Couchbase Server 1.8 replaces Membase Server 1.7 as our “flagship” database offering. In addition to the obvious rebranding, we’ve made substantial improvements in the cluster rebalancing process and fixed a number of nagging issues in 1.7.

In case you feel lost with which Couchbase products are which, read my 5 bullet points explanation.

Original title and link: Couchbase Server 1.8 Released, Rebranding and Some Improvements in Cluster Rebalancing (NoSQL database©myNoSQL)

Here are the 5 bullet points that would helped Couchbase clarify all the confusion about Couchbase, Membase, CouchDB:

1. We are working on Couchbase server 2.0. This is our next major release and the only product we will be focusing next. It represents the continuation of our current Membase server product.
2. We will not support anymore our distribution of CouchDB known as Couchbase Single Server. Damien Katz (and other Apache CouchDB committers on the Couchbase team) will not be involved anymore in the development of the Apache CouchDB.
3. Due to major changes, we will not offer a migration path for users of Couchbase Single Server to Couchbase server 2.0
4. We might release one or two updates to our Membase server that are addressing the most important issues.
5. We will provide a migration path to users of Membase server to Couchbase server 2.0

Original title and link: Couchbase: Clarifying Confusions in 5 Bullet Points (NoSQL database©myNoSQL)

Here’s the abstract of the patent filing submitted by MapR’s for a Map-Reduce Ready Distributed File System:

A map-reduce compatible disitrubuted file system that consists of successive component layers that each provide the basis on which the next layer is built provides transactional read-write -update semantics with file chunk replication and huge file-create rates. A primitive storage layer (storage pools) knits together raw block stores and provides a storage mechanism for containers and transaction logs. Storage pools are manipulated by individual file servers. Containers provide the fundamental basis for data replication, relocation, and transactional updates. A container location database allows containers to be found among all file servers, as well as defining precedence among replicas of containers to organize transactional updates of container contents. Volumes facilitate control of data placement, creation of snapshots and mirrors, and retention of a variety of control and policy information. Key-value stores relate keys to data for such purposes as directories, container location maps, and offset maps in compressed files.

You can get the complete PDF from here.

Original title and link: MapR’s Map-Reduce Ready Disitributed File System Patent Filing (NoSQL database©myNoSQL)

After reading about MarkLogic Packaging feature, I was wondering if managing configurations would not be better done with tools like Puppet or Chef instead of a custom built solution even if it comes packaged with your NoSQL database.

• You’ve been working on an application on your development machine. Now it’s time to move your application to the staging or testing servers. What follows is a tedious process of reviewing the settings on your development machine and applying them to the staging machine. How sure are you that you got all the indexes just right?
• You’ve got a certified configuration that you want to deploy onto a new cluster. Getting the hardware setup and installing the server itself isn’t too hard, but now you have to make sure that all the application servers and databases are setup. Can you see another tedious process coming?

If you’ve been involved or responsible for managing the configuration of a NoSQL database deployment, I’d really love to learn what solution and tools have been used.

Original title and link: NoSQL Databases Configuration Management (NoSQL database©myNoSQL)

All the basic steps of installing Cassandra, creating a keyspace and column families, inserting and manipulating data, querying data with CQL on a single page. Even if a bit old, you could continue next with my tutorial on getting started with Cassandra.

Original title and link: NoSQL Tutorials: Getting Started With Cassandra (NoSQL database©myNoSQL)

A question asked by many, but for now only a few shared their thoughts on Quora. Truth is there are many ways to defining a game changer technology: disruptive, innovative, impacting existing solution providers in the same market or in related markets, etc. Amazon DynamoDB could be all or none or a bit of each of these. But if the question implies a “winner-takes-it-all” answer, Sid Anand already answered it:

In the NoSQL world, it is by no means a winner-take-all battle. Distributed Systems are about compromises.

Leaving aside this type of questions, what I think it’s more relevant is learning who will be using Amazon DynamoDB and for what.

Original title and link: Will Amazon DynamoDB Be a Game Changer? (NoSQL database©myNoSQL)

Countandra features:

• Geographically distributed counting.
• Easy HTTP Based interface to insert counts.
• Hierarchical counting such as com.mywebsite.music.
• Retrieves counts, sums and square in near real time.
• Simple HTTP queries provides desired output in JSON format
• Queries can be sliced by period such as LASTHOUR, LASTYEAR and so on for MINUTELY, HOURLY, DAILY, MONTHLY values
• Queries can be classified for anything in hierarchy such as com, com.mywebsite or com.mywebsite.music

Inspired by Twitter’s Rainbird and built on top of Cassandra (1.0.1), Netty, Hector, and Joda.

Original title and link: Countandra: Cassandra-Based Hierarchical Distributed Counting Engine (NoSQL database©myNoSQL)

A different, unified look at the data model of the key-value stores, document databases, and column-family stores from Martin Fowler:

there’s a big similarity between the first three - all have a fundamental unit of storage which is a rich structure of closely related data: for key-value stores it’s the value, for document stores it’s the document, and for column-family stores it’s the column family. In DDD terms, this group of data is an aggregate.

The aggregate approach was present in the relational databases world for quite a while. It came in two flavors: views and denormalization. The first one worked well for non-distributed deployments, while the second is used everywhere the speed or the usage of joins was not an option.

Original title and link: Key-Value Stores, Document Databases, and Column Stores as Aggregate Oriented Databases (NoSQL database©myNoSQL)