Spark and GeoTrellis

This documentation series describes the use of the vast geotrellis.spark module.

On Distributed Computation


Distributed computing is difficult to get right. Luckily, we are able to lean on the RDD abstraction provided by Apache Spark to simplify matters somewhat. Still, the conceptual difficulties in geotrellis.spark are arguably as great as can be found in any part of the GeoTrellis library. As such, the discussion in this portion of the documentation assumes a passing familiarity with the key concepts of geotrellis.raster. If this is a difficulty, please refer to the documentation for the geotrellis.raster package.

Consider the (relatively) simple case of carrying out local addition on two raster tiles. In the abstract, this merely involves adding together corresponding values from two different Tiles. Practically, things can quickly become more complex: what if one Tile‘s data covers a larger extent than the other? In general, how do we determine what ‘corresponding values’ means in such a context? (Some specifics related to this question are covered in the geotrellis.spark documentation on joins)

What we need, then, is to pass around tiles as well as some kind of associated data. In addition, the Tile abstraction makes sense only in a particular place (in space and/or time) - the data in my Tile represents the elevation of terrain in this or that actual place which has such and such spatial relations to other Tiles that represent neighboring terrain elevations. If your application for finding directions displayed street data for Beijing in the middle of downtown Philadelphia, it would be extremely difficult to actually use. From the perspective of application performance during spatially-aware computations (say, for instance, that I want to compute the average elevation for every Tile‘s cell within a five mile radius of a target location) it is also useful to have an index which provides a sort of shortcut for tracking down the relevant information.

The need for intelligently indexed tiles is especially great when thinking about distributing work over those tiles across multiple computers. The tightest bottleneck in such a configuration is the communication between different nodes in the network. What follows is that reducing the likelihood of communication between nodes is one of the best ways to improve performance. Having intelligently indexed tilesets allows us to partition data according to expectations about which Tiles each node will require to calculate its results.

Hopefully you’re convinced that for a wide variety of GeoTrellis use-cases it makes sense to pass around tiles with indices to which they correspond as well as metadata. This set of concerns is encoded in the type system as RDD[(K, V)] with Metadata[M].

For more information on this type, see Tile Layers.

Writing Layers

The underlying purpose of is to provide reading and writing capability for instances of RDD[(K, V)] with Metadata[M] into one of the distributed storage formats.

GeoTrellis provides an abstraction for writing layers, LayerWriter, that the various backends implement. There are a set of overloads that you can call when writing layers, but generally you need to have the target LayerId that you will be writing to, and the RDD[(K, V)] with Metadata[M] that you want to write. Note that the K, V, and M concrete types need to have all of the context bounds satisfied; see the method signature in code or look to the implicit argument list in the ScalaDocs to find what the context bounds are (although if you are not using custom types, on the required imports should be necessary to satisfy these conditions). The overloaded methods allow you to optionally specify how the key index will be created, or to supply your own KeyIndex.

Key Index

A KeyIndex determines how your N-dimensional key (the K in RDD[(K, V)] with Metadtaa[M]) will be translated to a space filling curve index, represented by a Long. It also determines how N-dimensional queries (represented by KeyBounds with some minimum key and maximum key) will translate to a set of ranges of Long index values.

There are two types of key indexes that GeoTrellis supports, which represent the two types of space filling curves supported: Z-order Curves and Hilbert Curves. The Z-order curves can be used for 2 and 3 dimensional spaces (e.g. those represented by SpatialKeys or SpaceTimeKeys). Hilbert curves can represent N-dimensions, although there is currently a limitation in place that requires the index to fit into a single Long value.

In order to index the space of an RDD[(K, V)] with Metadata[M], we need to know the bounds of the space, as well as the index method to use.

The LayerWriter methods that do not take a KeyIndex will derive the bounds of the layer to be written by the layer itself. This is fine if the layer elements span the entire space that the layer will ever need to write to. If you have a larger space that represents the layer, for instance if you want to write elements to the layer that will be outside the bounds of the original layer RDD, you will need to create a KeyIndex manually that represents the entire area of the space.

