Pros and Cons of Hadoop MapReduce

The are some of the advantages and disadvantages of using MapReduce are (Lusblinksy et al., 2014; Sakr, 2014):


  • Hadoop is ideal because it is a highly scalable platform that is cost-effective for many businesses.
  • It supports huge computations, particularly in parallel execution.
  • It isolates low-level applications such as fault-tolerance, scheduling, and data distribution.
  • It supports parallelism for program execution.
  • It allows easier fault tolerance.
  • Has a highly scalable redundant array of independent nodes
  • It has a cheap unreliable computer or commodity hardware.
  • Aggregation techniques under the mapper function can exploit multiple different techniques
  • No read or write of intermediate data, thus preserving the input data
  • No need to serialize or de-serialize code in either memory or processing
  • It is scalable based on the size of data and resources needed for processing the data
  • Isolation of the sequential program from data distribution, scheduling, and fault tolerance


  • The product is not ideal for real-time process data. During the map phase, the process creates too many keys, which consume sorting time. 
  • Most of the MapReduce outputs are merged.
  • MapReduce cannot use natural indices.
  • It is a must to buffer all the records for a particular join from the input relations in repartition join.
  • Users of the MapReduce framework use textual formats that are inefficient.
  • There is a huge waste of CPU resources, network bandwidth, and I/O since data must be reprocessed and loaded at every iteration.
  • The common framework of MapReduce doesn’t support applications designed for iterative data analysis.
  • When a fixed point is reached, detection may be the termination condition that calls for more MapReduce job that incurs overhead.
  • The framework of MapReduce doesn’t allow building one task from multiple data sets.
  • Too many mapper functions can create an infrastructure overhead, which increases resources and thus cost 
  • Too few mapper functions can create huge workloads for certain types of computational nodes
  • Too many reducers can provide too many outputs, and too few reducers can provide too few outputs
  • It’s a different programming paradigm that most programmers are not familiar with
  • The use of available parallelism will be underutilized for smaller data sets


  • Lublinsky, B., Smith, K. T., & Yakubovich, A. (2013). Professional Hadoop Solutions. Vitalbook file.
  • Sakr, S. (2014). Large Scale and Big Data, (1st ed.). Vitalbook file.

Mobile & Distributed Database Management Systems

A transaction is a set of operations/transformations to be carried out on a database or relational dataset from one state to another. Once completed and validated to be a successful transaction, the ending result is saved into the database (Panda et al, 2011). Both ACID and CAP (discussed in further detail) are known as Integrity Properties for these transactions (Mapanga & Kadebu, 2013).

Mobile Databases

Mobile devices have become prevalent and vital for many transactions when the end-user is unable to access a wired connection. Since the end-user is unable to find a wired connection to conduct their transaction their device will retrieve and save information on the transaction either on a wireless connection or disconnected mode (Panda et al, 2011). A problem with a mobile user accessing and creating transactions with databases, is the bandwidth speeds in a wireless network are not constant, which if there is enough bandwidth connection to the end user’s data is rapid, and vice versa. There are a few transaction models that can efficiently be used for mobile database transactions: Report and Co-transactional model; Kangaroo transaction model; Two-Tiered transaction model; Multi-database transaction model; Pro-motion transaction model; and Toggle Transaction model. This is by no means an exhaustive list of transaction models to be used for mobile databases. 

According to Panda et al (2011), in a Report and Co-transactional Model, transactions are completed from the bottom-up in a nested format, such that a transaction is split up between its children and parent transaction. The child transaction once completed then feeds that information up to the chain until it reaches the parent. However, not until the parent transaction is completed is everything committed. Thus, a transaction can occur on the mobile device but not be fully implemented until it reaches the parent database. In the Kangaroo transaction model, a mobile transaction manager collects and accepts transactions from the end-user, and forwards (hops) the transaction request to the database server. Transaction made in this model is done by proxy in the mobile device, and when the mobile devices move from one location to the next, a new transaction manager is assigned to produce a new proxy transaction. The two-Tiered transaction model is inspired by the data replication schemes, where there is a master copy of the data but for multiple replicas. The replicas are considered to be on the mobile device but can make changes to the master copy if the connection to the wireless network is strong enough. If the connection is not strong enough, then the changes will be made to the replicas and thus, it will show as committed on these replicas, and it will still be made visible to other transactions. 

The multi-database transaction model uses asynchronous schemes, to allow a mobile user to unplug from it and still coordinate the transaction. To use this scheme, five queues are set up: input, allocate, active, suspend, and output. Nothing gets committed until all five queues have been completed. Pro-motion transactions come from nested transaction models, where some transactions are completed through fixed hosts and others are done in mobile hosts. When a mobile user is not connected to the fixed host, it will spark a command such that the transaction now needs to be completed in the mobile host. Though carrying out this sparked command is resource-intensive. Finally, the Toggle transaction model relies on software on a pre-determined network and can operate on several database systems, and changes made to the master database (global) can be presented different mobile systems and thus concurrency is fixed for all transactions for all databases (Panda et al, 2011).  

