Tools for reading data from Solr as a Spark RDD and indexing objects from Spark into Solr using SolrJ.

• Send objects from a Spark Streaming application into Solr
• Read the results from a Solr query as a Spark RDD
• Read a large results set from Solr using distributed deep paging as a Spark RDD

First, build the Jar for this project:

mvn clean package

To run the Twitter examples, you'll need to configure your Twitter API credentials in:

src/main/resources/twitter4j.properties

and then re-build the package (mvn package).

Now, let's populate a SolrCloud index with tweets:

./spark-submit --master local[2] --class com.lucidworks.spark.SparkApp \
  <PROJECT_PATH>/target/spark-solr-1.0-SNAPSHOT.jar \
  twitter-to-solr -zkHost localhost:9983 -collection collection1

After populating your collection with tweets, you can see an example of how to transform the results from a Solr query into a Spark RDD:

./spark-submit --master local[2] --class com.lucidworks.spark.SparkApp \
  <PROJECT_PATH>/target/spark-solr-1.0-SNAPSHOT.jar \
  query-solr -zkHost=localhost:9983 -collection=collection1 -query="*:*"

The com.lucidworks.spark.SolrRDD class transforms the results of a Solr query into a Spark RDD.

SolrRDD solrRDD = new SolrRDD(zkHost, collection);
JavaRDD<SolrDocument> solrJavaRDD = solrRDD.query(jsc, solrQuery, useDeepPagingCursor);

Once you've converted the results in an RDD, you can use the Spark API to perform analytics against the data from Solr. For instance, the following code extracts terms from the tweet_s field of each document in the results:

JavaRDD<String> words = solrJavaRDD.flatMap(new FlatMapFunction<SolrDocument, String>() {
  public Iterable<String> call(SolrDocument doc) {
    Object tweet_s = doc.get("tweet_s");
    String str = tweet_s != null ? tweet_s.toString() : "";
    str = str.toLowerCase().replaceAll("[.,!?\n]", " ");
    return Arrays.asList(str.split(" "));
  }
});

Also, notice in the example above, the third argument to the solrRDD.query() function indicates whether to use deep-paging cursors, which allows you to process very large result sets across a Spark cluster.

The com.lucidworks.spark.SolrSupport class provides static helper functions for send data to Solr from a Spark streaming application. The TwitterToSolrStreamProcessor class provides a good example of how to use the SolrSupport API. For sending documents directly to Solr, you need to build-up a SolrInputDocument in your Spark streaming application code.

    String zkHost = cli.getOptionValue("zkHost", cli.getOptionValue("zkHost", "localhost:9983"));
    String collection = cli.getOptionValue("collection", cli.getOptionValue("collection", "collection1"));
    SolrSupport.indexDStreamOfDocs(zkHost, collection, 100, docs);

The com.lucidworks.spark.SparkApp provides a simple framework for implementing Spark applications in Java. The class saves you from having to duplicate boilerplate code needed to run a Spark application, giving you more time to focus on the business logic of your application. To leverage this framework, you need to develop a concrete class that either implements RDDProcessor or extends StreamProcessor depending on the type of application you're developing.

Implement the com.lucidworks.spark.SparkApp$RDDProcessor interface for building a Spark application that operates on a JavaRDD, such as one pulled from a Solr query (see: SolrQueryProcessor as an example)

Extend the com.lucidworks.spark.SparkApp$StreamProcessor abstract class to build a Spark streaming application.