Spark Streaming is a popular engine for stream processing and its ability to compute data in memory makes it very attractive. However Spark Streaming is not self-sufficient, it relies on external data source and storage to output computation results. Therefore, in many cases the overall performance is limited by slow external components that are not able to keep up with Spark’s throughput and/or introduce unacceptable latency.

In this article we describe how we use GigaSpaces XAP in-memory datagrid to address this challenge. Code sources are available on github

Can be found here

Real-time processing is becoming very popular, and Storm is a popular open source framework and runtime used by Twitter for processing real-time data streams. Storm addresses the complexity of running real time streams through a compute cluster by providing an elegant set of abstractions that make it easier to reason about your problem domain by letting you focus on data flows rather than on implementation details.

Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

This pattern integrates XAP with Storm. XAP is used as stream data source and fast reliable persistent storage, whereas Storm is in charge of data processing. We support both pure Storm and Trident framework.

As part of this integration we provide classic Word Counter and Twitter Reach implementations on top of XAP and Trident.

Also, we demonstrate how to build highly available, scalable equivalent of Realtime Google Analytics application with XAP and Storm. Application can be deployed to cloud with one click using Cloudify.

Sources are available on github

Apache Kafka is a distributed publish-subscribe messaging system. It is designed to support persistent messaging with a O(1) disk structures that provides constant time performance even with many TB of stored messages. Apache Kafka provides High-throughput even with very modest hardware, Kafka can support hundreds of thousands of messages per second. Apache Kafka supports partitioning the messages over Kafka servers and distributing consumption over a cluster of consumer machines while maintaining per-partition ordering semantics. Many times Apache Kafka is used to perform parallel data load into Hadoop.

This pattern integrates GigaSpaces with Apache Kafka. GigaSpaces’ write-behind IMDG operations to Kafka making it available for the subscribers. Such could be Hadoop or other data warehousing systems using the data for reporting and processing. Sources are available on github

An interesting optimization trick from Storm internals which reminded me some algorithmic problem(see in the end).

For those who haven’t heard about Storm, in short it’s a distributed realtime computation system. Scalable, fault tolerant and guarantees that every message is fully processed.