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Exploratory integration between Spark and Anserini: provides the ability to "map" over documents in a Lucene index.

Build the repo:

$ mvn clean package

A demo mapping over the Core18 index of the Washington Post collection:

$ spark-shell --jars target/anserini-spark-0.0.1-SNAPSHOT-fatjar.jar
import io.anserini.spark._

val indexPath = "../Anserini/lucene-index.core18.pos+docvectors+rawdocs/"
val docids = new IndexLoader(sc, indexPath).docids
val docs =, doc => doc.getField("raw").stringValue())

The val docs has type:

docs: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[1]

It's an RDD... so you can now do whatever you want with it.


PySpark interfaces with the JVM via the Py4J project, which plays much nicer with Java than Scala. Thus, the Scala classes used above have been re-written in (roughly) equivelant Java code.

$ pyspark --jars target/anserini-spark-0.0.1-SNAPSHOT-fatjar.jar

# Import Java -> Python converter
from pyspark.mllib.common import _java2py

# The path of the Lucene index
INDEX_PATH = "/tuna1/indexes/lucene-index.core17.pos+docvectors+rawdocs/"

# The JavaIndexLoader instance
index_loader =, INDEX_PATH)

# Get the document IDs as an RDD
docids = index_loader.docids()

# Get the JavaRDD of Lucene Document as a Map (Document can't be serialized)
docs = lucene.docs2map(INDEX_PATH)

# Convert to a Python RDD
docs = _java2py(sc, docs)

docs is a <class 'pyspark.rdd.RDD'>... we can do whatever we want with it now too.

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