For example, say we have a spatio-temporal raster layer that only contains elements that partially inhabit the date range for which we will want the layer to encompass. We can use the TileLayout from the layer in combination with a date range that we know to be sufficient, and create a key index.

import geotrellis.raster.Tile
import geotrellis.spark._
import geotrellis.util._
import org.apache.spark.rdd.RDD
import org.joda.time.DateTime

val layer: RDD[(SpaceTimeKey, Tile)] with Metadata[TileLayerMetadata[SpaceTimeKey]] = ???

// Create the key index with our date range
val minDate: DateTime = new DateTime(2010, 12, 1, 0, 0)
val maxDate: DateTime = new DateTime(2010, 12, 1, 0, 0)

val indexKeyBounds: KeyBounds[SpaceTimeKey] = {
  val KeyBounds(minKey, maxKey) = layer.metadata.bounds.get // assuming non-empty layer

val keyIndex =

val writer: LayerWriter[LayerId] = ???
val layerId: LayerId = ???

writer.write(layerId, layer, keyIndex)

Reindexing Layers

If a layer was written with bounds on a key index that needs to be expanded, you can reindex that layer. The LayerReindexer implementation of the backend you are using can be passed in a KeyIndex, which can be constructed similarly to the example above.

Reading Layers

Layer readers read all or part of a persisted layer back into RDD[(K, V)] with Metadata[M]. All layer readers extend the FilteringLayerReader trait which in turn extends LayerReader. The former type should be used when abstracting over the specific back-end implementation of a reader with region query support, and the latter when referring to a reader that may only read the layers fully.

In order to read a layer correctly some metadata regarding the type and format of the values must be stored as well as metadata regarding layer properties. All layer readers lean on instances of AttributeStore to provide this functionality. As a convenience each concrete type of a LayerReader will provide a constructor that will instantiate an AttributeStore of the same type with reasonable defaults. For instance S3LayerReader constructor, which requires S3 bucket and prefix parameters, would instantiate an S3AttributeStore with the bucket and prefix.


import geotrellis.raster._
import geotrellis.spark._

val reader: FilteringLayerReader[LayerId] = S3LayerReader("my-bucket", "catalog-prefix")

val rdd: RDD[(SpatialKey, Tile)] with Metadata[TileLayerMetadata[SpatialKey]] =[SpatialKey, Tile, TileLayerMetadata[SpatialKey]](LayerId("NLCD", 10))

Type signature of rdd variable can be inferred from the assignment and may be omitted but the type parameters for the read method can not be inferred and are required. Furthermore, the method will use these explicitly provided type parameters to find implicit type class instances that will allow it to read records of that format.

It’s important to note that as a result of call to some IO will happen right away in order to read the layer attributes from the AttributeStore. However, the result of the call is an RDD, a description of the distributed collection at some point in the future. Consequently the distributed store (like HDFS or S3) will not touched until some spark “action” is called on either rdd or one of it’s decedents.

But what happens when IO gremlins strike and the type of the record stored does not match the type parameter? It depends. The layer reader will do its best to read the layer as instructed, possibly failing. Most likely this effort will result in org.apache.avro.AvroTypeException if the Avro schema of the specified value does not match the schema of the stored value or a spray.json.DeserializationException if the JSON format of the metadata does not match the JSON value stored in the AttributeStore. This behavior is somewhat unhelpful but it future proofs the persisted data in so far that records may be reified into types that differ from their original implementations and names, as long as correct their formats are specified correctly for the records written.

If the type of the layer can not be assumed to be known it is possible to inspect the layer through reader.attributeStore field.

val header = reader.attributeStore.readHeader[LayerHeader]
assert(header.keyClass == "geotrellis.spark.SpatialKey")
assert(header.valueClass == "geotrellis.raster.Tile")


In addition to there exists a reader.reader method defined as follows:

def reader[
  K: AvroRecordCodec: Boundable: JsonFormat: ClassTag,
  V: AvroRecordCodec: ClassTag,
  M: JsonFormat: GetComponent[?, Bounds[K]]
]: Reader[ID, RDD[(K, V)] with Metadata[M]] =
  new Reader[ID, RDD[(K, V)] with Metadata[M]] {
    def read(id: ID): RDD[(K, V)] with Metadata[M] =[K, V, M](id)

