Adv DB: CAP and ACID


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 transaction either on a wireless connection or disconnected mode (Panda et al, 2011).  A problem with a mobile user accessing and creating a transaction 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 in 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 successfully 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. 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, 2011).  ACID is used to assure reliability in the 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, ACID may be a bit constricting.  The web has independent services connected together relationally, but really hard to maintain (Khachana, 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.

Model Atomicity Consistency Isolation Durability
Report & Co-transaction model Yes Yes Yes Yes
Kangaroo transaction model Maybe No No No
Two-tiered transaction model No No No No
Multi-database Transaction model No No No No
Pro-motion Model Yes Yes Yes Yes
Toggle transaction model Yes Yes Yes Yes

Table 1: A subset of the information found in Panda et al (2011) dealing with mobile database system transaction models and how they use or 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).  These three properties are needed for distributed database management systems and is 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 in order to reduce delays 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 incoming requests (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, in order 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.
  • 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.

Column-oriented NoSQL databases

NoSQL (Not only Structured Query Language) databases are databases that are used to store data in non-relational databases i.e. graphical, document store, column-oriented, key-value, and object-oriented databases (Sadalage & Fowler, 2012; Services, 2015). Column-oriented databases are perfect for sparse datasets, ones with many null values and when columns do have data the related columns are grouped together (Services, 2015).  Grouping demographic data like age, income, gender, marital status, sexual orientation, etc. are a great example for using this NoSQL database. Cassandra, which is a column-oriented NoSQL database focuses on availability and partition tolerance, this means that as an AP system it can achieve consistency if data can be replicated and verified (Hurst, 2010).

Cassandra has been assessed for performance evaluation against other NoSQL databases like MongoDB and Raik for health care data analytics (Weider, Kollipara, Penmetsa, & Elliadka, 2013).  In this study, NoSQL database demands for health care data were two-fold:

  • Read/write efficiency of medical test results for a patient X (Availability)
  • All medical professionals should see the same information on patient X (Consistency)

A NoSQL graph database did not have the fit to use for the above demands, thus wasn’t part of this study.

The architecture of this project: nine partition nodes, where three by three nodes were used to mimic three data centers that would be used by 100 global health facilities, where data is generated at a rate of 1TB per month and must be kept for 99 years.

The dataset used in this project: a synthetic dataset that has 1M patients with 10M lab reports, averaging at seven lab reports per person, but randomly distributed of from 0-20 lab reports per person.

In meeting both of these two demands, Cassandra had a significantly higher throughput value than the other two NoSQL databases. Cassandra’s EACH_QUORUM write and LOCAL_QUORUM read options are part of their datacenter aware system, providing the great throughputs results, using the three synthetic datacenters. Testing consistency, by using Cassandra’s ONE for its write and read options at an eventual rate (slower consistency) or strong rate (faster consistency), shows that throughput increases with the eventual system. The choice to use either rate rests with the healthcare stakeholders.

The authors concluded that for their system and their requirements Cassandra had the highest throughput regardless of the level of consistency rates (Weider et al., 2013).  They also suggested that each of these tests should be adjusted based on the requirements from key stakeholders in the healthcare profession and that a small variation in the data model could change the results seen here.

In conclusion of this post, NoSQL databases provide huge advantages to data analytics over traditional relational database management systems. But, NoSQL databases must fit the needs of the stakeholders, and quantitative tests must be thoroughly designed to assess which NoSQL database will meet those needs.


  • Hurst, N. (2010). Visual guide to NoSQL systems. Retrieved from
  • Sadalage, P. J., Fowler, M. (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, 1st Edition. [Bookshelf Online].
  • Services, E. E. (2015). Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, 1st Edition. [Bookshelf Online].
  • Weider, D. Y., Kollipara, M., Penmetsa, R., & Elliadka, S. (2013, October). A distributed storage solution for cloud based e-Healthcare Information System. In e-Health Networking, Applications & Services (Healthcom), 2013 IEEE 15th International Conference on (pp. 476-480). IEEE.