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?
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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 :
Mapreduce vs Spark code
SparkSQL vs Hive
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:
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?
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:
Spark streaming
Apache storm
Apache Samza
Kafka streams
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:
App sends data to middleware until end of event. Event can end specified on periodicity or due to a business condition being met.
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.
Middleware logs all input
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:
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?
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.
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.
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.
Related
How effective is to use Apache NIFI for the ETL process having source as HDFS & destination as Oracle DB. What are the limitations of Apache NIFI compared other ETL tools such as Pentaho,Datastage,etc..
Main advantage of NiFi
The main advantages of NiFi:
Intuitive gui, which allows for easy inspection of the data
Strong delivery guarantees
Low latency, you can support both batch and streaming usecases
It can handle any format, not only limited to SQL tables, but can also move log files etc.
Schema aware, and can share schema with solutions like Kafka, Flink, Spark
Main limitation of NiFi
NiFi is really a tool for moving data around, you can do enrichments of individual records but it is typically mentioned to do 'EtL' with a small t. A typical thing that you would not want to do in NiFi is joining two dynamic data sources.
For joining tables, tools like Spark, Hive, or classical ETL alternatives are often used.
For joining streams, tools like Flink and Spark Streaming are often used.
Conclusion
NiFi is a great tool, you just need to make sure you use it for the right usecase. Where needed you can use other tools to complement it.
Extra strong full disclosure: I am an employee of Cloudera, the company that supports NiFi and other projects such as Spark and Flink. I have used other ETL tools before, but not to the same extent as NiFi.
Not sure about sqoop, I can explain the benifits of using Apache Nifi. In your case the data in HDFS could be of any format(Unstructured), Nifi has a capability to process and bring it to format of your choice so that you can directly save it to any RDBMS.
Nifi handles back-pressure in vary effective way to have lossless transmission.
One of the critical features that NiFi provides that our competitors generally don't is the ability to stop jobs and examine the flow and downstream systems while it's running. For you, this means you can test the flow against a test HDFS folder and a test Oracle DB, let some data go through, pause the flow and poke around Oracle to make sure it's to your liking after a matter of seconds or minutes instead of waiting for a "job to complete." It makes the process extremely agile.
Actually Nifi is very good tool. You can easily manipulate processors. In short time you can migrate huge data.
But for destinations such as RDBMS, there are always problems. I used to have a lot of problems about "non-killing" threads, you have to be very careful about stopping processes and the configuration of processors. Some processors like QueryDatabasetable consumes huge memory and the server goes down.
I have a different environments across a few Cloud providers, like windows servers, linux servers in rackspace, aws..etc. And there is a firewall between that and internal network.
I need to build a real time servers environment where all the newly generated IIS logs, apache logs will be sync to an internal big data environment.
I know there are tools like Splunk or Sumologic that might help but we are required to implement this logic in open source technologies. Due to the existence of the firewall, I am assuming I can only pull the logs instead push from the cloud providers.
Can anyone share with me what is the rule of thumb or common architecture for sync up tons of logs in NRT (near real time)? I heard of Apache Flume, Kafka and wondering if those are required or it is just a matter of using something like rsync.
You can use rsync to get the logs but you can't analyze them in the way Spark Streaming or Apache Storm does.
You can go ahead with one of these two options.
Apache Spark Streaming + Kafka
OR
Apache Storm + Kakfa
Have a look at this article about integration approaches of these two options.
Have a look this presentation, which covers in-depth analysis of Spark Streaming and Apache Storm.
Performance is dependent on your use case. Spark Steaming is 40x faster to Storm processing. But if you add "reliability" as key criteria, then data should be moved into HDFS first before processing by Spark Streaming. It will reduce final throughput.
Reliability Limitations: Apache Storm
Exactly once processing requires a durable data source.
At least once processing requires a reliable data source.
An unreliable data source can be wrapped to provide additional guarantees.
With durable and reliable sources, Storm will not drop data.
Common pattern: Back unreliable data sources with Apache Kafka (minor latency hit traded for 100% durability).
Reliability Limitations: Spark Streaming
Fault tolerance and reliability guarantees require HDFS-backed data source.
Moving data to HDFS prior to stream processing introduces additional latency.
Network data sources (Kafka, etc.) are vulnerable to data loss in the event of a worker node failure.
I have read in many places about Streaming data, but just trying to understand the challenges which are faced while processing it using Map Reduce technique?
i.e. the reason behind the existence of frameworks like Apache Flume, Apache Storm, etc.
Please share your advise & thoughts.
Thanks,
Ranit
There are many technologies out there, and many of them run on the Hadoop framework.
The older Hadoop services like Hive tend to be slow, and are usually used for batch jobs, not for streaming.
As streaming becomes more and more a necessity, other services have surfaced like Storm or Spark that are designed for faster execution and integration with messaging queues like Kafka for streaming.
In data analytics though, most of the time processing is not al real time: historical data may be processed in batch mode to extract models that are then used for real-time analytics, so a 'streaming' system is usually based on a Lambda Architecture http://lambda-architecture.net/
A service like Spark tries to integrate all of the components, with Spark Streaming for the speed layer, Spark SQL for the Serving layer, Spark MLLib for the modeling, all based on Hadoop Distributed File system (hdfs) for replicated large volume storage.