In effect we would be using a reader to produce a reader, but critically the read method on the constructed reader does not have any type class parameters. This is essentially a way to close over all of the formats for K, V, and M such that a “clean” reader can be passed to modules where those formats are not available in the implicit scope.


import geotrellis.vector._

val reader: FilteringLayerReader[LayerId] = S3LayerReader("my-bucket", "catalog-prefix")
val layerId = LayerId("NLCD", 10)

val rdd: RDD[(SpatialKey, Tile)] with Metadata[TileLayerMetadata[SpatialKey]] =
    .query[SpatialKey, Tile, TileLayerMetadata[SpatialKey]](LayerId("NLCD", 10))


When using the .query method the expected return types must still be provided just like we did when calling .read, however instead of producing an RDD it produced an instance of LayerQuery which is essentially a query builder in a fluent style, allowing for multiple ‘.where’ clauses to be specified. Only when .result is called will an RDD object be produced. When multiple .where clauses are used, the query specified their intersection.

This behavior allows us to build queries that filter on space and time independently.

import org.joda.time.DateTime

val time1: DateTime = ???
val time2: DateTime = ???

val rdd: RDD[(SpaceTimeKey, Tile)] with Metadata[TileLayerMetadata[SpaceTimeKey]] =
    .query[SpaceTimeKey, Tile, TileLayerMetadata[SpaceTimeKey]](LayerId("Climate_CCSM4-RCP45-Temperature-Max", 8))
    .where(Between(time1, time2))

Other query filters are supported through the LayerFilter type class. Implemented instances include:

  • Contains: Tile which contains a point
  • Between: Tiles between two dates
  • At: Tiles at a a specific date
  • Intersects: Tiles intersecting ...
  • KeyBounds
  • GridBounds
  • Extent
  • Polygon

Value Readers

Unlike layer readers, which produce a future distributed collection, an RDD, a tile reader for a layer is essentially a reader provider. The provided reader is able to read a single value from a specified layer.

import geotrellis.raster._
import geotrellis.spark._

val attributeStore = S3AttributeStore("my-bucket", "catalog-prefix")
val nlcdReader: Reader[SpatialKey, Tile] = S3ValueReader[SpatialKey, Tile](attributeStore, LayerId("NLCD", 10))
val tile: Tile =,2))

ValueReader class is very useful for creating an endpoint for a tile server because it both provides a cheap low latency access to saved tiles and does not require an instance of SparkContext to operate.

If you wish to abstract over the backend specific arguments but delay specification of the key and value types you may use an alternative constructor like os:

val attributeStore = S3AttributeStore("my-bucket", "catalog-prefix")
val readerProvider: ValueReader[LayerId] = S3ValueReader(attributeStore)
val nlcdReader: Reader[SpatialKey, Tile] = readerProvider.reader[SpatialKey, Tile](LayerId("NLCD", 10))
val tile: Tile =,2))

The idea is similar to the LayerReader.reader method except in this case we’re producing a reader for single tiles. Additionally it must be noted that the layer metadata is accessed during the construction of the Reader[SpatialKey, Tile] and saved for all future calls to read a tile.

Reader Threads

Cassandra and S3 Layer RDDReaders / RDDWriters are configurable by threads amount. It’s a programm setting, that can be different for a certain machine (depends on resources available). Configuration could be set in the reference.conf / application.conf file of your app, default settings available in a reference.conf file of each backend subproject (we use TypeSafe Config). For a File backend only RDDReader is configurable, For Accumulo - only RDDWriter (Socket Strategy). For all backends CollectionReaders are configurable as well. By default thread pool size per each configurable reader / writer equals by virtual machine cpu cores available. Word default means thread per cpu core, it can be changed to any integer value.

Default configuration example:

geotrellis.accumulo.threads { = default
  rdd.write       = default
geotrellis.file.threads { = default        = default
geotrellis.hadoop.threads { = default
geotrellis.cassandra.threads { = default
  rdd {
    write = default
    read  = default
geotrellis.s3.threads { = default
  rdd {
    write = default
    read  = default

Cassandra has additional configuration settings:

And additional connections parameters for Cassandra:

geotrellis.cassandra {
  keyspace             = "geotrellis"
  replicationStrategy  = "SimpleStrategy"
  replicationFactor    = 1
  localDc              = "datacenter1"
  usedHostsPerRemoteDc = 0
  allowRemoteDCsForLocalConsistencyLevel = false

Consider using hbase.client.scanner.caching parameter for HBase as it may increase scan performance.

