Storm Serialization with Avro (using Kryo Serializer)

Working with complex data events can be a challenge designing Storm topologies for real-time data processing. In such cases emitting single values for multiple and varying event characteristics soon reveals it’s limitations. For message serialization Storm leverages the Kryo serialization framework used by many other projects. Kryo keeps a registry of serializers being used for corresponding Class types. Mappings in that registry can be overridden or added making the framework extendable to diverse type serializations.

On the other hand Avro is a very popular “data serialization system” that bridges between many different programming languages and tools. While the fact that data objects can be described in JSON makes it really easy to use, Avro is often being used for it’s support of schema evolution. With support for schema evolution the same implementation (Storm topology) could be capable of reading different versions of the same data event without adaptation. This makes it a very good fit for Storm as a intermediator between data ingestion points and data storage in today’s Enterprise Data Architectures.

Storm Enterprise Data Architecture
Storm Enterprise Data Architecture

The example here does not provide complex event samples to illustrated that point, but it gives an end to end implementation of a Storm topology where events get send to a Kafka queue as Avro objects processesed natively by a real-time processing topology. The example can be found here. It’s a simple Hive Streaming example where stock events are read from a CSV file and send to Kafka. Stock events are a flat, none complex data type as already mentioned, but we’ll still use it to demo serialization with using Avro. Continue reading “Storm Serialization with Avro (using Kryo Serializer)”

Storm Flux: Easy Streaming Deployment

With Flux for Apache Storm deploying streaming topologies for real-time processing becomes less programmatic and more declarative. Using Flux for deployments makes it less likely you will have to re-compile your project just because you have re-configured or re-arranged your topology. It leverages YAML, a human-readable serialization format, to describe a topology on a whole. You might still need to write some classes, but by taking advantage of existing, generic implementations this becomes less likely.

While Flux can also be used with an existing topology, for this post we’ll take the Hive-Streaming example you can find here (blog post) to create the required topology from scratch using Flux. For experiments and demo purposes you can use the following Vagrant setup to run a HDP cluster locally. Continue reading “Storm Flux: Easy Streaming Deployment”

10 Resources About Storm

The event processing framework Apache Storm is the preferred approach for real-time Big Data. In use at large companies around the world it proofs it’s maturity every day at scale. This post collects some of the resources helpful in understanding what Storm is and contains also some sources that highlight the special relationship Kafka enjoys.

  1. Storm: Distributed and Fault-Tolerant Real-time Computation
  2. Evaluating persistent, replicated message queues (updated w/ Kafka)
  3. Apache storm vs. Spark Streaming
  4. Storm and Spark at Yahoo: Why Chose One Over the Other
  5. Common Topology Patterns
  6. Trident Tutorial
  7. Trident-ML
  8. Storm-R
  9. Storm-Pattern
  10. ZooKeeper Curator Client & Hint
  11. Hortonworks Storm as part of HDP

Continue reading “10 Resources About Storm”

Hive Streaming with Storm

With the release of Hive 0.13.1 and HCatalog, a new Streaming API was released as a Technical Preview to support continuous data ingestion into Hive tables. This API is intended to support streaming clients like Flume or Storm to better store data in Hive, which traditionally has been a batch oriented storage.

Based on the newly given ACID insert/update capabilities of Hive, the Streaming API is breaking down a stream of data into smaller batches which get committed in a transaction to the underlying storage. Once committed the data becomes immediately available for other queries.

Broadly speaking the API consists of two parts. One part is handling the transaction while the other is dealing with the underlying storage (HDFS). Transactions in Hive are handled by the the Metastore. Kerberos is supported from the beginning!

Some of the current limitations are:

  • Only delimited input data and JSON (strict syntax) are supported
  • Only ORC support
  • Hive table must be bucketed (unpartitioned tables are supported)

In this post I would like to demonstrate the use of a newly created Storm HiveBolt that makes use of the streaming API and is quite straightforward to use. The source of the here described example is provided at GitHub. To run this demo you would need a HDP 2.2 Sandbox, which can be downloaded for various virtualization environments here. Continue reading “Hive Streaming with Storm”