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terrastore: All content tagged as terrastore in NoSQL databases and polyglot persistence

11 Document-Oriented Databases Which Are 8: CouchDB, Jackrabbit, MongoDB, RavenDB

Such list would be even more useful with the following classification:

Production ready


Note: A special mention in this category for OrientDB and Terrastore which even if they might not be largely adopted they are still active projects probably counting a couple of production deployments.


Original title and link: 11 Document-Oriented Databases Which Are 8: CouchDB, Jackrabbit, MongoDB, RavenDB (NoSQL database©myNoSQL)

Griffon and NoSQL Databases

Andres Almiray:

The following list enumerates all NoSQL options currently supported by Griffon via plugins:

  • BerkeleyDB
  • CouchDB
  • Memcached
  • Riak
  • Redis
  • Terrastore
  • Voldemort
  • Neo4j
  • Db4o
  • Neodatis

The first 7 are Key/Value stores. Neo4j is a Graph based database. The last two are object stores. All of them support multiple datasources, data bootstrap and a Java friendly API similar to the one shown earlier.

Griffon is a Groovy-based framework for developing desktop applications. While the coolness factor of Java-based desktop apps is close to zero, having some multi-platform management utilities for these NoSQL databases might be interesting.

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


Project Voldermort and Terrastore: Key-Value vs Document Stores

It is an apples to oranges comparison, but it underlines, from a beginner perspective, the major differences between a pure key-value store (Project Voldemort) and a document database (Terrastore):

Being a simpler KV store than Terrastore, to my understanding Project Voldemort offers no ability to leverage the server to evaluate the Values. In order to, for example, produce a list of documents whose “publish date” is in the past, it is necessary to either fetch all documents and evaluate the publish date each time this operation is needed — or — manage a lookup list of document IDs that were “published” when the lookup list was created.

In the end, the author also emphasizes how important the first impression is: clean documentation, simple installation, etc.. Or differently put, an end user judges a project by how fast he can start using it.

Original title and link: Project Voldermort and Terrastore: Key-Value vs Document Stores (NoSQL databases © myNoSQL)


Terrastore 0.8.0 Released, Featuring Map/Reduce

After a short break, Terrastore has published a new version, 0.8.0, which brings quite a few interesting features, plus some performance, scalability, and stability enhancements:

  • map/reduce processing
  • active event listeners
  • adaptive ensemble scheduling
  • document and communication compression

Sergio Bossa, Terrastore lead developer, has shared more about this release ☞ here:

Terrastore map/reduce implementation targets all documents, or just a subset of documents specified by range, belonging to a single bucket, and is based on three phases: mapper, combiner and reducer. The mapper phase is initiated by the node which received the map/reduce request, the originator node: it locates the target documents and the nodes that hold them, then sends the map function to those node so that it can be applied in parallel on each node; the map function will take each target document as input argument, and return, for each document, a map of pairs as output. Then, each remote node runs the combiner phase, aggregating its local map results and returning a partial map of pairs. Finally, the originator node runs the reducer phase, aggregating all partial results.

You can download the new Terrastore from ☞ here.

Original title and link: Terrastore 0.8.0 Released, Featuring Map/Reduce (NoSQL databases © myNoSQL)

Terrastore and ElasticSearch to Replace MySQL, Memcached and Sphinx

Currently we are using PHP, MySQL, Sphinx, and Memcached to serve up pages so quick. […]

[…] Our (MY) final decision was to use Terrastore. I’m not sure if it is the fastest, but it is fast. The main reason is how easy it is to scale with growth, how well it protects the data and keeps multiple copies always available, and the fast release cycle which means it is always improving.

As a replacement for Sphinx , we have considered many, but have landed on ElasticSearch, which just so happens to have a direct integration with Terrastore. A no-brainer for us to choose ElasticSearch for our search and ranking algorithms.

While each piece is important, sometimes it is also about the combo.

Original title and link: Terrastore and ElasticSearch to Replace MySQL, Memcached and Sphinx (NoSQL databases © myNoSQL)


Terrastore Sweet Spot

From Sergio Bossa’s (@sbtourist) slides embedded below:

Terrastore is best suited for:

  • data hot spots
  • computational data
  • complex, rich or variable data
  • throw-away data

Quite generic, so I’d love to hear from those that are planning to use Terrastore in their projects.

Original title and link: Terrastore Sweet Spot (NoSQL databases © myNoSQL)

Terrastore: A Product as a Framework

Sergio Bossa in a recent interview:

Moreover, Terrastore is a kind of development platform too: you can customize and plug in your own components such as functions for executing custom update logic, predicates for conditionally reading and updating data, partitioning strategies for custom data placement and event listeners for automatic processing … everything in your JVM language of choice.

That’s an interesting alternative: the product as a framework, instead of the classical black-box product. RavenDB, another document database, offers a somehow similar approach using a plugin-based architecture.

Original title and link: Terrastore: A Product as a Framework (NoSQL databases © myNoSQL)


Terrastore: An Introductory Presentation

From Sven Johansson (@svjson):

The Terrastore deployment architecture slides are looking interesting:

Original title and link: Terrastore: An Introductory Presentation (NoSQL databases © myNoSQL)

Presentation: CRUD with Terrastore

Not sure how long is supposed to stay online, so check Mats Henricson’s slides on CRUD with Terrastore while they are ☞ here:

  • Built on top of Terracotta!
  • HTTP and Java API
  • Supports single-cluster and multi-cluster deployments
  • Elastic: You can add and remove nodes dynamically
  • Scalable: Automatic and transparent re-balancing
  • Schemaless
  • Easy to install and configure
  • Custom data partitioning
  • Event processing
  • Range queries
  • Server-side update functions
  • Per-document consistency

Original title and link for this post: Presentation: CRUD with Terrastore (published on the NoSQL blog: myNoSQL)

RavenDB in Production

Good question on Stackoverflow (it applies to the other document database, Terrastore too):

I’m having a hard time finding applications and websites that use RavenDB in production. Does anyone have first-hand experience with this, or examples of (well-known) sites that run on RavenDB?

I assume that both are too young to have production installations at this moment.

RavenDB in Production originally posted on the NoSQL blog: myNoSQL


Release: Terrastore 0.6.0, Improved Data Processing Capabilitites

Last week Terrastore has seen a new release, 0.6.0, featuring a few interesting improvements:

  • Reduced overall memory footprint, allowing for higher performance.
  • Improved range queries performance and scalability.
  • Reliable event publishing by integrating with the ActiveMQ message broker.
  • Several new predicate conditions and update functions, improving Terrastore built-in data processing capabilities.

You can find out more about Terrastore data processing capabilities in our interview with Terrastore lead developer, Sergio Bossa.

Terrastore Gets a Quickstart Option

To make things simpler for those planning to try out Terrastore, the installation now contains the option to create a simple cluster (1 master, 2 slaves) with a single command:

ant -f terrastore-install.xml quickstart -Dquickstart.dir=<install_dir>