GeoMesa Spark: Basic Analysis¶

This tutorial will show you how to:

Use GeoMesa with Apache Spark.
Write custom Scala code for GeoMesa to generate histograms and spatial densities of GDELT event data.

Background¶

Apache Spark is a “fast and general engine for large-scale data processing”. Spark presents an abstraction called a Resilient Distributed Dataset (RDD) that facilitates expressing transformations, filters, and aggregations, and efficiently executes the computation across a distributed set of resources. Spark manages the lineage of a block of transformed data so that if a node goes down, Spark can restart the computation for just the missing blocks.

GeoMesa has support for executing Spark jobs over data stored in GeoMesa. You can initialize a Spark RDD using standard CQL queries and by passing standard CQL functions to transform the data. In the spirit of the obligatory Word Count map-reduce example, we demonstrate two geospatial spins on word count. First, counting features by time resolution to compute a time series of spatial data, and second, aggregating by grid cell to rapidly generate density plots. Apache Spark enables us to express these transformations easily and succinctly.

Prerequisites¶

Warning

You will need access to a Hadoop 2.6 or later installation with Yarn as well as an Accumulo 1.9.1 or later database.

You will need to have ingested GDELT data using GeoMesa. Instructions are available in Map-Reduce Ingest of GDELT.

You will also need:

a Spark 2.0.0 or later distribution
an Accumulo user that has appropriate permissions to query your data
Java JDK 8,
Apache Maven 3.5.2 or later, and
a git client

The tutorial example below presumes that Spark is installed and configured, and that a cluster with Hadoop and Yarn is running. We will use spark-submit to run our jobs on the cluster.

Set Up Tutorial Code¶

Clone the geomesa-tutorials project, and go into the geomesa-examples-spark directory:

$ git clone https://github.com/geomesa/geomesa-tutorials.git $ cd geomesa-tutorials/geomesa-examples-spark

Note

The code in this tutorial is written in Scala.

Count Events by Day of Year¶

You will need to have ingested some GDELT data into Accumulo with GeoMesa, as described in Map-Reduce Ingest of GDELT or Global Database of Events, Language, and Tone (GDELT).

The com.example.geomesa.spark.CountByDay class in the src/main/scala directory is a self-contained example that may be submitted to Spark as an example of analysis. The libraries used are described in the GeoMesa Spark chapter of the GeoMesa manual. We describe this example code below.

Example Code¶

First, we set our connection parameters– you will need to modify these settings to match the configuration of your cluster:

val params = Map(
  "instanceId" -> "instance",
  "zookeepers" -> "zoo1,zoo2,zoo3",
  "user"       -> "user",
  "password"   -> "*****",
  "auths"      -> "USER,ADMIN",
  "tableName"  -> "geomesa.catalog")

We also define an ECQL filter used to select a subset of GDELT data from the GeoMesa Accumulo data store. The value of during should also be edited to match the range of GDELT data that you have ingested.

// Define a GeoTools Filter here
val typeName = "gdelt"
val geom     = "geom"
val date     = "dtg"

val bbox   = "-80, 35, -79, 36"
val during = "2014-01-01T00:00:00.000Z/2014-01-31T12:00:00.000Z"

val filter = s"bbox($geom, $bbox) AND $date during $during"

Within the main() method for the class, we create an AccumuloDataStore:

// Get a handle to the data store
val ds = DataStoreFinder.getDataStore(params).asInstanceOf[AccumuloDataStore]

and create the GeoTools Filter from the ECQL:

// Construct a CQL query to filter by bounding box
val q = new Query(typeName, ECQL.toFilter(filter))

We set up Spark:

// Configure Spark
val conf = new SparkConf().setAppName("testSpark")
val sc = SparkContext.getOrCreate(conf)

The GeoMesaSpark object provided by the geomesa-spark-core module uses the SPI to find an implementation of the SpatialRDDProvider interface. In this case, this will be an instance of AccumuloSpatialRDDProvider from the geomesa-accumulo-spark module, which will connect to Accumulo with the parameters provided. (For more information on this interface, see Spark Core in the GeoMesa manual.)

// Get the appropriate spatial RDD provider
val spatialRDDProvider = GeoMesaSpark(params)

Next, initialize an RDD[SimpleFeature] using this provider:

// Get an RDD[SimpleFeature] from the spatial RDD provider
val queryRDD = spatialRDDProvider.rdd(new Configuration, sc, params, q)

Finally, we construct our computation which consists of extracting the SQLDATE from each SimpleFeature and truncating it to the day resolution.

val dayAndFeature = queryRDD.mapPartitions { iter =>
  val df = new SimpleDateFormat("yyyyMMdd")
  val ff = CommonFactoryFinder.getFilterFactory2
  val exp = ff.property("SQLDATE")
  iter.map { f => (df.format(exp.evaluate(f).asInstanceOf[java.util.Date]), f) }
}

Then, we group by the day and count up the number of events in each group.

val countByDay = dayAndFeature.map( x => (x._1, 1)).reduceByKey(_ + _)
countByDay.collect().foreach(println)

Run the Tutorial Code¶

If you have not already done so, modify the CountByDay.scala class so that the parameter map points to your cloud instance, and ensure that the filter covers a valid range of your GDELT data.

Build (or rebuild) the JAR. This example JAR is shaded and will contain all of JARs needed to run the appropriate analysis in Spark.

$ mvn clean install

We can submit the job to our Yarn cluster using spark-submit:

$ /path/to/spark/bin/spark-submit --master yarn \
  --class com.example.geomesa.spark.CountByDay \
  target/geomesa-examples-spark-$VERSION.jar

Note

Note that $VERSION is the geomesa-tutorials version, not the GeoMesa version.

Alternatively, you may run the Spark job locally by setting --master 'local[*]. You should see a lot of Spark logging, and then the counts:

(20140117,57)
(20140120,38)
(20140113,407)
...

Parallel Computation of Spatial Event Densities¶

In the second demonstration, we compute densities of our feature by discretizing the spatial domain and counting occurrences of the feature in each grid cell. We use GeoHashes as our discretization of the world so that we can configure the resolution of our density by setting the number of bits in the GeoHash.

This code is does not exist in GeoMesa; it’s left as an exercise for the reader.

First, start with a similar RDD[SimpleFeature] as before but expand the bounding box.

val f = ff.bbox("geom", -180, -90, 180, 90, "EPSG:4326")
val q = new Query("gdelt", f)

val queryRDD = spatialRDDProvider.rdd(new Configuration, sc, params, q, None)

Project (in the relational sense) the SimpleFeature to a 2-tuple of (GeoHash, 1).

val discretized = queryRDD.map { f =>
   (geomesa.utils.geohash.GeoHash(f.getDefaultGeometry.asInstanceOf[Point], 25), 1)
}

Then, group by grid cell and count the number of features per cell.

val density = discretized.reduceByKey(_ + _)
density.collect.foreach(println)

The resulting density plot is visualized below.