At a cursory glance, these models seem similar but they vary strongly on how they implement the ACID properties in their transaction (see table 1) in the next section.

ACID Properties and their flaws

Jim Gray in 1970 introduced the idea of ACID transactions, which provide four guarantees: Atomicity (all or nothing transactions), Consistency (correct data transactions), Isolation (each transaction is independent of others), and Durability (transactions that survive failures) (Mapanga & Kedebu, 2013, Khachana et al, 2011; Connolly & Begg, 2015). ACID is used to assure reliability in a database system, due to a transaction, which changes the state of the data in the database. This approach is perfect for small relational centralized/distributed databases, but with the demand to make mobile transactions, big data, and NoSQL, the ACID may be a bit constricting. The web has independent services connected relationally, but hard to maintain (Khachana et al, 2011). An example of this is booking a flight for a CTU Doctoral Symposium. One purchases a flight, but then also may need another service that is related to the flight, like ground transportation to and from the hotel, the flight database is completely different and separate from the ground transportation system, yet sites like provide the service of connecting these databases and providing a friendly user interface for their customers. has its own mobile app as well. So taking this example further we can see how ACID, perfect for centralized databases, may not be the best for web-based services. Another case to consider is, mobile database transactions, due to their connectivity issues and recovery plans, the models aforementioned cover some of the ACID properties (Panda et al, 2011). This is the flaw for mobile databases, through the lens of ACID.

Table 1

Mobile Distributed Database Management Systems Transaction Models vs ACID.

Report & Co-transaction modelYesYesYesYes
Kangaroo transaction modelMaybeNoNoNo
Two-tiered transaction modelNoNoNoNo
Multi-database Transaction modelNoNoNoNo
Pro-motion ModelYesYesYesYes
Toggle transaction modelYesYesYesYes

Note: A subset of the information found in Panda et al (2011) dealing with mobile database system transaction models and how they use or do not use the ACID properties.

CAP Properties and their trade-offs

CAP stands for Consistency (just like in ACID, correct all data transactions and all users see the same data), Availability (users always have access to the data), and Partition Tolerance (splitting the database over many servers do not have a single point of failure to exist), which was developed in 2000 by Eric Brewer (Mapanga & Kadebu, 2013; Abadi, 2012; Connolly & Begg, 2015). These three properties are needed for distributed database management systems and are seen as a less strict alternative to the ACID properties by Jim Gary. Unfortunately, you can only create a distributed database system using two of the three systems so a CA, CP, or AP systems. 

CP systems have a reputation of not being made available all the time, which is contrary to the fact. 

Availability in a CP system is given up (or out-prioritized) when Partition Tolerance is needed. Availability in a CA system can be lost if there is a partition in the data that needs to occur (Mapanga & Kadebu, 2013). Though you can only create a system that is the best in two, that doesn’t mean you cannot add the third property in there, the restriction only talks applies to priority. In a CA system, ACID can be guaranteed alongside Availability (Abadi, 2012)Partitions can vary per distributed database management systems due to WAN, hardware, a network configured parameters, level of redundancies, etc. (Abadi, 2012). Partitions are rare compared to other failure events, but they must be considered. But, the question remains for all database administrators: 

Which of the three CAP properties should be prioritized above all others? Particularly if there is a distributed database management system with partitions considerations. Abadi (2012) answers this question, for mission-critical data/applications, availability during partitions should not be sacrificed, thus consistency must fall for a while.

Amazon’s Dynamo & Riak, Facebook’s Cassandra, Yahoo’s PNUTS, and LinkedIn’s Voldemort are all examples of distributed database systems, which can be accessed on a mobile device (Abadi, 2012). 

However, according to Abadi (2012), latency (similar to Accessibility) is critical to all these systems, so much so that a 100ms delay can significantly reduce an end user’s future retention and future repeat transactions. Thus, not only for mission-critical systems but for e-commerce, is availability during partitions key.

Unfortunately, this tradeoff between Consistency and Availability arises due to data replication and depends on how it’s done. 

According to Abadi (2012), there are three ways to do data replications: data updates sent to all the replicas at the same time (high consistency enforced); data updates sent to an agreed-upon location first through synchronous and asynchronous schemes (high availability enforced dependent on the scheme); and data updates sent to an arbitrary location first through synchronous and asynchronous schemes (high availability enforced dependent on the scheme). According to Abadi (2012), PNUTS sends data updates sent to an agreed-upon location first through asynchronous schemes, which improves Availability at the cost of Consistency. Whereas, Dynamo, Cassandra, and Riak send data updates sent to an agreed-upon location first through a combination of synchronous and asynchronous schemes. 

These three systems, propagate data synchronously, so a small subset of servers and the rest are done asynchronously, which can cause inconsistencies. All of this is done to reduce delay to the end-user. 

