Comparison between Apache Storm vs Spark Streaming

1. Objective

For processing real-time streaming data Apache Storm is the stream processing framework, while Spark is a general purpose computing engine. To handle streaming data it offers Spark Streaming. Hence, Streaming process data in near real-time. In this blog, we will cover the comparison between Apache Storm vs spark Streaming.  At first, we will start with introduction part of each. Afterwards, we will compare each on the basis of their feature, one by one.

Storm vs Spark Streaming

Battle between Apache Strom vs Spark Streaming

2. What is Apache Storm vs Spark Streaming

– Apache Storm

For processing real-time streaming data Apache Storm is the stream processing framework. Since it can do micro-batching using a trident. Also, “Trident” an abstraction on Storm to perform stateful stream processing in batches.

– Spark Streaming

Spark is a general purpose computing engine which performs batch processing. No doubt, by using Spark Streaming, it can also do micro-batching. Spark Streaming is an abstraction on Spark to perform stateful stream processing.

3. Comparison between Spark Streaming vs Apache Storm  

There is one major key difference between storm vs spark streaming frameworks, that is Spark performs data-parallel computations while storm performs task-parallel computations. There are many more similarities and differences between Strom and streaming in spark, let’s compare them one by one feature-wise:

3.1. Programming Language Options

Storm- Creation of  Storm applications is possible in Java, Clojure, and Scala.

Spark Streaming- Creation of Spark applications is possible in Java, Scala, Python & R.

3.2. Reliability

Storm- Supports “exactly once” processing mode. We can also use it in “at least once” processing and “at most once” processing mode as well.

Spark Streaming- Spark streaming supports “ exactly once” processing mode.

3.3. Processing Model

Storm- Through core storm layer, it supports true stream processing model.

Spark Streaming- For spark batch processing, it behaves as a wrapper.

3.4. State Management

Storm- It doesn’t offer any framework level support by default to store any intermediate bolt result as a state. Therefore, any application has to create/update its own state as and once required.

Spark Streaming- In spark streaming, maintaining and changing state via updateStateByKey API is possible. But, there is no pluggable method to implement state within the external system.

3.5. Primitives

Storm- Storm offers a very rich set of primitives to perform tuple level process at intervals of a stream. Through group by semantics aggregations of messages in a stream are possible. For example, right join, left join, inner join (default) across the stream are supported by storm.

Spark Streaming- There are 2 wide varieties of streaming operators, such as stream transformation operators and output operators. While we talk about stream transformation operators, it transforms one DStream into another. Output operators that write information to external systems.

3.6. Fault Tolerance

Storm- It is designed with fault-tolerance at its core. As if the process fails, supervisor process will restart it automatically. Because ZooKeeper handles the state management.

Spark Streaming- It is also fault tolerant in nature. Spark handles restarting workers by resource managers, such as Yarn, Mesos or its Standalone Manager.

3.7. Ease of Operability

Storm- It is not easy to deploy/install storm through many tools and deploys the cluster. It depends on Zookeeper cluster. Also, it can meet coordination over clusters, store state, and statistics. Moreover, Storm daemons are compelled to run in supervised mode, in standalone mode. While, Storm emerged as containers and driven by application master, in YARN mode.

Spark Streaming- Spark is fundamental execution framework for streaming.  Hence, it should be easy to feed up spark cluster of YARN.

3.8. Debuggability and Monitoring

Storm- Its UI support image of every topology. But, with the entire break-up of internal spouts and bolts. Moreover, Storm helps in debugging problems at a high level, supports metric based monitoring. Inbuilt metrics feature supports framework level for applications to emit any metrics. In addition, that can then be simply integrated with external metrics/monitoring systems.

Spark Streaming- The extra tab that shows statistics of running receivers & completed spark web UI displays. Moreover, to observe the execution of the application is useful. Also, this info in spark web UI is necessary for standardization of batch size are follows:

  • Processing Time – It is a time to process every batch of data.
  • Scheduling Delay – It is a time a batch stays in a queue for the process previous batches to complete.

3.9. Yarn Integration

Storm- Through Apache slider, storm integration alongside YARN is recommended. A YARN application “Slider” that deploys non-YARN distributed applications over a YARN cluster. Also, through a slider, we can access out-of-the-box application packages for a storm.

Spark Streaming- Spark also provides native integration along with YARN. All spark streaming application gets reproduced as an individual Yarn application.

3.10. Isolation

Storm- For a particular topology, each employee process runs executors. Mixing of several topology tasks isn’t allowed at worker process level. Even so, that supports topology level runtime isolation.

Spark Streaming- Spark executor runs in a different YARN container. Hence, JVM isolation is available by Yarn. Since 2 different topologies can’t execute in same JVM. Instead, YARN provides resource level isolation so that container constraints can be organized.

3.11. Latency

Storm- It provides better latency with fewer restrictions.

Spark Streaming- Latency is less good than a storm.

3.12. Low development Cost

Storm- We cannot use same code base for stream processing and batch processing

Spark Streaming- We can use same code base for stream processing as well as batch processing

4. Conclusion- Storm vs Spark Streaming

Hence, we have seen the comparison of Apache Storm vs Streaming in Spark. It shows that Apache Storm is a solution for real-time stream processing. Whereas,  Storm is very complex for developers to develop applications. Also, it has very limited resources available in the market for it.

Through Storm, only Stream processing is possible. Although the industry requires a generalized solution, that resolves all the types of problems, for example, batch processing, stream processing interactive processing as well as iterative processing. Thus, Apache Spark comes into limelight. Also, a general-purpose computation engine. Through it, we can handle any type of problem. As a result, Apache Spark is much too easy for developers. Also, we can integrate it very well with Hadoop. Therefore, Spark Streaming is more efficient than Storm. Hope you got all your answers regarding Storm vs Spark Streaming comparison.

For reference- Apache spark 

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