Relevance of Hadoop & Streaming solutions when Spark exists

Question

I am starting a big data initiative for my startup. In 2018 is there any reason to use Hadoop at all since Spark is touted to be way faster due to it primarily not writing the intermediate data to disk as Hadoop’s MR.

I realize Spark has a higher need for RAM But that would be just one time CAPEX costs that would pay for itself?

In general unless there are legacy projects why should one pick up Hadoop at all since Spark is available?

Would appreciate real world comparisons of the two, gotchas etc.?

Alternately are there use cases that Hadoop can solve but Spark cannot?

—————-comment below for actual problem————

I would use YARN as the resource manager with HDFS as the file system for Spark. Also realize that as Spark intersects quiet a bit with Hadoop ecosystem.

Comparos are :

1. Mapreduce vs Spark code
2. SparkSQL vs Hive
3. People mention Pig too but not a whole lot of people want to learn custom querying. And if I had to use Pig as a data scientist why wouldn’t I use say an Apache NiFi with Hadoop?

Also not sure how Spark handles the following:

1. If data does not fit in RAM then what ? Back to a disk based paradigm (not talking of streaming use cases here..) so no better than Mapreduce? How does Tez make MR2 better?
2. Hadoop 3 has support for Erasure coding to reduce data replication. What does Spark do?

Where I am unclear is the plethora of overlapping choices. For e.g. streaming alone has:

1. Spark streaming
2. Apache storm
3. Apache Samza
4. Kafka streams
5. CEP commercial tools.(ORacle CEP, TIBCO etc.)

A lot of them use DAG similar to Spark’s core engine so hard to pick one from the other.

Use case:

1. App sends data to middleware until end of event. Event can end specified on periodicity or due to a business condition being met.
2. Middleware must show real time addition of a value (simplifying) sent by users from their app instances. Accepted that middleware is the floor of the actual sum of values and real value can be higher. Plan to use Kafka streams here to have a consumer that adds all the inputs with minimal latency the consumer posts to a cache which is polled by apps to show current additive value.
3. Middleware logs all input
4. After event ends a big data paradigm scans through log data and database records to get accurate count by comparing all dB values and log entries (audit) and compare them to the Kafka shown value. Value calculated by this scheme is the final value.

Design choices:

1. I like Kafka because it decouples application middleware and is low latency high throughput messaging. Streams code is easy to write . Happy for someone to counter argue using Spark Streams Or Apache Storm or Apache Samza instead?
2. Application itself is Java code on Tomcat server with REST end points for iOS/ Android clients. Not doing client caching due to explicit liveliness of additive value.

Answer 1

You're confusing Hadoop with just MapReduce. Hadoop is an ecosystem of MapReduce, HDFS, and YARN.

First of all, Spark doesn't have a filesystem. That's primarily why Hadoop is nice, in my book. Sure, you can use S3, or many other cloud storages, or bare metal data stores like Ceph, or GlusterFS, but from what I've researched, HDFS is by far the fastest when processing data.

Maybe you're not familiar with the concept of rack locality that YARN offers. If you use Spark Standalone mode with any file system not mounted under the Spark executors, then all your data requests will need to be pulled over a network connection, therefore saturating the network, and causing a bottleneck, regardless of memory. Compare that to the Spark executors running on the YARN NodeManagers, HDFS datanodes are ideally also NodeManagers.

A similar problem - people say Hive is slow, SparkSQL is faster. Well, that's true if you run Hive with MapReduce instead of Tez or Spark execution modes.

Now, if you're wanting streaming and real-time events rather than the batch world commonly associated with Hadoop. You might want to research the SMACK stack.

Update

Pig as a data scientist why wouldn’t I use say an Apache NiFi with Hadoop

Pig is not comparable to NiFi.

You can use NiFi; nothing is stopping you. It would run closer to real-time than Spark micro batches. And it is a good tool to pair with Kafka.

plethora of overlapping choices

Yes, and you didn't even list them all... It's up to some BigData architect in your company to come up with a solution. You'll find that vendor support from Confluent is mostly for Kafka. I haven't seen them talking about Samza much. Hortonworks will support Storm, Nifi, and Spark, but they aren't running the latest version of Kafka if you want fancy features like KSQL. Streamsets is a similar company offering a tool competing with NiFi which consists of employees with backgrounds in other batch/streaming Apache projects.

Storm and Samza are two ways to do the same thing, as far as I know. I think Flink is more programmer friendly than Storm. I don't have experience with Samza, though I work closely with people who primarily are using Kafka Streams rather than it. And Kafka Streams isn't DAG based - it's just a high level Kafka library, embeddable in any JVM application.

If data does not fit in RAM then what ?

By default, it spills to disk... Spark has parameters to configure if you don't want disk to be touched. In which case, your jobs die of OOM more quickly, obviously.

How does Tez make MR2 better?

Tez isn't MR. It creates more optimized DAGs like Spark does. Go read about it.

Hadoop 3 has support for Erasure coding to reduce data replication. What does Spark do?

Spark has no filesystem. We already covered this. Erasure encoding is primarily for data at-rest, not during processing. I actually don't know if Spark supports Hadoop 3, yet.

Application itself is Java code on Tomcat server with REST end points for iOS/ Android clients

Personally, I would use Kafka Streams here because 1) You are using Java already 2) it's a standalone thread in your code that offers you to read/publish data from Kafka without Hadoop/YARN or Spark Clusters. It's not clear what your question has to do with Hadoop from your listed client-server archictecture, but feel free to string an additional line from a Kafka topic to a database/analytics engine of your choice. The Kafka Connect framework has many connectors for you to choose from.

You could also use NiFi as your mobile REST API to just ExposeHTTP and send requests to it, then route flows based on attributes in the data. Then, manipulate and publish to Kafka as well as other systems.

Answer 2

Spark and Hadoop works pretty similar in the way of solving MapReduce problems.

Hadoop is pretty relevant if you talk about HDFS point of view. The HDFS is a well known used solution for big data storage. But your question is about MapReduce.

Spark is the best option if you are talking about good machines with real good configuration of memory and network throughput. But we know that kind of machines are expensive and sometimes you best option is to use Hadoop to process your data. Spark is great and fast but sometimes you get crazy with Memory issues if you don't have a good cluster in case of fit too much data in the memory. Hadoop in this case can be better. But this problem year after year are less relevant.

So hadoop is here com complement Spark, Hadoop is not only MapReduce Hadoop is an ecosystem. Spark doesn't have a distributed file system, to Spark works well you need one, Spark doesn't have a resource manager, Hadoop has called Yarn. And Spark in a cluster mode need a resource manager.

Conclusion

Hadoop still relevant as an ecosystem but as only mapReduce I can say that is not been used anymore.