Going back to the example from the previous section, consistency in the web environment should be relaxed (Khachana et al, 2011). Further expanding on, if 7 users wanted to access the services at the same time they can ask which of these properties should be relaxed or not. One can order a flight, hotel, and car, and enforce that none is booked until all services are committed. Another person may be content with whichever car for ground transportation as long as they get the flight times and price they want. This can cause inconsistencies, information being lost, or misleading information needed for proper decision analysis, but systems must be adaptable (Khachana et al, 2011). They must take into account the wireless signal, their mode of transferring their data, committing their data, and load-balance of the incoming request (who has priority to get a contested plane seat when there is only one left at that price). At the end of the day, when it comes to CAP, Availability is king. It will drive business away or attract it, thus C or P must give, to cater to the customer. If I were designing this system, I would run an AP system, but conduct the partitioning when the load/demand on the database system will be small (off-peak hours), so to give the illusion of a CA system (because Consistency degradation will only be seen by fewer people). Off-peak hours don’t exist for global companies or mobile web services, or websites, but there are times throughout the year where transaction to the database system is smaller than normal days. So, making around those days is key. For a mobile transaction system, I would select a pro-motion transaction system that helps comply with ACID properties. Make the updates locally on the mobile device when services are not up, and set up a queue of other transactions in order, waiting to be committed once wireless service has been restored or a stronger signal is sought. 


  • Abadi, D. J. (2012). Consistency tradeoffs in modern distributed database system design: CAP is only part of the story. IEEE Computer Society, (2), 37-42.
  • Connolly, Thomas & Begg, Carolyn (2015). Database Systems: A Practical Approach to Design, Implementation, and Management, 6th Edition. Pearson Education, Inc., publishing as Addison-Wesley, Upper Saddle River, New Jersey.
  • Khachana, R. T., James, A., & Iqbal, R. (2011). Relaxation of ACID properties in AuTrA, The adaptive user-defined transaction relaxing approach. Future Generation Computer Systems, 27(1), 58-66.
  • Mapanga, I., & Kadebu, P. (2013). Database Management Systems: A NoSQL Analysis. International Journal of Modern Communication Technologies & Research (IJMCTR), 1, 12-18.
  • Panda, P. K., Swain, S., & Pattnaik, P. K. (2011). Review of some transaction models used in mobile databases. International Journal of Instrumentation, Control & Automation (IJICA), 1(1), 99-104.

Article Review: Knowledge Discovery through Text Analytics

The article “IT innovation adoption by enterprises: Knowledge Discovery through text analytics” was select as an article in the field of study. The path to get to this article is articulated below:

Science Direct (Elsevier) > Search Term: “Text Analytics”

This article was chosen due to my interest in text analytics of huge data sets, to help derive knowledge from this unstructured data set. This was a primary topic/interest for the dissertation, along with identifying another unconventional way to do literature reviews.


This study investigated two premises: (1) Is it possible to use text data mining techniques to conduct a more thorough and efficient literature review on any subject matter? (2) What are the drivers of IT Innovations? After having identified 472 quality articles spanning multiple fields in business administration and 30 years of knowledge.  The authors used a tool called Northernlight (, where they were able to answer both premises. 

The authors, state that current methods of most literature reviews are time-consuming, usually focus in the last five years and involve tons of attention. Most articles are scanned by title and abstracts before the researcher considers them to be read in their entirety. This method, as argued, is not useful. Thus, effective techniques consist of the use of “meaning extract” of a large set of documents (usually considered unstructured data sets) across various domains should help the researcher to obtain and discover knowledge efficiently. Therefore, the first premise deals with the utilization of text data mining techniques. These techniques shouldn’t just merely revolve around a core system of counting the identified keyphrases (or “themes”), but on automating “meaning extraction.” “Meaning extraction” measures the strength between keywords or phrases that are related to others. The end-user/researcher can apply rules to help enhance meaning extraction between sets of keywords. The authors conclude that these techniques are an excellent way to do a first-pass analysis. The first pass analysis can help generate more questions, which can lead to more future insights.

They prove the first premise by applying the Northernlight system towards IT innovation. The authors then used 472 data sets, in which IT innovation is mentioned in multiple disciplines across the field of business administration. By setting rules to identify keyword proximity to other keywords (or their equivalents) they were able to garner some insights into IT Innovation. Proximity could be measured as far as a sentence (~40 words) or a paragraph (~150 words). Thus, they determined that cost and complexity are the two most frequent IT innovation determinants (as well as complexity, compatibility, and relative advantage) based on an IT department’s perspective. However, on the enterprise level, perceived benefits, perceived usefulness, and ease of use, were determinants of IT innovation. Finally, organization size and top management support positively correlated with IT innovation with cost being negative towards IT innovation.


The obstacle that came in here was that some articles with really creative titles and were recently published came at a price. So the article that was chosen was still a good read, but one does wonder how good those papers are that have been priced/paywall. Is having a paywall from publicly funded sources be hidden behind a paywall. We paid for it through our taxes, why should we have to pay for it once the results are out.