Exploring the Functionality of MapReduce, Apache Spark and Hive in the Distributed Computing Paradigm

Big Data is now a phenomenon that has embedded itself into business, and affects how companies make their choices and create their plans. Since there is so much data created daily, organizations must have methods that can sort and analyze data quickly and efficiently. This is where technologies such as MapReduce, Apache Spark and SQL based systems such as Hive excel. These tools are designed to work for scales factor, for the volume or velocity and variety of big data.

In the course of this article, we will also explain what these technologies are, their benefits and demerits, and in what applications they can be actually used. At the end of this module, you should have further appreciation of how these tools integrate into the Big Data system.

Understanding Big Data

Big Data is data in a very large volume that needs very advanced techniques to handle it and cannot solve it with the current tools. Such datasets feature the three Vs – Volume, Velocity, and Variety, though the more recent definitions consider two Vs – Veracity (data quality) and Value (business insights).

Key Challenges of Big Data

• Storage: Storing and processing thousands of terabytes, or even petabytes of structured and unstructured data.
• Processing: Guaranteeing on time processing and access to information.
• Scalability: Scaling up landscape to manage exponential growth in data.

Now, with the emergence of the distributed systems and open-source semantic big data frameworks such as Hadoop, it became possible to speak about modern technologies for big data processing.

Big Data is not hype – it is about turning piles of data into useful information. Big Data is defined by the 5Vs

Volume: Due to the impossibility of describing the amounts of data produced with regular units or even with powers of ten, researchers use terabytes, petabytes, or exabytes.

Velocity: The rate of flow of data and hence the rate at which computations must be made.

Variety: How the data is classified: there are structured data, and there are semi-structured data, and there are unstructured data.

Veracity: The goodness and the validity of data that was collected.

Value: What could be learnt from the data and what could be practically done in light of the findings.

As the result of progress in distributed systems and the development of such scalable solutions as Hadoop, modern Big Data processing technologies were created.

MapReduce: The first name that can be associated with Distributed Computing

MapReduce, as developed by Google Company, is among the first ever platforms invented for handling large datasets in a distributed environment. It provides a simple programming model that splits data processing into two phases: Map and Reduce.

How MapReduce Works

Map Phase:

1. The input data is partitioned.
2. Chunking occurs so that each chunk generated is analyzed separately to create key and value variables.
3. Example: In implementing the power of word count using MapReduce, the map function accepts a line of text and produce key-value pairs ("word", 1).

Shuffle and Sort Phase:

1. The intermediate results also contain key-value pairs grouped by the key.
2. It stamps plausible all values in the same key are handed down to the same reducer.

Reduce Phase:

1. Reducers sum up the values of each key into the final values of the result.
2. Example: They all use Reduce where a non-aggregate key “word” sums up all the values to give the count.

Example: Word Count in MapReduce

Input:
"big data is amazing"
"big data is challenging"
Map Phase Output:
("big", 1), ("data", 1), ("is", 1), ("amazing", 1)
("big", 1), ("data", 1), ("is", 1), ("challenging", 1)
Reduce Phase Output:
("big", 2), ("data", 2), ("is", 2), ("amazing", 1), ("challenging", 1)        

Advantages of MapReduce

1. Scalability: Therefore, it uses distributed computing for handling several petabytes of data.
2. Fault Tolerance: A task that has been executed on several nodes but failed is retried in a different node without being manually reordered.
3. Batch Processing: It is best used when the result is not expected immediately in the process.

Limitations of MapReduce

1. Latency: Large overhead with respect to disk I/O for intermediate data.
2. Complexity: Frequently, developers have to write raw code for each task, they need to perform.
3. Iterative Processing: But not suitable for applications where you need to iterate through data several times, such as machine learning.

Apache Spark: The Next Evolution

Incidentally, in 2014, Apache Spark appeared and corrected the shortcomings of MapReduce by utilizing even faster in-memory computing and an easy-to-use Application Programming Interface. From the basis of MapReduce, Spark obtained a great number of enhancements in terms of performance, adaptability and usability.

Key Features of Spark

In-Memory Processing

• Spark also prefer intermediate data to be stored in memory and not on disk which greatly decreases latency.
• In iterative tasks such as machine learning this leads to massive speed benefits.

Rich Ecosystem

• Spark SQL: Query data using SQL.
• MLlib: Climb levels of learning and complete machine learning jobs.
• GraphX: Analyze graph-based data.
• Spark Streaming: Deal with data flows in real time.

