void validateDataFrameWithBeans(Bean bean, DataFrame df) {
StructType schema = df.schema();
Assert.assertEquals(
new StructField("a", DoubleType$.MODULE$, false, Metadata.empty()), schema.apply("a"));
Assert.assertEquals(
new StructField("b", new ArrayType(IntegerType$.MODULE$, true), true, Metadata.empty()),
schema.apply("b"));
ArrayType valueType = new ArrayType(DataTypes.IntegerType, false);
MapType mapType = new MapType(DataTypes.StringType, valueType, true);
Assert.assertEquals(new StructField("c", mapType, true, Metadata.empty()), schema.apply("c"));
Assert.assertEquals(
new StructField("d", new ArrayType(DataTypes.StringType, true), true, Metadata.empty()),
schema.apply("d"));
Row first = df.select("a", "b", "c", "d").first();
Assert.assertEquals(bean.getA(), first.getDouble(0), 0.0);
// Now Java lists and maps are converted to Scala Seq's and Map's. Once we get a Seq below,
// verify that it has the expected length, and contains expected elements.
Seq<Integer> result = first.getAs(1);
Assert.assertEquals(bean.getB().length, result.length());
for (int i = 0; i < result.length(); i++) {
Assert.assertEquals(bean.getB()[i], result.apply(i));
}
@SuppressWarnings("unchecked")
Seq<Integer> outputBuffer = (Seq<Integer>) first.getJavaMap(2).get("hello");
Assert.assertArrayEquals(
bean.getC().get("hello"),
Ints.toArray(JavaConverters.seqAsJavaListConverter(outputBuffer).asJava()));
Seq<String> d = first.getAs(3);
Assert.assertEquals(bean.getD().size(), d.length());
for (int i = 0; i < d.length(); i++) {
Assert.assertEquals(bean.getD().get(i), d.apply(i));
}
}
private <T> void assertExpectedTokens(LuceneAnalyzer analyzer, List<T> testData) {
JavaRDD<T> rdd = jsc.parallelize(testData);
Row[] pairs =
analyzer
.transform(jsql.createDataFrame(rdd, testData.get(0).getClass()))
.select("wantedTokens", "tokens")
.collect();
for (Row r : pairs) {
Assert.assertEquals(r.get(0), r.get(1));
}
}
@Override
public void execute(
JavaSparkContext ctx, SQLContext sqlContext, WorkflowContext workflowContext, DataFrame df) {
workflowContext.out("Executing NodePrintFirstNRows : " + id);
Row[] rows = df.take(n);
for (Row row : rows) {
workflowContext.out(row.toString());
}
super.execute(ctx, sqlContext, workflowContext, df);
}
@Override
public int run(SparkConf conf, CommandLine cli) throws Exception {
long startMs = System.currentTimeMillis();
conf.set("spark.ui.enabled", "false");
JavaSparkContext jsc = new JavaSparkContext(conf);
SQLContext sqlContext = new SQLContext(jsc);
long diffMs = (System.currentTimeMillis() - startMs);
System.out.println(">> took " + diffMs + " ms to create SQLContext");
Map<String, String> options = new HashMap<>();
options.put("zkhost", "localhost:9983");
options.put("collection", "ml20news");
options.put("query", "content_txt:[* TO *]");
options.put("fields", "content_txt");
DataFrame solrData = sqlContext.read().format("solr").options(options).load();
DataFrame sample = solrData.sample(false, 0.1d, 5150).select("content_txt");
List<Row> rows = sample.collectAsList();
System.out.println(">> loaded " + rows.size() + " docs to classify");
StructType schema = sample.schema();
CrossValidatorModel cvModel = CrossValidatorModel.load("ml-pipeline-model");
PipelineModel bestModel = (PipelineModel) cvModel.bestModel();
int r = 0;
startMs = System.currentTimeMillis();
for (Row next : rows) {
Row oneRow = RowFactory.create(next.getString(0));
DataFrame oneRowDF =
sqlContext.createDataFrame(Collections.<Row>singletonList(oneRow), schema);
DataFrame scored = bestModel.transform(oneRowDF);
Row scoredRow = scored.collect()[0];
String predictedLabel = scoredRow.getString(scoredRow.fieldIndex("predictedLabel"));
// an acutal app would save the predictedLabel
// System.out.println(">> for row["+r+"], model returned "+scoredRows.length+" rows,
// "+scoredRows[0]);
r++;
}
diffMs = (System.currentTimeMillis() - startMs);
System.out.println(">> took " + diffMs + " ms to score " + rows.size() + " docs");
return 0;
}
@Override
public Row next() {
Row row;
if (this.hasNext()) {
row = this.rows.next();
} else {
row = null;
}
if (this.incEnable) {
if (row.getLong(this.timestampIndex) > this.incMaxTS) {
this.incMaxTS = row.getLong(this.timestampIndex);
}
}
return row;
}
@Override
public ArrayList<String> call(JobContext jc) {
String inputFile = "src/test/resources/testweet.json";
SQLContext sqlctx = jc.sqlctx();
DataFrame input = sqlctx.jsonFile(inputFile);
input.registerTempTable("tweets");
DataFrame topTweets =
sqlctx.sql("SELECT text, retweetCount FROM tweets ORDER BY retweetCount LIMIT 10");
ArrayList<String> tweetList = new ArrayList<>();
for (Row r : topTweets.collect()) {
tweetList.add(r.toString());
}
return tweetList;
}
@Test
public void testCrosstab() {
DataFrame df = context.table("testData2");
DataFrame crosstab = df.stat().crosstab("a", "b");
String[] columnNames = crosstab.schema().fieldNames();
Assert.assertEquals("a_b", columnNames[0]);
Assert.assertEquals("2", columnNames[1]);
Assert.assertEquals("1", columnNames[2]);
Row[] rows = crosstab.collect();
Arrays.sort(rows, crosstabRowComparator);
Integer count = 1;
for (Row row : rows) {
Assert.assertEquals(row.get(0).toString(), count.toString());
Assert.assertEquals(1L, row.getLong(1));
Assert.assertEquals(1L, row.getLong(2));
count++;
}
}
@Override
public int compare(Row row1, Row row2) {
String item1 = row1.getString(0);
String item2 = row2.getString(0);
return item1.compareTo(item2);
}
