myNoSQL

Skytree Server connects to any number of existing data stores, including Hadoop, and, says Hack, is tens of thousands of times faster than existing tools, performing in minutes tasks that would have taken hours or days. As of now, it’s tuned to five specific use cases the company says are the most common — recommendation systems, anomaly/outlier identification, predictive analytics, clustering and market segmentation, and similarity search.

There’s a limited but free Skytree version available on demand, so I expect to read some more about it soon.

Original title and link: Skytree Launches a MacHine Learning Server (NoSQL database©myNoSQL)

via: http://gigaom.com/cloud/skytree-intros-machine-learning-for-the-masses/

Leaving aside for a second the aspect of immediate win for 10gen and quite possibly the visible benefits for the end users, I’m wondering if such partnerships (or the lack of them) could be part of the answer to the question why only some NoSQL databases are present in managed hosting offers.

Here’s how MongoLab is introducing this partnership:

MongoLab provides, as always, primary support for operational issues (e.g. password resets, service plan upgrades, maintenance and monitoring) and usage guidance (e.g. index recommendations, schema design).  Starting now, 10gen provides support escalation for code-level database and driver issues, acting as our backstop to provide patches or effective workarounds to issues that can not be solved by configuration or architecture changes.

From my NoSQL market observer position, it looks like a win-win-win situation.

Original title and link: 10gen Signs Partnerships to Strengthen MongoDB Hosting (NoSQL database©myNoSQL)

via: http://blog.10gen.com/post/18067595934/three-new-cloud-providers-join-the-mongodb-ecosystem

Dave Engberg has published on the Evernote Techblog a post explaining why the Atomicity, Consistency, and Durability characteristics of a single replicated MySQL/InnoDB deployment are essential to the way Evernote operates.

While it’s difficult to argue about a technical decision with so little details available, I still wanted to point out a couple of things:

1. Atomicity: most of the NoSQL databases offer atomic operation at the level of a single record. For distributed systems that do not want to rely on 2PC, it is the multi-row atomic operations that are not supported.

   The example presented in the post does not require multi-row transactions, but rather guaranteed client operation ordering. This is achievable in most NoSQL databases.

2. Consistency: the post talks about data consistency from the perspective of data integrity guarantees through usage of foreign keys.

   In the world of NoSQL similar behavior could be achieved by different data modeling solutions. Using Cassandra as an example for the notebook deletion scenario, one could store all the notes of a notebook in a single Cassandra row, thus making the delete operation safe.

   It’s also worth mentioning that many of the eventually consistent NoSQL databases offer different consistent read and write operations.

3. Durability: with just a few known exceptions, most NoSQL databases offer strong durability guarantees.
   

In conclusion, based only on the few details of the post, one could easily argument that a NoSQL database would fit the bill. But most of the time the reality behind is much different, making technical decisions a tad more complicated.

Original title and link: WhySQL: MySQL/InnoDB ACID Guarantees for Evernote (NoSQL database©myNoSQL)

via: http://blog.evernote.com/tech/2012/02/23/whysql/

Thomas James:

The GUID/UUID data type is great for replacing the numerical ID of a record with something that can stand up to the challenges of distributed data, but they are not very suitable for use by the end-user. In some applications you really do still want to be able to generate a reliable numerical sequence number, such as for an invoice number. […] It may seem like overkill to add an additional component to our software stack just to handle the task of keeping a counter, but Redis fits the mould perfectly in this case. Its also simple to setup and use, not adding much to the solution’s overhead. Especially considering that, like CouchDB, the redis instance is “in the cloud”.

I do feel this is overkill and one could generate the sequence at the application level. And even if there is a proposed feature for adding a 128-Bit K-ordered unique id generator to Redis, for high scale decentralized ID generation one should look into Twitter’s Snowflake or Boundary’s Flake.