Ease of Use

• Writing efficient code, I made available data manipulation easy with high-level APIs for Python, Java, Scala, and R.

Resilient Distributed Datasets (RDDs)

• RDDs are resilient aggregation of objects, which cannot be modified and can be compared across partitions.

Advantages of Apache Spark

1) Speed

One of the most important strengths of using Apache Spark in the data processing is its good processing speed. When it comes to data processing, one key element that is often correlated with efficiency, when working with big data, is speed. As for a reference, Apache Spark reportedly is 100X faster than Hadoop for Big data processing.

Apache Spark can accomplish this through in-memory (RAM) computing systems while Hadoop stores data in local memory. This makes Apache to manage more than petabytes of data of more than 8000 nodes at a time.

2) Ease of Use

Apache Spark can process large datasets by using API. It allows the building of parallel applications easily since it incorporates over 100 operators in these APIs.

3) Big Data Access

Note that acquiring Big Data is basic in accomplishing data processing tasks. Apache achieves this by looking for several means by which they can be made available. Data scientists and engineers, who write Spark code, are increasingly learning how to do that properly.

4) Machine Learning and Data Analysis

Apache Spark applies both Machine Learning and Data Analysis using the aspect of libraries. It is connected with an environment that provides capabilities for extracting and transforming data like structured.

5) Standard Libraries

As we expounded in the above advantage, Apache Spark comes with several standard libraries at an advanced level. These libraries help in Machine Learning, SQL queries and graph processing. This enables developers to work and achieve activities that have quantitative work flows in a smooth manner.

6) Promoting Career using Apache Spark

Businesses have to integrate Apache Spark to meet their data processing requirements. Such transformation opens up several possibilities for Data Engineers with similar competencies. All in all, companies’ demand for Spark developers has risen. As a result, they have flexible working hours and numerous incentives simply because they can hire people with talent. When wanting a career in Big Data, suggesting formative training to open opportunities concerning Apache Spark.

7) Open-source Community

Because Apache Spark is an open-source data processing platform, it raises the issue of an open-source community at them. Belonging to a community is useful in the learning process and regarding the recent advancements made with the field.

Limitations of Spark

While working on this tool, the user has to face some issues that are few limitations of Apache Spark. In general, this article is exclusively devoted to the analysis of Apache Spark and corresponding limitations and possible solutions. Okay, let me list down the following as the overall limitations of Apache Spark and explaining how to deal with the following Apache Spark limitations.

1) No File Management System

Apache Spark has no file management system at all and as such it has to work together with other platforms. Therefore, it relies on other formats like Hadoop or any other cloud-based format for the file management system. Exclusively, this is one of the major Apache Spark limitations.

2) No Real-Time Data Processing

Real-time data stream processing is not fully supported by the Spark system. In spark streaming the live data stream is grouped into sets which known as spark RDD which is Resilient Distributed Database. These RDDs can be processed using the operations such as join, map or reduce, and so on. After processing the result obtained from the batches and then converted in to batches again. In this way Spark streaming is nothing but micro-batch processing. Thus, it does not provide real-time processing capability fully, but close to it.

3) Expensive

When it comes to the cost-efficient relationship between big data and processing, it is not so simple to remember data. When using Spark development, memory management is notably high. In-memory requires extensive RAM, and due to this, Spark requires significant RAM for its functioning. Memory utilization is very intensive in Spark they do not make it very friendly to use. What is even worse is the need for additional memory in order to run Spark thereby making Spark costly.

4) Small Files Issue

One is small files issue when we word with Spark along with Hadoop. HDFS has few very large files and many a small file in Hadoop File System. When we use Spark with HDFS, this issue is not ignorable. However, with Spark, all the data is in the S3 in form of zip files. But now the issue is that such small zip files are expected to be uncompressed to aggregate the data files.

But in case of only complete file, we can compress the zip files all at one core will be there. They were spending much time when burning cores and unzipping files in sequence only. This is also because this time-consuming long procedure affects data processing. Due to large volume of data, volume of data shuffling is again very high to support efficient feed forward process.

5) Latency

The latency of Apache Spark is higher as it yields a lower throughput. In terms of latency Apache Flink has a lower latency while it has a higher throughput than Apache Flink that makes it better than Apache Spark.

6) The Lesser Number of Algorithms

In Apache Spark framework, MLib is the collection of machine learning algorithms in Spark. However, numbers of algorithms that are available in the Spark MLib are limited. Therefore, the limited number of available algorithms makes even the lesser available ones a limitation of Apache Spark.

