Big Data: Hadoop Distributed Framework

Lets move to various technologies that come under big data.

I am starting with hadoop, one of the most used (and criticized, not sure why) distributed frameworks for big data.
(FYI, As I am starting with the basics I am explaining hadoop 0.x and 1.x,  NOT 2.x or YARN)

Distributed Frameworks are building blocks of distributed systems and define their behavior in terms of various important aspects like architecture, synchronization algorithms, load balancing, fault tolerance , failure recovery algorithms, programming paradigms and a lot more...
 
Hadoop is composed of two components:

1. HDFS (Hadoop Distributed File System) : Its a distributed file system based on GFS (Google File System).
2. Map Reduce: Programming paradigm (way of programming). It has got two phases map and reduce, hence the name.

Hadoop as a distributed framework:

Architecture:
Hadoop has master slave architecture i.e. one node (I hope you understand what node is) acts as a master or a manager (an efficient one, not the one everyone's bitching about, just kidding) and all other nodes act as slave or worker.

1.  HDFS: For HDFS, namenode is master and datanode is slave. Datanode keeps the data, and namenode acts as index for the data.
2. Map Reduce: For map reduce, master is jobtracker and slave is tasktracker. Tasktracker runs the processes for map phase and reduce phase. Jobtracker acts as the manger and decides where to run which process.

Synchronization Algorithms:
As hadoop is a master slave architecture, obviously synchronization is done by master. Slaves send messages to master after some fixed interval. These messages are called heart beat messages, as they tell master that a slave is alive. These messages also contain other information like process completion (on tasktracker), space remaining (on datanode) etc.


Fault Tolerance and failure recovery:
As these nodes are commodity hardware, they are bound to fail.

1. Slave's data is replicated (copied) on multiple nodes to make the cluster fault tolerant. Whenever any one of them fails, master just redirects the reads to other node's copies. 
2. Master's data (index of the files on slaves) is copied on another node. On failure the copied data on the other node has to be copied back on master and has to be restarted by admin. (FYI this problem has been solved in hadoop 2.x)

Programming paradigm:
As hadoop needs to run the programs as distributed processes, it has a different way of programming. It is called map reduce. Everything is seen as a key value pair.
Map is used to create the right key value pair and reduce, as the name suggests aggregates the data. (Again FYI, this map is not the data structure map, its a phase)
e.g. bloggers log has to be examined to find the referring urls and number of references per url. Map phase will create each referring url as key and one(1) as value and reduce phase will add all the occurrences (or 1s) of each key or referring url
Map: <URL1,1>;<URL1,1>;<URL2,1>
Reduce:<URL1,1+1>;<URL2,1>


This is just the basic idea of hadoop. Each process and component will take 2-3 whole posts.

Ask questions if you don't understand something, you can even disagree with me. Looking forward to some response....
Stay tuned for more elaborate explanations and examples...

Why big data - II

Lets start where we left last time.

Lets elaborate how big data solves the following problems:

1. The problem of storing data as it increases: Instead of one server or computer, big Data systems are composed of multiple computers (called as nodes). They are simple commodity hardware (in less technical terms, PCs) and hence are cheaper and easy to scale. You don't need to change the configuration of your systems or servers if you work with big data, you just have to add one more system.
For e.g. Lets suppose you want to write a novel and you start writing it in a cheap notebook. But eventually notebook is not sufficient for your whole story. There are two ways to solve the problem:

1. Add more pages to the notebook, and when that is not possible just buy a new bigger, thicker and expensive notebook. Then copy everything from original notebook to the new notebook, and then resume your novel writing. This is called vertical scaling. Pretty silly way, but that's what the traditional approach with the servers.
2.  Buy a new cheap notebook, just resume your writing. Go on adding as many notebooks as you want, and just create an index. Index will tell which notebook has which part of novel or whats the sequence of the notebooks. That is called horizontal scaling. Pretty obvious and efficient way and is used by big data systems.

 

2. The problem of handling requests:As big data systems are composed of multiple computers (called as nodes), the handlers for requests increase. Now your single system is not your bottleneck. Big data synchronizer just sends your requests to the nearest node which is free or the best case scenario nearest node which is free and has the data that the request is to access.
Lets suppose a person grows different kinds of vegetables, and has different people working on different vegetables.

1. If he has one farm with one entry and one exit, there is a queue of workers waiting to enter the farm. Besides, to reach a particular vegetable section, workers has to go through all the sections in between. These workers are requests, data access requests to be more precise and those sections of different vegetables can be different databases or directories. And hence that way traditional systems cause long delays
2. If he has multiple smaller farms, lets say one for each type of vegetable (best case scenario). There are multiple entries and exists, and each worker can simply enter and start working, no delays of going through other sections to get the desired section.