RDD Joins

In geotrellis.spark we represent a raster layer as a distributed collection of non-overlapping tiles indexed by keys according to some TileLayout. For instance a raster layer is represented as RDD[(SpatialKey, Tile)]. With this setup, we can represent certain decisions about how operations between layers should be performed in terms of the sort of ‘join’ to be performed.

visualized joins

visualized joins

First, we’ll set the stage for a discussion of joins in geotrellis.spark with a discussion of how metadata is used in this context.


A previously tiled and saved RasterRDD read in through an instance of will be mixed in with the Metadata[RasterMetaData] trait. This metadata describes the TileLayout used by the layer, the extent it covers, the CRS of its projection, and what the CellType of each tile is. This metadata allows us to verify that we’re working with compatible layers.

import org.apache.spark._
import org.apache.spark.rdd._

import geotrellis.raster._

implicit val sc: SparkContext = ???

val reader : S3LayerReader[SpatialKey, Tile, TileLayerMetadata[SpatialKey]] =
  S3LayerReader.spatial("bucket", "prefix")

def getLayerId(idx: Int): LayerId = ???

val rdd1 =

val rdd2: RDD[(SpatialKey, Tile)] with Metadata[TileLayerMetadata] =

val rdd3: TileLayerRDD[SpaitalKey] =

Default Joins

GeoTrellis provides an API for interaction with RDDs of tiles as a single unit. Where possible, we attempt to provide symbolic methods where their meaning is obvious and explicit method names in all cases.

import geotrellis.spark.mapalgebra.local._

 rdd1 + 1           // add 1 to every cell in the tiled raster
 rdd1 localAdd 1    // explicit method name for above operation
 rdd1 + rdd2        // do a cell wise local addition between two rasters
 rdd1 localAdd rdd2 // explicit method name for above operation
 List(rdd1, rdd2, rdd3).localAdd
 // all results are of type RDD[(SpatialKey, Tile)]

Other supported operations can been found in the geotrellis.spark.mapalgebra package and its sub-packages.

In order to provide this concise and intuitive syntax for map algebra operations between two layers some assumptions need to be made regarding the mechanics of the join. So, by default, GeoTrellis will use the spark implementation of inner join deferring to spark for the production of an appropriate partitioner for the result. Thus, if two layers being operated on are not aligned the result of the operation will contain only the intersecting tiles.

Explicit Joins

In cases where it is important to control the type of join a more explicit method is required. We make a direct call to geotrellis.raster.mapalgebra.local.Add.apply to perform per tile operations.

Because all binary operations must have the shape of (V, V) => R we provide an extension method on RDD[(K, (V, V))] that decomposes the tuple resulting from the join and uses it to call a function taking two arguments.

import geotrellis.raster.mapalgebra.local._

// using spark API
rdd1.join(rdd2).mapValues { case (tile1: Tile, tile2: Tile) => Add(tile1, tile2) }

// using GeoTrellis method extensions
rdd1.join(rdd2).combineValues(Add(_, _))

Left Join

Another reason to want to control a join is to perform an update of a larger layer with a smaller layer, performing an operation where two intersect and capturing resulting values.

This case is captured by a left outer join. If the right-side of the join row tuple is None we return the left-side tile unchanged. Consequently the extension method updateValues will only accept operations with signature of (V, V) => V.

