Big Data... Is Hadoop the good way to start?

In the past 2 years, I have met many developers, architects that are working on “big data” projects. This sounds amazing, but quite often the truth is not that amazing.

TL;TR

You believe that you have a big data project?

• Do not start with the installation of an Hadoop Cluster -- the "how"
• Start to talk to business people to understand their problem -- the "why"
• Understand the data you must process
• Look at the volume -- very often it is not "that" big
• Then implement it, and take a simple approach, for example start with MongoDB + Apache Spark

The infamous "big data project"

A typical discussion would look like:

Me: “Can you tell me more about this project, what do you do with your data?”

Mr. Big Bytes: “Sure, we have a 40 nodes Hadoop cluster..."

Me: “This is cool but which type of data do you store, and what is the use case, business value?"

Mr. Big Bytes: “We store, all the logs of our applications, we have hundreds of gigabits…"

After a long blank:“We have not yet started to analyze these data. For now it is jut  'us, the IT team,' we store the data, like that soon we will be able to do interesting things with them"

You can meet the same person few months later; the cluster is still sitting here, with no activity on it. I even met some consultants telling me they received calls from their customer asking the following:

“Hmmm, we have an Hadoop cluster installed, can you help us to find what to do with it?"

Wrong! That is wrong!!!!! This means that the IT Team has spent lot of time for nothing, at least for the business; and I am not even sure the team has learned something technically.

Start with the "Why" not with the "How"!

The solution to this, could be obvious, start your “big data project” answering the “why/what” questions first! The “how”, the implementation, will come later.

I am sure that most of the enterprises will benefit of a so called “big data project”, but it is really important to understand the problems first. And these problems are not technical… at least at the beginning. So you must spend time with the business persons to understand what could help them. Let's take some examples.

You are working in a bank or insurance, business people will be more than happy to predict when/why customer will be leaving the company by doing some churn analysis; or it will be nice to be able to see when it makes lot of sense to sell new contracts, service to existing customers. 

You are working in retail/commerce, your business will be happy to see if they can adjust the price to the market, or provide precise recommendations to a user from an analysis of other customer behavior.

We can find many other examples. But as you can see we are not talking about technology, just business and possible benefits. In fact nothing new, compare with the applications you are building, you need first to have some requirements/ideas to build a product. Here we just need to have some "data input" to see how we can enrich the data with some business value.

Once you have started to ask all these questions you will start to see some input, and possible processing around them:

• You are an insurance, you customers has no contact with your representative, or the customer satisfaction is medium/bad; you start to see some customer name in quotes coming from price comparison website…. hmm you can guess that they are looking for a new insurance. 
• Still in the insurance, when your customer are close to the requirement age, or has teenagers learning how to drive, moving to college, you know that you have some opportunity to sell new contract, or adapt existing ones to the new needs
• In retail, you may want to look to all customers and what they have ordered, and based on this be able to recommend some products to a customer that "looks" the same.
• Another very common use case these days, you want to do some sentiment analysis of social networks to see how your brand is perceived by your community

As you can see now, we can start to think about the data we have to use and the type of processing we have to do on them.

Let's now talk about the "How"

Now that you have a better idea about what you want to do, it does not mean that you should dive into a large cluster installation.

Before that, you should continue to analyze the data:

• What is the structure of the data that I have to analyze?
• How big is my dataset?
• How much data I have to ingest on a period of time (minute, hour, day, ...)

All these questions will help you to understand better your application. This is where it is often interesting too, and we realize that for most of us the "big data" is not that big!

I was working the other day with a Telco company in Belgium, and I was talking about possible new project. I simply said:

• Belgium is what, 11+ millions of people
• If you store a 50kb object for each person this represent:
• Your full dataset will be 524Gb, yes not even a Terabyte!

Do you need a large Hadoop cluster to store and process this? You can use it, but you do not need to! You can find something smaller, and easier to start with.

Any database will do the job, starting with MongoDB. I think it is really interesting to start this project with a MongoDB cluster, not only because it will allow you to scale out as much as you need, but also because you will leverage the flexibility of the document model. This will allow you to store any type of data, and easily adapt the structure to the new data, or requirements.

Storing the data is only one part of the equation. The other part is how you achieve the data processing. Lately I am playing a lot with Apache Spark. Spark provides a very powerful engine for large scale data processing, and it is a lot simpler than Map Reduce jobs. In addition to this, you can run Spark without Hadoop! This means you can connect you Spark to your MongoDB, with the MongoDB Hadoop Connector and other data sources and directly execute job on your main database.