Original title and link: Generating Numerical Sequences With Redis (NoSQL database©myNoSQL)

via: http://www.thomasvjames.com/2012/02/relax-unwind-with-a-little-redis/

Romiko Derbynew comparing Gremlin and Neo4j Cypher:

• Simple graph traversals are much more efficient when using Gremlin
• Queries in Gremlin are 30-50% faster for simple traversals
• Cypher is ideal for complex traversals where back tracking is required
• Cypher is our choice of query language for reporting
• Gremlin is our choice of query language for simple traversals where projections are not required
• Cypher has intrinsic table projection model, where Gremlins table projection model relies on AS steps which can be cumbersome when backtracking e.g. Back(), As() and _CopySplit, where cypher is just comma separated matches
• Cypher is much better suited for outer joins than Gremlin, to achieve similar results in gremlin requires parallel querying with CopySplit, where as in Cypher using the Match clause with optional relationships
• Gremlin is ideal when you need to retrieve very simple data structures
• Table projection in gremlin can be very powerful, however outer joins can be very verbose

So in a nutshell, we like to use Cypher when we need tabular data back from Neo4j and is especially useful in outer joins.

Patrick Durusau

Original title and link: Gremlin vs Cypher (NoSQL database©myNoSQL)

via: http://romikoderbynew.com/2012/02/22/gremlin-vs-cypher-initial-thoughts-neo4j/

Arfon Smith¹:

A couple of months ago I wrote about how the astrophysics community should place more value on those individuals building tools for their community - the informaticians. One example of a tool that I don’t think is particularly well known in many areas of research is the Apache Hadoop software framework.

Hadoop is a great tool but it can be fiddly to configure. With Elastic MapReduce you can focus on the design of your map/reduce workflow rather than figuring out how to get your cluster setup. Next I’m planning on making some small changes to software used by radio astronomers to find astrophysical sources in data cubes of the sky to make it work with Hadoop Streaming - bring it on SKA!

Clearly Hadoop has issues. Meanwhile it helps local communities to plan for snow removal, geophysicists find oil in the oceans, and who knows exactly how many other similar problematic implementations are out there.

Peter Skomoroch

Original title and link: A Guide to Elastic MapReduce and Hadoop Streaming for Astrophysicists (NoSQL database©myNoSQL)

via: http://arfon.org/getting-started-with-elastic-mapreduce-and-hadoop-streaming

Mathias Meyer’s walk through the DynamoDB features and API with commentary:

Sorted range keys, conditional updates, atomic counters, structured data and multi-valued data types, fetching and updating single attributes, strong consistency, and no explicit way to handle and resolve conflicts other than conditions. A lot of features DynamoDB has to offer remind me of everything that’s great about wide column stores like Cassandra, but even more so of HBase. This is great in my opinion, as Dynamo would probably not be well-suited for a customer-facing system. And indeed, Werner Vogel’s post on DynamoDB seems to suggest DynamoDB is a bastard child of Dynamo and SimpleDB, though with lots of sugar sprinkled on top.

Think of it as an extended, better articulated and closer to the API version of my notes about Amazon DynamoDB.

Original title and link: A Tour of Amazon DynamoDB Features and API (NoSQL database©myNoSQL)

via: http://www.paperplanes.de/2012/1/30/a-tour-of-amazons-dynamodb.html

A new open source tool from Netflix, Priam—back in November, Netflix has released Curator, a ZooKeeper library—, used to simplify and automate the operations and management of a Cassandra cluster:

Priam is a co-process that runs alongside Cassandra on every node to provide the following functionality:

• Backup and recovery
  • snapshot and incremental backups
  • compression and multipart off-site uploading
  • data recovery and data testing

• Bootstrapping and automated token assignment

  Priam automates the assignment of tokens to Cassandra nodes as they are added, removed or replaced in the ring. Priam relies on centralized external storage (SimpleDB/Cassandra) for storing token and membership information, which is used to bootstrap nodes into the cluster. It allows us to automate replacing nodes without any manual intervention, since we assume failure of nodes, and create failures using Chaos Monkey. The external Priam storage also provides us valuable information for the backup and recovery process.

• Centralized configuration management: All our clusters are centrally configured via properties stored in SimpleDB, which includes setup of critical JVM settings and Cassandra YAML properties.

• RESTful monitoring and metrics: provides hooks that support external monitoring and automation scripts. They provide the ability to backup, restore a set of nodes manually and provide insights into Cassandra’s ring information. They also expose key Cassandra JMX commands such as repair and refresh.

Original title and link: Automating Cassandra Operations and Management With Netflix’s Priam Tool (NoSQL database©myNoSQL)

via: http://techblog.netflix.com/2012/02/announcing-priam.html