GeoMesa FileSystem Quick Start

This tutorial is the fastest and easiest way to get started with GeoMesa using the FileSytem data store (FSDS). The FSDS lets you store and query data without any database instance, using flat files. These files can be stored in HDFS, AWS, or even your local disk.

It is a good stepping-stone on the path to the other tutorials, that present increasingly involved examples of how to use GeoMesa.

About this Tutorial

In the spirit of keeping things simple, the code in this tutorial only does a few small things:

  1. Establishes a new (static) SimpleFeatureType

  2. Prepares the file system to store this type of data

  3. Creates a few thousand example SimpleFeatures

  4. Writes these SimpleFeatures to the file system

  5. Queries for a given geographic rectangle, time range, and attribute filter, writing out the entries in the result set

  6. Uses GeoServer to visualize the data (optional)


Before you begin, you must have the following installed and configured:

  • Java JDK 1.8

  • Apache Maven 3.6 or later

  • a GitHub client

Download and Build the Tutorial

Pick a reasonable directory on your machine, and run:

$ git clone
$ cd geomesa-tutorials


Make sure that you download or checkout the version of the tutorials project that corresponds to your GeoMesa version. See About Tutorial Versions for more details.

To ensure that the quick start works with your environment, modify the pom.xml to set the appropriate versions for Hadoop, etc.

For ease of use, the project builds a bundled artifact that contains all the required dependencies in a single JAR. To build, run:

$ mvn clean install -pl geomesa-tutorials-fsds/geomesa-tutorials-fsds-quickstart -am

Running the Tutorial

On the command line, run:

$ java -cp geomesa-tutorials-fsds/geomesa-tutorials-fsds-quickstart/target/geomesa-tutorials-fsds-quickstart-$VERSION.jar \
    org.geomesa.example.fsds.FileSystemQuickStart \
    --fs.path /tmp/fsds/                          \
    --fs.encoding parquet

The arguments above indicate:

  • fs.path the FSDS storage path. Each simple feature type will produce a sub-directory here.

  • fs.encoding the file storage encoding. The quick start comes pre-configured to use Apache’s Parquet encoding.

Once run, you should see the following output:

Loading datastore

Creating schema: GLOBALEVENTID:String,Actor1Name:String,Actor1CountryCode:String,Actor2Name:String,Actor2CountryCode:String,EventCode:String,NumMentions:Integer,NumSources:Integer,NumArticles:Integer,ActionGeo_Type:Integer,ActionGeo_FullName:String,ActionGeo_CountryCode:String,dtg:Date,geom:Point:srid=4326

Generating test data

Writing test data
Wrote 2356 features

Running test queries
Running query BBOX(geom, -120.0,30.0,-75.0,55.0) AND dtg DURING 2017-12-31T00:00:00+00:00/2018-01-02T00:00:00+00:00
01 719024913=719024913|||VICTORIA|AUS|173|2|1|2|4|Victoria Harbour, Ontario, Canada|CA|2018-01-01T00:00:00.000Z|POINT (-79.7667 44.75)
02 719024918=719024918|||EMPLOYER||020|10|1|10|3|Brooklyn, New York, United States|US|2018-01-01T00:00:00.000Z|POINT (-75.1704 42.3442)
03 719024923=719024923|||INDUSTRY||084|3|1|3|2|Pennsylvania, United States|US|2018-01-01T00:00:00.000Z|POINT (-77.264 40.5773)
04 719024924=719024924|||CORPORATION||115|2|1|2|4|Saskatoon, Saskatchewan, Canada|CA|2018-01-01T00:00:00.000Z|POINT (-106.667 52.1333)
05 719024925=719024925|||CORPORATION||172|2|1|2|4|Saskatoon, Saskatchewan, Canada|CA|2018-01-01T00:00:00.000Z|POINT (-106.667 52.1333)
06 719024927=719024927|||CANADIAN|CAN|100|24|2|14|4|Ottawa, Ontario, Canada|CA|2018-01-01T00:00:00.000Z|POINT (-75.7 45.4167)
07 719024928=719024928|||CANADIAN|CAN|100|6|1|6|1|United States|US|2018-01-01T00:00:00.000Z|POINT (-98.5795 39.828175)
08 719024929=719024929|||CANADA|CAN|190|4|1|4|4|Okanagan, British Columbia, Canada|CA|2018-01-01T00:00:00.000Z|POINT (-119.35 50.3667)
09 719024933=719024933|||POLICE||020|48|12|24|3|Wichita, Kansas, United States|US|2018-01-01T00:00:00.000Z|POINT (-97.3375 37.6922)
10 719024934=719024934|||POLICE||036|10|1|10|3|Bosque County, Texas, United States|US|2018-01-01T00:00:00.000Z|POINT (-97.6503 31.9002)

