原文地址：http://www.javacodegeeks.com/2015/02/streaming-big-data-storm-spark-samza.html

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There are a number of distributed computation systems that can process Big Data in real time or near-real time. This article will start with?a short description of?three?Apache frameworks, and attempt to provide a quick, high-level overview of some of their similarities and differences.

Apache Storm

In?Storm, you?design a graph of real-time computation called a?topology, and feed it to the cluster where the master node will distribute the?code among worker nodes to execute it. In a topology,?data is?passed around between?spouts?that emit data streams as immutable sets of key-value pairs called?tuples,?and?bolts?that transform those streams (count, filter etc.). Bolts themselves can optionally emit data to other bolts down the processing pipeline.

Apache Spark

Spark Streaming?(an extension of the core Spark API) doesn’t process?streams one at a time like Storm. Instead, it slices them in small batches of time intervals?before processing them. The Spark abstraction for a?continuous stream of data is called a?DStream?(for?Discretized Stream). A DStream is a micro-batch?of?RDDs?(Resilient Distributed Datasets). RDDs are distributed?collections that can be operated in parallel by arbitrary functions and by?transformations over a sliding window of data (windowed computations).

Apache Samza

Samza?’s approach to streaming is to process?messages as they are received, one at a time. Samza’s stream primitive is not a?tuple?or a?Dstream, but a?message. Streams are divided into?partitions?and each partition is an ordered sequence of read-only messages with each message having a unique ID (offset). The system?also supports?batching, i.e. consuming several messages from the same stream partition in sequence. Samza`s Execution & Streaming modules are both pluggable, although Samza?typically relies on Hadoop’s?YARN?(Yet Another Resource Negotiator) and?Apache Kafka.

Common Ground

All three real-time?computation systems are open-source,?low-latency,?distributed, scalable?and?fault-tolerant. They?all allow you to run your stream processing code through parallel?tasks?distributed across a cluster of computing machines with fail-over capabilities. They also provide?simple APIs?to abstract the complexity of the underlying implementations.

The three frameworks use different vocabularies for similar concepts:

Comparison Matrix

A few of the differences are summarized in the table below:

There are three general categories of?delivery patterns:

At-most-once: messages may be lost. This is usually the least desirable outcome.

At-least-once: messages may be redelivered (no loss,?but duplicates). This is good enough for many?use cases.

Exactly-once: each message is delivered once and only once (no loss, no duplicates). This is a desirable feature although?difficult to guarantee?in all cases.

Another aspect is?state management. There are?different strategies?to store state. Spark Streaming writes data into the distributed file system (e.g. HDFS). Samza uses?an embedded key-value store. With Storm,?you’ll have?to either roll your own state management at your application layer, or use?a higher-level abstraction called?Trident.

Use Cases

All three frameworks are particularly?well-suited to efficiently process continuous, massive amounts of real-time data. So which one to use? There are no hard rules, at most a few?general guidelines.

If you want a high-speed?event processing system that allows for incremental computations,?Storm?would be fine for that. If you further need to?run distributed computations on demand, while the client is waiting synchronously for the results, you’ll have?Distributed RPC?(DRPC) out-of-the-box. Last but not least, because Storm uses?Apache Thrift, you can write topologies in any programming language. If you need state persistence?and/or exactly-once delivery though, you should look at?the higher-level Trident API, which also offers micro-batching.

A few companies using Storm:?Twitter, Yahoo!, Spotify, The Weather Channel...

Speaking of micro-batching, if you must have?stateful computations, exactly-once delivery?and don’t mind a higher latency, you could consider?Spark?Streaming…specially if?you also plan for graph operations, machine learning or?SQL access. The Apache Spark stack lets you combine several?libraries with?streaming (Spark SQL,?MLlib,?GraphX) and provides?a convenient?unifying programming model. In particular,?streaming?algorithms?(e.g. streaming?k-means) allow?Spark to facilitate decisions in real-time.

A few companies using Spark:?Amazon, Yahoo!, NASA JPL, eBay Inc., Baidu…

If you have a large amount of state to work with (e.g. many gigabytes per partition),?Samza?co-locates storage and processing on the same machines, allowing to work efficiently with state that won’t fit in memory. The framework?also offers flexibility with its?pluggable?API:?its default execution, messaging and storage engines can each be replaced with?your choice of alternatives. Moreover, if you have a number of data processing stages?from different teams with different codebases, Samza ‘s fine-grained jobs would be particularly well-suited, since?they can be added/removed with minimal?ripple effects.

A few companies using Samza:?LinkedIn, Intuit, Metamarkets, Quantiply, Fortscale…

Conclusion

We only?scratched the surface of?The Three Apaches. We didn’t cover?a number of other features and more subtle differences between these frameworks.?Also, it’s important to keep in mind the limits of the above comparisons, as these systems are constantly evolving.

轉(zhuǎn)載于:https://www.cnblogs.com/davidwang456/p/4892213.html

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