22.6. GeoMesa PySpark¶

GeoMesa provides support for working with data stored in Accumulo using the Spark Python API.

22.6.1. Prerequisites¶

Spark 2.0 should be installed.
Python 2.7 or 3.x should be installed.
pip or pip3 should be installed.

22.6.2. Installation¶

The geomesa_pyspark package is not available for download. Build the artifact locally with the profile -Ppython. Then install using pip or pip3 as below. You will also need to put the geomesa-accumulo-spark-runtime JAR in a convenient location. This JAR bundles together the client libraries for GeoMesa Spark.

mvn clean install -Ppython
pip3 install geomesa-spark/geomesa_pyspark/target/geomesa_pyspark-$VERSION.tar.gz
cp  geomesa-accumulo/geomesa-accumulo-spark-runtime/target/geomesa-accumulo-spark-runtime_2.11-$VERSION.jar /path/to/

22.6.3. Using Geomesa PySpark¶

You may then access Spark using a Yarn master by default. Importantly, because of the way the geomesa_pyspark library interacts with the underlying Java libraries, you must set up the GeoMesa configuration before referencing the pyspark library.

import geomesa_pyspark
conf = geomesa_pyspark.configure(
    jars=['/path/to/geomesa-accumulo-spark-runtime_2.11-$VERSION.jar'],
    packages=['geomesa_pyspark','pytz'],
    spark_home='/path/to/spark/').\
    setAppName('MyTestApp')

conf.get('spark.master')
# u'yarn'

from pyspark.sql import SparkSession

spark = ( SparkSession
    .builder
    .config(conf=conf)
    .enableHiveSupport()
    .getOrCreate()
)

At this point you are ready to create a dict of connection parameters to your Accumulo data store and get a spatial data frame.

params = {
    "instanceId": "myInstance",
    "zookeepers": "zoo1,zoo2,zoo3",
    "user": "user",
    "password": "password",
    "tableName": "myCatalog"
}
feature = "mySchema"
df = ( spark
    .read
    .format("geomesa")
    .options(**params)
    .option("geomesa.feature", feature)
    .load()
)

df.createOrReplaceTempView("tbl")
spark.sql("show tables").show()

# Count features in a bounding box.
spark.sql("""
select count(*)
from tbl
where st_contains(st_makeBBOX(-72.0, 40.0, -71.0, 41.0), geom)
""").show()

You can terminate the Spark job on YARN using spark.stop().

22.6.4. Jupyter¶

To use the geomesa_pyspark package within Jupyter, you only needs a Python2 or Python3 kernel, which is provided by default. Substitute the appropriate Spark home and runtime JAR paths in the above code blocks. Be sure the GeoMesa Accumulo client and server side versions match, as described in Installing GeoMesa Accumulo.