// using spark API
rdd1.leftOuterJoin(rdd2).mapValues { case (tile1: Tile, optionTile: Option[Tile]) =>
  optionTile.fold(tile1)(Add(tile1, _))

// using GeoTrellis method extensions
rdd1.leftOuterJoin(rdd2).updateValues(Add(_, _))

Spatial Join

Given that we know the key bounds of our RDD, from accompanying TileLayerMetadata, before performing the join we may use a spark Partitioner that performs space partitioning. Such a partitioner has a number of benefits over standard HashPartitioner:

  • Scales the number of partitions with the number of records in the RDD
  • Produces partitions with spatial locality which allow:
    • Faster focal operations
    • Shuffle free joins with other spatially partitioned RDDs
    • Efficient spatial region filtering

Because the partitioner requires ability to extract Bounds of the original RDD from it’s Metadata it is able to provide the Bounds of the join result. Since the result of a join may be empty the user must match on the resulting Bounds object to find out if it’s EmptyBounds or KeyBounds[SpatialKey].

import geotrellis.spark.partitioner._

val joinRes: RDD[(SpatialKey, (Tile, Tile))] with Metadata[Bounds[SpatialKey]] =

val leftJoinRes: RDD[(SpatialKey, (Tile, Option[Tile])] with Metadata[Bounds[SpatialKey]] =

Manipulating Metadata

Metadata is provided when loading a layer from a GeoTrellis layer reader and is required when writing a layer through a GeoTrellis layer writer. The user bears responsibility that it is preserved and remains consistent through transformations if such behavior is desired.

The concrete implementation of RDD[(K, V)] with Metadata[M] signature in GeoTrellis is ContextRDD[K, V, M]

val rdd: RDD[(SpatialKey, Tile)] = rdd1 localAdd rdd2
val rddWithContext: RDD[(SpatialKey, Tile)] with Metadata[TileLayerMetadata] =
  ContextRDD(rdd, rdd1.metadata)

Preserving Metadata Through Operations

There are extension methods in RDD[(K, V]] with Metadata[M] that allow either changing rdd while preserving metadata or changing metadata while preserving the rdd.

// .withContext preserves the RDD context, the Metadata
val rddWithContext1: RDD[(SpatialKey, Tile)] with Metadata[TileLayerMetadata] =
  rdd1.withContext { _ localAdd rdd2 }

val rddWithContext2: RDD[(SpatialKey, Tile)] with Metadata[TileLayerMetadata] =
  rdd1.withContext { _ localAdd rdd2 localAdd rdd3 }

// .mapContext allows to chain changing Metadata after an operation
// example: localEqual will produce tiles with CellType of TypeBit
val rddWithContext3: RDD[(SpatialKey, Tile)] with Metadata[TileLayerMetadata] =
    .withContext { _ localEqual 123 }
    .mapContext { metadata: TileLayerMetadata => metadata.copy(cellType = TypeBit) }

Preserving Metadata Through Spatial Joins

Since spatial joins produce metadata, in contrast to vanilla spark joins, we must use .withContext wrapper at every transformation in order to allow the updated Bounds to flow to the end where they can be used.

For instance lets assume we wrote updateLayout that combines Bounds[SpatialKey] and LayoutDefinition from TileLayerMetadata to produce an RDD with updated, smaller TileLayout.

def updateLayout(md: TileLayerMetadata, bounds: Bounds[SpatialKey]): TileLayerMetadata = ???

val rddWithContext: RDD[(SpatialKey, Tile)] with Metadata[TileLayerMetadata] =
    .spatialJoin(rdd2).withContext { _.combineValues(Add(_, _)) }
    .spatialJoin(rdd3).withContext { _.combineValues(Add(_, _)) }
    .mapContext{ bounds => updateLayout(rdd1.metadata, bounds) }

Example Use Cases

Summaries from Temporal Overlap of Tiles

Sometimes you’d like to take a layer that has multiple tiles over the same spatial area through time, and reduce it down to a layer that has only value per pixel, using some method of combining overlapping pixels. For instance, you might want to find the maximum values of a pixel over time.

The following example shows an example of taking temperature data over time, and calculating the maximum temperature per pixel for the layer:

import geotrellis.raster._
import geotrellis.spark._
import geotrellis.util._

import org.apache.spark.rdd.RDD

val temperaturePerMonth: TileLayerRDD[SpaceTimeKey] = ???

val maximumTemperature: RDD[(SpatialKey, Tile)] =
    .map { case (key, tile) =>
      // Get the spatial component of the SpaceTimeKey, which turns it into SpatialKey
      (key.getComponent[SpatialKey], tile)
    // Now we have all the tiles that cover the same area with the same key.
    // Simply reduce by the key with a localMax

Stiching Tiles into a single GeoTiff

This example will show how to start with an RDD[(ProjectedExtent, Tile)] and end with a stitched together GeoTiff.


