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"Graph for Scala is intended to provide basic graph functionality that seamlessly fits into the Scala standard collections library. Like the other members of scala.collection, Graph for Scala is an in-memory container that exposes a user-friendly interface without sacrificing functionality or flexibility. Graph for Scala also has ready-to-go implementations of JSON-Import/Export and Dot-Export - more popular graph serialization formats are coming soon. In addition, other powerful tools such as graph databases emulation and distributed graph processing are due to be supported."

What is Graphite? Graphite is a highly scalable real-time graphing system. As a user, you write an application that collects numeric time-series data that you are interested in graphing, and send it to Graphite's processing backend, carbon, which stores the data in Graphite's specialized database. The data can then be visualized through graphite's web interfaces. Who should use Graphite? Graphite is actually a bit of a niche application. Specifically, it is designed to handle numeric time-series data. For example, Graphite would be good at graphing stock prices because they are numbers that change over time. However Graphite is a complex system, and if you only have a few hundred distinct things you want to graph (stocks prices in the S&P 500) then Graphite is probably overkill. But if you need to graph a lot of different things (like dozens of performance metrics from thousands of servers) and you don't necessarily know the names of those things in advance (who wants to maintain such huge configuration?) then Graphite is for you.

"Apache Giraph is an iterative graph processing system built for high scalability. For example, it is currently used at Facebook to analyze the social graph formed by users and their connections. Giraph originated as the open-source counterpart to Pregel, the graph processing architecture developed at Google and described in a 2010 paper. Both systems are inspired by the Bulk Synchronous Parallel model of distributed computation introduced by Leslie Valiant. Giraph adds several features beyond the basic Pregel model, including master computation, sharded aggregators, edge-oriented input, out-of-core computation, and more. With a steady development cycle and a growing community of users worldwide, Giraph is a natural choice for unleashing the potential of structured datasets at a massive scale."

"Blueprints is a property graph model interface. It provides implementations, test suites, and supporting extensions. Graph databases and frameworks that implement the Blueprints interfaces automatically support Blueprints-enabled applications. Likewise, Blueprints-enabled applications can plug-and-play different Blueprints-enabled graph backends."

big-data graph graph-processing architecture development programming

"Apache Hama is a pure BSP (Bulk Synchronous Parallel) computing framework on top of HDFS (Hadoop Distributed File System) for massive scientific computations such as matrix, graph and network algorithms. Today, many practical data processing applications require a more flexible programming abstraction model that is compatible to run on highly scalable and massive data systems (e.g., HDFS, HBase, etc). A message passing paradigm beyond Map-Reduce framework would increase its flexibility in its communication capability. Bulk Synchronous Parallel (BSP) model fills the bill appropriately. Some of its significant advantages over MapReduce and MPI are: * Supports message passing paradigm style of application development * Provides a flexible, simple, and easy-to-use small APIs * Enables to perform better than MPI for communication-intensive applications * Guarantees impossibility of deadlocks or collisions in the communication mechanisms"