Example: Word Count in Spark

Spark provides a simpler API for the same task as MapReduce.

from pyspark import SparkContext

sc = SparkContext("local", "WordCount")
text = sc.textFile("input.txt")
counts = text.flatMap(lambda line: line.split(" ")) \
             .map(lambda word: (word, 1)) \
             .reduceByKey(lambda a, b: a + b)
counts.saveAsTextFile("output")
        

This code achieves the same result as MapReduce but with fewer lines of code and better performance.

Hive: SQL for Big Data

Apache Hive was designed to facilitate managing large datasets based on their processing method and also to act as SQL like tool to facilitate querying on data stored in Hadoop. Hive also acts as a front end to MapReduce and can be understood as SQL like queries to MapReduce jobs.

Key Features

1. Hive supports the computing engines MapReduce, Tez and spark.
2. Hive is a comparatively potent batch processing tool, which is based upon Hadoop Distributed File System and can be employed as data warehouse.
3. Hive also employs structure data in its MySQL like HIVE query language that is fairly easy to code. From the 100 lines of Java code that are used in querying structured data, we make use of only 4 lines of HQL.
4. HQL is similar to SQL and actually is not a procedural language.
5. As for the structure, the table is analogous to the one in RDBMS. It also supports partitioning as well as bucketing processes.
6. As of now, partition, bucket and tables are the three types of data structures are available for Hive.
7. Apache Hive incorporates ETL, extract, transform and load services. Prior to Hive, ETL had been done using python.
8. Hive enables the user to query files stored in HDFS, HBase, S3 and other systems.
9. Hive is also capable of handling tremendous amount of data right up to the petabytes.?
10. It is not very difficult to integrate our own MapReduce code with Hive in order to analyze unstructured information.

Advantages of Hive

• Familiarity: SQL interface minimizes the learning outcome for the users who are not conversant with programming.
• Scalability: Can handle terabytes of data well.
• Extensibility: Is capable of Supporting custom functions and script.

Limitations of Hive

• Batch Processing: It does not work well with real-time data set.
• Latency: Depends on MapReduce that causes complications.

Example Query in Hive

Suppose you have a table sales with columns product, region, and sales amount.

SELECT region, SUM(sales_amount) 
FROM sales 
GROUP BY region;
        

This query calculates the total sales for each region by converting it into a series of MapReduce jobs.

Comparative Analysis: MapReduce, Spark, and Hive

Real-World Use Cases

E-Commerce:

Problem: Treating millions of uses interactions to help them find what they are looking for.

Solution: Spark deals with streaming and clickstream data, whereas Hive has a batch data processing feature.

Healthcare:

Problem: Patient data keeping and record processing.

Solution: Hive organizes resources to query, and Spark enables prediction mechanisms.

?Finance:

Problem: Preventing transaction frauds.

Solution: In general, real-time capability of Spark helps to identify anomalies immediately.

?Conclusion

Today, technologies such as MapReduce, Spark, and Hive are the fundamental of data analytics. Each tool offers unique strengths tailored to specific use cases:

• MapReduce: Safe for high volume standby computation transactions.
• Spark: Suitable for operations that require repeated analyses or instant results.
• Hive: This tool is very suitable for querying large databases using standard SQL query language.

Knowing these tools allows businesses to take drive the maximum value from the data they have and gain a competitive edge in today’s digital world.

References

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Verma, A. (2018). What are the Limitations of Apache Spark? [online] Whizlabs Blog. Available at: https://www.whizlabs.com/blog/apache-spark-limitations/.

Databricks. (n.d.). What is Apache Hive Used For? [online] Available at: https://www.databricks.com/glossary/apache-hive.

GeeksforGeeks. (2020). Apache HIVE - Features And Limitations. [online] Available at: https://www.geeksforgeeks.org/apache-hive-features-and-limitations/.

GeeksforGeeks. (2020). MapReduce - Understanding With Real-Life Example. [online] Available at: https://www.geeksforgeeks.org/mapreduce-understanding-with-real-life-example/.

Today Software Magazine. (n.d.). Hadoop MapReduce deep diving and tuning. [online] Available at: https://www.todaysoftmag.com/article/1358/hadoop-mapreduce-deep-diving-and-tuning.

www.knowledgehut.com. (n.d.). Features of Apache Spark | Apache Spark Tutorial. [online] Available at: https://www.knowledgehut.com/tutorials/apache-spark-tutorial/apache-spark-features.