Apache Spark vs Hadoop They are two of the most important and well-known products of the Big Data family.
Photo credits: OlgaYakovenko
Although there are those who see these two frameworks as competitors in the big data space., It's not so easy to make a comparison Spark vs. Hadoop. They do many things in the same way, But there are some areas where they don't overlap.. As an example, Apache Spark does not have a file system and, therefore, is based on the Hadoop distributed file system.
If you check Google Trends, you can see that Hadoop is more popular compared to Apache Spark. But nevertheless, companies like Yahoo, Intel, Baidu, Trend Micro and Groupon are already using Apache Spark.
Apache Spark vs Hadoop are comparable in different parameters. Are you interested in knowing which are the fields that make the difference?
Spark vs. Hadoop. The battle is served
Spark vs Hadoop puzzle solving is served in three keys:
a) Usability. One of the most common problems when contrasting both frameworks is related to their ease of use. Which is easier to use? Spark vs Hadoop? In this circumstance Apache Spark would outperform its opponent since it comes equipped with truly simple APIs for Scala, Python, Java and Spark SQL. At the same time, provides information in REPL format about commands. For his part, although it is true that MapReduce has plugins like Pig and Hive that make it somewhat easier to use, In the end what happens is that Simple logic needs more programming (programs must be written in Java), So what is gained in usability on the one hand would be lost on the other..
b) Performance. This point is perhaps the most difficult to solve in any comparison between Spark and Hadoop.. The point is that, Since both process data differently, It is not easy to determine who achieves the best performance. To make a choice you should pay attention that:
Talking about Spark – spark:
- Works in memory And that's why all processes are accelerated.
- But you need more memory for storage.
- Your performance may be affected by the need to use heavy applications.
In the case of Hadoop:
- The data is on the disk and that slows everything down.
- The advantage is that, compared to the other alternative, Storage needs are lower.
- By taking care of erasing data when it is no longer needed, No significant performance losses for heavy applications.
c) Security. If in usability Spark surpassed Hadoop, In this circumstance it has nothing to do. Hadoop has no rivals What:
- Provides your users with all the benefits of the advances made in Hadoop security projects. (Knox Gateway or Sentry are some examples).
- HDFS supports service level authorization, ensuring proper permissions for file-level clients.
- Y, at the same time … has Hadoop HILO
For his part, Spark must run on HDFS to enter file-level permissions, and, at the same time for security benefits, must resort to Hadoop YARN.
But then, Who can be considered the winner of the Spark vs Hadoop competition? Each dominates the other in different areas. As an example, Hadoop would be the right choice when the memory size is significantly smaller than the data size; But if you are looking for speed, you couldn't consider any alternative other than Spark. Which do you prefer?? Do you think Spark could end up replacing MapReduce? Does it seem more likely that Hadoop will continue to enjoy its hegemony??