Returned 669 total features

Running query BBOX(geom, -120.0,30.0,-75.0,55.0) AND dtg DURING 2017-12-31T00:00:00+00:00/2018-01-02T00:00:00+00:00
Returning attributes [GLOBALEVENTID, dtg, geom]
01 719024913=719024913|2018-01-01T00:00:00.000Z|POINT (-79.7667 44.75)
02 719024918=719024918|2018-01-01T00:00:00.000Z|POINT (-75.1704 42.3442)
03 719024923=719024923|2018-01-01T00:00:00.000Z|POINT (-77.264 40.5773)
04 719024924=719024924|2018-01-01T00:00:00.000Z|POINT (-106.667 52.1333)
05 719024925=719024925|2018-01-01T00:00:00.000Z|POINT (-106.667 52.1333)
06 719024927=719024927|2018-01-01T00:00:00.000Z|POINT (-75.7 45.4167)
07 719024928=719024928|2018-01-01T00:00:00.000Z|POINT (-98.5795 39.828175)
08 719024929=719024929|2018-01-01T00:00:00.000Z|POINT (-119.35 50.3667)
09 719024933=719024933|2018-01-01T00:00:00.000Z|POINT (-97.3375 37.6922)
10 719024934=719024934|2018-01-01T00:00:00.000Z|POINT (-97.6503 31.9002)

Returned 669 total features

Running query EventCode = '051'
01 719024909=719024909|||MELBOURNE|AUS|051|10|1|10|4|Melbourne, Victoria, Australia|AS|2018-01-01T00:00:00.000Z|POINT (144.967 -37.8167)
02 719024963=719024963|||CITIZEN||051|6|2|6|4|City Of Sydney, New South Wales, Australia|AS|2018-01-01T00:00:00.000Z|POINT (151.217 -33.8833)
03 719025168=719025168|AUSTRALIAN|AUS|||051|18|1|10|4|Sydney, New South Wales, Australia|AS|2018-01-01T00:00:00.000Z|POINT (151.217 -33.8833)
04 719025178=719025178|AUSTRALIA|AUS|COMMUNITY||051|20|2|20|4|Sydney, New South Wales, Australia|AS|2018-01-01T00:00:00.000Z|POINT (151.217 -33.8833)
05 719025248=719025248|BUSINESS||||051|10|1|10|1|Australia|AS|2018-01-01T00:00:00.000Z|POINT (135 -25)
06 719025509=719025509|COMMUNITY||AUSTRALIA|AUS|051|2|1|2|1|Australia|AS|2018-01-01T00:00:00.000Z|POINT (135 -25)
07 719025555=719025555|DENMARK|DNK|||051|2|1|2|1|Australia|AS|2018-01-01T00:00:00.000Z|POINT (135 -25)
08 719025634=719025634|FIJI|FJI|||051|2|1|2|1|Fiji|FJ|2018-01-01T00:00:00.000Z|POINT (178 -18)
09 719025965=719025965|MIDWIFE||||051|10|1|10|4|Sydney, New South Wales, Australia|AS|2018-01-01T00:00:00.000Z|POINT (151.217 -33.8833)
10 719025036=719025036|||SENATE||051|5|1|5|2|Alabama, United States|US|2018-01-01T00:00:00.000Z|POINT (-86.8073 32.799)

Returned 138 total features

Running query EventCode = '051' AND dtg DURING 2017-12-31T00:00:00+00:00/2018-01-02T00:00:00+00:00
Returning attributes [GLOBALEVENTID, dtg, geom]
01 719024909=719024909|2018-01-01T00:00:00.000Z|POINT (144.967 -37.8167)
02 719024963=719024963|2018-01-01T00:00:00.000Z|POINT (151.217 -33.8833)
03 719025168=719025168|2018-01-01T00:00:00.000Z|POINT (151.217 -33.8833)
04 719025178=719025178|2018-01-01T00:00:00.000Z|POINT (151.217 -33.8833)
05 719025248=719025248|2018-01-01T00:00:00.000Z|POINT (135 -25)
06 719025509=719025509|2018-01-01T00:00:00.000Z|POINT (135 -25)
07 719025555=719025555|2018-01-01T00:00:00.000Z|POINT (135 -25)
08 719025634=719025634|2018-01-01T00:00:00.000Z|POINT (178 -18)
09 719025965=719025965|2018-01-01T00:00:00.000Z|POINT (151.217 -33.8833)
10 719024994=719024994|2018-01-01T00:00:00.000Z|POINT (79.25 30.25)