What I like also about this approach, you can when you dataset starts to grow, and it become harder to process all the data on your operational database, you can easily add Hadoop; and keep most of your data processing layer intact, and only change the data sources information. In this case you will connect MongoDB and Hadoop to get/push the data into HDFS, once again using the MongoDB Hadoop Connector.

Conclusion

Too many times, projects are driven by technology instead of focusing on the business value. This is particularly true around big data projects. So be sure you start by understanding the business problem, and find the data that could help to solve it.

Once you have the business problem and the data, select the good technology, that could be very simple, simple files and python scripts, or more often a database like MongoDB with a data processing layer like Spark. And start to move to Hadoop when it is really mandatory... a very, very, very, large dataset. 

Introduction to MongoDB Geospatial feature

This post is a quick and simple introduction to Geospatial feature of MongoDB 2.6 using simple dataset and queries.

Storing Geospatial Informations

As you know you can store any type of data, but if you want to query them you need to use some coordinates, and create index on them. MongoDB supports three types of indexes for GeoSpatial queries:

• 2d Index : uses simple coordinate (longitude, latitude). As stated in the documentation: The 2d index is intended for legacy coordinate pairs used in MongoDB 2.2 and earlier. For this reason, I won't detail anything about this in this post. Just for the record 2d Index are used to query data stored as points on a two-dimensional plane
   
• 2d Sphere Index : support queries of any geometries on an-earth-like sphere, the data can be stored as GeoJSON and legacy coordinate pairs (longitude, latitude). For the rest of the article I will use this type of index and focusing on GeoJSON.
   
• Geo Haystack : that are used to query on very small area. It is today less used by applications and I will not describe it in this post.

So this article will focus now on the 2d Sphere index with GeoJSON format to store and query documents.

So what is GeoJSON?

You can look at the http://geojson.org/ site, let's do a very short explanation. GeoJSON is a format for encoding, in JSON, a variety of geographic data structures, and support the following types:  Point , LineString , Polygon , MultiPoint , MultiLineString , MultiPolygon and Geometry.

The GeoJSON format  is quite straightforward based, for the simple geometries, on two attributes: type and coordinates. Let's take some examples:

The city where I spend all my childhood, Pleneuf Val-André, France, has the following coordinates (from Wikipedia)

 48° 35′ 30.12″ N, 2° 32′ 48.84″ W

This notation is a point, based on a latitude & longitude using the WGS 84 (Degrees, Minutes, Seconds) system. Not very easy to use by application/code, this is why it is also possible to represent the exact same point using the following values for latitude & longitude:

48.5917, -2.5469

This one uses the WGS 84 (Decimal Degrees) system. This is the coordinates you see use in most of the application/API you are using as developer (eg: Google Maps/Earth for example)

By default GeoJSON, and MongoDB use these values but the coordinates must be stored in the longitude, latitude order, so this point in GeoJSON will look like:

{
  "type": "Point",
  "coordinates": [
    -2.5469,  
    48.5917 
  ]
}

This is a simple "Point", let's now for example look at a line, a very nice walk on the beach :

{
  "type": "LineString",
  "coordinates": [
    [-2.551082,48.5955632],
    [-2.551229,48.594312],
    [-2.551550,48.593312],
    [-2.552400,48.592312],
    [-2.553677, 48.590898]
  ]
}

So using the same approach you will be able to create MultiPoint, MultiLineString, Polygon, MultiPolygon. It is also possible to mix all these in a single document using a GeometryCollection. The following example is a Geometry Collection of MultiLineString and Polygon over Central Park:

{
  "type" : "GeometryCollection",
  "geometries" : [
    {
      "type" : "Polygon",
      "coordinates" : [
         [
	  [ -73.9580, 40.8003 ],
          [ -73.9498, 40.7968 ],
	  [ -73.9737, 40.7648 ],
	  [ -73.9814, 40.7681 ],
	  [ -73.9580, 40.8003  ]
	 ]
       ]
    },
    {
      "type" : "MultiLineString",
      "coordinates" : [
         [ [ -73.96943, 40.78519 ], [ -73.96082, 40.78095 ] ],
 	 [ [ -73.96415, 40.79229 ], [ -73.95544, 40.78854 ] ],
         [ [ -73.97162, 40.78205 ], [ -73.96374, 40.77715 ] ],
         [ [ -73.97880, 40.77247 ], [ -73.97036, 40.76811 ] ]
       ]
     }
  ]
}

Note: You can if you want test/visualize these JSON documents using the http://geojsonlint.com/ service. 

Now what? Let's store data!

