Java API
In addition to the Scala API, some APIs can also be accessed from Java.
Creating a Spark Session
When accessing Spark from java, a SparkSession needs to be created, similar to this:
SparkSession spark = SparkSession
.builder()
.master("local[*]")
.appName("Java API")
.config("spark.couchbase.connectionString", "127.0.0.1")
.config("spark.couchbase.username", "Administrator")
.config("spark.couchbase.password", "password")
.config("spark.couchbase.implicitBucket", "travel-sample")
.getOrCreate();Properties and configuration are set in the same way.
Using SparkSQL DataFrames
The DataFrame APIs can be accessed very similarly from Java compared to Scala.
Dataset<Row> airlines = spark.read()
.format("couchbase.query")
.option(QueryOptions.Filter(), "type = 'airline'")
.option(QueryOptions.Bucket(), "travel-sample")
.load();
airlines.show(3);Note that since later version of Spark 2, a DataFrame is just an alias for a Dataset<Row> and can be interacted with in the same way.
If executed against the travel-sample bucket, this will print:
+-------------+--------+-------------+----+----+-----+-----------+-------+
| __META_ID|callsign| country|iata|icao| id| name| type|
+-------------+--------+-------------+----+----+-----+-----------+-------+
| airline_10|MILE-AIR|United States| Q5| MLA| 10|40-Mile Air|airline|
|airline_10123| TXW|United States| TQ| TXW|10123|Texas Wings|airline|
|airline_10226| atifly|United States| A1| A1F|10226| Atifly|airline|
+-------------+--------+-------------+----+----+-----+-----------+-------+Please see the corresponding scala sections for DataFrame on how to configure the data source and which properties can be applied.
Using SparkSQL Datasets
Since Datasets work with actual Java objects, first create one:
public static class Airline implements Serializable {
private String name;
private String callsign;
private String country;
public String getName() {
return name;
}
public void setName(String name) {
this.name = name;
}
public String getCallsign() {
return callsign;
}
public void setCallsign(String callsign) {
this.callsign = callsign;
}
public String getCountry() {
return country;
}
public void setCountry(String country) {
this.country = country;
}
@Override
public String toString() {
return "Airline{" +
"name='" + name + '\'' +
", callsign='" + callsign + '\'' +
", country='" + country + '\'' +
'}';
}
}Next, you can convert a DataFrame to a Dataset through the .as() API:
Dataset<Airline> airlines = spark.read()
.format("couchbase.query")
.option(QueryOptions.Filter(), "type = 'airline'")
.option(QueryOptions.Bucket(), "travel-sample")
.load()
.as(Encoders.bean(Airline.class));
airlines
.limit(3)
.foreach(airline -> {
System.out.println("Airline:" + airline);
});This will print:
Airline:Airline{name='40-Mile Air', callsign='MILE-AIR', country='United States'}
Airline:Airline{name='Texas Wings', callsign='TXW', country='United States'}
Airline:Airline{name='Atifly', callsign='atifly', country='United States'}