a single server or multiple servers, offering faster access to data but limited scalability compared to Couchbase.

  2. Data Persistence: Another major difference is their approach to data persistence. Couchbase is designed to persist data on disk, ensuring durability and consistency even in the event of node failures. On the other hand, Ehcache primarily stores data in-memory and may offer options for disk persistence as a secondary storage mechanism, making it more suitable for temporary caching purposes rather than long-term data storage.

  3. Support for Data Replication: Couchbase provides built-in support for data replication, allowing data to be automatically synchronized across nodes to ensure data consistency and high availability. In contrast, Ehcache may require additional configurations or plugins to enable data replication between cache instances, making it less robust in distributed environments compared to Couchbase.

  4. Scalability: In terms of scalability, Couchbase is designed to horizontally scale by adding more nodes to the cluster, allowing the database to handle increased load and storage capacity efficiently. Ehcache, on the other hand, may have limitations in scaling out due to its reliance on a single server or limited cluster configurations, making it less suited for large-scale applications that require high performance and scalability.

  5. Consistency Models: Couchbase supports strong consistency models like linearizable consistency, ensuring that clients always receive the most up-to-date data from the database. In contrast, Ehcache may offer eventual consistency models, where data updates are eventually propagated to all cache instances, potentially leading to inconsistencies in data retrieval under high load or network partitions.

  6. Query Language Support: Couchbase provides a query language called N1QL (pronounced "nickel") for flexible and powerful querying of JSON data stored in the database, offering SQL-like syntax with support for joins, aggregations, and indexes. Ehcache does not natively support complex query capabilities and is primarily used for simple key-value lookups and caching, limiting its functionality for applications that require advanced querying capabilities.

In Summary, Couchbase and Ehcache differ in architecture, data persistence, data replication support, scalability, consistency models, and query language capabilities, making each suitable for specific use cases in web development.

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Advice on Couchbase, Ehcache

Gabriel
Gabriel

CEO at Naologic

Nov 2, 2020

Decided

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.

247k views247k
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
Ehcache
Ehcache

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.

Ehcache is an open source, standards-based cache for boosting performance, offloading your database, and simplifying scalability. It's the most widely-used Java-based cache because it's robust, proven, and full-featured. Ehcache scales from in-process, with one or more nodes, all the way to mixed in-process/out-of-process configurations with terabyte-sized caches.

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
GitHub Stars
-
GitHub Stars
2.1K
GitHub Forks
-
GitHub Forks
585
Stacks
505
Stacks
616
Followers
606
Followers
160
Votes
110
Votes
4
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
  • 4
    Terrible query language
Pros
  • 1
    Container doesn't have to be running for local tests
  • 1
    Simpler to run in testing environment
  • 1
    Way Faster than Redis and Elasticache Redis
  • 1
    Easy setup
Integrations
Hadoop
Hadoop
Kafka
Kafka
Elasticsearch
Elasticsearch
Kubernetes
Kubernetes
Apache Spark
Apache Spark
No integrations available

What are some alternatives to Couchbase, Ehcache?

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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