3. The problem of distributing computation: Big data systems not just distribute the requests, but also distributes the data. They not only distribute directory or database, they distribute the single units of computation like file or table. So, even if your file is too large to fit in  the memory of one system, it doesn't matter because now it is not in one system but is split and distributed in different systems with indexes. So if you want to access only a part of your file or your table, then you don't need to read the whole file and seek the point. Also, if you want to do some manipulation, your execution is working on different parts of your file and hence it is being worked on in not only distributed but also in parallel manner.
Lets say there is a log of all the events happening on amazon for one day. Now you want to find out the average sales of each product per hour.

1. Traditional systems will read the file in chunks (of the size which can fit into memory at a time), then will do grouping and aggregations on each chunk sequentially. This results in lot of I/O delays and processing delays, and hence is really time consuming.
2. Big data systems will just split the log file into some fixed size chunks, and will give each chunk to different nodes. Now the groupings and aggregation is done on different nodes in parallel. Now the log chunks can fit into memory, so no I/O delays, and the process is not sequential, hence much lesser processing delays.

Hope this time you got the whole concept, not just the gist. If you are not convinced, just comment and ask. I'll be more than happy to help.
Also please provide your valuable suggestions as well.

Stay Tuned for more...

Why Big data?

After my last post if you decide that you actually need big data, then lets start the deep dive level I.

When do you need big data or when does your traditional systems fail:

As the data size increases, the problem of storing it arises. This is usually solved by increasing your memory size. Then comes the problem of computation on such huge data, you increase your RAM. When that stops working, you switch to servers with loads of memory and RAM.

Then starts a problem of handling requests. As the size of organization or website popularity increases, number of users increase and ultimately number of requests for data access increase. But your server no matter how powerful it is, is after all a single device and  there's a limit on how much it can handle at once. It then becomes a bottleneck (the most used and boring term in the books). Its as if hundreds of cars are trying to enter a city which is big enough to accommodate the cars and its people (no problem there), but the highway to enter the city is jam packed.

Now you need a system that distributes the requests and data. Here comes your distributed RDBMS, you still have the benefits of RDBMS and you have distributed you databases across different systems, your requests will now be distributed to different systems which will process your request and will provide you with data you want. Its like you create different entrance routes to different parts of the city, all the traffic gets distributed between these entrances on the basis of the part of city a person wants to go to. But it distributes the requests, not computation.

Now starts the ultimate problem, distributing your computation or data manipulation. Lets say you have a table of 100 GB of data, and you have to do a computation on it, e.g. you want do a sum along with group by on it.While distributed RDBMS distributes the databases, it doesn't distribute your table. As your table is on a single machine, your computation, no matter how big is also on a single machine. Your system goes madly slow and you ofcourse go mad. So,none of the above solutions can help you. And here comes the ultimate rescuer Big data. Say Hellooo people :)



Why Big Data:

Here's how big data will solve the problem. It will break your table into multiple splits to be put on multiple systems of your cluster (nodes of the cluster to be precise) and the computation will be done on multiple nodes instead of one. Now you have data at multiple nodes, and each part is being processed by one node. Now, not only your data, or your requests, but also your computation is distributed.
Result: Neither you nor your system goes mad, as its not one system at all, so no limitation or bottleneck

Its ok if you don't fully understand it until now, its enough to get the gist of it. Stay Tuned for more...

Big Data: Do you need it

Big data is one of the most misunderstood terms now a days. Every article and book on big data starts with how much data has become available. Facts and figures are given on how dramatically data volume has increased over the years with internet boom. And all this is what created the need for big data technologies. Its not fully wrong but its misdirecting, and is misinterpreted big time.

It is often interpreted that need for big data arose as a result of the need to store that much volume of data. But actually it wasn’t just the storage, but the computation that was biggest problem. Need for big data is defined by the amount of data you need to process at a time.

Lets start with an example, there is a e-commerce company, having lots of data about everything starting from products, their prices, categories etc. to user profiles, user logs. They are using a RDBMS for storing their product and user data, and simple log files for user logs.
They have loads of data, probably a few hundred GBs, for products and user data. Now do they need big data for this? The answer surprisingly is no. Data is in hundreds of GBs but they don’t need to process this data all at once. At most they probably need to join 4-5 tables(or less if they have efficient schema). Big data will harm them more than helping them, they can simply use distributed RDBMS(no synchronization needed).
On the other hand if they decide to analyze their user logs, find out patterns to decide marketing strategies. Thats a real choice, whether to use big data or not, based on the volume of logs per day and how far in the history they want to go. If they are popular and have GBs of data per day, and:

1. want to analyze data for just one day: just load the relevant part to RDBMS every hour or so 
2. want to analyze data for a month: probably won’t work with RDBMS in their case (will still work if logs are small, just 2-3 GBs)
3. want to analyze data for one or more years: Its better to switch to big data tools, too cumbersome to handle computations on such huge data by one system.

Thus the biggest question before starting the switch to big data every one need to ask is whether it is actually needed. If you answer right it might save a whole lot of trouble.