#.driver.extraJavaOptions=-Dhdp.version=current history.fs.logDirectory=hdfs\:///kylin/spark-history eventLog.dir=hdfs\:///kylin/spark-history Below is the default configurations, which is also the minimal config for a sandbox (1 executor with 1GB memory) usually in a normal cluster, need much more executors and each has at least 4GB memory and 2 cores: These properties will be extracted and applied when runs submit Spark job E.g, if you configure “.mory=4G”, Kylin will use “–conf =4G” as parameter when execute “spark-submit”.īefore you run Spark cubing, suggest take a look on these configurations and do customization according to your cluster. Kylin embedes a Spark binary (v2.1.0) in $KYLIN_HOME/spark, all the Spark configurations can be managed in $KYLIN_HOME/conf/kylin.properties with prefix “-conf.”.
If this property isn’t set, Kylin will use the directory that “hive-site.xml” locates in while that folder may have no “hbase-site.xml”, will get HBase/ZK connection error in Spark. Converting works for list or tuple with shapely -conf-dir=/usr/local/apache-kylin-2.1.0-bin-hbase1x/hadoop-conf
To create Spark DataFrame based on mentioned Geometry types, please use GeometryType from module. plot ( figsize = ( 10, 8 ), column = "value", legend = True, cmap = 'YlOrBr', scheme = 'quantiles', edgecolor = 'lightgray' )Ĭreating Spark DataFrame based on shapely objects ¶ Supported Shapely objects ¶ shapely object GeoDataFrame ( df, geometry = "geometry" ) gdf. sql ( "SELECT *, st_geomFromWKT(geom) as geometry from county" ) df = counties_geom. createOrReplaceTempView ( "county" ) counties_geom = spark. Import geopandas as gpd from pyspark.sql import SparkSession from geospark.register import GeoSparkRegistrator spark = SparkSession. For documentation please look at GeoSpark websiteįor example use GeoSparkSQL for Spatial Join. Examples ¶ GeoSparkSQL ¶Īll GeoSparkSQL functions (list depends on GeoSparkSQL version) are available in Python API. To specify Schema with geometry inside please use GeometryType() instance (look at examples section to see that in practice). Based on GeoPandas DataFrame, Pandas DataFrame with shapely objects or Sequence with shapely objects, Spark DataFrame can be created using spark.createDataFrame method.
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To turn on GeoSparkSQL function inside pyspark code use GeoSparkRegistrator.registerAll method on existing instance ex.Īfter that all the functions from GeoSparkSQL will be available, moreover using collect or toPandas methods on Spark DataFrame will return Shapely BaseGeometry objects. If jars was not uploaded manually please use function upload_jars()
Use KryoSerializer.getName and GeoSparkKryoRegistrator.getName class properties to reduce memory impact, reffering to GeoSpark docs. :param spark:, spark session instanceįunction uses findspark Python module to upload newest GeoSpark jars to Spark executor and nodes.Ĭlass which handle serialization and deserialization between GeoSpark geometries and Shapely BaseGeometry types.Ĭlass property which returns .KryoSerializer string, which simplify using GeoSpark Serializers.Ĭlass property which returns .GeoSparkKryoRegistrator string, which simplify using GeoSpark Serializers. To check available functions please look at GeoSparkSQL section. Class method registers all GeoSparkSQL functions (available for used GeoSparkSQL version). GeoSparkRegistrator.registerAll(spark: ) -> bool Creating Spark DataFrame based on shapely objects
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