Returned 138 total features

Cleaning up test data

Looking at the Code

The source code is meant to be accessible for this tutorial. The main logic is contained in the generic org.geomesa.example.quickstart.GeoMesaQuickStart in the geomesa-tutorials-common module, which is datastore agnostic. Some relevant methods are:

  • createDataStore get a datastore instance from the input configuration

  • createSchema create the schema in the datastore, as a pre-requisite to writing data

  • writeFeatures use a FeatureWriter to write features to the datastore

  • queryFeatures run several queries against the datastore

  • cleanup delete the datastore directory and dispose of the datastore instance.

The quick start uses a small subset of GDELT data. Code for parsing the data into GeoTools SimpleFeatures is contained in

  • getSimpleFeatureType creates the SimpleFeatureType representing the data

  • getTestData parses an embedded TSV file to create SimpleFeature objects

  • getTestQueries illustrates several different query types, using CQL (GeoTools’ Contextual Query Language)

Visualize Data (optional)

There are two options to visual the data ingested by this quick start. The easiest option is to use the export command of the GeoMesa FSDS tools distribution. For a more production ready example, you can alternatively stand up a GeoServer and connect it to your FSDS instance.

Visualize Data With Leaflet


To successfully run this command you must have a computer that is connected to the internet in order to access external Leaflet resources.

The export command is a part of the GeoMesa FSDS command-line tools. In order to use the command, ensure you have the command-line tools installed as described in Setting up the FileSystem Command Line Tools. The export command provides the leaflet format which will export the features to a Leaflet map that you can open in your web browser. To produce the map, run the following command from the GeoMesa FSDS tools distribution directory:

bin/geomesa-fs export               \
    --feature-name gdelt-quickstart \
    --output-format leaflet         \
    --path /tmp/fsds/

Where the connection parameters are the same you used above during the quickstart. To view the map simply open the url provided by the command in your web browser. If you click the menu in the upper right of the map you can enable and disable the heatmap and feature layers as well as the two provided base layers.

Visualizing quick-start data with Leaflet

Visualizing quick-start data with Leaflet

Visualize Data With GeoServer

You can use GeoServer to access and visualize the data stored in GeoMesa. In order to use GeoServer, download and install version 2.22.2. Then follow the instructions in Installing GeoMesa FileSystem in GeoServer to enable GeoMesa.

Register the GeoMesa Store with GeoServer

Log into GeoServer using your user and password credentials. Click “Stores” and “Add new Store”. Select the FileSystem (GeoMesa) vector data source, and fill in the required parameters.

Basic store info:

  • workspace this is dependent upon your GeoServer installation

  • data source name pick a sensible name, such as geomesa_quick_start

  • description this is strictly decorative; GeoMesa quick start

Connection parameters:

  • these are the same parameter values that you supplied on the command line when you ran the tutorial; they describe how to connect to the FSDS location where your data reside

Click “Save”, and GeoServer will search your FSDS directory for any GeoMesa-managed feature types.

Publish the Layer

GeoServer should recognize the gdelt-quickstart feature type, and should present that as a layer that can be published. Click on the “Publish” link.

You will be taken to the “Edit Layer” screen. You will need to enter values for the data bounding boxes. In this case, you can click on the link to compute these values from the data.

Click on the “Save” button when you are done.

Take a Look

Click on the “Layer Preview” link in the left-hand gutter. If you don’t see the quick-start layer on the first page of results, enter the name of the layer you just created into the search box, and press <Enter>.

Once you see your layer, click on the “OpenLayers” link, which will open a new tab. You should see a collection of red dots similar to the following image:

Visualizing quick-start data with GeoServer

Visualizing quick-start data with GeoServer

Tweaking the display

Here are just a few simple ways you can play with the visualization:

  • Click on one of the red points in the display, and GeoServer will report the detail records underneath the map area.

  • Shift-click to highlight a region within the map that you would like to zoom into.

  • Click on the “Toggle options toolbar” icon in the upper-left corner of the preview window. The right-hand side of the screen will include a “Filter” text box. Enter EventCode = '051', and press on the “play” icon. The display will now show only those points matching your filter criterion. This is a CQL filter, which can be constructed in various ways to query your data. You can find more information about CQL from GeoServer’s CQL tutorial.