Once you have a GeoJSON document you just need to store it into your document. For example if you want to store a document about JFK Airport with its location you can run the following command:

db.airports.insert(
  {
    "name" : "John F Kennedy Intl",
    "type" : "International",
    "code" : "JFK",
    "loc" : {
      "type" : "Point",
      "coordinates" : [ -73.778889, 40.639722 ]
    }
}

Yes this is that simple! You just save the GeoJSON as one of the attribute of the document, (loc in this example)

Querying Geospatial Informations

Now that we have the data stored in MongoDB, it is now possible to use the geospatial information to do some interesting queries. 

For this we need a sample dataset. I have created one using some open data found in various places. This dataset contains the following informations:

• airports collection with the list of US airport (Point)
• states collection with the list of US states (MultiPolygon)

I have created this dataset from various OpenData sources ( http://geocommons.com/ , http://catalog.data.gov/dataset ) and use toGeoJSON to convert them into the proper format.

Let's install the dataset:

1. Download it from here
2. Unzip geo.zip file
3. Restore the data into your mongoDB instance, using the following command
   mongorestore geo.zip

MongoDB allows applications to do the following types of query on geospatial data:

• inclusion
• intersection
• proximity

Obviously, you will be able to use all the other operator in addition to the geospatial ones. Let's now look at some concrete examples. 

Inclusion

Find all the airports in California. For this you need to get the California location (Polygon) and use the command $geoWithin in the query. From the shell it will look like :

use geo

var cal = db.states.findOne(  {code : "CA"}  );

db.airports.find( 
  { 
    loc : { $geoWithin : { $geometry : cal.loc } } 
  },
  { name : 1 , type : 1, code : 1, _id: 0 } 
);

Result:

{ "name" : "Modesto City - County", "type" : "", "code" : "MOD" }
...
{ "name" : "San Francisco Intl", "type" : "International", "code" : "SFO" }
{ "name" : "San Jose International", "type" : "International", "code" : "SJC" }
...

So the query is using the "California MultiPolygon" and looks in the airports collection to find all the airports that are in these polygons. This looks like the following image on a map:

You can use any other query features or criteria, for example you can limit the query to international airport only sorted by name :

db.airports.find( 
  { 
    loc : { $geoWithin : { $geometry : cal.loc } },
    type : "International" 
  },
  { name : 1 , type : 1, code : 1, _id: 0 } 
).sort({ name : 1 });

Result:

{ "name" : "Los Angeles Intl", "type" : "International", "code" : "LAX" }
{ "name" : "Metropolitan Oakland Intl", "type" : "International", "code" : "OAK" }
{ "name" : "Ontario Intl", "type" : "International", "code" : "ONT" }
{ "name" : "San Diego Intl", "type" : "International", "code" : "SAN" }
{ "name" : "San Francisco Intl", "type" : "International", "code" : "SFO" }
{ "name" : "San Jose International", "type" : "International", "code" : "SJC" }
{ "name" : "Southern California International", "type" : "International", "code" : "VCV" }

I do not know if you have looked in detail, but we are querying these documents with no index. You can run a query with the explain()to see what's going on. The $geoWithin operator does not need index but your queries will be more efficient with one so let's create the index:

db.airports.ensureIndex( { "loc" : "2dsphere" } );

Run the explain and you will se the difference.

Intersection

Suppose you want to know what are all the adjacent states to California, for this we just need to search for all the states that have coordinates that "intersects" with California. This is done with the following query:

var cal = db.states.findOne(  {code : "CA"}  );

db.states.find(

  { 

    loc : { $geoIntersects : { $geometry : cal.loc  }  } ,

    code : { $ne : "CA"  }  

  }, 

  { name : 1, code : 1 , _id : 0 } 

);

Result:

{ "name" : "Oregon", "code" : "OR" }
{ "name" : "Nevada", "code" : "NV" }
{ "name" : "Arizona", "code" : "AZ" }

Same as before $geoIntersect operator does not need an index to work, but it will be more efficient with the following index:

db.states.ensureIndex( { loc : "2dsphere" } );

Proximity

The last feature that I want to highlight in this post is related to query with proximity criteria. Let's find all the international airports that are located at less than 20km from the reservoir in NYC Central Park. For this you will be using the $near operator.

db.airports.find(
{
loc : {
$near : {
$geometry : { 
type : "Point" , 
coordinates : [-73.965355,40.782865]  
}, 
$maxDistance : 20000
}
}, 
type : "International"
},
{
name : 1,
code : 1,
_id : 0
}
);

Results:

{ "name" : "La Guardia", "code" : "LGA" } { "name" : "Newark Intl", "code" : "EWR"


So this query returns 2 airports, the closest being La Guardia, since the $near operator sorts the results by distance. Also it is important to raise here that the $near operator requires an index.

Conclusion

In this first post about geospatial feature you have learned:

• the basic of GeoJSON
• how to query documents with inclusion, intersection and proximity criteria.

