Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontal scalable. The original intention has been modern web-scale databases.
The movement began early 2009 and is growing rapidly. Often more characteristics apply as:
Usually do not require a fixed table schema nor do they use the concept of joins
All NoSQL offerings relax one or more of the ACID properties (will talk about the CAP theorem)
Large data volumes
Google’s “big data”
Scalable replication and distribution
Potentially thousands of machines
Potentially distributed around the world
Queries need to return answers quickly
Mostly query, few updates
Open source development
Asynchronous Inserts & Updates
Schema-less
ACID transaction properties are not needed – BASE
CAP Theorem
Consistency
“is equivalent to requiring requests of the distributed shared memory to act as if they were executing on a single node, responding to operations one at a time.”
Not the same as “ACID”
Linearizability ~ operations behave as if there were no concurrency.
Does not mention transactions
When no updates occur for a long period of time, eventually all updates will propagate through the system and all the nodes will be consistent
For a given accepted update and a given node, eventually either the update reaches the node or the node is removed from service
Known as BASE (Basically Available, Soft state, Eventual consistency), as opposed to ACID
Soft state – copies of a data item may be inconsistent
Eventually Consistent – copies becomes consistent at some later time if there are no more updates to that data item
Available
“every request received by a non-failing node in the system must result in a response.”
says nothing about the content of the response. It could be anything; it need not be “successful” or “correct”.
Partition Tolerant
any guarantee of consistency or availability is still guaranteed even if there is a partition.
if a system is not partition-tolerant, that means that if the network can lose messages or any nodes can fail, then any guarantee of atomicity or consistency is voided.
Typical NoSQL API
Basic API access:
get(key) -- Extract the value given a key
put(key, value) -- Create or update the value given its key
delete(key) -- Remove the key and its associated value
execute(key, operation, parameters) -- Invoke an operation to the value (given its key) which is a special data structure (e.g. List, Set, Map .... etc).
{
"_id": "guid goes here",
"_rev": "314159",
"type": "abstract",
"author": "Keith W. Hare"
"title": "SQL Standard and NoSQL Databases",
"body": "NoSQL databases (either no-SQL or Not Only SQL)
are currently a hot topic in some parts of
computing.",
"creation_timestamp": "2011/05/10 13:30:00 +0004"
}
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"_id"
GUID – Global Unique Identifier
Passed in or generated by CouchDB
"_rev"
Revision number
Versioning mechanism
"type", "author", "title", etc.
Arbitrary tags
Schema-less
Could be validated after the fact by user-written routine
Graph, Object Stores
Neo4J
db4o
Couch DB
An Apache project create by….Damien Katz…
A document database server, accessible via a RESTful JSON API.
Ad-hoc and schema-free with a flat address space.
Distributed, featuring robust, incremental replication with bi-directional conflict detection and management.
Recently merged with Membase
The CouchDB file layout and commitment system features all
Document updates (add, edit, delete) are serialized, except for binary blobs which are written concurrently.
CouchDB read operations use a Multi-Version Concurrency Control (MVCC) model where each client sees a consistent snapshot of the database from the beginning to the end of the read operation.
Eventually Consistent
Common Advantages of CouchDB
Cheap, easy to implement (open source)
Data are replicated to multiple nodes (therefore identical and fault-tolerant) and can be partitioned
Down nodes easily replaced
No single point of failure
Easy to distribute
Don't require a schema
Can scale up and down
Relax the data consistency requirement (CAP)
CouchDB vs MongoDB :
CouchDB vs Couchbase :
Earlier there was an affinity towards the similar names. Behind each of these names there is a story. Damien Katz initiated the Couchbase, who was actually the founder of CouchDB, a combination of CouchDB and Membase is called Couchbase, leading to make an easily scalable and high performance database.
1. Open Source Type
An apache open source project written in the Erlang language is CouchDB and is freely downloadable by the user. Another open source language is couch base, but it has community, enterprise and developer editions as a group of components.
2. Database Lock
For ensuring the table or a row, DBMS will use lock and that is what traditional DBMS is all about. In the CouchDB there is no lock as it uses a concept called MVCC (Multi Version Concurrency Control). Pessimistic locking is used by Couchbase on the other hand.
3.Query language
Couchbase has its own query language called N1QL, a SQL-like query language for JSON. CouchDB doesn’t have Query language. They both have similar views that is multi-dimensional/geospatial.
4. Topology
Couchbase topology is distributed and it means it is built from forms and scratch a cluster of nodes. The owner for a portion of hash space has each node in the cluster.
On the other hand Couch DB is imitated and is master-master replication, making multi-site application easy for deployment.
In the form of key-document MongoDB is widely used in application development. It has pros and cons over CouchDB and Couch database.
couchbase vs mongodb :
Couchbase vs Cassandra :
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