Flume helps in directing the data from source to hdfs for raw storage, but in order to process it, Storm or Spark are used.
Hope that helps.
Your question is open eneded. But I assume you want to understand the challenges of processing streaming data in Map Reduce environment.
1) Map Reduce is primarily designed for batch processing. It is for processing high volume of data which is at rest in disk.
2) The streaming data is a high velocity of data, which are coming from various sources like Web Application Click Stream, Social Media Logs, Twitter Tags, Application logs.
3) The stream of events might be processed either stateless manner ( assuming every event is unique) or in a stateful manner (collect the data for 2 seconds and processes them) but batch applications does not have any such requirement.
4) Streaming applications wants delivery / process guarantee. For example, the frameworks must provide "exactly once" delivery/process mechanism, so that it processes all the stream events without fail. It is not a challenge in batch processing since all the data is available locally.
5) External Connectors : Streaming frameworks must support external connectivity to read data in realtime from various sources as we discussed in (2). This is not a challenge in batch, since the data is locally available.
Hope this helps.
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I hear lot about Hadoop but when it comes for defining what it is i get confused. Because definition defers form point to point.
Is Hadoop something that serves files from server to client?
Ex: If we implement Hadoop for a MAILDIR where emails are stored, Can Hadoop help in accessing the emails and serving it to client in super fast speed? Is this how it can be used?
Can you tell me in simple words what is Hadoop and its uses?
Dude You are messing this up.
Hadoop is an open-source software framework for storage and large-scale processing of data-sets on clusters of commodity hardware. Hadoop is an Apache project being built and used by a global community of contributors and users.
The Apache Hadoop framework is composed of the following modules
Hadoop Common – contains libraries and utilities needed by other Hadoop modules
Hadoop Distributed File System (HDFS) – a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster.
Hadoop YARN – a resource-management platform responsible for managing compute resources in clusters and using them for scheduling of users' applications.
Hadoop MapReduce – a programming model for large scale data processing.
For the end-users, though MapReduce Java code is common, any programming language can be used with "Hadoop Streaming" to implement the "map" and "reduce" parts of the user's program.Apache Pig, Apache Hive, Apache Spark among other related projects expose higher level user interfaces like Pig Latin and a SQL variant respectively. The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell-scripts.
The Hadoop distributed file system (HDFS) is a distributed, scalable, and portable file-system written in Java for the Hadoop framework. Each node in a Hadoop instance typically has a single namenode; a cluster of datanodes form the HDFS cluster. The situation is typical because each node does not require a datanode to be present. Each datanode serves up blocks of data over the network using a block protocol specific to HDFS. The file system uses TCP/IP sockets for communication. Clients use remote procedure call (RPC) to communicate between each other.
HDFS stores large files (typically in the range of gigabytes to terabytes) across multiple machines. It achieves reliability by replicating the data across multiple hosts, and hence theoretically does not require RAID storage on hosts (but to increase I/O performance some RAID configurations are still useful). With the default replication value, 3, data is stored on three nodes: two on the same rack, and one on a different rack. Data nodes can talk to each other to rebalance data, to move copies around, and to keep the replication of data high.
The HDFS file system is not restricted to MapReduce jobs.It can be used for other applications including the HBase database, the Apache Mahout machine learning system, and the Apache Hive Data Warehouse system. Hadoop can in theory be used for any sort of work that is batch-oriented rather than real-time, that is very data-intensive, and able to work on pieces of the data in parallel.
Commercial applications of Hadoop includes :
Log and/or clickstream analysis of various kinds
Marketing analytics
Machine learning and/or sophisticated data mining
Image processing
Processing of XML messages
Web crawling and/or text processing
General archiving, including of relational/tabular data, e.g. for compliance
You can refer to YDN to have a good startup in understanding the hadoop framework.
i just started to learn Hadoop and have gone through some sites and i often found that
"Hadoop is not a real-time platform" even in SO also
I mess with this and i really cant understand about it . Can any one help me and explain me about this?
Thanks all
Hadoop was initially designed for batch processing. That means, take a large dataset in input all at once, process it, and write a large output. The very concept of MapReduce is geared towards batch and not real-time. But to be honest, this was only the case at Hadoop's beginning, and now you have plenty of opportunities to use Hadoop in a more real-time way.
First I think it's important to define what you mean by real-time. It could be that you're interested in stream processing, or could also be that you want to run queries on your data that return results in real-time.
For stream processing on Hadoop, natively Hadoop won't provide you with this kind of capabilities, but you can integrate some other projects with Hadoop easily:
Storm-YARN allows you to use Storm on your Hadoop cluster via YARN.
Spark integrates with HDFS to allow you to process streaming data in real-time.
For real-time queries there are also several projects which use Hadoop:
Impala from Cloudera uses HDFS but bypasses MapReduce altogether because there's too much overhead otherwise.
Apache Drill is another project that integrates with Hadoop to provide real-time query capabilities.
The Stinger project aims to make Hive itself more real-time.
There are probably other projects that would fit into the list of "Making Hadoop real-time", but these are the most well-known ones.
So as you can see, Hadoop is going more and more towards the direction of real-time and, even if it wasn't designed for that, you have plenty of opportunities to extend it for real-time purposes.