You can now play more with this for example integrate this into an application that expose data into some UI, or see how you can use the geospatial operators into a aggregation pipeline.

db.person.find( { "role" : "DBA" } )

Wow! it has been a while since I posted something on my blog post. I have been very busy, moving to MongoDB, learning, learning, learning…finally I can breath a little and answer some questions.

Last week I have been helping my colleague Norberto to deliver a MongoDB Essentials Training in Paris. This was a very nice experience, and I am impatient to deliver it on my own. I was happy to see that the audience was well balanced between developers and operations, mostly DBA.

What! I still need DBA?

This is a good opportunity to raise a point, or comment a wrong idea: the fact that you are using MongoDB, or any other NoSQL datastore does not mean that you do not need a DBA… Like any project, an administrator is not mandatory, but if you have one it is better. So even when MongoDB is pushed by development team it is very important to understand the way the database works, and how to administer, monitor it.

If you are lucky enough to have real operations teams, with good system and database administrators use them! They are very important for your application.

Most DBA/System Administrators have been maintaining systems in production for many years. They know how to keep your application up and running. They also most of the time experienced many “disasters”, and then recover (I hope).

Who knows, you may encounter big issues with your application and you will be happy to have them on your side at this moment.

"Great, but the DBA is slowing down my development!"

I hear this, sometimes, and I had this feeling in the past to as developer in large organization. Is it true?

Developers and DBA are today, not living in the same worlds:

• Developers want to integrate new technologies as soon as possible, not only because it is fun and they can brag about it during meetups/conferences; but because these technologies, most of the time, are making them more productive, and offer better service/experience to the consumer 
• DBA, are here to keep the applications up and running! So every time they do not feel confident about a technology they will push back. I think this is natural and I would be probably the same in their position. Like all geeks, they would love to adopt new technologies but they need to understand and trust it before.

System administrators, DBAS look at the technology with a different angle than developers.

Based on this assumption, it is important to bring the operation team as early as possible when  the development team wants to integrate MongoDB or any new data store. Having the operation team in the loop early will ease the global adoption of MongoDB in the company.

Personally, and this will show my age, I have seen a big change in the way developers and DBAs are working together.

Back in the 90's, when the main architecture was based on client/server architecture  developers and DBAs where working pretty well togethers; probably because they were speaking the same language: SQL was everywhere.  I had regular meetings wit

Then, since mid 2000, mots of applications have moved to a web based architecture , with for example Java middleware, and the developers stopped working with DBAs. Probably because the abstraction data layer provided by the ORM exposed the database as a "commodity" service that is supposed to work: "Hey Mr DBA, my application has been written with the best middleware technology on the market, so now deal with the performance and scalability! I am done!"

Yes it is a cliché, but I am sure that some of you will recognize that.

Nevertheless each time I can, I have been pushing developers to talk more to administrators and look closely to their database!

A new era for operations and development teams

The fast adoption of MongoDB by developers, is a great opportunity to fix what we have broken 10 years ago in large information systems:

• Let's talk again!

MongoDB has been built first for developers. The document oriented approach gives lot of flexibility to quickly adapt to change. So anytime your business users need a new feature you can implement it, even if this change impact the data structure. Your data model is now driven and controlled by the application, not the database engine.

However, the applications still need to be available 24x7, and performs well. These topics are managed - and shared- by administrator and developers! This has been always the case but, as I described it earlier, it looks like some of us have forgotten that.

Schemas design, change velocity, are driven by the application, so the business and development teams, but all this impacts the database, for example:

• How storage will grow ?
• Which indexes must be created to speed up my application?
• How to organize my cluster to leverage the infrastructure properly:
• Replica-Set organization (and related write concerns, managed by developer)
• Sharding options
• And the most important of them : backup/recovery strategies

So many things that could be managed by the project team, but if you have an operation team with you, it will be better to do that as a single team.

You, the developer, are convinced that MongoDB is the best database for your projects! Now it is time to work with the ops team and convince them too.  So you should for sure explain why MongoDB is good for you as developer, but also you should highlight all the benefits for the operations, starting with built-in high-availability with replica sets, and easy scalability with sharding. MongoDB is also here to make the life of the administrator easier! I have shared in the next paragraph a lit of resources that are interesting for operations people.

Let’s repeat it another time, try to involve the operation team as soon as possible, and use that as an opportunity to build/rebuild the relationship between developers and system administrators!

Resources

You can find many good resources on the Site to helps operations or learn about this:

• Documentation : Operations
• Online Training :
• M102: MongoDB for DBAs
• M202: MongoDB Advanced Deployment and Operations
• And many others such as White Papers and Webinars.