Stitching together an RDD can produce a tile that is far bigger than the driver program’s memory can handle. You should only do this with small layers, or a filtered RDD.

import geotrellis.raster._
import geotrellis.raster.resample._
import geotrellis.spark._
import geotrellis.spark.tiling._
import geotrellis.vector._
import org.apache.spark.HashPartitioner
import org.apache.spark.rdd.RDD

val rdd: RDD[(ProjectedExtent, Tile)] = ???

// Tile this RDD to a grid layout. This will transform our raster data into a
// common grid format, and merge any overlapping data.

// We'll be tiling to a 512 x 512 tile size, and using the RDD's bounds as the tile bounds.
val layoutScheme = FloatingLayoutScheme(512)

// We gather the metadata that we will be targeting with the tiling here.
// The return also gives us a zoom level, which we ignore.
val (_: Int, metadata: TileLayerMetadata[SpatialKey]) =

// Here we set some options for our tiling.
// For this example, we will set the target partitioner to one
// that has the same number of partitions as our original RDD.
val tilerOptions =
    resampleMethod = Bilinear,
    partitioner = new HashPartitioner(rdd.partitions.length)

// Now we tile to an RDD with a SpaceTimeKey.

val tiledRdd =
  rdd.tileToLayout[SpatialKey](metadata, tilerOptions)

// At this point, we want to combine our RDD and our Metadata to get a TileLayerRDD[SpatialKey]

val layerRdd: TileLayerRDD[SpatialKey] =
  ContextRDD(tiledRdd, metadata)

// Now we can save this layer off to a GeoTrellis backend (Accumulo, HDFS, S3, etc)
// In this example, though, we're going to just filter it by some bounding box
// and then save the result as a GeoTiff.

val areaOfInterest: Extent = ???

val raster: Raster[Tile] =
    .filter()                            // Use the filter/query API to
    .where(Intersects(areaOfInterest))   // filter so that only tiles intersecting
    .result                              // the Extent are contained in the result
    .stitch                 // Stitch together this RDD into a Raster[Tile]


Median Filter over Multiband Imagery

This example shows how to take some multiband imagery that exists in a layer, filter it with some upper bound threshold, and then apply a 5x5 median filter.

import geotrellis.spark._
import geotrellis.raster._
import geotrellis.raster.mapalgebra.focal.Square

val imageLayer: MultibandTileLayerRDD[SpaceTimeKey] = ???
val neighborhood = Square(2)

val resultLayer: MultibandTileLayerRDD[SpaceTimeKey] =
    .withContext { rdd =>
      rdd.mapValues { tile => { (band, z) =>
          if(z > 10000) NODATA
          else z
      .mapValues { bufferedTile =>
        bufferedTile.tile.mapBands { case (_, band) =>
          band.focalMedian(neighborhood, Some(bufferedTile.targetArea))

Region Query and NDVI Calculation

import geotrellis.raster._
import geotrellis.spark._
import geotrellis.util._
import geotrellis.vector._
import org.joda.time.DateTime

val region: MultiPolygon = ???
val layerReader: FilteringLayerReader[LayerId] = ???
val layerId: LayerId = LayerId("layerName", 18) // Querying zoom 18 data

val queryResult: MultibandTileLayerRDD[SpaceTimeKey] =
  layerReader.query[SpaceTimeKey, MultibandTile, TileLayerMetadata[SpaceTimeKey]](layerId)
    .where(Between(new DateTime(2016, 3, 1, 0, 0, 0), new DateTime(2016, 4, 1, 0, 0)))

val raster: Raster[Tile] =
    .withContext { rdd =>
        .mapValues { tile =>
          // Assume band band 4 is red and band 5 is NIR
          tile.convert(DoubleConstantNoDataCellType).combine(4, 5) { (r, nir) =>
            (nir - r) / (nir + r)
        .map { case (key, tile) => (key.getComponent[SpatialKey], tile) }