Natural selection of database technology through the years

1Charles Darwin in his landmark book “The Origin of Species’ discusses how flora and fauna evolved through the centuries. The different features of each individual species would undergo a process of natural selection by which modification of attributes would naturally occur that would enable the species to adapt and propagate through time. Those modifications that failed to adapt would naturally become extinct.

In this post I discuss how database (DB) technology has evolved over the years. As new requirements arose database technology has had to adapt and newer paradigms have evolved. However, unlike species which became extinct the older versions still exist as they continue o address the earlier problems that remain today.

Here is a short & brief history of evolution of databases

Relational databases: Relational databases had their genesis when E.F Codd of IBM came up with a relational model of organizing data. In this model all data is organized as tables with several rows. In a relational model each row has several columns and one of the columns contains a unique value for each row called primary key. Relational databases have ruled the enterprise domain for more than 3 decades. An enterprise’s data is organized as a set of related tables. Users can query the database using Structured Query Language or SQL.

I remember in the late 1980’s when I started to work in the industry, programming jobs were much sought after by all of us engineering graduates. In those days database jobs were ‘uncool’ and system programming jobs dealing with writing assemblers, compilers were the really cool jobs. I was also susceptible to this prevailing opinion and stayed away from databases. As fate would have it I eventually moved into telecom and telecom protocol work in which I worked for more than 2 decades and have largely maintained my distance from DB.

However it recent times I did want to look brush up whatever little I knew of DB. Recently I was listening to the Coursera course “Introduction to Data Science‘ by Bill Howe. In one of the lectures the professor uttered something that really caught my fancy. He mentions that SQL is probably the closest to natural language. How true! Once the DB schema and tables have been set up, querying the DB for all sorts of data can be done in SQL which is close to natural language. For e.g.

SELECT a,b,c from TABLE S,T where condition X1 AND/OR condition X2

The power of DBs comes from the fact that all the data is organized as tables and enables one to retrieve any sort of data from it. Trying to accomplish this with any other high level programming language would take several hundreds of lines of code and we would have to write functions for each in individual query.

NoSQL databases: However the utility of relational databases decreases as we scale to hundreds of Gbs of data. In this age of the internet and the worldwide web data is easily of the order of several terabytes to a few petabytes. For e.g. Weather modelling, Social networks like FB,Twitter or LinkedIn all need to operate on millions of status updates or tweets per day. Traditional relational databases cannot handle such large sets of data. This is where the concept of NoSQL DB came into existence. NoSQL databases typically store data as key, value pairs. The singular advantage of NoSQL is that the database can scale horizontally or in other words the performance does not degrade with large increases in data size. In NoSQL databases data is hashed and uniformly distributed across commodity servers through a technique known as ‘consistent hashing’. Also data in NoSQL databases is replicated across servers. This architecture of NoSQL databases is based on common, commodity servers which are expected to crash. However this would not affect the NoSQL DB to function correctly. The strength of NoSQL databases comes from the fact that servers can join or leave the NoSQL DB without affecting the functioning of the DB. Some of the more popular examples of NoSQL DB are CouchDB, MongoDB, Riak, Voldemort, Dynamo etc.Do take a look at my post “When NoSQL makes better sense that MySQL

NewSQL: This variation of DB came into existence as there was a need for extremely fast performance for computing tasks like analytics etc. These DBs exist completely in memory and so the access is blazingly fast. The most famous of DBs of this paradigm is SAP’s HANA.

Graph Databases: Graph databases are the recent entrants into database technology. This strain of databases came into existence to handle associative data more efficiently. In a graph database data is represented as a graph. Nodes in the graph can be entities and edges can be relationships. A search on a graph database will result in a traversal from a specified start node to a specified terminating node. “Friends’ in Facebook, ‘followers/following’ in Twitter and ‘connections’ in LinkedIn all use Graph Database to map association and enable easy search. Graph Databases is what allows these databases to make recommendations like ‘You may know’. E.g. of Graph Database Google’s Graph DB, Neo4j

As we move ahead database technology will continue to evolve into newer architectures to handle

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Reducing to the Map-Reduce paradigm- Thinking Web Scale – Part 1

In physics there are 4 types of forces – gravitational forces among celestial bodies, electro-magnetic forces and strong and weak forces at the sub-atomic level. The equations that seem to work among large bodies don’t seem to apply at the sub-atomic level though there have been several attempts at grand unification theories

Similarly in computing we have: – computing at personal level, enterprise level, data-center level and a web scale level. The problems and paradigms at each level are very different and unique. The sequential processing, relational database accesses or network speeds at the local area network level are very different to the parallel processing requirements, NoSQL based storage accesses  and WAN latencies.

