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cassandra: All content about cassandra in NoSQL databases and polyglot persistence

Improving Secondary Index Write Performance in Cassandra 1.2

Sam Tunnicliffe’s describes the old and new, optimized behavior of secondary indexes writes in Cassandra 1.2:

While secondary indexes can add a lot of flexibility to the way data is modelled and accessed, they do add complexity on the server side as the indexes need to be kept in sync with the primary data. Until recently, this has led to some significant trade offs in write throughput and IO utilisation as we always had to perform a read before the write in order to update any relevant secondary indexes. In Cassandra 1.2, this area has been substantially reworked to remove the need for read-before-write. New index entries are now written at the same time as the primary data is updated and old entries removed lazily at query time. Overall, this has lead to some decent performance improvements.

Original title and link: Improving Secondary Index Write Performance in Cassandra 1.2 (NoSQL database©myNoSQL)

via: http://www.datastax.com/dev/blog/improving-secondary-index-write-performance-in-1-2


Graph Based Recommendation Systems at eBay

Slidedeck from eBay explaining how they have implemented a graph based recommendation system based on,—surprise! not a graph database—Cassandra.

Original title and link: Graph Based Recommendation Systems at eBay (NoSQL database©myNoSQL)


RSS Reader With Cassandra and Netflix OSS Tools

This RSS reader app from Netflix can be a very good excuse to use Cassandra, some of the open source projects from Netflix and why not create an alternative to Google’s Reader which is declared defunct or alive every couple of months:

Recipes_RSS_arch

Projects you’ll use: Cassandra with Astyanax, Archaius, Blitz4j, Eurka, Governator, Hystrix, Karyon, Ribbon, Servo. As for myself, I’ve already checked out the code.

Original title and link: RSS Reader With Cassandra and Netflix OSS Tools (NoSQL database©myNoSQL)

via: http://techblog.netflix.com/2013/03/introducing-first-netflixoss-recipe-rss.html


Cassandra at Adobe: The Profile Cache Servers

The team I know at Adobe has invested a lot into HBase and they are offering their services globally. But according to this PDF, in a true polyglot database manner, it looks like other parts of the Adobe business have opted for a different solution: Cassandra. The size of the cluster mentioned in the whitepaper is pretty small, 16 nodes, but what is interesting is that these are beafy servers using solid state drives:

The PCS is comprised of large servers using solid state drives (SSDs) for storage […] The PCS is basically Cassandra with a set of custom APIs built on top of it.

Original title and link: Cassandra at Adobe: The Profile Cache Servers (NoSQL database©myNoSQL)


Adding Value Through Graph Analysis Using Titan and Faunus

Interesting slidedeck by Matthias Broecheler introducing 3 graph-related tools developed by Vadas Gintautas, Marko Rodriguez, Stephen Mallette and Daniel LaRocque:

  1. Titan: a massive scale property graph allowing real-time traversals and updates
  2. Faunus: for batch processing of large graphs using Hadoop
  3. Fulgora: for global running graph algorithms on large, compressed, in-memory graphs

The first couple of slides are also showing some possible use cases where these tools would prove their usefulness:

Original title and link: Adding Value Through Graph Analysis Using Titan and Faunus (NoSQL database©myNoSQL)


Brief Intro to Cassandra in 27 Slides

If you never looked into Apache Cassandra, Michaël Figuière’s slidedeck will give you a quick into Cassandra’s main concepts.

Apache Cassandra 1.2 introduces some new features such as a Binary Protocol and Collections datatype that together with the now finalized CQL3 query language provide a new interface to communicate with Cassandra that dramatically shrink its learning curve and simplify its daily use while still relying on its highly scalable architecture and storage engine. This presentation will iterate over all these new features including an overview of CQL3 query language, a look at the new client architecture, and an update on data modeling best practices. Then we’ll see how to implement an enterprise application using this new interface so that the audience can realize that a number of design principles are inspired from those commonly used with relational databases while some other entirely different, due to Cassandra partitioning approach.

Original title and link: Brief Intro to Cassandra in 27 Slides (NoSQL database©myNoSQL)


A Quick Tour of Internal Authentication and Authorization Security in DataStax Enterprise and Apache Cassandra

Robin Schumacher describes the new security features added to Apache Cassandra and DataStax Enterprise:

This article will concentrate on the new internal authentication and authorization (or permission management) features that are part of both open source Cassandra as well as DataStax Enterprise. Authentication deals with validating incoming user connections to a database cluster, whereas authorization concerns itself with what a logged in user can do inside a database.

I’m happy to see NoSQL databases entering the space of security as this would ease their way inside enterprises. But I fear a bit the moment when the marketing message will change from “it’s too early to provide security features” to “the first enterprise grade NoSQL database”.

Original title and link: A Quick Tour of Internal Authentication and Authorization Security in DataStax Enterprise and Apache Cassandra (NoSQL database©myNoSQL)

via: http://www.planetcassandra.org/blog/post/a-quick-tour-of-internal-authentication-and-authorization-security-in-datastax-enterprise-and-apache-cassandra


From SimpleDB to Cassandra: Data Migration for a High Volume Web Application at Netflix

Prasanna Padmanabhan and Shashi Madapp posted an article on the Netflix blog describing the process used to migrate data from Amazon SimpleDB to Cassandra:

There will come a time in the life of most systems serving data, when there is a need to migrate data to a more reliable, scalable and high performance data store while maintaining or improving data consistency, latency and efficiency. This document explains the data migration technique we used at Netflix to migrate the user’s queue data between two different distributed NoSQL storage systems.

