Tug’s Blog

My journey in Big Data, Hadoop, NoSQL and MapR

Convert CSV File to Apache Parquet… With Drill

| Comments

A very common use case when working with Hadoop is to store and query simple files (CSV, TSV, …); then to get better performance and efficient storage convert these files into more efficient format, for example Apache Parquet.

Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem. Apache Parquet has the following characteristics:

  • Self-describing
  • Columnar format
  • Language-independent

Let’s take a concrete example, you can find many interesting Open Data sources that distribute data as CSV files- or equivalent format-. So you can store them into your distributed file system and use them in your applications/jobs/analytics queries. This is not the most efficient way especially when we know that these data won’t move that often. So instead of simply storing the CSV let’s copy this information into Parquet.

How to convert CSV files into Parquet files?

You can use code to achieve this, as you can see in the ConvertUtils sample/test class. You can use a simpler way with Apache Drill. Drill allows you save the result of a query as Parquet files.

The following steps will show you how to do convert a simple CSV into a Parquet file using Drill.

Apache Drill : How to Create a New Function?

| Comments

Apache Drill allows users to explore any type of data using ANSI SQL. This is great, but Drill goes even further than that and allows you to create custom functions to extend the query engine. These custom functions have all the performance of any of the Drill primitive operations, but allowing that performance makes writing these functions a little trickier than you might expect.

In this article, I’ll explain step by step how to create and deploy a new function using a very basic example. Note that you can find lot of information about Drill Custom Functions in the documentation.

Let’s create a new function that allows you to mask some characters in a string, and let’s make it very simple. The new function will allow user to hide x number of characters from the start and replace then by any characters of their choice. This will look like:

1
MASK( 'PASSWORD' , '#' , 4 ) => ####WORD

You can find the full project in the following Github Repository.

As mentioned before, we could imagine many advanced features to this, but my goal is to focus on the steps to write a custom function, not so much on what the function does.

MongoDB : Playing With Arrays

| Comments

As you know, you have many differences between relational and document databases. The biggest, for the developer, is probably the data model: Row versus Document. This is particularly true when we talk about “relations” versus “embedded documents (or values)”. Let’s look at some examples, then see what are the various operations provided by MongoDB to help you to deal with this.

Introduction to MongoDB Security

| Comments

Last week at the Paris MUG, I had a quick chat about security and MongoDB, and I have decided to create this post that explains how to configure out of the box security available in MongoDB.

You can find all information about MongoDB Security in following documentation chapter:

In this post, I won’t go into the detail about how to deploy your database in a secured environment (DMZ/Network/IP/Location/…)

I will focus on Authentication and Authorization, and provide you the steps to secure the access to your database and data.

I have to mention that by default, when you install and start MongoDB, security is not enabled. Just to make it easier to work with.

The first part of the security is the Authentication, you have multiple choices documented here. Let’s focus on “MONGODB-CR” mechanism.

The second part is Authorization to select what a user can do or not once he is connected to the database. The documentation about authorization is available here.

Let’s now document how-to:

  1. Create an Administrator User
  2. Create Application Users

For each type of users I will show how to grant specific permissions.

Everybody Says “Hackathon”!

| Comments

TLTR:

  • MongoDB & Sage organized an internal Hackathon
  • We use the new X3 Platform based on MongoDB, Node.js and HTML to add cool features to the ERP
  • This shows that “any” enterprise can (should) do it to:
    • look differently at software development
    • build strong team spirit
    • have fun!

Introduction

I have like many of you participated to multiple Hackathons where developers, designer and entrepreneurs are working together to build applications in few hours/days. As you probably know more and more companies are running such events internally, it is the case for example at Facebook, Google, but also ING (bank), AXA (Insurance), and many more.

Last week, I have participated to the first Sage Hackathon!

In case you do not know Sage is a 30+ years old ERP vendor. I have to say that I could not imagine that coming from such company… Let me tell me more about it.

How to Create a Pub/sub Application With MongoDB ? Introduction

| Comments

In this article we will see how to create a pub/sub application (messaging, chat, notification), and this fully based on MongoDB (without any message broker like RabbitMQ, JMS, … ).

So, what needs to be done to achieve such thing:

  • an application “publish” a message. In our case, we simply save a document into MongoDB
  • another application, or thread, subscribe to these events and will received message automatically. In our case this means that the application should automatically receive newly created document out of MongoDB

All this is possible with some very cool MongoDB features: capped collections and tailable cursors,

Big Data… Is Hadoop the Good Way to Start?

| Comments

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

Introduction to MongoDB Geospatial Feature

| Comments

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:

1
2
3
4
5
6
7
{
  "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 :

1
2
3
4
5
6
7
8
9
10
{
  "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:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
{
  "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:

1
2
3
4
5
6
7
8
9
10
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
1
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 :

1
2
3
4
5
6
7
8
9
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:

1
2
3
4
5
{ "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 :

1
2
3
4
5
6
7
db.airports.find(
{
  loc : { $geoWithin : { $geometry : cal.loc } },
  type : "International"
},
{ name : 1 , type : 1, code : 1, _id: 0 }
).sort({ name : 1 });

Result:

1
2
3
4
5
6
7
{ "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:

1
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:

1
2
3
4
5
6
7
8
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:

1
2
3
{ "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:

1
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.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
db.airports.find(
{
  loc : {
    $near : {
      $geometry : {
        type : "Point" ,
        coordinates : [-73.965355,40.782865]
      },
      $maxDistance : 20000
    }
  },
  type : "International"
},
{
  name : 1,
  code : 1,
  _id : 0
}
);

Results:

1
2
{ "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.