Here is the first of my posts on paradigms at the Web Scale.

The internet now contains in excess of 1 billion hosts.  This is based on a report in the World Fact Book published in 2012.

In these 1 billion and odd hosts there are at least ~1.5 billion pages that have been indexed. There must be several hundred million that are not indexed by the major search engines.

Search engines like Google, Bing or Yahoo have to work on several hundred million pages.  Similarly social web sites like Facebook, Twitter or LinkedIn have to deal with several hundred million users who constantly perform status updates, upload images, tweet etc. To handle large quantities of data efficiently and quickly there is a need for web scale algorithms.

One such algorithm is the map-reduce, that had its origins in Google. The map reduce essentially consists of a set of mappers which take as input a key-value pair and outputs 0 or more key value pairs. The reducer takes all tuples with the same key and combines them based on some function and emits a key value pair


Map-reduce, and its open source avatar, Hadoop, are now used routinely to solve several large scale problems. To be honest, I was and still am, puzzled whether the 2 simple tasks types of mapping & reducing can be used for a large variety of problems. However, it appears so.

I would have assumed that there would have been other flavors, maybe an ‘identify-update’, ‘determine-solve’ or some such equivalent, unless a large set of problems can be expressed as some combination of the map reduce paradigm.

Anyway here a few examples for which the map reduce algorithm is useful.

Word Counting: The standard example for map-reduce is the word counting program. In this the map reduce algorithm generates a list of words with their corresponding word count from a set of input files. The Map task reads each document and breaks it into a sequence of words (w1, w2, w3 …). It then emits a key value pair as follows

(w1,1),(w2,1),(w3,1),(w1,1) and so on. If a word is repeated in the document it occurs multiple times in the output.  Now the entire key, value pairs are grouped by keys and sent to one of the reducer tasks. Each reducer will then sum all the values thus giving the total for each word.


Matrix multiplication: Big Data is a typical challenge in the web where there is a need to determine patterns and trends in mountains of data. Machine learning algorithms are utilized to determine structure in data that has 3 characteristics of volume, variety and velocity. Machine learning algorithms typically depend on matrix operations. Map-reduce is ideally suited for this and one of the original purposes of Google for map-reduce was with matrix multiplication.

Let us assume that we have a n x n matrix M whose element in row i and column j is mij

Also let us assume that there is a vector ‘v’ whose jth element is vj . Then the matrix vector product can be is the vector x of the length n whose ith element is given as

xi = ∑ mijvj


Map function: The map function applies to each single element of the matrix M. For each element mij the map task outputs a key-value pair as follows (i, mijvj).  Hence we will have a key-value pairs for all ‘i’ from 1 to n.

Reduce function:  The reduce function takes all pairs with the same key ‘i’ and sum it up.

Hence each reducer will generate

xi = ∑ mijvj

(Reference: Mining of Massive Datasets– Anand Rajaraman, Jure Leskovec, Jeffrey D Ullman)

This link gives a good write-up on a matrix x matrix multiplication,

Map-reduce for Relational Operations: Map-reduce can be used to perform a number of operations on large scale data that are used in database operations. Multiple database operations can be performed on large scale data like selection, projection, union, intersection, difference, natural join, grouping etc.

Here is a an example taken from ‘Web Intelligence & Big Data’ course from Coursera any Gautam Shroff.

Let us assume that there are 2 tables ‘Sales by address’ and “City by address’ and the need is to find the total ‘Sales by City’. The SQL query for this

SELECT SUM(Sale),City FROM Sales, City WHERE Sales.Addr_id = Cities.Addr_id GROUP BY City

This can be done by 2 map-reduce tasks.

The first map-reduce task GROUPs BY Sales as follows

Map1: The first map task will emit (Address, rest of record (SALE/City))

Reduce1: The first reduce task will SUM (Sales) by Address for every City. Clearly this will have multiple occurrences of City.

At this point we will have the sum of the sales for every city. However each city can occur multiple times. Now we have to GROUP BY City

Map2: Now the mapper emits the (City, rest of record (SALES)

Reduce2: The 2nd reduce now SUMS all the sales for each city.

Clearly the map-reduce algorithm does solve some major areas. It is extremely useful when there is a need to perform the same operation on multiple documents. It would definitely be useful in building the inverted index or in Page rank. Also, map-reduce is very powerful in handling matrix operations. Large class of problems like machine learning, computer vision all use matrices extensively and map-reduce is extremely critical when it has done in large volumes of data.  Besides, the ability of map-reduce to perform a large set of database operations is something that can be used in many situations in the web.

However it is no silver bullet for all types of problems.

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