The steps involved are what you’d expect for a large data set migration:

  1. forklift
  2. incremental replication
  3. consistency checking
  4. shadow writes
  5. shadow writes and shadow reads for validation
  6. end of life of the original data store (SimpleDB)

If you think of it, this is how a distributed, eventually consistent storage works (at least in big lines) when replicating data across the cluster. The main difference is that inside a storage engine you deal with a homogeneous system with a single set of constraints, while data migration has to deal with heterogenous systems most often characterized by different limitations and behavior.

In 2009, Netflix performed a similar massive data migration operation. At that time it involved moving data from its own hosted Oracle and MySQL databases to SimpleDB. The challenges of operating this hybrid solution were described in a the paper Netflix’s Transition to High-Availability Storage Systems authored by Sid Anand.

Sid Anand is now working at LinkedIn where they use Databus for low latency data transfer. But Databus’s approach is very similar.

Original title and link: From SimpleDB to Cassandra: Data Migration for a High Volume Web Application at Netflix (NoSQL database©myNoSQL)

via: http://techblog.netflix.com/2013/02/netflix-queue-data-migration-for-high.html?m=1


DataStax's Reaction to MySQL 5.6: Oracle’s MySQL Misses the NoSQL Mark

Jonathan Ellis in a post about MySQL 5.6 and how Oracle got the whole NoSQL wrong, considering NoSQL is, in this exact order, about scaling, continuous availability, flexibility, performance, and queryability:

The big news for MySQL 5.6 was the inclusion of “NoSQL” features in the form of a memcached api for get and put operations.

In cases like this, it’s tough to tell whether Oracle got this so wrong deliberately to sow confusion in the market, or because they really think that’s what NoSQL is about.

I know Jonathan Ellis has always had very strong opinions about the technical superiority of Cassandra and Cassandra is indeed a very solid solution, but I’m always reluctant to calling a competitor stupid and using the myopic argument “if I’m good at X and suck at Y, then what everyone is looking for is only X”.

Original title and link: DataStax’s Reaction to MySQL 5.6: Oracle’s MySQL Misses the NoSQL Mark (NoSQL database©myNoSQL)

via: http://www.datastax.com/dev/blog/oracles-mysql-misses-the-nosql-mark


Cassandra Performance in Review

Jonathan Ellis:

I honestly think Cassandra is one to two years ahead of the competition, but I’m under no illusions that Cassandra itself is perfect.

You cannot start the year without taking a stab at your competitors. At least from the performance point of view and even if they’re not really competitors—MongoDB, Riak, HBase.

The NoSQL market is ant-size compared to the database market and while easier to convince people to change from NoSQL to NoSQL, the products that will thrive are those that will be able to constantly convert people from outside of this small universe.

Original title and link: Cassandra Performance in Review (NoSQL database©myNoSQL)

via: http://www.datastax.com/dev/blog/2012-in-review-performance


System Level and Functional Requirements for the Backend Database of a User Engagement Platform

Very good and practical analysis of what the requriments of a user engagement platform are for the backend database from both the system level and functional point of views. The ideal case is also spelled out, but I don’t think there’s one product out there that could do all of these:

So, today’s and tomorrow’s engagement services should accommodate, heavy write loads, heavy read loads, heavy aggregate(counter), modify and read loads. What becomes apparent if we look at user engagement services in this way is that aggregation needs to be a first class function of engagement services that is near real time, scalable and highly available.

Original title and link: System Level and Functional Requirements for the Backend Database of a User Engagement Platform (NoSQL database©myNoSQL)

via: http://tech-blog.flipkart.net/2013/01/nosql-for-a-user-engagement-platform/


11 Interesting Releases From the First Weeks of January

The list of releases I wanted to post about has been growing fast these last couple of weeks, so instead of waiting leaving it to Here it is (in no particular order1):

  1. (Jan.2nd) Cassandra 1.2 — announcement on DataStax’s blog. I’m currently learning and working on a post looking at what’s new in Cassandra 1.2.
  2. (Jan.10th) Apache Pig 0.10.1 — Hortonworks wrote about it
  3. (Jan.10th) DataStax Community Edition 1.2 and OpsCenter 2.1.3 — DataStax announcement
  4. (Jan.10th) CouchDB 1.0.4, 1.1.2, and 1.2.1 — releases fixing some security vulnerabilities
  5. (Jan.11th) MongoDB 2.3.2 unstable — announcement. This dev release includes support for full text indexing. For more details you can check:

    […] an open source project extending Hadoop and Hive with a collection of useful user-defined-functions. Its aim is to make the Hive Big Data developer more productive, and to enable scalable and robust dataflows.


  1. I’ve tried to order it chronologically, but most probably I’ve failed. 

Original title and link: 11 Interesting Releases From the First Weeks of January (NoSQL database©myNoSQL)